energies Article
A Novel Method for Idle-Stop-Start Control of Micro Hybrid Construction Equipment—Part B: A Real-Time Comparative Study Truong Quang Dinh 1, *, James Marco 1 , Hui Niu 1 , David Greenwood 1 , Lee Harper 2 and David Corrochano 2 1 2
*
Warwick Manufacturing Group (WMG), University of Warwick, Coventry CV4 7AL, UK;
[email protected] (J.M.);
[email protected] (H.N.);
[email protected] (D.G.) JCB, Rocester, Straffordshire ST14 5JP, UK;
[email protected] (L.H.);
[email protected] (D.C.) Correspondence:
[email protected]; Tel.: +44-24-7657-4902
Received: 29 July 2017; Accepted: 18 August 2017; Published: 23 August 2017
Abstract: Micro hybrid propulsion (MHP) technologies have emerged as promising solutions for minimisation of fuel consumption and pollutant emissions of off-highway construction machines (OHCMs). Their performance and economic feasibility strongly depend on the way they utilize the idle-stop-start control (ISSC) concept. The ISSC design process and performance evaluation are particularly challenging due to the peculiar structures and dynamics of OHCMs compared to other vehicles and, therefore, require significant development time and efforts. This paper is the second of a two-part study focusing on prediction-based idle-start-stop control (PISSC) for micro hybrid OHCMs. In part A, the powertrain model and the procedure to design the PISSC system have been presented. The PISSC-based engine control performance has been investigated through numerical simulations with the designed model. In this Part B, a hardware-in-the-loop (HIL) test platform is established in HIL Control Laboratory for the rapid validation of the proposed technique in terms of the fuel/pollutant emission saving in real-time. First, the powertrain architecture and PISSC algorithm presented in Part A are briefly reviewed. Second, the process to build the HIL test platform is clearly stated. Third, experiments and analysis are carried out for a number of comparative studies to validate the superiority and practical applicability of the PISSC approach. Keywords: construction machine; hybridization; idle-stop-start; hardware-in-the-loop; real-time
1. Introduction In the past two decades, the use of fuel has not been considered a significant portion of construction firms’ operating budgets. However together with the recent price rises and the fuel supply crisis, this is no longer the case [1]. Most construction equipment fleet owners are now concerned with not only improving productivity, but also reducing the fuel consumption of their machines. Furthermore, pollutant emissions from this kind of machines has also strongly stimulated research and development activities on greener machines. Among the technology roadmaps toward this objective, exploitation of micro hybrid propulsion (MHP), well implemented in automobiles [2–8], has been drawing more and more attention due to its advantages, including high energy performance, low fuel use and emissions while requiring low cost and effort for installation [9]. For a MHP system, the key function is known as stop-start control (SSC) or idle-stop-start control (ISSC). This enables the internal combustion engine (ICE) to be automatically shut down (with both SSC and ISSC) or driven with low power consumption (only with ISSC) while working without load or with reduced workloads, respectively, and immediately turned on if needed.
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In order to underpin the SSC/ISSC research theme of construction equipment, two critical issues need to be addressed. The first is how to detect whether or not a machine has a low or zero workload to make decisions to reduce power of the ICE or shut it down to avoid the unnecessary fuel use [10–12]. The second is how to design an algorithm capable of recognizing periodically repeated working cycles of the machine, which are normal in many construction tasks, to make the engine control decisions sooner [12]. This will therefore maximize the potential energy savings of the machine. These both two issues can be well resolved using the recent work described by the authors in part A of this project [12]—the so-called prediction-based idle-stop-start control (PISSC) which has been introduced for application to a micro hybrid excavator for the first-time. Through the art presented in part A [12], the target machine has been discussed and the powertrain model has been properly constructed. The PISSC scheme has been then built to identify online the cyclic working profiles based on the machine states. This information is then used to predict any engine mode changes ahead of time. Consequently, the ICE can be switched almost directly into a new mode once the first few machine states of this mode are captured. The PISSC performance has been briefly validated with simulation results. In order to realize the proposed control approach in the production of an engine control unit (ECU) as well as to further identify the potential fuel and emissions saving of the machine using this approach, additional tests in real-time and a comprehensive analysis need to be done. However, time, costs and safety are the critical concerns for conducting this kind of tests and analysis directly on real construction equipment. Hardware-in-the-loop (HIL) test platforms have been widely used by both researchers and original equipment manufacturers (OEMs) in terms of product design (hardware and software) and rapid prototyping. An HIL platform is based on system knowledge and mimics the operation conditions that the system will actually face in the real world. When performing a HIL experiment, a physical plant is then substituted by a precisely equivalent computer model, running in real-time on a hardware appropriately equipped with inputs/outputs (I/Os) capable of interfacing with control systems and other equipment. Thus, both the system modelling and control works can be then tested and/or validated quickly and safely in the real environment using this HIL setup. Hence, HIL testing methodology is the most time efficient and cost effective way to bring research and development of new systems and control algorithms one step closer to the real applications and productions [13]. In vehicle development, the HIL test method is a well-known firm fixture in the product design progress as a method of testing electronic control unit software. Several commercial tool chains of HIL platforms for vehicles such as LABCAR, dSPACE, VeHIL or Autonomie are known [14]. These kinds of tools can be utilized not only to test functionalities but also optimize the structure/parameters of one or even a network of control units [13,15]. Consequently, they can improve the design success rate while eliminating the risk of development errors [14]. Especially in industrial vehicles and off-highway machines where the supervisory controllers may contain more than 20 electronic control units connected to more than two controller area network (CAN) buses [15], HIL platforms capable of evaluating and/or optimizing the control system via CAN buses are indispensable to perform regression tests on multiple variants and multiple test cases quickly, safety and closely mimicking practical applications [16]. To address the challenge of verifying the PISSC strategy in actual conditions as well as acknowledging the importance of HIL-based testing method, this paper—the second half (Part B) of ous research on the PISSC concept focuses on a design of an advanced HIL test platform representative of a real construction machine with basic engine control functions and a real-time evaluation of the proposed methodology in terms of fuel and emission reduction. First, the powertrain architecture, modelling and PISSC strategy presented in [12] are briefly reviewed in Section 2. In the next section, the process to design the HIL test platform using dSPACE and CAN buses is clearly presented. Experiments and analysis on the HIL platform for a number of test cases and comparative control approaches are carried out in Section 4 to validate the superiority and practical applicability of the PISSC. Finally, concluding remarks are given in Section 5.
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2. Powertrain and PISSC Review 2.1. Powertrain Architecture Architecture 2.1. Powertrain Based and evaluation from the the experts on the Based on on the the numerical numerical analysis analysis and evaluation from experts on the ISSC ISSC potential potential of of main main construction machine classes, a typical heavy excavator from JCB has been selected as the target construction machine classes, a typical heavy excavator from JCB has been selected as the target machine Its powertrain powertrain is is simply simply presented presented in The powertrain powertrain consists DC machine [12]. [12]. Its in Figure Figure 1. 1. The consists of of an an ICE, ICE, aa DC starter, alternator, aa lead-acid lead-acid battery, battery, and drivetrain including including hydrodynamic hydrodynamic and and hydrostatic hydrostatic starter, aa DC DC alternator, and aa drivetrain transmissions (HDT and HST), clutches and loads. The output shaft of the engine is coupled transmissions (HDT and HST), clutches and loads. The output shaft of the engine is coupled to to the the alternator belt transmission transmission and The starter starter with with battery battery is is used used alternator by by aa belt and then then connected connected to to the the drivetrain. drivetrain. The to During the the ICE the starter-engine engagement is is controlled controlled by by to crank crank the the engine. engine. During ICE cranking cranking phase, phase, the starter-engine engagement a pinion-ring gear mechanism and an over-running clutch. A solenoid valve is used to engage the a pinion-ring gear mechanism and an over-running clutch. A solenoid valve is used to engage the pinion pinion with with the the ring ring gear gear on on aa flywheel flywheel of of the the engine. engine. During During the the machine machine operation, operation, loads loads coming coming from the HST and HDT can be represented by a time-variant dynamic load connected to the from the HST and HDT can be represented by a time-variant dynamic load connected to the engine engine output Specifications of output shaft. shaft. Specifications of the the main main powertrain powertrain components components are are given given in in Table Table 11[9]. [9]. STORAGE MANAGEMEN T UNIT VREF
Signal
Exci tati on Sy stem (Vol tag e Regulator + Power Stabi lizer)
Converter
Energy Mechanic
Alternator
Battery
Fly wheel
STARTING CONTROL wS_REF
HDT ICE C
Servo Contro ller
Servo Driv e
Starter
HST
Figure 1. Powertrain configuration of the targeted excavator [12]. Figure 1. Powertrain configuration of the targeted excavator [12]. Table 1. Specifications of the main powertrain components. Table 1. Specifications of the main powertrain components. Component Specifications Component Specifications ratio 2.8:1 Belt transmission Transmission Belt transmission Transmission ratio 2.8:1 Transmission ratio 11:1 Pinion-ring gear transmission Transmission ratio 11:1 44.7 kg Flywheel mass Pinion-ring gear transmission Flywheel mass 44.7 kg Four-stroke diesel engine 1.9 L Four-stroke diesel engine 1.9 L 2400 rpm MaxMax speedspeed 2400 rpm Engine Engine Gross power 41 kW, power kW 38.4 kW Gross power 41net kW, net38.4 power Compression ratio 16.7:1 Compression ratio 16.7:1 12 V working voltage 12 V working voltage Starter 2 kW power Starter 2 kW power Alternator Current 80 A Alternator Current 80 A Lead-acid battery Lead-acid battery Battery 12 V nominal voltage 75 Battery 12Ah V capacity nominal voltage 75 Ah capacity
2.2. PISSC-Based Engine Control Design Review 2.2. PISSC-Based Engine Control Design Review Remark 1. For the PISSC design, four ICE working modes are defined as displayed in Figure 2 which is based Remark 1. For the PISSC design, four ICE working modes are defined as displayed in Figure 2 which is based on the ICE torque-speed bands to manage the engine operation [12]: on the ICE torque-speed bands to manage the engine operation [12]:
•
τ e and ne are the engine torque and speed, respectively. Their high, medium and low levels are in turn defined as: τ eH , τ eM , τ eL and neH , neM , neL .
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τe and ne are the engine torque and speed, respectively. Their high, medium and low levels are in turn 4 of 25 τeM , τeL and neH , neM , neL . Normal mode (NOR): the machine requests the engine to run with full power capability. The starter does Normal mode (NOR): machine requests the engine to run with full power capability. The starter does not operate during thisthe mode. not duringthe thismachine mode. does not request the full engine power. The cylinder deactivation control Idleoperate mode (IDL): Idle mode (IDL): the machine nottherequest full engine power. control technology can be employed to does reduce enginethe power. The starter doesThe notcylinder operateddeactivation during this mode. technology can be employed to reduce the engine power. The starter does not operated during this mode. Stop mode (STO): there is no command given from the human machine interface (HMI) module and the Stop mode (STO): there is no command given from the human machine interface (HMI) module and the ICE torque and speed are below the low torque and speed levels, respectively (the ICE torque is mainly come ICE torque and speed are below the low torque and speed levels, respectively (the ICE torque is mainly from the system inertia). During this mode, the engine can be shut down to save the fuel and the starter is come from the system inertia). During this mode, the engine can be shut down to save the fuel and the also turned off. starter is also turned off. Start mode (STA): the engine is cranked from zero speed to its normal idle speed (around 900 rpm). The Start mode (STA): the engine is cranked from zero speed to its normal idle speed (around starter is firstly turned on to crank the engine to a low idle speed (around 250 rpm). At this low idle speed, 900 rpm). The starter is firstly turned on to crank the engine to a low idle speed (around the starter is turned off while the injector starts to inject fuel to continue to crank the engine to the desired 250 rpm). At this low idle speed, the starter is turned off while the injector starts to inject fuel to continue idle speed. to crank the engine to the desired idle speed. A machine working cycle is a sequence of machine stages in which each machine stage is corresponding to A machine working cycle is a sequence of machine stages in which each machine stage is corresponding to one of the four defined ICE modes. The selection of ICE modes is performed by a rule-based controller. one of the four defined ICE modes. The selection of ICE modes is performed by a rule-based controller.
Energies 2017, 10, defined as:1250 τeH ,
• • • • • •
• •
• •
τeH
τe NOR
τeM IDL
NOR
τe
L
STO
IDL
ne L
ne
M
ne
H
ne
Figure 2. ICE mode definition based on ICE torque-speed bands [12]. Figure 2. ICE mode definition based on ICE torque-speed bands [12].
The PISSC-based engine control principle is described in Figure 3. The PISSC functions as the The PISSC-based engine control principle is described in Figure 3. The PISSC functions as the ICE speed controller which defines the working regions for the ECU. During operation, the signals ICE speed controller which defines the working regions for the ECU. During operation, the signals from the HMI (including braking and acceleration commands from the pedals and actuator from the HMI (including braking and acceleration commands from the pedals and actuator commands commands from joysticks) and ICE sensors (torque and speed) are acquired to determine the current from joysticks) and ICE sensors (torque and speed) are acquired to determine the current ICE working ICE working mode using the ICE mode definition based on Remark 1. A timer is enabled to measure mode using the ICE mode definition based on Remark 1. A timer is enabled to measure durations of durations of the ICE modes which are then used to determine the machine working cycles. When a the ICE modes which are then used to determine the machine working cycles. When a cyclic working cyclic working profile is identified online, the grey prediction model (GPM) is activated to estimate profile is identified online, the grey prediction model (GPM) is activated to estimate the coming the coming engine mode after only a few new machine states corresponding to a new ICE mode engine mode after only a few new machine states corresponding to a new ICE mode (compared to the (compared to the current mode) are recorded. The prediction-based and timer-based mode decisions current mode) are recorded. The prediction-based and timer-based mode decisions are both input to are both input to a rule-based controller (RBC) to make a final decision on ICE mode switching. This a rule-based controller (RBC) to make a final decision on ICE mode switching. This decision is then decision is then sent to the ECU in order to drive the engine in its highest fuel efficiency zone without sent to the ECU in order to drive the engine in its highest fuel efficiency zone without sacrificing the sacrificing the machine performance. machine performance.
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START
(**)
HMI & sensors’ data observation
(*)
Cycle Data Rolling Operation
GPM (***)
Mode Definition
Actual State
Yes
Correct Estimation? No
Prediction-based Mode Switch Flag
Estimated Mode
No
New Working Cycle? Yes
Timer-based Mode Switch Flag
Mode Decision using RBC
Reset GPM with New Cycle
PISSC Machine Operation
ECU
No
Work Loads
Stop? Yes END
Figure 3. PISSC-based engine control principle [12].
2.2.1. 2.2.1. Cycle Cycle Data Data Rolling Rolling Operation Operation This module includes includes three three main main functions: functions: working identification, raw raw data data preparation preparation This module working cycle cycle identification, and activation control for the GPM: and activation control for the GPM: • • • • • •
A working cycle can be then identified by the iterative searching algorithm proposed in [12]. A working cycle can be then identified by the iterative searching algorithm proposed in [12]. Once Once a periodic cycle is determined, the recorded machine stages are rearranged into a matrix a periodic cycle is determined, the recorded machine stages are rearranged into a matrix form of form of cycle number, stages’ indices and timestamps within a cycle. cycle number, stages’ indices and timestamps within a cycle. For the cyclic operation, to estimate a coming machine stage (its index in the working cycle has For the cyclic operation, to estimate a coming machine stage (its index in the working cycle has been defined), a vector with n raw data points which are the timestamps of the same stage within been defined), a vector with n raw data points which are the timestamps of the same stage within n historical cycles is required. n historical cycles is required. The GPM is only activated when the recorded stage matrix contains historical data of n cycles. The GPM isa only whenwhich the recorded stagefrom matrix data ofthe n cycles. If existing new activated machine stage is different thecontains defined historical stage sequence, cycle If existing a new machine stage which is different from the defined stage sequence, the cycle definition is reset by removing the oldest machine stage and adding the newest one. The GPM definition is reset byand removing the oldest machine stage again and adding the newest one. The GPM is is then de-activated the search algorithm is applied to identify the new working cycle. then de-activated and the search algorithm is applied again to identify the new working cycle.
2.2.2. Grey Prediction Model 2.2.2. Grey Prediction Model Grey models are powerful estimation tools to deal with partially unknown systems [17–21]. In Grey models are powerful estimation tools to deal with partially unknown systems [17–21]. In this this research, the grey prediction model has been built to estimate the ICE coming modes once the research, the grey prediction model has been built to estimate the ICE coming modes once the machine machine is repeatedly operated with the same cycle. The internal flow diagram of this GPM in is repeatedly operated with the same cycle. The internal flow diagram of this GPM in combination with combination with the cycle data rolling operation module in Figure 3 is described in Figure 4. The the cycle data rolling operation module in Figure 3 is described in Figure 4. The detailed mathematical detailed mathematical equations of this GPM is clearly introduced in [12]. Based on the grey theory equations of this GPM is clearly introduced in [12]. Based on the grey theory [17,18,22], a minimum [17,18,22], a minimum number of past data, more than four, is required to perform a grey prediction. number of past data, more than four, is required to perform a grey prediction. Here, the GPM receives Here, the GPM receives the historical timestamp vector of the coming machine stage (over the n the historical timestamp vector of the coming machine stage (over the n historical cycles) to estimate historical cycles) to estimate the starting time of this stage at present. For each m repeated cycles (m ≥ n), duration of the first machine stage in the first cycle could be largely different from durations of this stage in the lateral cycles in case the first and last stages of a cycle are the same. Thus, n has
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the starting time of this stage at present. For each m repeated cycles (m ≥ n), duration of the first machine stage in the first cycle could be largely different from durations of this stage in the lateral Energies 2017, 10, 1250 6 of 25 cycles in case the first and last stages of a cycle are the same. Thus, n has been selected as 6 [12] and the GPM takes most fiveonly datatakes points perform prediction. a prediction is been only selected as the 6 [12] andrecent the GPM thetomost recentthe five data pointsOnce to perform the prediction. Once a prediction is performed, accuracy is evaluated by comparing theactual performed, the prediction accuracy is evaluatedthe byprediction comparing the estimated timestamp with the estimated timestamp with the actual at which the first machine state of this stage is5%, detected. If the time at which the first machine state of time this stage is detected. If the accuracy is within it means the accuracy is within 5%, it means the machine keeps running with the same cycle and, the ICE mode machine keeps running with the same cycle and, the ICE mode decision and data rolling operation decision and data rolling operation are executed based on the GPM output. The engine then is shifted are executed based on the GPM output. The engine then is shifted to the new mode immediately. to the new mode immediately. On the contrary, the machine may work with a new cycle and On the contrary, the machine may work with a new cycle and subsequently, the mode decision and subsequently, the mode decision and data rolling operation need to be executed using the data timer-based rolling operation need to be executed using the timer-based mode detection [12]. In this case, mode detection [12]. In this case, the engine continuously runs in the current mode and, the engine continuously runs inmode the current modethe and, only can shifted to thedefined new mode only can be shifted to the new after checking machine statesbeover a duration by the after checking the machine states over a duration defined by the timer. timer. (*)
(*)
(**)
Timer based ICE mode detection (Remark 1)
Cycle Defined?
New Cycle Detection?
Yes Working cycle definition (Remark 1)
Yes
Add new data to raw stage data sequence (holding)
No
Store >= N data points & Cycle not defined?
(****)
Yes + Remove oldest value from raw stage data sequence + Clear cycle definition
Full data sequence for n cycles?
Yes
No Full data sequence for n cycles?
(****)
New machine state w.r.t. next ICE mode?
Cycle Data Rolling Operation
Yes
No Correct Estimation?
(***)
Yes
Rolling operation with data vector of the coming machine stage’s time stamps: replace oldest value by GPM-based stage detection result
Generate a raw input grey sequence: the vector of current machine stage’s timestamps (n recent cycles)
Generate a new sequence using accumulated generating algorithm (AGO) & Remark 2
Generate a background series using adaptive weights
Establish grey different equation & calculate [a, b] using least square estimation & Remark 2
Rolling operation with data vector of the coming machine stage’s time stamps: replace oldest value by timer-based stage detection result (Remark 1)
GPM
Setup grey prediction model & Calculate model output at (k+sk)th step using inverse AGO (IAGO)
Produce the predicted timestamp value of the coming machine stage
Note: (*) and (**) are the inputs of the Cycle Data Rolling Operation while
(***)
(***) is the output of the GPM shown in Figure 3
Figure 4. Flow diagram of the cycle data rolling operation and GPM modules in Figure 3 [12]. Figure 4. Flow diagram of the cycle data rolling operation and GPM modules in Figure 3 [12].
2.2.3. Rule-Based Controller
2.2.3. Rule-Based Controller
To support the RBC design, the following variables are defined:
To support the RBC design, the following variables are defined: •
• •
•
HMI (HMI flag) is a variable which is unit if there exists at least a signal sent from the HMI and is zero vice versa. HMImodule, (HMI flag) is a variable which is unit if there exists at least a signal sent from the HMI • TMR is value of the timer which counts number of continuous states of the engine which are module, and is zero vice versa. within the same working mode, different from the current mode. This TMR is reset once having TMR is value of the timer which counts number of continuous states of the engine which are more than TMR new engine states or a new engine mode is selected. TMR is the pre-defined within the same working mode, the current mode. Thisa decision TMR is reset once having threshold value for TMR and, different is the timefrom in seconds needed to make on ICE mode moreswitching than TMR new engine states or a new engine mode is selected. TMR is the pre-defined (in case the GPM is not activated). Here, TMR is selected as 3. threshold for TMR and, isa the time in seconds needed tothe make a decision ICE mode • PRE isvalue a variable representing correct estimation of the GPM (if estimation erroron is within switching (in caseacceptable the GPMrange). is not activated). TMRand is selected 3. correct. a pre-defined Then, PRE =Here, 1 if correct PRE = 0 ifasnot • IGN is a variable representing the engine state, IGN 1 if GPM ICE is (if onthe andestimation IGN = 0 if ICE is off. PRE is a variable representing a correct estimation of=the error is within a
pre-defined acceptable range). Then, PRE = 1 if correct and PRE = 0 if not correct.
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IGN is a variable representing the engine state, IGN = 1 if ICE is on and IGN = 0 if ICE is off. SOC is the battery state of charge representing the energy remained in the battery (in percentage). In order to enable stop-start operation, this SOC should be greater than a minimum level SOCmin which allows the starter can crank the engine when STA mode is enabled. In this case, SOCmin is set to 70%.
In this study, the prediction is only considered for the STO mode and the engine can be moved directly into NOR and IDL modes to adapt to the HMI commands. By updating all the declared variables and using Remark 1, four rules are then established for the RBC as follows:
•
Rule for NOR mode: the ICE is shifted to NOR mode if:
•
i τeL < τe (t) ≤ τeM ∧ ne (t) ≤ neM ∨ τe (t) ≤ τeL ∧ (HMI = 1)
(2)
Rule for STO mode: the ICE is shifted to STO mode if:
•
(1)
Rule for IDL mode: the ICE is shifted to IDL mode if: h
•
τeM < τe (t) ≤ τeH ∨ τeL < τe (t) ≤ τeM ∧ neM < ne (t) ≤ neH
τe (t) ≤ τeL ∧ (HMI = 0) ∧ (PRE = 1) ∨ (PRE = 0) ∧ TMR > TMR
(3)
Rule for STA mode: the ICE is shifted to STA mode if:
(HMI = 1) ∧ (IGN = 0)
(4)
3. HIL Platform for Real-Time Engine Controller Evaluation 3.1. Design Requirements and Specifications The HIL platform is desired to perform two main tasks:
• •
Rapid prototyping and validation of the engine control system for either traditional or hybrid (mainly micro/mild hybrid) propulsion mechanisms. Functional and non-functional testing of different ISS-based engine control schemes, such as timer-based ISSC and prediction-based ISSC.
To meet these requirements, the following specifications need to be considered when designing the HIL system:
•
•
•
Rapid control prototyping tool chain: it is necessary to equip the HIL setup with a fast, efficient and flexible way of deploying, calibrating and validating of the developed control systems in a real-time environment. It also necessarily offers a re-use of plant models, control logics and data when moving between model-in-the loop (MIL) simulations and HIL experiments. A graphic user interface (GUI) is needed to allow control developers to interact with the HIL system easily. In addition, functions for automatically performing experiments and data acquisition are essential to minimize the development time and efforts. Therefore, the HIL should be equipped with proper rapid control prototyping (hardware and software) tool chains. Proper powertrain model for validation and analysis: in order to achieve reliable test results compared to the actual machine performance, it is required to use a high fidelity powertrain model which represents well the simulated system and is executable in real time to perform a closed-loop virtual reality environment.
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Real-time communication: to investigate the capability of the ICE control methods in actual working conditions, a CAN bus interface needs to be used for the data interaction between the powertrain model and control systems. Energies 2017, 10, 1250
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3.2. HIL Platform Design 3.2. HIL Platform Design
3.2.1. HIL Architecture 3.2.1. HIL Architecture
From the requirement and specification analysis, the HIL platform has been built in the HIL From the requirement and specification analysis, the HIL platform has been built in the HIL Control Laboratory at WMG—the University of Warwick using dSPACE machines as shown in Figure 5. Control Laboratory at WMG—the University of Warwick using dSPACE machines as shown in Here, a MicroAutoBox (MAB) with Real Time Interfaces (RTI) Blocksets (dSPACE, Wixom, MI, USA) Figure 5. Here, a MicroAutoBox (MAB) with Real Time Interfaces (RTI) Blocksets (dSPACE, Wixom, and Simulink Coder of MATLAB code generation) (MathWorks, MA,Massachusetts, USA) are utilized MI, USA) and Simulink Coder of(automatic MATLAB (automatic code generation) (MathWorks, for building theutilized ICE speed controllers as C-codes to run on as theC-codes MAB. Each controller consists USA) are for building the ICE speed controllers to run on the MAB. Eachof the normal powerconsists controlofmode (of the traditional and the ISSC mode (can be timer-based controller the normal power control machine) mode (of the traditional machine) and the ISSC mode or prediction-based strategy). Meanwhile, a SCALEXIO with RTIamulti-blocksets (can be timer-based or prediction-based strategy). Meanwhile, SCALEXIO withand RTI ConfigurationDesk multi-blocksets and ConfigurationDesk of dSPACE, and Simulink Coder are the employed to generate the powertrain of dSPACE, and Simulink Coder are employed to generate powertrain model’s C-code for the model’stoC-code for the real system. addition, the HMI SCALEXIO represent the SCALEXIO real system.toInrepresent addition,the C-codes of theIn HMI modelC-codes and theofenvironmental model and the environmental model are generated to support the control system validation. The model are generated to support the control system validation. The MAB and SCALEXIO communicate MAB and SCALEXIO communicate via CAN buses which are based on the actual CAN interface of via CAN buses which are based on the actual CAN interface of the real machine. Next, a personal the real machine. Next, a personal computer (PC) installed ControlDesk is used to activate both the computer (PC) installed ControlDesk is used to activate both the controllers and plant model and controllers and plant model and manage the interaction between these components. Furthermore, manage the interaction between these components. the systemtool testing with data the system testing with data management is completely Furthermore, controlled by a simulation chain which management is completely controlled by a simulation tool chain which will be discussed later. will be discussed later.
Management & Monitoring via Control Desk (PC)
Engine Speed Controller – Normal Mode C-Code
Engine Speed Controller – ISSC Mode C-Code
Signal Bus (CAN)
MicroAutoBox
Signal Bus (CAN)
HMI (Driver) Model C-Code
Environment Model C-Code
ECU-Integrated Powertrain Model C-Code
SCALEXIO
Figure5.5.Structure Structure of of the Figure theHIL HILsystem. system.
3.2.2. Real-Time Powertrain Model
3.2.2. Real-Time Powertrain Model
From the results in [12], the powertrain model can be constructed using the advanced
From the results in [12], (Siemens, the powertrain model be constructed using the advanced co-simulation tool—AMESim Austin, TX, USA) can and Simulink of MATLAB. This model is co-simulation tool—AMESim (Siemens, Austin, TX, USA) and Simulink of MATLAB. This is the combination of sub-models in which the sub-models of ICE, starter, pinion-ring gear and model belt the combination sub-models which of ICE, starter, pinion-ringofgear and belt transmissions,ofexternal loads,inand ECUthe are sub-models built in AMESim while the sub-models battery, alternator external and electrical load are builtare in built Simulink using Simscape. Forsub-models the MIL simulations [12], transmissions, loads, and ECU in AMESim while the of battery,inalternator by using the black-box model generationusing modeSimscape. of AMESim,For the the AMESim set wasin packaged and electrical load are built in Simulink MIL model simulations [12], byasusing the black-box model and converted into the S-function. The major advancement of this model type is the black-box model generation mode of AMESim, the AMESim model set was packaged as the the capability of working independently in Simulink environment (without linking to AMESim), black-box model and converted into the S-function. The major advancement of this model type is resulting in the shorter simulation time and the flexibility for control development. In this study for the capability of working independently in Simulink environment (without linking to AMESim), the HIL experiments, the AMESim model set constructed in [12] is converted into another S-function (having similar control utilities as the MIL one) using real-time code generation mode of AMESim.
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resulting in the shorter simulation time and the flexibility for control development. In this study for the HIL experiments, the AMESim model set constructed in [12] is converted into another S-function Energies 2017, 10, 1250 9 of 25 (having similar control utilities as the MIL one) using real-time code generation mode of AMESim. Energies 2017,S-function 10, 1250 9 of 25 The generated is then able theSimulink Simulink sub-models either to in run The generated S-function is then abletotocombine combine with with the sub-models either to run thein the real-time mode of MATLAB/Simulink or to enable the automatic code generation of the whole model real-time mode of MATLAB/Simulink or to enable the automatic code generation of the whole model The generated S-function is then able to combine with the Simulink sub-models either to run in the to support the HIL The powertrain model is therefore completed in Simulink as depicted to support the experiments. HIL experiments. The powertrain model is therefore completed in Simulink as real-time mode of MATLAB/Simulink or to enable the automatic code generation of the whole model depicted in Figure 6. in Figure 6. to support the HIL experiments. The powertrain model is therefore completed in Simulink as depicted in Figure 6.
Figure 6. Real-time Simulinkpowertrain powertrain model and Simscape. Figure 6. Real-time Simulink modelusing usingAMESim AMESim and Simscape. Figure 6. Real-time Simulink powertrain model using AMESim and Simscape.
3.2.3. CAN Interface
3.2.3. CAN Interface
CAN interface of the research machine is developed based on the AUTomotive Open System 3.2.3.The CAN Interface
The CAN interface of the research machine is developed based on the AUTomotive Open Architecture (AUTOSAR) [23] and demonstrated via Figure 7. As a standard control process, the The CAN interface of the research machine is developed based on 7. theAs AUTomotive Open System System Architecture and demonstrated viainstrumentation Figure a(bottom standard control process, machine control (AUTOSAR) system can be[23] divided into four layers: layer, mainly Architecture (AUTOSAR) [23] and demonstrated via Figure 7. As a standard control process, the the machine control system can be divided into four layers: instrumentation (bottom layer, mainly consisting of machine actuators and sensors), low component control (electronic control units for machine control system can be divided into four layers: instrumentation (bottom layer, mainly consisting of machine actuators and sensors), low component control control units basic functionalities and safety operations), supervisory control (such as the(electronic ICE speed controller and for consisting of machine actuators and sensors), low component control (electronic control units for for low control loop interaction) and planning (top layer, including HMIcontroller for task and basicfunctions functionalities and safety operations), supervisory control (such as the ICE speed basic functionalities and safety operations), supervisory control (such as the ICE speed controller and assignment, performance visualizationand and data management). Due to HMI the use of network functions for low loop interaction) (top layer, forHMI task assignment, functions forcontrol low control loop interaction)planning and planning (top including layer, including for task communication, the sampling periods are defined differently as the orders of 10 millisecond seconds performance visualization and data management). Due to the use assignment, performance visualization and data management). Dueoftonetwork the use communication, of network depending on the process control levels (increased with the rise of control level) to eliminate the the sampling periods defined differently as the ordersasofthe 10orders millisecond secondsseconds depending communication, theare sampling periods are defined differently of 10 millisecond network traffic as well as to maximize the overall machine performance. depending on the process control levels (increased with the rise of control level) to eliminate the as on the process control levels (increased with the rise of control level) to eliminate the network traffic network traffic as well as to maximize the overall machine performance. well as to maximize the overall machine performance. Cycle Period [s]
Cycle Period [s] Ignition Commands
Planning
Joysticks’ Commands Ignition Commands Pedals’ Commands
Planning
Joysticks’ Commands
M-CAN1
Supervisory Control
Pedals’ Commands Engine Speed Controller
M-CAN1
Supervisory Control
Engine Speed Controller
Component Control Component Control Instrumentation Instrumentation
Electronic Control Units Electronic Control Units Machine Actuators & Sensors Machine Actuators &0 Sensors
M-CAN2 M-CAN2
Figure 7. Hierarchical decomposition and CAN structure of the0 machine control system. FigureHierarchical 7. Hierarchical decompositionand and CAN CAN structure ofofthe control system. Figure decomposition structure themachine machine system. To deal 7. with the complex machine structure with thousands of points,control the communication module of the real machine is managed by two database CAN (dbc) files named M-CAN1 and To deal with the complex machine structure with thousands of points, the communication M-CAN2 using the standardmachine vehicle CAN protocol, (Society of Engineers standard) To deal with the complex structure withSAE thousands of Automotive points, the communication module module of the real machine is managed by two database CAN (dbc) files named M-CAN1 and These two dbc files are employed to define nodes with messages and their constituent signals of theJ1939. real machine is managed by two database CAN (dbc) files named M-CAN1 and M-CAN2 M-CAN2 using the standard vehicle CAN protocol, SAE (Society of Automotive Engineers standard) J1939. These two dbc files are employed to define nodes with messages and their constituent signals
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using the standard vehicle CAN protocol, SAE (Society of Automotive Engineers standard) J1939. These two dbc files are employed to define nodes with messages and their constituent signals of the top two layers and the bottom two layers in Figure 7, respectively. Hence, the M-CAN1 and M-CAN2 are utilized for the intercommunication of the HIL system. The signal formats of the plant model’s torque, speed and fuel consumption rate outputs and the HMI outputs required for the ICE speed controller (as its inputs) can be obtained directly from the messages of the two CAN buses. Meanwhile to support the utilization of the proposed ISSC approach, the following message is created and stored in the M-CAN1 for the ICE speed controller outputs:
• •
• • • •
The message called ISSC_cmds contains five signals which are sent to the ECU to drive the engine: ICE_crank_cmd, ICE_brake_cmd, ICE_ISS_enable, ICE_mode_cmd and ICE_speed_cmd. ICE_crank_cmd (0 or 1, 1-bit length): a rising edge from 0 to 1 will activate the starter and injectors to crank the engine from zero to idle speed or working speed; on the contrary, a falling edge from 1 to 0 will shut down the engine. ICE_brake_cmd (0 or 1, 1-bit length): a value of 1 will lock the engine shaft during the STO mode (ICE_crank_cmd = 0) and, vice versa. ICE_ISS_enable (0 or 1, 1-bit length): a value of 1 will active the ISS-based engine control mode and, vice versa. ICE_mode_cmd (0, 1 or 2, 2-bit length): the engine will run in NOR, IDL or STO mode if the value of ICE_mode_cmd is in turn 2, 1 or 0. ICE_speed_cmd (0 to 100, 8-bit length): the ICE torque command is computed using the following relation (please note that, value of 1 corresponds to the maximum speed command): q ICE_speed_cmd =
JX12 + JX22 + JY12 + JY22 2
+
AP × (100 − BP) 2
(5)
where: JX1 , JY1 , JX2 and JY2 are in turn the signals from the two 2-axis joysticks (within range [0, 100], to drive the hydraulic actuators of the machine); AP and BP are in turn the signals of the acceleration and brake pedals (within range [0, 100], to control the machine traction). In addition, for the energy and emission performance analysis, another message named “ICE_emiss” is generated in the M-CAN2 for the plant model emission outputs. In summary, all the necessary messages and signals employed for the HIL system are summarized in Table 2. Table 2. CAN message-signal design for HIL system. Message Name
Signal Name
Signal Range (-)
Resolution (bit)
Cycle Period (ms)
Display
M_Ign_key
0/1/2
2
500
Ignition key
0/1/2
H_ECU_LJ
JS1X_pos JS1Y_pos
0–100 0–100
8 8
100 100
Joystick 1 X angle percent Joystick 1 Y angle percent
0–100% 0–100%
H_ECU_RJ
JS2X_pos JS2Y_pos
0–100 0–100
8 8
100 100
Joystick 2 X angle percent Joystick 2 Y angle percent
0–100% 0–100%
H_ECU_P
Acc_pedal Brk_pedal
0–100 0–100
8 8
100 100
Acceleration pedal angle percent Brake pedal angle percent
0–100% 0–100%
ISSC_cmds
ICE_crank_cmd ICE_brake_cmd ICE_ISS_enable ICE_mode_cmd ICE_speed_cmd
0/1 0/1 0/1 0/1/2 0–100
1 1 1 2 8
100 100 100 100 100
ICE cranking command ICE brake command ISS control enable ICE mode command ICE speed command percent
0/1 0/1 0/1 0/1/2 0–100%
L_ECU_E
ICE_speed
0–8032
16
20
ICE speed response
0–8032 rpm
L_ECU_E
ICE_torque
0–100
8
20
ICE torque response in percent
0–100%
L_ECU_E
ICE_Fuel_rate
0–100
16
20
ICE fuel consumption rate
0–100 g/s
Signal Mean
Physical Range & Unit
M-CAN1
M-CAN2
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Table 2. Cont. Message Name
Signal Name
Signal Range (-)
Resolution (bit)
Cycle Period (ms)
Signal Mean
Physical Range & Unit
ICE_emiss
ICE_Emis_CO2 ICE_Emis_CO ICE_Emis_HC ICE_Emis_NOx ICE_Emis_Soot
0–350 0–350 0–350 0–350 0–350
8 8 8 8 8
20 20 20 20 20
ICE CO2 emission ICE CO emission ICE HC emission ICE NOx emission ICE Soot emission
0–350 g/s 0–350 mg/s 0–350 mg/s 0–350 mg/s 0–350 mg/s
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The cycle periods of all the signals are defined based on the machine control hierarchy (Figure 7) The cycle periods of all the signals are defined based on the machine control hierarchy and the actual machine’s CAN format, M-CAN1 and M-CAN2. (Figure 7) and the actual machine’s CAN format, M-CAN1 and M-CAN2. 3.2.4. HIL3.2.4. Platform HIL Platform to the makeHIL the HIL platformcloser closer to environment, it has been structured In order In toorder make testtest platform toreal realmachine machine environment, it has been structured as displayed in Figure 8a. Two CAN channel sets (each set contains a CAN High and a CAN as displayed in Figure 8a. Two CAN channel sets (each set contains a CAN High and Low a CAN Low channel) have been selected from both the MAB and SCALEXIO to exchange the information between channel) have been selected from both the MAB and SCALEXIO to exchange the information between these machines. these machines.
Test Case Selection
CAN Monitoring
Data Monitoring / Recording
PC CANalyzer
MAB
SCALEXIO CAN1H CAN1L
{ISSC_cmds}
CAN2H {H_ECU_LJ; H_ECU_RJ; H_ECU_P}
CAN2L
{L_ECU_E; ICE_emiss}
(a) Platform structure
SCALEXIO MAB
Break-Out Boxes
CANalyzer
(b) Actual platform setup Figure 8. Hardware-in-the-loop test platform.
Figure 8. Hardware-in-the-loop test platform.
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As a closed-loop control system, the controller embedded in the MAB sends all the ICE driving commands to the plant modelsystem, embedded in the SCALEXIO viainCAN1 set and receives all the HMI As a closed-loop control the controller embedded the MAB sends all the ICE driving signals and to the torque-speed responses viaSCALEXIO CAN2 set. via Breakout ofreceives both theallMAB and commands theICE plant model embedded in the CAN1 boxes set and the HMI SCALEXIO perform theresponses connection. ensureset. the Breakout real-time boxes operation as well network signals and are theused ICE to torque-speed viaToCAN2 of both theas MAB and traffic, the communication statusthe between these two devices arereal-time monitored using aas CANalyzer which SCALEXIO are used to perform connection. To ensure the operation well as network is connected to the SCALEXIO sendsthese all the message their states to the PC. traffic, the communication statusand between two devicestransmissions are monitoredand using a CANalyzer which The actual HIL is finally demonstrated Figure 8b. transmissions and their states to the PC. is connected to setup the SCALEXIO and sends allin the message The actual HIL setup is finally demonstrated in Figure 8b. 3.2.5. Simulation Tool Chain for MIL and HIL Testing 3.2.5. Simulation Tool Chain for MIL and HIL Testing In order to minimize the user time and efforts, a simulation tool chain with a common user In order to minimize the user and efforts, simulation tool user interface is necessary to enable userstime to configure anda perform tests viachain eitherwith MILaorcommon HIL method interface is necessary to enable users to configure and perform tests via either MIL or HIL method automatically. In this research, a simulation tool chain (shortened as STC) has been generated using automatically. this research, a simulation chaingeneration (shortenedand as STC) has been generated MATLAB and In Python scripts for automatic tool test case automatic testing on bothusing MIL MATLAB Python scripts for automatic and automatic testing on both MIL and and HIL and platforms. The users do not test needcase to generation interact directly with ConfigurationDesk HIL platforms. The users do not needto toperform interact the directly with ConfigurationDesk ControlDesk applications of dSPACE HIL tests. The structure of thisand STCControlDesk is described applications in Figure 9. of dSPACE to perform the HIL tests. The structure of this STC is described in Figure 9. START
Defined Working Cycles
Working Cycle Generation
User to Input
Plant Models & Controllers Project Navigator & Creation Yes
MIL Test?
No
Parameter Initialisation
ConfigurationDesk & ControlDesk Start
MIL Simulation Execution
GUI Generation in ControlDesk
Data Monitoring ?
Yes
Parameter Initialisation
No Result Plotting & Capturing
Data Visualisation & Capturing
Real-Time Experiment Execution
Data Analysis & KPI Table Generation Data Formatting & Saving
END
Figure 9. Working principle of the simulation tool chain. chain.
As shown in Figure 9, the STC firstly enables the user to input some key parameters, such as number of test cases, number of working cycles, control modes and model options. The STC allows
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As shown in Figure 9, the STC firstly enables the user to input some key parameters, such as number of test cases, number of working cycles, control modes and model options. The STC allows working cycles to be defined in a simple excel file format and to be re-used without adaptation in 1250 13 of 25 both MILEnergies and 2017, HIL10,phases of development. By selecting a specific working cycle, corresponding test casesworking are automatically generated based on the defined number of test cases (discussed in the cycles to be defined in a simple excel file format and to be re-used without adaptation in next section). All and theHIL projects generated By files are then created and organized in a default both MIL phasesand of development. selecting a specific working cycle, corresponding test format. cases are automatically generated based on thetest, defined number ofsimulations test cases (discussed in the next Next, depending on the selection of MIL or HIL closed-loop are performed within the section). All the projects and generated files are then created and organized in a default format. Next, MATLAB/Simulink environment or real-time experiments are performed using dSPACE machines, depending on the selection of MIL or HIL test, closed-loop simulations are performed within the respectively. A flag-based control logic is implemented to ensure the synchronous operation between MATLAB/Simulink environment or real-time experiments are performed using dSPACE machines, the MAB respectively. and SCALEXIO. For each MIL the sampling rate the is fixed at 10 ms while,between for the HIL test, A flag-based control logictest, is implemented to ensure synchronous operation the communication defined by filessampling and CAN interface (introduced 3.2.3). the MAB and is SCALEXIO. For the eachtwo MIL dbc test, the rate is fixed at 10 ms while, forin theSection HIL test,data the monitoring communication is defined the two during dbc filesthe and CAN interface with (introduced in platform, In addition, functions are by supported tests. Especially the HIL Section 3.2.3) In addition, data monitoring functions are supported during the tests. Especially with a GUI consisting of a basic control panel for the user inputs (as demonstrated in Figure 10a) and a the HIL platform, a GUI consisting of a basic control panel for the user inputs (as demonstrated in standardFigure graph10a) setand of ainputs and outputs of the system plant and controller (as demonstrated in standard graph set of inputs and outputs of the system plant and controller (as Figure 10b–d) is self-generated to allow the user to to interact directly thedirectly HIL machines if necessary demonstrated in Figure 10b–d) is self-generated allow the user towith interact with the HIL machines if necessary (such as to manually re-configure the platform or do specific tests). Finally, all collected (such as to manually re-configure the platform or do specific tests). Finally, all the results are the results are collected and analyzed using pre-defined key performance indicators (KPIs). These and analyzed using pre-defined key performance indicators (KPIs). These analyzed and standardized analyzed and standardized results are stored using MAT-file formats in MATLAB for an easy of results are stored using MAT-file formats in MATLAB for an easy of access and reuse. access and reuse.
(a) GUI control panel
M_Ign_key Signal
JS1Y_pos Signal
Acc_pedal Signal
Brk_pedal Signal
(b) Message status and plots of HMI inputs
Figure 10. Cont.
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ICE_speed_cmd Signal
ICE_crank_cmd Signal
ICE_mode_cmd Signal
ICE_brake_cmd Signal
ICE_ISS_enable Signal
(c) Message status and plots of controller outputs
ICE_speed Signal
ICE_torque Signal
ICE_Fuel_rate Signal
ICE emission Signals
(d) Message status and plots of ICE responses and emissions Figure 10. GUI designed for real-time experiment using HIL platform.
Figure 10. GUI designed for real-time experiment using HIL platform.
4. HIL Experiments and Analysis
4. HIL Experiments and Analysis 4.1. Setup of Comparative ICE Speed Controllers and Test Case Definition The accuracy of the plant model has been validated in the part A of this study [12] and therefore, 4.1. Setup of Comparative ICE Speed Controllers and Test Case Definition
it can be used for the development of the ICE control algorithm. In order to evaluate the capability of
proposed approach, other ICEvalidated speed controllers selected for a comparative The the accuracy ofcontrol the plant model hastwo been in thehave partbeen A of this study [12] and therefore, study with the PISSC. These two controllers tagged as CONC and TISSC are in turn denoted the it can be used for the development of the ICE control algorithm. In order to evaluate the capability of conventional controller, the timer-based idle-stop-start controller. The CONC is the simple open-loop the proposed control approach, other two speed controllers havemoving been selected comparative control algorithm which is currently usedICE for the target machine without the enginefor intoa high study with the PISSC. These two controllers tagged as CONC and TISSC are in turn the idle or stop/start mode. In this case, the ICE output power is only driven by the HMI commands. denoted The TISSC is upgraded from the CONC in which the ICE idle-stop-start function is added using the conventional controller, the timer-based idle-stop-start controller. The CONC is the simple open-loop timer-based controlislogics described byfor the the variable TMR in the RBC (Sectionmoving 2.2.3). Functionalities control algorithm which currently used target machine without the engine into high of these controllers and their main logics are summarized in Table 3. idle or stop/start mode. In this case, the ICE output power is only driven by the HMI commands. The TISSC is upgraded from the CONC in which the ICE idle-stop-start function is added using the timer-based control logics described by the variable TMR in the RBC (Section 2.2.3). Functionalities of these controllers and their main logics are summarized in Table 3.
Table 3. Functionalities of comparative ICE speed controllers. Controller Name
Working Modes
Controller Meaning
CONC
Conventional controller
TISSC
Timer-based ISS controller
PISSC
Prediction-based ISS controller
Normal Idle √ √ √
√
√
Algorithm
Stop
Start
√
√
√
√
Traditional logics without ISS Timer-based logics Prediction-based logics
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For the control system investigation, a working cycle consisting of all the four working modes defined from [12] is employed and described in Table 4. As shown in this table, each row is a machine phase in the cycle corresponding to a specific working mode (which is automatically identified by the controller) while the columns are the timestamps of phases’ starting, loads and HMI signals. An excel file with similar number of rows and columns is then built to represent this table. Next, two test scenarios have been then generated:
•
•
The first test scenario: the defined cycle is used to create NC continuously and repeatedly working cycles. Here, the first three phases of the defined cycle are only executed one time as they represent the operator first time entering the machine to turn it on manually via the ignition key. The remained phases are then repeated NC times to perform the first test case with NC cycles (the phase number 18 of cycle n-th is connected to phase 4 of cycle (n + 1)-th). This scenario is utilized to investigate the differences between MIL results and real-time HIL results, and to compare the performance of the PISSC to the performances of the other control methods. The second test scenario: a set of NT new working cycles are randomly generated from the one defined in Table 4 and stored in additional NT excel files in such a way that their first three phases and the total cycle durations are the same while the other 15 phases, 4 to 18 (or row 5th to row 19th in Table 4), are randomly mixed for different sequences with different phases’ durations. These new cycles are then employed to construct NT corresponding test cases based on the same principle as the first scenario. This scenario is utilized to assessing the energy/emission saving capability of the proposed ICE control approach in different conditions. Table 4. A simulated working cycle for heavy excavator. Phase No.
Phase Time Stamp (s)
Machine Key 1
Mech Load 2 M (%)
Elec Load 3 E (%)
JX 1 (%)
JX 2 (%)
JY 1 (%)
JY 2 (%)
AP (%)
BP (%)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
0.01 3 4 5 10 15 20 25 30 40 45 50 70 75 85 90 110 115
0 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
0 0 0 50 50 50 50 50 100 100 50 0 20 20 20 0 20 50
0 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
0 0 0 100 0 100 0 100 0 20 20 0 100 20 0 0 100 0
0 0 0 0 100 100 0 100 0 20 20 0 100 20 0 0 0 100
0 0 0 100 0 0 100 0 0 0 0 0 0 0 0 0 0 100
0 0 0 0 100 0 100 0 0 0 0 0 0 0 0 0 100 0
0 0 0 0 0 0 0 0 100 50 0 0 0 100 50 0 0 0
0 0 0 100 100 100 100 0 0 30 100 100 100 0 30 100 100 100
1
Machine key is with three positions: 0 (machine off), 1 (machine on), 2 (manual ignition); 2 Simulated load applied to ICE shaft = ICE internal load (inertia and frictions come from the model) + M × A pre-defined external load/100; 3 Simulated electrical load = E × A predefined electrical load/100.
4.2. Correlation between MIL and HIL Results In this section, as the first stage of evaluating the machine performance and energy/emission saving potential using the designed simulation tool chain, the comparison between MIL and HIL tests has been carried out to ensure the reliability of the results. For this purpose, the first test scenario (defined in Section 4.1) was selected to simulate the system work tasks and loads while only one control option—the PISSC was chosen to drive the plant model. The total simulation time was set to 1558 s (NC = 14). The simulation and real-time experiments with the same test case using the MIL and HIL platforms, respectively, were then performed. For the correlation examination, a continuous data set over one cycle period was extracted from the test results. In this case to show the uninterrupted working profile by repeating the defined cycle, data of cycle 7th from phases 6 to 18 and a part of cycle
Acc. & Brake [%] Joystick 1 [%] Joystick 2 [%] Joystick 2 Difference [%] Joystick 1 Difference [%] Acc. & Brake Difference [%]
(defined in Section 4.1) was selected to simulate the system work tasks and loads while only one control option—the PISSC was chosen to drive the plant model. The total simulation time was set to 1558 s (NC = 14). The simulation and real-time experiments with the same test case using the MIL and HIL platforms, respectively, were then performed. For the correlation examination, a continuous data set Energies 2017, 10, 1250 16 of 25 over one cycle period was extracted from the test results. In this case to show the uninterrupted working profile by repeating the defined cycle, data of cycle 7th from phases 6 to 18 and a part of cycle 8th (phases 5) been haveselected been selected and extracted from the whole test results. And theinputs HMI 8th (phases 4 and 45)and have and extracted from the whole test results. And the HMI inputs of thiswere cyclefirstly werecompared firstly compared as shown in 11. Figure 11. As in this at figure, at 770,18th phase of this cycle as shown in Figure As seen in seen this figure, 770, phase of 18th of cycle 7th was automatically linked to phase 4th of cycle 8th to perform the continuous test. cycle 7th was automatically linked to phase 4th of cycle 8th to perform the continuous test. The figure The alsothat indicates that the inputs (acceleration pedal, brake andsignals) joystickofsignals) the also figure indicates the inputs (acceleration pedal, brake pedal andpedal joystick the twooftests two tests quite similar. were small only some small during differences duringtransitions, the signal varied transitions, were quitewere similar. There wereThere only some differences the signal from varied to 7% of thesignal maximum signal level (100%). These were the signal delays by 0.4% tofrom 7% of0.4% the maximum level (100%). These were the signal delays caused by thecaused different the different settingsrates of sampling rates and HIL tests (0.1 s as forshown HMI signals, settings of sampling of the MIL (0.01ofs)the andMIL HIL(0.01 testss) (0.1 s for HMI signals, in Table as 2). shown in Table 2). Next, the controller outputs and simulated ICE performances were carefully Next, the controller outputs and simulated ICE performances were carefully analyzed in order to analyzed in order to evaluate theMIL correlation between evaluate the correlation between and HIL results.MIL and HIL results. 100 MIL - Acc. HIL - Acc. MIL - Brake HIL - Brake
75 50 25 0 5 0 -5 MIL-HIL Acc. Difference; 100
MIL-HIL Brake Difference MIL - JS1X MIL - JS1Y HIL - JS1X HIL - JS1Y
75 50 25 0 5 0 -5
MIL-HIL JS1X Difference;
MIL-HIL JS1Y Difference
100
MIL - JS2X MIL - JS2Y HIL - JS2X HIL - JS2Y
75 50 25 0 5 0 -5 670
MIL-HIL JS2X Difference; 680
690
700
MIL-HIL JS2Y Difference 710
720 730 Time [s]
740
750
760
770
780
Figure 7th. Figure 11. 11. Comparison Comparison of of HMI HMI inputs inputs and and loads loads of of MIL MIL and and HIL HIL tests tests during during cycle cycle 7th.
4.2.1. 4.2.1. PISSC PISSC Outputs Outputs Correlation Correlation The PISSC outputs outputs of of the theMIL MILand andHIL HILtests testsduring duringthe theextracted extracted period were firstly evaluated The PISSC period were firstly evaluated as as shown in Figure 12. As seen in the second sub-plot from the top of this figure, the MIL and HIL shown in Figure 12. As seen in the second sub-plot from the top of this figure, the MIL and HIL ICE ICE speed commands slight differences which were similar to those of the HMI signals (plotted speed commands had had slight differences which were similar to those of the HMI signals (plotted in Figure 11). This was because the ICE speed command was derived directly from the HMI commands (using Equation (5)). Therefore, the differences only appeared during the transitions of speed command set points caused by the changes of HMI commands. Meanwhile, the other control outputs, including IEC cranking command, ICE brake command and ICE mode command (as depicted in the top and the last two sub-plots of Figure 12), were calculated using the PISSC logics (Section 2.2) based on the feedback ICE torque and speed. Different torque and speed profiles would lead to the different decisions on the control outputs and will be discussed in the next section. Herein, the ICE mode commands were assigned with values of 0, 1 and 2 which represented the STO, IDL and NOR modes (as shown in the bottom sub-plot of Figure 12, the STA mode was automatically integrated into NOR or IDL modes).
Speed Command [%] Cranking Command [-]
depicted in the top and the last two sub-plots of Figure 12), were calculated using the PISSC logics (Section 2.2) based on the feedback ICE torque and speed. Different torque and speed profiles would lead to the different decisions on the control outputs and will be discussed in the next section. Herein, the ICE mode commands were assigned with values of 0, 1 and 2 which represented the STO, IDL and NOR Energies 2017,modes 10, 1250 (as shown in the bottom sub-plot of Figure 12, the STA mode was automatically 17 of 25 integrated into NOR or IDL modes). 1.0
0.5
0.0 120
MIL HIL MIL HIL MIL-HIL Difference
100 80 60 40 20 0
Brake Command [-]
1.0
Mode Command [-]
-20
2.0
MIL HIL
0.5
0.0
1.5 1.0 0.5 0.0 670
MIL HIL 680
690
700
710
720
730
740
750
760
770
780
Time [s]
Figure Figure 12. 12. Comparison Comparison of of PISSC PISSC outputs outputs of of MIL MIL and and HIL HIL tests tests during during cycle cycle 7-th. 7-th.
4.2.2. 4.2.2. ICE ICE Dynamics Dynamics Correlation Correlation Next, Next, in in order order to to understand understand the the control control decisions decisions as as well well as as the the resulted resulted ICE ICE performances, performances, the the correlation correlation between between MIL MIL and and HIL HIL ICE ICE dynamics dynamics were were analyzed analyzed in in Figure Figure 13. 13. Compared Compared to to the the MIL MIL result, some delays delays in in the the real-time real-time simulated simulated ICE ICE speed, speed, especially especially during during the the cranking cranking result, there there were were some periods. the ICE speed response waswas about 2 s at speedspeed level.level. This periods. The Themaximum maximumdelay delayinin the ICE speed response about 2 the s atlimited the limited problem came from the uses of sampling rates. The sampling rate was fixed at 10 ms for the MIL This problem came from the uses of sampling rates. The sampling rate was fixed at 10 ms for test the while in while the HIL the test, largethe sampling rates were used for the for communication between the MAB MIL test in test, the HIL large sampling rates were used the communication between the (100 ms for the controller outputs) and SCALEXIO (20 ms for the plant model outputs) which MAB (100 ms for the controller outputs) and SCALEXIO (20 ms for the plant model outputs) which represented theactual actual CAN architecture and setting of machine the real (as machine (asin discussed in represented the CAN architecture and setting of the real discussed Section 3.2.3). Section 3.2.3). As a result, this led to similar delay phenomena in the HIL-ICE torque (the second As a result, this led to similar delay phenomena in the HIL-ICE torque (the second sub-plot of Figure 13) sub-plot of Figure 13) fuel and,consumption therefore, the consumption (the sub-plot and, therefore, the HIL rateHIL (the fuel bottom sub-plot of rate Figure 13).bottom In addition, due of to Figure 13). In addition, due to the slower change of speed command, a slower speed response of the the slower change of speed command, a slower speed response of the ICE in the HIL test compared to ICE in the HIL test compared to the MIL test resulted, and the simulated internal torques of ICE the MIL test resulted, and the simulated internal torques of ICE model, including inertia and friction model, inertia friction lowerthe than those of of the MIL test ICE [9]. summing Thus, the torques,including could lower thanand those of thetorques, MIL testcould [9]. Thus, amplitude simulated amplitude of simulated ICE summing torque during HIL cranking could be lower than the MIL770 one,s. torque during HIL cranking could be lower than the MIL one, for example the period around for example period around 770 s. These werethe sent back to the PISSC to These torquethe and speed signals were sent torque back toand thespeed PISSCsignals to derive control outputs of the derive the control outputs of the coming step. Subsequently, the ICE cranking, mode and brake coming step. Subsequently, the ICE cranking, mode and brake commands of the HIL and MIL tests commands of had the HIL and MIL tests (in Figure 12) hadICE lower consistency compared to the ICE speed (in Figure 12) lower consistency compared to the speed commands. commands.
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ICE Speed [rpm]
2000 1500 1000 500 0
MIL HIL
Fuel Consumption Rate [g/s]
ICE Torque [Nm]
200 150 100 50 0 -50 -100 4
MIL HIL MIL HIL
2
0 670
680
690
700
710
720
730
740
750
760
770
780
Time [s]
Figure cycle 7th. 7th. Figure 13. 13. Comparison Comparison of of ICE ICE dynamics dynamics of of MIL MIL and and HIL HIL tests tests during during cycle
4.2.3. 4.2.3. ICE ICE Emissions’ Emissions’ Rates Rates Correlation Correlation Finally, the correlation of ICE ICE emissions’ emissions’ rates rates between between the the MIL MIL and and HIL HIL tests tests has has been been carried carried Finally, the correlation of out out and and plotted plotted in in Figure Figure 14. 14. Like Like the the fuel fuel consumption consumption rate rate variation, variation, the the HIL HIL emission emission results results were were mostly similar to the MIL results but contained transition delays (maximum around 2 s during mostly similar to the MIL results but contained transition delays (maximum around 2 s during each each cranking phase). It also pointed out that, by using the proposed idle-stop-start control logics, the cranking phase). It also pointed out that, by using the proposed idle-stop-start control logics,fuel the consumption rate rate (the(the bottom sub-plot of Figure 13)13) could bebeeliminated fuel consumption bottom sub-plot of Figure could eliminatedand andtherefore, therefore, the the zero zero emissions couldbe beachieved achievedduring duringthe the periods that machine worked and without emissions could periods that machine worked withwith low low loadload and without HMI HMI commands. The correlation between MIL and HIL results were then summarized in Table 5. commands. The correlation between MIL and HIL results were then summarized in Table 5. From this From table, it can bethe seen that the largest differences from theICE simulated table, this it can be seen that largest differences came from came the simulated speeds ICE and speeds torque and due torque due to the used of actual CAN communication method. However, it did not impact heavily to the used of actual CAN communication method. However, it did not impact heavily on the ICE on the ICE fuel consumptions and emissions. This was because the main signal delays only came fuel consumptions and emissions. This was because the main signal delays only came from the large from the state large transitions machine state transitions duringphases. the cranking phases. The average fueland consumption machine during the cranking The average fuel consumption emissions and emissions differences of the two test methods varied around 1.9% to 3.7%, except the NOx and differences of the two test methods varied around 1.9% to 3.7%, except the NOx and Soot emissions. Soot emissions. Amongthe the emissions, the difference in NOx levels was the highest with 6.693% Among the emissions, difference in NO x levels was the highest with 6.693% although the NOx although the NO x emission rate change was quite similar to the others (as shown in Figure 14). emission rate change was quite similar to the others (as shown in Figure 14). Meanwhile the Soot levels’ Meanwhile the negative Soot levels’ difference wasreason negative −2.206%. The reason these emissions were difference was at − 2.206%. The wasatthese emissions were was highly emitted during an highly emitted during an ICE cranking phase. With a typical diesel engine, retarding fuel injection ICE cranking phase. With a typical diesel engine, retarding fuel injection timing can effective reduce timing can effective reduce NO formation. However, this results in an increase of soot production, NO formation. However, this results in an increase of soot production, and vice versa [24]. Therefore, and vice versa [24]. Therefore, the large mismatch between the MIL and HIL cranking resulted in the the large mismatch between the MIL and HIL cranking resulted in the large variations of both NOx large variations of both NOx and Soot levels in the opposite direction. and Soot levels in the opposite direction. Table 5. Summary of correlation between MIL and HIL results during cycle 7th. Table 5. Summary of correlation between MIL and HIL results during cycle 7th. Factor
Factor’s Unit
MIL Test
HIL Test
HMI commands HMI commands ICE cranking command ICE cranking command ICE spee command ICE brake command ICE mode command ICE speed
% % %% % % % rpm
Followed commands Followed commands Followed command Followed command Followed command Followed command Followed command Faster response
Followed commands Followed commands Followed Followedcommand command Followed command Followed command Followed command Slower response
Factor
Factor’s Unit
MIL Test
HIL Test
Average Difference between Average MILDifference and HIL between MIL and HIL 0.699% (RMSE) 0.699% (RMSE) 0.101% (RMSE) 0.101% (RMSE) 1.428% (RMSE) 0.101% (RMSE) 0.209% (RMSE) 268.273 rpm (RSME)
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Table 5. Cont. Factor Energies 2017, 10, 1250
Total
Factor’s Unit
ICE spee command ICEtorque brake command ICE ICE mode command fuel consumption ICE speed torque TotalICE CO2 Total fuel consumption TotalTotal COCO2 TotalTotal HC CO Total Total NOxHC Total NOx TotalTotal SootSoot
% % Nm % L rpm gNm L mgg mg mg mg mg mg mg mg
MIL Test
HIL Test
Followed command Followed command Faster response Followed command 1.617 Faster response Faster response 4237.597 1.617 100,858.124 4237.597 17,099.2 100,858.124 17,099.2 48,62.868 48,62.868 189.162 189.162
Followed command Followedresponse command Slower Followed command 1.577 Slower response Slower response 4133.869 1.577 97,109.689 4133.869 16,764.519 97,109.689 16,764.519 4537.388 4537.388 193.336 193.336
Average Difference between MIL and HIL
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1.428% (RMSE) 0.101%Nm (RMSE) 31.021 (RSME) 0.209% (RMSE) 2.448% (Relative) 268.273 rpm (RSME) 31.021 Nm(Relative) (RSME) 2.448% 2.448% (Relative) 3.717%(Relative) 2.448% (Relative) 1.957% (Relative) 3.717%(Relative) 1.957% (Relative) 6.693% (Relative) 6.693% (Relative) −2.206% (Relative) − 2.206% (Relative)
RSME: squareerror. error. RSME:root root mean mean square
Soot Rate [mg/s]
NOx Rate [mg/s]
HC Rate [mg/s]
CO Rate [mg/s]
CO2 Rate [g/s]
12 9
MIL HIL
6 3 0 120 80 40 0 40
MIL HIL MIL HIL
20 0 15 10
MIL HIL
5 0 6 4
MIL HIL
2 0 670
680
690
700
710
720
730
740
750
760
770
780
Time [s]
Figure Figure 14. 14. Comparison Comparison of of ICE ICE emission emission rates rates of of MIL MIL and and HIL HIL tests tests during during cycle cycle 7th. 7th.
Overall, it can be concluded that the consistency between MIL and HIL results is acceptable and Overall, it can be concluded that the consistency between MIL and HIL results is acceptable and therefore, it proves the real-time applicability of the proposed control approach. therefore, it proves the real-time applicability of the proposed control approach. 4.3. 4.3. Comparison Comparison of of ICE ICE Performances Performances Using Using Different Different Controllers Controllers Next, the evaluation of the proposed control scheme Next, the evaluation of the proposed control scheme over over the the other other control control schemes, schemes, CONC CONC and and TISSC, has been performed through the HIL experiments using the first test scenario during 1558 TISSC, has been performed through the HIL experiments using the first test scenario during 1558s.s. The PISSC was was firstly firstly validated. validated. As The prediction prediction function function of of the the PISSC As presented presented in in Section Section 2.2.2, 2.2.2, the the GPM GPM was activated after 6 repeated cycles. The comparison of ICE performance during cycles 6 and 7 was activated after 6 repeated cycles. The comparison of ICE performance during cycles 6 and 7 was was carried out as in Figure 15. The result indicates that the prediction worked correctly from cycle 7th. carried out as in Figure 15. The result indicates that the prediction worked correctly from cycle 7th. The engine was then controlled by the predictor instead of the timer with 3-s threshold (Section 2.2.3). The engine was then controlled by the predictor instead of the timer with 3-s threshold (Section 2.2.3). Subsequently, the engine was shut down almost immediately after the controller detected a few Subsequently, the engine was shut down almost immediately after the controller detected a few machine states. Thus comparing to the previous cycle, the additional fuel saving during around 3 s machine states. Thus comparing to the previous cycle, the additional fuel saving during around 3 s per cycle could be achieved from the 7th cycle (as depicted in the bottom sub-plot). per cycle could be achieved from the 7th cycle (as depicted in the bottom sub-plot).
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20 of 25 20 of 25 Time Vector of Cycle 6 [s]
559
569
579
589
599
609
619
629
639
649
659
669
700
710
720
730
740
750
760
770
780
ICE Speed [rpm]
2000 1500 1000 500 0
PISSC - Cycle 6 PISSC - Cycle 7
Fuel Consumption Rate [g/s]
ICE Torque [Nm]
200 150 100 50 0 -50 -100 4
PISSC - Cycle 6 PISSC - Cycle 7 PISSC - Cycle 6 PISSC - Cycle 7
2
0 670
680
690
Time Vector of Cycle 7 [s]
Figure Figure 15. 15. Comparison Comparison of of ICE ICE dynamics dynamics of of HIL HIL tests tests using using PISSC PISSC during during cycles cycles 6th 6th and and 7th. 7th.
Next, the performance comparison between the three controllers was performed as shown in the Next, the performance comparison between the three controllers was performed as shown in following figures. Figure 16 displays the evaluation of controllers’ outputs during the working cycle the following figures. Figure 16 displays the evaluation of controllers’ outputs during the working 7th. With the CONC, the engine was cranked only one time by the operator while the ICE brake and cycle 7th. With the CONC, the engine was cranked only one time by the operator while the ICE mode commands were always set to 0 and 2 during the experiment, respectively. The ICE power brake and mode commands were always set to 0 and 2 during the experiment, respectively. The ICE output was driven by only the HMI signals, represented by the speed command, and the loads. With power output was driven by only the HMI signals, represented by the speed command, and the the TISSC, the engine can be shifted in to idle or stop mode during the “zero” HMI commands and loads. With the TISSC, the engine can be shifted in to idle or stop mode during the “zero” HMI low load conditions (such as period 713 to 724 s or 753 to 764 s). This controller used the timer logics commands and low load conditions (such as period 713 to 724 s or 753 to 764 s). This controller used to make the decisions based on the ICE states over the 3-s sliding time windows (for examples, the timer logics to make the decisions based on the ICE states over the 3-s sliding time windows periods of 710 to 713 s and 750 to 753 s). Meanwhile as explained through Figure 15, the ICE mode (for examples, periods of 710 to 713 s and 750 to 753 s). Meanwhile as explained through Figure 15, decision was made quicker (around 3 s) using the proposed method. These differences resulted in the ICE mode decision was made quicker (around 3 s) using the proposed method. These differences the different ICE dynamics as shown in Figure 17. This figure shows that the significant amounts of resulted in the different ICE dynamics as shown in Figure 17. This figure shows that the significant unnecessary energy during the machine idle could be saved through the TISSC and PISSC. Herein, amounts of unnecessary energy during the machine idle could be saved through the TISSC and PISSC. the fuel consumption was calculated by the ICE model which was based on the actual fuel Herein, the fuel consumption was calculated by the ICE model which was based on the actual fuel consumption map of the engine given in Table 1. The injector control methodology (such as injection consumption map of the engine given in Table 1. The injector control methodology (such as injection phase, time and speed) to optimize the engine consumption during low idle speed region is not the phase, time and speed) to optimize the engine consumption during low idle speed region is not the focus of this study. By using either the TISSC or PISSC, for low workloads, the engine could be shifted focus of this study. By using either the TISSC or PISSC, for low workloads, the engine could be shifted in to the IDL mode by deactivating one cylinder. Once there was no commands from the HMI and in to the IDL mode by deactivating one cylinder. Once there was no commands from the HMI and no no external loads, the engine could be fully shut down as it worked with very low efficiency and zero external loads, the engine could be fully shut down as it worked with very low efficiency and zero productivity. As a consequence, the ICE emissions, including CO2, CO, HC, NOx and Soot, could be productivity. As a consequence, the ICE emissions, including CO2 , CO, HC, NOx and Soot, could be reduced by replacing the CONC (the solid-red lines) with either the TISSC (the dot-blue lines) or the reduced by replacing the CONC (the solid-red lines) with either the TISSC (the dot-blue lines) or the PISSC (the dash-dot-magenta lines) as plotted in Figure 18. It also comes as no surprise that the PISSC PISSC (the dash-dot-magenta lines) as plotted in Figure 18. It also comes as no surprise that the PISSC could save more fuel and emissions compared to the TISSC due to the use of GPM. could save more fuel and emissions compared to the TISSC due to the use of GPM.
Mode Command Mode Command [-][-] Brake Brake Command Cranking Command Speed Command [%]Cranking Command [-][-] Speed Command [-][-] Command [%]
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1.0 1.0 0.5 0.5 0.0 0.0 120 120 100 100 80 80 60 60 40 40 20 20 0 0 -20 -20 1.0 1.0 0.5 0.5
CONC CONC TISSC TISSC PISSC PISSC
CONC CONC TISSC TISSC PISSC PISSC CONC CONC TISSC TISSC PISSC PISSC
0.0 0.0 2.0 2.0 1.5 1.5 1.0 1.0 0.5 0.5 0.0 0.0 670 670
CONC CONC TISSC TISSC PISSC PISSC 680 690 680 690
700 700
710 710
720 730 720 730 Time [s] Time [s]
740 740
750 750
760 760
770 770
780 780
Figure cycle 7th. Figure 16. 16. Comparison Comparison of of HIL-ICE HIL-ICE control control inputs inputs using using difference difference controllers controllers during during Figure 16. Comparison of HIL-ICE control inputs using difference controllers during cycle cycle 7th. 7th.
ICE Speed [rpm] ICE Speed [rpm]
2000 2000 1500 1500 1000 1000 500 500
Fuel Consumption Rate [g/s] Fuel Consumption Rate [g/s]
ICE Torque [Nm] ICE Torque [Nm]
0 0 200 200 150 150 100 100 50 50 0 0 -50 -50 -100 -100 4 4
CONC CONC TISSC TISSC PISSC PISSC CONC CONC TISSC TISSC PISSC PISSC
CONC CONC TISSC TISSC PISSC PISSC
2 2 0 0 670 670
680 680
690 690
700 700
710 710
720 730 720 730 Time [s] Time [s]
740 740
750 750
760 760
770 770
780 780
Figure 17. Comparison of HIL-ICE dynamics using difference controllers during cycle 7th. Figure during cycle cycle 7th. 7th. Figure 17. 17. Comparison Comparison of of HIL-ICE HIL-ICE dynamics dynamics using using difference difference controllers controllers during
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10
CONC TISSC PISSC
CO Rate [mg/s]
0 320
HC Rate [mg/s]
CO2 Rate [g/s]
20
90
240 160
CONC TISSC PISSC
80
Soot Rate [mg/s]
NOx Rate [mg/s]
0
60
CONC TISSC PISSC
30 0 15 10
CONC TISSC PISSC
5 0 6 4
CONC TISSC PISSC
2 0 670
680
690
700
710
720
730
740
750
760
770
780
Time [s]
Figure Figure 18. 18. Comparison Comparison of of HIL-ICE HIL-ICE emission emission rates rates using using difference difference controllers controllers during during cycle cycle 7th. 7th.
The ICE performances using the compared controllers were then numerically analyzed through The ICE performances using the compared controllers were then numerically analyzed through a a set of KPI factors, including the ICE stop/start numbers with durations, amounts of fuel used/saved set of KPI factors, including the ICE stop/start numbers with durations, amounts of fuel used/saved with their costs, and emission factors with their saving amounts, as shown in Table 6. As seen in this with their costs, and emission factors with their saving amounts, as shown in Table 6. As seen in this table, the CONC without ISS function did not perform any stop-start action. There was only 0.14 s of table, the CONC without ISS function did not perform any stop-start action. There was only 0.14 s ICE stop time because of the delay in ICE response when the machine was started the first time of ICE stop time because of the delay in ICE response when the machine was started the first time manually by the operator. With both the TISSC and PISSC, the number of ICE stop-start during the manually by the operator. With both the TISSC and PISSC, the number of ICE stop-start during the whole test was 28 (or, two times per cycle). The ICE run time was therefore reduced from 1553.87 s whole test was 28 (or, two times per cycle). The ICE run time was therefore reduced from 1553.87 s (with the CONC) to 1243.78 s (with the TISSC). Compared to the CONC performance, the TISSC (with the CONC) to 1243.78 s (with the TISSC). Compared to the CONC performance, the TISSC could could lead to a fuel savings of 0.329 L (equivalent to a saving of £0.382, while considering only lead to a fuel savings of 0.329 L (equivalent to a saving of £0.382, while considering only stop-start) and stop-start) and 3.111 L (equivalent to a saving of £3.609, while considering the impact of 3.111 L (equivalent to a saving of £3.609, while considering the impact of idle-stop-start) over the whole idle-stop-start) over the whole test. By utilizing the PISSC method, the ICE stop time could be test. By utilizing the PISSC method, the ICE stop time could be extended to 356.41 s resulting in to an extended to 356.41 s resulting in to an additional fuel saving of 20.974% compared to the TISSC additional fuel saving of 20.974% compared to the TISSC (equivalent to an additional saving of £0.118 (equivalent to an additional saving of £0.118 over 14 cycles). Furthermore, with the use of ISS control over 14 cycles). Furthermore, with the use of ISS control techniques, the emissions were remarkably techniques, the emissions were remarkably decreased. More than 43% of CO2, CO, HC and soot was decreased. More than 43% of CO2 , CO, HC and soot was cut-off while that of NOx was around 29%. cut-off while that of NOx was around 29%. This was because the engine only emitted small amounts This was because the engine only emitted small amounts of NO during low idles (with slow injection of NOx during low idles (with slow injection rate). Through all xthe KPI factors, the PISSC showed its rate). Through all the KPI factors, the PISSC showed its superiority in managing the ICE operation superiority in managing the ICE operation over both the CONC and TISSC. over both the CONC and TISSC. Table after 14 14 cycles. cycles. Table 6. 6. KPI KPI analysis analysis for for the the ICE ICE performances performances using using different different controllers controllers after KPI KPI Stop count Stop count Start count Start count Run Runtime time Stoptime time Stop Fuel used Fuel used Fuel saved Total fuel saved Fuel saved % Total fuel saved Total fuel saved % Total fuel saved 2 % Total saved Fuelfuel cost used % Total fuel saved 2 Fuel cost used Fuel cost saved Total fuel cost saved
KPI’s Unit s s s s L (Litres) LL(Litres) L L % L % %1 ) £ (Pound % £ (Pound 1) £ £ KPI’s Unit
CONC 1 1 0 0 1553.870 1553.870 0.140 0.140 4.790 04.790 0 0 0 0 0 5.5570 0 5.557 0 0 CONC
TISSC 29 29 28 28 1243.780 1243.780 310.230 310.230 1.680 1.680 0.329 3.111 0.329 64.939 3.111 0 64.939 1.948 0 1.948 0.382 3.609 TISSC
PISSC PISSC 29 29 28 28 1197.600 1197.600 356.410 356.410 1.577 1.577 0.378 3.213 0.378 67.073 3.213 20.974 67.073 1.830 20.974 1.830 0.439 3.727
Notes Notes Include initial state of ICE Include initial state of ICE Not include 1st ICE start using key Not include 1st ICE start using key Total ICE run Total ICE run timetime Total ICE timetime Total ICEstop stop Total fuel consumed by ICE Total fuel consumed by ICE Against no stop-start function Against CONC’s total fuelfunction used Against no stop-start Against CONC’s total fuel used Against CONC’s total fuel used Against TISSC’s total fuel used Against total Total fuelCONC’s cost for ICE fuel fuel usageused Against TISSC’s total fuel used Total fuel cost for ICE fuel usage Against no stop-start function Against CONC’s total fuel cost
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Table 6. Cont. KPI
KPI’s Unit
CONC
TISSC
PISSC
Notes
Fuel cost saved Total fuel cost saved Total fuel cost saved 2 CO2 emission CO2 emission reduced CO emission CO emission reduced HC emission HC emission reduced NOx emission NOx emission reduced Soot emission Soot emission reduced
£ £ £ g % g % g % g % g %
0 0 0 12,554.780 0 183.033 0 38.934 0 6.472 0 1.188 0
0.382 3.609 0 4401.885 64.939 102.813 43.828 18.485 52.522 4.582 29.211 0.284 76.083
0.439 3.727 0.118 4133.869 67.073 97.110 46.944 16.764 56.941 4.537 29.895 0.193 83.721
Against no stop-start function Against CONC’s total fuel cost Against TISSC’s total fuel cost Total ICE CO2 emission Against CONC’s total CO2 Total ICE CO emission Against CONC’s total CO Total ICE HC emission Against CONC’s total HC Total ICE NOx emission Against CONC’s total NOx Total ICE soot emission Against CONC’s total soot
1
The fuel price is calculated based upon the average diesel price in UK (£1.16/L—source [21]).
4.4. Analysis on Fuel and Emission Saving Potential Finally, to confirm the capability of the designed PISSC strategy, the fuel and esmission saving potential of the proposed ICE control aproach has been assessed through a series of real-time tests with different test cases. The second test scenario (Section 4.1) was used to generate randomly ten working cycles from the orgininal cycle (Table 4). Due to the differences in sequences and durations of the machine phases (the total cycle durations were still the same), the fuel economy and saving potential of the machine could be directly affected. By using these working cycles, the corresponding ten test cases were generated to perform the ten real-time tests with the PISSC on the HIL platform for 14 cycles. By selecting some KPI factors defined in Table 7 and defining a new KPI named “Stop/Run Ratio” (denotes the ratio between the total ICE stop time and run time in %), the simulated performances of the engine have been summarized in Table 7. The analysis pointed out that the number of ICE stop-start was significantly depended on the duration of machine in idles and the setting of timer-threshold. In this case, the ICE would not be stopped if the engine stayed less than 3 s in idle conditions without loads and HMI commands (as shown in Test 1 and Test 3). With the remained tests, the stop/run ratio was varied from a minimum of 6.948% to a maximum of 35.274%. By linking to the KPI analysis in Table 6, it can be evidently concluded that the proposed PISSC has strong ability and capability for real-time ICE control to minimize the fuel consumption and emissions of the engine. Table 7. Summary of the PISSC-based ICE performances with respect to 10 random test cases after 14 cycles. KPI
Test 1
Test 2
Test 3
Test 4
Test 5
Test 6
Test 7
Test 8
Test 9
Test 10
Stop count
1
15
1
15
15
15
15
29
15
15
Start count
0
14
0
14
14
14
14
28
14
14
Run time
1553.860
1305.400
1553.860
1316.560
1025.410
1445.760
1307.550
1195.110
1004.440
1379.350
Stop time
0.150
248.610
0.150
237.450
528.600
108.250
246.460
358.900
549.570
174.660
Stop/run ratio
0.003
15.957
0.003
15.241
33.928
6.948
15.819
23.036
35.274
11.211
Fuel used
2.285
1.911
2.284
1.928
1.463
1.979
1.828
1.592
1.434
1.939
Fuel saved
0.000
0.264
0.000
0.252
0.561
0.115
0.261
0.381
0.583
0.185
Fuel cost used
2.651
2.217
2.650
2.237
1.697
2.295
2.121
1.846
1.663
2.249
Fuel cost saved
0.000
0.306
0.000
0.292
0.650
0.133
0.303
0.442
0.676
0.215
CO2 emission
5989.410
5009.129
5986.143
5052.952
3834.291
5185.733
4791.329
4171.151
3758.256
5080.823
CO emission
149.357
123.200
149.303
124.174
93.789
124.237
116.016
97.816
92.804
123.858
HC emission
23.777
20.248
23.768
20.448
15.574
20.510
19.183
16.965
15.421
20.398
NOx emission
7.663
6.070
7.654
6.108
4.586
6.143
5.686
4.507
4.496
6.130
Soot emission
0.075
0.107
0.072
0.113
0.103
0.163
0.126
0.192
0.106
0.134
5. Conclusions As the two parts of the study on the innovative energy management strategy—PISSC for OHCMs (with the concept proposed in Part A [12]), its real-time performance has been carefully investigated
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in this Part B. The HIL test platform is properly developed using dSPACE machines and CAN communication method for the rapid design and validation of different ICE control methods in the actual real-time environment. The simulation tool chain has been built to allow the automatic generation of the MIL and HIL tests and automatic data observation and analysis. The correlation between MIL and HIL results has been firstly assessed to prove the applicability of the designed control approach. Next, the dominance of the PISSC over the conventional approach and TISSC in managing the ICE operation has been validated through the number of real-time tests with the KPI analysis. Finally, the capability of the PISSC in terms of energy and emission saving has been proven by the numerical analysis over the test series with random test cases. As the statistic calculation in [12] based on the reports of the US construction sector market [25], a 10% of fuel saving of OHCMs around the world could lead to a huge reduction of 35.42 million metric tons of CO2 evaporating into the atmosphere. From the HIL results in Table 7, an average of 15.742% of machine operation due to idle could be cut-off by using the PISSC technique. Associating this factor with the machine stop and run times in Table 6 and using a simple linear interpolation, a corresponding amount in excess of 30% of total fuel consumption could be saved by replacing the CONC with the PISSC. Hence, this result affirms convincingly that the proposed approach offers high potential for energy and emission saving targets in the off-highway sector. As a future research direction of this study, three main activities will be carried out. First, the integration of PISSC and the engine start control method developed by the authors in [9] will be taken into account for the smooth ICE start and the development of micro/mild hybrid machines. Second, a sensitivity study on the timer-threshold setting for the ICE stop decision will be done to improve the performance of the proposed control approach. Third, an 11-ton excavator has been selected to implement and validate the PISSC logics in actual construction tasks. Acknowledgments: This research is supported by Innovate UK through the Off-Highway Intelligent Power Management (OHIPM), Project Number: 49951-373148 in collaboration with the WMG Centre High Value Manufacturing (HVM), JCB and Pektron. Author Contributions: Truong Quang Dinh is the main author of this work. James Marco and Hui Niu contributed to the control logics, testing and analysis while David Greenwood contributed to powertrain architecture modelling and simulations. Lee Harper and David Corrochano supported the actual machine data, electrical component models, CAN setup and control logics. Conflicts of Interest: The authors declare no conflict of interest.
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