Transparency of a Geographically Distributed Test Platform for ... - KIT

energies Article

Transparency of a Geographically Distributed Test Platform for Fuel Cell Electric Vehicle Powertrain Systems Based on X-in-the-Loop Approach Wenxu Niu 1 , Ke Song 1,2, *, Qiwen Xiao 3 , Matthias Behrendt 3 , Albert Albers 3 and Tong Zhang 1 1 2 3

*

School of Automotive Studies, Tongji University, Shanghai 201804, China; [email protected] (W.N.); [email protected] (T.Z.) National Fuel Cell Vehicle and Powertrain System Engineering Research Center, Tongji University, Shanghai 201804, China Institute of Product Engineering (IPEK), Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany; [email protected] (Q.X.); [email protected] (M.B.); [email protected] (A.A.) Correspondence: [email protected]

Received: 15 August 2018; Accepted: 7 September 2018; Published: 12 September 2018







Abstract: X-in-the-loop is a new vehicle development and validation method for increasingly complex vehicle systems, which integrates the driver and the environment. In view of recent developments in fuel cell electric vehicle powertrain systems, Tongji University and Karlsruhe Institute of Technology have jointly developed a set of distributed test platforms based on the X-in-the-loop approach. This platform contains models and test equipment for a fuel cell electric vehicle powertrain system. Due to the involvement of remote connection and the Internet, test with connected test benches will suffer great uncertainty cause of signal transfer delay. To figure out this uncertainty, the concept of transparency is introduced. Four parameters were selected as transparency parameters in this distributed test platform. These include vehicle speed, fuel cell output power, battery output power, and electric motor torque under several different configuration settings. With the help of transparency theory and statistical methodology, especially Analysis of Variance (ANOVA), the transparency of these four parameters was established, vehicle speed, electric motor torque, battery power, and fuel cell power are affected by network state, the degree of influence is enhanced in turn. Using new defined parametric and non-parametric methods, this paper identifies the statistical significance and the transparency limitations caused by Internet under these several configurations. These methods will generate inputs for developer setting the distributed test configuration. These results will contribute to optimize the process of geographically distributed validation and joint development. Keywords: X-in-the-loop; fuel cell electric vehicle; powertrain system; distributed test platform; transparency; nonparametric detection

1. Introduction With the development of the automobile industry, the complexity of automobiles is increasing. Therefore, the development of vehicles is no longer a concern of individual companies, but has become a primary focus requiring the combined efforts of a number of companies. This is especially true in the case of electric vehicles, which require additional interdisciplinary cooperation from companies that have never dealt with the automotive industry before. Nowadays, collaboration in vehicle development can take place not only between developers in different locations, but also across companies. This multi-site product development is being studied at some research institutes as an important research field. The trend of globalization, the increasing complexity of automotive Energies 2018, 11, 2411; doi:10.3390/en11092411

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products, the rapid change in technologies [1,2] as well as the regionalization of technical expertise [2], have presented new challenges for the automotive industry. Cooperative testing and validating tests between multiple regions or countries has now become a usual practice in industry. One way of realizing such a cooperation is to connect the globally distributed testing and validation parts through the Internet, which assures agreement on what parts to assemble when distributed parts are brought together for local tests [3]. Further advantages of this method include a guarantee that participants will share confidential information eliminates inclusion of those who are unwilling to share their knowledge or prototypes with partners who realize the importance of IP cooperation. Aimed at simplifying the increasingly complex vehicle system, X-in-the-loop development and validation theory has been led by Professor Albers at the Karlsruhe Institute of Technology. He and other scholars have integrated simulation models and real components, making full use of existing tools and methods to assess the impacts of drivers and the external environment on electric vehicle requirements and development processes [3–5]. The core benefit of the theory is its ability to enable continuance when a test component is missing through models or code, which can replace the missing parts [6,7]. In this way, hardware and software integration tests were successful [8–10]. By breaking through the limitation of physical connection, the concept of “X-in-the-loop” has been extended to “X-in-the-distance-loop”, which provides an innovative and inspirational way of testing and validating in the vehicle development process, creating more possibilities for the future of the electric vehicle and other advanced vehicles in the automotive industry [3,4]. However, despite the advances, questions remain unanswered, including: how to evaluate the effectiveness of the X-in-the distance loop’s development and validation theory, how to determine the difference between remote testing and local testing, and how to measure this difference. Remote development and validation with X-in-the-loop are used in other applications such as space manipulator remote operation and remote surgery, with comparative procedures. Evaluating indicators in these areas can be used as references as similarities surface between these technologies and the new remote automotive technology. In the field of space manipulator remote operation, the concept “transparency” refers to accuracy of the force and displacement between the robot slave arm and the main arm, which allows the operator to offer input based on his or her subjective, intellectual and experienced connectivity to the direct operation of the environment [11]. Combining the practical problems of teleoperation manipulators and space exploration, Lawrence et al. [12,13] put forward a measure of transparency, which measures the subjective feelings of remote distributed system operators. Thus, system transparency will be ideal when transmission impedance and environmental impedance are equal. In addition, Natori et al. [14,15] used the correspondence between the position and the force signal, which is named H matrix, to measure the transparency. It is worth noting that the above methods are aimed at linear systems, which are for parameters of a set value. In a distributed nonlinear system, the coupling relationship is complicated; thus, it is hard to calculate impedance or H matrix, which makes a transparency description method for non-linear systems necessary. Since in the linear system, force and position are measures of subjective feelings, in a nonlinear system, it is necessary to look for quantitative relationships between variables and subjective feelings. In the field of psychology, the Weber-Fechner law shows the relationship between psychological and physical quantities, that is, the difference between the threshold of a change with an amount of stimulation changes, while still showing a certain regularity. Φ is the original amount of stimulation, ∆Φ is the difference in threshold at this time, and C is the ratio of ∆Φ and Φ, also known as Weber rate [16]. The Weber-Fechner Law has been introduced into the field of robot tactile perception, and the Friedman statistical method is used to measure the comprehensive performance of the robot arm [17]. Here quantitative relationships between variables and subjective feelings are established. Furthermore, the Analysis of Variance (ANOVA) method is used to measure and compare the transparency of the system [18]. Assuming the existence of n different types of experimental conditions (here, this specifically refers to different structural conditions), in each type of experimental condition, the experiments repeat m times. According to the mean value of the standard deviation, p-value was

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carried out by using the analysis of variance method. Furthermore, the robustness and transparency Energies 2018, 11, x 3 of 23 were discussed according to the p-value. Here levels of p-value represent whether the transparency of objects to be compared is consistent. deviation, p-value was carried out by using the analysis of variance method. Furthermore, the In this paper, distributed testdiscussed platformaccording is locatedto intheChina andHere Germany with Internet robustness andthe transparency were p-value. levels of p-value data exchange interaction. Because Internet data transmission has the characteristics of uncertain represent whether the transparency of objects to be compared is consistent. delay andIn packet loss, the additional uncertainty is added to the system Whether the additional this paper, distributed test platform is located in China and[19,20]. Germany with Internet data exchange data transmission characteristics of uncertain delay uncertainty caninteraction. be ignoredBecause shouldInternet be discussed. To find outhas thethe differences between remote operation and operation, packet loss,four additional uncertainty is added were to theselected, system [19,20]. Whether thespeed, additional and local transparency parameters including vehicle fuel cell uncertainty can be ignored should be discussed. To find out the differences between remote operation output power, battery output power and electric motor torque under several different configuration andinlocal fourtest transparency including vehicle speed, fuel cell settings thisoperation, distributed platform.parameters With the were help selected, of the transparency theory and statistical output power, battery output power and electric motor torque under several different configuration method, transparency comparisons of these four parameters were carried out. Using parametric settings in this distributed test platform. With the help of the transparency theory and statistical and non-parametric detection, the statistical significance and transparency limitations caused by the method, transparency comparisons of these four parameters were carried out. Using parametric and Internet under these configurations were determined. non-parametric detection, the statistical significance and transparency limitations caused by the Internet under these configurations were determined.

2. System Model

2. System Model

2.1. Structure of Distributed Test Platform 2.1. Structure of Distributed Test Platform Tongji University in Shanghai, China and Karlsruhe Institute of Technology (KIT) in Karlsruhe, Tongji University China Karlsruhe Technology (KIT) in Karlsruhe, Germany jointly developedinaShanghai, distributed testand platform for aInstitute fuel cellofelectric vehicle powertrain system Germany jointly developed a distributed test platform for a fuel cell electric vehicle powertrain based on X-in-the-loop approach. This platform aimed at calculating the energy consumption of system on X-in-the-loop approach. This platform at calculating the energy consumption the fuel cell based drivetrain system and validating the fuelaimed economy of the fuel cell vehicle drivetrain of the fuel cell drivetrain system and validating the fuel economy of the fuel cell vehicle drivetrain and system. Another goal was to remotely connect the distributed platform’s developed environment system. Another goal was to remotely connect the distributed platform’s developed environment and data transfer capability between two computers—one in China at Tongji Universtiy and the other in data transfer capability between two computers—one in China at Tongji Universtiy and the other in Germany at KIT. In KIT, we used a MiniHiL as a vehicle axle to connect the road and transmission Germany at KIT. In KIT, we used a MiniHiL as a vehicle axle to connect the road and transmission and toand create a model running at real-time tointegrate integratethe the E-motor transmission. to create a model running at real-timemachine machine rates rates to E-motor andand transmission. The next task was to include the driver model and energy resource model with a fuel cell battery, The next task was to include the driver model and energy resource model with a fuel cell and and battery, whichwhich were placed in a real-time machine in Tongji. Since we tried to compare the results from different were placed in a real-time machine in Tongji. Since we tried to compare the results from different connection settings, thesewere two placed parts were placedsides on both sides of theorvirtual or physical connection settings, these two parts on both of the virtual physical model as a model as requirement. a configurationThe requirement. The whole of this distributed test platform shown configuration whole structure of structure this distributed test platform is shownis in Figure 1. in Figure 1.

Figure 1. Structure of distributed testplatform platformfor for fuel fuel cell powertrain system. Figure 1. Structure of distributed test cellelectric electricvehicle vehicle powertrain system.

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According to testtargets, targets,the thevirtual virtualmodels sidescan canbe becombined combinedflexibly. flexibly. Accordingto totest modelsor orhardware hardwareon onboth bothsides sides can be combined flexibly. According Aimed at transparency research, on the Chinese side of the driver model, the electric control unit (ECU) Aimedatattransparency transparencyresearch, research,on onthe theChinese Chineseside sideof ofthe thedriver drivermodel, model,the theelectric electriccontrol control unit Aimed unit model, batterybattery modelmodel and fuel cell model were chosen, while on German side, the drivetrain model, (ECU)model, model, battery model and fuel cellmodel model werechosen, chosen, while onGerman German side, thedrivetrain drivetrain (ECU) and fuel cell were while on side, the drivetrain hardware (MiniHiL(MiniHiL test bench) and vehicle model weremodel chosen. The chosen. data flow between both model, drivetrain drivetrain hardware (MiniHiL test bench) and vehicle model were chosen. The data flow flow model, hardware test bench) and vehicle were The data sides and models are shown in Figure 2. betweenboth bothsides sidesand andmodels modelsare areshown shownin inFigure Figure2.2. between

Figure 2. Dataflow flow ofdistributed distributed testplatform platform aimedatattransparency transparency research. Figure Figure2.2.Data Data flowof of distributedtest test platformaimed aimed at transparencyresearch. research.

2.2.Model 2.2. ModelofofObjects Objects 2.2. 2.2.1. PowertrainConfiguration Configuration of Demo Fuel Cell Electric Vehicle 2.2.1.Powertrain Configurationof ofDemo DemoFuel FuelCell CellElectric ElectricVehicle Vehicle 2.2.1. This article focuses on vehicle’s powertrain system shown in Figure 3.3. The The power power sources This article article focuses focuses on on aaa vehicle’s vehicle’s powertrain powertrain system system shown shown in in Figure Figure 3. power sources sources This consist of a fuel cell range extender and a battery system for the sake of quick dynamic response and consistof ofa fuel cell range extender and a battery system for the sake of quick dynamic response and consist durability. The fuel cell system can drive the motors and simultaneously charge the battery [21]. durability.The Thefuel fuelcell cellsystem systemcan candrive drivethe themotors motorsand andsimultaneously simultaneouslycharge chargethe thebattery battery[21]. [21]. durability.

electricvehicle vehiclepowertrain powertrainsystem. system. Figure3.3.Structure Structureof offuel fuelcell cellelectric electric vehicle powertrain system. Figure

2.2.2. The Longitudinal Dynamic Model of Vehicle 2.2.2.The TheLongitudinal LongitudinalDynamic DynamicModel Modelof ofVehicle Vehicle 2.2.2. In order velocity and complete remote driver simulation, it is necessary to build Inorder orderto tosimulate simulateactual actual velocity and complete remote driver simulation, necessary to In to simulate actual velocity and complete remote driver simulation, ititisisnecessary to a longitudinal dynamic model of the electric vehicle. The longitudinal dynamic equation of the vehicle buildaalongitudinal longitudinaldynamic dynamicmodel modelof ofthe theelectric electricvehicle. vehicle.The Thelongitudinal longitudinaldynamic dynamicequation equationof ofthe the build is shown in Equation (6) [22]:(6) vehicle shown inEquation Equation (6)[22]: [22]: vehicle isisshown in ηT .2 .. 1 ggη TTTtqtqtqigiη TT = m v g f R +11 Cw Aρ a x22+ δm x mvvgfgfRR++ 2CCwwAAρρaaxx ++δδmx mx  rt ==m 22 rtrt

Thevehicle vehicleparameters parametersare arelisted listedin inTable Table11[23]. [23]. The

(1) (1) (1)

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Table 1. Vehicle parameters. Name

Parameter

weight The vehicle parametersVehicle are listed in Table 1 [23].

1600

Transmission ratio 1 Table 1. Vehicle parameters. Transmission system efficiency 92 Name Tire radius

Parameter 0.3

Vehicle weight coefficient Rolling resistance Transmission ratio Air resistance Transmission system coefficient efficiency Frontal Tire radius area Rolling coefficient 10 resistance °C sea level air density Air resistance coefficient Rotational massarea conversion factor Frontal ◦ Driveair torque 10 C sea level density Rotational mass conversion factor Vertical speed Drive torque Verticalspeed acceleration Vertical Vertical acceleration

1600 0.01 1 0.35 92 2.8 0.3 0.01 1.2 0.35 1.05 2.8 1.21.05 --

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Unit

Symbol

kg

mv

-

ig

%

ηT

mUnit

rtSymbol

fR - kg Cw - % m2 m A ρa N·s2·m--4 - 2 δ m 2 − 4 T N·m N ·s ·m tq m/s x N·m 2 m/s x m/s m/s2

mv ig ηT rt fR Cw A ρa δ Ttq . x .. x

According to the longitudinal dynamic equation, vehicle can be obtained based on drive torque. According to the longitudinal dynamic equation, vehicle can be obtained based on drive torque. 2.2.3. Fuel Cell Model 2.2.3.Proton Fuel Cell Model membrane fuel cells (PEMFC) use hydrogen and oxygen as fuels to generate exchange electrical energy through electrochemical Thehydrogen oxidationand reaction of as thefuels anode and the Proton exchange membrane fuel cells reactions. (PEMFC) use oxygen to generate reduction reaction of the cathode are shown in the following formulas [24]: electrical energy through electrochemical reactions. The oxidation reaction of the anode and the reduction reaction of the cathode are shown in the following + − formulas [24]:

2H 2 → 4H +4e +

2H2 → 4H +4e

−

(2)

O2 +4H+ +4e− → 2H2 O

O 2 +4H +4e → 2H 2 O

(2) (3) (3)

2H +O →2H 2H2 O 2H22+ O22 → 2O

(4) (4)

+

−

covers theoretical theoretical knowledge knowledge including material science, fluid mechanics, The fuel cell covers thermodynamics and electrochemistry, and its working mechanism can hardly be described by a thermodynamics clear mathematical relation. The The complexity complexity of of the the fuel fuel cell cell subsystem subsystem also makes the dynamic response of offuel fuelcells cells worse more affected by surrounding their surrounding working environment. At response worse andand more affected by their working environment. At present, a simplified is established with reference to the order toadescribe apresent, simplified model is model established with reference to the models ofmodels [25,26]. of In[25,26]. order toIndescribe fuel cell a fuel cell system more accurately, identification and data are methods common to methods to system more accurately, parameterparameter identification and data fitting arefitting common improve improve the of accuracy of the simplified anditstoapplicability make its applicability easier to understand. the accuracy the simplified model andmodel to make easier to understand. When the When the load current due toeffect, the charging effect, the fuel cell’s bipolar plateasurface load current changes, duechanges, to the charging the fuel cell’s bipolar plate surface produces slowly produces voltage. a slowlyThe changing voltage. The R equivalent resistance Ra iswith connected in parallel withina changing equivalent resistance in parallel a capacitor C, as shown a is connected capacitor C,equivalent as shown in the specific equivalent diagram of Figure 4. the specific model diagram of Figure model 4.

Figure 4. Fuel cell equivalent circuit model diagram.

The relationship between the fuel cell internal variables is shown as follows:

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6 of 23 t −  U Ra = U C = I ⋅ Ra ×  1 − e Ra C  

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   

6 of (5)23

t The relationship between the fuel cell internal variables is− shown as follows:   U m = E − I ⋅ Rint − I ⋅ Ra ×  1 − e Ra C      t   URa = UC = I · R a × 1 − e− Ra C

(6) (5)

 U stack = U m ⋅ n  t Um = E − I · Rint − I · R a × 1 − e− Ra C

(7) (6)

t  = Um2 · n 2  −  Ustack RaC Pfc _ out = U stack × I = n ×h EI − I Rint − I Ra 1 −e  t i   Pf c_out = Ustack × I = n × EI − I 2 Rint − I 2R a 1 − e− Ra C

(7) (8) (8)

The The fuel fuel cell cell symbols symbols are are shown in Table 2. Table Table 2. Fuel Fuel cell cell symbols. symbols. Name Name equivalent resistance equivalent resistance equivalent capacitor equivalent capacitor output current current output R a voltage Ra voltage C voltage voltage C fuel cell cell single single voltage voltage fuel electromotive electromotive force force of of single single cell cell internal internal resistance resistance fuel fuel cell cell output output voltage voltage number of single number of single cell cell output power of the fuel cell output power of the fuel cell

Symbol Symbol Ra Ra C C II ܷUோೌ Ra U Ucc U Umm EE RRint int Ustack stack U nn Pfc_out Pfc_out

The The output output power-efficiency power-efficiency curve curve of of fuel fuel cell cell is is shown shown in in Figure 5. Other Other fuel fuel cell cell values values are are shown in Table 3. shown in Table 60

Efficiency [%]

58

56

54

52

50 0

1000

2000

3000

4000

Fuel cell power [W]

5000

Figure Figure 5. 5. Output Outputpower-efficiency power-efficiency curve curve of of fuel fuel cell. cell. Table 3. Fuel cell values. Table 3. Fuel cell values. Name Name Single fuel fuel cell cell internal internal resistance resistance Single Single Single fuel fuel cell cell equivalent equivalentresistance resistance Single Single fuel fuel cell cell open open circuit circuitvoltage voltage Single Single fuel fuel cell cell equivalent equivalentcapacitor capacitor Fuel Fuel cell cell rated rated power power Fuel cell peak power

Value Unit Unit Value 0.0003 0.0003 ΩΩ 0.0006 ΩΩ 0.0006 1.037 1.037 VV 33 FF 6 kW 6 kW 6.5 kW

6000

7000

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2.2.4. Battery The battery can make up for the lack of dynamic response of the fuel cell and absorb the energy of the brake [27–29]. Here a packaged ternary polymer lithium battery model is used. Battery and fuel cell are connected in parallel, using a power following strategy. In this paper, the analytic model of the battery will be used. The input is battery current and temperature, and the output will be voltage and State of Charge (SOC). By stationary state the charging mode of our vehicle is constant current-constant voltage (CC-CV) cycle. For this battery model, the following assumptions exist: the internal resistance of the battery model is constant, that is, the internal resistance value is kept constant during the charging and discharging process of the battery, and is also independent of the charge and discharge current; there is no memory effect in the battery. The battery model is mainly divided into three modules: SOC calculation module, voltage calculation module, and a thermal calculation module. The input and output of the battery model and energy management strategy can be expressed as: R Qc − idt SOC = × 100% (9) Qc   Ub = OCV (SOC ) − Udrop (i ) · Ns (10) Pb_in = Pin − η · Pf c_out

(11)

Pout = η · Pf c_out + Pb_out

(12)

The battery symbols are shown in Table 4. Table 4. Battery symbols. Name

Symbol

capacity of battery cell battery cell current cell open circuit voltage voltage drop in battery cell battery output power battery utilization efficiency function battery demand power battery voltage system demand power efficiency of fuel cell DC/DC output power of fuel cell and battery

Qc i OCV Udrop Pb_out fP Pb_in Ub Pin η Pout

The maximum capacity of the battery is related to temperature. The cell open circuit voltage-SOC diagram is shown in Figure 6. Other battery values are shown in Table 5. Energies 2018, 11, x 8 of 23 4 ←1

OCV [V]

3.5

3

2.5

2

1.5 0

0.1

0.2

0.3

0.4

0.5

SOC

0.6

0.7

0.8

0.9

Figure Power battery battery cell Figure 6. 6. Power cell open open circuit circuit voltage-SOC voltage-SOC curve. curve.

Table 5. Battery values. Name Maximum current

Value 500

Unit A

1

2

1.5 0

0.1

0.2

0.3

0.4

0.5

SOC

0.6

0.7

0.8

0.9

1

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Figure 6. Power battery cell open circuit voltage-SOC curve. Table Table 5. 5. Battery Battery values. values. Name Name Maximum Maximumcurrent current Maximum Maximumcharge chargecurrent current Numberof ofseries seriesbatteries batteries Number Numberof ofparallel parallelbatteries batteries Number Battery cell radius Battery cell radius Batterycell cellheight height Battery Battery cell capacity Battery cell capacity C-rate (charge-discharge current/rated capacity) C-rate (charge-discharge current/rated capacity)

Value Value 500 500 −45 −45 100 100 20 20 0.013 0.013 0.065 0.065 2.3 × 3600 2.3 × 3600 1 1

Unit Unit A A A A --m m m m Ah·s Ah·s C C

2.2.5. Electric Motor 2.2.5. Electric Motor The drivetrain contains containsfour fourin-wheel in-wheel electric motorss. Here, the electric is a The drivetrain electric motorss. Here, the electric motormotor modelmodel is a quasiquasi-steady-state model. The relationship between torque, rotating speed, andefficiency efficiencyisis shown shown in steady-state model. The relationship between torque, rotating speed, and in Figure 7. The electric motor values are shown in Table 6. Figure 7. The electric motor values are shown in Table 6.

1

Efficiency [%]

0.8 0.6 0.4 0.2 0 500 400 300 200 100 0

-800

-600

Rotating speed [r/min]

-400

-200

0

200

400

600

800

Torque [N*m]

Relationship between torque, rotating speed, and efficiency. Figure 7. Relationship efficiency. Table 6. Electric motor values. Table 6. Electric motor values. Name Name Drive DriveType Type In-Wheel Motor In-Wheel MotorRated/Peak Rated/Peak Power Power

Value Value 4 In-Wheel Motor 44×× 0.8/4 2.5 0.8/4 ××2.5

Unit Unit –– kW kW

Test Bench Bench 2.2.6. Drivetrain Test configurations are implemented on the high power test benches, they can Before the new test configurations first be tested tested on on relatively relatively lower lower power power test test benches. benches. The goal is to create a development development and test compromising thethe operation on the test test stands. For environment for for new newtest testconfigurations configurationswithout without compromising operation on the stands. For this purpose, a Mini Hardware-in-the-Loop test bench (MiniHiL), which is shown in Figure 8, was used to create a development environment for new test configurations. Notably, the Mini-HiL proved to be a particularly suitable test bench for this preliminary study. Further considering the combination of hardware and software, corresponding to the drivetrain model, the MiniHiL test bench was used to replace the drivetrain model. The MiniHiL test bench consists of two electric motors with 1.5 kW power and 6000 min−1 peak speed, with a connecting shaft. Here, one motor can be used as a driving motor, and the other motor can be used as a loading motor. The electric motor parameters are shown in Table 7 [30].

to be a particularly suitable test bench for this preliminary study. Further considering the combination of hardware and software, corresponding to the drivetrain model, the MiniHiL test bench was used to replace the drivetrain model. The MiniHiL test bench consists of two electric motors with 1.5 kW power and 6000 min−1 peak speed, with a connecting shaft. Energies 2018,motor 11, 2411can be used as a driving motor, and the other motor can be used as a loading motor. 9 of 23 Here, one The electric motor parameters are shown in Table 7 [30].

Figure 8. MiniHiL Figure 8. MiniHiL test test bench. bench. Table 7. Table 7. Electric Electric motor motor parameters. parameters. Parameter Parameter Ratedpower power Rated Ratedtorque torque Rated Maximumrotating rotatingspeed speed Maximum Rated Ratedrotating rotatingspeed speed Maximum Maximumtorque torque

Value Value 1.5 1.5kW kW 2.4 2.4Nm Nm 6000 6000r/min r/min 2500r/min r/min 2500 10.3Nm Nm 10.3

2.3. Communication Settings In order between thethe hardware and and software for efficiency and longorder totoensure ensurecommunication communication between hardware software for efficiency and distance communication quality between the two Usera Datagram Protocol (UDP)(UDP) was used long-distance communication quality between thePCs, two aPCs, User Datagram Protocol was with privateprivate network (VPN) (VPN) components to ensure speed communication and security. used virtual with virtual network components to the ensure theofspeed of communication and Between the MiniHiL platform and the PC on the KIT side, the Controller Area Network (CAN) security. Between the MiniHiL platform and the PC on the KIT side, the Controller Area Network communication protocol was used meet the MiniHiL platform communication standards. In (CAN) communication protocol was to used to meet the MiniHiL platform communication standards. addition, considering thethe actual PCs, computing In addition, considering actual PCs, computingability abilityand andstorage storagecapacity, capacity,all all PCs PCs used used the Ode3 solver, and the simulation simulation step step was was 0.001 0.001 s. s. 3. Configuration Settings 3.1. Configuration Configuration Settings Settings on 3.1. on Location, Location, Hardware Hardware and and Software Software Any difference difference in in the the whole whole structure structure configuration configuration can can have have aa potential potential impact impact on ontransparency; transparency; Any hence, itit is configurations, including different locations, hardware and hence, isimportant importanttotoset setseveral severaldifferent different configurations, including different locations, hardware software. A variable-control approach was used to measure the impact of different configurations. and software. A variable-control approach was used to measure the impact of different The configuration on location, hardware and software shown in Table 8 and details are configurations. Thesettings configuration settings on location, hardwareare and software are shown in Table 8 described afterwards. Because of some hardware factors of the physical object, this paper analyzes the and details are described afterwards. Because of some hardware factors of the physical object, this network impact from the two settings of simulation and hardware test. Therefore, in the following table, the tests were divided into A1–A4, (simulation test), B1–B3 (physical test).

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Table 8. Configuration settings on location, hardware and software. Serial Number

Location

Roundtrip Delay

Operating Environment

Configuration A1 Configuration A2 Configuration A3 Configuration A4 Configuration B1 Configuration B2 Configuration B3

Same location Same location Same location China and Germany Same location Same location China and Germany

0 0.4s Real time campus Ethernet delay Real time Internet delay 0 0.4s Real time Internet delay

Simulation environment Simulation environment Simulation environment Simulation environment Simulation environment with MiniHiL Simulation environment with MiniHiL Simulation environment with MiniHiL

3.1.1. Configuration A1 Configuration A1 is under the MATLAB/Simulink simulation environment, which ran a fuel cell powertrain system model, including a driver model, vehicle longitudinal dynamic model, fuel cell model, power battery model and an electric drive system model. All models ran on the same PC. The conditions were tested as per the worldwide harmonized light duty test cycle (WLTC). This configuration is a total simulation environment without hardware components. This configuration is simulation environment standard. 3.1.2. Configuration A2 Configuration A2 is a MATLAB/Simulink simulation environment, under WLTC operating conditions, running the fuel cell powertrain system model, including two modules—each with different models: Module I contains the driver model, the fuel cell model, and the battery model. Module II contains the vehicle longitudinal dynamic model and the electric drive system model as module II. Between the two modules, a one-way 0.2 s delay (roundtrip 0.4 s) is added. The significance of Configuration A2 is that the ideal state output of the fuel cell powertrain system under the fixed delay condition is given in the simulation environment. 3.1.3. Configuration A3 Based on the Configuration A2, Module I and Module II are set in two PCs. Both PCs are in Tongji University. The significance of Configuration A3 is that the output of the fuel cell powertrain system under an Ethernet delay condition is given in a simulation environment. 3.1.4. Configuration A4 Based on Configuration A3, PC1 and PC2 were placed in Tongji and KIT. With Configuration A4 the output of the various parts of the fuel cell powertrain system under long-distance conditions is given. 3.1.5. Configuration B1 Configuration B1 follows the set up in Configuration A2 except that the drivetrain model is replaced by a MiniHiL test bench. The MiniHiL test bench is shown in Figure 4. All the components are in the same place. The delay between two modules is set to 0. This configuration gives the output of each part of the fuel cell powertrain system under the condition of a real electric motor, and other parts are in the form of a simulated model. 3.1.6. Configuration B2 Configuration B2 has a similar setup to Configuration A3 and Configuration B1. The drivetrain model is replaced by the MiniHiL test bench. Other models of the operating environment remain unchanged. The one-way 0.2 s delay (roundtrip 0.4 s) between the two PCs is set.

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3.1.7. Configuration B3 3.1.7. Configuration B3

Under Configuration B3, the MiniHiL test bench is placed in KIT and the other components are Under Configuration B3, the MiniHiL test bench is placed in KIT and the other components are in in Tongji. Configuration B3 gives the output of each part of the fuel cell powertrain system under the Tongji. Configuration B3 gives the output of each part of the fuel cell powertrain system under the condition of the Internet. condition of the Internet.

3.2. Responses under Different Configuration 3.2. Responses under Different Configuration Settings Settings To analyzethe the responses responses under different configuration settings,settings, the roundtrip under different To analyze under different configuration the time roundtrip time under configuration settingssettings is measured and shown in shown Figure 9in and Table 99.and Table 9. different configuration is measured and Figure 6

Configuration A3

Round trip delay [s]

5

Configuration B3 Configuration A4

Configuration A2 Configuration A1 Configuration B2 Configuration B1

4

3

2

1

0 0

200

400

600

800

1000

1200

1400

1600

1800

Time [s]

Figure Roundtriptime time under under different settings. Figure 9. 9. Roundtrip differentconfiguration configuration settings. Table 9. Roundtrip delay parameters.

Table 9. Roundtrip delay parameters. Configuration Number of Min Max Average Standard Configuration Type Type Number of Packets Delay Average Standard Deviation PacketsMin Delay Delay Max Delay Delay Delay Deviation

Configuration A1 180,001 Configuration A1 ConfigurationConfiguration A2 180,001 A2 ConfigurationConfiguration A3 180,001 A3 Configuration A4 Configuration A4 180,001 Configuration B1 Configuration B1 180,001 Configuration B2 Configuration B2 180,001 Configuration B3 Configuration B3 180,001

180,001 180,001 180,001 180,001 180,001 180,001 180,001

0.0000 0.0000 0.4000 0.4000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.4000 0.4000 0.0000 0.0000

0.0000 0.0000 0.4000 0.4000 4.6000 4.6000 4.4200 4.4200 0.0000 0.0000 0.4000 0.4000 5.0400 5.0400

0.0000

0.0000 0.4000 0.4000 0.3524 0.3524 0.5205 0.5205 0.0000 0.0000 0.4000 0.4000 0.5330

0.5330

0.0000 0.0000 0.2630 0.2513 0.0000 0.0000 0.2571

0.0000 0.0000 0.2630 0.2513 0.0000 0.0000 0.2571

As shown in Figure 9 and Table 9, the roundtrip delay of each configuration setting is different.

As shown in Figure 9 and Table 9, the roundtrip delay of each configuration setting is different. It is worth noting that the hardware itself also has an impact on the round trip time. Under the It is worth noting that the hardware itself also has an impact on the round trip time. Under the condition that the delay of each configuration setting is different, the vehicle speed, fuel cell output condition that the delay of each configuration setting is different, the vehicle speed, fuel cell output power, battery output power, and the output torque of the electric motor are shown in Figures 10–13. power, battery output power, and the output torque of the electric motor are shown in Figures 10–13.

Configuration B3

180,001

0.0000

5.0400

0.5330

0.2571

As shown in Figure 9 and Table 9, the roundtrip delay of each configuration setting is different. It is worth noting that the hardware itself also has an impact on the round trip time. Under the Energies 2018,that 11, 2411 12 of 23 condition the delay of each configuration setting is different, the vehicle speed, fuel cell output power, battery output power, and the output torque of the electric motor are shown in Figures 10–13.

configurations. Speed response response of fuel cell powertrain system under different configurations. Energies 2018,Figure 11, x 10. Speed

Fuel cell power [W]

12000

Configuration A1 Configuration A2 Configuration A3 Configuration A4

10000 8000 6000 4000 2000 0 0

200

10000

Fuel cell power [W]

12 of 23

400

600

800

1000

1200

1400

1600

1800

600

800

1000

1200

1400

1600

1800

Time [s] (a)

Configuration B1 Configuration B2 Configuration B3

8000 6000 4000 2000 0 0

200

400

Time [s] (b)

Figure 11. 11. (a) (a) Fuel Fuel cell cell output output power power under under Configurations Configurations A1–A4; A1–A4; (b) (b) Fuel Fuel cell cell output output power power under under Figure Configurations B1–B3. B1–B3. Configurations 4

x 10 Configuration A1 Configuration A2 4 Configuration A3 Configuration A4 2

Battery power [W]

6

0 -2 0

200

4

10

x 10

400

600

800 1000 1200 1400 1600 1800 Time [s] (a)

0 0

200

400

600

800

1000

Time [s] (b)

1200

1400

1600

1800

Energies 2018, 11, 2411 Figure 11. (a) Fuel cell output power under Configurations A1–A4; (b) Fuel cell output power under 13 of 23

Configurations B1–B3. 4

x 10 Configuration A1 Configuration A2 4 Configuration A3 Configuration A4 2

Battery power [W]

6

0 -2 0

200

400

600

800 1000 1200 1400 1600 1800 Time [s] (a)

4

Battery power [W]

10

x 10

5

Configuration B1 Configuration B2 Configuration B3

0

-5 0

200

400

600

800 1000 1200 1400 1600 1800 Time [s] (b)

Figure Battery outputpower powerunder underConfigurations Configurations A1–A4; power under Figure 12. 12. (a) (a) Battery output A1–A4;(b) (b)Battery Batteryoutput output power under Configurations B1–B3. Configurations Energies 2018, 11, x B1–B3. 13 of 23

Torque [Nm]

100

50

0 Configuration A1 Configuration A2 Configuration A3 Configuration A4

-50

-100 0

200

400

600

800

1,000

Time [s]

1,200

1,400

1,600

1,800

(a)

Torque [Nm]

100

50

0

-50

-100 0

Configuration B1 Configuration B2 Configuration B3 200

400

600

800

1,000

Time [s]

1,200

1,400

1,600

1,800

(b) Figure Electric motortorque torqueunder under Configurations Configurations A1–A4; motor torque under Figure 13. 13. (a)(a) Electric motor A1–A4;(b) (b)Electric Electric motor torque under Configurations B1–B3. Configurations B1–B3.

While under Configuration A1–A4, all the components are in the form of a model. Under Configurations B1–B3, the Mini-HiL test bench is added, and the diagrams of fuel cell power, battery power and electric motor torque are divided into two parts. 4. Transparency Analysis

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While under Configuration A1–A4, all the components are in the form of a model. Under Configurations B1–B3, the Mini-HiL test bench is added, and the diagrams of fuel cell power, battery power and electric motor torque are divided into two parts. 4. Transparency Analysis 4.1. Nonparametric Statistical Analysis Method From the above results, the system with several configurations is a nonlinear system. To evaluate the transparency of different configurations, a statistical analysis method, preferably the ANOVA method, should be considered. The ANOVA method was used in this research to measure the transparency of the system. With the ANOVA method, seven kinds of configurations can be regarded as seven different experimental conditions. In each type of experimental condition, the experiment is repeated three times. The standard deviation of the parameters is shown in the Tables 10–13. The data processing platform is IBM SPSS Statistics 19. Table 10. The standard deviation of vehicle speed under different configurations (unit: m/s). Criterion: Configuration A1

Test 1 Test 2 Test 3 Average value

Criterion: Configuration B1

Configuration A2

Configuration A3

Configuration A4

Configuration B2

Configuration B3

0.0990 0.0990 0.0990 0.0990

0.1633 0.1970 0.2236 0.1946

0.1541 0.1720 0.1502 0.1588

0.1798 0.1870 0.1726 0.1798

0.8356 0.1668 0.1655 0.3893

Table 11. The standard deviation of fuel cell power under different configurations (unit: W). Criterion: Configuration A1

Test 1 Test 2 Test 3 Average value

Criterion: Configuration B1

Configuration A2

Configuration A3

Configuration A4

Configuration B2

Configuration B3

248.4924 248.4924 248.4924 248.4924

566.8660 488.0595 456.3407 503.7554

419.0964 372.1072 396.2455 395.8164

365.9171 293.0996 587.1804 415.3990

2686.9 2644.1 2653.3 2661.4

Table 12. The standard deviation of power battery power under different configurations (unit: W). Criterion: Configuration A1

Test 1 Test 2 Test 3 Average value

Criterion: Configuration B1

Configuration A2

Configuration A3

Configuration A4

Configuration B2

Configuration B3

547.2865 547.2865 547.2865 547.2865

2164.2 1044.4 1184.2 1464.3

830.0621 930.7343 861.0848 873.9604

1102.7 1278.9 1121.2 1167.6

3428.1 1624.4 1609.4 2200.6

Table 13. The standard deviation of electric motor torque under different configurations (unit: Nm). Criterion: Configuration A1

Test 1 Test 2 Test 3 Average value

Criterion: Configuration B1

Configuration A2

Configuration A3

Configuration A4

Configuration B2

Configuration B3

3.5937 3.5937 3.5937 3.5937

4.3321 4.1277 4.3115 4.2571

5.8564 5.8548 5.9193 5.8768

4.5379 4.2710 4.7106 4.5065

36.2328 16.9457 39.9875 31.0553

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The variance homogeneity test is an important prerequisite for the ANOVA. The ANOVA method requires that the samples under the respective processing conditions come from the normal distribution respectively. Therefore, using the results of Tables 10–13 the variance homogeneity tests were carried out. Test results are shown in Tables 14–17. Table 14. Variance homogeneity test result of vehicle speed. Criterion

Levene

df1

df2

Significance

Configuration A1 Configuration B1

0.936 3.508

3 2

720,000 540,000

0.422 0.030

Table 15. Variance homogeneity test result of fuel cell output power. Criterion

Levene

df1

df2

Significance

Configuration A1 Configuration B1

6.973 750.686

3 2

720,000 540,000

0.000 0.000

Table 16. Variance homogeneity test result of battery output power. Criterion

Levene

df1

df2

Significance

Configuration A1 Configuration B1

28.844 120.278

3 2

720,000 540,000

0.000 0.000

Table 17. Variance homogeneity test result of electric motor output torque. Criterion

Levene

df1

df2

Significance

Configuration A1 Configuration B1

546.631 542.999

3 2

720,000 540,000

0.000 0.000

As shown in Tables 14–17, except for vehicle speeds under Configurations A1–A4, vehicle speeds under Configuration B1–B3, fuel cell power, battery power and electric motor torque, variance is not homogeneous. Since the ANOVA method requires that the samples under each processing condition should come from the normal distribution population, the ANOVA method cannot be used directly. Non-parametric analysis, also known as the distribution of a free test, was used to mainly solve the overall distribution of the unknown statistical inference and can complete the lower level of measurement data inferred. Common nonparametric methods include the Mann-Whitney U test, Kolmogorov-Smirnov test, Kruskal-Wallis test, etc. Here, the Kruskal-Wallis test is used. The Kruskal-Wallis test is a nonparametric test method that determines whether or not the distribution on the p-value is the same by observing values from multiple independent population samples. Hence, we set the expectation and variance of rank sum Ti in Group i: n i ( N + 1) 2

(13)

ni ( N − ni )( N + 1) 12

(14)

µ Ti = σT2i = H=

k

Ti − µ Ti

i =1

σT2 i

∑


2

=

Ti2 12 − 3( N + 1) ∑ N ( N + 1) ni

(15)

where µ Ti = expectation of rank sum Ti in group i; σTi = variance of rank sum Ti in group I H = test statistic. Here H is set to follow the message: “Distribution is the same in the different configurations” [18]. After H is calculated, the p-value can be determined by a look-up table. According to this method the above data including vehicle speed, fuel cell power, battery power and electric motor torque are processed.

i =1

σT

i

N ( N + 1)

ni

where ߤ ்೔ = expectation of rank sum Ti in group i; ߪ்೔ = variance of rank sum Ti in group I H = test statistic. Here H is set to follow the message: “Distribution is the same in the different configurations” [18]. After H is calculated, the p-value can be determined by a look-up table. Energies 2018, 11, 2411 16 of 23 According to this method the above data including vehicle speed, fuel cell power, battery power and electric motor torque are processed. 4.2. Non-Parametric Test of Vehicle Speed 4.2. Non-parametric Test of Vehicle Speed Assuming that “vehicle speed distribution is the same in the different configurations”, if p-value Assuming that the “vehicle speed is distribution is the same in the different configurations”, if p-value is greater than 0.05, hypothesis established. is greater thancharts 0.05, the hypothesis established. The box in Figure 14a,bisshow vehicle speed expressed in two categories to analyze the The box charts in Figures 14a,b show vehicleconfigurations speed expressed categories to In analyze median, quartile, and tentacles lines of the different andin to two calculate p-value. Figurethe 14, median, quartile, and tentacles lines of the different configurations and to calculate p-value. In Figure the median, quartile, and tentacles lines of the different configurations are approximately same. 14,Configurations the median, quartile, andone-way tentaclesANOVA lines ofmethod the different approximately same. In A1–A4 the is usedconfigurations and p = 1.000 >are 0.050. In Configurations In Configurations A1–A4 the one-way ANOVA method is used and p = 1.000 > 0.050. In B1–B3, p = 0.008 < 0.050. Configurations B1–B3, p = 0.008 < 0.050.

Energies 2018, 11, x

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(a)

(b) Figure 14. 14. Box B1–B3. Figure Box charts charts of of vehicle vehicle speed speed under under (a) (a) Configurations Configurations A1–A4 A1–A4 and and (b) (b) Configurations Configurations B1–B3.

Table 18 18shows showsthe thecomparison comparisonofoftwo twoconfigurations. configurations.AsAs can seen from Figure Table Table can bebe seen from Figure 14 14 andand Table 18, 18, two the two significant differences between theconfigurations two configurations of Configurations are the significant differences between the two of Configurations A1–A4 A1–A4 are greater greater than 0.05, as asthe well as significant the two significant differences between Configurations are0.05; less than 0.05, as well two differences between Configurations B1–B3 areB1–B3 less than than 0.05; thus, no significant difference in the transparency of Configurations A1–A4 for the thus, no significant difference exists in theexists transparency of Configurations A1–A4 for the variable of variable of the vehicle speed, but the transparency of Configurations B1–B3 is significantly the vehicle speed, but the transparency of Configurations B1–B3 is significantly different. different. Table 18. Comparison of two configurations under different configurations. Sample1-Sample2 Configuration A1–A3 Configuration A3–A4 Configuration A2–A3 Configuration A1–A4 Configuration A1–A2 Configuration A2–A4 Configuration B1–B2 Configuration B1–B3 Configuration B2–B3

Test Statistic 529.265 −667.577 743.746 −138.311 −214.480 76.169 1450.539 1312.237 −138.302

Std. Test Statistic 0.611 −0.771 0.859 −0.160 −0.248 0.088 2.794 2.528 −0.266

Sig. 0.541 0.441 0.390 0.873 0.804 0.930 0.005 0.011 0.790

Adj. Sig. 1.000 1.000 1.000 1.000 1.000 1.000 0.016 0.034 1.000

4.3. Nonparametric Test of Fuel Cell Power Assuming that “fuel cell power distribution is the same in the different configurations”, if the pvalue is greater than 0.05, the hypothesis is approved. The box charts in Figures 15a,b show fuel cell

Table 18 shows the comparison of two configurations. As can be seen from Figure 14 and Table 18, the two significant differences between the two configurations of Configurations A1–A4 are greater than 0.05, as well as the two significant differences between Configurations B1–B3 are less than 0.05; no significant difference exists in the transparency of Configurations A1–A4 for Energies 2018,thus, 11, 2411 17 ofthe 23 variable of the vehicle speed, but the transparency of Configurations B1–B3 is significantly different. Table 18. Comparison of of two two configurations configurations under Table 18. Comparison under different different configurations. configurations. Sample1-Sample2 Sample1-Sample2 Configuration A1–A3 Configuration A1–A3 Configuration A3–A4 Configuration A3–A4 Configuration A2–A3 Configuration A2–A3 Configuration A1–A4 Configuration A1–A4 Configuration A1–A2 Configuration A1–A2 Configuration A2–A4 Configuration A2–A4 Configuration B1–B2 Configuration B1–B2 Configuration B1–B3 Configuration B1–B3 Configuration B2–B3 Configuration B2–B3

Test Test Statistic Statistic 529.265 529.265 − 667.577 −667.577 743.746 743.746 − 138.311 −138.311 − 214.480 −214.480 76.169 76.169 1450.539 1450.539 1312.237 1312.237 − 138.302 −138.302

Std.Test TestStatistic Statistic Std. 0.611 0.611 −0.771 −0.771 0.859 0.859 − 0.160 −0.160 − 0.248 −0.248 0.088 0.088 2.794 2.794 2.528 2.528 −0.266 −0.266

Sig. Adj. Adj. Sig. Sig. Sig. 0.541 0.541 1.000 1.000 0.441 1.000 1.000 0.441 0.390 1.000 1.000 0.390 0.873 1.000 0.873 1.000 0.804 1.000 0.804 1.000 0.930 1.000 0.930 1.000 0.005 0.016 0.005 0.016 0.011 0.034 0.011 0.790 0.034 1.000 0.790 1.000

4.3. Nonparametric Test of Fuel Cell Power 4.3. Nonparametric Test of Fuel Cell Power Assuming that “fuel cell power distribution is the same in the different configurations”, if the Assuming that “fuel cell power distribution is the same in the different configurations”, if the pp-value is greater than 0.05, the hypothesis is approved. The box charts in Figure 15a,b show fuel cell value is greater than 0.05, the hypothesis is approved. The box charts in Figures 15a,b show fuel cell power expressed in two categories to analyze the median, quartile, and tentacles lines of the different power expressed in two categories to analyze the median, quartile, and tentacles lines of the different configurations and to calculate the p-value. configurations and to calculate the p-value.

Energies 2018, 11, x

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(a)

(b) Figure 15. Box charts of fuel cell power under (a) Configurations A1–A4 and (b) Configurations B1–B3. Figure

In In Configurations Configurations A1–A4 A1–A4 and and Configurations Configurations B1–B3, B1–B3, pp == 0.000 0.000