IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 66, NO. 12, DECEMBER 2017
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Integrated Power Management and Aftertreatment System Control for Hybrid Electric Vehicles With Road Grade Preview Yao Ma
and Junmin Wang
Abstract—This paper presents a model-based and integrated strategy for hybrid electric vehicle (HEV) power management and aftertreatment control with preview information of road grade. The HEV power management has been well studied for decades to optimize vehicle fuel economy by properly determining the power split ratio between internal combustion engine and electric motor. Meanwhile, the HEV tailpipe emissions have also been decreased, thanks to the implementation of aftertreatment systems, of which selective catalytic reduction (SCR) systems have been widely equipped in ground vehicles powered by diesel engines to reduce NOx emissions. For SCR systems, major efforts are dedicated to design the efficient ammonia dosing strategies for removing NOx without generating excessive ammonia slip at the tailpipe. By coordinately controlling the HEV power management and aftertreatment systems, it is possible to achieve lower fuel consumption and emissions from tailpipe. In addition, the proposed control strategy incorporates the preview road grade information to calculate the optimal torque–split ratio and ammonia dosing amount such that the overall performance can be improved for the trip. The road grade impacts on vehicle fuel consumption and emissions are investigated in this paper. A model-based controller with an explicit consideration of road grade has been analytically developed and verified in simulation environment. The performance of the proposed controller is evaluated under US06 test cycle and comparison results are presented to demonstrate the effectiveness of the proposed design. Controller’s real-time implementation potential is also discussed in this paper. Index Terms—Emissions, HEV power management, road preview, SCR.
I. INTRODUCTION HE regulations of fuel consumption and NOx emissions for Diesel powered ground vehicles have been strictly enforced over the years by environmental regulatory bodies for the sake of public health as well as energy sustainability [1], [2]. To achieve better fuel economy and emission performance, hybrid electric vehicle (HEV) emerges as one of the top candidates in the past decades for its superior fuel economy and low
T
Manuscript received May 11, 2017; revised August 25, 2017 and September 19, 2017; accepted October 7, 2017. Date of publication October 16, 2017; date of current version December 14, 2017. The review of this paper was coordinated by Dr. D. Cao. (Corresponding author: Junmin Wang.) The authors are with the Department of Mechanical and Aerospace Engineering, The Ohio State University, Columbus OH 43210 USA (e-mail: ma.506@ osu.edu;
[email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TVT.2017.2763587
, Senior Member, IEEE
emissions [3]. The core and well-studied HEV power management problem is to properly split torque output between electric motor (EM) and internal combustion engine (ICE) such that the overall power demand can be met efficiently. At the same time, the battery state of charge (SOC) should remain within boundaries to avoid depletion and overcharging which consequently lead to battery degradation and serious safety issues [4]. HEV power management algorithms can be generally classified into two subcategories: rule-based algorithms and optimization-based algorithms. Rule-based control strategies are empirically tuned by offline acquired data to attain the best possible performance over a specified driving cycle. The control action is determined by a set of predefined rules and depending on different working conditions the controller can switch between several possible working modes. In [5], a state machine model is proposed to characterize ten different vehicle operating modes and make corresponding actions of engine, clutch, and motor. In [6], a fuzzy logic controller is developed for HEV with a parallel configuration. A set of rules are made per driver command, SOC, and EM speed, and they are implemented in a Sugeno-Takagi [7] type fuzzy logic controller to improve the system overall efficiency. In [8], an offline-trained neural network controller is deployed to control a Toyota Prius over both standard and random driving cycles. The controller is trained by a high-fidelity simulator and covers most working conditions. The experimental results show less battery SOC variance and better fuel economy. The obvious advantages of rule-based methods include the easy implementation, computational efficiency, and lower hardware cost. However, the inherent nature of its inflexibility to adapt to different driving scenarios has often limited its real-world performance and applications. Optimization-based methods solve the power management problem by minimizing a cost function. The cost function is defined by a set of performance indices including fuel consumption, emissions, SOC variance, etc. with tuned weighting factors. The control action is calculated as a solution to the optimization problem either analytically or numerically. In [9], authors derived a gear-shifting and power-split strategy to globally optimize the cost function over a driving cycle using dynamic programing method. However, dynamic programming is a non-causal solution as it requires full knowledge of the entire driving cycle. Together with its heavy computational burden, the real-time implementation of such algorithm is not feasible. In [10]–[12], authors suggest equivalent consumption
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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 66, NO. 12, DECEMBER 2017
minimization strategy (ECMS) method for real-time implementation purpose. The method formulates a cost function with an equivalent factor to combine electricity and fuel consumptions as a single index. The cost function is then minimized instantaneously using only the current system variables. Thanks to its causality and computational efficiency, the method can be employed in real-time applications. In [13], authors introduce a model predictive control (MPC) based power management system for a power-split HEV. The controller solves an open-loop optimization problem over a prediction horizon. Since the model is linearized with a quadratic cost function, the optimization problem can be solved online. Most HEV power management strategies address fuel economy and engine efficiency without explicit consideration of vehicle emissions. As reported, there is a known tradeoff between Diesel engine efficiency and NOx emissions [14], [15]. The fundamental reason is that the temperature, pressure, and fuel-oxygen conditions under which a Diesel engine runs most efficiently also favor the conversion from nitrogen and oxygen to NOx . In this case, exhaust aftertreatment systems are critical to meet the stringent tailpipe emission standards. Selective catalytic reduction (SCR) system, as one of the most promising techniques, has seen growing popularity in Diesel engine powertrains [16]. SCR systems utilize urea as reductant agent to convert NOx into nitrogen and water through the help of catalyst. To maximize the conversion efficiency of SCR system without generating excessive ammonia slip at tailpipe, the urea dosing rate needs to be carefully arranged. Model-based control strategies for SCR systems can be found in literature [17]–[22]. The primary task is to regulate the catalyst ammonia coverage ratio to a desirable level to fulfill the NOx reduction and ammonia slip requirement simultaneously. The controller will first determine a proper reference ammonia coverage ratio given the operating conditions of catalyst. Secondly the urea dosing amount will be calculated to track the reference ammonia coverage ratio. In [19], a feedback and feedforward controller is designed to deal with SCR dynamics with unknown disturbances. In [21], a nonlinear MPC is designed with a constraint on ammonia coverage ratio. The NOx and NH3 emission requirements can be met at same time within the derived boundaries. In [22] authors design a Lyapunov-based controller to track the reference coverage ratio. The proof of asymptotic stability is presented as well. As illustrated above, the overall control problem consists of several conflicting objectives hence standalone control design for either power management system or aftertreatment system will only offer suboptimal performance. To obtain the best fuel economy and tailpipe emission performance simultaneously, all tradeoffs need to be evaluated as an integrated system [23]–[27]. In [23], authors introduce a MPC-based control strategy to minimize a cost function consists of fuel consumption, SOC variance, and SCR temperature deviation from reference. The SCR efficiency is simplified as a static map of temperature. In [24], the cost function is defined as sum of fuel cost and urea cost subject to NOx emission and SOC variance constraints. The optimal control problem is solved in a fashion similar to ECMS method. In [25], the authors propose three different energy management strategies to control optimal engine operating point, minimal CO2 and minimal NOx , respectively.
The road grade has a significant influence on vehicle fuel economy and tailpipe emission performance [28], [29] as it essentially changes power demand and operating conditions over the trip. As a result, the SCR operating condition and battery SOC dynamics are altered as well. Under varying road grade profile, it is thus crucial for the controller to adapt to the road condition and execute in a proactive manner to improve the fuel economy and lower the emissions over the entire trip. Thanks to the development of geographic information system (GIS) and global positioning system (GPS), the road grade profile can be made available to vehicle in advance. The preview geographic information can then be utilized in the controller design to compensate for the road grade effect. In [30], the authors consider road grade as a Markov-chain model and propose a stochastic MPC-based energy management strategy. The goal is to minimize the fuel consumption and battery SOC variance. The formulated problem can be solved by stochastic dynamic programming technique and updated by road segment. The approach in [31] uses the road preview information to optimize a shifting schedule for an automatic transmission such that the fuel economy and drivability can be improved. The controller is implemented as a standard MPC formulation. In [32] authors solve the minimal fuel consumption problem by generating an optimal velocity trajectory given a road grade profile. Based on the fuel consumption and vehicle models developed in this paper, the optimization problem is solved analytically. The contribution of this study is to develop an integrated power management system for Diesel-powered HEVs with parallel configuration. Different from the traditional HEV power management strategies, the proposed method features SCR system dynamics and road grade prediction. By effectively synergizing EM, SCR, and predictive road information, the proposed power management system can determine the optimal powersplit ratio and urea dosing strategy to achieve the best overall fuel economy and NOx emissions performance over the preview road segment while maintaining battery SOC. Meanwhile, unlike many computationally-heavy optimization-based methods, the proposed algorithm can be conducted in real time thanks to its simple and efficient formulation. The remaining part of this paper is organized as follows. In Section II, the system modeling approach of each component is introduced. In Section III, the development of power management controller is presented. Simulation studies and comparison results are demonstrated in Section IV. At last concluding remarks are made in Section V. II. SYSTEM MODELING A general schematic diagram of a parallel Diesel HEV with SCR aftertreatment system is presented in Fig. 1. Major components include Diesel engine, electric motor, battery, SCR system, powertrain, vehicle and the designed controller. A. Vehicle Dynamics In this paper, a vehicle longitudinal dynamic model is considered [33]. M ax = Fx − Rx − DA − W sin θ,
(1)
MA AND WANG: INTEGRATED POWER MANAGEMENT AND AFTERTREATMENT SYSTEM CONTROL FOR HEV WITH ROAD GRADE PREVIEW
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TABLE I ENGINE PARAMETERS Engine Parameters
Values
Bore Stroke Displacement Compression Ratio Peak Torque Rated Power
98 mm 105 mm 6.4 L 17.5 882 Nm @ 2000 r/min 263 kW @ 3000 r/min
[36] respectively. Some of its key parameters are shown in Table I. Fig. 1.
Schematic diagram of an HEV.
D. Electric Motor and Battery Model
where Fx is the tractive force from ground; Rx is rolling resistance force; DA is aerodynamic drag force; W is gravity force; θ is road grade; ax is longitudinal acceleration; M is vehicle effective mass: M = Mv eh × fM ,
(2)
where Mv eh is vehicle total mass; fM is mass factor incorporating equivalent mass of rotating components based on operating gear. An empirical equation is often taken as: fM = 1 + 0.01 + 0.0025Ntf 2 ,
(3)
where Ntf is combined ratio of transmission and final drive. The power delivered by vehicle is calculated as: Pv eh = M ax × v,
(4)
B. Powertrain Ignoring the wheel dynamics, the torque at wheels can be calculated as (5)
where Rw is the radius of wheel and Tw is the total torque at wheels. Tw is translated from engine and electric motor through transmission and final drive, Tw = (Ten g + TE M ) × Ntf × ηp .
PE M = TE M ωE M ηE M ,
(6)
Ten g and TE M are engine torque output and electric motor torque output respectively. ηp is the powertrain mechanical efficiency. The braking energy regeneration used in the model is a static motor efficiency map. A more comprehensive evaluation can be found in [34]. C. Transient Engine Fuel Consumption and Torque Prediction Model A medium-duty diesel engine model is employed in this study. The model is capable of capturing engine transient dynamics and predicting torque output. The instantaneous fuel consumption is also calculated in this model. A detailed description and experimental validation of the model can be found in [35] and
(7)
where PE M is EM power; TE M is EM output torque; ωE M is EM angular speed; ηE M is EM efficiency and is a function of EM torque and EM speed characterized by a static map. The battery SOC is calculated in current integrating form: tf t I (t) dt ∗ , (8) SOC = SOC − 100 × 0 Q where SOC ∗ is the initial SOC; Q is the battery charge capacity; I is current; t0 and tf are trip start and end time. Based on the open-circuit model, the battery power is: Pbatt = VO C I − RI 2 ,
where v is vehicle velocity.
T w = R w × Fx ,
A typical equivalent circuit model is adopted for electric motor and battery. The electric motor power is calculated as:
(9)
where Pbatt is battery output power and approximately equals to EM power in (7); VO C is battery open-circuit voltage; R is battery internal resistance. The current can then be solved in VO C − VO C 2 − 4Pbatt R . (10) I= 2R E. Emission and SCR Model The emission generated by the Diesel engine is predicted by an adaptive neuro-fuzzy inference system (ANFIS). It takes engine speed and torque as inputs and predicts engine-out NOx concentration. The model implements a fuzzy inference system (FIS) with membership function parameters trained by experimental data. As shown in Fig. 2, the model is capable of predicting emission generation. An SCR system is equipped to reduce the excessive NOx . A brief introduction of SCR working mechanism is presented here for the sake of completeness. Urea solution, known as Diesel Exhaust Fluid (DEF), is injected at upstream of SCR. Through the process of evaporation, thermal decomposition of solid urea and hydrolysis isocyanic acid [37], DEF will be converted to gaseous ammonia. The converted ammonia is absorbed and desorbed by the catalyst simultaneously, as described in: N H3 + θf r ee ↔ N H3∗ ,
(11)
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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 66, NO. 12, DECEMBER 2017
The reaction rates can change substantially depending on SCR temperature, exhaust oxygen concentration, and exhaust NOx component. In this paper, (11), (13), (14), (16), and (19) are considered in SCR modeling. The detailed reasoning can be found in [38]. Per Continuous Stirred Tank Reactor (CSTR) assumption and mass conservation law, a four-state SCR model is presented in (20) shown at the bottom of this page, where CN O , CN H 3 , θN H 3 are the NO concentration, NH3 concentration, ammonia coverage ratio; Θ is the ammonia storage capacity and modeled by empirical equation: Θ = S1 e−S 2 T ,
Fig. 2.
Engine-out emission model validation.
where ammonia absorption N H3∗ and desorption N H3 are shown as forward and reverse reactions respectively. θf r ee is the number of free catalyst sites. The ammonia coverage ratio θN H 3 is defined as the absorbed ammonia MN H 3 divided by catalyst storage capacity Θ θN H 3 =
MN H 3 . Θ
(12)
Absorbed ammonia N H3∗ can take participate in DeNOx reactions. Major reactions are listed below: 4N H3∗ + 4N O + O2 → 4N2 + 6H2 O
(13)
2N H3∗
(14)
+ N O + N O2 → 2N2 + 3H2 O
4N H3∗ + 3N O2 → 3.5N2 + 6H2 O
(15)
In addition, undesired oxidation of ammonia and NO can impair SCR performance as it consumes more ammonia and generates other toxic NOx product. The reactions include:
(21)
with S1 , S2 being positive constants; δi is the ammonia desorption efficiency; T denotes the exhaust gas temperature; SV is the exhaust gas space velocity; CN H 3 ,in represents the ammonia concentration at the SCR inlet. ri = ki exp[−Ei /(RT )] where i = 1, 2, 3, 4F, 4R, 5 indicate the reaction rates of reactions (13), (14), (16), (11), and (19) respectively; ki is a positive constant and Ei is the activation energy. The model is calibrated with experimental data and comparison results can be found in [18]. The model (20) can be further simplified for easier online implementation. Model simplification can be conducted by neglecting NOx and NH3 dynamics since they are much faster than the ammonia coverage ratio dynamics [39], which depending upon the exhaust gas flow rate can range from seconds to tens of seconds. The resulting simplified model consists of three algebraic equations and one ordinary differential equation as (22) shown at the bottom of the page. III. INTEGRATED POWER MANAGEMENT SYSTEM DEVELOPMENT As emphasized above, the designed power management system will incorporate the feature of electric motor, aftertreatment system and road profile together to achieve a better overall fuel economy and emission performances. In this section, the cooperative torque-split and ammonia dosing strategy will be introduced.
4N H3∗ + 3O2 → 2N2 + 6H2 O
(16)
4N H3∗
+ 5O2 → 4N O + 6H2 O
(17)
4N H3∗ + 4O2 → 2N2 O + 6H2 O
(18)
A. Torque-Split Ratio Control
2N O + O2 ↔ 2N O2
(19)
The torque-split ratio is the critical factor deciding the torque distribution between Diesel engine and electric motor. The
⎡
⎤ ⎡ ⎤ C˙ N O −r1 CN O CO 2 θN H 3 Θ − 12 r2 CN O CN O 2 θN H 3 Θ − r5 CN O CO 2 − SV CN O + SV CN O ,in ⎢ ˙ ⎥ ⎥ ⎢ CN O 2 ⎥ ⎢ − 12 r2 CN O CN O 2 θN H 3 Θ + r5 CN O CO 2 − SV CN O 2 + SV CN O 2 ,in ⎥ ⎢ ⎥=⎢ ⎢ ⎥, ⎢ C˙ ⎥ ⎣ ⎦ −CN H 3 [Θr4F (1 − θN H 3 ) + SV ] + r4R ΘθN H 3 δ2 + SV CN H 3 ,in ⎣ N H3 ⎦ −θN H 3 (r4F CN H 3 + r3 Co 2 δ1 + r4R δ2 + r1 CN O CO 2 + r2 CN O CN O 2 ) + r4F CN H 3 θ˙N H 3
(20)
⎤ ⎡ ⎤ 0 −r1 CN O CO 2 θN H 3 Θ − 12 r2 CN O CN O 2 θN H 3 Θ − r5 CN O CO 2 − SV CN O + SV CN O ,in ⎢ 0 ⎥ ⎢ ⎥ − 12 r2 CN O CN O 2 θN H 3 Θ + r5 CN O CO 2 − SV CN O 2 + SV CN O 2 ,in ⎢ ⎥ ⎢ ⎥ ⎢ ⎥=⎢ ⎥. ⎣ 0 ⎦ ⎣ ⎦ −CN H 3 [Θr4F (1 − θN H 3 ) + SV ] + r4R ΘθN H 3 δ2 + SV CN H 3 ,in −θN H 3 (r4F CN H 3 + r3 Co 2 δ1 + r4R δ2 + r1 CN O CO 2 + r2 CN O CN O 2 ) + r4F CN H 3 θ˙N H
(22)
⎡
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MA AND WANG: INTEGRATED POWER MANAGEMENT AND AFTERTREATMENT SYSTEM CONTROL FOR HEV WITH ROAD GRADE PREVIEW
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following definition is adopted throughout this paper: EM % =
TE M , Tr
(23)
where Tr is the torque requested from driver; EM % is the percentage of torque provided by electric motor. In real-world operation, Tr is usually hard to predict as it directly relates to driver behavior. Each driver has different driving style and even the same driver can behave differently in similar situations. The unpredictable distraction can also affect driver’s decision. The underlying uncertainty of human driver has drawn many research interests over decades [40]–[43]. Without dwelling into that topic, a proportional-integrative-derivative (PID) model is used to emulate driver’s braking and accelerator pedal actions for general purpose. It is assumed that the driver will strive to follow the driving cycle speed trajectory and driver uncertainty is neglected. The requested torque Tr is calculated as:
Fig. 3.
Schematic diagram of SCR controller.
Fig. 4.
Ammonia coverage ratio tracking performance.
Tr = α (max (Ten g ) + max (TE M )) + β (min (TE M )) , α, β ∈ [0, 1]
(24)
where α and β are accelerator pedal and brake pedal positions of the driver. When driver demands negative torque, the electric motor will provide braking torque and partially regenerate electric energy; when driver demands positive torque, the engine and/or electric motor will generate propulsion power based on torque-split ratio, namely: TE M = Tr · EM % Ten g = Tr · (1 − EM %) .
(25)
The key is to determine a proper EM % under different operating conditions. As mentioned before, the road grade has a great effect on vehicle working condition. Hence the following design featuring road grade is proposed in (26) shown at the bottom of the page where G is road grade; up , un are designed parameters and will be determined by solving an online optimization problem introduced later in this section. On a flat road, the SOC will be regulated to its initial level by a tuned PID controller. The proposed design implies the torque-split ratio should explicitly reflect road grade profile while avoiding unnecessary SOC variance. Typically, a hybrid power management system will charge the battery before an uphill and use the electric motor to power the vehicle when it climbs up and regenerate the electric energy through braking when it goes downhill. The strategy works well when a single hill scenario is considered. However, during the whole driving cycle the road grade may change frequently. In this case the simple charge-deplete strategy can easily trigger battery SOC constraints and result in a higher accumulated fuel consumption. To achieve the best overall fuel economy for the entire trip, the road profile needs to be considered as a whole.
EM % =
⎧ ⎪ ⎪ ⎨ ⎪ ⎪ ⎩
∗
kp (SOC − SOC) + ki
B. SCR Ammonia Dosing Control The ammonia dosing controller involves a two-level structure: ammonia coverage ratio reference generator and ammonia coverage ratio tracking controller. Fig. 3 shows the detailed schematic diagram of the SCR controller. 1) Ammonia Coverage Ratio Tracking: The function of tracking controller is to regulate the catalyst ammonia coverage ratio to a reference level within a reasonable amount of time. Based on the model (20), a Lyapunov-based tracking controller is proposed in authors’ previous work [22]. Asymptotic stability of the tracking error is proved with a fast convergence rate. However, the proposed tracking controller requires many sensor measurements including NOx and NH3 concentrations at tailpipe which may not be available on all vehicles. For the sake of generality, a well-tuned PID controller is adopted in this part. The tracking performance and control input are presented in Figs. 4 and 5. 2) Ammonia Coverage Ratio Reference: To evaluate the SCR NOx removal performance, we define the following efficiency: η =1−
CN O , CN O ,in
(27)
where CN O is the NO emission at tailpipe; CN O ,in is NO emission generated by engine; η denotes the NOx reduction up G ∗
(SOC − SOC) dt + un G
G>0 ∗ kd d(S O Cdt−S O C )
G = 0, G
