Connected and Automated Vehicles (CAV) technology presents significant opportunities for energy saving in the transportation sector. CAV technology forecasts vehicle and powertrain power needs under various terrain, ambient, and traffic conditions. Integration of the CAV technology in Hybrid Electric Vehicles (HEVs) provides the opportunity for optimal vehicle operation. Indeed, Hybrid Electric Vehicle powertrains present high degrees of flexibility and possibility for choosing optimum powertrain modes based on the predicted traction power needs. In modeling complex CAV powertrain dynamics, the modeler needs to consider short-time scale powertrain dynamics, such as engine transients, and hysteresis of mode-switching for a multi-mode HEV. Therefore, the powertrain dynamics essential for developing powertrain controllers for a class of connected HEVs is presented. To this end, control-oriented powertrain dynamic models for a test vehicle consisting of full electric, hybrid, and conventional engine operating modes are developed. The resulting powertrain model can forecast vehicle traction torque and energy consumption for the specified prediction horizon of the test vehicle. The model considers different operating modes and associated energy penalty terms for mode switching. Thus, the vehicle controller can determine the optimum powertrain mode, torque, and speed for forecasted vehicle operation via utilizing connectivity data. The powertrain model is validated against the experimental data and shows prediction error of less than 5% for predicting vehicle energy consumption. The model is used to create energy penalty maps that can be used for CAV control, for example fuel penalty map for engine torque changes (10–40 Nm) at each engine speed. The results of model-based optimization show optimum switching delays ranging from 0.4 to 1.4 s to avoid hysteresis in mode switching.
GaoDWMiCEmadiA. Modeling and simulation of electric and hybrid vehicles. Proc IEEE2007; 95(4): 729–745.
4.
WilcuttsMAKarl HedrickJ. Model-based control for automotive cold start applications. Veh Syst Dyn2007; 45(5): 399–411.
5.
DuWZhaoSJinL, et al. Parameter optimization of rule-based control strategy for multi-mode hybrid electric vehicle. Proc IMechE, Part D: J Automobile Engineering2020; 234(10–11): 2706–2716.
6.
DuSYangYLiuC, et al. Multi-objective real-time optimization energy management strategy for plug-in hybrid electric vehicle. Proc IMechE, Part D: J Automobile Engineering2019; 233(5): 1067–1080.
7.
WangPLiJYuY, et al. Energy management of plug-in hybrid electric vehicle based on trip characteristic prediction. Proc IMechE, Part D: J Automobile Engineering2020; 234(8): 2239–2259.
8.
ZhangYZhaoHHuangK, et al. Hybrid optimization and its applications for multi-mode plug-in hybrid electric vehicle. Proc IMechE, Part D: J Automobile Engineering2020; 234(1): 228–244.
9.
LiuJPengH. Modeling and control of a power-split hybrid vehicle. IEEE Trans Control Syst Technol2008; 16(6): 1242–1251.
10.
TremblayODessaintLA. Experimental validation of a battery dynamic model for EV applications. World Electr Veh J2009; 3(2): 289–298.
JurkovicSRahmanKPatelN, et al. Next generation Voltec electric machines; design and optimization for performance and rare-earth mitigation. SAE Int J Altern Powertrains2015; 4: 336–342.
13.
XiongRDuanYCaoJ, et al. Battery and ultracapacitor in-the-loop approach to validate a real-time power management method for an all-climate electric vehicle. Appl Energy2018; 217: 153–165.
14.
ZhaoJHuYMuldoonS, et al. “InfoRich” eco-driving control strategy for connected and automated vehicles. In: 2019 American control conference (ACC), Philadelphia, PA, USA, 2019, pp.4621–4627.
15.
HanJVahidiASciarrettaA. Fundamentals of energy efficient driving for combustion engine and electric vehicles: an optimal control perspective. Automatica2019; 103: 558–572.
16.
ShaoYMohd ZulkefliMASunZ. Vehicle and powertrain optimization for autonomous and connected vehicles. Mech Eng2017; 139(9): S19–S23.
17.
ShaoYSunZ. Optimal vehicle speed and gear position control for connected and autonomous vehicles. In: 2019 American control conference (ACC), Philadelphia, PA, USA, 2019, pp.545–550.
18.
NikzadfarKBakhshinezhadNMirMohammadSadeghiSA, et al. An optimal gear shifting strategy for minimizing fuel consumption based on engine optimum operation line. SAE technical paper 2019-01-5055, 2019.
19.
OglieveCJMohammadpourMRahnejatH. Optimisation of the vehicle transmission and the gear-shifting strategy for the minimum fuel consumption and the minimum nitrogen oxide emissions. Proc IMechE, Part D: J Automobile Engineering2017; 231: 883–899.
20.
LuCDongJHuL. Energy-Efficient adaptive cruise control for electric connected and autonomous vehicles. IEEE Intell Transp Syst Mag2019; 11(3): 42–55.
21.
WuFYuYWangJ, et al. Predictive energy-saving optimization based on nonlinear model predictive control for cooperative connected vehicles platoon with V2V communication. Energy2019; 189.
22.
BatraMMcPheeJAzadNL. Real-time model predictive control of connected electric vehicles. Veh Syst Dyn2019; 57(11): 1720–1743.
23.
BatraMMaitlandAMcPheeJ, et al. Non-linear model predictive anti-jerk cruise control for electric vehicles with slip-based constraints. In: 2018 annual American control conference (ACC), Milwaukee, WI, USA, 2018, pp.3915–3920.
24.
YiZShirkM. Data-driven optimal charging decision making for connected and automated electric vehicles: a personal usage scenario. Transp Res Part C Emerg Technol2018; 86: 37–58.
25.
GuptaSRajakumar DeshpandeSTufanoD, et al. Estimation of fuel economy on real-world routes for next-generation connected and automated hybrid powertrains. SAE technical paper 2020-01-0593, 2020.
26.
MaGGhasemiMSongX. Integrated powertrain energy management and vehicle coordination for multiple connected hybrid electric vehicles. IEEE Trans Vehicular Technol2018; 67(4): 2893–2899.
27.
HomChaudhuriBLinRPisuP. Hierarchical control strategies for energy management of connected hybrid electric vehicles in urban roads. Transp Res Part C Emerg Technol2016; 62: 70–86.
28.
OnckenJChenB. Real-Time model predictive powertrain control for a connected plug-In hybrid electric vehicle. IEEE Trans Vehicular Technol2020; 69(8): 8420–8432.
29.
ZhaoLMahbubAMMalikopoulosAA. Optimal vehicle dynamics and powertrain control for connected and automated vehicles. In: 2019 IEEE conference on control technology and applications (CCTA), Hong Kong, China, 2019, pp.33–38.
30.
WangHSachevaKTrippJ, et al. Optimal map-based mode selection and powertrain control for a multi-mode plug-in hybrid electric vehicle. In: 2018 14th IEEE/ASME international conference on mechatronic and embedded systems and applications (MESA), Oulu, 2018, pp.1–6.
31.
ChauCKElbassioniKTsengCM. Drive mode optimization and path planning for plug-in hybrid electric vehicles. IEEE Trans Intell Transp Syst2017; 18(12): 3421–3432.
32.
DoshiNHanoverDHemmatiS, et al. Modeling of thermal dynamics of a connected hybrid electric vehicle for integrated HVAC and powertrain optimal operation. In: Proceedings of the ASME 2019 dynamic systems and control conference, Park City, UT, USA, 8–11 October 2019, vol. 2, V002T23A005. ASME.
33.
HemmatiSDoshiNHanoverD, et al. Integrated cabin heating and powertrain thermal energy management for a connected hybrid electric vehicle. Appl Energy2021; 283: 116353.
34.
YadavR. Modeling and analysis of energy consumption in Chevrolet Volt Gen II hybrid electric vehicle. Master’s Thesis, Michigan Technological University, 2018.
35.
BhatPKOnckenJYadavR, et al. 2019, Generation of optimal velocity trajectory for real-time predictive control of a multi-mode PHEV. In: 2019 IEEE 90th vehicular technology conference (VTC2019-Fall), Honolulu, HI, USA, 2019, pp.1–5.
36.
HendricksESorensonSC. Mean value modelling of spark ignition engines. SAE technical paper 900616, 1998.
RajamaniR. (2012) Mean Value Modeling of SI and Diesel Engines. In: Vehicle Dynamics and Control. Mechanical Engineering Series. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-1433-9_9
39.
ShaoYSunZ. Energy-Efficient connected and automated vehicles: real-time traffic prediction-enabled co-optimization of vehicle motion and powertrain operation. IEEE Vehicular Technol Mag2021; 16(3): 47–56.
40.
WipkeKBCuddyMRBurchSD. ADVISOR 2.1: a user-friendly advanced powertrain simulation using a combined backward/forward approach. IEEE Trans Vehicular Technol1999; 48(6): 1751–1761.
ChanCC. The state of the art of electric, hybrid, and fuel cell vehicles. Proc IEEE2007; 95(4): 704–718.
43.
KimNDuobaMKimN, et al. Validating volt PHEV model with dynamometer test data using autonomie. SAE Int J Passenger Cars Mech Syst2013; 6(2): 985–992.
44.
ConlonBMBlohmTHarpsterM, et al. The next generation “voltec” extended range EV propulsion system. SAE Int J Altern Powertrains2015; 4: 248–259.
45.
KnauffMMcLaughlinJDafisC, et al. Simulink model of a lithium-ion battery for the hybrid power system testbed. In: Electric ship technologies symposium, 2007, ESTS ‘07, Arlington, Virginia, USA, June 2007. IEEE.
46.
RobertsABrooksRShipwayP. Internal combustion engine cold-start efficiency: a review of the problem, causes and potential solutions. Energy Convers Manag2014; 82: 327–350.
47.
ParkSZhangDMouraS. Hybrid electrochemical modeling with recurrent neural networks for Li-ion batteries. In: 2017 American control conference (ACC), Seattle, WA, USA, 2017, pp.3777–3782.
48.
GimaZTKatoDKleinR, et al. Analysis of online parameter estimation for electrochemical Li-ion battery models via reduced sensitivity equations. In: 2020 American control conference (ACC), Denver, CO, USA, 2020, pp.373–378.
