To achieve rational coordination of the front and rear axle torque of commercial vehicles equipped with hydraulic hybrid motor-assisted systems (HHMAS), and further enhance the vehicle’s fuel efficiency and traction capability. A hydraulic pump displacement control strategy based on energy-efficiency-orientation (EEO) is proposed for driving force distribution. Firstly, the efficiency is analyzed from the perspective of system energy flow. Then, considering the time-varying efficiency characteristics of the hydraulic variable pump and hydraulic motor, the stochastic dynamic optimization problem is modeled as a markov decision process, and a value function estimation method is introduced to approximate the optimal energy efficiency of the entire HHMAS. Finally, the results indicate that the EEO strategy can enhance the fuel economy of vehicles, with a fuel-saving rate of 6.3% compared to the wheel-speed-following-control (WSFC) strategy. In addition, the proposed strategy exhibits superior traction capacity and climbing ability compared to the traditional WSFC strategy.
De RobbioRCamerettiMCMancarusoE. Investigation by modelling of a plug-in hybrid electric commercial vehicle with diesel engine on WLTC. Fuel2022; 317: 123519.
2.
SongDFLiLXZengXH, et al. Hardware-in-the-loop validation of speed synchronization controller for a heavy vehicle with Hydraulics AddiDrive System. Adv Mech Eng2018; 10: 1687814018767169.
3.
YangJLiuBZhangTZ, et al. Application of energy conversion and integration technologies based on electro-hydraulic hybrid power systems: a review. Energy Convers Manage2022; 272: 116372.
4.
LiuHLLiDFChenGP, et al. Research on power coupling characteristics and acceleration strategy of electro-hydrostatic hydraulic hybrid power system. Proc IMechE, Part I: J Systems and Control Engineering2021; 235: 1445–1459.
5.
ZhangHXWangFXuB, et al. Extending battery lifetime for electric wheel loaders with electric-hydraulic hybrid powertrain. Energy2022; 261: 125190.
6.
PanHYQiLFZhangZT, et al. Kinetic energy harvesting technologies for applications in land transportation: a comprehensive review. Appl Energy2021; 286: 116518.
7.
XiongHYTanZRZhangRH, et al. A new dual axle drive optimization control strategy for electric vehicles using vehicle-to-infrastructure communications. IEEE Trans Industr Inform2020; 16: 2574–2582.
8.
HuJYLiJQHuZY, et al. Energy-efficient torque-allocation strategy for a 6 x 6 vehicle using electric wheels. Etransportation2021; 10: 100136.
9.
JinLQTianDTZhangQX, et al. Optimal torque distribution control of multi-axle electric vehicles with in-wheel motors based on DDPG algorithm. Energies2020; 13: 1331.
10.
TorinssonJJonassonMYangDR, et al. Energy reduction by power loss minimisation through wheel torque allocation in electric vehicles: a simulation-based approach. Veh Syst Dyn2022; 60: 1488–1511.
11.
WangJNGaoSLWangK, et al. Wheel torque distribution optimization of four-wheel independent-drive electric vehicle for energy efficient driving. Control Eng Pract2021; 110: 104779.
12.
HajilooRKhajepourAZenginH, et al. A coupled force predictive control of vehicle stability using front/rear torque allocation with experimental verification. Veh Syst Dyn2022; 60: 2541–2563.
13.
SunXDHuCCZhuJG, et al. MPTC for PMSMs of EVs with multi-motor driven system considering optimal energy allocation. IEEE Trans Magn2019; 55: 1–6.
14.
ChoJHuhK.Torque vectoring system design for hybrid electric-all wheel drive vehicle. Proc IMechE, Part D: J Automobile Engineering2020; 234: 2680–2692.
15.
GaoBZYanYJChuH, et al. Torque allocation of four-wheel drive EVs considering tire slip energy. Sci China Inform Sci2022; 65: 122202.
16.
ZhiJNWangXWShiQL, et al. Torque allocation strategy based on economy and stability for electric vehicle considering controllability after motors failure. Proc IMechE, Part D: J Automobile Engineering2023; 237: 2759–2779.
17.
ChenGPLiuHL.Design and energy utilization of electro-hydrostatic hydraulic hybrid system for battery bus. Proc IMechE, Part D: J Automobile Engineering2021; 235: 759–772.
18.
EckertJJBarbosaTPSilvaFL, et al. Optimum fuzzy logic controller applied to a hybrid hydraulic vehicle to minimize fuel consumption and emissions. Expert Syst Appl2022; 207: 117903.
19.
WangPXRuiXTYuHL.Study on dynamic track tension control for high-speed tracked vehicles. Mech Syst Signal Process2019; 132: 277–292.
20.
SongDFYangDPZengXH, et al. A coordinated control of hydraulic hub-motor auxiliary system for heavy truck. Measurement2021; 175: 109087.
21.
ZengXHLiGHYinGD, et al. Model predictive control-based dynamic coordinate strategy for hydraulic hub-motor auxiliary system of a heavy commercial vehicle. Mech Syst Signal Process2018; 101: 97–120.
22.
ZengXZhangXYangD, et al. Dynamic coordination control for hydraulic hub-motor auxiliary system based on NMPC algorithm. Measurement2022; 191: 110795.
23.
QuSYFassbenderDVaccaA, et al. A high-efficient solution for electro-hydraulic actuators with energy regeneration capability. Energy2021; 216: 119291.
24.
LiMTFossRStelsonKA, et al. Design, dynamic modeling, and experimental validation of a novel alternating flow variable displacement hydraulic pump. IEEE ASME Trans Mechatron2019; 24: 1294–1305.
25.
ZengXHQianQFChenHX, et al. A unified quantitative analysis of fuel economy for hybrid electric vehicles based on energy flow. J Clean Prod2021; 292: 126040.
26.
LeeSMBoomsmaTK.An approximate dynamic programming algorithm for short-term electric vehicle fleet operation under uncertainty. Appl Energy2022; 325: 119793.
27.
UcinskiD.D-optimal sensor selection in the presence of correlated measurement noise. Measurement2020; 164: 107873.
28.
ZhangWWangJXXuZY, et al. A generalized energy management framework for hybrid construction vehicles via model-based reinforcement learning. Energy2022; 260: 124849.
29.
ZhangHLPengJKDongHX, et al. Hierarchical reinforcement learning based energy management strategy of plug-in hybrid electric vehicle for ecological car-following process. Appl Energy2023; 333: 120599.
30.
DandachiGCerfSHadjadj-AoulY, et al. A robust control-theory-based exploration strategy in deep reinforcement learning for virtual network embedding. Comput Netw2022; 218: 109366.
