Dual Heuristic Dynamic Programming Based Energy Management Control for Hybrid Electric Vehicles
Yaqian Wang and
Xiaohong Jiao
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Yaqian Wang: Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment, Yanshan University, Qinhuangdao 066004, China
Xiaohong Jiao: Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment, Yanshan University, Qinhuangdao 066004, China
Energies, 2022, vol. 15, issue 9, 1-19
Abstract:
This paper investigates an adaptive dynamic programming (ADP)-based energy management control strategy for a series-parallel hybrid electric vehicle (HEV). This strategy can further minimize the equivalent fuel consumption while satisfying the battery level constraints and vehicle power demand. Dual heuristic dynamic programming (DHP) is one of the basic structures of ADP, combining reinforcement learning, dynamic programming (DP) optimization principle, and neural network approximation function, which has higher accuracy with a slightly more complex structure. In this regard, the DHP energy management strategy (EMS) is designed by the backpropagation neural network (BPNN) as an Action network and two Critic networks approximating the control policy and the gradient of value function concerning the state variable. By comparing with the existing results such as HDP-based and rule-based control strategies, the equivalent consumption minimum strategy (ECMS), and reinforcement learning (RL)-based strategy, simulation results verify the robustness of fuel economy and the adaptability of the power-split optimization of the proposed EMS to different driving conditions.
Keywords: dual heuristic dynamic programming (DHP); hybrid electric vehicle (HEV); backpropagation neural network (BPNN); energy management strategy (EMS) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (5)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:9:p:3235-:d:804621
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