Hierarchical energy management strategy based on adaptive dynamic programming for hybrid electric vehicles in car-following scenarios

Wenbin Tang, Yaqian Wang, Xiaohong Jiao and Lina Ren

Energy, 2023, vol. 265, issue C

Abstract: In order to improve the driving safety and fuel economy of hybrid electric vehicles (HEVs) in car-following scenarios, this paper proposes a hierarchical energy management strategy (EMS) based on adaptive dynamic programming (ADP). In the upper-layer speed planning, the speed prediction model of the preceding vehicle is established utilizing a back-propagation neural network (BPNN), and the host vehicle speed is planned based on heuristic dynamic programming (HDP) real-time through the predicted preceding vehicle speed and the current state of the host vehicle. In the lower-layer energy management control, the EMS based on dual heuristic dynamic programming (DHP) is used to realize the power allocation of each power source according to the planned speed to reduce fuel consumption. In both HDP and DHP algorithms, BPNN is used to implement the critic network (CN) and action network (AN) to obtain a more accurate control variable and faster convergence speed. By comparing the results with various existing EMSs in the different driving cycles, the simulation results show that the proposed EMS ensures real-time planning speed and good following performance while reducing energy consumption.

Keywords: Adaptive dynamic programming (ADP); Predictive cruise control (PCC); Back-propagation neural network (BPNN); Energy management strategy (EMS); Hybrid electric vehicle (HEV) (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:265:y:2023:i:c:s0360544222031504

DOI: 10.1016/j.energy.2022.126264

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