Adaptive deep reinforcement learning energy management for hybrid electric vehicles considering driving condition recognition

Dehai Zhang, Junhui Li, Ningyuan Guo, Yonggang Liu, Shiquan Shen, Fuxing Wei, Zheng Chen and Jia Zheng

Energy, 2024, vol. 313, issue C

Abstract: Adaptive energy management strategy (EMS) can adjust the underlying control scheme of hybrid electric vehicles (HEVs) according to different external conditions, thus maintaining high performance consistently. For this reason, this study designs a double-layer EMS for a power-split HEV, which combines deep reinforcement learning (DRL) with driving condition recognition (DCR) to achieve efficient operation of powertrain under different driving conditions. At the upper layer, the long-term dependencies in velocity sequences are captured through the sequential networks to enhance the accuracy of DCR. At the lower layer, multi-objective reward functions are formulated, and the optimal weight parameters are identified for different driving conditions. Furthermore, the deep deterministic policy gradient (DDPG) algorithm is employed to dynamically optimize power flow. The simulation experiments are conducted to verify the feasibility and the energy-saving effects of the proposed strategy. The results indicate that integrating variations in driving conditions into the energy allocation of HEVs can further improve the fuel economy and maintain battery health. Specifically, the method enhances the fuel economy by an average of 10.76 %, compared to a number of traditional benchmark strategies.

Keywords: Adaptive energy management strategy; Driving condition recognition (DCR); Multi-objective optimization; Long short time memory (LSTM); Deep deterministic policy gradient (DDPG) (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544224038647
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:313:y:2024:i:c:s0360544224038647

DOI: 10.1016/j.energy.2024.134086

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().