Review of energy management strategies for hybrid electric vehicles based on driving information

doi:10.3785/j.issn.1008-973X.2026.07.016

Journal of ZheJiang University (Engineering Science)

2026, Vol. 60

Issue (7): 1539-1556 DOI: 10.3785/j.issn.1008-973X.2026.07.016

Review of energy management strategies for hybrid electric vehicles based on driving information

Qiao LUO1(

),Jun CHEN1,Jie GENG2,Chaoyang TANG3,Chunyun FU1,*(

)

1. College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China
2. Automotive Digitalization Department, Shenyang MXNavi Co Ltd, Shenyang 110169, China
3. New Power Development Department, Chongqing Chang’an Automobile Co Ltd, Chongqing 400023, China

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Abstract

Energy management of hybrid vehicles faces multiple challenges arising from dynamic traffic environments, and the key breakthrough lies in the efficient fusion of driving information. Existing studies offer insufficient analysis into the coupling mechanisms between driving information and strategy frameworks. To address this, the evolution from traditional methods to data-driven paradigms was considered to investigate the coupling mechanisms between driving information and energy management strategies in both traditional and deep reinforcement learning frameworks. The nonlinear impacts of driving styles, driving conditions, and road environments on vehicle energy consumption were analyzed, and the necessity of driving information fusion was clarified. The action mechanisms of driving information under the rule-based and the optimization-based traditional strategies were compared and analyzed, which revealed the adaptability limitations of traditional strategies. Focusing on the deep reinforcement learning framework, the driving information integration methods of feature engineering fusion and end-to-end mapping were summarized, and the role of driving information in improving the model performance of multi-objective optimization, scenario generalization, and transfer learning were demonstrated. Future studies should balance the discretization accuracy and the computational efficiency in traditional methods, while exploring distributed control architectures, rule-data hybrid-driven models, and world-model-driven training data augmentation techniques in the deep reinforcement learning frameworks.

Key words： driving information energy management strategy deep reinforcement learning rule optimization hybrid electric vehicle

Received: 01 April 2025 Published: 23 May 2026

CLC:

U 469.7

Fund: 重庆市自然科学基金创新发展联合基金资助项目（CSTB2023NSCQ-LZX0169）.

Corresponding Authors: Chunyun FU E-mail: luo_qiao@cqu.edu.cn;fuchunyun@cqu.edu.cn

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Cite this article:

Qiao LUO,Jun CHEN,Jie GENG,Chaoyang TANG,Chunyun FU. Review of energy management strategies for hybrid electric vehicles based on driving information. Journal of ZheJiang University (Engineering Science), 2026, 60(7): 1539-1556.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2026.07.016 OR https://www.zjujournals.com/eng/Y2026/V60/I7/1539

基于行车信息的混合动力汽车能量管理策略综述

混合动力汽车能量管理面临由动态交通环境引发的多重挑战，其突破关键在于行车信息的高效融合. 现有研究对行车信息与策略框架的耦合机理缺乏深入解析，为此，结合从传统方法至数据驱动范式的演进过程，探讨传统框架与深度强化学习框架下行车信息与能量管理策略的耦合机制. 解析驾驶风格、行驶工况及道路环境对整车能耗的非线性影响，阐明行车信息融合的必要性；对比分析基于规则与基于优化的传统策略中行车信息的作用机理，揭示传统策略的适应性局限；聚焦深度强化学习框架，总结特征工程融合与端到端映射这2类行车信息整合方法，并说明行车信息在多目标优化、场景泛化以及迁移学习中对模型性能的提升作用. 未来研究须在传统方法中平衡离散化精度与计算效率，同时在深度强化学习框架下探索分布式控制架构、规则-数据混合驱动模型及世界模型驱动的训练数据增强技术.

关键词： 行车信息, 能量管理策略, 深度强化学习, 规则优化, 混合动力汽车

Fig.1 Simplified energy flow diagram of hybrid electric vehicles

Fig.2 Schematic diagram of driving information acquisition in intelligent transportation system

Tab.1 Characteristics of methods for optimizing rule-based EMS using driving information

Tab.2 Characteristics of methods for optimizing optimization-based EMS using driving information

Tab.3 Comparison of driving information fusion methods based on feature fusion

Tab.4 Comparison of driving information fusion methods based on multi-source perception mapping


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