基于行车信息的混合动力汽车能量管理策略综述

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

浙江大学学报(工学版)

2026, Vol. 60

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

机械工程

基于行车信息的混合动力汽车能量管理策略综述

罗桥1(

),陈俊1,耿杰2,唐朝阳3,傅春耘1,*(

)

1. 重庆大学机械与运载工程学院，重庆 400044
2. 沈阳美行科技股份有限公司汽车数字化部，辽宁沈阳 110169
3. 重庆长安汽车股份有限公司新动力开发部，重庆 400023

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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摘要：

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

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

收稿日期: 2025-04-01 出版日期: 2026-05-23

CLC:

U 469.7

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

通讯作者: 傅春耘 E-mail: luo_qiao@cqu.edu.cn;fuchunyun@cqu.edu.cn

作者简介: 罗桥（2001—），男，硕士生，从事新能源汽车控制研究. orcid.org/0009-0003-6654-9899. E-mail：luo_qiao@cqu.edu.cn

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引用本文:

罗桥,陈俊,耿杰,唐朝阳,傅春耘. 基于行车信息的混合动力汽车能量管理策略综述[J]. 浙江大学学报(工学版), 2026, 60(7): 1539-1556.

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.

链接本文:

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

图 1 混合动力汽车能量流简图

图 2 智能交通系统中行车信息获取示意图

表 1 利用行车信息优化基于规则的EMS的方法特点

表 2 利用行车信息优化基于优化的EMS的方法特点

表 3 基于特征融合的行车信息融合方法对比

表 4 基于多源感知映射的行车信息融合方法对比

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