基于端云协同优化的混合动力车辆实时能量管理策略研究

doi:10.3901/JME.2025.16.227

机械工程学报 ›› 2025, Vol. 61 ›› Issue (16): 227-238.doi: 10.3901/JME.2025.16.227

• 运载工程 • 上一篇

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基于端云协同优化的混合动力车辆实时能量管理策略研究

杨超, 杜雪龙, 王伟达, 杨刘权, 项昌乐

北京理工大学机械与车辆学院北京 100081

接受日期:2024-08-15 出版日期:2025-04-16 发布日期:2025-04-16
作者简介:杨超(通信作者)，男，1986年出生，博士，教授，博士研究生导师。主要研究方向为车辆机电复合传动综合控制技术、线控底盘域控制技术、智能驾驶车辆决策规划技术、飞行车辆技术。E-mail：cyang@bit.edu.cn;杜雪龙，男，1997年出生，博士研究生。主要研究方向为混合动力系统能量优化控制。E-mail：dxl954074081@163.com
基金资助:
国家自然科学基金资助项目(52275047，51975048)

Research on Real-time Energy Management Strategy for Hybrid Electric Vehicles based on Edge-cloud Collaborative Optimization

YANG Chao, DU Xuelong, WANG Weida, YANG Liuquan, XIANG Changle

School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081

Accepted:2024-08-15 Online:2025-04-16 Published:2025-04-16

摘要/Abstract

摘要： 改善燃油经济性与降低计算资源的占用是提升网联式混合动力车辆性能的重要途径。为了实现这一目标，提出一种基于端云协同优化的混合动力车辆实时能量管理策略。为了有效利用云端存储的驾驶工况信息，设计一种子工况划分方法，利用划分后的子工况构建拟合函数以减轻车端的在线优化负担。提出触发式端云协同优化策略，推导获取事件触发周期并据此设计综合事件触发机制，实现非触发时刻降低计算量和参数更新频次，触发时刻结合拟合解完成动作修正改善车辆实时运行性能。结果表明，所提出的触发式端云协同优化策略在不同测试场景下能够显著改善车辆的燃油经济性，同时有效降低了控制动作输出的计算成本，策略的应用潜力在混合动力试验台架上得到了验证。

关键词: 网联式混合动力车辆, 能量管理策略, 事件触发, 端云协同优化

Abstract: Improving vehicle fuel economy and reducing the occupation of computing resources are crucial measures to elevate the performance of connected hybrid electric vehicles. To achieve this goal, a real-time energy management strategy for hybrid electric vehicles based on edge-cloud collaborative optimization is proposed here. In order to effectively utilize the driving information stored in the cloud, a sub-cycle division method is designed, and the divided sub-cycles are applied to construct the fitting function, alleviating the online optimization burden of the vehicle. A trigger-based edge-cloud collaborative optimization strategy is proposed. The trigger period is derived and a comprehensive event-triggered mechanism is designed accordingly to reduce the computational load and parameter updating frequency during non-trigger moments, and modify the action in combination with the fitting solution at trigger moments to improve the real-time operation performance of the vehicle. Results demonstrate that the proposed trigger-based edge-cloud collaborative optimization strategy significantly improves the fuel economy of the vehicle and effectively reduces the computational cost of outputting control actions in various testing scenarios. The application potential of the proposed strategy is validated on the hybrid test bench.

Key words: connected hybrid electric vehicles, energy management strategy, event-triggered, edge-cloud collaborative optimization

中图分类号:

U469

杨超, 杜雪龙, 王伟达, 杨刘权, 项昌乐. 基于端云协同优化的混合动力车辆实时能量管理策略研究[J]. 机械工程学报, 2025, 61(16): 227-238.

YANG Chao, DU Xuelong, WANG Weida, YANG Liuquan, XIANG Changle. Research on Real-time Energy Management Strategy for Hybrid Electric Vehicles based on Edge-cloud Collaborative Optimization[J]. Journal of Mechanical Engineering, 2025, 61(16): 227-238.

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基于端云协同优化的混合动力车辆实时能量管理策略研究

Research on Real-time Energy Management Strategy for Hybrid Electric Vehicles based on Edge-cloud Collaborative Optimization

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