基于强化学习的P2结构混动车辆能量优化控制

Reinforcement Learning-Based Energy Optimization Control for Hybrid Vehicles with P2 Structure

胡作磊 ¹童紫威 ²刘平 ³施伟⁴

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作者信息

1. 湖南大学信息科学与工程学院,湖南长沙 410082
2. 湖南大学信息科学与工程学院,湖南长沙 410082;湖南锦和园林建设工程有限公司,湖南长沙 410148
3. 中国(湖南)自由贸易试验区长沙片区临空管理委员会,湖南长沙 410137
4. 长沙医学院信息工程学院,湖南长沙 410219
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摘要

针对P2结构混动车辆,提出了一种基于强化学习的自适应等效燃油消耗最小策略(RL-EC-MS),通过两个智能体实现等效因子的自适应更新和车辆扭矩的动态分配,以适应不断变化的驾驶需求.通过MATLAB/Simulink仿真平台对比了 RL-ECMS与传统ECMS和基于规则的控制策略.结果表明,RL-ECMS在FTP75和FTP75-Highway两种典型驾驶工况下均能实现更低的燃油消耗,且不影响车辆性能.同时测试了未经训练的ECE典型工况,结果表明本文所提算法同样具有良好的泛化性与鲁棒性.

Abstract

A reinforcement learning-based adaptive equivalent fuel consumption minimization strategy(RL-ECMS)is proposed for P2-structured hybrid vehicles,which realizes adaptive updating of the equivalence factor and dynamic allocation of vehicle torque through two intelligences to adapt to the changing driving demands.The RL-ECMS is compared with the conventional ECMS and rule-based control strategies through the MATLAB/Simulink simulation platform.The results show that the RL-ECMS can achieve lower fuel consumption under both FTP75 and FTP75-Highway typical driving conditions without affecting vehicle performance.The untrained ECE typical conditions are also tested,and the result shows that the algorithm proposed in this paper also has good generalization and robustness.

关键词

混合动力车辆/能量优化/强化学习/自适应控制

Key words

hybrid vehicles/energy optimization/reinforcement learning/aadaptive control

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基金项目

国家自然科学基金资助项目(201601420565)

出版年

2024

计算技术与自动化

湖南大学

计算技术与自动化

CSTPCD

影响因子：0.295

ISSN：1003-6199

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