中国电机工程学报2024，Vol.44Issue(12)：4788-4798,中插15,12.DOI:10.13334/j.0258-8013.pcsee.230023
基于近端策略优化算法的燃料电池混合动力系统综合价值损耗最小能量管理方法
Comprehensive Value Depletion Minimization Energy Management Method for Fuel Cell Hybrid Systems Based on Proximal Policy Optimization Algorithm
摘要
Abstract
In order to reduce the fuel economy cost of fuel cell hybrid systems of city EMUs and improve the durability of the fuel cell,this paper proposes an energy management method based on proximal policy optimization algorithm.The method models the hybrid system energy management problem as a Markov decision process,and sets the reward function with the optimization objective of minimizing the comprehensive value depletion considering both fuel economy and fuel cell durability.Then,a deep reinforcement learning algorithm with high convergence speed,the proximal policy optimization algorithm,is used to solve the problem and achieve a reasonable and effective distribution of load power between the fuel cell and lithium battery,and finally,the actual operating conditions of EMUs are used for experimental verification.The experimental results show that the proposed method reduces the comprehensive value depletion by 19.71%and 5.87%under the training condition compared with the equivalent hydrogen consumption minimum and the Q-learning respectively,and reduces the comprehensive value depletion by 18.05%and 13.52%under the unknown condition respectively.The results show that the proposed method can effectively reduce the comprehensive value depletion and has good adaptability to working conditions.关键词
燃料电池混合动力系统/深度强化学习/综合价值损耗/近端策略优化算法/能量管理Key words
fuel cell hybrid system/deep reinforcement learning/comprehensive value depletion/proximal policy optimization/energy management分类
信息技术与安全科学引用本文复制引用
李奇,刘鑫,孟翔,谭逸,杨明泽,张世聪,陈维荣..基于近端策略优化算法的燃料电池混合动力系统综合价值损耗最小能量管理方法[J].中国电机工程学报,2024,44(12):4788-4798,中插15,12.基金项目
国家自然科学基金项目(52377123) （52377123）
四川省自然科学基金项目(2022NSFSC0027) （2022NSFSC0027）
中国国家铁路集团有限公司科研开发计划重点课题(N2021J030). Project Supported by National Natural Science Foundation of China(52377123) （N2021J030）
Natural Science Foundation of Sichuan Province(2022NSFSC0027) （2022NSFSC0027）
Key Research and Development Project of China National Railway Group Co.,Ltd.(N2021J030). （N2021J030）
