当前位置: X-MOL 学术IEEE Trans. Transp. Electrif. › 论文详情
Ensemble Reinforcement Learning-Based Supervisory Control of Hybrid Electric Vehicle for Fuel Economy Improvement
IEEE Transactions on Transportation Electrification ( IF 5.444 ) Pub Date : 2020-04-28 , DOI: 10.1109/tte.2020.2991079
Bin Xu; Xiaosong Hu; Xiaolin Tang; Xianke Lin; Huayi Li; Dhruvang Rathod; Zoran Filipi

This study proposes an ensemble reinforcement learning (RL) strategy to improve the fuel economy. A parallel hybrid electric vehicle model is first presented, followed by an introduction of ensemble RL strategy. The base RL algorithm is $Q$ -learning, which is used to form multiple agents with different state combinations. Two common energy management strategies, namely, thermostatic strategy and equivalent consumption minimization strategy, are used as two single agents in the proposed ensemble agents. During the learning process, multiple RL agents make an action decision jointly by taking a weighted average. After each driving cycle iteration, $Q$ -learning agents update their state-action values. A single RL agent is used as a reference for the proposed strategy. The results show that the fuel economy of the proposed ensemble strategy is 3.2% higher than that of the best single agent.
更新日期:2020-06-23

 

全部期刊列表>>
材料学研究精选
Springer Nature Live 产业与创新线上学术论坛
胸腔和胸部成像专题
自然科研论文编辑服务
ACS ES&T Engineering
ACS ES&T Water
屿渡论文,编辑服务
杨超勇
周一歌
华东师范大学
南京工业大学
清华大学
中科大
唐勇
跟Nature、Science文章学绘图
隐藏1h前已浏览文章
中洪博元
课题组网站
新版X-MOL期刊搜索和高级搜索功能介绍
ACS材料视界
x-mol收录
福州大学
南京大学
王杰
左智伟
湖南大学
清华大学
吴杰
赵延川
中山大学化学工程与技术学院
试剂库存
天合科研
down
wechat
bug