Real-Time Energy Management Strategy for Fuel Cell Vehicles Based on DP and Rule Extraction

Author:

Liu Yanwei1ORCID,Wang Mingda1,Tan Jialuo1,Ye Jie2,Liang Jiansheng3ORCID

Affiliation:

1. School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China

2. School of Mechatronics Engineering, Foshan University, Foshan 528225, China

3. Automotive Engineering Research Institute, BYD Co., Ltd., Shenzhen 518118, China

Abstract

Energy management strategy (EMS), as a core technology in fuel cell vehicles (FCVs), profoundly influences the lifespan of fuel cells and the economy of the vehicle. Aiming at the problem of the EMS of FCVs based on a global optimization algorithm not being applicable in real-time, a rule extraction-based EMS is proposed for fuel cell commercial vehicles. Based on the results of the dynamic programming (DP) algorithm in the CLTC-C cycle, the deep learning approach is employed to extract output power rules for fuel cell, leading to the establishment of a rule library. Using this library, a real-time applicable rule-based EMS is designed. The simulated driving platform is built in a CARLA, SUMO, and MATLAB/Simulink joint simulation environment. Simulation results indicate that the proposed strategy yields savings ranging from 3.64% to 8.96% in total costs when compared to the state machine-based strategy.

Funder

The Natural Science Foundation of Guangdong Province

Publisher

MDPI AG

Reference29 articles.

1. Development trend and research status of the hydrogen fuel cell vehicle;Gao;Mater. Rep.,2022

2. A comprehensive review on hybrid power system for PEMFC-HEV: Issues and strategies;Qu;Energy Convers. Manag.,2018

3. A comprehensive review of energy management optimization strategies for fuel cell passenger vehicle;Teng;Int. J. Hydrogen Energy,2020

4. Research on energy management control strategy based on fuel cell electric vehicle;Nie;Mechatronics,2019

5. Energy management strategy for battery/supercapacitor/fuel cell hybrid source vehicles based on finite state machine;Wang;Appl. Energy,2019

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3