Improved Deep Learning-Based Energy Management Strategy for Battery-Supercapacitor Hybrid Electric Vehicle With Adaptive Velocity Prediction
Author:
Affiliation:
1. Department of Aeronautics, Mechanical and Electronic Convergence Engineering, Kumoh National Institute of Technology, Gumi-si, South Korea
Funder
Ministry of SMEs and Start-Ups, South Korea
National Research Foundation of Korea
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/9668973/09998547.pdf?arnumber=9998547
Reference42 articles.
1. Power split strategies for hybrid energy storage systems for vehicular applications
2. Remaining Useful Life Prediction for Supercapacitors Using an Optimized End-to-End Deep Learning Approach
3. Optimal control of hybrid electric vehicles based on Pontryagin’s minimum principle;kim;IEEE Trans Control Syst Technol,2011
4. A Comparative Study of LSTM and DNN for Stock Market Forecasting
5. A Novel ECMS and Combined Cost Map Approach for High-Efficiency Series Hybrid Electric Vehicles
Cited by 10 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. A soft actor-critic reinforcement learning framework for optimal energy management in electric vehicles with hybrid storage;Journal of Energy Storage;2024-10
2. Utilizing machine learning and deep learning for enhanced supercapacitor performance prediction;Journal of Energy Storage;2024-10
3. Battery Range Estimation in Electric Vehicles Using Machine Learning and Deep Learning Techniques;2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS);2024-06-28
4. Machine Learning and Optimization in Energy Management Systems for Plug-In Hybrid Electric Vehicles: A Comprehensive Review;Energies;2024-06-21
5. Adaptive power allocation strategy for hybrid energy storage system based on driving pattern recognition;Journal of Energy Storage;2024-04
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