Artificial Intelligence for Power Electronics in Electric Vehicles: Challenges and Opportunities

Author:

Paret Paul1,Finegan Donal2,Narumanchi Sreekant1

Affiliation:

1. Center for Integrated Mobility Sciences, National Renewable Energy Laboratory , 15013 Denver West Parkway, Golden, CO 80401

2. Center for Energy Conversion and Storage Systems, National Renewable Energy Laboratory , 15013 Denver West Parkway, Golden, CO 80401

Abstract

Abstract Progress in the field of power electronics within electric vehicles has generally been driven by conventional engineering design principles and experiential learning. Power electronics is inherently a multidomain field where semiconductor physics and electrical, thermal, and mechanical design knowledge converge to achieve a practical realization of desired targets in the form of conversion efficiency, power density, and reliability. Due to the promising nature of artificial intelligence in delivering rapid results, engineers are starting to explore the ways in which it can contribute to making power electronics more compact and reliable. Here, we conduct a brief review of the foray of artificial intelligence in three distinct subtechnologies within a power electronics system in the context of electric vehicles: semiconductor devices, power electronics module design and prognostics, and thermal management design. The intent is not to report an exhaustive literature review, but to identify the state of the art and opportunities for artificial intelligence to play a meaningful role in power electronics design from a mechanical and thermal standpoint, as well as to discuss a few promising future research directions.

Funder

National Renewable Energy Laboratory

Publisher

ASME International

Subject

Electrical and Electronic Engineering,Computer Science Applications,Mechanics of Materials,Electronic, Optical and Magnetic Materials

Reference54 articles.

1. Artificial Intelligence Based Forecast Models for Predicting Solar Power Generation;Mater. Today: Proc.,2018

2. Machine Learning Ensembles for Wind Power Prediction;Renewable Energy,2016

3. Routing of Electric Vehicles With Intermediary Charging Stations: A Reinforcement Learning Approach;Front. Big Data,2021

4. Operating Electric Vehicle Fleet for Ride-Hailing Services With Reinforcement Learning;IEEE Trans. Intell. Transp. Syst.,2020

5. Machine Learning Approaches for EV Charging Behavior: A Review;IEEE Access,2020

Cited by 7 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. Automated Strategies for Improving Power Efficiency in Low Power Electronics;2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC);2024-01-29

2. A Comprehensive Analysis of Artificial Intelligence Integration in Electrical Engineering;2024 5th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI);2024-01-18

3. Enhancement of heat transfer efficiency in Li-ion battery packs through response surface optimization of heat pipes;Next Energy;2024-01

4. AI-Driven Urban Energy Solutions—From Individuals to Society: A Review;Energies;2023-12-09

5. Why Electric Vehicles Are the Future of Transportation;2023 IEEE International Conference on ICT in Business Industry & Government (ICTBIG);2023-12-08

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