人工智能在动力电池健康状态预估中的研究综述

doi:10.3901/JME.2024.04.391

机械工程学报 ›› 2024, Vol. 60 ›› Issue (4): 391-408.doi: 10.3901/JME.2024.04.391

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人工智能在动力电池健康状态预估中的研究综述

戴国洪¹, 张道涵¹, 彭思敏², 苗一凡², 卓悦², 杨瑞鑫³, 于全庆⁴

1. 常州大学机械与轨道交通学院常州 213164;
2. 盐城工学院电气工程学院盐城 224051;
3. 北京理工大学机械与车辆学院北京 100081;
4. 哈尔滨工业大学(威海)汽车工程学院威海 264209

收稿日期:2023-06-10 修回日期:2023-12-08 出版日期:2024-02-20 发布日期:2024-05-25
通讯作者: 彭思敏,男,1980年出生,博士,副教授,硕士研究生导师。主要研究方向为电池储能系统控制与管理、新能源汽车控制技术。E-mail:siminpeng@ycit.edu.cn
作者简介:戴国洪,男,1966年出生,博士,教授,博士研究生导师。主要研究方向为计算机软件及计算机应用、机械工业。E-mail:dgh@cczu.edu.cn;张道涵,男,1998年出生。主要研究方向为人工智能技术在电池状态估计中的应用。E-mail:daohanzhang2023@163.com
基金资助:
国家自然科学基金(52177210); 中国博士后科学基金(2021M690395); 江苏高校“青蓝工程”(2021-11); 盐城工学院校级科研(xjr2021052)资助项目

Overview of Artificial Intelligence in Health Prediction of Power Battery

DAI Guohong¹, ZHANG Daohan¹, PENG Simin², MIAO Yifan², ZHUO Yue², YANG Ruixin³, YU Quanqing⁴

1. School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164;
2. School of Electrical Engineering, Yancheng Institute of Technology, Yancheng 224051;
3. School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081;
4. School of Automotive Engineering, Harbin Institute of Technology at Weihai, Weihai 264209

Received:2023-06-10 Revised:2023-12-08 Online:2024-02-20 Published:2024-05-25

摘要/Abstract

摘要： 目前先进的电动汽车开发和应用已成为实现“脱碳”的关键技术。准确的电池健康状态(State of health,SOH)预估可有效地表征动力电池性能,对电动汽车动力电池维护和寿命管理具有重要意义。近年来,以深度学习、强化学习和大数据技术等为代表的新一代人工智能技术在电动汽车电池状态预估的应用已成为研究热点。首先简要介绍人工智能技术、SOH的含义以及影响SOH主要因素,然后分别从电池单体与电池系统的角度对几种人工智能模型在SOH预估中的研究进行总结与讨论,最后结合大数据、云计算、区域链等新兴技术,对电池健康状态预估问题进行展望,为提升当前动力电池全生命周期管理能力提供一些思路。

关键词: 人工智能, 健康状态, 电池系统, 现状与趋势

Abstract: The development and application of advanced electric vehicles has become the key technology to achieve “decarbonization”. Accurate state of health(SOH) prediction of battery can effectively characterize its operation performance. It is of great significance to the maintenance and life management of battery in electric vehicle. In recent years, a new generation of artificial intelligence technology represented by deep learning, reinforcement learning and big data technology has become a research hotspot in the application of battery state prediction. The basic theory of artificial intelligence technology and SOH and SOH influence factors is briefly introduced. Several main artificial intelligence algorithms in SOH prediction are summarized and discussed from the perspective of battery cell and battery system respectively. Finally, combined with emerging technologies such as big data, cloud computing and regional chain, some battery SOH prediction problems are discussed, which provides some ideas for breaking through the bottleneck of current power battery full life cycle management technology.

Key words: artificial intelligence, state of health, battery system, status and trend

中图分类号:

TM912

戴国洪, 张道涵, 彭思敏, 苗一凡, 卓悦, 杨瑞鑫, 于全庆. 人工智能在动力电池健康状态预估中的研究综述[J]. 机械工程学报, 2024, 60(4): 391-408.

DAI Guohong, ZHANG Daohan, PENG Simin, MIAO Yifan, ZHUO Yue, YANG Ruixin, YU Quanqing. Overview of Artificial Intelligence in Health Prediction of Power Battery[J]. Journal of Mechanical Engineering, 2024, 60(4): 391-408.

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人工智能在动力电池健康状态预估中的研究综述

Overview of Artificial Intelligence in Health Prediction of Power Battery

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