上海交通大学学报

上海交通大学学报 >

2024 , Vol. 58 >Issue 12: 1925 - 1934

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2023.141

新型电力系统与综合能源

基于差分热伏安法和高斯过程回归的锂离子电池健康状态估计

  • 朱浩然 ,
  • 陈自强 ,
  • 杨德庆
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  • 1.上海交通大学 海洋工程国家重点实验室,上海 200240
    2.上海交通大学 三亚崖州湾深海科技研究院,海南 三亚 572024
朱浩然(1999—),硕士生,从事锂离子电池健康状态监测研究.
陈自强,研究员,博士生导师;E-mail:chenziqiang@sjtu.edu.cn.

收稿日期: 2023-04-17

  修回日期: 2023-07-25

  录用日期: 2023-09-18

  网络出版日期: 2023-09-26

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State of Health Estimation of Li-Ion Batteries Based on Differential Thermal Voltammetry and Gaussian Process Regression

  • ZHU Haoran ,
  • CHEN Ziqiang ,
  • YANG Deqing
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  • 1. State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
    2. SJTU Yazhou Bay Institute of Deepsea SCI-TECH, Shanghai Jiao Tong University, Sanya 572024, Hainan, China

Received date: 2023-04-17

  Revised date: 2023-07-25

  Accepted date: 2023-09-18

  Online published: 2023-09-26

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摘要

锂离子电池在工作过程中会发生容量衰退甚至恶化等现象,实现电池健康状态(SOH)的有效估计是电池管理系统发展的关键挑战.提出一种数据驱动模型与特征参数相融合的锂离子电池健康状态估计方法,使用差分热伏安(DTV)法对锂离子电池实验数据进行预处理,提取6个有用的特征,建立以不同核函数的两步高斯过程回归(GPR)为核心的SOH估计模型.结果表明,建立的模型能在更好地逼近实验值的同时缩短训练和预测时间,SOH估计的平均绝对误差在0.67%~0.97%之间,相比单步GPR降低了20%~30%.因此,该模型对锂离子电池健康状态的估计有较高的鲁棒性和准确性.

关键词： 锂离子电池; 健康状态; 差分热伏安法; 高斯过程回归

本文引用格式

朱浩然 , 陈自强 , 杨德庆 . 基于差分热伏安法和高斯过程回归的锂离子电池健康状态估计[J]. 上海交通大学学报, 2024 , 58(12) : 1925 -1934 . DOI: 10.16183/j.cnki.jsjtu.2023.141

Abstract

Lithium-ion batteries experience capacity decline or even deterioration during the working process. Effective estimation of battery health status is a key challenge in the development of battery management systems. This paper proposes a method for estimating the state of health (SOH) of lithium-ion batteries based on the fusion of data-driven models and characteristic parameters. Using differential thermal voltammetry(DTV) to preprocess the experimental data of lithium-ion batteries, this method extracts six useful features, and establishes a SOH estimation model based on two-step Gaussian process regression (GPR) with different kernel functions. The results show that the established model can better approximate the experimental value and shorten the training and prediction time. The average absolute error of SOH estimation is 0.67%—0.97%, which is 20%—30% lower than that of single-step GPR. Therefore, the model has a high robustness and accuracy in estimating the state of health of lithium-ion batteries.

Key words： lithium-ion battery; state of health (SOH); differential thermal voltammetry (DTV); Gaussian process regression (GPR)

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