基于ECM和SGPR的高鲁棒性锂离子电池健康状态估计方法
TM912; 锂离子电池健康状态(SOH)的准确估计对于保障电池系统安全运行具有重要意义.针对传统SOH估计方法在可变工况下失效的问题,提出了 一种基于等效电路模型和稀疏高斯过程回归的锂离子电池SOH在线估计方法.通过两个在线滤波器,在恒流充电过程中动态地辨识了锂离子电池等效电路模型的各项参数,构建了工况不敏感的健康因子,结合稀疏高斯过程回归实现SOH的间接估计.该方法在多种工况下使用统一的信号处理方法和特征映射模型,兼具鲁棒性强和冗余度低的优点.实验结果表明,该方法在多种工况下的平均绝对误差不超过0.94％,均方根误差不超过1.12％,与现有方法相比,该方法在综合性能上具有显著优势....
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| Published in | 上海交通大学学报 Vol. 58; no. 5; pp. 747 - 759 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | Chinese |
| Published |
上海交通大学海洋工程国家重点实验室,上海 200240
28.05.2024
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1006-2467 |
| DOI | 10.16183/j.cnki.jsjtu.2022.221 |
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| Abstract | TM912; 锂离子电池健康状态(SOH)的准确估计对于保障电池系统安全运行具有重要意义.针对传统SOH估计方法在可变工况下失效的问题,提出了 一种基于等效电路模型和稀疏高斯过程回归的锂离子电池SOH在线估计方法.通过两个在线滤波器,在恒流充电过程中动态地辨识了锂离子电池等效电路模型的各项参数,构建了工况不敏感的健康因子,结合稀疏高斯过程回归实现SOH的间接估计.该方法在多种工况下使用统一的信号处理方法和特征映射模型,兼具鲁棒性强和冗余度低的优点.实验结果表明,该方法在多种工况下的平均绝对误差不超过0.94％,均方根误差不超过1.12％,与现有方法相比,该方法在综合性能上具有显著优势. |
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| AbstractList | TM912; 锂离子电池健康状态(SOH)的准确估计对于保障电池系统安全运行具有重要意义.针对传统SOH估计方法在可变工况下失效的问题,提出了 一种基于等效电路模型和稀疏高斯过程回归的锂离子电池SOH在线估计方法.通过两个在线滤波器,在恒流充电过程中动态地辨识了锂离子电池等效电路模型的各项参数,构建了工况不敏感的健康因子,结合稀疏高斯过程回归实现SOH的间接估计.该方法在多种工况下使用统一的信号处理方法和特征映射模型,兼具鲁棒性强和冗余度低的优点.实验结果表明,该方法在多种工况下的平均绝对误差不超过0.94％,均方根误差不超过1.12％,与现有方法相比,该方法在综合性能上具有显著优势. |
| Abstract_FL | Accurately estimating the state of health(SOH)of lithium-ion batteries is of great significance in ensuring the safe operation of the battery system.Addressing the issue where traditional SOH estimation methods fail under variable working conditions,an online SOH estimation method for lithium-ion batteries based on equivalent circuit model(ECM)and sparse Gaussian process regression(SGPR)is proposed.During the constant current charging process,the parameters of the ECM of lithium-ion battery are dynamically identified by two online filters,based on which,a condition-insensitive health indicator is constructed.In combination with the SGPR,the indirect SOH estimation is achieved.This method uses the unified signal processing method and feature mapping model under various working conditions,and features strong robustness with low redundancy.The experimental results show that the average absolute error of the method proposed under various working conditions does not exceed 0.94％,and the root mean square error stays below 1.12％.When benchmarked against existing methods,this method has significant advantages in comprehensive performance. |
| Author | 陈自强 崔显 |
| AuthorAffiliation | 上海交通大学海洋工程国家重点实验室,上海 200240 |
| AuthorAffiliation_xml | – name: 上海交通大学海洋工程国家重点实验室,上海 200240 |
| Author_FL | CUI Xian CHEN Ziqiang |
| Author_FL_xml | – sequence: 1 fullname: CUI Xian – sequence: 2 fullname: CHEN Ziqiang |
| Author_xml | – sequence: 1 fullname: 崔显 – sequence: 2 fullname: 陈自强 |
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| ClassificationCodes | TM912 |
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| Copyright | Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
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| DOI | 10.16183/j.cnki.jsjtu.2022.221 |
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| DocumentTitle_FL | A Highly Robust State of Health Estimation Method for Lithium-lon Batteries Based on ECM and SGPR |
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| Keywords | 健康状态 sparse Gaussian process regression(SGPR) health indicator state of health(SOH) 锂离子电池 健康因子 粒子滤波 particle filter lithium-ion battery 稀疏高斯过程回归 |
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| Title | 基于ECM和SGPR的高鲁棒性锂离子电池健康状态估计方法 |
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