在电池单元间变化的情况下,对并联电池单元的电池模块进行健康状况评估

IF 15 1区 工程技术 Q1 ENERGY & FUELS
Qinan Zhou , Dyche Anderson , Jing Sun
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引用次数: 0

摘要

对并联电池单元的锂离子电池模块进行健康状态(SOH)估算是一个具有挑战性的问题,尤其是在电池单元之间存在变化的情况下。增量容量分析法(ICA)和差分电压分析法(DVA)在电池单元层面非常有效,但在仅有模块层面测量数据的情况下,仍缺少一种可推广的方法将其扩展到模块层面的 SOH 估算。本文提出了一种新方法,并证明了通过从模块级 ICA 和 DVA 中系统地选择多个特征,可以在存在单元间变化的情况下,高精度、高置信度地估算模块级 SOH。首先,提出了一种基于信息论的特征选择算法,为模块级 SOH 估算找到一组最佳特征。其次,提出了一种基于相关性向量回归(RVR)的模块级 SOH 估计模型,在保持模型稀疏性的同时,提供点估计值和三西格玛可信区间。随着更多选定特征的加入,所提出的方法以更高的模型复杂度为代价,获得了更好的估计精度和更高的可信度。当应用于一个大型实验数据集时,所提出的方法和由此产生的稀疏模型可得出模块级 SOH 估计值,均方根误差为 0.5%,平均三西格玛值为 1.5%。由于所有训练过程都是在机外完成的,因此所提出的方法在机上实施时具有较低的计算复杂度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
State of health estimation for battery modules with parallel-connected cells under cell-to-cell variations

State of health (SOH) estimation for lithium-ion battery modules with cells connected in parallel is a challenging problem, especially with cell-to-cell variations. Incremental capacity analysis (ICA) and differential voltage analysis (DVA) are effective at the cell level, but a generalizable method to extend them to module-level SOH estimation remains missing, when only module-level measurements are available. This paper proposes a new method and demonstrates that, with multiple features systematically selected from the module-level ICA and DVA, the module-level SOH can be estimated with high accuracy and confidence in the presence of cell-to-cell variations. First, an information theory-based feature selection algorithm is proposed to find an optimal set of features for module-level SOH estimation. Second, a relevance vector regression (RVR)-based module-level SOH estimation model is proposed to provide both point estimates and three-sigma credible intervals while maintaining model sparsity. With more selected features incorporated, the proposed method achieves better estimation accuracy and higher confidence at the expense of higher model complexity. When applied to a large experimental dataset, the proposed method and the resulting sparse model lead to module-level SOH estimates with a 0.5% root-mean-square error and a 1.5% average three-sigma value. With all the training processes completed offboard, the proposed method has low computational complexity for onboard implementations.

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来源期刊
Etransportation
Etransportation Engineering-Automotive Engineering
CiteScore
19.80
自引率
12.60%
发文量
57
审稿时长
39 days
期刊介绍: eTransportation is a scholarly journal that aims to advance knowledge in the field of electric transportation. It focuses on all modes of transportation that utilize electricity as their primary source of energy, including electric vehicles, trains, ships, and aircraft. The journal covers all stages of research, development, and testing of new technologies, systems, and devices related to electrical transportation. The journal welcomes the use of simulation and analysis tools at the system, transport, or device level. Its primary emphasis is on the study of the electrical and electronic aspects of transportation systems. However, it also considers research on mechanical parts or subsystems of vehicles if there is a clear interaction with electrical or electronic equipment. Please note that this journal excludes other aspects such as sociological, political, regulatory, or environmental factors from its scope.
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