Battery health state estimation method based on data feature mining

IF 0.8 4区工程技术 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC

Ieice Electronics Express Pub Date : 2023-11-10 DOI:10.1587/elex.20.20230370

Geng Chamin, Zhang Tianhai, Chen Bo, Zhou Qingfu

引用次数: 0

Abstract

The health status estimation of lithium-ion battery is a challenging through measurement. To establish a connection between battery health status and data features, a battery State of Health (SOH) estimation method based on data feature mining is proposed. Four features are extracted from the battery charging curve, and the grey correlation analysis is used to determine the high correlation between features and health status. The method combines a Backpropagation (BP) neural network with Genetic Algorithm (GA) for feature training and learning, enabling the estimation of battery SOH. The feasibility of the proposed method is validated using the NASA battery dataset. The results show that the battery SOH estimation method proposed in this paper outperforms the traditional BP neural network method achieving accurate estimation.

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基于数据特征挖掘的电池健康状态估计方法

锂离子电池的健康状态评估是一个具有挑战性的测量问题。为了建立电池健康状态与数据特征之间的联系，提出了一种基于数据特征挖掘的电池健康状态(SOH)估计方法。从电池充电曲线中提取4个特征，利用灰色关联分析确定特征与健康状态之间的高相关性。该方法结合反向传播(BP)神经网络和遗传算法(GA)进行特征训练和学习，实现对电池SOH的估计。利用NASA电池数据集验证了该方法的可行性。结果表明，本文提出的电池SOH估计方法优于传统的BP神经网络方法，实现了准确的估计。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Ieice Electronics Express 工程技术-工程：电子与电气

CiteScore

1.50

自引率

37.50%

发文量

119

审稿时长

1.1 months

期刊介绍： An aim of ELEX is rapid publication of original, peer-reviewed short papers that treat the field of modern electronics and electrical engineering. The boundaries of acceptable fields are not strictly delimited and they are flexibly varied to reflect trends of the fields. The scope of ELEX has mainly been focused on device and circuit technologies. Current appropriate topics include: - Integrated optoelectronics (lasers and optoelectronic devices, silicon photonics, planar lightwave circuits, polymer optical circuits, etc.) - Optical hardware (fiber optics, microwave photonics, optical interconnects, photonic signal processing, photonic integration and modules, optical sensing, etc.) - Electromagnetic theory - Microwave and millimeter-wave devices, circuits, and modules - THz devices, circuits and modules - Electron devices, circuits and modules (silicon, compound semiconductor, organic and novel materials) - Integrated circuits (memory, logic, analog, RF, sensor) - Power devices and circuits - Micro- or nano-electromechanical systems - Circuits and modules for storage - Superconducting electronics - Energy harvesting devices, circuits and modules - Circuits and modules for electronic displays - Circuits and modules for electronic instrumentation - Devices, circuits and modules for IoT and biomedical applications