Li-Ion Battery State of Health Estimation Based on Short Random Charging Segment and Improved Long Short-Term Memory

IF 1.1 4区工程技术 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC IET Signal Processing Pub Date : 2023-10-23 DOI:10.1049/2023/8839034

Aina Tian, Zhe Chen, Zhuangzhuang Pan, Chen Yang, Yuqin Wang, Kailang Dong, Yang Gao, Jiuchun Jiang

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引用次数: 1

Abstract

Lithium-ion batteries have been used in a wide range of applications, including electrochemical energy storage and electrical transportation. In order to ensure safe and stable battery operation, the State of Health (SOH) needs to be accurately estimated. In recent years, model-based and data-driven methods have been widely used for SOH estimation, but due to the uncertainty of battery charging conditions in practice, it is difficult to obtain a fixed local segment. In this paper, the charging curve is first divided into several equal voltage difference segments based on charging segment voltage difference ΔV in order to solve the random charging segment problem. Time interval of equal charge voltage difference of the voltage curve, coefficient of variation and euclidean distance of the charging capacity difference curve are extracted as health features. The improved flow direction algorithmlong short term memory-based SOH assessment method is proposed and verified by the Oxford battery degradation dataset and experimental battery degradation dataset with a maximum error of 0.6%.

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基于短时随机充电段和改进长短期记忆的锂离子电池健康状态估计

锂离子电池在电化学储能和电力运输等领域有着广泛的应用。为了保证电池安全稳定的运行，需要对电池的健康状态(SOH)进行准确的估算。近年来，基于模型和数据驱动的SOH估计方法得到了广泛的应用，但由于实践中电池充电条件的不确定性，难以获得固定的局部分段。本文首先根据充电段电压差ΔV将充电曲线划分为若干等电压差段，以解决随机充电段问题。提取电压曲线等充电电压差的时间间隔、充电容量差曲线的变异系数和欧氏距离作为健康特征。提出了改进的流动方向算法-基于长短期记忆的SOH评估方法，并通过牛津电池退化数据集和实验电池退化数据集进行了验证，最大误差为0.6%。

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来源期刊

IET Signal Processing 工程技术-工程：电子与电气

CiteScore

3.80

自引率

5.90%

发文量

审稿时长

9.5 months

期刊介绍： IET Signal Processing publishes research on a diverse range of signal processing and machine learning topics, covering a variety of applications, disciplines, modalities, and techniques in detection, estimation, inference, and classification problems. The research published includes advances in algorithm design for the analysis of single and high-multi-dimensional data, sparsity, linear and non-linear systems, recursive and non-recursive digital filters and multi-rate filter banks, as well a range of topics that span from sensor array processing, deep convolutional neural network based approaches to the application of chaos theory, and far more. Topics covered by scope include, but are not limited to: advances in single and multi-dimensional filter design and implementation linear and nonlinear, fixed and adaptive digital filters and multirate filter banks statistical signal processing techniques and analysis classical, parametric and higher order spectral analysis signal transformation and compression techniques, including time-frequency analysis system modelling and adaptive identification techniques machine learning based approaches to signal processing Bayesian methods for signal processing, including Monte-Carlo Markov-chain and particle filtering techniques theory and application of blind and semi-blind signal separation techniques signal processing techniques for analysis, enhancement, coding, synthesis and recognition of speech signals direction-finding and beamforming techniques for audio and electromagnetic signals analysis techniques for biomedical signals baseband signal processing techniques for transmission and reception of communication signals signal processing techniques for data hiding and audio watermarking sparse signal processing and compressive sensing Special Issue Call for Papers: Intelligent Deep Fuzzy Model for Signal Processing - https://digital-library.theiet.org/files/IET_SPR_CFP_IDFMSP.pdf