Research on the output characteristics and SOC estimation method of lithium-ion batteries over a wide range of operating temperature conditions

IF 9.4 1区工程技术 Q1 ENERGY & FUELS

Energy Pub Date : 2025-01-24 DOI:10.1016/j.energy.2025.134726

Xiong Shu , Yongjing Li , Kexiang Wei , Wenxian Yang , Bowen Yang , Ming Zhang

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引用次数: 0

Abstract

With the rapid growth of the EV market, the use of lithium-ion batteries (LIBs) has increased significantly. However, the safety of these battery systems remains a concern. Accurate estimation of the state of charge (SOC) is crucial to enhance battery safety and longevity. In this paper, the impact of temperature on LIB performance is investigated and it is found that temperature variations can lead to inaccurate SOC estimation. To address this issue, LIB performance and capacity degradation at different operating temperatures are experimentally studied, and Electrochemical impedance spectroscopy (EIS) characteristics are analyzed. Based on the analysis results, an SOC estimation method, combining recursive least squares with forgetting factor (FFRLS) and adaptive extended Kalman filtering (AEKF) with temperature compensation, is proposed in the study. This method is tested respectively at 0 °C, 25 °C and 45 °C, demonstrating the feasibility and higher prediction accuracy of the proposed method across a wide temperature range.

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大范围工作温度条件下锂离子电池输出特性及荷电状态估算方法研究

随着电动汽车市场的快速增长，锂离子电池（LIBs）的使用量大幅增加。然而，这些电池系统的安全性仍然令人担忧。准确估计电池的荷电状态（SOC）对于提高电池的安全性和寿命至关重要。本文研究了温度对锂电池性能的影响，发现温度变化会导致电池荷电状态估计不准确。为了解决这一问题，实验研究了锂电池在不同工作温度下的性能和容量退化，并分析了电化学阻抗谱（EIS）特性。在分析结果的基础上，提出了一种结合带遗忘因子的递推最小二乘（FFRLS）和带温度补偿的自适应扩展卡尔曼滤波（AEKF）的SOC估计方法。在0°C、25°C和45°C下分别对该方法进行了测试，验证了该方法在较宽温度范围内的可行性和较高的预测精度。

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

Energy 工程技术-能源与燃料

CiteScore

15.30

自引率

14.40%

发文量

审稿时长

14.2 weeks

期刊介绍： Energy is a multidisciplinary, international journal that publishes research and analysis in the field of energy engineering. Our aim is to become a leading peer-reviewed platform and a trusted source of information for energy-related topics. The journal covers a range of areas including mechanical engineering, thermal sciences, and energy analysis. We are particularly interested in research on energy modelling, prediction, integrated energy systems, planning, and management. Additionally, we welcome papers on energy conservation, efficiency, biomass and bioenergy, renewable energy, electricity supply and demand, energy storage, buildings, and economic and policy issues. These topics should align with our broader multidisciplinary focus.