基于双卡尔曼滤波算法的锂离子电池健康评估研究

Qifeng Zhang
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引用次数: 3

摘要

锂离子动力电池因其高能量、低污染、高安全性等优点,已成为新能源汽车的主要动力来源之一。动力电池的健康状态(SOH)是电动汽车动力电池系统最重要的性能指标。然而,现有的电池SOH估计多采用脱机卡尔曼滤波算法,无法准确描述实际工况。本文分析了锂离子电池的电路模型,设计了锂离子电池充放电测试系统,设计了双卡尔曼滤波算法在线估计方法对电池健康状况进行评估。实验结果表明,该设计算法具有较高的精度和收敛性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Evaluating the Health of Lithium-ion Battery Based on Double Kalman Filter Algorithm
Lithium-ion power battery has become one of the main power sources of new energy vehicles due to its high energy, low pollution and high safety. State of health (SOH) of power battery is the most important performance index of electric vehicle power battery system. However, the existing estimation of battery SOH mostly uses the off-line Kalman filter algorithm and it is unable to accurately describe the actual working condition. In this paper, the circuit model of lithium-ion battery is analyzed, the charge and discharge test system of lithium-ion battery is designed, and the online estimation method of double Kalman filter algorithm is designed to evaluate the battery health. Experimental results show that the design algorithm has higher accuracy and convergence.
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