基于D-UKF的18650型锂电池健康状态评估

Yifei Cai, Qiuting Wang, Wei Qi
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引用次数: 6

摘要

提出了一种锂电池健康状态(SOH)的精确估计方法。基于等效电路模型和电池内部电化学特性对电池模型进行了改进。在我们的研究中,设计了双无气味卡尔曼滤波(D-UKF)算法来同时计算荷电状态(SOC)和SOH。主要特点是基于电池内阻推导出电池SOH估计模型。定义为UKF1和UKF2的两个滤波器共同计算SOC和欧姆电阻的实值,以获得准确的SOH值。实验结果表明,新模型在不同工况下考虑了不同的电池内阻值。此外,我们的研究验证了基于D-UKF的新估计方法的性能和可行性。
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D-UKF based state of health estimation for 18650 type lithium battery
An accurate estimation method of State of Health (SOH) for lithium battery is presented in this paper. The battery model is improved based on equivalent circuit model and battery internal electrochemical characteristics. In our study, Double Unscented Kalman Filtering (D-UKF) algorithm is designed to calculate State of Charge (SOC) and SOH at the same time. The main feature is the battery SOH estimation model is derived based on battery internal resistances. Two filters defined as UKF1 and UKF2 are working together to calculate the real-value of SOC and Ohmic resistance to obtain the accurate SOH value. The experimental results indicate that our new battery model considers different value of battery internal resistances on different working condition. Besides, our study verifies the performance and feasibility of new estimation method based on D-UKF.
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