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D-UKF based state of health estimation for 18650 type lithium battery

机译:基于D-UKF的18650型锂电池健康状况评估

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摘要

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.
机译:本文提出了一种准确的锂电池健康状态(SOH)估算方法。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,双无味卡尔曼滤波(D-UKF)算法设计为可同时计算充电状态(SOC)和SOH。主要功能是基于电池内阻推导电池SOH估算模型。定义为UKF1和UKF2的两个滤波器一起工作以计算SOC和欧姆电阻的实际值,以获得准确的SOH值。实验结果表明,我们的新电池模型在不同工作条件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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