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Estimating SOC and SOH of lithium battery based on nano material
Ferroelectrics ( IF 0.6 ) Pub Date : 2021-09-09 , DOI: 10.1080/00150193.2021.1905731
Xiaoqiang Zhang 1 , Gongxing Yan 2
Affiliation  

Abstract

Nano-carbon materials are widely used and studied in a new generation of energy storage systems. Lithium batteries are widely used in transportation, power networks, and mobile devices. This paper mainly studies the estimation methods of SOC and SOH of lithium batteries based on nano materials. The SOC state equation and output equation of the lithium battery are established by the ampere integration method, and the parameters in the system are observed to achieve the purpose of observing the charging state of the lithium battery online. Using the internal resistance and SOC of the cell model as state parameters, the nonlinear transfer of equal and covariance is handled. The working voltage was modified using the lithium battery model, and different control methods were implemented for different SOC situations. According to the experimental data, the voltage estimation error is less than 2.1%, which can meet the actual use requirements. It was found that there are different types of lithium-ion batteries, and the model obtained by training samples of lithium-ion batteries reduces the prediction accuracy when predicting other lithium-ion batteries.



中文翻译:

基于纳米材料的锂电池SOC和SOH估算

摘要

纳米碳材料在新一代储能系统中得到广泛应用和研究。锂电池广泛应用于交通、电力网络和移动设备。本文主要研究基于纳米材料的锂电池SOC和SOH的估算方法。采用安培积分法建立锂电池的SOC状态方程和输出方程,对系统中的参数进行观测,达到在线观测锂电池充电状态的目的。使用电池模型的内阻和SOC作为状态参数,处理等量和协方差的非线性传递。使用锂电池模型修改工作电压,针对不同的SOC情况实施不同的控制方法。根据实验数据,电压估算误差小于2.1%,可以满足实际使用要求。发现锂离子电池有不同的类型,通过锂离子电池的训练样本得到的模型在预测其他锂离子电池时降低了预测精度。

更新日期:2021-09-10
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