SOC and SOH Monitoring Algorithms for Lithium Batteries Using Multilayer Neural Networks

Jong-Hyun Lee, Hyun-Sil Kim, Insoo Lee
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Abstract

This paper presents a battery monitoring system using a multilayer neural network (MNN) for state of charge (SOC) estimation and state of health (SOH) diagnosis. In this system, the MNN utilizes experimental discharge voltage data from lithium battery operation to estimate SOH and uses present and previous voltages for SOC estimation. From experimental results, we know that the proposed battery monitoring system performs SOC estimation and SOH diagnosis well.
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基于多层神经网络的锂电池SOC和SOH监测算法
提出了一种利用多层神经网络(MNN)进行充电状态(SOC)估计和健康状态(SOH)诊断的电池监测系统。在该系统中,MNN利用锂电池运行的实验放电电压数据来估计SOH,并使用当前和以前的电压来估计SOC。实验结果表明,所提出的电池监测系统具有较好的SOC估计和SOH诊断功能。
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