Study on SOC Estimation of Lithium Battery Based on Improved BP Neural Network

Y. Hu, Zhiping Wang
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引用次数: 9

Abstract

In this paper, the state of charge (SOC) is estimated by charging the battery at constant current and voltage, collecting the current, voltage, temperature and internal resistance during the experiment. The influence of internal resistance on SOC prediction of lithium batteries is mainly considered. In this paper, the Improved BP neural network is used to carry out simulation experiments. The experimental results show that the prediction accuracy is higher and the simulation effect is better than that without considering the internal resistance.
基于改进BP神经网络的锂电池荷电状态估计研究
本文通过对电池进行恒流恒压充电,收集实验过程中的电流、电压、温度和内阻,来估算电池的荷电状态(SOC)。主要考虑了内阻对锂电池荷电状态预测的影响。本文采用改进的BP神经网络进行仿真实验。实验结果表明,与不考虑内阻时相比,该方法的预测精度更高,仿真效果更好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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