Battery state of health estimation using the generalized regression neural network

Jie Zhou, Zhiwei He, Mingyu Gao, Yuanyuan Liu
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引用次数: 14

Abstract

Batteries have been widely used in the field of electric vehicles. So prediction of the state of health (SOH) is important to the safe and efficient use of them. In this study, SOH is estimated by the generalized regression neural network (GRNN). GRNN is established by the radial basis neurons and linear neurons. The network has the advantages of approximation ability and the learning speed. In this test, there are 12 pieces of Li-ion batteries. Constant current charging and discharging are performed on them, until the capacity drops to below 80% of nominal. The SOH of the battery is estimated by the data that obtained from the operation. The data from the test shows that the recharging time by the constant current on the battery, the instantaneous voltage drops in discharge, and the output energy under a certain depth of discharge (DOD) are important to estimate the SOH of battery. The data from the 6 pieces of batteries are performed to train the GRNN. And the feasibility of this method is verified by the data from the other batteries. The test shows the difference of the SOH of the battery can be estimated accurately by this method, and it has great significance in the performance improvement of the battery management system.
基于广义回归神经网络的电池健康状态估计
电池在电动汽车领域得到了广泛的应用。因此,健康状况的预测对安全有效地使用它们具有重要意义。本文采用广义回归神经网络(GRNN)对SOH进行估计。GRNN由径向基神经元和线性神经元组成。该网络具有逼近能力强和学习速度快的优点。在这个测试中,有12块锂离子电池。对其进行恒流充电和放电,直到容量降至标称容量的80%以下。电池的SOH是通过从操作中获得的数据来估计的。试验数据表明,电池恒流充电时间、放电时瞬时电压降、一定放电深度(DOD)下的输出能量是估算电池SOH的重要指标。使用6块电池的数据来训练GRNN。并通过其他电池的数据验证了该方法的可行性。试验结果表明,该方法能准确估算出电池SOH的差异,对电池管理系统的性能提升具有重要意义。
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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