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State of Charge Estimation for LiFePO4 Battery Using Artificial Neural Network
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Abstract
En 中文
An artificial neural network based state of charge (SOC) estimation method for LiFePO4 battery is proposed. The artificial neural network is one of the best tools applied to state estimate. In this paper two types of typical neural networks, namely, back propagation (BP) neural network and radial basis function (RBF) neural network are investigated. The proposed SOC estimation method uses the input data of the terminal voltage, discharging current, and temperature of battery to estimate the SOC for LiFePO4 battery under different discharging conditions. To demonstrate the effectiveness of the proposed estimation method, the method has been tested on 3.2V, 10AH LiFePO4 batteries under several different discharging conditions. The experimental data are found to be in close agreement. The test results show that the proposed method is efficient and reliable. Copyright (C) 2012 Praise Worthy Prize S.r.l. - All rights reserved.
Keywords:
State of Charge
LiFePO4 Battery
Back Propagation Neural Network
Radial Basis Function Neural Network
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