SoC Estimation for Lithium-Ion Battery Using Recurrent NARX Neural Network and Genetic Algorithm

2019 4th Asia Conference on Power and Electrical Engineering

能源学;电工学

Chuangxin, Guo^1 ; Gen, Yuan^1^2 ; Chengzhi, Zhu^3 ; Xueping, Wang^2 ; Xiu, Cao^2

School of Electrical Engineering, Zhejiang University, Zhejiang Province, Hangzhou

310027, China^1

School of Computer Science, Fudan University, Yangpu District, Shanghai

200433, China^2

State Grid Zhejiang Electric Power Company, LTD., Zhejiang Province, Hangzhou

310007, China^3

关键词: Auto-regressive;    Hidden layer neurons;    Mean absolute error;    NARX neural network;    Remaining capacity;    Root mean square errors;    State of charge;    Statistical errors;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/486/1/012076/pdf
DOI  :  10.1088/1757-899X/486/1/012076

来源: IOP

【 摘 要 】

State of charge (SOC) is an important indicator for assessing the remaining capacity of the battery. An accurate SOC estimation is crucial for ensuring the safe operation of lithium batteries and preventing from over-charging or over-discharging in electric vehicle (EV) industry. However, to estimate an accurate capacity of SOC of the lithium batteries has become a major concern for the EV industry. In this paper, a recurrent nonlinear autoregressive external input neural network(NARXNN) model optimized by genetic algorithm(GA) is proposed to improve accuracy of SOC of lithium battery by finding the optimal value of input delays, feedback delays, and hidden layer neurons. The NARXNN based GA model is compared with the NARXNN in performance using statistical error values of mean absolute error and root mean square error are used to check the performance of the SOC estimation. The results show that the NARXNN based genetic algorithm outperforms NARXNN in estimating SOC with high accuracy.