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

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... ptimized 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.