State of charge estimation of lithium-ion battery based on improved Hausdorff gradient using wavelet neural networks

Abstract

In this paper, a parameter optimization algorithm via the wavelet neural network (WNN) based on fractal derivative is proposed to estimate the state of charge (SOC) from the measured current and voltage data for lithium-ion batteries. The improved Hausdorff derivative is applied to the parameter tuning of a WNN to improve the performance of the SOC estimation. The local property of the improved Hausdorff derivative is used to obtain the chain rule which is consistent with the integer-order derivative, and the parameter tuning formulas in WNN based on the improved Hausdorff gradient are obtained. The adaptive order tuning method is proposed and applied to optimize the parameter tuning. Compared with the integer-order optimization method, this method speeds up the optimization of the parameters and improves the optimization accuracy of SOC estimation. The experiment results verify the effectiveness of the proposed SOC estimation method via the WNN based on fractal derivative.