State of charge estimation of lithium-ion batteries using optimized Levenberg-Marquardt wavelet neural network

Bizhong Xia, Deyu Cui, Zhen Sun, Zizhou Lao, Ruifeng Zhang, Wei Wang, Wei Sun, Yongzhi Lai and Mingwang Wang

Energy, 2018, vol. 153, issue C, 694-705

Abstract: State of charge (SOC) is one of the most critical parameters for indication of the remaining energy which is vital important for the safety and reliability of power system. In this paper, Levenberg-Marquardt (L-M) algorithm optimized multi-hidden-layer wavelet neural network (WNN) model and a series of novel intelligent SOC estimation methods using L-M based WNN are proposed. Particle swarm optimization (PSO) algorithm is used to optimize L-M based three-layer WNN (LMWNN) for SOC estimation problem. Furthermore, it is validated that L-M based multi-hidden-layer WNN (LMMWNN) has better performance than LMWNN. Basing the specific characteristic of SOC estimation, the LMMWNN method is optimized by combining piecewise-network method (PLMMWNN) and seven-point linear smoothing method (smoothed PLMMWNN). Under single driving cycle, such as the New European Driving Cycle(NEDC), the mean absolute error of PLMMWNN can be decreased to 0.6% and the maximum absolute error 5%. A comparison study of the series of WNN-based methods with BP neural network (BPNN) and extend Kalman filter (EKF) is conducted. The robustness evaluation, which is based on untrained driving cycles test, measurement noise test and piecewise training and batteries test, indicates the good performance on estimation accuracy, applicability and robustness of the proposed methods.

Keywords: State of charge; Lithium-ion battery; Wavelet neural network; Levenberg-Marquardt algorithm; Particle swarm optimization; Multi-hidden-layer (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (29)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:153:y:2018:i:c:p:694-705

DOI: 10.1016/j.energy.2018.04.085

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