The State of Charge Estimation of Power Lithium Battery Based on RBF Neural Network Optimized by Particle Swarm Optimization

Zhixiang  Hou; Ping  Xie; Jiqiang  Hou

doi:10.6180/jase.2017.20.4.10

The State of Charge Estimation of Power Lithium Battery Based on RBF Neural Network Optimized by Particle Swarm Optimization

Zhixiang Hou This email address is being protected from spambots. You need JavaScript enabled to view it.¹, Ping Xie¹ and Jiqiang Hou¹

¹College of Automobile and Mechanical Engineering, Changsha University of Science and Technology, Changsha, Hunan 410004, P.R. China

Received: April 13, 2017
Accepted: September 5, 2017
Publication Date: December 1, 2017

Download Citation: ||https://doi.org/10.6180/jase.2017.20.4.10

ABSTRACT

In order to ensure the safe and stable operation of electric vehicles (EV), it is necessary to accurately estimate the state of charge (SOC) of power lithium battery for electric vehicle. Because of the nonlinear relationship between SOC and its influencing factors, RBF neural network has obvious advantages in solving nonlinear problems, so in this paper, an SOC estimation method of power battery based on RBF neural network is proposed. In order to improve the accuracy of SOC estimation, we use particle swarm optimization (PSO) to optimize the RBF neural network model and identify the value of RBF network center vector and the weights through global optimal searching ability of PSO algorithm. The results simulation show that the SOC model based on PSO-RBF neural network has good estimation accuracy.

Keywords: Electric Vehicle, State-of-charge Estimation, Radial Basis Function Neural Network, Particle Swarm Optimization Algorithm

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