SOC Estimation of Lithium Battery Based on IPSO-BP Neural Network. (November 2020)

• Record Type: Journal Article
• Authors: Mao, Xinjian; Song, Shaojing; Ding, Feng; Tang, Pan
• Is Part Of: Journal of physics. Volume 1684(2020)
• Publication Date: 2020-11
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1684/1/012152
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Based on the analysis of the accurate estimation method of the state of charge (SOC) of lithium battery for electric vehicles, aiming at the shortcomings of back propagation (BP) neural network model, an algorithm based on Improved Particle Swarm Optimization (IPSO) is proposed to optimize the parameters of BP neural network. In this algorithm, the particle swarm optimization algorithm is optimized by introducing shrinkage factor to limit the particle speed, so as to determine the initial parameters of BP neural network. Finally, the battery estimation model is established by using the data set of lithium battery published by NASA PCoE, and the simulation test is carried out by using MATLAB platform. The results show that the method can effectively reduce the SOC error and control the error within 2%. It has practical significance for SOC estimation in battery management system.