Research on SOC Estimation Method for Lithium-Ion Batteries Based on Neural Network

Abstract

With the increasingly serious problem of environmental pollution, new energy vehicles have become a hot spot in today’s research. The lithium-ion battery has become the mainstream power battery of new energy vehicles as it has the advantages of long service life, high-rated voltage, low self-discharge rate, etc. The battery management system is the key part that ensures the efficient and safe operation of the vehicle as well as the long life of the power battery. The accurate estimation of the power battery state directly affects the whole vehicle’s performance. As a result, this paper established a lithium-ion battery charge state estimation model based on BP, PSO-BP and LSTM neural networks, which tried to combine the PSO algorithm with the LSTM algorithm. The particle swarm algorithm was utilized to obtain the optimal parameters of the model in the process of repetitive iteration so as to establish the PSO-LSTM prediction model. The superiority of the LSTM neural network model in SOC estimation was demonstrated by comparing the estimation accuracies of BP, PSO-BP and LSTM neural networks. The comparative analysis under constant flow conditions in the laboratory showed that the PSO-LSTM neural network predicts SOC more accurately than BP, PSO-BP and LSTM neural networks. The comparative analysis under DST and US06 operating conditions showed that the PSO-LSTM neural network has a greater prediction accuracy for SOC than the LSTM neural network.

Keywords

• battery management system
• new energy vehicle
• state of charge
• particle swarm optimization
• PSO-LSTM neural network