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.

To improve the discharge equalization efficiency of the battery and prevent the occurrence of overdischarge, in this paper, the 18,650 ternary lithium battery is taken as the object of investigation, and an alternating equalization control system for the discharge process of serial cells is proposed. The system implements the alternating discharge of serial cells by switching on and off, using state of charge (SOC) as the equalization variable, and eventually completes the equalization control of the entire battery pack. Discharge simulations were performed in Matlab/Simulink for faulty and normal operating conditions of the battery pack, respectively. The findings indicate that even in the presence of a malfunction, the battery pack can continue to operate continuously for a while; in contrast, under ideal circumstances, the battery pack is capable of maintaining SOC balance throughout the discharge process. Eventually, five batteries are used to construct the experimental platform for the alternating equalization system. The battery pack can still perform selective discharge under fault conditions until the battery pack reaches the discharge cutoff condition. Under normal conditions, the maximum SOC difference of all five batteries can stabilize at about 1%. The experimental results show that the proposed equalization control system can achieve the equalization of battery discharge and prolong the discharge time, and can prevent the occurrence of battery over discharge.