Abstract
In electric vehicles, accurate SOC can provide accurate and reliable residual power for drivers, which is the target value of a judgment method to effectively avoid overcharge or over discharge of battery pack[4]. However, due to the nonlinear characteristic of SOC value, its specific value cannot be directly measured by instruments and equipment. The final estimation of SOC value can only be carried out by monitoring battery parameters such as current and voltage. In order to achieve high-precision prediction of the state of charge (SOC) of lithium iron phosphate (LiFePO4) batteries, this paper uses an algorithm combining particle swarm optimization optimized by simulated annealing and BP neural network. Through MATLAB simulation, it can be seen that this method is more accurate than particle swarm optimization used to optimize BP neural network.
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