Fault Diagnosis of Lithium Battery for Electric Vehicle Based on BP Neural Network Optimized by Bat-particle Swarm Optimization Algorithm
In order to diagnose the fault of lithium battery of electric vehicle,on the basis of analyzing the fault characteristics and causes of lithium battery failure,a fault diagnosis model of lithium battery for electric vehicle was established,which included lithium battery fault sample collection and processing,BP neural network,fault feature coding output and fault type diagnosis.The bat-particle swarm optimization algorithm was used to optimize the initial structure parameters of the BP neural network,and the improved BP algorithm and fault samples were used to train and test the BP neural network.The simulation results show that compared with the BP algorithm,genetic algorithm and particle swarm algorithm,the bat-particle swarm optimization BP neural network has the highest fault diagnosis accuracy,the shortest training time and the smallest training error.
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