国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

The accurate estimation of the lithium battery’s state can prolong the service life of the battery and reduce the occurrence of safety accidents. In order to improve the accuracy of back propagation (BP) neural network to estimate the state of charged (SOC), an improved method is proposed to optimize the initial weights of BP neural networks by using genetic particle swarm optimization algorithm (GA-PSO). The K-mean algorithm is introduced to optimize the error caused by the randomness of the initial particle distribution in the genetic particle swarm algorithm, and seeks the relationship between the initial weights and the output error of the BP neural network algorithm, the optimal particle swarm optimization is carried out in the particle swarm generated by the genetic particle swarm optimization algorithm, which can reduce the error. According to the analysis of the charging and discharging data of the 18650 lithium battery and the 200 sets of data is produced by the BP neural network training that is optimized by the original genetic particle swarm optimization algorithm. Then the initial weight characteristic formula of BP neural network with lithium battery characteristics is obtained. And the feasibility of the improved BP neural network method is verified by the cosimulation of MATLAB and FPGA .The method also optimizes the GA-PSO, and reduces the error caused by the uncertainty of initial values.