Power battery remaining service life prediction method based on particle swarm optimization

The invention belongs to the field of new energy automobile batteries, and particularly relates to a power battery remaining service life prediction method based on a particle swarm optimization algorithm, and the method comprises the steps: determining a decline point of battery life attenuation and battery quality related operation data on the basis of analyzing the working principle of a new energy automobile power battery; the collected data is denoised to obtain high-quality battery operation data; determining a battery remaining life prediction health factor index; constructing a new energy automobile power battery residual life prediction model based on a Bi-LSTM network; optimizing model parameters by using a particle swarm algorithm to obtain an optimal prediction model; and finally, taking the capacity, internal resistance and temperature of the new energy automobile power battery as input data of the model, and predicting the remaining life of the new energy automobile power battery. The method pro