Lithium battery state-of-charge intelligent prediction method based on improved grey wolf particle filtering

The invention provides a lithium battery state-of-charge intelligent prediction method based on improved grey wolf particle filtering, and the method comprises the following steps: S10, constructing a high-fidelity second-order autoregression model, and carrying out the model parameter identification; and S20, taking identification parameters of the high-fidelity second-order autoregression model as state observation quantities, and substituting the state observation quantities into an improved grey wolf particle filtering algorithm to carry out iterative calculation so as to complete intelligent prediction of the state of charge of the lithium battery. According to the method, estimation precision and calculation complexity are comprehensively considered, the lithium battery SOC intelligent prediction method based on improved grey wolf particle filtering is provided, calculation of lithium battery SOC intelligent prediction is achieved by combining establishment of an SOC estimation model on the basis of ful