Lithium ion battery health state and residual life prediction method

The invention discloses a lithium battery health state and residual life prediction method, which comprises the following steps of: collecting voltage, current, temperature and capacity data of charging and discharging of a lithium ion battery monomer under different working conditions, and eliminating redundant feature information in original data by adopting a KPCA (Kernel Principal Component Analysis) method; and establishing a lithium ion battery health state model and a residual life prediction model based on the time convolutional network and the Reformer model. The method comprises the following steps: initializing a population of a badger algorithm by adopting Logistic chaotic mapping, and introducing an updating strategy based on dimension learning to obtain an improved badger algorithm; and optimizing hyper-parameters of the battery health state model and the residual life prediction model by using an improved badger algorithm, and predicting the battery health state by using the optimized battery h