Lithium battery charge state assessment method based on finite difference expansion Kalman algorithm

The invention discloses a lithium battery charge state assessment method. The method includes the first step of setting an initial value and carrying out Cholesky decomposition on each covariance, the second step of state one-step prediction, the third step of covariance one-step prediction, the fourth step of gain filtering, the fifth step of updating the optimized value of a state, and the sixth step of updating filtering covariance. Compared with the prior art, the precision of the method is higher than that of first-order spreading of the Taylor series, effective error information caused by model linearization is fully made use of, and strong robustness for model parameter changes is achieved.