Estimation of state-of-charge and state-of-health for lithium-ion battery based on improved firefly optimized particle filter

Accurate estimation of state-of-charge and state-of-health is extremely important for the lithium-ion batteries used in electric vehicles. First, this article proposes an online parameter identification method based on joint forgetting factor recursive least squares-adaptive extended Kalman filter and calculate state-of-charge by adaptive extended Kalman filter in real time. Through simulation tests, the minimum error of this method is 0.45%, which verifies its high accuracy. Then an improved firefly algorithm solved the particle dilution problem is combined with particle filter to estimate state-of-health for the first time. Compared with particle filter, the estimation accuracy of improved firefly algorithm optimized particle filter is better in the experiment, its esti-mated error is as low as 0.79% and predicted cycles is consistent with the failure cycles in test. In addition, the convergence of the algorithm is verified by the experiment.