SOC and SOH Identification Method of Li-ion Battery Based on SWPSO-DRNN

To realize accurate estimation of SOC (state of charge) and SOH (state of health), Li-ion battery’s operating characteristic is analyzed in this paper while fully considering temperature, degree of aging and other practical factors that could impact their operating status. On the basis of nonlinear autoregressive with exogenous inputs (NARX) architecture, an improved dynamic recurrent neural network (DRNN) with ability of dynamic mapping is established, which is more suitable than static network for estimating the batteries’ state with strongly nonlinear and dynamic behaviors. Meanwhile, a self-adaptive weight particle swarm optimization (SWPSO) algorithm is introduced for training the network. Compared with gradient descent algorithm, the SWPSO algorithm could improve error convergence speed and avoid falling into local optimum. Validation results highlights that the presented method is able to improve the estimation accuracy of the SOC and SOH under different conditions including temperature, current and degree of aging, and has strong robustness and ability of generalization.