A Novel State of Charge Estimation Algorithm for Lithium-Ion Battery Using Recurrent Equilibrium Network

Accurate estimation of state of charge (SOC) is essential for the applications of lithium-ion battery. Although many machine learning-based SOC estimation algorithms have been proposed, the two common and long-lasting problems remain unsolved, i.e., tedious neural network training process and unsatisfying robustness to measurement noises. To solve these two problems while providing highly accurate SOC estimation, this article proposes a novel recurrent equilibrium network-based algorithm. Direct parameterization technique is employed to substantially simplify the neural network training. The proposed algorithm produces outstanding SOC estimation results under varying temperatures. Its efficacy is verified by experimental results.