Robustness analysis of State-of-Charge estimation methods for two types of Li-ion batteries

Battery State of Charge (SOC) estimation is an important function for battery management systems and critical for the reliable operations of batteries. This paper analyzes the robustness of SOC estimation algorithms for two types of Li-ion batteries under varying loading conditions, temperatures and aging levels. Based on the model templates identified in an earlier research, the model parameters are determined. The Extended Kalman Filter (EKF) technique is then adopted as the SOC estimation algorithm. The robustness of the estimator against varying loading profiles and temperatures is evaluated and compared against the Coulomb counting method. We subsequently used data from cells that have significantly aged to assess the robustness of the SOC estimation algorithm. Finally, the need for model parameter updates is analyzed.

► Analytic functions describing the battery model parameters are optimized.
► EKF based on the optimal analytic model is adopted as the SOC estimator.
► The robustness of the SOC estimator against varying loading profiles is evaluated.
► The robustness of the SOC estimator against varying temperatures is analyzed.
► The robustness of the SOC estimator against varying aging levels is assessed.