Online SOC and SOH estimation for multicell lithium-ion batteries based on an adaptive hybrid battery model and sliding-mode observer

This paper proposes an adaptive hybrid battery model-based high-fidelity state of charge (SOC) and state of health (SOH) estimation method for rechargeable multicell batteries. The hybrid battery model consists of an enhanced Coulomb counting algorithm for SOC estimation and an electrical circuit battery model. A variable-length sliding window least squares (VSWLS)-based online parameter identification algorithm is designed to estimate the electrical parameters of the electrical battery model, which are then used as the parameters of an adaptive discrete-time sliding-mode observer (ADSMO) for terminal and open-circuit voltage estimation of a battery cell. The error of the SOC estimated from the enhanced Coulomb counting algorithm is then corrected by using the SOC obtained from the ADSMO-estimated open-circuit voltage. This leads to an accurate, robust real-time SOC estimation. In addition, the maximum capacity of the cell is estimated to determine the SOH of the cell. The proposed method is validated by simulation and experimental results for a four-cell cylindrical lithium-ion battery pack.