Online State of Charge Estimation of Lithium-ion Battery Cells: A Multiple Model Adaptive Estimation Approach

Accurate state of charge (SOC) estimation of Lithium-ion battery cells is critical since they are commonly used in a variety of applications. However, the complex chemical reactions inside the cell makes its model nonlinear, which increases the difficulty of the SOC estimation. An accurate online estimation technique to determine the SOC estimate can improve the safety of Lithium-ion cells. More importantly, cell performance and life cycle can also be improved. In this work, the nonlinear state estimation problem of determining SOC is converted to a linear estimation problem solved in a parallel fashion. Multiple model adaptive estimation (MMAE) technique based on a bank of Kalman filters is used to adaptively estimate the SOC. Simulation results for a LiFePO4 Lithium-ion battery cell demonstrate that this technique provides smaller estimation error compared to the Extended Kalman filter.