Model-based state of X estimation of lithium-ion battery for electric vehicle applications

In developing an efficient battery management system (BMS), an accurate and computationally efficient battery states estimation algorithm is always required. In this work, the highly accurate and computationally efficient model-based state of X (SOX) estimation method is proposed to concurrently estimate the different battery states such as state of charge (SOC), state of energy (SOE), state of power (SOP), and state of health (SOH). First, the SOC and SOE estimation is performed using a new joint SOC and SOE estimation method, developed using a multi-time scale dual extended Kalman filter (DEKF). Then, the SOP estimation using T-method and 2RC battery model is performed to evaluate the non-instantaneous peak power during charge/discharge. Finally, the battery current capacity estimation is performed using a simple coulomb counting method (CCM)-based capacity estimation with a sliding window. The performance of the proposed SOX estimation method is compared and analyzed. The experimental results show that the estimated SOC and SOE error is less than 1% under considered dynamic load profile at three different temperatures. After the final convergence, the estimated capacity maximum value absolute error is +/- 0.08 Ah. In addition, the low value of evaluated mean execution time (MET) justifies the high computational efficiency of the proposed method.