Sleek dual Extended Kalman Filter for Battery State of Charge and State of Health Estimation in Electric Vehicle Applications

Accurate battery state estimation is crucial for the performance, safety, and durability of electric vehicle (EV) battery management systems (BMS). The model-based dual extended Kalman filter (DEKF) has been widely used for concurrent state of charge (SOC) and state of health (SOH) estimation. However, tuning the process and measurement covariance matrices of the DEKF is challenging and typically done through a trial and error process. In this work, a sleek version of the standard DEKF is formulated relying on a second-order equivalent circuit battery model (ECM) to estimate the SOC and SOH of EV batteries. The proposed sleek DEKF estimates the capacity fading of the battery. The main advantage of the proposed formulation is the significant reduction in tuning effort. On the other hand, to account for the non-negligible resistance increase over battery lifespan, the ohmic resistance is here formulated as a function of the state of charge and available capacity. Finally, the effectiveness of the proposed method is demonstrated over laboratory data reproducing real-world driving scenarios. The results show that the proposed DEKF obtains high accuracy, comparable to the standard DEKF.