An Optimized FPGA-Based Hardware Accelerator for Physics-Based EKF for Battery Cell Management

Battery technology is the cornerstone of HEVs. Model Predictive Control coupled with physics-based model (PBM) is an effective technique for battery management systems (BMS). In this case, extended Kalman filter (EKF) is used as the state observer for the highly non-linear PBM. Thus far, the sheer computational complexity of PBM hinders it from being utilized for portable BMS. In this paper, we introduce a novel and efficient FPGA-based hardware accelerator for physics-based EKF, to address the computational complexity of PBM.