Fast UD factorization-based RLS online parameter identification for model-based condition monitoring of lithium-ion batteries

This paper proposes a novel parameter identification method for model-based condition monitoring of lithium-ion batteries. A fast UD factorization-based recursive least square (FUDRLS) algorithm is developed for identifying time-varying electrical parameters of a battery model. The proposed algorithm can be used for online state of charge, state of health and state of power estimation for lithium-ion batteries. The proposed method is more numerically stable than conventional recursive least square (RLS)-based parameter estimation methods and faster than the existing UD RLS-based method. Moreover, a variable forgetting factor (VF) is included in the FUDRLS to optimize its performance. Due to its low complexity and numerical stability, the proposed method is suitable for the real-time embedded Battery Management System (BMS). Simulation and experimental results for a polymer lithium-ion battery are provided to validate the proposed method.