Lithium‐Ion Battery Parameter Estimation With Adaptive Honey Badger Optimization and State‐of‐Charge Prediction Using the Extended Kalman Filter

Abstract

Accurate parameter identification and terminal‐voltage estimation are essential for precise state‐of‐charge estimation and effective control of lithium‐ion battery management systems. Conventional optimization methods, such as Particle Swarm Optimization (PSO) and Honey Badger Optimization (HBO), are prone to local optima under certain nonlinear operating conditions and are less effective at identifying parameter values that minimize the root mean square error (RMSE) in nonlinear battery models at specific temperatures. Hence, this study introduces the Adaptive Honey Badger Optimization (AHBO) algorithm by incorporating Sine‐chaotic population, adaptive exploration, adaptive exploitation, diversity preservation, and conditional recomputation mechanisms, thereby maintaining an effective balance between global exploration and local exploitation. It has been achieved through adaptively regulated search dynamics and a diversity‐dependent exponential‐decay mechanism. The proposed AHBO method, with static OCV–SoC values, is used for parameter identification and battery voltage estimation in the equivalent circuit models of the One‐RCH and Two‐2RCH configurations. The results demonstrate that AHBO achieves lower terminal‐voltage estimation RMSE values, ranging from 7.85 to 16.64 mV for the Two‐RCH configuration over the temperature range of 45°C to −15°C, compared with PSO and HBO. In addition, the proposed method reduces the convergence time by approximately 70%–75% and enhances the dynamic terminal‐voltage response, particularly at low temperatures. After parameter identification, a comparative study of state‐of‐charge estimation was conducted using the Extended Kalman Filter (EKF) and the Gated Recurrent Unit (GRU) methods. The EKF exhibited a physically consistent model‐based SoC response with RMSE values of 0.87% at 45°C, 1.18% at 15°C, and 1.48% at −15°C. However, the GRU demonstrated better estimation accuracy at the intermediate temperature of 15°C (RMSE: 0.73%) and comparable accuracy at −15°C (RMSE: 1.40%), while showing reduced accuracy at 45°C (RMSE: 1.36%).