Internal Temperature Estimation of Power Lithium Batteries Based on GA-EKF Algorithm

Accurate estimation of internal battery temperature is vital for the secure functioning of power batteries. In order to obtain real-time and precise internal battery temperature, A technique for estimating the internal temperature of lithium-ion batteries is put forward, based on a combination of genetic optimization algorithm and extended Kalman filter algorithm. By combining the Bernardi battery heat generation model with the thermal circuit model, an equivalent thermal model for lithium-ion batteries is formulated. The correlation between internal and external temperatures of the battery is determined using the state equation analysis method. The genetic algorithm (GA) is employed to identify the parameters of the thermal model and validate the model accuracy. The estimation of internal battery temperature is achieved by integrating the GA algorithm with the extended Kalman filter (EKF) algorithm. Experimental trials performed on power batteries featuring embedded temperature sensors illustrate the efficacy of the proposed approach in accurately estimating the internal temperature of lithium-ion power batteries. with an estimation error of less than 1°C. This method provides a robust guarantee for real-time safety monitoring of power batteries.