Temperature Prediction of Lithium-ion Battery by CPSO-UKF

In this study, the temperature estimation of lithium-ion batteries is proposed by Chaos Particle Swarm Algorithm-Unscented Kalman Filter (UKF). 18650-type lithium-ion batteries are widely used in electric vehicles due to their compact design and long life. The accurate estimation of the temperature parameter of these batteries is critical for reasons such as balancing the performance and predicting chemical degradation. Therefore, in this study, the temperature parameter estimation of an 18650-type lithium-ion battery is made by UKF-based methods. Due to the intensive and mathematical processing load of the UKF method, the parameter values are determined by Chaos Particle Swarm Optimization (PSO) methods, and their estimation performances are compared. The parameter values such as alpha, kappa, and R matrix of the UKF method are determined by Particle Swarm Optimization (PSO), Chaos Particle Swarm Optimization (CPSO), Comprehensive Learning Particle Swarm Optimization (CLPSO), and hyperparameter values determined by trial and error. The hyperparameter values obtained from these four different methods were applied to the UKF method separately and their estimation performances were compared. The CPSO-UKF method became the most successful method by reaching an accuracy of 99.99228% in estimation according to the R2 metric. The success of the proposed method is also given with other regression metrics.