Research on Electric Vehicle Parameter Identification Method Based on Battery and Ultracapacitor

The accuracy of the model relies heavily on parameter identification. In order to accurately identify the model parameters of batteries and ultracapacitors, three representative intelligent optimization algorithms: Grey Wolf Optimization Algorithm (GWO), Particle Swarm Optimization Algorithm (PSO), and Genetic Algorithm (GA) are selected in this study to identify the parameters of battery and ultracapacitor models, respectively. The results of the study show that the Grey Wolf optimization algorithm demonstrates significant advantages in improving the overall prediction accuracy of the battery and supercapacitor models. Specifically, the Grey Wolf optimization algorithm reduces the root mean square error (RMS), an evaluation metric, by at least 7.4% and 13.8% compared to the particle swarm optimization algorithm and the genetic algorithm, respectively. Therefore, the parameter identification of battery and supercapacitor models using the Grey Wolf optimization algorithm not only has better accuracy and reliability but also provides strong support for the subsequent research of electric vehicle energy storage systems.