A comparative study of fractional-order models for lithium-ion batteries using Runge Kutta optimizer and electrochemical impedance spectroscopy. (April 2023)

• Record Type: Journal Article
• Authors: Wang, Yujie; Zhao, Guanghui
• Is Part Of: Control engineering practice. Volume 133(2023)
• Publication Date: 2023-04
• Subjects: Lithium-ion battery -- Fractional-order model -- Parameter identification -- Runge Kutta optimizer; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2023.105451
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: With the popularity of new energy vehicles and various electronic products, lithium-ion batteries are widely used in daily life. For lithium-ion batteries, an accurate model is the basis of battery management. In the process of continuous pursuit for model accuracy, it often leads to an increase in model complexity. Fractional-order models can effectively balance model accuracy and complexity between electrochemical models and equivalent circuit models, thus having good application prospects. In this paper, four typical fractional-order models for lithium-ion batteries are compared, and the Runge Kutta optimizer (RUN) algorithm, which has fast convergence speed and high precision, is employed to identify the model parameters. The accuracy for predicting terminal voltage and the time required for parameter identification process are compared respectively under different dynamic conditions for each model. Finally, the model impedance spectrum fitted results and model parameter values are discussed through electrochemical impedance spectroscopy test. This paper provides guidance on the modeling and parameter identification for lithium-ion batteries through analyzing different fractional-order models and introducing impedance spectroscopy to help explore the battery characteristics.