Co-estimation of lithium-ion battery state-of-charge and state-of-health based on fractional-order model. (15th August 2023)

• Record Type: Journal Article
• Authors: Ye, Lihua; Peng, Dinghan; Xue, Dingbang; Chen, Sijian; Shi, Aiping
• Is Part Of: Journal of energy storage. Volume 65(2023)
• Publication Date: 2023-08-15
• Subjects: Fractional-order -- Adaptive algorithm -- Unscented Kalman filter -- State estimation -- Cooperative estimation; Energy storage -- Periodicals; Energy storage -- Research -- Periodicals; 621.3126
• DOI: 10.1016/j.est.2023.107225
• Languages: English
• ISSNs: 2352-152X

Abstract

Abstract: Accurate estimation of lithium-ion battery state of charge and state of health has been the focus of research in battery management systems. Effectively improving the estimation accuracy of battery state parameters is crucial for the safe and stable driving of electric vehicles. In this paper, based on the fractional-order model, a multi-scale cooperative estimation method based on the dual adaptive Unscented Kalman filter is proposed. Firstly, according to the fast and slow time-varying characteristics of the measured parameters, the dual filters perform state estimation of SOC and SOH according to different time scales, alternately updating the information parameters by nesting them with each other and optimizing the noise terms with an adaptive algorithm. Meanwhile, the influence of temperature on the battery parameters is considered, and experimental analysis of the battery is carried out using a variety of different temperatures and operating conditions, as well as the accuracy and stability of the algorithm are verified by simulation. The experimental and simulation results show that the co-estimation method has good accuracy and robustness in both high and low-temperature environments. Highlights: A fractional-order equivalent circuit model is used to improve the accuracy of the battery model. A multi-scale collaborative estimation method based on a dual adaptive unscented Kalman filter is proposed. The memory factor is combined with an adaptive unscented Kalman filter via fractional order calculus. The system noise is optimally corrected using an adaptive algorithm.