State-of-health estimation of lithium-ion battery based on fractional impedance model and interval capacity. (July 2020)

• Record Type: Journal Article
• Authors: Yang, Qingxia; Xu, Jun; Li, Xiuqing; Xu, Dan; Cao, Binggang
• Is Part Of: International journal of electrical power & energy systems. Volume 119(2020)
• Publication Date: 2020-07
• Subjects: State of health (SOH) -- Lithium-ion battery -- Fractional impedance model (FIM) -- Interval capacity; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2020.105883
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: A simplified impedance model based on Grünwald-Letnikov definition is introduced. Model parameters are identified with a voltage-tracing error rate less than 0.2%. A novel SOH estimation method is proposed with a small error region [−1.5%, 1.5%]. Abstract: Lithium-ion batteries are being used in electric vehicles with very demanding duty schedules. The estimation of battery state of health is very important, so that it has become a research hotspot. This paper deals with the problem of lithium-ion battery state-of-health estimation based on a simplified fractional impedance model and the battery's interval capacity. A simplified fractional impedance model based on the Grünwald-Letnikov definition is introduced, and the least-squares genetic algorithm is utilized to identify the model parameters with a voltage-tracing error rate less than 0.2%. In order to validate the battery ageing performance, a battery test-bench has been established, and an accelerated ageing experiment has been carried out. Based on the identified model parameters and interval capacity combination with a voltage range from 3.95 V to 4.15 V, a back propagation neural network is introduced to estimate the battery state of health with an error margin of [−1.5%, 1.5%]. The effectiveness of the proposed method is verified through simulations and experiments.