Aging diagnosis-oriented three-scale impedance model of lithium-ion battery inspired by and reflecting morphological evolution. (March 2023)

• Record Type: Journal Article
• Authors: Yang, Bowen; Wang, Dafang; Zhang, Bi; Chen, Shiqin; Sun, Xu; Wang, Tao
• Is Part Of: Journal of energy storage. Volume 59(2023)
• Publication Date: 2023-03
• Subjects: Lithium-ion battery -- State of health -- Impedance modeling -- Degradation mechanisms -- Aging diagnosis; Energy storage -- Periodicals; Energy storage -- Research -- Periodicals; 621.3126
• DOI: 10.1016/j.est.2022.106357
• Languages: English
• ISSNs: 2352-152X

Abstract

Abstract: Numerous mutually coupled factors contribute to the aging of lithium-ion battery (LIB). Inspired by the observation of microscopic morphological changes and their mechanistic analysis, this paper considers the evolving agglomerate fracture and growing SEI film as the main driving forces, and the aging of LIB and consequential impedance changes are represented with the fracture expansion of secondary particle and thickness growth of SEI layer, leading to a full-cell impedance model suitable for describing aging behavior of LIB, which is capable of effectively approximating the electrochemical impedance spectrum (EIS) throughout the usable life. By analyzing the identified parameters with regard to the state of health (SOH), the fracture depth of cathode agglomerate and the thickness of anode SEI film exhibit trends consistent with the morphological development on electrode materials and the deduction of multi-physical mechanisms, and such strong correlation is conveniently used to estimate the SOH of LIB. A large number of qualitative studies are emerging regarding the aging of LIB, and this paper aims to supplement research on the aging modeling and diagnosis of SOH in the frequency-domain by unifying the cause and effect from a quantitative perspective. Highlights: Two crucial yet less implemented metrics are identified through the morphological evolution observed in LIB. A SOH-diagnosis-friendly model is established by quantifying and incorporating the influence of the two metrics. As both cause and effect, the two metrics show strong correlation with aging, and support the diagnosis of SOH well.