Accelerated degradation model for C-rate loading of lithium-ion batteries. (May 2019)

• Record Type: Journal Article
• Authors: Saxena, Saurabh; Xing, Yinjiao; Kwon, Daeil; Pecht, Michael
• Is Part Of: International journal of electrical power & energy systems. Volume 107(2019)
• Page Start: 438
• Page End: 445
• Publication Date: 2019-05
• Subjects: Lithium-ion battery -- C-rate -- Accelerated testing -- Reliability -- Degradation modeling -- Mixed-effects regression; 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.2018.12.016
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: The main focus is accelerated testing and degradation modeling of Li-ion batteries. Discharge C-rate variable has been investigated for accelerating the battery testing. The model is inspired by the physics of SEI layer formation. The model utilizes historical degradation data for better generalization capability. The non-linear mixed effect regression technique has been used to capture the battery-to-battery variations. Abstract: As Li-ion batteries are used in increasingly diverse applications, their performance and reliability become more critical. Reliability testing of Li-ion batteries involves battery capacity fade monitoring over repeated charging/discharging cycles. Cycling at a nominal charge/discharge current requires an extensive amount of time and resources, and hence a battery qualification process based on battery cycle testing may cause delays in time to market. Discharge C-rate variable can be used for accelerating Li-ion battery cycle testing. This paper develops an accelerated capacity fade model for Li-ion batteries under multiple C-rate loading conditions, to translate the performance and degradation of a battery population at accelerated C-rate conditions to normal C-rate conditions. A nonlinear mixed-effects regression modeling technique is used to take into account the variability of repeated capacity measurements on individual batteries in a population. The model is validated using the experimental data from two battery populations that have been fielded.