Challenges in data‐based degradation models for lithium‐ion batteries. (3rd February 2020)

• Record Type: Journal Article
• Authors: Galatro, Daniela; Silva, Carlos Da; Romero, David A.; Trescases, Olivier; Amon, Cristina H.
• Is Part Of: International journal of energy research. Volume 44:Number 5(2020)
• Page Start: 3954
• Page End: 3975
• Publication Date: 2020-02-03
• Subjects: data‐based models -- degradation -- lithium‐ion batteries; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Power resources -- Research -- Periodicals; 621.042
• DOI: 10.1002/er.5196
• Languages: English
• ISSNs: 0363-907X

Abstract

Summary: This work summarizes the findings resulting from applying an aging modeling approach to four different capacity loss experimental datasets of lithium‐ion batteries (LIBs). This approach assumes that the degradation trajectory of the capacity is a function of three variables: time, kinetic constant, and time‐dependent factor. The analysis shows that the time‐dependent factor α is cell‐chemistry dependent and cannot be averaged for calendar and cycling modes and combined modes. This factor was also found to be a function of the stress factors. A quadratic model was used to obtain the kinetic constants per test, and statistical metrics were provided to evaluate the quality of the fitting, which was significantly affected when using averaged values of α and refitted kinetic constants. A set of test matrices is proposed for calendar, cycling, and mixed aging modes to overcome the challenges of data‐based models developed from accelerated test approaches for modeling aging in LIBs. This work also proposes a methodology to develop these data‐based aging models.