Saurabh Saxena1, Yinjiao Xing1, Daeil Kwon2, and Michael Pecht1
1CALCE, Center for Advanced Life Cycle Engineering, Department of Mechanical Engineering, University of Maryland, College Park, Maryland 20740, USA
2Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, South Korea
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.