From accelerated aging tests to a lifetime prediction model: Analyzing lithium-ion batteries

As lithium-ion batteries play an important role for the electrification of mobility due to their high power and energy density, battery lifetime prediction is a fundamental aspect for successful market introduction. This work shows the development of a lifetime prediction model based on accelerated aging tests. To investigate the impact of different voltages and temperatures on capacity loss and resistance increase, calendar life tests were performed. Additionally, several cycle aging tests were performed using different cycle depths and mean SOC. Both the calendar and the cycle test data were analyzed to find mathematical equations that describe the aging dependence on the varied parameters. Using these functions an aging model coupled to an impedance-based electrical-thermal model was built. The lifetime prognosis model allows analyzing and optimizing different drive cycles and battery management strategies. The cells modeled in this work were thoroughly tested taking into account a wide range of influence factors. As validation tests on realistic driving profiles show, a robust foundation for simulation results is granted. Together with the option of using temperature profiles changing over the seasons, this tool is able to simulate battery aging in various applications.

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