Lithium-ion batteries (LiBs) have already become main energy storage devices for a variety of applications such as cellular phones, laptops, electric vehicles (EVs), and energy storage systems (ESSs). Because they have not only high energy density but also long cycle life performance with low self-discharging behaviour. Furthermore, the market size of EVs and ESSs is also expected to grow up exponentially in the near future. So, it is strongly required to estimate or predict long-term cycle life of LIBs under various operating conditions. However, due to time and facility limitation, it is almost impossible to confirm cycle lives for longer than 10 years and at various temperatures. That is why accelerated life testing methods and computation modeling have been spotlighted immensely. In particular, there have been reported various battery models, however, most of them did not show high accuracy to predict long-term electrochemical performance owing to the lack of in-depth analysis of LIB cells.

Hence, we attempt to build an advanced cycle life model based on physicochemical analysis data during cycling. First, hundreds of 18650 LIB cells are cycled at different c-rates, temperature, and depth-of-discharge, and their degradation behaviour will be gathered. And then, based on Newman’s works, the degradation equations about solid electrolyte interphase(SEI) growth and crack on the anode, an electrolyte depletion, and cathode electrolyte interphase(CEI) growth on the cathode, will be built and applied to this model.