Battery lifetime of electric vehicles by novel rainflow-counting algorithm with temperature and C-rate dynamics: Effects of fast charging, user habits, vehicle-to-grid and climate zones

Abstract

The adoption of electric vehicles is expected to soon widespread to cope with energy transition needs; however, concerns on battery lifetime arise, especially related to charging behaviors, vehicle usage habits, vehicle-to-grid and weather conditions. In fact, lifetime battery modeling is a challenging dynamic to characterize, as it involves complex chemical processes related to charging, discharging and temperature dynamics over long time spans that are often difficult to dominate, given the large uncertainties. Having a fatigue-like behavior, the battery aging has sometimes been modeled using rainflow-counting algorithms, yet traditional modeling is not holistic and approximations are used, especially when considering temperature or current dynamics. Based on experimental data, this paper aims at developing a holistic battery degradation model based on rainflow-counting algorithm to properly account for all major determinants of capacity loss, namely cycling usage, calendar lifetime, dynamic temperature and battery current. The approach is coupled with a physical-electro-thermal modeling of the vehicle system, developed in Modelica language, to accurately simulate the intertwined thermal and electrical behavior of the system subject to different usage charging behaviors, including slow and fast charging, as well as vehicle-to-grid application. The proposed case study shows the expected lifetime of electric vehicles to be comparable with of traditional cars (10–20y) and that the proposed temperature-dependent battery modeling enables reducing estimation errors up to 27%. A sensitivity on different climate zones has been considered and results suggest that cool climates can increase life expectancy by 30% with respect to hot climates in typical Italian contexts.