Lithium- ion technologies have become the most attractive and selected choice
for battery electric vehicles. However, the understanding of battery aging is still
a complex and nonlinear experience which is critical to the modeling method
ologies. In this work, a comprehensive lifetime modeling twin framework fol
lowing semi- empirical methodology has been developed to predict the crucial
degradation outputs accurately in terms of capacity fade and resistance increase.
The constructed model considers all the relevant aging influential factors for
commercial nickel manganese cobalt (NMC) Li- ion cells based on long- term
laboratory- level investigation and combines both the cycle life and the calendar
life aspects. To demonstrate robustness, the model is validated with a real- life
worldwide harmonized light- duty test cycle (WLTC). The model can precisely
predict the capacity fade and the internal resistance growth with a root- mean-
squared error (RMSE) of 1.31% and 0.56%, respectively. The developed model
can be used as an advanced online tool forecasting the lifetime based on dynamic