Practical Mixed Parameter Obtention Framework for Li-Ion Battery Electrochemical and Degradation Models Based on Parameter Classification and Physical Measurement

Prediction of EV performance through lifetime is a crucial task. However, accurate prediction of states of LIBs remains a challenge due to the complexity of detailed electrochemical models, absence of a single universally accepted approach for the identification of model parameters and limitations in invasive and non-invasive parameter obtention methods. Classification and estimation of the most relevant parameters under different steps will enhance the identifiability of parameters in P2D models operating under various conditions. On the other hand, reliable estimation of the internal state of batteries can be drawn with proper integration of material parameters into the battery model. In this study, a systematic classification framework of parameter obtention was proposed through multi-steps for P2D electrochemical and degradation models. Furthermore, a practical mixed approach is developed for parameter obtention of P2D models with optimization-based calibration methodology and experimentally characterized parameters. The electrochemical P2D model with degradation mechanisms is calibrated and validated with best parameter set that fits electrochemical response of the cell. Two different experiments for calendar and cycling aging were also conducted to reflect different aging and impact of temperature on the degradation mechanisms. The comparison of selected parameters from cell characterization measurements and calibration results demonstrates that cathode capacity loading and N/P ratio differ by 6.93% and 14.56% while electrode thicknesses variations between measured and calibrated value for anode and cathode are 1.4% and 13.4 %. Results of degradation model show that by calibration of cycling ageing with only the first 100 cycles, the error in the cycling aging prediction remains under 1.2% in 600 cycles. The results of this study demonstrate an efficient and practical parameter classification and obtention framework based on multi-step approach and present a practical mixed electrochemical and degradation model by utilizing the correct set of material parameters in the calibration process.