Using Digitalization to Reduce Laboratory Testing Time for Lithium-Ion Cells

The development of lithium-ion batteries for electric vehicles and other applications requires numerous complex and time-consuming research efforts. Numerical modeling can significantly reduce both the scope and duration of laboratory testing by enabling rapid prediction of cell behavior under various operating conditions. In this study, it is demonstrated that the parameters of the Newman–Tiedemann–Gu–Kim (NTGK) battery model can be determined using only extreme discharge current values, omitting intermediate currents. This approach increases the average voltage error by 0.23% but reduces the average temperature error by 0.22%. Additionally, the use of limited experimental data leads to extrapolation errors at an 8 A discharge current from 1.20% to 0.65% for voltage and from 7.04% to 5.78% for temperature. Furthermore, the proposed model enables accurate prediction of the state of charge (SoC) and battery temperature evolution without additional measurements under realistic driving conditions, such as the Worldwide Harmonized Light-Duty Vehicle Test Cycle (WLTC).