Customer-centric aging simulation for 48 V lithium-ion batteries in vehicle applications


Valentin Steininger, Peter Hüsson, Katharina Rumpf, Dirk Uwe Sauer, eTransportation, Vol. 16, 15 March 2022.



In the automotive industry, a battery management system’s state prediction algorithms can change during the development cycle of a car. Monitored aggregated customer data thus includes inconsistent state of health values which have been generated by different algorithms. To facilitate comparability over the collected data, databased models can be trained on selected input variables to provide reference state of health values. This study aims to generate virtual customer driving data of mild-hybrid electric vehicles using automotive simulation models and stochastic customer driving profiles in order to establish a simulation database for model training purposes on one hand and to conduct lifetime simulations for new vehicles in the market on the other. Mapping algorithms between load profile libraries and derived statistical features from a field customer database ensure a realistic representation of individual customer driving behavior. We validated our toolchain using collected trip data from a testing fleet and checked for statistical plausibility of the simulation data. Moreover, lifetime simulation results of selected customers show significant differences in aging implications due to individual driving behavior and environmental conditions. Therefore, during a 10-year simulation, the average aging rate per driven kilometer of an Asian customer is about 33% higher compared to a European customer.