In automotive industry the state prediction algorithms of a battery management system (BMS) can change during the development cycle of a car. Moreover, even serial production cars happen to have different software versions depending on their date of production. Monitored aggregated customer data sets thus include state of health (SOH) values i.e. capacity retention and resistance increase, which have been generated by different BMS algorithms. To ensure comparability over the whole vehicle production cycle, databased models are needed which can be applied on selected input variables such as temperature and energy histogram data or depth of discharge counters.
This study aims at generating virtual customer driving data by using automotive simulation models and stochastic customer driving profiles. The resulting data points will be collected in a database for model training purposes. These purposes can either be creating reference models for existing collected data or also forecasting the impact of future customer behaviour.
Synthesizing aggregated driving data requires vehicle simulation models to compute the battery load profile from speed and temperature input profiles. Those load profiles can then be propagated into an ageing model which outputs the corresponding SOH factors caused by the initial driving profile. Since vehicle simulation is computationally exhaustive, extracted battery load profiles are multiplied and slightly modified using Markov chains. With this method a higher flexibility with regard to battery profile variability demand can be achieved. All necessary signals are then collected and transformed into the aggregated driving data format.
Finally, the simulated and real customer databases are analysed towards congruence to get a better understanding of the actual customer behaviour. The generated database is going to be used in future studies such as investigations of extrapolated time horizons and databased ageing prediction with aggregated customer data.