Abstract
Battery electric vehicles will become more and more important in future, but up to now they lack sufficient driving range. While a conventional powered range extender can be used to enable driving over extended distances, the operation of the combustion engine with the depleted battery is not optimal in terms of consumption and emissions.
The predictive operating strategy that is presented in this paper is able to minimise consumption and emissions as well as to guarantee a desired electric power reserve when reaching the final destination. It incorporates an iterative optimisation algorithm which calculates the most efficient operation mode of the range extender vehicle for a given route. The individual driver’s behaviour and the current traffic conditions that are identified by a self-learning algorithm are used to enhance map data for a precise wheel power prediction.
Test drives were conducted with a prototype vehicle that was equipped with the developed predictive operating strategy. A specific route was driven with differing driving styles. The test drives started with the traction battery at the same state of charge to ensure comparability of the results. For different drivers the prediction of the energy demand led to different results respecting the individual driving style and the differing environmental conditions. Thus the predictive operating strategy was able to derive the ideal range extender usage for each different driver on the predicted route.
KEYWORDS – Range Extender, Operating Strategy, Adaptive Prediction, Driving Behaviour, Map Data