Essential predictive information for high fuel efficiency and local emission free driving with PHEVs

Autoren

MSc Tobias Schürmann, Dr.-Ing. Daniel Görke, Dipl.-Ing. Stefan Schmiedler, Daimler AG;
Dipl.-Ing. Tobias Gödecke, Prof. Dr.-Ing. Kai André Böhm, Hochschule Esslingen;
Prof. Dr.-Ing. Michael Bargende, Universität Stuttgart

Jahr

2019

Zusammenfassung

An intelligent selection of the operating modes can improve the fuel efficiency of plugin hybrid electric vehicles (PHEVs) and allow them to drive local emission free. In order to align these goals and hence to improve the mobility especially with air pollution problems in urban areas, predictive information about future driving situations is necessary. To achieve this target and furthermore to design and calibrate predictive control strategies accordingly, the sensitivity of predictive information on the fuel efficiency is analyzed in the presented simulation study. Traffic simulations are used which enable reproducible driving situations regarding traffic, traffic control and driving characteristics by their parameterizable settings. By calculating fuel optimal strategies with
Dynamic Programming (DP) for a PHEV in P2 topology, the impact of predictive information about future driving situations on the fuel efficiency is evaluated. The results show which driving situations are suitable for charging and discharging and assess the efficiency of local emission free driving by comparing the fuel savings to the costs of the electric energy demand.

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