Modelling Commercial Driving and Charging Behaviour based on Telematic Data

IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe), pp. 1–5

Abstract

The electrification of heavy and medium duty vehicles is essential for reducing road transport emissions, but poses challenges due to the high energy demand and synchronised usage patterns. To assess grid impacts, detailed charging and driving profiles are required. This paper presents how the driving and charging behaviour of electric commercial vehicles can be modelled using real-world telematic data from a logistics fleet. A bottom-up probabilistic approach is used to derive realistic driving profiles via Markov chains. These profiles are used to simulate charging behaviour under different strategies that have been developed within this work. The simulation results demonstrate a high degree of temporal simultaneity of charging events, especially in the evening, and show that certain charging strategies can significantly reduce peak loads without increasing total energy demand. A sensitivity analysis highlights the critical role of battery capacity in ensuring vehicle availability. The presented modelling framework provides a robust tool that is useful for assessing infrastructure needs, grid impacts, and operational strategies for electric commercial vehicle fleets.

Authors 5

1. Nils Poll Aachen

   RWTH Aachen University

   Affiliation as printed

   IAEW at RWTH Aachen University,Aachen,Germany

2. Andreas Bong Aachen

   RWTH Aachen University

   Affiliation as printed

   IAEW at RWTH Aachen University,Aachen,Germany

3. Julian Bigalke Aachen

   RWTH Aachen University

   Affiliation as printed

   IAEW at RWTH Aachen University,Aachen,Germany

4. Jan Pfisterer
   Affiliation as printed

   Rhenus SE & Co. KG,Holzwickede,Germany

5. Andreas Ulbig Aachen

   RWTH Aachen University

   Affiliation as printed

   IAEW at RWTH Aachen University,Aachen,Germany