Freight transport accounts for about half of all distances travelled in Europe. Therefore, freight transport is one of the decisive factors for reducing greenhouse gases and air pollutants. For this reason, the electrification of road freight transport is being promoted as part of the project “BEV Goes eHighway—[BEE]”. The data basis for the modelling used in this project is an electric drive axle for a heavy commercial vehicle, which was developed in the “Concept-ELV²” project. Based on the results of the previous project, the methodological tools that were developed are presented in this paper. These allow a wide range of possible powertrain topologies to be considered at the concept stage of development based on an estimation of future system characteristics. For this purpose, the components are automatically designed taking into account the mutual influence of the requirements and are evaluated in the context of the holistic system. This publication focuses on the efficiency and thermal evaluation of the transmission stages of the addressed electric drive units and validates the developed models using a pototypically designed electric commercial vehicle axle.

The imperative for electrification of road transport, driven by global climate targets, underscores the need for innovative powertrain systems in heavy-duty vehicles. When developing new electric drive modules, individual operational requirements need to be considered instead of generalized usage profiles, as heavy-duty vehicles experience significantly differing loads depending on their field of operation. Real driving data, representing the demands of different application scenarios, offers great potential for digital replication of driving conditions at different stages of simulation and physical validation. Application- and vehicle-specific longitudinal requirements during operation are particularly relevant for the dimensioning of powertrain components. Road gradient and mass estimation assist in the description of these operating conditions, allowing for detailed modeling of the real load conditions. An incorporation of real driving data instead of solely relying on standardized cycles has the potential of tailoring components to the target lead users and applications. While some operating conditions can be recorded by vehicle manufacturers, these are usually not accessible by third parties. In this paper, the authors present an innovative methodology of estimating vehicle parameters for the generation of representative driving profiles for implementation into a consecutive powertrain design process. The approach combines the measurement of real driving data with state estimation. The authors show that the presented methodology enables the generation of driving profiles with less than 25% deviation from the original data set.

EBook: Concept ELV² – Development of an Electric Drive Axle for Heavy Commercial Vehicles (ISBN 978-3-18-092384-0) von aus dem Jahr 2021