This webinar will present a model created in Ansys Twin Builder to simulate and predict the efficiency and dynamic behavior of electric vehicles. The model supports early design decisions and operation optimization strategies such as route selection, custom vehicle configuration, energy recovery, and assessment and mitigation of driver behavior. The model is applied to the case of a light delivery truck in an urban route, including system-level models for the driver, electric powertrain, battery pack, longitudinal dynamics behavior and a robust speed-reference block.
A representative driving cycle for a delivery truck was selected from an NREL database, and an artificially created elevation profile was incorporated into the model, allowing the inspection of the grading effect on both dynamic and average performance. Grading analysis is an important factor to consider on commercial vehicles, as the grade may prevent the vehicle from reaching reference speed, or even starting the vehicle in severe slopes. In addition, negative gradients may present great opportunities for energy recovery. Key performance indicators were extracted from the study, such as gradeability, startability, average consumption, regenerative energy, peak power and range.
The simplified electric drive model was then replaced by accurate FEA-generated performance maps and losses were taken into account, allowing general performance evaluation and cooling system sizing.