Power Prediction in Electric Drivetrains for Enhanced Driveability

Autoren

Dr. Gerd Kaiser, Rudolf Fitz, Dr. Tobias Gemassmer, GKN Driveline International GmbH, Lohmar
Patrick Berninghaus, Bockmuehl-Kabel GmbH & Co. KG, Hattingen

Zusammenfassung

To improve driveability of hybrid electric vehicles, unexpected load changes must be avoided. This contribution presents an algorithm that determines the torque of the traction motor based on a battery model, powertrain losses, and auxiliary components. Additionally, a prediction algorithm estimates the maximum usable torque for 10 seconds. A model has been developed for both driving and regenerative operations. Important side effects regarding load-dependent battery voltage drops, powertrain efficiencies, supporting generators, and DCDC converters are all considered in the model. The torque prediction is based on thermal perspectives in order to maximize the powertrain torque while maintaining temperature limits. System components are protected, and vehicle controllers can be adapted, based on calculated torque limitations.

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