Temperature-adaptive torque allocation for all wheel drive electric trucksGhazali, Mohammad (Avtor) Vukotić, Mario (Avtor) Miljavec, Damijan (Avtor) Hartavi, Ahu Ece (Avtor) electric machinethermal modelenergy optimizationelectrificationheavy duty truckstorque allocationElectrification of heavy-duty trucks is of great interest since it would bring substantial benefits in terms of reduce emissions (>80%) of road transport) and noise pollution. This study investigates energy-optimized torque allocation for a 370 kW all-wheel-drive electric truck under variable temperatures. The novel approach incorporates electric machine temperature dynamics into the modelling process, recognizing that the electric machine temperature varies with load and speed. A high-fidelity, multi-physical model of the permanent magnet synchronous machine is developed enabling analysis of electric, magnetic, mechanical, and thermal phenomena, including their cross-influence. The model dynamically updates electric machines’ characteristics and component loss rates as a function of temperature and integrates this information into a control strategy that adaptively allocates torque between axles. To the best of the authors’ knowledge, this temperature-adaptive torque control represents a novel contribution. Therefore, the novel strategy optimally distributed torque between axles based on the temperature-dependent loss characteristics of each motor to minimize overall energy consumption. The result is compared with two conventional techniques: (1) fixed torque ratio distribution, and (2) fixed-temperature optimal torque allocation using efficiency maps generated at −20℃ and +50℃. For the Eskisehir cycle, the proposed method reduces power consumption by up to 2% and 3%, respectively, within operating temperature range of −20℃ to 180℃. Further analysis of a hypothetical cycle indicates that energy savings may increase to 3% and 7%, demonstrating the drive cycle’s decisive effect. This work advances the integration of thermal dynamics into vehicle level control, offering practical pathways to improve efficiency up to 7%, in electric heavy duty vehicles.20252025-09-19 12:50:10Članek v reviji173660sl