Skugor, Branimir (University of Zagreb, Faculty of Mechanical Engineering and Nava), Grden, Luka (University of Zagreb, Faculty of Mechanical Engineering and Nava), Dabcevic, Zvonimir (University of Zagreb, Faculty of Mechanical Engineering and Nava), Deur, Josko (University of Zagreb)

Neural Network-Based Control Strategy for Energy-Efficient Front-Rear Torque Vectoring in Electric Vehicles with Multiple Motors and Disconnect Clutches

Scheduled for presentation during the Regular Session "Vehicle dynamics " (WeB2), Wednesday, June 18, 2025, 14:40−15:00, Jos

AAC 2025 11th IFAC International Symposium on Advances in Automotive Control, June 15-18, 2025, Eindhoven, Netherlands

This information is tentative and subject to change. Compiled on June 1, 2025

Abstract

The paper deals with neural network (NN)-based energy-efficient front/rear torque vectoring (TV) for an electric vehicle (EV) with multiple motors and disconnect clutches. The NN model reflects a binary classification problem, formulated to distinguish between four-wheel (4WD) and two-wheel drive (2WD) powertrain configurations depending on current vehicle velocity and total torque demand inputs. The model provides a probability of EV being set in 2WD configuration with disconnected clutches on the opposite axle. By introducing a probability threshold on the NN model output, a proper trade-off between energy efficiency and suppression of clutch state switching frequency can be posed. The NN model is trained based on the globally optimal dataset obtained by off-line executed dynamic programming (DP) optimizations over multiple certification driving cycles. The proposed NN-based TV control strategy is tested against the DP benchmark and a rule-based (RB) TV baseline.