An Improved Baseline for Torque Vectoring Controller Comparisons of Four-Wheel-Independent-Drive Electric Vehicles

The current approach for controller comparisons utilises the passive vehicle as a baseline, which does not fully demonstrate the advantages of four-wheel-independent-drive electric vehicles. This paper presents an algorithm to improve upon existing baseline torque vectoring strategies. Two controllers which were simple and easy to implement were selected: a normal-force proportional controller, and a causal controller. The maximum requirements for these were measurements of steering angle, lateral acceleration, and longitudinal acceleration. This ensured the tested strategies were easily implementable on a vehicle with low additional overhead. The torque scaling algorithm aims to improve the longitudinal performance of the vehicle compared to the standard torque vectoring controllers without modification. By improving longitudinal dynamics with limited loss in lateral dynamics, the overall performance of the vehicle can be improved depending on the given driving task, providing a better reference point. The torque vectoring controllers increased lateral performance compared to the passive vehicle. With the addition of the torque scaling algorithm, lateral performance remained within 1% of the respective controllers. Over an autocross lap, the algorithm (used with the normal-force proportional controller) produced the fastest lap time and second-lowest energy consumption. This was an improvement of over 1/10
^th
s (over 60s lap) in lap time and a 9% reduction in energy consumption compared to the passive vehicle.