A Comparative Study of the Effect of Intelligent Control Based Torque Vectoring Systems on EVs with Different Powertrain Architectures

Abstract

Intelligent Transportation Systems (ITS) is currently one of the most active research areas, being electric vehicles (EVs) and their vehicle dynamics enhancement key topics. For this purpose, the development of optimal Advanced Driver- Assistance Systems (ADAS) and Advanced Vehicle Dynamics Control Systems is required. However, as electrified propulsion systems offer multiple topologies (and higher complexity), this task becomes much more difficult. In this context, the use of intelligent control techniques has been proposed as a suitable alternative to offer both performance and flexibility. In order to demonstrate the advantages of intelligent approaches and their ability to adapt to different scenarios, this work presents a comparative study of the performance of Intelligent Control based torque vectoring (TV) algorithms in electric vehicles with three different powertrain topologies: Front Wheel Driven (FWD), Rear Wheel Driven (RWD) and Four/All Wheel Driven (AWD). The same TV approach has been used for all topologies, and a skid-pad test has been selected as a critical manoeuvre for evaluating the lateral dynamics of each topology, which has been simulated using a high fidelity vehicle simulator. Results show that the same intelligent control approach can be used for different topologies without retuning its parameters, enhancing the vehicle dynamics for all cases. This demonstrates the flexibility of intelligent approaches due to their reduced model dependency. Additionally, results show that each architecture promotes a different type of dynamic behaviour in the vehicle: understeering behaviour for the FWD, oversteering behaviour for the RWD and a neutral behaviour for the AWD.