Intelligent Torque Vectoring Approach for Electric Vehicles with Per-Wheel Motors

Abstract

Transport electrification is currently a priority for authorities, manufacturers, and research centers around the world. The development of electric vehicles and the improvement of their functionalities are key elements in this strategy. As a result, there is a need for further research in emission reduction, efficiency improvement, or dynamic handling approaches. In order to achieve these objectives, the development of suitable Advanced Driver-Assistance Systems (ADAS) is required. Although traditional control techniques have been widely used for ADAS implementation, the complexity of electric multimotor powertrains makes intelligent control approaches appropriate for these cases. In this work, a novel intelligent Torque Vectoring (TV) system, composed of a neuro-fuzzy vertical tire forces estimator and a fuzzy yaw moment controller, is proposed, which allows enhancing the dynamic behaviour of electric multimotor vehicles. The proposed approach is compared with traditional strategies using the high fidelity vehicle dynamics simulator Dynacar. Results show that the proposed intelligent Torque Vectoring system is able to increase the efficiency of the vehicle by 10%, thanks to the optimal torque distribution and the use of a neuro-fuzzy vertical tire forces estimator which provides 3 times more accurate estimations than analytical approaches.

Efficient Neural Network Implementations on Parallel Embedded Platforms Applied to Real-Time Torque-Vectoring Optimization Using Predictions for Multi-Motor Electric Vehicles

“…This can be very conveniently exploited by reassigning a part of the torque from the interior wheels to the exterior ones. As has also already assessed by means of race-track tests in previous works [6,39,54,55], this does not only avoid the inner wheel to spin because of the lower traction capacity but it also generates an additional yaw moment, helping the vehicle rotate over its vertical axis towards the direction of the curve, thus mitigating understeer-i.e., the front wheels turning the car less than expected according to their steering angle- [38,56].…”

“…Powertrains with independent motors for each of the wheels on a same axis enable the implementation of refined Torque-Vectoring algorithms. Torque-Vectoring relies on controlling the torque of each wheel aiming not only to enhance cornering performance but also to enhance stability and consequently increasing safety [38,39], as will be further discussed in Section 3.…”

Evaluación de un algoritmo de torque vectoring con capacidad de frenado regenerativo

“…En general, estas mediciones no son posibles y estos valores deben ser estimados. En este trabajo, se ha implementado el estimador neuroborroso basado en el propuesto en [12]. Este estimador requiere variables dinámicas del vehículo fácilmente medibles mediante una plataforma inercial y un GPS, y ha sido ampliamente validado mediante varias maniobras en [12], demostrando su correcto desempeño (3% de error medio).…”

“…Al ser la eficiencia una variable clave a considerar junto con la estabilidad en los trabajos de TV, en este trabajo se presenta un sistema de TV basado en lógica difusa con capacidades de frenado regenerativo. El sistema desarrollado hace uso de un estimador de fuerzas verticales en los neumáticos, el cual se presentó en [12]. El cor-recto funcionamiento del sistema propuesto se ha validado a través de una maniobra de emergencia, comparando los resultados con otros dos escenarios: un vehículo con el mismo sistema de TV pero deshabilitando el frenado regenerativo; y un vehículo sin sistema de TV.…”

On Nonlinear Model Predictive Control for Energy-Efficient Torque-Vectoring

“…A wide literature shows the vehicle dynamics benefits of TV in terms of handling and cornering stability, see [5]- [15] . The variety of proposed TV algorithms includes feedforward and proportional integral derivative (PID) controllers [12], H∞ controllers [15], sliding mode controllers [16]- [17], linear quadratic controllers [18], and intelligent controllers [19]. Owing to the increasing computational capabilities of recent embedded platforms, model predictive control (MPC) has become a viable solution for TV [20]- [22], even if many of the available MPC TV implementations still use rather simplified linearized or nonlinear prediction models.…”