Direct yaw-moment control of an in-wheel-motored electric vehicle based on body slip angle fuzzy observer

A stabilizing observer-based control algorithm for an in-wheel-motored vehicle is proposed, which generates direct yaw moment to compensate for the state deviations. The control scheme is based on a fuzzy rule-based body slip angle (beta) observer. In the design strategy of the fuzzy observer, the vehicle dynamics is represented by Takagi-Sugeno-like fuzzy models. Initially, local equivalent vehicle models are built using the linear approximations of vehicle dynamics for low and high lateral acceleration operating regimes, respectively. The optimal beta observer is then designed for each local model using Kalman filter theory. Finally, local observers are combined to form the overall control system by using fuzzy rules. These fuzzy rules represent the qualitative relationships among the variables associated with the nonlinear and uncertain nature of vehicle dynamics, such as tire force saturation and the influence of road adherence. An adaptation mechanism for the fuzzy membership functions has been incorporated to improve the accuracy and performance of the system. The effectiveness of this design approach has been demonstrated in simulations and in a real-time experimental settin

Simulation of Electric Vehicles Combining Structural and Functional Approaches

In this paper the construction of a model that represents the behavior of an Electric Vehicle is described. Both the mechanical and the electric traction systems are represented using Multi-Bond Graph structural approach suited to model large scale physical systems. Then the model of the controllers, represented with a functional approach, is included giving rise to an integrated model which exploits the advantages of both approaches. Simulation and experimental results are aimed to illustrate the electromechanical interaction and to validate the proposal.Fil: Silva, Luis Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Rio Cuarto. Facultad de Ingeniería. Grupo de Electronica Aplicada; ArgentinaFil: Magallán, Guillermo Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Rio Cuarto. Facultad de Ingeniería. Grupo de Electronica Aplicada; ArgentinaFil: de la Barrera, Pablo Martin. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Rio Cuarto. Facultad de Ingeniería. Grupo de Electronica Aplicada; ArgentinaFil: de Angelo, Cristian Hernan. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Rio Cuarto. Facultad de Ingeniería. Grupo de Electronica Aplicada; ArgentinaFil: Garcia, Guillermo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Rio Cuarto. Facultad de Ingeniería. Grupo de Electronica Aplicada; Argentin

Energy-efficient torque-vectoring control of electric vehicles with multiple drivetrains

The safety benefits of torque-vectoring control of electric vehicles with multiple drivetrains are well known and extensively discussed in the literature. Also, several authors analyze wheel torque control allocation algorithms for reducing the energy consumption while obtaining the wheel torque demand and reference yaw moment specified by the higher layer of a torque-vectoring controller. Based on a set of novel experimental results, this study demonstrates that further significant energy consumption reductions can be achieved through the appropriate tuning of the reference understeer characteristics. The effects of drivetrain power losses and tire slip power losses are discussed for the case of identical drivetrains at the four vehicle corners. Easily implementable yet effective rule-based algorithms are presented for the set-up of the energy-efficient reference yaw rate, feedforward yaw moment and wheel torque distribution of the torque-vectoring controller

On the Energy Efficiency of Electric Vehicles with Multiple Motors

Electric Vehicles (EVs) with multiple motors permit to design the steady-state cornering response by imposing reference understeer characteristics according to expected vehicle handling quality targets. To this aim a direct yaw moment is generated by assigning different torque demands to the left and right vehicle sides. The reference understeer characteristic has an impact on the drivetrain input power as well. In parallel, a Control Allocation (CA) strategy can be employed to achieve an energy-efficient wheel torque distribution generating the reference yaw moment and wheel torque. To the knowledge of the authors, for the first time this paper experimentally compares and critically analyses the potential energy efficiency benefits achievable through the appropriate set-up of the reference understeer characteristics and wheel torque CA. Interestingly, the experiments on a four wheel-drive EV demonstrator show that higher energy savings can be obtained through the appropriate tuning of the reference cornering response rather than with an energy efficient CA

On the energy efficiency of electric vehicles with multiple motors

Electric Vehicles (EVs) with multiple motors permit to design the steady-state cornering response by imposing reference understeer characteristics according to expected vehicle handling quality targets. To this aim a direct yaw moment is generated by assigning different torque demands to the left and right vehicle sides. The reference understeer characteristic has an impact on the drivetrain input power as well. In parallel, a Control Allocation (CA) strategy can be employed to achieve an energy-efficient wheel torque distribution generating the reference yaw moment and wheel torque. To the knowledge of the authors, for the first time this paper experimentally compares and critically analyses the potential energy efficiency benefits achievable through the appropriate set-up of the reference understeer characteristics and wheel torque CA. Interestingly, the experiments on a four wheel-drive EV demonstrator show that higher energy savings can be obtained through the appropriate tuning of the reference cornering response rather than with an energy efficient CA

Energy efficient torque vectoring control

Tire forces are at the heart of the dynamic qualities of vehicles. With the advent of electric vehicles the precise and accurate control of the traction and braking forces at the individual wheel becomes a possibility and a reality outside test labs and virtual proving grounds. Benefits of individual wheel torque control, or torque-vectoring, in terms of vehicle dynamics behavior have been well documented in the literature. However, very few studies exist which analyze the individual wheel torque control integrated with vehicle efficiency considerations. This paper focuses on this aspect and discusses the possibilities and benefits of integrated, energy efficient torque vectoring control. Experiments with a four-wheel-drive electric vehicle show that considerable energy savings can be achieved by considering drivetrain and tire power losses through energy efficient torque vectoring control

Model predictive torque vectoring control with active trail-braking for electric vehicles

In this work we present the development of a torque vectoring controller for electric vehicles. The proposed controller distributes drive/brake torque between the four wheels to achieve the desired handling response and, in addition, intervenes in the longitudinal dynamics in cases where the turning radius demand is infeasible at the speed at which the vehicle is traveling. The proposed controller is designed in both the Linear and Nonlinear Model Predictive Control framework, which have shown great promise for real time implementation the last decades. Hence, we compare both controllers and observe their ability to behave under critical nonlinearities of the vehicle dynamics in limit handling conditions and constraints from the actuators and tyre-road interaction. We implement the controllers in a realistic, high fidelity simulation environment to demonstrate their performance using CarMaker and Simulink

Understeer characteristics for energy-efficient fully electric vehicles with multiple motors

Electric vehicles with multiple motors allow torque-vectoring, which generates a yaw moment by assigning different motor torques at the left and right wheels. This permits designing the steady-state cornering response according to several vehicle handling quality targets. For example, as widely discussed in the literature, to make the vehicle more sports-oriented, it is possible to reduce the understeer gradient and increase the maximum lateral acceleration with respect to the same vehicle without torque-vectoring. This paper focuses on the novel experimentally-based design of a reference vehicle understeer characteristic providing energy efficiency enhancement over the whole range of achievable lateral accelerations. Experiments show that an appropriate tuning of the reference understeer characteristic, i.e., the reference yaw rate of the torque-vectoring controller, can bring energy savings of up to ~11% for a case study four-wheel-drive electric vehicle demonstrator. Moreover, during constant speed cornering, it is more efficient to significantly reduce the level of vehicle understeer, with respect to the same vehicle with even torque distribution on the left and right wheels