Comparison of steering geometries for multi-wheeled vehicles by modelling and simulation

2010 IEEE Vehicle Power and Propulsion Conference

Abdessemed

et al. 2010

The use of an Electric Differential (ED) constitutes a technological advance in vehicle design along with the concept of more electric vehicles. EDs have the advantage of replacing loose and heavy mechanical differentials and transmissions with lighter and smaller electric motors directly coupled to the wheels via a single gear or an in-wheel motor. This paper deals then with an Electric Differential System (EDS) for an Electric Vehicle (EV) directly driven by dual induction motors in the rear wheels. A sensorless control technique is preferred to a position or speed encoder-based control one to reduce the overall cost and to improve the reliability. The EDS main feature is the robustness improvement against system uncertainties and road conditions. The EDS control performances are validated through experiments on a dSPACE-based test bench. The experimental results show that the proposed controller is able to track the speed reference and the curvature angle with good static and dynamic performances.

Section: The Electric Differential and Its Implementationmentioning

confidence: 99%

Design and implementation of an Electric Differential for traction application

2010 IEEE Vehicle Power and Propulsion Conference

Abdessemed

et al. 2010

Section: The Electric Differential and Its Implementationmentioning

confidence: 99%

Analysis, Modeling and Neural Network Traction Control of an Electric Vehicle without Differential Gears

2007 IEEE International Electric Machines &Amp; Drives Conference

Abdessemed

et al. 2007

This paper presents system analysis, modeling and simulation of an EV with two independent rear wheel drives. The traction control system is designed to guarantee the EV dynamics and stability in case of no differential gears. Using two electrics in-wheel motors give the possibility to have a torque and speed control in each wheel. This control level improves the EV stability and the safety. The proposed traction control system uses the vehicle speed, which is different from wheels speed characterized by slip in the driving mode, an input. In this case, a generalized neural network algorithm is proposed to estimate the vehicle speed. In terms of the analysis and the simulations carried out, the conclusion can be drawn that the proposed system is feasible. Simulation results on a test vehicle propelled by two 37-kW induction motors showed that the proposed control approach operates satisfactorily.

“…In fact, the Ackermann steering geometry is a geometric arrangement of linkages in the steering of a car or other vehicles designed to solve the problem of wheels on the inside and outside of a turn needing to trace out circles of different radii. Modern cars do not use pure Ackermann-Jeantaud steering, partly because it ignores important dynamic and compliant effects, but the principle is sound for low speed maneuvers [16]. It is illustrated in Fig.…”

Section: The Neural Network Controllermentioning

confidence: 99%

SDTC Neural Network Traction Control of an Electric Vehicle without Differential Gears

Khoucha

2007 IEEE Vehicle Power and Propulsion Conference

et al. 2007

This paper proposes a Sensorless Direct Torque Control (SDTC) neural network traction control approach of an Electric vehicle (EV) without differential gears (electrical differential system). The EV is in this case propelled by two induction motor (one for each wheel). Indeed, using two electric in-wheel motors give the possibility to have a torque and speed control in each wheel. This control level improves the EV stability and the safety. The proposed traction control system uses the vehicle speed that is different from wheels speed characterized by slip in the driving mode, as an input. In terms of the analysis and the simulations carried out, the conclusion can be drawn that the proposed system is feasible. Simulation results on a test vehicle propelled by two 37-kW induction motors showed that the proposed SDTC neural network approach operates satisfactorily.