Abstract
The coordinated control of vehicle actuators is gaining more importance as new platforms are becoming available, with chassis endowed with many different actuators that may help controlling the vehicle motion. Further, in-wheel motors (IWMs) allow using a single system to apply both positive and negative torques at the wheels, which can be actuated independently one from the other. In electric vehicles (EVs), moreover, such a freedom in the actuation mechanisms opens the way to the combined optimization of performance and energy consumption issues. In this paper, the problem of torque allocation for maximizing the vehicle performance in EVs is addressed. The proposed strategy is compared against a benchmark, a-causal optimal solution showing that only a negligible loss of performance is experienced.

Abstract
This paper presents a new control system, based on field programmable gate array technology, targeting the power-train control of multi-motor electric vehicles (EVs). The control chip builds around a reusable intellectual property core named propulsion control unit, which features motor control functions with field-orientation methods, and energy loss minimization of induction motors. In order to improve the EV safety, the control system was extended with a wheel slip controller based on the sliding mode framework. The robustness to parametric and modeling uncertainties is the main attraction in this design, thanks to a simple connection that was found between the driving torque request and the model uncertainty. To overcome the chattering issue, which arrives from the discontinuous nature of the sliding control, the conditional integrator approach was employed, enabling a smooth transition to a Proportional+Integral control law, with anti-windup, when the tire slip is close to the setpoint. The controller asymptotic stability and robustness was analytically investigated through the Lyapunov method. Experimental results, obtained with a multi-motor EV prototype under low grip conditions, demonstrate a good slip regulation and robustness to disturbances.

Abstract
This paper proposes the concepts of sliding mode schemes for fault tolerant control. The theoretical ideas developed in the paper were applied to a multi-motor electric vehicle (EV). To address this problem, we started by revisiting the modeling of vehicle dynamics focusing on nonlinear single-track model. The vehicle response to external perturbation forces, which are generated by unbalanced right and left traction forces due to malfunctions in motor drive, is described using the transfer functions analysis. The designing of a sliding mode controller for handling faults is described. The proposed scheme shows that certain motor drive failures can be handled directly without reconfiguring the controller. Simulation results obtained with CarSim vehicle model show the effectiveness of the fault tolerant control in various driving scenarios.

Abstract
Fault detection has been an open problem in power converters for several years. In this paper, we aim at detecting faults by means of left invertibility techniques. Using a mathematical model of the power converter it is possible to exploit the principle of injectivity of I/O map, which allows the recovery of unknown inputs applied to the system from the measure of the outputs. To achieve this end, we utilize the existing current and voltage sensors of the converter without the need for any additional sensors. Finally, a case-study of a multiport converter is presented and simulated to illustrate the methodology. Experimental results obtained under realistic conditions illustrate the effectiveness of the scheme and prove that fault detection based on the inverse method is possible.

Abstract
This paper presents a comparative study between two non-causal algorithms for the energy management problem of electric vehicles, endowed with batteries and supercapacitors(SCs). Toward that goal, an optimization-based energy problem is formulated, which targets the minimization of the source's energy losses throughout a given driving cycle. This problem is solved, firstly, with the help of a fast (but locally optimal) non-linear programming solver; and, secondly, with a slow, but globally optimal, dynamic programming (DP) approach. Simulation results will demonstrate that, despite the different theoretical properties associated with these two solver approaches, both generate similar solutions. In the second part of the work, we will develop a filter-based energy management algorithm, i.e., employ batteries to provide the low-frequency content of the power demand, while SCs cover the high-frequency demand. Our approach builds on the idea of adapting the filter's time constant throughout the vehicle's journey, using, for that purpose, a fuzzy logic algorithm and the information of the state of the vehicle. In comparison with the traditional fixed time-constant approach, the simulation results show that under some conditions the adaptive time-constant algorithm has the potential to reduce the energy losses of the sources by up to 62%. © 2013 IEEE.

Abstract