This paper presents a new control system, based on field programmable gate array technology, targeting the powertrain 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.

This paper presents a new modulation and control strategies for the high-frequency link matrix converter (HFLMC). The proposed method aims to achieve controllable power factor in the grid interface as well as voltage and current regulation for a battery energy storage device. The matrix converter (MC) is a key element of the system, since it performs a direct ac to ac conversion between the grid and the power transformer, dispensing the traditional dc-link capacitors. Therefore, the circuit volume and weight are reduced and a longer service life is expected when compared with the existing technical solutions. A prototype was built to validate the mathematical analysis and the simulation results. Experimental tests developed in this paper show the capability of controling the grid currents in the synchronous reference frame in order to provide grid services. Simultaneously, the battery current is well regulated with small ripple, which makes this converter ideal for battery charging of electric vehicles and energy storage applications.

This paper is concerned with the study of combined sizing and energy management algorithms for electric vehicles (EVs) endowed with batteries and supercapacitors (SCs). The main goal is to find the number of cells of each source that minimizes the installation and running costs of the EV, taking into account the performance requirements specified for the vehicle and the technical constraints of the energy sources. To tackle this problem, two methodologies will be investigated. The first considers a filter-based approach to perform the power split among the sources; it will be shown that, under some practical assumptions, the resultant sizing problem can be posed as a linear programming problem and solved using efficient numerical techniques. The second methodology employs an optimal noncausal energy management, which, when integrated with the sizing problem, yields a nonlinear optimization problem. These two methodologies will be then applied to size the storage unit of a small EV. The results indicate that the filter-based approach, although simple and numerically efficient, generally requires an oversized storage unit. Furthermore, it was also concluded that, if the range requirements of the EV are not very high (below 50 km, in our case study), the use of SCs enables energy savings of up to 7.8%.

Among the many opportunities offered by electric vehicles (EVs), the design of power trains based on in-wheel electric motors represents, from the vehicle dynamics point of view, a very attractive prospect, mainly due to the torque-vectoring capabilities. However, this distributed propulsion also poses some practical challenges, owing to the constraints arising from motor installation in a confined space, to the increased unsprung mass weight and to the integration of the electric motor with the friction brakes. This last issue is the main theme of this work, which, in particular, focuses on the design of the anti-lock braking system (ABS). The proposed structure for the ABS is composed of a tyre slip controller, a wheel torque allocator and a braking supervisor. To address the slip regulation problem, an adaptive controller is devised, offering robustness to uncertainties in the tyre–road friction and featuring a gain-scheduling mechanism based on the vehicle velocity. Further, an optimisation framework is employed in the torque allocator to determine the optimal split between electric and friction brake torque based on energy performance metrics, actuator constraints and different actuators bandwidth. Finally, based on the EV working condition, the priorities of this allocation scheme are adapted by the braking supervisor unit. Simulation results obtained with the CarSim vehicle model, demonstrate the effectiveness of the overall approach.

In spite of several technical advances made in recent years by the automotive industry, the driver's behaviour still influences significantly the overall fuel consumption. With the rise of smartphones adoption, there are also new opportunities to increase the awareness to this issue. The main aim of this paper is to present a new smartphone application that will help drivers reduce the fuel consumption of their vehicles. This is accomplished by using the smartphone's sensors and the vehicle state to detect the driving pattern and suggest new behaviours in real time that will lead to a more efficient driving experience. The preliminary results show the potential for significant energy savings and their relevance for changing the drivers' behaviour.

Typically, the unique objective pursued in either active or passive balancing is equalization of single cell charge. However, a balancing circuit may offer more control features, like virtual equalization of single cell internal resistance or thermal balancing. Such control features for balancing systems are evaluated in this paper by means of convex optimization. More than one hundred cases in a pure EV application are evaluated. Balancing circuits' efficiency models are implemented and realistic cell-to-cell parameter distributions are considered based on experimental data. Different battery sizes and driving cycles are considered. Balancing circuit topology is taken into account by selecting a specific category of energy transfer: cell-to-heat, bypass, cell-to-pack, pack-to-cell, cell-to-cell shared, cell-to-cell distributed or cell-to-pack-to-cell. In general, better results in terms of energy losses, available capacity or temperature are obtained for the last three categories, even for moderate balancing currents. In particular, remarkable improvements are observed under conditions of high power demand with high variability, i.e., smaller battery sizes and more demanding driving cycles.