The popularity of electric vehicles is still growing. The investment in R&D has been focused on the development of new and cheaper motors like Switched Reluctance Machines (SRM). This article presents an overview of SRM converter topologies critically compared for EV application in terms of controllability, costs, fault tolerance and complexity. The studied topologies are classified into two groups: hard-switching and soft-switching. But also, other features like the amount of components (switches, diodes, etc.) and modes of operation are taken into account.

Switched Reluctance Machines (SRM) are emerging as a possible alternative in terms of cost and supply stability to rare earth based electric vehicle traction systems. However, because of the huge amounts of energy stored and transferred back and forth between the DC source and the SRM, large DC-link capacitors must be used as buffers, which increases overall costs and size. This paper proposes a novel modulation technique which forces the exchange of energy between phases while decreasing the energy transfer between the DC bus and the SRM. This means lower DC bus currents (capacitor size and cost reduction) and lower Joule-effect conduction losses (better efficiency). The proposed modulation has been validated experimentally in a test bench and compared with the conventional torque-sharing function.

Switched Reluctance Machines (SRM) are considered promising rare-earth free candidates for the next generation electrified vehicles. One of the main drawback of this technology is the need of a large DC-link capacitor to balance the energy transferred back and forth between the DC source and the SRM. There are interesting novel modulations to reduce the current of the DC bus, focused on the capacitor size and cost reduction but leaving aside the thermal analysis and lifetime improvements. Carrying out the required dynamic multi-physics simulations for that purpose becomes highly time consuming and complex, especially when standardized or real driving conditions are needed to be taken into account. This paper proposes a simulation methodology, simple to implement and with a relatively low computational cost, to estimate the lifetime of an automotive DC-link capacitor, with the current load it delivers as the starting point. The presented methodology has also been used to validate a novel SRM modulation technique and to compare it, in terms of reliability, with the conventional torque sharing function.

In this paper, a smart machine-learning-based energy management system (MLBEMS) is developed for a hybrid energy storage system (HESS). This HBESS consists of batteries with high-energy (HE) and high-power (HP) characteristics, to provide grid-supporting services. The aim of the MLBEMS is to improve the overall battery lifetime and achieve state-of-charge (SoC) balancing for two different use cases (UC). UC1 involves enhanced frequency regulation for the Pan-European grid, while UC2 pertains to an electric vehicle (EV) charging station with photovoltaic (PV) generation. The designed MLBEMS is compared with a rule-based energy management system (RBEMS) from the literature with similar use cases. To ensure optimal power sharing between the battery modules, an optimization model is created using real battery aging data. Using a genetic algorithm, optimal power sharing is achieved for various initial SoC conditions. The generated dataset is subsequently utilized to train a machine-learning regression model, and the resulting prediction function is imported into MATLAB/Simulink. In UC1, MLBEMS achieved a 39.3% better SoC balancing compared to RBEMS, along with 36.5% and 22.6% higher battery lifetimes for HE and HP batteries, respectively. Similarly, for UC2, MLBEMS achieved a 68.5% improvement in SoC balancing, along with 53.6% and 45.8% higher battery lifetimes for HE and HP batteries, respectively.

The popularity of electric, hybrid and fuel-cell vehicles is still growing. The investment in R&D has been focused on the development of new and more flexible configurations, and more reliable and less expensive systems. This article presents an overview of the powertrain system technologies on EV (BEV, HEV and FCEV) topologies, emphasizing on new systems with the greater projection.

This paper provides three different research contributions applied to a Wind Turbine patented in 1606 by the inventor Jerónimo de Ayanz y Beaumont. The windmill under study is the Ayanz Wind Turbine with screw blades. The first contribution consists of an experimental characterization of the Ayanz Wind Turbine, incorporating the enclosure proposed at the patent and showing that the efficiency of the wind turbine is increased between 70% and 90% due to the enclosure being employed. As not many details about the shape of the screw blades are provided at the patent, in this article the nowadays well-studied and commercially available Archimedes Spiral Wind Turbine blade is utilized. It has been observed that by using an enclosure with a cylindrical shape, not only the efficiency of the wind turbine is increased, but the visual impact is reduced as seeing the blades rotating is avoided, which is a very important fact for many potential individual users of this wind turbine. In addition, it also enables the use of a protective mesh for birds, almost totally reducing the probability of bird deaths. The second contribution consists in a simple and low-cost Maximum Power Point Tracking (MPPT) strategy for the wind turbine, which only uses an AC three-phase impedance to capture the maximum energy from the wind, enabling to eliminate the DC-DC converter and microprocessor employed typically for this purpose. Due to this, the cost, complexity, failure rate, and power losses of the electronic power circuit are reduced which is very welcomed for small-scale wind turbines. Finally, the last contribution is a protection electronic circuit that fulfills several objectives: to brake the wind turbine under high winds and to disconnect and protect it when over-currents occur and when the voltage range of the batteries connected to the wind turbine is outside their safety range.