This paper introduces a novel active gate driver (AGD) for switching control of the silicon carbide (SiC) MOSFETs. The new gate driver improves the current and voltage profiles by suppression of the overshoot problems. The main innovation of the proposed AGD is the modification of gate-source voltage slope in two stages for both turn-on and turn-off transitions with a simple closed-loop control, which directly implies to the control of di/dt and dv/dt. The new gate driver is validated by experimental results. Moreover, an analysis of performance and electromagnetic interference (EMI) is realized. The experimental tests have been developed with 100 kHz of switching frequency and 200 V of dc-bus, in hard-switching conditions. The results show that the proposed AGD can reduce EMI problems with a minimum side effect on the efficiency of the SiC MOSFETs.

Silicon carbide (SiC)-based switching devices provide significant performance improvements in many aspects, including lower power dissipation, higher operating temperatures, and faster switching; compared with conventional Si devices, all these features contribute to these devices generating interest in applications for electric traction systems. The topology that is frequently used in these systems is the voltage source inverter (VSI), but the use of SiC devices in the current source inverter topology (CSI), which is considered as an emerging topology, generates interest. This paper presents a method for improving total harmonic distortion (THD) in the currents of output and efficiency in SiC current source inverter for future application in an electric traction system. The method that is proposed consists of improving the coupling of a bidirectional converter topology, voltage current (V-I) and CSI. The V-I converter serves as a current regulator for the CSI, and allows for the recovery of energy. The method involves an effective selection of the switching frequencies and phase angles for the carrier signals that are present in each converter topology. With this method, it is expected to have a reduction of the total harmonic distortion, THD in the output currents. In addition, a comparative analysis between converters with all-SiC technology and converters with hybrid technology is realized, to verify the impact of the SiC devices in the power converters efficiency.

In this paper, a feedback active gate driver (AGD) based on a multistage technique for improving the switching trajectory of silicon carbide (SiC) MOSFET devices is presented. The main purpose of this technique is to reduce the oscillations and overshoot in high-frequency with low switching losses. Besides, the gate driver is designed considering the current load variations with simple circuit structure. An AGD validation has been developed by using simulations. The results have shown the behaviour of the AGD and feasibility. Therefore, the AGD can reduce the oscillations to avoid electromagnetic interference (EMI) and also decrease the overshoots caused by high di/dt current and dv/dt voltage slopes. In addition, the tests showed that the proposed AGD can be developed with low switching losses in high-frequency applications and medium power.

The state of charge of a battery depends on many magnitudes, but only voltage and intensity are included in mathematical equations because other variables are complex to integrate into. The contribution of this work was to obtain a model to determine the state of charge with these complex variables. This method was developed considering four models, the multilayer feed-forward backpropagation models of two and three input variables used supervised training, with the variable-learning-rate backpropagation training function, five and seven neurons in the hidden layer, respectively, achieving an optimal training. Meanwhile, the radial basis neural network models of two and three input variables were trained with the hybrid method, the propagation constant with a value of 1 and 80 neurons in the hidden layer. As a result, the radial basis neural network with the variable-learning-rate training function, considering the discharge temperature, was the one with the best performance, with a correlation coefficient of 0.99182 and a confidence interval of 95% (0.98849; 0.99516). It is then concluded that artificial neural networks have high performance when modeling nonlinear systems, whose parameters are difficult to measure with time variation, so estimating them in formulas where they are omitted is no longer necessary, which means an accurate SOC.

Electric vehicles are currently the focus of the researchers all over the world. High harmful gas emissions from conventional vehicles and the rising of new clean and competitive energy sources have leaded to an increasing interest of vehicle providers and consumers. In this paper, a practical example of an hybrid vehicle is addressed. The hybrid vehicle is driven by an array of batteries and a 5 kW fuel cell. The changes done in the vehicle chassis, the necessary power electronic converter and the control and monitoring systems are explained in this paper. Some experimental results are shown in order to show the vehicle performance. The vehicle range has been approximately doubled using the hybrid electric- fuel cell system. The proposed hybrid vehicle, tested under real weather conditions, has demonstrated a high robustness and reliability.