• Energy Research

This article proposes a boost converter and its analytical calculation method for switched reluctance motor (SRM) drives. In the proposed converter, each conventional asymmetric half-bridge driver for each phase has a passive frontend circuit. The front-end circuit consists of parallel type capacitors to store the energy and boost the phase voltages. With the boosted voltage, the SRM achieves lower torque ripple and lower coil energy recovery re percentage to the main power supply. An analytical method is used to calculate and analyze the motor drive for each phase. Optimization methods are used for controlling the proposed SRM drive to generate maximum torque. The motor, its drive and control are then simulated in a physical modeling simulation environment (MATLAB/Simscape™). The simulation results verified the analytical calculation method and confirmed the torque ripple improvement and reduction of coil magnetic energy re-flow to the source with the proposed converter.

(2020). doi:10.1016/j.etran.2020.100060

This paper represents the optimization of an advanced battery model parameter minimization tool for estimation of lithium-ion battery model parameters. This system is called extended Levenberg–Marquardt. The proposed system is able to predict the nonlinearity of lithium-ion batteries accurately. A fitting percentage of over 99% between the simulation and experimental results can be achieved. Then, this paper contains a new second-order electrical battery model for lithium-ion batteries, extracted on the basis of experimental study and able to predict the battery behavior precisely. Further, in this paper, an extended comparative study of the performances of the various existing electrical battery models in the literature (Rint, RC, Thevenin, and FreedomCar) for lithium-ion batteries against the new developed battery model is presented, on the basis of the optimized battery parameter minimization tool. These battery models have been validated at different working conditions. From the analysis, one can observe that the new proposed battery model is more accurate than the existing ones and that it can predict the battery behavior during transient and steady-state operations. Finally, the new battery model has been validated at different working temperatures. The analysis shows that the error percentage between 0% and 90% depth of discharge at 40 °C is less than 1.5% and at 0 °C less than 5%.

In critical applications of electrical machines, ensuring validity and safety is paramount to prevent system failures with potentially hazardous consequences. The integration of machine learning (ML) technologies plays a crucial role in monitoring system performance and averting failures. Among various motor types, permanent magnet synchronous motors (PMSMs) are widely favored for their versatile speed range, enhanced power density, and ease of control, finding applications in both industrial settings and electric vehicles. This study focuses on the detection and classification of the percentage of broken magnets in PMSMs using a pre-trained AlexNet convolutional neural network (CNN) model. The dataset was generated by combining finite element methods (FEMs) and short-time Fourier transform (STFT) applied to stator phase currents, which exhibited significant variations due to diverse broken magnet structures. Leveraging transfer learning, the pre-trained AlexNet model underwent adjustments, including the elimination and rearrangement of the final three layers and the introduction of new layers tailored for electrical machine applications. The resulting pre-trained CNN model achieved a remarkable performance, boasting a 99.94% training accuracy and 0.0004% training loss in the simulation dataset, utilizing a PMSM with 4% magnet damage for experimental validation. The model’s effectiveness was further affirmed by an impressive 99.95% area under the receiver operating characteristic (ROC) curve in the experimental dataset. These results underscore the efficacy and robustness of the proposed pre-trained CNN method in detecting and classifying the percentage of broken magnets, even with a limited dataset.

With the increasing demand for electric vehicles, the requirements of the market are changing ever faster. Therefore, there is a need to improve the electric car’s design time, where simulations could be an appropriate tool for this task. In this paper, the modeling and simulation of an inverter for an electric vehicle are presented. Four different modeling approaches are proposed, depending on the required simulation speed and accuracy in each case. In addition, these models can provide up to 150 different electric modeling and three different thermal modeling variants. Therefore, in total, there were 450 different electrical and thermal variants. These variants are easily selectable and usable and offer different options to calculate the electrical parameters of the inverter. Finally, the speed and accuracy of the different models were compared and the obtained results presented.

This paper proposes an optimal design for hybrid grid-connected Photovoltaic (PV) Battery Energy Storage Systems (BESSs). A smart grid consisting of PV generation units, stationary Energy Storage Systems (ESSs), and domestic loads develops a multi-objective optimization algorithm. The optimization aims at minimizing the Total Cost of Ownership (TCO) and the Voltage Deviation (VD) while considering the direct and indirect costs for the prosumer, and the system stability with regard to intermittent PV generation. The optimal solution for the optimization of the PV-battery system sizing with regard to economic viability and the stability of operation is found while using the Genetic Algorithm (GA) with the Pareto front. In addition, a fuzzy logic-based controller is developed to schedule the charging and discharging of batteries while considering the technical and economic aspects, such as battery State of Charge (SoC), voltage profile, and on/off-peak times to shave the consumption peaks. Thus, a hybrid approach that combines a Fuzzy Logic Controller (FLC) and the GA is developed for the optimal sizing of the combined Renewable Energy Sources (RESs) and ESSs, resulting in reductions of approximately 4% and 17% for the TCO and the VD, respectively. Furthermore, a sensitivity cost-effectiveness analysis of the complete system is conducted to highlight and assess the profitability and the high dependency of the optimal system configuration on battery prices.

This article reviews the design and evaluation of different DC-DC converter topologies for Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs). The design and evaluation of these converter topologies are presented, analyzed and compared in terms of output power, component count, switching frequency, electromagnetic interference (EMI), losses, effectiveness, reliability and cost. This paper also evaluates the architecture, merits and demerits of converter topologies (AC-DC and DC-DC) for Fast Charging Stations (FCHARs). On the basis of this analysis, it has found that the Multidevice Interleaved DC-DC Bidirectional Converter (MDIBC) is the most suitable topology for high-power BEVs and PHEVs (> 10kW), thanks to its low input current ripples, low output voltage ripples, low electromagnetic interference, bidirectionality, high efficiency and high reliability. In contrast, for low-power electric vehicles (<10 kW), it is tough to recommend a single candidate that is the best in all possible aspects. However, the Sinusoidal Amplitude Converter, the Z-Source DC-DC converter and the boost DC-DC converter with resonant circuit are more suitable for low-power BEVs and PHEVs because of their soft switching, noise-free operation, low switching loss and high efficiency. Finally, this paper explores the opportunity of using wide band gap semiconductors (WBGSs) in DC-DC converters for BEVs, PHEVs and converters for FCHARs. Specifically, the future roadmap of research for WBGSs, modeling of emerging topologies and design techniques of the control system for BEV and PHEV powertrains are also presented in detail, which will certainly help researchers and solution engineers of automotive industries to select the suitable converter topology to achieve the growth of projected power density.