This paper comprehensively reviews the current status of multidisciplinary technologies in electric vehicles. Because the electric vehicle market will expand dramatically in the coming few years, research accomplishments in power electronics technology for electric vehicles will be highly attractive. Challenges in power electronics technology for driving electric vehicles, charging batteries, and circuit topologies are being explored. This paper aims primarily to address the practical issues of the future electric vehicles and help researchers obtain an overview of the latest techniques in electric vehicles, focusing on power electronics-based solutions for both current and future electric vehicle technologies. In this work, different medium-and high-voltage DC-AC inverter topologies are investigated and compared in terms of power losses and component requirements. Recent research on electric vehicle power converters is also discussed, with highlighting on soft-switching and multilevel inverters for electric vehicle motor drives. In this paper, a methodical overview and general classification of DC-AC power converters are presented. In specific topologies, drawbacks such as voltage stresses on active switches and control complications may occur, which can make them difficult for immediate commercialization. However, various modified circuit topologies have been recommended to overcome these drawbacks and enhance the system performance.

In this paper, a new multi-port DC-DC power converter used to deal with the intermittent nature and slow response in renewable energy applications is proposed. The proposed converter integrates a DC-DC converter and a DC-AC inverter, and the proposed circuit integrates various renewable energy sources in addition to the energy storage unit. By combining renewable energy sources with a statistical trend to offset each other, the impact of the intermittency can be considerably minimized. This combination increases the overall system reliability and usability. Moreover, integrating such systems with energy storage systems can overcome the slow response issue of renewable sources. It can provide the additional energy required by the load or absorb the extra energy provided by the power sources, which greatly improves the dynamics of the overall system. The proposed converter can reduce the system cost and size and improve the efficiency and reliability. The operation principle is studied in detail, and the design considerations are provided. The proposed architecture and its control strategy were analyzed and studied using the Simulink/MATLAB environment. Finally, the feasibility of the proper operation of the studied converter was experimentally verified based on the results of experimental studies conducted on a 300 W prototype implemented in a laboratory.

The disadvantage of photovoltaic (PV) power generation is that output power decreases due to the presence of clouds or shade. Moreover, it can only be used when the sun is shining. Consequently, there is a need for further active research into the maximum power point tracking (MPPT) technique, which can maximize the power of solar cells. When the solar cell array is partially shaded due to the influence of clouds or buildings, the solar cell characteristic has a number of local maximum power points (LMPPs). Conventional MPPT techniques do not follow the actual maximum power point, namely, the global maximum power point (GMPP), but stay in the LMPP. Therefore, an analysis of the occurrence of multiple LMPPs due to partial shading, as well as a study on the MPPT technique that can trace GMPP, is needed. In order to overcome this obstacle, the grey wolf optimization (GWO) method is proposed in order to track the global maximum power point and to maximize the energy extraction of the PV system. In addition, opposition-based learning is integrated with the GWO to accelerate the MPPT search process and to reduce convergence time. Simultaneously, the DC link voltage is controlled to reduce sudden variations in voltage in the event of transients of solar radiation and/or temperature. Experimental tests are presented to validate the effectiveness of the proposed MPPT method during uniform irradiance and partial shading conditions. The proposed method is compared with the perturbation and observation method.

Electric vehicles (Evs) offer promising benefits in reducing emissions and enhancing energy security; however, accurately estimating their load presents a challenge in optimizing grid management and sustainable integration. Moreover, EV load estimation is context-specific, and generalized methods are inadequate. To address this, our study introduces a tailored three-step solution, focusing on the Middle East, specifically Saudi Arabia. Firstly, real survey data are employed to estimate driving patterns and commuting behaviors such as daily mileage, arrival/departure time at home and workplace, and trip mileage. Subsequently, per-unit profiles for homes and workplaces are formulated using these data and commercially available EV data, as these locations are preferred for charging by most EV owners. Finally, the developed profiles facilitate EV load estimations under various scenarios with differing charger ratios (L1 and L2) and building types (residential, commercial, mixed). Simulation outcomes reveal that while purely residential or commercial buildings lead to higher peak loads, mixed buildings prove advantageous in reducing the peak load of Evs. Especially, the ratio of commercial to residential usage of around 50% generates the lowest peak load, indicating an optimal balance. Such analysis aids grid operators and policymakers in load estimation and incentivizing EV-related infrastructure. This study, encompassing data from five Saudi Arabian cities, provides valuable insights into EV usage, but it is essential to interpret findings within the context of these specific cities and be cautious of potential limitations and biases.

Improvements in the high accuracy of micro‐phasor measurement unit (μPMU) and synchrophasor unit provide the distribution network a special perspective to access full observability in the presence of renewable distribution generations (DGs). In this regard, modelling the uncertainty effects for accurate fault detection in smart grids has become a novel turning point. This research work attempts to develop a novel stochastic architecture based on unscented transform (UT), which requires a very few number of μPMUs, to communicate in a secured blockchain‐enabled structure to allocate and detect the smart grid irregularities and faults. Such a blockchain‐based technique can be beneficial for encrypting the data transmitted among the μPMUs in the smart grid consisting of a variety of distributed energy sources (DERs) and high impedance fault events, while incorporating the uncertainties associated with the load and individual installation sensors. The proposed stochastic structure benefits from voltage measurement and uses its angle in event placement that performs more efficiently than the conventional sensors based on high‐voltage and current metering. The simulation results show the great precision and effectiveness of the proposed model during various fault scenarios in smart grid.