With the widespread use of lithium-ion batteries in a wide range of consumer electronics products, the CE industry has undergone a dramatic shift. The Li-ion battery has emerged as the heart of electric cars, and the focus has now shifted to the automotive sector. Liquid crystal displays have evolved over time to meet the demands of automobiles. International research groups and the performance of production electric vehicles are used to discuss and inform vehicle-driven battery targets. There is still a lot of room for improvement in terms of energy, life expectancy, cost, safety, and fast-charging capabilities for LIBs suited for the automotive sector. In this study, a review of lithium-ion battery applications in electric vehicles is presented.

Recent years have seen a surge in interest in DC microgrids as DC loads and DC sources like solar photovoltaic systems, fuel cells, batteries, and other options have become more mainstream. As more distributed energy resources (DERs) are integrated into an existing smart grid, DC networks have come to the forefront of the industry. DC systems completely sidestep the need for synchronization, reactive power control, and frequency control. DC systems are more dependable and productive than ever before because AC systems are prone to all of these issues. There is a lot of unrealized potential in DC power, but it also faces some significant challenges. Protecting a DC system is difficult because there is no discrete location of where the current disappears. DC microgrid stability that is dependent on inertia must also be considered during the planning stage. The problems that DC microgrids have include insufficient power quality and poor communication. The power quality, inertia, communication, and economic operations of these value streams, as well as their underlying architectures and protection schemes, are all extensively discussed in this paper. This review paper examines the pros and cons of both grid-connected and isolated DC microgrids. In addition, the paper compares the different kinds of microgrids in terms of power distribution and energy management agency, such as the prerequisites for a DC microgrid’s planning, operation, and control that must be met before state-of-the-art systems can be implemented.

In the present paper, a novel meta-heuristic algorithm, namely quasi-oppositional search-based political optimizer (QOPO), is proposed to solve a non-convex single and bi-objective economic and emission load dispatch problem (EELDP). In the proposed QOPO technique, an opposite estimate candidate solution is performed simultaneously on each candidate solution of the political optimizer to find a better solution of EELDP. In the bi-objective EELDP, QOPSO is applied to simultaneously minimize fuel costs and emissions by considering various constraints such as the valve-point loading effect (VPLE) and generator limits for a generation. The effectiveness of the proposed QOPO technique has been applied on three units, six units, 10-units, 11-units, 13-units, and 40-unit systems by considering the VPLE, transmission line losses, and generator limits. The results obtained using the proposed QOPO are compared with those obtained by other techniques reported in the literature. The relative results divulge that the proposed QOPO technique has a good exploration and exploitation capability to determine the optimal global solution compared to the other methods provided in the literature without violation of any constraints and bounded limits.

Load flow analysis is an essential tool for the reliable planning and operation of interconnected power systems. The constant increase in power demand, apart from the increased intermittency in power generation due to renewable energy sources without proportionate augmentation in transmission system infrastructure, has driven the power systems to function nearer to their limits. Though the power flow (PF) solution may exist in such circumstances, the traditional Newton–Raphson based PF techniques may fail due to computational difficulties owing to the singularity of the Jacobian Matrix during critical conditions and faces difficulties in solving ill-conditioned systems. To address these problems and to assess the impact of large-scale photovoltaic generator (PVG) integration in power systems on power flow studies, a derivative-free quasi-oppositional heap-based optimization (HBO) (QOHBO) technique is proposed in the present paper. In the proposed approach, the concept of quasi-oppositional learning is applied to HBO to enhance the convergence speed. The efficacy and effectiveness of the proposed QOHBO-PF technique are verified by applying it to the standard IEEE and ill-conditioned systems. The robustness of the algorithm is validated under the maximum loadability limits and high R/X ratios, comparing the results with other well-known methods suggested in the literature. The results thus obtained show that the proposed QOHBO-PF technique has less computation time, further enhancement of reliability in the presence of PVG, and has the ability to provide multiple PF solutions that can be utilized for voltage stability analysis.

Electric vehicles offer a promising alternative to traditional internal combustion engine vehicles, mitigating air and noise pollution while reducing reliance on petroleum resources. However, the widespread adoption of electric vehicles faces several challenges, including high upfront costs, limited driving range, and the availability of charging infrastructure. The shift toward electric vehicle motors that do not rely on rare-earth elements is an important and massive engineering undertaking. Permanent magnet synchronous motors, which use copper windings instead of permanent magnets to generate the excitation field, offer an alternative approach to reducing rare-earth material usage, with research focusing on optimizing their design and control for electric vehicle applications. Induction motors are being reconsidered for the majority of electric vehicle models due to their robust design, established manufacturing infrastructure, and absence of rare-earth magnets, offering a viable alternative with ongoing research focused on improving their efficiency and power density. New electric vehicle (EV) motors using rotors outfitted with electromagnets (i.e., wire coils) are perhaps the most promising near-term solution for producing powerful motors without REEs altogether. This paper presents an overview of electric vehicles with the possible inclusion of rare-earth-free elements.

Intelligent inverters have the capability to interact with the grid and supply supplemental services. Solar inverters designed for the future will have the ability to self-govern, self-adapt, self-secure, and self-heal themselves. Based on the available capacity, the ancillary service rendered by a solar inverter is referred to as moonlighting. Inverters that communicate with the grid but are autonomous can switch between the grid forming mode and the grid following control mode as well. Self-adaptive grid-interactive inverters can keep their dynamics stable with the assistance of adaptive controllers. Inverters that interact with the grid are also capable of self-adaptation Grid-interactive inverters may be vulnerable to hacking in situations in which they are forced to rely on their own self-security to determine whether malicious setpoints have been entered. To restate, an inverter can be referred to as a “smart inverter” when it is self-tolerant, self-healing, and provides ancillary services. The use of artificial intelligence in solar plants in addition to moon-lighting capabilities further paves the way for its flexibility in an environment containing a smart grid. This perspective paper presents the present as well as a more futuristic outlook of solar plants that utilize artificial intelligence while moonlighting advanced capabilities as smart inverters to form the core of a smart grid. For the first time, this perspective paper presents all the novel ancillary applications of a smart inverter while employing Artificial intelligence on smart inverters. The paper’s emphasis on the Artificial Intelligence associated with PV inverters further makes them smarter in addition to ancillary services.