Resource Availability and Implications for the Development of Plug-In Electric Vehicles

A Review of Solid-State Proton-Polymer Batteries: Materials and Characterizations

The ever-increasing global population necessitates a secure and ample energy supply, the majority of which is derived from fossil fuels. However, due to the immense energy demand, the exponential depletion of these non-renewable energy sources is both unavoidable and inevitable in the approaching century. Therefore, exploring the use of polymer electrolytes as alternatives in proton-conducting batteries opens an intriguing research field, as demonstrated by the growing number of publications on the subject. Significant progress has been made in the production of new and more complex polymer-electrolyte materials. Specific characterizations are necessary to optimize these novel materials. This paper provides a detailed overview of these characterizations, as well as recent advancements in characterization methods for proton-conducting polymer electrolytes in solid-state batteries. Each characterization is evaluated based on its objectives, experimental design, a summary of significant results, and a few noteworthy case studies. Finally, we discuss future characterizations and advances.

Biomass Alginate Derived Oxygen-Enriched Carbonaceous Materials with Partially Graphitic Nanolayers for High Performance Anodes in Lithium-Ion Batteries

Lithium-ion batteries with high reversible capacity, high-rate capability, and extended cycle life are vital for future consumer electronics and renewable energy storage. There is a great deal of interest in developing novel types of carbonaceous materials to boost lithium storage properties due to the inadequate properties of conventional graphite anodes. In this study, we describe a facile and low-cost approach for the synthesis of oxygen-doped hierarchically porous carbons with partially graphitic nanolayers (Alg-C) from pyrolyzed Na-alginate biopolymers without resorting to any kind of activation step. The obtained Alg-C samples were analyzed using various techniques, such as X-ray diffraction, Raman, X-ray photoelectron spectroscopy, scanning electron microscope, and transmission electron microscope, to determine their structure and morphology. When serving as lithium storage anodes, the as-prepared Alg-C electrodes have outstanding electrochemical features, such as a high-rate capability (120 mAh g^-1 at 3000 mA g^-1) and extended cycling lifetimes over 5000 cycles. The post-cycle morphologies ultimately provide evidence of the distinct structural characteristics of the Alg-C electrodes. These preliminary findings suggest that alginate-derived carbonaceous materials may have intensive potential for next-generation energy storage and other related applications.

Energy Anxiety in Decentralized Electricity Markets: A Critical Review on EV Models

The automobile sector is a promising avenue for enhancing energy security, economic opportunity, and air quality in India. Before penetrating a large number of electric vehicles (EV) into the power grid, a thorough investigation and assessment of significant parameters are required, as additional nonlinear and EV loads are linked to the decentralized market. Many automobile companies have already invested in electric vehicle research; hence, a detailed analysis on range anxiety and grid connectivity concerns are the important factors affecting the future of the electric vehicle industry. In this paper, the initial review is about the decentralized market in India and sustainable aspects of electric mobility based on the Indian context, as it is a developing nation with an enormous resource and scope for EV markets. With recent literature from the last three years, the substantial constraints observed in benefits and challenges are reviewed. The financial stability aspects and the incentives to overcome the barriers to EV adoption are briefly discussed. From the review, it has come to the limelight that infrastructure availability, technology, load demand, and consumer behaviour are all major obstacles in the electric vehicle ecosystem. For the overall design and study of the vehicle to grid (V2G) infrastructure, this paper also provides insight into the representation of electric vehicles in different energy-efficient models and their categorization while connecting to the grid. The methodology adopted for energy-efficient models includes lifecycle emissions, economy, smart charging, real-time optimization, aggregated EV resource modelling, and a support vector machine (SVM)-based method. This paper gives a positive impact on EV fleet integration and electric mobility in general, as it critically reviews the influential parameters and challenges. This classification depends on crucial parameters that are at the frontline of EV grid integration research. This review is a solution to enhance grid stability in regard to new EV models. With the advanced electric motors development and renewed battery technology models, longer-distance automobiles are now available on the market. This paper investigates the constraints of EV grid integration and analyzes different EV models to ease the grid stability for a decentralized market.

Differential Evolution Algorithm for Optimizing the Energy Usage of Vertical Transportation in an Elevator (VTE), Taking into Consideration Rush Hour Management and COVID-19 Prevention

This research aimed to develop an effective algorithm to minimize the energy use of vertical transportation in elevators while controlling the number of passengers in the elevator waiting area and the number of passengers in the elevator during rush hour, thus maintaining social distancing to limit the spread of COVID-19. A mobile application and Internet of Things (IoT) devices were used to electronically communicate between the elevator’s control system and the passengers. IoT devices were used to reduce the number of passengers waiting for an elevator and passengers’ waiting time, while the energy consumption of the lift was reduced by using passenger scheduling and elevator stopping strategies. Three mathematical models were formulated to represent the different strategies used to cause the elevator to stop. These strategies were normal (allowing the elevator to stop at every floor), odd–even (some elevators are allowed to stop at odd floors and others are allowed to stop at even floors of the building), and high–low (some elevators are allowed to stop at high floors and others are allowed to stop at low floors of the building). Lingo v.11 and the differential evolution algorithm (DE) were used to address the optimal scheduling of the passengers and the elevators. The computational results show that the odd–even strategy had a 13.91–23.71% lower energy consumption compared with the high–low and normal strategies. Furthermore, the use of DE consumed 6.67–7.99% less energy than the use of Lingo.v11. Finally, the combination of DE and the designed application reduced the number of waiting passengers, the average passenger waiting time, and the total energy consumption by 74.55%, 75.12%, and 45.01%, respectively.