The Electric transit buses (ETBs) are the main source for public transit to get the cleaner environment. The ETBs have gained popularity around the globe since the last decade. The ETBs are 14 % of the total transit buses around the world. The ETBs are heavy EVs and need more power to charge the high capacity batteries. The batteries used in these buses have the energy capacities between 80 kWh to 550 kWh. Such high capacity batteries take more to get charged. In order to increase the adoption of ETBs, a suitable number of high power dc charging infrastructures need to be installed in various main cities of the nations. The ETBs can operate with battery alone or fuel cell or combination of these two. Such ETBs are named as battery electric transit bus (BETB), fuel cell electric transit bus (FCETB) and hybrid electric transit bus (HETBs). As of 2020, out of total ETBs around the globe, 70 % are HETBs, 20 % are FCETBs and 10 % are BETBs. In this paper, the key parameters and comparison among these three batteries are shown. The major EV market holders are China, US, Europe and japan. The charging protocol used in the US and Europe is combined charging system. The charger capacity has reached up to 500 kW as of 2020. With such high power chargers it takes only less than half an hour to charge the high power batteries.

Sufficient public parking lots (PLs) are essential for developing of sustainable cities. Different factors such as location, accessibility, safety, and environmental effects must be considered to ensure PLs stability. New technologies such as intelligent parking systems, electric vehicle (EV) charging stations (CSs), and green infrastructure make PLs more sustainable and efficient. In addition to providing parking spaces for ordinary cars (OCs), PLs provide charging services for EVs. After completing charging, EVs can be transferred to another place in the PL to provide charging service for more EVs. This problem is a motivation to present an optimization process for park scheduling in this paper. The proposed process is based on minimizing the number of required chargers. The considered constraints in the optimal scheduling process include providing the requested charging service and parking space for all EVs and OCs. The required parking space is determined based on the available databases and the simultaneous presence of vehicles in the PL. Statistical simulations produce different scenarios of vehicles in PL. The findings demonstrate that the suggested approach enhances the utilization of EV charging infrastructure in PLs. It can address the issue of random parking in public places and determine the parking routine.

The use of electric vehicles is expanding as one of the essential solutions to reduce greenhouse gas emissions and save gasoline fuel consumption. Therefore, the modern transportation industry in many countries is changing and evolving under the influence of electric vehicles' economic and environmental benefits. To accelerate the process of this evolution, proper design and development of the charging infrastructure are crucial. In addition, equipping charging stations with renewable energy sources and managing the intermittent nature of the production of these sources can lead to the improvement of environmental goals. The design and development of the charging infrastructure and the increase in electric vehicles bring various challenges to the power and transportation networks. In this regard, it is essential to provide appropriate solutions for various key issues such as congestion management, determining the location and optimal capacity of charging stations, and managing charging demand, considering both networks' requirements. This paper comprehensively discusses the latest achievements and significant developments in this fascinating and rapidly developing research area by introducing the objectives, methods, and data used. By highlighting the existing challenges, we pave the way for revealing research gaps and helping researchers address the problems.

At present, electric vehicles (EVs) are increasingly recognized as a viable alternative to conventional internal combustion engine vehicles, primarily due to their superior environmental sustainability, particularly regarding carbon emissions, and their cost-effectiveness attributed to lower energy consumption. Consequently, the market share of electric vehicles has witnessed substantial growth in recent years, which has in turn heightened the demand for charging infrastructure. Conversely, the rising number of electric vehicles necessitating recharging-especially during peak demand periods-poses challenges such as prolonged waiting times at public charging stations and increased strain on the power distribution network. To address these issues and enhance network efficiency, the concept of Mobile Charging Stations (MCS) has emerged, offering flexible charging solutions in terms of both time and location. This paper introduces an innovative approach for the allocation and deployment of MCSs in areas with high demand, aimed at alleviating the burden on public charging stations. A mathematical model grounded in the Location-or-Routing Problem (LoRP) has been formulated, employing various truck-based and van-based mobile charging stations to collaboratively service demand points near public charging facilities. This strategy seeks to attain various achievements, including the reduction of network load and waiting times at charging stations while simultaneously expanding coverage to improve customer satisfaction. Based on conducted experiments, a comprehensive evaluation and analysis of the proposed model demonstrate that the LoRP significantly outperforms traditional models in terms of both coverage and cost efficiency.

In recent years, electric vehicles have attracted significant attention due to environmental issues. Charging stations installation requires a systematic consideration of relevant issues such as determination of the location and size of charging stations. On the other hand, it is necessary to encourage private investors to invest in charging stations installations and to provide proper conditions for them so that they can profit from their investment. In this paper, the fast charging station (FCS) planning problem is modeled as a mixed integer nonlinear programming (MINLP), in which the objective function of distribution company (DISCO) and FCS owner (FCSO) have been considered, separately. In the proposed model, the location and size of FCSs as well as the price of transacted energy between DISCO and FCSO are determined, such that the objective functions of DISCO and FCSO are optimized. In the proposed method, queuing theory and user equilibrium based traffic assignment model are used to determine the size of FCSs. Then, the problem of multi-objective planning of FCSs has been investigated, considering the objectives of DISCO and FCSO. In addition, the final solution is chosen from the Pareto front solutions based on the economic and operational indices. Finally, the efficiency of the proposed method is demonstrated by numerical results.

Electric motorcycles (EM) are promising solutions for eco-friendly vehicles, but there are some dilemmas caused by the fossil-based energy used for charging and the limited charging infrastructure. This article proposes solving these dilemmas by designing a Solar-Powered Mobile Battery Swap Charging Station (MBSCS) for EM infrastructure. MBSCS will integrate solar power plants as a sustainable energy source and using battery swap system to accommodate EM. Design thinking methodology is used to develop the initial design of MBSCS and technical indicator assessment through focus group discussions with expert panelists. Simulations are conducted using PVSyst software to evaluate various system variants defined according to the selected components. The results of this study provide the MBSCS initial design, technical indicators to assess the MBSCS system, simulation results, and optimal system variant configuration. The findings of this study will mainly contribute to a solution for EM challenges and offer an environmentally friendly charging infrastructure. This study is expected to serve as an alternative solution for future mobile charging stations designed to answer the limited charging infrastructure as well as to demonstrate the potential use of portable solar power plant to overcome dependence on fossil-based energy.

In this article, a scheme for charging quantum batteries using the chain-STIRAP technique is proposed. For this purpose, first, a five-level quantum battery is considered and four pulses are used to charge this battery. It has been shown that by properly adjusting the maximum intensity and the time delay between pulses, the conditions of the chain -STIRAP technique can be established and charged the five-level quantum battery properly, so that the maximum amount of ergotropy is achieved. In this scheme, small changes in the parameters of the pulses, including the time delay between the pulses and the maximum value of the pulses, do not have much effect on the final ergotropy of the system. It is also shown that the proposed method in this scheme can be extended to charge quantum batteries with more than five levels.

In this paper, the effect of connecting the charging station of electric vehicles is investigated considering the optimal charging and discharging scheduling at the station. Accordingly, in the first section, the planning process of the station aimed at maximizing the mutual benefits.In the second part, uncertainty was noted in the program's presence at the station based on the production of different scenarios. The statistical simulation method has been used to model the uncertainties in the problem. Possible scenarios are selected from generated scenarios considering the diversity of vehicle presence plans, station’s load curves, market price, and network load levels. Therefore, the equivalent load is calculated. In the third section, the effect of station loading on the distribution network was investigated based on equivalent loads (extracted based on optimal planning of the first part). Comparison of the station loading effect is carried out for IEEE 33-bus network in the MATLAB based on the loss indexes (loss cost) and the voltage drop of the buses. The results indicate loss reduction due to optimal planning at the station and its application in appropriate buses.