Electric vehicle (EV) penetration is accelerating in an unprecedented way, but the insufficient charging infrastructure to cover all locations hinders the improvement of the EV market. Currently, due to the small EV to internal combustion engine vehicle ratio, installing fixed charging stations (FCSs) at all locations is not financially viable. Lack of available FCSs increases the range anxiety and overall charging time, which are two major barriers to the large-scale adoption of electric cars. As a remedy, mobile charging stations (MCSs) can play a vital role in speeding up the process of moving toward more EV adoption by providing charging services at EV users’ convenient times and locations. This paper aims to disseminate information of papers and technical reports working on MCS, discuss the benefits, analyze the challenges, and finally propose possible research topics on MCS as a flexible method of charging to provide support to FCSs. The study reveals that utilizing MCS services is a cost-effective technology for charging facilities owners to improve the utilization rate of charging equipment and for the power grid to reduce the adverse effects of EV penetration.

Mobile charging stations for electric vehicles — A review

There is a range of anxiety-related phenomena among users and potential buyers of electric vehicles. Chief among them is the fear of the vehicle stopping and its users getting “stuck” before reaching their designated destination. The limited range of an electric vehicle makes EV users worry that the battery will drain while driving and the vehicle will stall on the road. It is therefore important to know the factors that could further reduce the range during daily vehicle operation. The purpose of this study was to determine the effect of selected parameters on a battery’s depth of discharge (DOD). In a simulation study of an electric vehicle, the effects of the driving cycle, ambient temperature, load, and initial state of charge of the accumulator on the energy consumption pattern and a battery’s depth of discharge (DOD) were analyzed. The simulation results confirmed that the route taken has the highest impact on energy consumption. The presented results show how significantly the operating conditions of an electric vehicle affect the energy life. This translates into an electric vehicle’s range.

Parameters Influencing on Electric Vehicle Range

The fixed public charging stations (FCS) network is challenged by widespread of electric vehicle (EV) uses. Therefore, there is exploitation of the many parks spread over the territory of a smart city by means of mobile charging stations (MCS). That can be set up or moved anywhere as needed. This allows for the rapid expansion of the charging infrastructure. In this work, we propose an architecture system consisting of a set of algorithms to manage electric vehicle charging plans in terms of minimizing journey time, including waiting and charging time at charging stations (CS). Thus, During the CS selection decision, the system takes into consideration the amount of sufficient energy for the EV to reach the specified CS, the remaining amount of energy in stock if the selected CS is the MCS type, the CS Real-time status, and the first-come-first-served policy based on providing charge seats in CS. Moreover, the dynamically system regulates each FCS at its peak time of its MCS operation, ensuring a semi-permanent equilibrium in electrical grid usage and reducing congestion by changing the flow of vehicles that are directed towards FCSs for charging. The evaluation results demonstrate, in the context of the Helsinki City scenario, the effectiveness of the proposed system and algorithms, in terms of achieving the above-mentioned objectives.

A Smart Coordination System Integrates MCS to Minimize EV Trip Duration and Manage the EV Charging, Mainly at Peak Times

Optimal routing for electric vehicle charging systems with stochastic demand: A heavy traffic approximation approach

The advancement of new fixed charging stations (FCS) has facilitated electric vehicle (EV) charging and increased EV adoption. However, FCSs construction is constrained by budget, power grid facilities, site size, etc. Thus, many places are not suitable for building FCSs. Moreover, EV users still deal with high charging time and low charger availability challenges, especially in urban areas with huge populations and various types of charging demands. A solution to address these challenges is utilizing different EV charging technologies. This paper proposes mobile charging stations (MCS) as complementary charging technology to FCSs. A mixed-integer linear model is developed to solve the EV charging management (EVCM) problem. In addition to receiving the charging services at FCSs, the proposed optimization model lets MCSs serve EV users at their convenient times and locations. The results confirm that MCSs are more economical than FCSs for many users, considering the EV users’ time value. Moreover, MCSs can reduce the users’ cost and the stress on the power network during peak hours, considering their energy arbitrage capability.

Mobile Charging Station: A Complementary Charging Technology for Electric Vehicles

Today, smart cities are turning to electric transport, carpooling and zero emission zones. The growing number of electric vehicles on the roads makes it increasingly necessary to have a public charging infrastructure. On the other hand, the main limitations of electric vehicles are the limited range of their batteries and their relatively long charging times. To avoid having problems to recharge, electric vehicle drivers must plan their journeys more thoroughly than others. At the goal of optimizing trip time, drivers need to automate their travel plans based on a smart charging solution, which will require the development of new Vehicle-to-Grid applications that will allow at the charging stations to dynamically interact with the vehicles. In this paper, we propose an architecture based on an algorithm allowing the management of charging plans for electric vehicles traveling on the road to their destination, in order to minimize the duration of the drivers’ journey including waiting and charging times. The decision taken by the algorithm based on the exploration of the data of each public supply station according to its location, number of vehicles in the queue, number of charging sockets, and rates of service.

https://www.mdpi.com/2032-6653/10/4/67/pdf

Application Design Aiming to Minimize Drivers’ Trip Duration through Intermediate Charging at Public Station Deployed in Smart Cities

The main limitations of electric vehicles are the limited scope of the battery and their relatively long charging times. This may cause discomfort to drivers of electric vehicles due to a long waiting period at the service of the charging station, during their trips. In this paper, we suggest a model system based on argorithms, allowing the management of charging plans of electric vehicles to travel on the road to their destination in order to minimize the duration of the drivers' journey. The proposed system decision to select the charging station, during advance reservation of electric vehicles, take into account the time of arrival of electric vehicles at charging stations, the expected charging time at charging stations, the local status of the charging stations in real time, and the amount of energy sufficient for the electric vehicle to arrive at the selected charging station. Furthermore, the system periodically updates the electric vehicule reservations to adjust their recharge plans, when they reach their selected earlier station compared to other vehicules requesting new reservations, or they may not arrive as they were forecast, due to traffic jams on the road or certain reluctance on the part of the driver.

https://www.beei.org/index.php/EEI/article/download/1564/1280

System architecture to select the charging station by optimizing the travel time considering the destination of electric vehicle drivers in smart cities

The limited capacity of the charging infrastructure remains the most formidable obstacle to the wide use of electric vehicles, hinders their ability and reduces their efficiency, especially in long-distance roads such as highways. Thus, the faced problem lies essentially in real time, which can become very important when the waiting queue at the charging station is long. Therefore, a strategy will have to be found to assign each vehicle to the most suitable station in a methodical and appropriate manner. In this paper, we will try to propose a communication system design with an architecture based on a decision algorithm to minimize trip duration, and which implicitly includes waiting time. This algorithm takes into account several parameters that are updated over time, such as the previous reservations, the waiting time in the charging stations, the energy needed to reach the destination etc. The performance of our design system will be illustrated through a programed and applied simulation in the frame of the Moroccan highway Rabat- Agadir.

Decision algorithm for managing electric vehicle charge reservation requests at highway service stations

The electric vehicle (EV) is considered an attractive alternative to a conventional vehicle, due to its potential beneficiation in decreasing carbon emission. But the battery range anxiety is a key challenge to its wide adoption and also the EV drivers spend so much time in public charging stations (CS) to charge especially during peak times. In this paper, we propose a charging station selected system (C3S) to control and manage EVs charging plans. Moreover, the C3S system proposed consists of a set of algorithms that are proposed to recommend a suitable CS for EV charging requests. The CS selection is based on minimizing travel time and takes into account in real-time the queuing time at CS, EVs' charging reservations, and the predicted time of EVs' future charging requests. Besides, we proposed three different strategies for predicting the EVs incoming and controlling the uncertainty matter of the dynamic arrival of EV charging requests. As part of the Helsinki City scenario, the evaluation results demonstrate the performance, especially at peak times, of our proposed C3S with regard to the CS recommendation which has the minimum total trip duration.

/pdf/a-smart-system-combining-real-and-predicted-data-to-2ibroi5l.pdf

Ibrahim El-Fedany

Papers

A Smart Coordination System Integrates MCS to Minimize EV Trip Duration and Manage the EV Charging, Mainly at Peak Times

Application Design Aiming to Minimize Drivers’ Trip Duration through Intermediate Charging at Public Station Deployed in Smart Cities

System architecture to select the charging station by optimizing the travel time considering the destination of electric vehicle drivers in smart cities

Decision algorithm for managing electric vehicle charge reservation requests at highway service stations

A smart system combining real and predicted data to recommend an optimal electric vehicle charging station