Search Results (30)

The energy transition of the road transport sector is now a strategic priority for achieving global decarbonization targets. In particular, the highway sector offers the opportunity to integrate sustainable solutions without additional land consumption, thanks to the availability of relevant areas that are already covered by infrastructure. This study proposes a large-scale analysis of the potential photoelectric energy that can be produced within highway infrastructures, with the aim of evaluating the contribution that these assets can make to electric mobility. The analysis was conducted using geographic information systems (GISs), applied to the case study of the A3 Napoli–Pompei–Salerno highway. The processing of topographical, orographic, and solar data has made it possible to identify a total surface area of approximately 27,100 m² that is potentially suitable for the installation of photovoltaic systems, distributed among service areas, toll stations, car parks, and side sections. This result highlights the concrete possibility of making the most of the energy potential of highway infrastructure, promoting self-production and local consumption models to power the electric vehicle charging network, thus contributing directly to the reduction of emissions and the sustainability of the transport system. Full article

It is believed that the deployment of autonomous vehicles in Europe has the potential to achieve safer, more sustainable and more equitable mobility. This study provides new insight into the hopes and expectations of citizens across five European countries for autonomous transport systems via a qualitative co-creation activity. A diverse and inclusive sample of 147 citizens was invited to generate their own ideas about how self-driving transport could be usefully deployed. Participants were asked to describe, in detail, what the vehicle would be like, what kind of mobility service it would provide, and who the target users would be. Structured qualitative analysis of the answers identified 337 distinct scenarios for autonomous vehicles across seven archetypes. In this paper, we describe a number of features of autonomous services that citizens expect and analyze the differences between demographic groups. We found that citizens across all five countries share a vision of autonomous mobility as electric- or hydrogen-powered, with a much greater use of shared mobility than is currently the case. This understanding of the expectations of a diverse group of citizens offers valuable insights for industry and policymaker actors to implement regarding future technology and transport investment and policy and service development. Full article

Electric vehicle (xEV) battery durability significantly impacts the long-term operation, consumer satisfaction, and market adoption of xEVs. As driving range diminishes over time, it affects vehicle service life and lifecycle GHG emissions. Measuring the full service life of xEV batteries in laboratory tests presents technical and logistical challenges, necessitating representative measurements for parameterizing numerical models. These models are crucial for predicting long-term performance and rely on high-quality experimental data. While performance and aging trends under extreme temperatures are documented, cell thermal contact conditions suitable for direct model input are not well characterized. This study investigates lithium-ion cells from three xEV types, cycled at constant currents from C/40 to 1C, at temperatures between −15 °C and +45 °C, over 1000 cycles in a multi-year campaign. Stable isothermal cell temperatures were achieved using custom-built liquid immersion baths with forced convection, highlighting fundamental electrochemical behaviors by decoupling complex self-heating not typically monitored in air environments. The data inform and validate physics-based models on temperature-dependent performance and durability, providing operational limits to enhance cell and battery thermal management design and educate xEV consumers about conditions affecting performance, range, and durability. Full article

Any sharing mobility service aims to make urban mobility sustainable to help reduce environmental impacts and improve the quality of life for all in cities. Many transport services are not currently self-sustainable. The Life for Silver Coast (LifeSC) opened its mobility services on 22 May 2021 and offered electric mobility services during the summer for a few cities in Tuscany. E-bikes and e-scooters can be financially neutral, and even profitable, thanks to the low costs of the vehicles, but they only see a high utilization rate in winter. Shared electric cars, meanwhile, are not profitable. A new shared service that is viable must be profitable to become widely adopted and significantly contribute to sustainability. A few key characteristics have been identified, and one has been tested with a new business model that combines ride-sharing and car-sharing. The innovative Ride Sharing Algorithm (RSA) has been tested based on data from a potential city, Monterondo, where many commuters travel daily to Rome by train. The Italian census and local survey data allowed for the simulation of the scheduling of vehicle rides and an evaluation of the economic results, which could be positive if enough interest for such a system exists among the people, as at least 400 commuters from Monterotondo go to the train station daily in the morning and return in the afternoon. Such a transport demand would justify a new commercial sharing service by using the model tested with the RSA algorithm. Full article

Carsharing has received considerable attention as a sustainable mobility paradigm. Various service designs and dynamic business environments have increased the decision complexity for the carsharing business. Therefore, carsharing operators require a tool for assessing business development from holistic perspectives. This research provides a framework for outlining the requirements of the carsharing system with holistic perspectives of stakeholders being considered, as well as to derive assessment metrics for examining carsharing development. To create the framework, the system modeling tool, context diagram, was adopted to map out the interactions of externalities with the system and the requirements of the system. Eight assessment metrics: the market condition, business advantage, parking condition, electric vehicle deployment, self-serving configuration, vehicle reservation, vehicle maintenance, and pricing scheme, were eventually identified from the system modeling. From these dimensions, we review 24 carsharing cases from China, Europe, Japan, and the United States, and we summarize discrepancies among different marketplaces and some managerial insights on carsharing development, such as carsharing motivators and inhibitors, innovations in respect of different business backgrounds, approaches of increasing parking privileges, approaches of increasing electrifications, essential digital features, reservation regimes, methods of vehicle maintenances, and service pricing regimes. Full article

Worldwide, in many cities, electric vehicles (EVs) have started to spread as a green alternative in transportation. Several well-known automakers have announced their plans to switch to all-electric engines very soon, although for EV drivers, battery range is still a significant concern—especially when driving on long-distance trips and driving EVs with limited battery ranges. Cities have made plans to serve this new form of transportation by providing adequate coverage of EV charging stations in the same way as traditional fuel ones. However, such plans may take a while to be fully deployed and provide the required coverage as appropriate. In addition to the coverage of charging stations, cities need to consider the potential loads over their power grids not only to serve EVs but also to avoid any shortages that may affect existing clients at their various locations. This may take a decade or so. Consequently, in this work, we propose a novel city-friendly navigation model that is oriented to serve EVs in particular. The methodology of this model involves reading real-time power loads at the grid’s transformer nodes and accordingly choosing the routes for EVs to their destinations. Our methodology follows a real-time pricing model to prioritize routes that pass through less-loaded city zones. The model is developed to be self-aware and adaptive to dynamic price changes, and hence, it nominates the shortest least-loaded routes in an automatic and autonomous way. Moreover, the drivers have further routing preferences that are modeled by a preference function with multiple weight variables that vary according to a route’s distance, cost, time, and services. Different from other models in the literature, this is the first work to address the dynamic loads of the electricity grids among various city zones for load-balanced EV routing in an automatic way. This allows for the easy integration of EVs through a city-friendly and anxiety-free navigation model. Full article

Urban transport systems play a major role in the development of today’s societies, but they require technological changes to reduce their environmental impact. The problem lies in their level of autonomy, which is why electrical energy production systems are proposed for self-consumption, efficiently feeding their accumulators. As the energy provided by photovoltaic installations has lower recharge speeds, conventional systems with high transfer amperage and higher voltage are required. For this reason, solar installations are used for additional services and to support their autonomy. The present work tries to find the best solution for both constant voltage and peak current systems. Once found, these solutions will be applied in real time for the dynamic recharging of battery packs, trying to achieve vehicles that are progressively more energetically autonomous. To solve these situations, a new computational method for calculating voltage and amperage has been developed in this work, based on Dijkstra’s minimum path search algorithm on graph theory, adapted to electrical circuits. Once this algorithm has been established, the panel performance analysis sensors, developed at the University of Malaga, are combined with different electronic solutions described in this article (Wi-Fi relay devices using esp8266 chips or feeding these relays through panels and establishing the voltage drop to switch the connection), achieving precise and sufficiently fast solutions at very low cost. Both series and parallel transitions are possible, depending on the type of energy generation required. The theoretical solutions using Minkowski paths, analyzed in the past, have been simulated and subsequently constructed in this paper, indicating the diagrams necessary for their realization. Full article

Electromobility is generally seen as an efficient means of decarbonizing the transport sector. Ensuring both a broad propagation of electric vehicles and a stable energy system requires intelligent charging strategies in the form of use cases. Most use cases do not combine both the prospect of profit and systemic advantages. This paper analyzes combinations of use cases that merge different use cases to combine profitability and systemic benefits. We present a novel methodological approach for analyzing and comparing the synergies of different use case combinations. The focus is on evaluating the potential for reducing the technical implementation effort resulting from the simultaneous implementation of two to three different use cases. Our findings show that the simultaneous implementation of complex use cases, often involving in-front-of-meter pooling of vehicles, produces the greatest synergies. Combinations that include ancillary services and spot market trading lead to considerable reductions in the implementation effort. Balancing profitability and systemic benefits with little absolute effort requires combinations that include use cases implemented behind-the-meter, for example, optimization of self-consumption. Challenges in the implementation of the combinations investigated arise primarily from technical hurdles and the fact that some use cases have not yet been fully defined in regulatory terms. Full article

Electric vehicles (EVs) and photovoltaic (PV) systems have been progressively incorporated into the grid in recent years principally due to two factors: reduced energy costs and lower pollutants. Numerous studies have investigated how integrating PV and EVs into the grid may affect specific people. It is crucial to understand that the electricity grid will experience the combined effects of PV–EV integration as PV and EV penetration increases. The primary motivation for PV’s integration with Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) services is the aim to reduce charging costs from discharging; moreover, another prerequisite must be satisfied before PV arrays will be able to provide V2G services. The range between the driving limit and EV battery degradation should be reasonable. The way EVs charge and discharge will be impacted by these factors. Numerous analyses are required in order to control the power between various source and load scenarios. In order to balance grids and manage frequency, controllers such as Improved Particle Swarm Optimization (IPSO), Improved Ant Colony Optimization (IACO), and Improved Mayfly Optimization (IMO) are used. As a result, V2G/G2V helps feed electricity back into the grid. By providing the proper duty cycle ratio, the proposed controller regulates converter switching. This study allowed for the performance analysis and operation simulation of a grid-connected PV/EV/Grid system. The purpose of this system was to maximize PV self-consumption while maintaining power quality characteristics like harmonics, grid voltage/current, and power factor. Full article

The boom seen in artificial intelligence in recent years has led to a revolution in the automotive industry. Numerous automakers around the world, such as Tesla, Toyota, Honda, and BMW, have achieved giant strides in the development of e-autonomous vehicles. Consequently, shared electric automated vehicle mobility (SEAVM) systems, which are a crucial part of future innovative transportation solutions, have attracted significant attention from the research community, particularly from a design perspective. However, the flexibility of shared automated mobility systems may lead to a self-operating technology issue (unequal distribution of vehicles), since users in these systems can pick up and drop off electric vehicles wherever they like. With this in mind, this paper addressed the issues of autonomous repositioning and the assignment of shared autonomous electric vehicle systems to balance a system’s network and fulfill its demand. Modeling, analysis and assessment of the system’s performance were carried out using stochastic Petri nets formalism, which included determining the average time areas were empty/congested and the number of unserved consumers, and estimating the redistribution service launch moment. Furthermore, many simulation scenarios were analyzed, including repositioning and without repositioning scenarios, in order to evaluate the efficiency of the model and to show the potential of using Petri nets as a probabilistic formalism approach for the modeling of e-automated mobility systems. Full article

Risk evaluation is an important part of the product design and product manufacturing process; it entails the pursuit of the highest product quality and preventing failure under the constraints of limited resources. The failure mode and effects analysis approach is one of the most widely applied risk evaluation tools that uses the product of the three risk elements of product failure items, severity, occurrence probability, and detection probability, to calculate the risk priority number, the priority of failure risk. However, the typical failure mode and effects analysis method ignores the consideration of objective weights, which may lead to incorrect evaluation results. Moreover, the method of expressing information about product failure provided by experts also directly affects the results of risk assessment. To comprehensively assess the risk of product failure, in this study, the hybrid of the Fermatean fuzzy set and entropy method was used to prioritize product failure items risk. This study used a service failure mode and effects analysis numerical example of self-service electric vehicles to illustrate and test the correctness of the proposed new hybrid Fermatean fuzzy set and entropy method. The mathematical operation results were also compared with the listing of different calculation methods. The test results prove that the proposed new hybrid Fermatean fuzzy set and entropy method can fully consider the cognitive information provided by experts to provide more accurate risk ranking results of failure items. Full article

Congestion is an ongoing problem for many urban centres worldwide (such as London), leading to excessive delays, noise and air pollution, frustrated drivers, and high energy consumption. The carbon footprint of conventional transport systems can be high as a result and transport is among the highest contributors of greenhouse gas emissions. Therefore, with the growing interest in developing connected fully autonomous vehicles (ConFAVs), there is a pressing need to consider their effects within the congested urban setting. To address this, the current research study was designed to investigate the potential for ConFAVs in providing a sustainable transport solution. During this research, a simulation model was developed, calibrated, and validated using field data collected from several sites in East London, using the graphical user interface (GUI) simulation software PTV VISSIM to simulate the proposed driving and car following behaviour, which included the platooning of these ConFAVs, to assess how they could improve the level of service of the roads. Using the new model, this research addresses the shortcomings of two other adaptations of the Wiedemann 99 car-following models by changing the ConFAV’s behaviour to be more cautious when travelling behind a human driven vehicle, and less cautious when behind another ConFAV. As little is known about the transitional period from zero autonomy to full autonomy on the already congested road network, due to the fact that these vehicles are typically tested in small numbers (often one at a time in a controlled environment), the present research study introduced ConFAVs to the simulated network gradually and in large numbers at 20% intervals (namely 0% where there are no ConFAVs, 20%, 40%, 60%, 80%, and finally 100% where all vehicles within the network were ConFAVs). The average delays and subsequent level of service for the roads within the networks were then assessed against each ConFAV penetration level. This helped understand how the network’s efficiency changes when the number of ConFAVs increases, and the potential benefits for these self-driving vehicles on congestion and the ensuing greenhouse gas emissions. The model showed that a reduction in delay of up to 100% can be achieved by introducing ConFAVs, which translates to a significant reduction in greenhouse gas emissions. This, coupled with the fact that ConFAVs are predominantly electric, points to a future sustainable road transport system. The primary purpose of this research would be to investigate the potential of ConFAVs in reducing traffic congestion and, as a result, greenhouse gas emissions. Full article

Considering the limited driving range and inconvenient energy replenishment way of battery electric vehicle, fuel cell electric vehicles (FC EVs) are taken as a promising way to meet the requirements for long-distance low-carbon driving. However, due to the limitation of FC power ability, a battery is usually adopted as the supplement power source to fill the gap between the requirement of driving and the serviceability of FC. In consequence, energy management is essential and crucial to an efficient power flow to the wheel. In this paper, a self-optimizing power matching strategy is proposed, considering the energy efficiency and battery degradation, via implementing a deep deterministic policy gradient. Based on the proposed strategy, less energy consumption and longer FC and battery life can be expected in FC EV powertrain with optimal hybridization degree. Full article

Sales of electric vehicles, for commercial use and personal use, keep rising. In parallel of the development of the associated Electric Vehicle Charging Infrastructure (EVCI), systems for controlling the charging of EVs will have to be developed in order to reduce the impact of such a development on the power grid. In this paper, we present a supervision system that controls the electric vehicle charging of employees of CEA Cadarache research center. The EVCI of Cadarache, set up in 2016, is constituted of more than 80 22-kW AC charging points spread over 30 zones. This EVCI currently supplies more than 376 vehicles including taxis, service vehicles as well as employees’ vehicles. This infrastructure is one of the largest private EVCIs in the region. The supervision system controls electric vehicle (EV) charging in real-time according to two objectives: respecting user preferences, by fully charging the EV battery, and synchronizing the power consumption of a fraction of the EVCI, i.e., 24 charging points, with the power production of a solar photovoltaic plant. This paper details the supervision system that is used to carry out these experiments and presents experimental results. These results show that it is technically feasible to increase (up to 60 percentage points) the self-production ratio while satisfying EV users. Full article

The vehicle-to-grid concept emerged very quickly after the integration of renewable energy resources because of their intermittency and to support the grid during on-peak periods, consequently preventing congestion and any subsequent grid instability. Renewable energies offer a large source of clean energy, but they are not controllable, as they depend on weather conditions. This problem is solved by adding energy storage elements, implementing a demand response through shiftable loads, and the vehicle-to-grid/vehicle-to-home technologies. Indeed, an electric vehicle is equipped with a high-capacity battery, which can be used to store a certain amount of energy and give it back again later when required to fulfill the electricity demand and prevent an energy shortage when the main-grid power is limited for security reasons. In this context, this paper presents a comparative study between two home microgrids, in one of which the concept of vehicle-to-home is integrated to provide a case study to demonstrate the interest of this technology at the home level. The considered microgrid is composed of renewable energy resources, battery energy storage, and is connected to the main grid. As the vehicle is not available all day, in order to have consistent results, its intervention is considered in the evening, night, and early morning hours. Two case studies are carried out. In the first one, the vehicle-to-home concept is not taken into account. In this case, the system depends only on renewable resources and the energy storage system. Subsequently, the electric vehicle is considered as an additional energy storage device over a few hours. Electric vehicle integration brings an economic contribution by reducing the cost, supporting the other MG components, and relieving the main grid. Simulation results using real weather data for two cities in France, namely Brest and Toulon, show the effectiveness of the vehicle-to-home concept in terms of cost, energy self-sufficiency, and continuity of electrical service. Full article