Fleet Of Electric Vehicles Research Articles

Advancing sustainable EV charging infrastructure: A hybrid solar-wind fast charging station with demand response

This study aims to design an efficient hybrid solar-wind fast charging station with an energy storage system (ESS) to maximize station efficiency and reduce grid dependence. The research employs Monte Carlo simulation to capture renewable energy source (RES) uncertainties, models stochastic electric vehicles (EVs) driver behavior and fleet diversity, and utilizes an Erlang B queuing model for EV load demand estimation. A hybrid optimization approach combining the Binary-Gravitational Search Optimization Algorithm (B-GOA) and the Non-dominated Crowding Sort Optimization Algorithm (NCSOA) is implemented for efficient optimization in a combined binary-continuous solution space. Results demonstrate superior performance of the proposed approach compared to existing methods, with high-RES penetration significantly reducing grid reliance. This study contributes to advancing sustainable EV charging infrastructure development and enhancing overall grid stability through improved load flexibility and demand response management. The analysis reveals Scenario IV as the most economically viable, with the highest Net Present Value of 1,025,895.32€. Most scenarios favor 5 chargers (44–46 kW) and 4 Type 3 wind generators. Battery capacity varies widely (108–372 kWh), as does grid connection (0–292 kW). Despite varying initial investments, Scenarios II-VII show a consistent 4-year Internal Rate of Return, indicating good economic potential across different configurations.

A comprehensive simultaneous allocation algorithm of charging stations and vehicle to grid operation in radial networks

The widespread adoption of electric vehicles (EVs) helps improve air quality by minimizing pollutants and promoting sustainable transportation practices. The integration of a substantial fleet of EVs leads to an increase in the installation of charging stations. The unplanned allocation of these loads impacts the distribution systems, such as increasing power loss. This paper introduces an algorithm that proposes an optimal allocation strategy for charging stations (CSs) in a distribution system. The allocation process aims to minimize the total apparent energy losses and ensure that the system voltage profile remains within limits. Load profile, charging and discharging profiles of CSs are considered. Vehicle-to-Grid (V2G) mode is one of the merits of integrating EVs in the grid, so this feature has been used to maintain system voltage stability and minimize energy loss. Power rating and locations of V2G mode are optimally determined to guarantee power quality indices. A hybrid algorithm of genetic algorithm (GA) and Self-Adaptive Multi-Population Elitist JAYA (SAMPE-JAYA) is developed to simultaneously allocate CSs and V2G in the systems. The proposed algorithm is verified on standard systems, IEEE 33, and 69-bus systems. The proposed algorithm is verified on standard IEEE 33-bus and 69-bus systems. V2G integration with CSs results in a reduction of energy losses by 6.33% and 22.25%, respectively, and voltage deviation improvements to 7.61% and 7.88% for the two systems.

Optimization of PV benefits for Electric Vehicles Charging Station Using a Deterministic Method and Fuzzy Inference System Method

Utilizing renewable energy, specifically Photovoltaic (PV), for Electric Vehicle (EV) charging presents diverse technical and economic opportunities, reflecting a recent trend in innovation and research to address global transportation challenges while highlighting the value of renewable sources. In this context, this paper aims to devise an optimal power management strategy for an EV charging station powered by a PV-based microgrid (MG). The main purpose is to improve the benefits of PV production in recharging EVs to increase self-consumption, decrease dependency on the power grid, and reduce energy costs. Production data from a real-scale MG at the Catholic University in France were used as a case study. This MG comprises a heterogeneous fleet of EVs, PV sources, stationary storage, EV charging stations, and a connection to the power grid. The optimization of PV benefits relies on leveraging the knowledge of EV characteristics to modulate their charge profile. In this context, two methods are used and compared for the modulation of the EVs' charging profile: a deterministic method and a Fuzzy Inference System (FIS)-based method. For each method, different scenarios emerge based on the knowledge or lack thereof of the EVs' characteristics. This methodology permits the optimal distribution of energy among multiple EVs and regulates the necessary power to achieve the desired charge level upon departure. It encourages charging through PV production, resulting in reduced energy costs. The comparison of total costs reveals notable differences between the deterministic and FIS approaches for both slow and fast charging modes. In the case of slow charging, the deterministic approach achieves savings of -863.4 c€/day, whereas the FIS method delivers greater savings of -898.7 c€/day. For fast charging, the deterministic approach yields savings of -524.6 c€/day, while the FIS method results in even higher savings of -540.7 c€/day. These results highlight that the FIS approach enables better cost management, primarily due to more efficient utilization of PV energy. Additionally, simulation results from MATLAB/Simulink confirm the effectiveness of the FIS-based method, which improves PV energy utilization to 98.20%, compared to 81.60% with the deterministic approach. This highlights the superior efficiency of the proposed FIS.

Reinforcement Learning for EV Fleet Smart Charging with On-Site Renewable Energy Sources

In 2020, the transportation sector was the second largest source of carbon emissions in the UK and in Newcastle upon Tyne, responsible for about 33% of total emissions. To support the UK’s target of reaching net zero emissions by 2050, electric vehicles (EVs) are pivotal in advancing carbon-neutral road transportation. Optimal EV charging requires a better understanding of the unpredictable output from on-site renewable energy sources (ORES). This paper proposes an integrated EV fleet charging schedule with a proximal policy optimization method based on a framework for deep reinforcement learning. For the design of the reinforcement learning environment, mathematical models of wind and solar power generation are created. In addition, the multivariate Gaussian distributions derived from historical weather and EV fleet charging data are utilized to simulate weather and charging demand uncertainty in order to create large datasets for training the model. The optimization problem is expressed as a Markov decision process (MDP) with operational constraints. For training artificial neural networks (ANNs) through successive transition simulations, a proximal policy optimization (PPO) approach is devised. The optimization approach is deployed and evaluated on a real-world scenario comprised of council EV fleet charging data from Leicester, UK. The results show that due to the design of the rewards function and system limitations, the charging action is biased towards the time of day when renewable energy output is maximum (midday). The charging decision by reinforcement learning improves the utilization of renewable energy by 2–4% compared to the random charging policy and the priority charging policy. This study contributes to the reduction in battery charging and discharging, electricity sold to the grid to create benefits and the reduction in carbon emissions.

A Stochastic Approach to the Power Requirements of the Electric Vehicle Charging Infrastructure: The Case of Spain

Battery electric vehicles represent a technological pathway for reducing carbon emissions in personal road transport. However, for the widespread adoption of this type of vehicle, the user experience should be similar to that of combustion engine vehicles. To achieve this objective, a robust and reliable public charging infrastructure is essential. In Spain, the electric recharging infrastructure is growing quickly in metropolitan areas but much more slowly on roads and highways. The upcoming charging stations must be located along high-volume traffic corridors and in proximity to the Trans-European Transport Network. The main contribution of this research is to offer a method for examining the essential electricity infrastructure investments required in scenarios involving substantial electric vehicle adoption. The methodology includes a sensitivity analysis of fleet composition and market share, recharging user behavior, charging station density, and vehicle efficiency improvements. To this end, the authors have developed a simplified probabilistic model, addressing the effect of the involved parameters through a comprehensive scenario analysis. The results show that the actual number of high-capacity charging plugs on Spanish roads is significantly lower than the European regulation requirements for the year 2030 considering an electric vehicle market share according to the Spanish Integrated National Energy and Climate Plan 2021–2030 objectives and it is far from the necessary infrastructure to cover the expected demand according to the traffic flow. Under these circumstances, the charging peak power demand reaches over 7.4% of the current Spanish total power demand for an electric vehicle fleet, which corresponds to only 12% of the total.

Layered Graph Models for the Electric Vehicle Routing Problem With Nonlinear Charging Functions

AbstractElectric vehicle routing problems (EVRPs) involve the routing of a fleet of electric vehicles (EVs) to visit a set of customers while typically minimizing the total travel and charging time. Due to their limited autonomy, EVs may need to recharge their batteries en‐route at charging stations (CSs). Thus, routing decisions also include which CSs to visit, and how much energy to charge during those visits. These decisions are compounded by the fact that charging times follow a nonlinear charging function with respect to the EV's state of charge (SoC). We propose a layered graph representation for the EVRP with nonlinear charging functions (EVRP‐NL). Specifically, the layers correspond to discretized SoC values. Therefore, the arcs' energy consumption is approximated to match those values. We develop two compact formulations based on the layered graph. Furthermore, we introduced two charging policies that facilitate aligning charging duration with practical considerations. Computational results demonstrate the effectiveness of our formulations. Our best formulation effectively handles instances with up to 40 customers. On those instances, compared to the state‐of‐the‐art compact formulation, our formulation solves 13 more instances to optimality with less than half of the computational time. Considering instances solved by both formulations to optimally, the approximation entailed by our formulation yields a 0.94% deviation on average. Since our best performing formulation is compact, it may be readily used by a broad audience. Furthermore, as the majority of algorithms for the EVPR and its variants are heuristics, our formulation could be beneficial in evaluating the performance of these methods.

Managing grid impacts from increased electric vehicle adoption in African cities

Electric vehicles are pivotal for global climate solutions, particularly in emerging markets like Africa. Despite the continent’s clean energy potential, electric vehicle adoption faces unique challenges due to inefficiencies and reliability issues of distribution power grids. Here, we analyze the impacts of expanding electric vehicle fleets—private, commercial, and paratransit—on Nairobi’s power grid. We simulate traffic patterns, charging behaviors, and transformer utilization using local mobility data. Our results show that while electric commercial and paratransit fleets may improve power system efficiency, widespread private EV adoption could significantly strain the grid, increasing peak loads and transformer aging. Smart charging strategies could mitigate these issues, reducing potential transformer replacement costs by up to 40%. Our study highlights the importance of tailored demand management and infrastructure planning to support EV growth in African cities, providing critical insights for policymakers, utilities, and transport planners to facilitate sustainable electric mobility transitions.

Designing a Bidirectional Power Flow Control Mechanism for Integrated EVs in PV-Based Grid Systems Supporting Onboard AC Charging

This paper investigates the potential use of Electric Vehicles (EVs) to enhance power grid stability through their energy storage and grid-support capabilities. By providing auxiliary services such as spinning reserves and voltage control, EVs can significantly impact power quality metrics. The increasing energy consumption and the global imperative to address climate change have positioned EVs as a viable solution for sustainable transportation. Despite the challenges posed by their variable energy demands and rising numbers, the integration of a smart grid environment with smart charging and discharging protocols presents a promising avenue. Such an environment could seamlessly integrate a large fleet of EVs into the national grid, thereby optimizing load profiles, balancing supply and demand, regulating voltage, and reducing energy generation costs. This study examines the large-scale adoption of EVs and its implications for the power grid, with a focus on State of Charge (SOC) estimation, charging times, station availability, and various charging methods. Through simulations of integrated EV–PV charging profiles, the paper presents a lookup-table-based data estimation approach to assess the impact on power demand and voltage profiles. The findings include multiple charging scenarios and the development of an optimal control unit designed to mitigate the potential adverse effects of widespread EV adoption.

A continuous approximation model for the electric vehicle fleet sizing problem

A continuous approximation model for the electric vehicle fleet sizing problem

Commercial EV fleet smart charging for cost reduction and renewables integration: A case study in Germany

The transition to electric mobility reduces transportation carbon emissions but presents challenges in integrating growing electricity demand with renewable electricity generation and power systems. Smart charging emerges as a key technology to minimize operational costs and support higher utilization of green electricity for electric vehicles (EVs). Market analysis indicates a trend towards dynamic electricity tariffs, incentivizing EV charging at times favourable to the electricity grid, yet existing literature fails to address smart charging technology in sync with times of lower electricity prices and higher renewables integration. This work examines different smart charging strategies for a commercial EV fleet in Germany, considering a Real-Time Pricing (RTP) tariff indexed to day-ahead market prices and renewable electricity integrations. HOMER Grid is used to model these smart charging strategies and generate new EV load profiles. Direct and indirect CO2 emissions and renewable electricity usage ratios are calculated based on detailed electricity grid and self consumption data. The resulting Levelized Cost of Energy (LCOE) is minimized, by adjusting solar PV and battery capacities. The adoption of an RTP tariff decreases the LCOE from €0.530 to €0.314 per kWh. A smart charging strategy designed to reduce peak demand and schedule charging at lower price times further reduces the LCOE to €0.283 per kWh. The lowest LCOE of €0.281 per kWh is achieved with 34.4 kWp of solar PV capacity and no batteries. The carbon impact is minimized with larger solar PV and battery capacities. The performed work indicates a favourable framework for EV charging integrators to maximize cost savings and renewable energy integration for an EV fleet charging use case.

Multiple energy planning in the energy hub considering renewable sources, electric vehicles and management in the daily electricity market with wind multi-objective optimization algorithm

This article presents a decision-making framework leveraging randomness to manage short-term intelligent energy hub (EH) within regenerated power systems integrating EH input electricity and natural gas. EH outputs, addressing electricity and heat needs, are managed via battery storage systems (BSS) and electric vehicle (EV) fleets in regulation markets. The energy hub operator optimizes decisions concerning natural gas and energy, minimizing costs in electricity supply and thermal energy carriers across day-ahead (DA) markets and regulations. Emphasizing the benefits of demand-side management planning in cost reduction, the model incorporates a conditional value at risk (CVaR) method to assess and manage uncertainties. Moreover, leveraging electric vehicle battery storage capacity is explored to mitigate wind and solar energy production uncertainties. An energy optimization strategy based on a Multi-Objective Wind-Driven Optimization (MOWDO) is proposed, demonstrating the effectiveness of the model and method across various system conditions and scenarios. Results indicate that increasing the number of EVs from 2000 to 10,000 reduces the expected cost by 1.5 % and the CVaR by 1.5 %. Additionally, optimizing the BSS discharge cost coefficient from $45/MWh to $15/MWh decreases the expected cost by 0.5 % and the CVaR by 0.3 %. The system achieves a 20 % reduction in peak-hour electricity purchases by leveraging the BSS and EV fleet during high price conditions. These findings underscore the positive impact of demand-side management programs in enhancing renewable resource utilization amidst uncertainty.

The Electric Vehicle Routing and Overnight Charging Scheduling Problem on a Multigraph

In the electric vehicle (EV) routing and overnight charging scheduling problem, a fleet of EVs must serve the demand of a set of customers with time windows. The problem consists in finding a set of minimum cost routes and determining an overnight EV charging schedule that ensures the routes’ feasibility. Because (i) travel time and energy consumption are conflicting resources, (ii) the overnight charging operations take considerable time, and (iii) the charging infrastructure at the depot is limited, we model the problem on a multigraph where each arc between two vertices represents a path with a different resource consumption trade-off. To solve the problem, we design a branch-price-and-cut algorithm that implements state-of-the-art techniques, including the ng-path relaxation, subset-row inequalities, and a specialized labeling algorithm. We report computational results showing that the method solves to optimality instances with up to 50 customers. We also present experiments evaluating the benefits of modeling the problem on a multigraph rather than on the more classical 1-graph representation. History: Accepted by Andra Lodi, Area Editor for Design and Analysis of Algorithms—Discrete. Funding: This work was supported by the Natural Sciences and Engineering Research Council of Canada through the Discovery grants [Grant RGPIN-2023-03791]. It was also partially funded by HEC Montréal through the research professorship on Clean Transportation Analytics. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0404 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0404 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Evaluating Synergies between Electric Vehicles and Photovoltaics: A Comparative Study of Urban Environments

Electric vehicles (EVs) and photovoltaics (PVs) are expected to be broadly adopted in future power systems. However, the temporal variability of EV load and PV production presents challenges for integrating them into the power grid. This study evaluates and assesses the synergies between EVs and PV systems to maximize solar energy utilization for EV load coverage. The configurations studied include EV charging via the national grid as a reference case (Case 1) and two solar energy harvesting options: EVs powered directly by vehicle-mounted PVs (Case 2) and EV chargers connected to residential PV installations (Case 3). These cases are evaluated across different urban environments with large EV fleets and dissimilar weather conditions: Berlin and Los Angeles. A customized operation profile based on the worldwide harmonized light-duty test cycle (WLTC) and a charge-right-away (CRA) strategy is used. Energy performance analysis is conducted through dynamic simulations using the Modelica language, with environmental and economic indices derived. Key findings highlight the superior performance of residential PV systems in both cities compared to current solar EV technologies, with both solutions offering significant benefits over the reference case. Cases 2 and 3 result in a 44% and 59% reduction in annual energy consumption, greenhouse gas emissions, and charging costs in Berlin, while in Los Angeles, the reductions are 67% and 98%. The average daily solar driving range reaches 20.3% in Berlin and 30.4% in Los Angeles.

Optimal Charging Control of Electric Vehicle Fleets Based on Demand Aggregation and User-Oriented Disaggregation Respecting Data Privacy

Optimal Charging Control of Electric Vehicle Fleets Based on Demand Aggregation and User-Oriented Disaggregation Respecting Data Privacy

Transport Fleet Electrification Development Conditions—Perspective of Transport, Shipping, and Logistics Industry in Poland

The development of electric vehicle fleets is an important element of today’s economic, social, and ecological development. This multidimensional sustainable process, although not easy, generates many tangible benefits for various stakeholders, ranging from environmental to financial and competence issues. Despite the fact that the phenomenon of transformation toward rational energy management is gaining momentum on a global scale, there is a significant disproportion in terms of development levels depending on the origin of the economy. The aim of this research article is to identify the key factors affecting the development of fleet electrification in the transport, shipping, and logistics (TSL) sector in Poland. Based on a literature review, a fleet development framework was developed using a PESTEL (political, economic, social, technological, environmental, and legal) analysis and evaluated by TSL companies. According to the conclusions drawn, the most important stimulants are economic factors and technological factors, which limit the development of electrified transport fleets in the TSL industry. Based on this, the authors propose various solutions to improve economic profitability and technological conditions. In addition, it was found that the attitudes of the decision makers at transport companies and cooperation within the TSL sector are also important.

A Multidisciplinary Approach for the Sustainable Technical Design of a Connected, Automated, Shared and Electric Vehicle Fleet for Inner Cities

The increasing volume of personal motorized vehicles (PMVs) in cities has become a serious issue leading to congestion, noise, air pollution and high land consumption. To ensure the sustainability of urban transportation, it is imperative to transition the current transportation paradigm toward a more sustainable state. Transitions within socio-technical systems often arise from niche innovation. Therefore, this paper pursues the technical optimization of such a niche innovation by applying a technical sustainability perspective on an innovative mobility and logistics concept within a case study. This case study is based on a centrally managed connected, automated, shared and electric (CASE) vehicle fleet which might replace PMV use in urban city centers of the future. The key technical system components of the envisioned mobility and logistics concept are analyzed and optimized with regard to economic, ecological and social sustainability dimensions to maximize the overall sustainability of the ecosystem. Specifically, this paper identifies key challenges and proposes possible solutions across the vehicle components as well as the orchestration of the vehicles’ operations within the envisioned mobility and logistics concept. Thereby, the case study gives an example of how different engineering disciplines can contribute to different sustainability dimensions, highlighting the interdependences. Finally, the discussion concludes that the early integration of sustainability considerations in the technical optimization efforts of innovative transportation systems can provide an important building block for the transition of the current transportation paradigm to a more sustainable state.

Discovering the Properties of a Problem of Scheduling Battery Charging Jobs to Minimize the Total Time with the Use of Harmonic Numbers

In this work, we consider a problem from the field of power-aware scheduling in which a fleet of electric vehicles have to be charged in a minimum time. Each vehicle is equipped with a lithium-ion battery of a given capacity. The initial power used for charging each battery is known, whereas it is assumed that the power drops to zero at the moment when the battery gets fully loaded. The power usage function is linear and decreasing. The charging jobs are nonpreemptable and independent, whereas the total available amount of power is limited. The objective is to minimize the schedule length. In this paper, we analyze the case of a problem with identical jobs that already cover a wide variety of practical situations. By employing inverses of natural numbers, similar to harmonic series, we prove two properties of this case, and we also discuss the phenomenon of the stabilization of the difference between the start times of two successive jobs in a schedule. We also take under examination a few special cases of the problem. Some conclusions and directions for future research are given.

Multi-time scales prediction of aggregated schedulable capacity of electric vehicle fleets based on enhanced Prophet-LGBM algorithm

The fast-increasing applications of renewable energy and electric vehicles (EVs) pose significant challenges to the safe and economic operation of the power grid. However, through Virtual Power Plant (VPP) technology, EV fleets can be aggregated into large-scale EV generalized energy storage systems, providing scarce dispatchable flexible resources for the future power system. Rapid and accurate prediction of the Aggregated Schedulable Capacity of EV (EVASC) fleets is a crucial technology for VPP with Electric Vehicles (EV-VPP) to participate in various ancillary services of the power system. In this paper, orienting for various dispatch scenarios in the power system, a multi-time scale prediction model of the EVASC for EV-VPPs is presented based on the enhanced Prophet algorithm combining a light gradient boosting machine (LGBM) algorithm. The enhanced Prophet-LGBM algorithm is initially proposed here by incorporating the LGBM algorithm into the traditional Prophet algorithm in series and adding extra sequential features in its input, such as temperature, weather, and alike. In this way, the problem of overfitting and low capability in addressing additional input features in the traditional Prophet is overcome. The proposed enhanced Prophet-LGBM-based prediction method of the EVASC for EV-VPPs is validated using 1.8 million actual charging records of over 4000 charging piles for half a year at the provincial level. The 30-day simulation results of the day ahead and intraday predictions of the EVASC for EV-VPPs show that much higher performances in the prediction accuracy can be achieved, specifically in holidays.

Power Quality Enhancement by Mitigating Load Imbalance from Random Electric Vehicle Fleet at Electric Vehicle Charging Stations

Power Quality Enhancement by Mitigating Load Imbalance from Random Electric Vehicle Fleet at Electric Vehicle Charging Stations

Optimal Operation of an Industrial Microgrid within a Renewable Energy Community: A Case Study of a Greentech Company

The integration of renewable energy sources in the European power system is one of the main goals set by the European Union. In order to ease this integration, in recent years, Renewable Energy Communities (RECs) have been introduced that aim to increase the exploitation of renewable energy at the local level. This paper presents an Energy Management System (EMS) for an industrial microgrid owned and operated by a greentech company located in the north of Italy. The company is a member of an REC. The microgrid is made of interconnected busbars, integrating photovoltaic power plants, a fleet of electric vehicles, including company cars and delivery trucks supporting Vehicle-to-Grid (V2G), dedicated charging stations, and a centralized battery energy storage system. The industrial site includes two warehouses, an office building, and a connection to the external medium-voltage network. The EMS is designed to optimize the operation of the microgrid and minimize the operating costs related to the sale and purchase of energy from the external network. Furthermore, as the company is a member of an REC, the EMS must try to follow a desired power exchange profile with the grid, suggested by the REC manager, with the purpose of maximizing the energy that is shared within the community and incentivized. The results demonstrate that, when minimizing only costs, local self-consumption is favored, leading to a Self-Sufficiency Rate (SSR) of 65.37%. On the other hand, when only the adherence to the REC manager’s desired power exchange profile is considered in the objective function, the SSR decreases to 56.43%, net operating costs increase, and the energy shared within the REC is maximized.