Fleet Of Electric Vehicles Research Articles

Accelerating a consensus-based EV smart charging algorithm by user priority clustering

The rapid increase in Electric Vehicle (EV) adoption leads to significant challenges for charging station operators due to the unbalance between the growing demand for charging services and their limited power capacity connected to the grid. To tackle the challenge, this paper applies a two-step architecture in which an energy management system (EMS) in the first step determines the available capacity to charge the present fleet of EVs, after which in the second step this capacity is distributed over the individual EVs. To optimally allocate available power and satisfy user requirements, a user priority-based charging management strategy is proposed. Such priority can optimally order the set of EVs to be charged based on user input. However, relying solely on user-estimated input can be prone to incorrect priority due to inaccurate estimations. Therefore, we introduced a clustering of charging sessions based on the user-provided estimated information in the proposed priority function. These clusters relax the effect of inaccurately estimated user input by grouping charging sessions with similar behavior. Our proposed priority can increase user satisfaction by up to 30%, as measured by the percentage of requested energy that is delivered to users, compared to scenarios without priority, 10% more than priority based solely on estimation information, and 8% more than first-come-first-serve. To avoid exposing the personal behavior of the user, we introduce a decentralized approach, applying a consensus-based method that allows local control per EV charging station and only requires minimal communication of a consensus variable with neighboring charging stations. By introducing the priority in the communication weight matrix of the consensus-based algorithm, we accelerate the convergence rate more than 10 times. Simulation results based on real-world data sets in Belgium demonstrate the proposed method scales well with growing fleet sizes.

Hybrid genetic algorithm-simulated annealing based electric vehicle charging station placement for optimizing distribution network resilience

Rapid placement of electric vehicle charging stations (EVCSs) is essential for the transportation industry in response to the growing electric vehicle (EV) fleet. The widespread usage of EVs is an essential strategy for reducing greenhouse gas emissions from traditional vehicles. The focus of this study is the challenge of smoothly integrating Plug-in EV Charging Stations (PEVCS) into distribution networks, especially when distributed photovoltaic (PV) systems are involved. A hybrid Genetic Algorithm and Simulated Annealing method (GA-SAA) are used in the research to strategically find the optimal locations for PEVCS in order to overcome this integration difficulty. This paper investigates PV system situations, presenting the problem as a multicriteria task with two primary objectives: reducing power losses and maintaining acceptable voltage levels. By optimizing the placement of EVCS and balancing their integration with distributed generation, this approach enhances the sustainability and reliability of distribution networks.

Optimization Strategies for Electric Vehicle Charging and Routing: A Comprehensive Review

Based on information from recent research, by 2045, Electric Vehicles (EV) will dominate the roads with presence of more than 80% of its kind. Hence, these vehicles' grid level penetration will increase proportionally, which challenges the existing grid infrastructure in terms of its reliability and energy management capabilities. New techniques to store and consume massive quantities of energy from the power grid, as well as infusing the captive energy within the EV in response to grid demands, are emerging with the advent of electric vehicles. Everything could be handled smoothly only if we schedule the EV operation (charging/discharging) more optimally and efficiently using scheduling algorithms. Despite the existence of many routings and charging schedule computations, nature-inspired optimization approaches might play a critical role in responding to such routing challenges. Researchers have created several optimum scheduling approaches, such as Dynamic Programming, Differential Evolutionary Optimization Techniques, Collaborative Optimization Scheduling, Two-stage optimal scheduling strategy, and so on. The optimum schedule review examines the operation of an EV fleet while considering uncertainty sources and varied EV operating circumstances by integrating heuristic and meta-heuristic techniques. This paper exhibits a deep review on the various EV optimal scheduling techniques and adopted algorithms which are the emerging best practices like predictive analytics, dynamic routing, user centric planning, multi-objective optimization, etc. that reflect the industry's focus on leveraging advanced technologies, data-driven decision-making, and collaborative approaches to enhance the efficiency and sustainability of electric vehicle routing and charging scheduling.

Delivery routing for a mixed fleet of conventional and electric vehicles with road restrictions

The enforcement of stringent regulations capping carbon emissions has prompted city logistics enterprises to substitute electric vehicles (EVs) for conventional vehicles (CVs). For city logistics enterprises with a mixed fleet of CVs and EVs, this study investigates the delivery routing problem with road restrictions (DRPRR) for accessible time windows and bearing weight. We formulate the DRPRR as a mixed integer programming model to minimise the total operations cost. The model consists of the set-up cost of CVs and EVs, the diesel cost of CVs, the electricity cost of EVs, the carbon tax cost, and the penalty cost of vehicles waiting for roads to be accessible. To effectively solve the model, an adaptive large neighbourhood search (ALNS) algorithm is developed that consists of tailored destroy and repair operators with two alternative solution acceptance criteria. Numerical experiments are conducted to validate the effectiveness of the proposed model and ALNS algorithm. In small-scale instances, the ALNS with the Metropolis criterion finds the best solutions for 41 of 48 instances while maintaining a deviation of less than 2.5% from CPLEX in the remaining 7 instances, and its running time is significantly shorter than CPLEX. In large-scale instances, the ALNS with the Metropolis criterion has stronger solving ability and better stability than benchmark algorithms (i.e. GA-LS, LNS, and ALNS with a threshold acceptance criterion). We also address a real-world case and conduct a sensitivity analysis to provide useful managerial insights. Specifically, implementing road restrictions on accessible time windows and the carbon tax policy simultaneously is more appropriate from the comprehensive perspective of market activity and carbon emissions.

Strategic Model for Charging a Fleet of Electric Vehicles with Energy from Renewable Energy Sources

The ever-growing number of electric vehicles requires increasing amounts of energy to charge their traction batteries. Electric vehicles are the most ecological when the energy for charging them comes from renewable energy sources. Obtaining electricity from renewable sources such as photovoltaic systems is also a way to reduce the operating costs of an electric vehicle. However, to produce cheap electricity from renewable energy sources, you first need to invest in the construction of a photovoltaic system. The article presents a strategic model for charging a fleet of electric vehicles with energy from photovoltaic systems. The model is useful for sizing a planned photovoltaic system to the energy needs of a vehicle fleet. It uses the Metalog family of probability distributions to determine the probability of producing a given amount of energy needed to power electric vehicle chargers. Using the model, it is possible to determine the percentage of energy from photovoltaic systems in the total energy needed to charge a vehicle fleet. The research was carried out on real data from an operating photovoltaic system with a peak power of 50 kWp. The approach presented in the strategic model takes into account the geographical and climatic context related to the location of the photovoltaic system. The model can be used for various renewable energy sources and different sizes of vehicle fleets with different electricity demands to charge their batteries. The presented model can be used to manage the energy produced both at the design stage of the photovoltaic system and during its operation.

Considerations on reducing emissions by increasing the use of electric vehicles in urban area

The pace between city development, increasing number of working places and emissions control is a challenge nowadays in urban environment. The gradual increase of electric cars ownership is essential for reducing the emissions level in urban areas. The city policy is of high importance for encouraging the use of electric cars, especially for trip purposes to work and business. The paper deals with the effects of increasing the fleet of electric vehicles on the emissions level in urban areas. The effect of considering a certain share of electric car trips to work and business is analyzed. Considering this, the concept of “green city corridors” can be introduced and considered as a priority for the transport policy in urban areas.

A ride time-oriented scheduling algorithm for dial-a-ride problems

This paper offers a new algorithm to efficiently optimize scheduling decisions for dial-a-ride problems (DARPs), including problem variants considering electric and autonomous vehicles (e-ADARPs). The scheduling heuristic, based on linear programming theory, aims at finding minimal user ride time schedules in worst-case quadratic time. The algorithm can either return feasible routes or it can return incorrect infeasibility declarations, on which feasibility can be recovered through a specifically-designed heuristic. The algorithm is furthermore supplemented by a battery management algorithm that can be used to determine charging decisions for electric and autonomous vehicle fleets. Timing solutions from the proposed scheduling algorithm are obtained on millions of routes extracted from DARP and e-ADARP benchmark instances. They are compared to those obtained from a linear program, as well as to popular scheduling procedures from the DARP literature. The proposed procedure mostly yields optimal solutions, with nearly-optimal solutions occurring in only 27 out of 21.5 million cases on DARP instances. Additionally, it demonstrates an average speed improvement of around 60% compared to a linear program and performs comparably to benchmark scheduling approaches from the DARP literature, while outperforming them in solution quality.

Electric vehicles in urban delivery fleets: How far can they go?

The goal of this study is to provide insights into the expected role of medium-duty electric vehicles (EVs) in urban delivery fleets and to analyze the effectiveness of EV subsidies on EV fleet penetration and tailpipe emissions. To meet this goal, we propose a modeling framework that determines the minimum-cost fleet size and fleet mix (of EVs and conventional vehicles) and vehicle routes for a profit-maximizing delivery company. Second, we conduct extensive analyses using this modeling framework and Southern California network data; we vary the EV driving range, per-mile cost of EVs, demand rate, service region size/structure, driver working hours, and network travel times. We find that the optimal fleet mix nearly always includes EVs and conventional vehicles. Moreover, we find that EV subsidies have limited effectiveness with current EV batteries and service regions designed around conventional vehicles. Hence, improving EV battery technology is critical to electrifying urban delivery fleets.

Optimization of Grid Energy Balance Using Vehicle-to-Grid Network System

This paper proposes a methodological way to compensate for the imbalance between energy generation and consumption using a battery block from electric vehicles as an energy reservoir through the well-known vehicle-to-grid system (V2G). This method is based on a simulation process developed by the authors that takes into consideration the daily fluctuations in energy consumption as well as the power level generated by an energy source, either conventional, renewable, or hybrid. This study shows that for very large electric vehicle fleets, the system is rendered non-viable, since the remaining energy in the battery block that allows the electric vehicle to be usable during the daytime avoids having to compensate for the energy grid imbalance, only allowing it to cover a percentage of the energy imbalance, which the proposed methodology may optimize. The analysis of the proposed methodology also shows the viability of the system when being applied to a small fleet of electric vehicles, not only compensating for the energy imbalance but also preserving the required energy in the battery of the electric vehicle to make it run. This method allows for predicting the optimum size of an electric vehicle battery, which depends on the energy generation level, coverage factor of the energy imbalance, and size of the electric vehicle fleet.

Economic and environmental benefits of automated electric vehicle ride-hailing services in New York City

A precise, scalable, and computationally efficient mathematical framework is proposed for region-wide autonomous electric vehicle (AEV) fleet management, sizing and infrastructure planning for urban ride-hailing services. A comprehensive techno-economic analysis in New York City is conducted not only to calculate the societal costs but also to quantify the environmental and health benefits resulting from reduced emissions. The results reveal that strategic fleet management can reduce fleet size and unnecessary cruising mileage by up to 40% and 70%, respectively. This alleviates traffic congestion, saves travel time, and further reduces fleet sizes. Besides, neither large-battery-size AEVs nor high-power charging infrastructure is necessary to achieve efficient service. This effectively alleviates financial and operational burdens on fleet operators and power systems. Moreover, the reduced travel time and emissions resulting from efficient fleet autonomy create an economic value that exceeds the total capital investment and operational costs of fleet services.

Identification and Mitigation of Predominant Challenges in the Utilization of Aged Traction Batteries within Stationary Second-Life Scenarios

As the production of battery cells experiences exponential growth and electric vehicle fleets continue to expand, an escalating number of traction batteries are nearing the conclusion of their operational life for mobility purposes, both presently and in the foreseeable future. Concurrently, the heightened interest in sustainable energy storage solutions has spurred investigations into potential second-life applications for aging traction batteries. Nonetheless, the predominant practice remains the removal of these batteries from electric vehicles, signifying the end of their life cycle, and their subsequent incorporation into recycling processes, with limited consideration for life-extending measures. This study seeks to elucidate the reasons behind the deprioritization of battery repurposing strategies. Therefore, the research team conducted two industry studies with over 20 battery experts from Europe, revealing concerns about the economic viability of repurposing batteries for stationary storage applications. A literature review of studies published since 2016 confirmed the industry’s struggles to address this issue theoretically. In conclusion, a research question was formulated, and a solution approach was delineated to assess the economic prospects of aged traction batteries within the industry’s landscape in the future. This solution approach encompasses pertinent market analysis, the identification of representative second-life applications, as well as the formulation of a methodology for evaluating the residual value of these batteries.

Charge Scheduling of Electric Vehicle Fleets: Maximizing Battery Remaining Useful Life Using Machine Learning Models

Reducing greenhouse emissions can be done via the electrification of the transport industry. However, there are challenges related to the electrification such as the lifetime of vehicle batteries as well as limitations on the charging possibilities. To cope with some of these challenges, a charge scheduling method for fleets of electric vehicles is presented. Such a method assigns the charging moments (i.e., schedules) of fleets that have more vehicles than chargers. While doing the assignation, the method also maximizes the total Remaining Useful Life (RUL) of all the vehicle batteries. The method consists of two optimization algorithms. The first optimization algorithm determines charging profiles (i.e., charging current vs time) for individual vehicles. The second algorithm finds the charging schedule (i.e., the order in which vehicles are connected to a charger) that maximizes the RUL in the batteries of the entire fleet. To reduce the computational effort of predicting the battery RUL, the method uses a Machine Learning (ML) model. Such a model predicts the RUL of an individual battery while taking into account common stress factors and fabrication-related differences per battery. Simulation results show that charging a single vehicle as late as possible maximizes the RUL of that single vehicle, due to the lower battery degradation. Simulations also show that the ML model accurately predicts the RUL, while taking into account fabrication-related variability in the battery. Additionally, it was shown that this method schedules the charging moments of a fleet, leading to an increased total RUL of all the batteries in the vehicle fleet.

Optimalitas Rute pada Pengiriman Multiperjalanan dengan Armada Kendaraan Listrik Heterogen

Electric vehicles are emerging as a key trend in sustainable mobility, mitigating emissions, and reducing dependence on fossil fuels. The challenge in optimizing route modeling lies in some limitations, such as battery range, charging time, and the diversity of electric vehicle types. This article explores the optimality of routes in a multiple-trip distribution system using a heterogeneous fleet of electric vehicles. The electric vehicle routing problem is formulated as a mixed-integer linear programming aiming to find the most cost-efficient optimal route. A notable feature of the model allows electric vehicle fleets to undertake additional travel to complete distribution tasks, i.e., multiple trips. The model is implemented in two illustrative examples involving the delivery of goods using homogeneous and heterogeneous electric vehicle fleets characterized by loading and battery capacities. Each case includes one depot, 8 and 10 customers, and 2 battery swapping stations, solved using the branch-and-bound method through Lingo 18.0. Simulation results indicate that battery capacity and the presence of battery swapping stations significantly influence the routes selection.

An Empirical Study of the Policy Processes behind Norway’s BEV-Olution

Norway’s large battery electric vehicle (BEV) market and fleet are not the result of a comprehensive policy plan. Using the multiple streams (MS) framework and document analysis, it was identified that the most important Norwegian BEV policy decisions were made using inadequate policy processes that fall outside of traditional politics. This is contrary to the MS framework postulate that three independent streams of problems, policy solutions, and politics must align to pave the way for new policies. Politicians had limited information about the effects of policies they introduced in this “learning by doing process”. Impact assessments were rarely made. The decision rationale was often not documented. The future market expectation and thus the national budget consequences were low when important policy decisions were made, whereas the political gain was high. The processes were more aligned with traditional politics after 2014. The ambitious ZE vehicle targets for 2025 and the climate policy targets for 2030 locked in incentives, despite rising tax losses. In sum, these developments created the world’s largest per-capita BEV market. To avoid negative issues and keep the BEV policies’ potential to support the BEV transition, politicians should ensure that sufficient knowledge is available when making decisions about future policies. Such decisions should be taken transparently within traditional politics, be properly assessed as with EU policy processes, and regularly reviewed as with the California ZEV mandate process. The required knowledge should be developed in open-access research.

The stochastic share-a-ride problem with electric vehicles and customer priorities

We introduce a stochastic share-a-ride problem in which a fleet of electric vehicles (EV) in a ride-hailing system are dynamically dispatched to serve passenger and parcel orders in a shared manner. We assume uncertain demands of both passenger and parcel orders and consider that passenger orders have priority over parcel orders. Passengers must be transported directly from their origins to destinations, while parcels can share a vehicle with other orders. The operator of the ride-hailing platform needs to decide whether to accept a newly arrived service request, how to assign orders to vehicles, and how to route and charge the EVs. To develop dynamic policies for the problem, we formulate the problem as a Markov decision process (MDP) and propose a reinforcement learning (RL) approach to solve the problem. We develop action-space restriction and state-space aggregation schemes to facilitate the implementation of the RL algorithm. We also present two rolling horizon heuristic methods to develop dynamic policies for our problem. We conduct computational experiments based on real-world taxi data from New York City. The computational results show that our RL policies perform better than the three benchmark policies in terms of serving more orders and collecting more rewards. Our RL policies are able to make high-quality decisions more efficiently when compared with the rolling horizon policies.

Development of an efficient vehicle-to-grid method for massive electric vehicle aggregation

The growing adoption of renewable energy and electric vehicles (EVs) has contributed to environmental sustainability; nevertheless, integration of these products into the power grid has become complex owing to their unpredictable nature and variable energy demands. A significant challenge lies in the realization of large-scale, coordinated control of EVs to serve as an alternative to traditional energy storage systems. This challenge is underscored by the complexity of optimization in large-scale cooperative control problems and the difficulty in reducing such problems to an easily manageable and practical level in real-world application. In response to these challenges, a practical mechanism for the integration of EVs into a vehicle-to-grid concept is proposed in this study. In this approach, the constraints involved in merging multiple EVs into a fictitious clustered energy storage unit, which are often neglected, are given renewed focus. An iterative multi-stage optimization method is introduced that includes an EV aggregation clustering model, multi-tier optimization model, and recursive framework. Here, marked efficacy for larger EV fleets is demonstrated for this method, providing optimal charging and discharging schedules for each vehicle while a high degree of precision is maintained. With the proposed technique, validated through numerous case studies using historical data, the global optimum solution is largely approximated, with a marginal deviation of 4 %. In addition, robustness of the model is demonstrated under varying pricing scenarios, with a computation time that increases in a linear manner, rather than exponentially, as the number of EVs increases. Meanwhile, compared with conventional methods, the technique proposed in this study has the capacity to fulfill the charging demands of all users with reduced charging expense, demonstrating the high precision and efficacy of the technique.

Charging infrastructure for electric vehicles in New Zealand

The uptake of electric vehicles (EV) is closely interlinked with the availability of charging infrastructure. Fast charging stations (FCS) can facilitate the uptake of EV, but their installation requires significant investments affecting their profitability. An optimal determination of charging locations and capacities based on associated costs can help to provide infrastructure efficiently. As initial FCS affect installations and costs in later years, a strategic infrastructure development plan over multiple years is required to expand the infrastructure to satisfy the charging demand of a growing EV fleet, while pursuing the goal of minimal costs. Based on former work, a model for the investment-optimal allocation and sizing of FCS over multiple years (including costs for grid connection) is developed. For the first time, a multi-periodic capacitated Arc-Cover Path-Cover formulation with an investment-optimal objective is proposed and applied to New Zealand. The results are analysed with respect to the locations and sizes of FCS, the impact on the coverage of traffic flows, and the related trends over time. They indicate that FCS are to be located along highly trafficked corridors in densely populated regions for being applied profitably. The chosen station locations have below-average installation costs and are capable of covering high shares of EV traffic on inter-regional and highly frequented routes.

Can Electric Vehicle Carsharing Bridge the Green Divide? A Study of BlueLA’s Environmental Impacts among Underserved Communities and the Broader Population

This study aims to evaluate the potential of electric vehicle (EV) carsharing services to address social and environmental disparities in urban transportation through an evaluation of BlueLA, a one-way station-based carsharing service in Los Angeles, California. BlueLA provides a clean and affordable mobility option in underserved communities that face significant air quality burdens and have historically been excluded from environmental benefits. By incorporating BlueLA trip activity data from January 2021 to December 2022 (n = 59,112 trips) and an online user survey implemented in early December 2022 (n = 215 responses), we estimate the impacts of BlueLA on personal vehicle ownership patterns, vehicle miles traveled (VMT), and associated greenhouse gas (GHG) emissions. The results show an overall net reduction in VMT and GHG emissions of 463,845 miles and 656 metric tons, respectively, among the BlueLA user population (3074 registered users). When disaggregating impacts by BlueLA member type, our findings show a net reduction of 234 and 371 metric tons in GHG emissions for Standard (general population) and Community (low-income qualified) members, respectively. Additionally, our socio-demographic analysis highlights clear disparities between these two member groups, with Community members typically having lower incomes (i.e., 74% earning below USD 50,000 annually); lower educational attainment (i.e., 46% with at most an associate’s degree); and larger households (i.e., 23% living in households of four or more) compared to Standard members (i.e., 19% earning below USD 50,000, 24% with at most an associate’s degree, and 9% in households of four or more). Moreover, when comparing the VMT and associated GHG emissions due to BlueLA, we find that the presence of BlueLA reduces VMT and GHG emissions by 34% and 48% respectively, and each BlueLA vehicle replaces 16 personally owned vehicles (shed and postponed purchases). Last, when comparing the emissions produced by the electric fleet of BlueLA to those of a comparable fleet of internal combustion engine vehicles, we find that the use of an EV fleet reduces GHG emissions by 43% in comparison. The BlueLA carsharing service has led to notable net reductions in VMT and thus GHG emissions, with a major share of these reductions observed among Community members.

Trends in the design and development of the infrastructure of electric filling stations

The article addresses trends in the design and development of infrastructure of charging stations. The article analyzes international experience of the development of electric transport (Germany, Great Britain, China). National concepts, principles of production development, as well as private business in this aspect are analyzed. A separate emphasis is placed on the fleet of electric vehicles, the most developed market segments (passenger cars, buses, commercial vehicles) are discussed. Problematic issues and stop factors in the development of electric transport are emphasized. Scientific novelty based on defining components of the development of charging infrastructur, practical recommendations for the development of infrastructure for electric transport and formulation practical recommendations on creating a network of charging stations along federal highways, as well as roadside service facilities.

RENEWABLE HORIZONS: INVESTIGATING FEASIBILITY IN CONSUMPTION TRENDS

The European Union's ambitious target to achieve zero emissions from passenger cars by 2035 has sparked significant strategic overhauls among European car manufacturers, pushing them towards a 100% electric vehicle fleet. Concurrently, a forthcoming heating law, slated to take effect from 2024 initially in Germany, aims to drastically curtail the installation of gas and oil heating systems in favour of heat exchangers. These legislative shifts are already straining the power grid, necessitating a substantial expansion of renewable energy capacity. Projections indicate an ongoing surge in energy demand, underscoring the imperative of a global transition away from fossil fuels. However, this endeavour poses formidable challenges for nations worldwide. This study delves into the current state of renewable energy and its projected evolution, stressing the imperative of significantly boosting renewable energy deployment to achieve sustainable electrification of passenger vehicles globally. It highlights the need to triple the share of renewable energy sources, quadruple efforts across all transportation sectors, and increase the overall transition away from fossil fuels fifteenfold. While conventional renewables such as solar, wind, hydro, and biofuels remain integral, emerging technologies like nuclear fusion offer promising avenues. Nonetheless, attaining complete independence from fossil fuels may prove elusive, considering non-energy applications of crude oil and other fossil resources. The study also assesses potential ecological ramifications and balances the environmental impacts of these energy transitions.