Electric Vehicle Charging Research Articles

A Novel Arithmetic Optimization Technique for Electric Vehicle Charging

With the increased usage of Electric vehicles in recent days, the optimal usage of battery both in terms of electrical and economic perspective is very important. Though several battery optimization techniques are evolving day by day, the method to achieve an efficient control of all parameters like battery current, power and power loss has not yet been achieved. This paper proposes a novel approach of implementing an arithmetic optimization scheme for FTP 75 drive cycle source. In this connect, the details of the arithmetic operators are discussed and their relations with electrical parameters are framed from the fundamental equations. A Pseudo-code has been developed and implemented for identifying the optimum charging point from different search spaces. The simulation results obtained for different electrical parameters with respect to different arithmetic operators, emphasizes the enhanced operation of electric vehicles at optimum charging points.

A Hybrid Technique for Bidirectional Smart Charging of EVs Using BLDC Motor and Bidirectional Converter

This paper introduces a novel hybrid approach for bidirectional smart charging of electric vehicles (EVs) powered by brushless DC (BLDC) motors. The proposed technique, named Atomic Orbital Search-Rat Swarm Optimizer (AOS-RSO), leverages the Atomic Orbital Search (AOS) for optimizing energy storage management and the Rat Swarm Optimizer (RSO) for enhancing motor control. The modular hybrid energy storage system (HESS) is designed with five modules, each consisting of a bidirectional DC-DC converter connecting a battery and super capacitor module. A multi-level cascaded converter (MCC) connects the HESS to the BLDC motor inverter of the EV drive, controlling the DC bus voltage. The AOS-RSO approach efficiently manages the state-of-charge (SOC) across the modules, achieving a balanced SOC of 98%. Implemented in MATLAB, the method is evaluated in both open and closed-loop systems and compared with existing techniques. Results show the proposed method achieves 96% efficiency in simulation and 94% in experiments, with a CPU time of 2.84 s. This approach significantly advances smart charging solutions for EVs, offering improved energy management and operational efficiency.

Divergent imaginings: Transitioning to decarbonised mobility in ‘post-coalonial’ County Durham

ABSTRACT What happens when the various parties involved in constructing decarbonised futures’ infrastructure diverge in their imaginaries? Much of the published research on the sociotechnical imaginaries relating to electric vehicles (EVs) describes the creation of the future of decarbonised transport as a process mired in conflict, with various interested parties represented as strenuously disagreeing in their assessment of the most efficacious solution. The aim of the article is to offer an alternative account, based upon data gathered through participant observation, interviews, and grey literature. It describes the sort of personal transportation futures currently being imagined in the United Kingdom. The focus is specifically on the installation of electric vehicle charge points. The author contrasts Whitehall’s national vision for this infrastructure with the ‘post-coalonial’ vision of officers of Durham County Council in North East England have articulated an alternative, a ‘post-coalonial’ vision, and finds that the vision of both the British civil service and Government of the United Kingdom focused on private ownership and commuting, while Durham County Council envisioned publicly accessible charge points that enabled various types of different journeys. Despite the striking differences the conclusion is that contrary to the findings of previous studies the existence of these divergent infrastructural imaginaries led not to conflict but to co-existence.

Differential Game Theory in Electric Vehicle Charging Optimization

Differential Game Theory is a strong way to solve the hard optimization problems that come up when charging electric vehicles (EVs) in smart grids. This method takes into account the strategic exchanges between many parties, such as EV users, charging station operators, and grid operators, each of whom has their own goals. In this study, we look at how differential game theory can be used to find the best charging plans for electric vehicles. Our goal is to lower total energy costs, make the grid less crowded, and get more energy from green sources. We take into account how power prices change, the different types of green energy that are available, and how EVs move around by modeling the charging process as a difference game. The game-theoretic method lets you come up with plans where each player maximizes their own reward while taking into account what other players do. To find the best price methods for different situations, such as peak and off-peak hours, different amounts of green energy usage, and different pricing plans, we use Nash equilibrium solutions. Our findings show that differential game theory can successfully balance the different needs of parties, which can lead to EV charging systems that work better and last longer. Furthermore, the suggested way not only makes smart grids work better, but it also helps lower carbon emissions by supporting the use of clean energy. In addition, this method helps us understand how to create reward systems that match individuals' goals with world improvement goals. This makes it possible for EV charging strategies to work together better. This study shows that differential game theory could be a very useful tool in the development of smart and environmentally friendly transportation systems.

Techno-Economic Analysis of Grid-Connected Highway Solar EV Charging Station

AbstractSolar electric vehicle (EV) charging stations offer a promising solution to an environmental issue related to EVs by supplying eco-friendly electricity. Herein, we designed and analyzed a grid-connected highway solar EV charging station for 2022, 2030, and 2050 under two scenarios: Current policy scenario with restricted grid sales and policy mitigation scenario allowing grid sale. Future systems consider changes in EV charging station, grid CO2 emissions, carbon prices, and renewable costs. In 2022, high PV and battery costs led to a grid-only system. By 2030 and 2050, significant reductions in PV costs enabled systems with substantial PV capacity, especially under the policy mitigation scenario. Economic analysis showed that enabling grid sales reduces net present cost (NPC) by 40% in 2030 and 35% in 2050 compared to the current policy scenario, with significantly lower levelized cost of energy. CO2 emissions from EV operation are projected to be 44% and 3% of 2022 levels by 2030 and 2050, respectively. The policy mitigation scenario further reduces CO2 emissions by 22% in 2030 and 25% in 2050 compared to the current policy scenario. Sensitivity analyses reveal that increased grid sale capacity leads to higher renewable penetration and lower NPC but also increased grid congestion, highlighting the need for efficient grid management. Policymakers should consider revising regulations to support higher PV penetration and manage grid congestion. This study supports the transition to renewable systems and underscores the importance of policy measures in achieving sustainable energy goals.

A Novel Heuristic Algorithm Integrating Battery Digital-Twin-Based State-of-Charge Estimation for Optimized Electric Vehicle Charging

The increasing need for effective electric vehicle (EV) charging solutions in the context of transportation electrification has become a significant challenge. This system introduces an innovative algorithm, named Energy Distribution and Node Allocation using Evolutionary and Resourceful Optimization (ENDEAVOR), designed to elevate the efficiency of EV charging through the integration of a battery’s digital twin. This cutting-edge algorithm offers precise estimations of EV charging time, seamlessly updating both the State of Charge (SOC) via the Unscented Kalman Filter (UKF) and the internal battery resistance using parameterization, while sending this information to the cloud. ENDEAVOR optimizes charging-node allocation and intelligently distributes energy among incoming EVs based on their specific charging requests, all within the context of renewable-energy-sourced charging stations. The incorporation of a digital twin for the battery confers several benefits, including highly accurate SOC and charging-time estimates that ultimately enhance the overall efficiency of the charging process. This algorithm further optimizes energy distribution, resulting in significantly improved charging-time predictions, reduced wait times for users, and an enhanced overall experience for the user. The day-to-day implications of these enhancements are remarkable, culminating in substantial annual energy savings of approximately 180 units. ENDEAVOR has the potential to revolutionize the landscape of EV charging.

EV Charging Prediction in Residential Area Based on SE‐GRU‐MA Model Consider Multi‐Source Data Feature Mining

The number of electric vehicle (EV) in residential areas is growing rapidly, resulting in large‐scale charging of EVs connected to the distribution network. This poses a challenge to the safe and stable operation of the distribution network. In order to cope with this challenge, it is crucial to achieve accurate EV charging load prediction. However, current researches on EV charging load prediction suffer from insufficient data feature mining and lower prediction accuracy. To address this issue, this paper proposes a SE‐GRU‐MA residential EV charging load prediction method that incorporates multi‐source data feature mining. The proposed method employs a multi‐source data feature mining approach based on Pearson correlation analysis, which enhances the training efficiency and prediction accuracy of the prediction model. Additionally, this study develops a prediction model based on SE‐GRU‐MA hybrid network to achieve accurate EV charging load prediction. Simulation cases on actual history data validate that the proposed feature mining method can effectively promote prediction accuracy, and proposed SE‐GRU‐MA prediction model exhibits superior prediction capability in comparison to existing models. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Prediction of electric vehicle charging demand using enhanced gated recurrent units with RKOA based graph convolutional network

Prediction of electric vehicle charging demand using enhanced gated recurrent units with RKOA based graph convolutional network

Load Analysis and Prediction of Electric Vehicle Charging Stations Supported by RF-CNN Algorithm

Load Analysis and Prediction of Electric Vehicle Charging Stations Supported by RF-CNN Algorithm

Renewable energy driven on-road wireless charging infrastructure for electric vehicles in smart cities: A prototype design and analysis

The adoption of electrical vehicles (EVs) has increased due to the increasing concerns about fossil fuel shortages, rising costs, and environmental impacts. This increasing adoption of EVs significantly increase power demand on utility grids. Moreover the EVs require more time for charging compared to fossil fuel vehicles. To address these challenges, this study proposes a renewable energy-based on-road wireless charging (ORWC) infrastructureto reducing charging time and utilizing renewable energy within a smart city framework. This study develops a prototype of the ORWC system and examines its techno-economic and environmental benefits. Moreover, the study also evaluates annualized energy share, energy economics, and cost-effectiveness. The results of the proposed study indicate that the ORWC system in combination with RERs, effectively reduces the cost of energy from $0.112/kWh to $0.30/kWh and lowers carbon emissions from 76.9 tons/year to 29.5 tons/year. Furthermore, the results also reveals optimal energy distribution among different energy resources PV, BESS, and the grid. These results highlight that ORWC infrastructure can reduce peak load demand on existing grids and enhance the power density of EV charging systems. This has potential implications for future-oriented fast charging stations and policy development to support the widespread adoption of EVs.

Spatio‐temporal dynamic navigation for electric vehicle charging using deep reinforcement learning

AbstractThis paper considers the real‐time spatio‐temporal electric vehicle charging navigation problem in a dynamic environment by utilizing a shortest path‐based reinforcement learning approach. In a data sharing system including transportation network, an electric vehicle (EV) and EV charging stations (EVCSs), it is aimed to determine the most convenient EVCS and the optimal path for reducing the travel, charging and waiting costs. To estimate the waiting times at EVCSs, Gaussian process regression algorithm is integrated using a real‐time dataset comprising of state‐of‐charge and arrival‐departure times of EVs. The optimization problem is modelled as a Markov decision process with unknown transition probability to overcome the uncertainties arising from time‐varying variables. A recently proposed on‐policy actor–critic method, phasic policy gradient (PPG) which extends the proximal policy optimization algorithm with an auxiliary optimization phase to improve training by distilling features from the critic to the actor network, is used to make EVCS decisions on the network where EV travels through the optimal path from origin node to EVCS by considering dynamic traffic conditions, unit value of EV owner and time‐of‐use charging price. Three case studies are carried out for 24 nodes Sioux‐Falls benchmark network. It is shown that phasic policy gradient achieves an average of 9% better reward compared to proximal policy optimization and the total time decreases by 7–10% when EV owner cost is considered.

An Optimized Strategy for the Integration of Photovoltaic Systems and Electric Vehicles into the Real Distribution Grid

The increasing spread of photovoltaic systems for private households (PVs) and electric vehicles (EVs) in order to reduce carbon emissions significantly impacts operation conditions in existing distribution networks. Variable and unpredictable PVs can stress distribution network operation, mainly manifested in voltage violations during the day. On the other hand, variable loads such as EV chargers which have battery storage in their configuration have the ability of storying a surplus energy and, if it is necessary, support a distribution network with energy, commonly known as vehicle-to-grid concept (V2G), to help voltage stability network enhancement. This paper proposes an optimal power flow (OPF)-based model for EV charging to minimize power exchange between the superior-10 kV grid and the observed distribution feeder. The optimization procedure is realized using the co-simulation approach that connects power flow analysis software and optimization method. Three different scenarios are observed and analysed. The first scenario is referred to as a base case without optimization. The second and third scenarios include optimal EV charging and discharging patterns under different constraints. To test the optimization model, a 90-bus unbalanced distribution feeder modelled based on real-life examples is used. The obtained results suggest that this optimization model does not only significantly reduce the power exchange between an external network and the distribution feeder but also improves voltage stability and demand curve in the distribution feeder.

Enhancing distribution system performance by optimizing electric vehicle charging station integration in smart grids using the honey badger algorithm

The global surge in electric vehicle (EV) adoption has driven significant research into electric vehicle charging stations (EVCS) due to their environmentally friendly attributes, including low CO₂ emissions. However, integrating EVCS into existing distribution grids presents challenges such as power losses and voltage instability, especially with the increasing incorporation of renewable distributed generation (RDG) sources and battery energy storage systems (BESS). This study introduces a novel honey badger optimization algorithm (HBOA), designed to enhance solution convergence and optimize multi-objective criteria efficiently. HBOA strategically places EVCS while considering vehicle-to-grid (V2G) capabilities and user driving behaviors over a full 24-hour cycle, effectively addressing uncertainties and dynamic conditions. Simulations on modified IEEE 69-bus and Indian 28-bus radial distribution systems (RDS) demonstrate significant results: in the IEEE 69-bus system, power loss is reduced by 62.0%, the voltage stability index (VSI) increases from 0.7139 to 0.8311, and CO₂ emissions decrease by 66.0%. In the Indian 28-bus system, power loss decreases by 55.5%, with VSI improving from 0.7394 to 0.9964, leading to a 50.0% reduction in CO₂ emissions. The proposed smart microgrid (MG) structure incorporates interconnected MGs for residential, commercial, and industrial sectors, emphasizing the efficacy of RDGs in mitigating the impact of EVCS on RDS. The advantages of the HBOA method lie in its superior optimization capabilities, which enhance system performance, reduce operational costs, and promote sustainability, highlighting the proposed methodology’s potential for future integration of EVCS in distribution networks.

How grid reinforcement costs differ by the income of electric vehicle users

The simultaneous charging of many electric vehicles in future mobility scenarios may lead to peaks and overloads threatening grid stability. The necessary infrastructure investments vary by the number and model type of vehicles driven and the residents’ charging preferences. These attributes significantly depend on socio-economic factors such as income. Using power flow simulations based on real-life driving profiles, we predict massive cost asymmetries with an investment demand up to 33-fold in higher-income compared to lower-income neighborhoods. Many grid operators may redistribute these costs through an across-the-board electricity price increase for all households. In times of rising electricity prices, these unwanted inequitable costing allocations could lead to severe challenges and energy poverty. Policymakers should consider countermeasures like dynamic electricity pricing schemes, income-based electric vehicle subsidies, or improved charging network access to ensure energy equity in future mobility scenarios. Our analysis of the impact of socio-economic factors on electric vehicle grid infrastructure and their quantification contributes to the energy equity discussion.

Optimizing Electric Vehicle Charging Infrastructure through Machine Learning: A Study of Charging Patterns and Energy Consumption

Optimizing Electric Vehicle Charging Infrastructure through Machine Learning: A Study of Charging Patterns and Energy Consumption

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.

Integration of electric vehicles in smart grids: Challenges and opportunities in achieving carbon neutrality goals

Abstract. The integration of electric vehicles (EVs) into smart grids represents a pivotal shift towards sustainable transportation, offering substantial environmental benefits and new challenges for grid stability and energy management. This paper proposes a dual-layer economic dispatch model emphasizing source-load collaboration for carbon reduction, utilizing carbon trading mechanisms and demand response strategies to enhance grid stability and carbon efficiency. The Vehicle-to-Grid (V2G) technology emerges as a key solution, enabling EVs to contribute to grid reliability and facilitating renewable energy integration. Through analysis of EV charging behavior and demand response mechanisms, the study underscores the critical role of EVs in achieving carbon neutrality goals and the necessity of innovative solutions to integrate EVs seamlessly into the grid. This research highlights the importance of collaborative efforts among policymakers, utilities, and stakeholders to address the challenges and seize the opportunities presented by EV integration, paving the way for a low-carbon energy future.

Optimizing EV fast charging infrastructure: integrating high-gain interleaved converter with advanced MPPT

ABSTRACT Modernizing the Electric Vehicle (EV) charging infrastructure is essential for the widespread adoption of electric mobility. This research addresses the imperative need for enhancing the power performance of photovoltaic (PV) grid-connected inverters for EV fast charging applications. The system integrates Voltage-Oriented Control (VOC) for the PV inverter, a High-Gain Interleaved (Single Ended Primary Inductance Converter) SEPIC-Cuk (HGISC) converter, and a Bald Eagle Search Optimized Adaptive Neuro Fuzzy Inference System (BESO-ANFIS)) algorithm. VOC with Proportional Resonant (PR) ensures optimized energy transfer to grid, while the HGIBC converter enhances power performance. The proposed BESO-ANFIS Maximum Power Point Tracking (MPPT) dynamically tracks the PV array’s Maximum Power Point (MPP) under varying conditions. Additionally, a bidirectional battery converter in the energy storage system optimizes power usage. The synergistic implementation of these advanced controller results in a comprehensive solution, showcasing improved power output, grid integration, and efficient solar energy utilization for EV fast charging. MATLAB simulations and experiments demonstrate 97% efficiency and reduced Total Harmonic Distortion (THD) of 0.98%, positioning this research as an advanced solution for EV charging infrastructure enhancement.

Exploration of the application of solar energy technology in the field of new energy vehicles

Due to the harsh effects of fossil fuels on the world, researchers are paying more attention to the application of new energy sources. This paper explores the possibility to support electric vehicles with solar energy by demonstrating the design of a solar cooling system and a solar parking lot in a large flat area and workspace. The use of solar energy in electric vehicles can help control some systems such as cooling systems. Solar panels are used in some large flat parking area to collect and invert solar energy for parking electric vehicles’ charging. Solar panels are able to help charge electric vehicles during the workday by setting them on the surface of working area. However, due to the limitations of solar energy, solar panels are very much in need of good weather and direct sunlight. Therefore, solar energy cannot be the only source to support other powered vehicles. In the future, developments in other areas such as inverters and converters, solar panels, and nanotechnology may make the use of solar energy in green energy vehicles possible.

A Variant of the Growing Neural Gas Algorithm for the Design of an Electric Vehicle Charger Network

The Growing Neural Gas (GNG) algorithm constitutes an incremental neural network model based on the idea of a Self-Organizing Map (SOM), that is, unsupervised learning algorithms that reduce the dimensionality of datasets by locating similar samples close to each other. The design of an electric vehicle charging network is an essential aspect in the transition towards more sustainable and environmentally friendly mobility. The need to design and implement an efficient network that meets the needs of all users motivates us to propose the use of a model based on GNG-type neural networks for the design of the network in a specific geographical area. In this paper, a variant of this iterative neural network algorithm is used with the objective that, from an initial dataset of points in the plane, it calculates a new simplified dataset with the main characteristic that the final set of points maintains the geometric shape and topology of the original set. To demonstrate the capabilities of the algorithm, it is exemplified in a real case, in which the design of an electric vehicle charging network is proposed. This network is built by applying the algorithm, taking as the original set of points the ones formed by the nodes of the gas station network in the geographical area studied. Several tests of running the algorithm for different sizes of the final dataset are performed, showing the differences between the original network and the computationally generated one.