Electric Vehicle Charging Patterns Research Articles

Electric vehicle fast charging station energy management system for radial distribution network with a photo-voltaic distributed generator (PV-DG)

As the demand for electric vehicles (EVs) increases in all countries, automobile companies are launching different types of EVs. Installation of EVCS in the present power system network without modification can cause an extra burden on the present network. However, this extra burden can be reduced if EVCS gets a supply of renewable energy resources. This paper proposes energy management schemes by installing a photo-voltaic distributed generator (PV-DG) and energy storage system (ESS) at an EVCS. An IEEE-33 bus system for the Saudi Arabia United Kingdom rural location has been selected to introduce real-time energy management schemes. In this radial distribution network (RDN), three EVCS of different capacities are installed at different locations to maintain the stability of the network. The power flow program is used in the MATLAB environment to check the stability of the network. Accurate solar data, traffic patterns, and EV charging patterns in Saudi Arabia are collected and used for energy management at EVCS. Three cases are used for comparison: EVCS annual charges, EVCS annual benefits, and grid annual fuel charges. All proposed cases are also simulated in MATLAB using MATPOWER6 to check the impact on voltage profile and power line flows.

Advanced intelligent optimization for electric vehicle charging stations integrated with photovoltaic system and battery storage in commercial buildings

This paper presents a sophisticated four-stage optimization and intelligent control algorithm tailored for two-way electric vehicle charging (EVC) stations integrated with advanced photovoltaic systems and fixed battery energy storage in commercial buildings. The primary objective is to minimize operating costs while prioritizing customer satisfaction within a dynamic and uncertain energy landscape. Our algorithm optimizes the scheduled charging and discharging of electric vehicles (EVs), local battery storage (BS) units, grid power supply, and deferred loads to balance instantaneous supply and demand. The first stage focuses on developing optimal energy management plans for the day ahead, considering factors such as projected energy production, anticipated EVC demand, and building energy consumption patterns. Building on this foundation, the second stage introduces multilayer EV charging price structures and optimizes participation rewards for discharging, dynamically addressing EV charging patterns and price sensitivities. Approaching the commissioning timeline, the third stage refines energy management plans for the upcoming hours using real-time data and forecasts, adapting to evolving conditions for optimal resource allocation. The final stage involves real-time control and the implementation of optimized programs, dynamically adjusting charge/discharge processes, grid interactions, and load deferral to maintain supply-demand balance and minimize operating costs. Our algorithm enhances system resilience in unpredictable conditions, providing compelling incentives for active EV user participation. Coordinating the integrated system efficiently, including the commercial building’s energy load, ensures reliable service to customers while reducing costs. Extensive case studies and a comparative analysis validate the algorithm’s efficiency in significantly reducing operating costs and enhancing resilience to uncertainty. The paper concludes by highlighting the algorithm’s pioneering role in intelligent EV charging station (CHS) management, offering a cost-effective, customer-oriented, and dynamic energy control strategy for advancing global energy practices.

A Review of Bidirectional Charging Grid Support Applications and Battery Degradation Considerations

Electric vehicles (EVs) are crucial in mitigating global emissions by replacing internal combustion engines. The capacity of EV batteries, coupled with their charging infrastructure, offers the added advantage of supplying flexible demand capacity and providing demand response benefits to the power grid, which is essential as overall demand increases. EVs ready for vehicle-to-everything (V2X) applications and chargers that support them enhance this flexibility by allowing for varied storage applications. However, to fully harness these benefits, it is vital to consider EV drivers’ charging habits and optimize the charging and discharging controls to minimize battery life impact. This study examines various V2X applications in North America and their effects on battery longevity, considering EV charging patterns. Additionally, it investigates advanced aging-aware optimization algorithms for managing bidirectional charging.

Analysis of Spatiotemporal Characteristics and Influencing Factors of Electric Vehicle Charging Based on Multisource Data

Amid the global shift towards sustainable development, this study addresses the burgeoning electric vehicle (EV) market and its infrastructure challenges, particularly the lag in public charging facility development. Focusing on Wuhan, it utilizes big data to analyze EV charging behavior’s spatiotemporal aspects and the urban environment’s influence on charging efficiency. Employing a random forest regression and multiscale geographically weighted regression (MGWR), the research elucidates the nonlinear interaction between urban infrastructure and charging station usage. Key findings include (1) a direct correlation between EV charging patterns and urban temporal factors, with notable price elasticity; (2) the predominant influence of commuting distance, supplemented by the availability of fast-charging options; and (3) a strategic proposal for increasing slow-charging facilities at key urban locations to balance operational costs and user demand. The study combines spatial analysis and charging behavior to recommend enhancements in public EV charging infrastructure layouts.

Load Frequency Control and Optimization of Load Allocation for Multi-Area Power System with Electric Vehicle Charging Load

The integration of electric vehicles (EVs) into power systems has introduced new challenges for load frequency control due to the additional charging load they impose. This research article investigates the design and analysis of automatic load frequency control in a two-area power system, considering the presence of EV charging load. The study employs Artificial Neural Network (ANN) based PI control to manage both traditional load demand and the dynamic charging requirements of EVs. Maintaining a stable power system frequency and balancing generation with the EV charging load have become crucial tasks. Automatic Generation Control (AGC) or Load Frequency Control (LFC) systems need to adapt and account for the variability and uncertainty associated with EV charging patterns. The integration of ANN-based PI control provides an intelligent and adaptive approach to address these challenges. Using MATLAB, a power system model is simulated to evaluate the effectiveness of the proposed control scheme. This investigation conducts a comparative analysis of the system's frequency responses under various scenarios, including different EV charging load profiles. It highlights the benefits and challenges of utilizing ANN-based PI control to manage the combined load of traditional demand and EV charging. Moreover, the load distribution among the distribution stations varied from 0.08% to 12.50% when compared between particle swarm optimization and genetic algorithm, respectively. By considering the dynamic interaction between power system operation and EV charging, this research aims to enhance the reliability, efficiency and sustainability of power systems in the context of evolving transportation trends and the increasing electrification of vehicles.

Prediction of EV Charging Behavior using BOA-based Deep Residual Attention Network

In smart city applications, electric vehicles (EVs) are rapidly gaining popularity due to their ability to help cut down on carbon emissions. Numerous environmental conditions, including terrain, traffic, driving style, temperature, and so on, affect the amount of energy an EV needs to operate. However, the burden on power grid infrastructure from widespread EV deployment is one of the biggest obstacles. Smart scheduling algorithms can be used to handle the rising public charging demand. Scheduling algorithms can be improved using data-driven tools and procedures to study EV charging behaviour. Predictions of behaviour, including temperature, departure time, and energy requirements, have been the focus of research on past charging data. Weather, traffic, and surrounding events are all factors that have been mostly ignored but which could improve representations and predictions. The DRA-Net, or Deep Residual Attention Network, was developed by the researchers and is used to recognize EV charging patterns. To minimize data loss, the Res-Attention component utilized tighter connections and smaller convolutional kernels (3 x 3). In addition, an Artificial Butterfly Optimisation Algorithm (BOA) model is used to fine-tune the DRA-Net's hyper-parameters. We highlight the significance of traffic and weather info for charging behaviour predictions, and the study's experimental forecasts show a considerable improvement over prior work on the same dataset. The future of electric vehicle (EV) research has been mapped out thanks to in-depth study, and as a result, EVs will soon significantly impact the auto industry.

Towards Smart EV Charging: Assessing the Flexibility Provision Potential of Electric Vehicle Charging Stations for Cost-Effective Grid Responsiveness

In the dynamic shift towards a sustainable global energy landscape, electric vehicle (EV) charging stations emerge as crucial components, fostering clean mobility while holding untapped potential for grid support. This study investigates the flexibility potential of charging stations, aiming to provide ancillary services to the grid. The methodology, encompassing diverse factors from parking scenarios to technical regulations, serves a dual purpose: probabilistically forecasting the station’s engagement in services and assessing its capacity to mitigate grid events. Building on prior research in frequency maintenance and voltage stability, the methodology creates a comprehensive framework, challenging the synthesis of this knowledge with the dynamic nature of EV charging patterns. Leveraging a two-year dataset from a specific charging station, the research builds upon previous statistical analyses conducted in a prior study. In this continuation, the study employs a variety of statistical analyses and machine learning models, including XGBoost, to explore time-based, energy-based, and behavior-based perspectives. Results showcase increased charging sessions and energy consumption, emphasizing the growing role of EVs in the energy landscape. The study presents simulation algorithms for ancillary service potential, revealing the impact of a EV charging management proposal on daily power consumption, client satisfaction, net profit, and pricing dynamics. The intricate interplay between charging stations, electric grid providers, and users underscores the need for strategic considerations to optimize economic viability and user satisfaction in the evolving electric vehicle charging landscape. As the electric mobility era unfolds, this study provides a crucial approach for forecasting capabilities and estimating economic gains, contributing to the sustainable integration of electric vehicles into the global energy ecosystem.

Comparative Analysis and Optimal Operation of an On-Grid and Off-Grid Solar Photovoltaic-Based Electric Vehicle Charging Station

One of the key strategies for decarbonization and green transportation is using electric vehicles (EVs). However, challenges like limited charging infrastructure, EV battery characteristics, and grid integration complexities persist. This study proposes a mixed-integer linear programming (MILP) approach to optimize a grid-connected solar PV-based commercial EV charging station (SPEVCS) with a battery energy storage system (BESS) for profit maximization. The MILP model efficiently manages SPEVCS operations, considering solar power fluctuations, EV charging patterns, and BESS usage. By coordinating charging schedules, grid stability is reinforced, and excess solar power can be lucratively managed. Comparing grid-connected and off-grid SPEVCS scenarios highlights grid integration benefits. Solar power mismatches with optimal charging periods pose a challenge, addressed here by BESS utilization and import/export of deficit/surplus power from/to the grid. The proposed framework incorporates solar power forecasts and probabilistic EV arrival predictions, enhancing decision accuracy. This approach fosters viable commercial EV charging, promotes green transportation, and reinforces grid resilience.

Analysis of Tariffs and the Impact on Voltage Variations in Low-Voltage Grids with Smart Charging and Renewable Energy

The rapid increase in electric vehicles (EVs) and installed photovoltaic systems (PV) has resulted in new challenges for electric systems, e.g., voltage variations in low-voltage grids. Grid owners cannot directly control the power consumption of the end consumers. However, by the design of transparent tariffs, economic incentives are introduced for the end consumers to adjust their EV charging patterns. In this work, the main objective is to design a time-of-use pricing tariff to reduce the voltage variations in a low-voltage grid when introducing PVs and EVs with smart charging. Data from an existing low-voltage grid and hourly data from household power consumption, together with models of PV and EV charging, are used to simulate the voltage fluctuations based on the modified electric consumption. The results show that a time-of-use pricing tariff taking into consideration maximum peak power is important to reduce grid voltage variations. Another observation is that the use of economic incentives, such as subsidies when selling power from the household, combined with V2G technology can be economical for households but increases the voltage variations in the grid.

Techno-economic and environmental assessment of renewable energy sources, virtual synchronous generators, and electric vehicle charging stations in microgrids

Optimum design for microgrids that include renewable energy sources (RESs) is a complex process that requires optimization across a wide range of factors, including economic, technological, and environmental effectiveness. The inverter-connected RES units lack the rotational mass required to maintain the system’s inertia and can result in significant frequency instability during disturbances. The battery energy storage system-based virtual synchronous generator (BESS-VSG) is a unique approach to address this challenge since it mimics a conventional synchronous generator (SG) using the inverter regulation concept. Furthermore, given the recent rise in electric vehicles (EVs), EV owners’ charging patterns impact the microgrid’s operational, financial, and technological aspects. Therefore, this research offers a novel Modified Multi-objective Salp Swarm Optimization Algorithm (MMOSSA)-based technique for optimal photovoltaic (PV), wind turbine (WT), BESS-VSG, and EV charging station (EVCS) allocation on microgrids. The objective function is designed to optimize the total net present cost (TNPC), levelized cost of electricity (LCOE), energy loss, frequency deviation, voltage stability indicator (VSI), and carbon emissions. The proposed strategy is evaluated on two real-world grid networks: Masirah Island in Oman and Ankara in Turkey, where wind prospects, solar potential, and EV charging patterns are conflicting. This study investigated PV/BESS-VSG, WT/BESS-VSG, and PV/WT/BESS-VSG configurations regarding the weather and load variability for the two test networks. The obtained results demonstrate that the PV/WT/BESS-VSG is the best choice for both the test networks, with TNPC values of 261030.9 $ and 303840.5 $, respectively, and LCOE values of 0.0383 $/kWh and 0.0469 $/kWh, respectively. The carbon emissions are reduced by 88.8% and 87.3% for the two test networks, respectively. In addition, with the accurate PV/WT/VSG design and appropriate EVCS placements, the microgrids’ renewable fraction, the quantity of sold energy, and technical characteristics improved significantly. Moreover, employing numerical analysis, different loss of power supply probability (LPSP) values, spacing index, and hyper-volume index, the suggested MMOSSA method is compared to five relevant multi-objective optimization strategies. The findings demonstrate that the MMOSSA Pareto fronts offer better solutions across all the assessments.

Data-Driven Model for Identifying Factors Influencing Electric Vehicle Charging Demand: A Comparative Analysis of Early- and Maturity-Phases of Electric Vehicle Programs in Korea

With increasing concerns about urban pollution, electric vehicles (EVs) have offered an alternative mode of transportation that reduces urban pollution levels. Previous studies have sought to identify the various factors influencing EV charging patterns to deploy an appropriate charging infrastructure. However, limited attention has been paid to the investigation of different charging patterns identified in different regions at different phases of the EV program. This study aims to fill this research gap in the literature by developing binary logistic models that account for the factors influencing charging demands in different regions of Korea, i.e., Jeju-do and Gangneung-si. To this end, we collected historical data on EV charging transactions in these study regions and analyzed them to evaluate the difference in charging demands. The developed models suggest that the charging demand varies with charger characteristics and charging time. Moreover, different charging patterns in different regions can be explained by the different travel behaviors of those who use EVs for different trip purposes. These findings provide an important implication suggesting that policymakers should consider a stepwise strategy to construct charging infrastructure at the appropriate scale and configuration, depending on the phase of the EV program.

Ensemble Classification Model for EV Identification From Smart Meter Recordings

Electric vehicles (EVs) often consume large amounts of energy, and uncoordinated charging of many EVs may lead to grid overload, adversely impacting other customers. Electricity distributors require full visibility on the EV distribution to better manage operation planning of their distribution grid. However, they often have incomplete knowledge of EV presence in their network. Identifying EV customers (charging at home) using smart meter data is a nontrivial task for the grid network and energy scheduling. The difficulties include recognizing charging patterns, balancing the number of EV and non-EV customers during modeling, and building an efficient classification model. In this article, we propose a periodic pattern recognition method to extract useful EV charging patterns. Real world smart meter datasets are unbalanced with few EVs and majority of energy customers are those without EVs. We improve Kmedoids evaluated by dynamic time warping to obtain the representative non-EV training samples so that balanced samples over EV and non-EV customers can be obtained. We develop an ensemble classification model (ECM) by taking advantages of multiple classifiers, in which the optimization consists of obtaining the optimal subset of periodic patterns and the optimal parameters in each classifier and the optimal weights for combining classifiers. The superiority of the proposed ECM is demonstrated in comparison to several baseline models.

Impact of demand growth on decarbonizing India's electricity sector and the role for energy storage

Global energy sector decarbonization efforts are contingent on technology choices for energy production and end-use in emerging markets such as India, where air conditioning (AC) is expected to be a major driver for electricity demand growth. Here, we assess the impact of demand and supply side-drivers on the long-term evolution of the electricity sector in India under various technology and policy scenarios. Our analysis is based on developing: (a) multiple demand scenarios produced from a bottom-up forecasting model with high temporal resolution that capture structural changes in electricity consumption resulting from (AC) and electric vehicle (EV) adoption, and (b) a multi-period power system capacity expansion model with high temporal resolution of grid operations to model supply-side evolution in response to changes in demand and technology and policy factors. Such a framework allows us to, for example, quantify the impacts of improving appliance efficiency standards for AC systems or shifts in EV charging patterns on power system decarbonization prospects. Under projected renewables and Li-ion storage cost declines, our modeling points to solar and wind generation contributing substantially (46–67%) to meet annual electricity demand in India by 2030. However, without appropriate policy measures to phase out existing coal generation, even such rapid adoption of variable renewable energy coupled with one or more technological levers such as low-cost energy storage and demand-side measures such as setting aggressive AC efficiency standards and deploying distribution level storage, are insufficient to reduce annual CO2 emissions in 2050 vs. 2020 because of the relatively higher growth rate of projected electricity demand over this period. This suggests that deep decarbonization of India's power sector will require policy measures beyond efforts related to accelerating renewables deployment.

A Game-Theoretic Approach for Dynamic Service Scheduling at Charging Facilities

Electric vehicle (EV) charging patterns are highly uncertain in both location, time, and duration particularly in association with the predicted high demand for electric mobility in the future. An EV can be charged at home, at charging stations near highway ramps, or on parking lots next to office buildings, shops, airports, among other locations. Charging time and duration can be fixed and continuous or flexible and intermittent. EV user preferences of charging services depend on many factors (e.g., charging prices, choice of destinations), causing EV charging patterns to shift in real-time. Hence, there is a need for a highly flexible EV charging network to support the rapid adoption of the technology. This study presents a dynamic scheduling scheme for EV charging facilities considering uncertainties in charging demand, charger availability, and charging rate. The problem is formulated as a dynamic programming model that minimizes the travel and waiting costs and charging expenses while penalizing overcharging attempts. An integrated generalized Nash equilibrium technique is introduced to solve the problem that incorporates a Monte Carlo tree search algorithm to efficiently capture the uncertainties and approximate the value function of the dynamic program. Numerical experiments on hypothetical and real-world networks confirm the solution quality and computational efficiency of the proposed methodology. This study will promote EV adoption and support environmental sustainability by helping users lower the charging spot search burden via a real-time, user-adaptive optimizer. Stakeholders can retrieve charger utilization and pricing data and get feedback on their charging network policies.

EVStationSIM: An end-to-end platform to identify and interpret similar clustering patterns of EV charging stations across multiple time slices

Transport electrification introduces new opportunities in supporting sustainable mobility. Fostering Electric Vehicle (EV) adoption integrates vehicle range and infrastructure deployment concerns. An understanding of EV charging patterns is crucial for optimizing charging infrastructure placement and managing costs. Clustering EV charging events has been useful for ensuring service consistency and increasing EV adoption. However, clustering presents challenges for practitioners when first selecting the appropriate hyper-parameter combination for an algorithm and later when assessing the quality of clustering results. In a clustering process, the ground truth data is normally not available for practitioners to validate different modeling decisions. Consequently, it is difficult to judge the effectiveness of the discovered patterns because there is no objective method to compare them. This work proposes an end-to-end platform prototype named “EVStationSIM” that allows for the creation of relative rankings of similar clustering results. The ultimate goal is to support users/practitioners by allowing them to identify and interpret similar clustering patterns of EV charging stations using multiple time slices. The performance of this proposed platform is demonstrated with a case study using real-world EV charging event data from charging station operators in New Brunswick, Canada. The case study illustrates how generated results can assist in downstream analytical tasks such as planning infrastructure allocation expansions.

A Novel Model to Predict Electric Vehicle Rapid Charging Deployment on the UK Motorway Network

Recent transformations from internal combustion engines (ICE) to electric vehicles (EVs) are challenged by limited the driving range per charge, thereby requiring the improvement or substantial deployment of rapid charging infrastructure to stimulate sufficient confidence in EV drivers. This study aims to establish the necessary level of EV motorway service station infrastructure for the United Kingdom (UK) based market. The investigation is founded on increasing the appropriate rapid charger availability and shorter charging times. EV charging patterns are determined, focusing on two Volkswagen iD3 EV models by measuring power curves across field-based rapid chargers at one-minute intervals. Datasets are analysed throughout rapid charging field tests. Additionally, variance synthesis is applied to establish variables within this study’s assessment for rapid charger capacity requirements in the UK. The operational performance for the utilised rapid chargers is correspondingly recorded, whilst the EV range is calculated at 3 miles per kWh, revealing a mean power delivery rate of just 27 kW per hour using a 50 kW rapid charger. Time-of-day charging sessions are used to generate data that is then amalgamated into our previous study data, confirming that rapid charging points on UK motorways are used primarily for EV journey range extension. If fully utilised for an entire 24h period, 434 chargers (with a variance consolidation number of 81) are required to service the UK-based motorway EV user base. Moreover, this study establishes that simply replacing current fuel pumps with individual rapid chargers on a like-for-like basis reduces availability and support for novel and existing users and may impact short-term grid availability.

Modeling electric vehicle charging patterns: A review

Electric vehicles (EVs) offer an opportunity to move towards greenhouse gas emission reduction targets by decarbonizing the transport sector as well as reduce local air pollution. However, uncontrolled and simultaneous charging of a significant number of EVs could pose a challenge to electricity grids and generation-load adequacy. Studying these impacts requires a predictive model of EV fleet recharging. Here we review techniques for EV charging pattern modeling and the types of studies they are used for. The paper also introduces the wide range of parameters (vehicle types, charging points, plug-in behavior, etc.) that modeling studies can factor in, and the EV smart charging simulation approaches available. We conclude by proposing a framework for future research on EV load prediction models.JEL classification: C02, C65, L62, L94, Q40.

Voltage and energy control in distribution systems in the presence of flexible loads considering coordinated charging of electric vehicles

Maintaining a desirable energy and voltage profile in large-scale power distribution systems has been remained a challenging concern particularly due to the rushing arrival of uncertainties and high proliferation of intermittent renewables. This challenge has been exacerbated by an increased interest in the adoption of highly-uncertain electric vehicle (EV) loads as distributed energy storage (DES) devices since the owners' attitudes and EV's charging and discharging patterns are radically random. In this paper, in addition to the stochastic modeling of the random behavior of EV's charging patterns, EVs' charging statuses are optimally coordinated to support the control of voltage and energy in the system with the adaptive deployment of controllable loads. Moreover, fast and normal charging modes of EVs and the corresponding charging and discharging challenges to the grid are investigated. Distinct scenarios of EVs-only and EV-controllable loads are proposed and investigated through solving an optimization problem. In doing so, improved mixed real and binary vector-based swarm optimization algorithm is used to optimize the distribution system's operation while addressing the impacts of EVs' coordination on energy and voltage control (EVC). The efficiency and applicability of the proposed algorithm are tested and verified on the IEEE 69-bus and 119-bus test systems.

Deep reinforcement learning control of electric vehicle charging in the presence of photovoltaic generation

In recent years, the importance of electric mobility has increased in response to climate change. The fast-growing deployment of electric vehicles (EVs) worldwide is expected to decrease transportation-related CO2 emissions, facilitate the integration of renewables, and support the grid through demand–response services. Simultaneously, inadequate EV charging patterns can lead to undesirable effects in grid operation, such as high peak-loads or low self-consumption of solar electricity, thus calling for novel methods of control. This work focuses on applying deep reinforcement learning (RL) to the EV charging control problem with the objectives to increase photovoltaic self-consumption and EV state of charge at departure. Particularly, we propose mathematical formulations of environments with discrete, continuous, and parametrized action spaces and respective deep RL algorithms to resolve them. The benchmarking of the deep RL control against naive, rule-based, deterministic optimization, and model-predictive control demonstrates that the suggested methodology can produce consistent and employable EV charging strategies, while its performance holds a great promise for real-time implementations.

Hourly Power Grid Variations, Electric Vehicle Charging Patterns, and Operating Emissions.

Light-duty vehicles emit ∼20% of net US greenhouse gases. Deployment of electric vehicles (EVs) can reduce these emissions. The magnitude of the reduction depends significantly on EV charging patterns and hourly power grid variations. Previous US EV studies either do not use hourly grid data, or use data from 2012 or earlier. Since 2012, US grids have undergone major emission-relevant changes, including growth of solar from ∼1 to ∼20% of generation in California, and >30% reduction of coal power countrywide. This study uses hourly grid data from 2018 and 2019 (alongside hourly charging, driving, and temperature data) to estimate EV use emissions in 60 cases spanning the US. The emission impact of charging pattern varies by region. In California and New York, respectively, overnight EV charging produces ∼70% more and ∼20% fewer emissions than daytime charging. We quantify error from two common approximations in EV emission analysis, ignoring hourly variation in grid power and ignoring temperature-driven variation in fuel economy. The combined error exceeds 10% in 30% of cases, and reaches 50% in California, home to half of US EVs. A novel EV emission approximation is introduced, validated (<1% error), and used to estimate EV emissions in future scenarios.