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Journal articles on the topic 'EV usage forecast'

Author: Grafiati

Published: 14 March 2023

Last updated: 31 July 2025

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1

Mahlberg, Justin Anthony, Jairaj Desai, and Darcy M. Bullock. "Evaluation of Electric Vehicle Charging Usage and Driver Activity." World Electric Vehicle Journal 14, no. 11 (2023): 308. http://dx.doi.org/10.3390/wevj14110308.

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As the country moves toward electric vehicles (EV), the United States is in the process of investing over USD 7.5 billion in EV charging stations, and Indiana has been allocated $100 million to invest in their EV charging network. In contrast to traditional “gas stations”, EV charging times, depending on the charger power delivery rating, can require considerably longer dwell times. As a result, drivers tend to pair charging with other activities. This study looks at two EV public charging locations and monitors driver activity while charging, charge time, and station utilization over a 2-mont

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Manoj, Vasupalli, M. Ramasekhara Reddy, G. Nooka Raju, Ramakrishna Raghutu, P. A. Mohanarao, and Aakula Swathi. "Machine Learning Models for Predicting and Managing Electric Vehicle Load in Smart Grids." E3S Web of Conferences 564 (2024): 02009. http://dx.doi.org/10.1051/e3sconf/202456402009.

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The integration of electric vehicles (EVs) into smart grids provides major issues and prospects for effective energy management. This research examines the actual utilization of machine learning models to forecast and manage EV demand in smart grids, intended to increase grid effectiveness and dependable operation. We acquire and preprocess different datasets, considering elements such as time of usage, characteristics of the environment, and user behaviors. Multiple machine learning models, combining neural networks, support vector machines, and forests that are random, are developed and rate

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Jaržemskis, Andrius, and Ilona Jaržemskienė. "European Green Deal Implications on Country Level Energy Consumption." Folia Oeconomica Stetinensia 22, no. 2 (2022): 97–122. http://dx.doi.org/10.2478/foli-2022-0021.

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Abstract Research background: The European Green deal set by the European Commission has launched new business models in sustainable development. Major contributions are expected in the road transport sector; as far as conventional internal combustion creates significant input in Green House Gas emission inventories. Each EU member state has an obligation to reduce GhG emission by accelerating Electric Vehicle development. In order to foster growth of EVs, there is the need of significant investment into charging infrastructures. The article propose the model of forecasting of investment based

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Sneddon, Simon. "Paradise lost? The red right hand of green technology." Journal of Human Rights and the Environment 14, no. 2 (2023): 169–93. http://dx.doi.org/10.4337/jhre.2023.02.03.

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This article addresses issues of climate and environmental injustice through the lens of electric vehicle (EV) usage. The current market for batteries relies very heavily on Lithium-Ion (Li-Ion) batteries, as they provide ‘high efficiency and low cost’. In 2020, BloombergNEF forecast that by 2040, 58 per cent of global passenger vehicles sales would be EVs, with demand for batteries rising commensurately. Between 2010 and 2021, the average unit price for EV batteries fell from $1200 to $132 per kW/h. The article considers the environmental impacts of EVs and assesses the extent to which impact

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Syed Nasir, SN, JJ Jamian, R. Ayop, and MW Mustafa. "Enhancing power loss by optimal coordinated extensive CS operation during off-peak load at the distribution system." E3S Web of Conferences 231 (2021): 01003. http://dx.doi.org/10.1051/e3sconf/202123101003.

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Minimise dependency of energy from depleted non-renewable had pushed the usage of electric vehicle (EV). However, the presence of charging station (CS) may cause another impact such as higher power loss, especially involving uncoordinated CS. The impact becomes vital when the numbers of CS to charge the EV increased dramatically. From research, CS at residential usually operated during off-peak load. Furthermore, the variation of the charging pattern that difficult to perceive had added severe condition. Thus, the exploration of the mitigation method is necessary to avoid the stress at the exi

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Alanazi, Fayez, Talal Obaid Alshammari, and Abdelhalim Azam. "Optimal Charging Station Placement and Scheduling for Electric Vehicles in Smart Cities." Sustainability 15, no. 22 (2023): 16030. http://dx.doi.org/10.3390/su152216030.

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Electric vehicles (EVs) have emerged as a transformative solution for reducing carbon emissions and promoting environmental sustainability in the automotive industry. However, the widespread adoption of EVs in the United States faces challenges, including high costs and unequal access to charging infrastructure. To overcome these barriers and ensure equitable EV usage, a comprehensive understanding of the intricate interplay among social, economic, and environmental factors influencing the placement of charging stations is crucial. This study investigates the key variables that contribute to d

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Castro, José F. C., Augusto C. Venerando, Pedro A. C. Rosas, et al. "Operation Model Based on Artificial Neural Network and Economic Feasibility Assessment of an EV Fast Charging Hub." Energies 17, no. 13 (2024): 3354. http://dx.doi.org/10.3390/en17133354.

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The energy transition towards a low-emission matrix has motivated efforts to reduce the use of fossil fuels in the transportation sector. The growth of the electric mobility market has been consistent in recent years. In Brazil, there has been an accelerated growth in the sales rate of new electric (and hybrid) vehicles (EVs). Fiscal incentives provided by governments, along with the reduction in vehicle costs, are factors contributing to the exponential growth of the EV fleet—creating a favorable environment for the dissemination of new technologies and enabling the participation of players f

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Al-lami, Ammar, Adám Török, Anas Alatawneh, and Mohammed Alrubaye. "Future Energy Consumption and Economic Implications of Transport Policies: A Scenario-Based Analysis for 2030 and 2050." Energies 18, no. 12 (2025): 3012. https://doi.org/10.3390/en18123012.

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The transition to sustainable transport poses significant challenges for urban mobility, requiring shifts in fuel consumption, emissions reductions, and economic adjustments. This study conducts a scenario-based analysis of Budapest’s transport energy consumption, emissions, and monetary implications for 2020, 2030, and 2050 using the Budapest Transport Model (EFM), which integrates COPERT and HBEFA within PTV VISUM. This research examines the evolution of diesel, gasoline, and electric vehicle (EV) energy use alongside forecasted fuel prices, using the ARIMA model to assess the economic impac

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Karpurapu, Sailatha, and J. Naga Venkata Raghuram. "Synergizing Green Transitions: Exploring EV Usage Risks in South India through the UTAUT2 Model." Qubahan Academic Journal 4, no. 1 (2024): 26–37. http://dx.doi.org/10.58429/qaj.v4n1a370.

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Climate change can be combated, and sustainable transportation can be promoted by electric vehicles (EVs). Despite their versatility, they face multiple challenges when it comes to their uptake and habitual use. A comprehensive framework to clarify these factors, specifically in relation to EVs, is available through the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Due to perceived risks, many users are hesitant to use electric vehicles despite their potential benefits. to develop and empirically test a model that forecasts the elements that affect consumers' acceptance of ele

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Dukpa, Andu, Boguslaw Butrylo, and Bala Venkatesh. "Comparative Analysis and Optimal Operation of an On-Grid and Off-Grid Solar Photovoltaic-Based Electric Vehicle Charging Station." Energies 16, no. 24 (2023): 8086. http://dx.doi.org/10.3390/en16248086.

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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 coordinat

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Gopinath S., P. Vasuki, V. Arun, Purushottama TL, Billa Pardhasaradhi, C. Shilaja,. "Intelligent Integration: Harnessing Artificial Intelligence for Enhanced Performance and Efficiency in Electric Vehicles." Journal of Electrical Systems 20, no. 5s (2024): 376–85. http://dx.doi.org/10.52783/jes.2042.

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The transition towards electric vehicles (EVs) necessitates the development of efficient and reliable charging infrastructure. This paper presents an AI-driven approach to optimize EV infrastructure, focusing on five key aspects: profiling, augmentation, forecasting, explainability, and charging efficiency. Profiling involves understanding EV drivers' behaviors and preferences, facilitating targeted infrastructure development. Augmentation utilizes AI algorithms to identify optimal locations for new charging stations or upgrades based on usage patterns and demand forecasts. Forecasting models

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Mumtahina, Umme, Sanath Alahakoon, Peter Wolfs, and Jiannan Liu. "Constructing Australian Residential Electricity Load Profile for Supporting Future Network Studies." Energies 17, no. 12 (2024): 2908. http://dx.doi.org/10.3390/en17122908.

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This paper examines how Australian residential load profiles may evolve in the short to medium term future. These profiles can be used to support simulation studies of the future Australian network within an environment that is transitioning to renewable energy and broader use of electricity as a tool for decarbonisation. The daily profiles rely heavily on the Australian Energy Market Operator (AEMO) forecasts for future annual energy usage. The period from 2024 to 2050 will be transformational. In the residential networks, two secular trends are particularly important in expanding residential

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13

D. Ramya and K. Umadevi. "A Complete Review on DC-to-DC Converter Topologies for Energy Sustainable Electro-mobility under Environmentally Heterogeneous Power Conditions." Journal of Environmental Nanotechnology 13, no. 3 (2024): 161–70. http://dx.doi.org/10.13074/jent.2024.09.242714.

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The electric vehicle is an upcoming technology that upgrades the biosphere and diminishes pollution across the globe. Electric Vehicles powered by batteries mitigate the problem of the emission of greenhouse gases and air pollution. Research has been undertaken lately to integrate sources of renewable energy to electrify the E-Vehicle to reduce the dependency on fossil fuels, making it an eco-friendly and nil carbon emission transport system. The increased usage and the forecasted growth of E- -vehicles urge the research to be centered on power electronic converters to attain highly efficient,

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14

Kabir, Mohammad Anowarul, Md Rasel Ahmed, and Faysal Ahmed. "STRATEGIC US ENERGY MARKET INSIGHTS: A DATA-DRIVEN ANALYSIS OF U.S. FUEL CONSUMPTION, PRICING TRENDS, AND THE RISE OF ELECTRIC VEHICLES." American Journal of Interdisciplinary Studies 06, no. 01 (2025): 174–207. https://doi.org/10.63125/6bka3w37.

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This project explores the evolving landscape of the U.S. energy market with a focus on identifying strategic growth opportunities for Duncan Oil Company, a family-owned regional fuel distributor. Due to the company’s limited public data availability and its niche market presence, a comprehensive analysis based solely on internal metrics was not feasible. Consequently, the study relied heavily on publicly accessible datasets from the U.S. Energy Information Administration (EIA), spanning from 1983 through September 2024. These datasets, while extensive, presented challenges such as missing valu

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15

Effah, Francis Boafo, Daniel Kwegyir, Daniel Opoku, Peter Asigri, and Emmanuel Asuming Frimpong. "Short-Term EV Charging Demand Forecast with Feedforward Artificial Neural Network." JURNAL NASIONAL TEKNIK ELEKTRO, July 31, 2023. http://dx.doi.org/10.25077/jnte.v12n2.1094.2023.

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The global increase in greenhouse gas emissions from automobiles has brought about the manufacture and usage of large quantities of electric vehicles (EVs). However, to ensure proper integration of EVs into the grid, there is a need to forecast the charging demand of EVs accurately. This paper presents a short-term electric vehicle charging demand forecast using a feedforward artificial neural network optimized with a modified local leader phase spider monkey optimization (MLLP-SMO) algorithm, a proposed variant of spider monkey optimization. A proportionate fitness selection is employed to im

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16

Zeng, Yulin, Yi Fang, Yuhong Liu, and Hohyun Lee. "INTEGRATING SOCIODEMOGRAPHICS INTO TRIP CHAIN MODELS FOR RESIDENTIAL EV CHARGING SCHEDULE SIMULATION WITH LLMS." ASME Journal of Engineering for Sustainable Buildings and Cities, July 9, 2025, 1–11. https://doi.org/10.1115/1.4069121.

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Abstract Accurately forecasting electric vehicle (EV) charging demand is critical for managing peak loads and ensuring grid stability in regions with increasing EV adoption. Residential household peak energy usage and EV charging patterns vary significantly across areas, influenced by geographic accessibility, sociodemographics factors, charging preferences, and EV attributes. Averaging data across regions can overlook these differences, leading to underestimation of charging demand disparities and risking grid overload during peak periods. This study introduces a spatio-temporal trip chain ba

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17

TENG, TANG. "Electric Vehicle Presence Discovery." April 30, 2019. https://doi.org/10.5281/zenodo.2655067.

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The global popularization of electric vehicles (EVs) poses an opportunity for the construction of micro-grid and smart community within energy internet on competent the massive and concentrated energy switching and routing in the local environment. Smart EV charging is a promising solution to manage EV charging load that relies on an accurate prediction of EV charging demands. Evaluation of household EV charging demand is a primary factor for designing smart EV charging solutions on household and neighbourhood level. However, this subject has not been adequately discussed in the research commu

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18

Yuan, Yukun, Mian Jia, Yue Zhao, and Shan Lin. "Stochastic Model Predictive Control-based Electric Taxi Fleet Coordination under Solar Power Uncertainty." ACM Transactions on Cyber-Physical Systems, June 17, 2025. https://doi.org/10.1145/3744748.

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As electric vehicles (EV) gradually replace fuel vehicles and provide transportation services in cities, e.g., electric taxi fleets, solar-powered charging stations with energy storage systems have been deployed to provide charging services for EV fleets. The mixture of solar-powered and traditional charging stations brings efficiency challenges to charging stations and reliability challenges to power systems. In this paper, we explore e-taxis’ mobility and charging demand flexibility to co-optimize service quality of e-taxi fleets and system cost of charging infrastructures, e.g., solar power

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19

Iqbal, Muhammad Aamir, Maria Malik, Wajeehah Shahid, et al. "Ab-initio study of pressure influenced elastic, mechanical and optoelectronic properties of Cd0.25Zn0.75Se alloy for space photovoltaics." Scientific Reports 12, no. 1 (2022). http://dx.doi.org/10.1038/s41598-022-17218-8.

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AbstractThe optoelectronic properties of the ternary Cd0.25Zn0.75Se alloy are reported under the influence of a high pressure ranging from 0 to 25 GPa, within a modified Becke–Jhonson potential using density functional theory. This alloy has a cubic symmetry, is mechanically stable, and its bulk modulus rises with pressure. It is observed to be a direct bandgap material with a bandgap energy that increases from 2.37 to 3.11 eV with rise in pressure. Pressure changes the optical and electronic properties, causing the absorption coefficient to rise and absorb visible green-to-violet light. The s

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20

Ibeh, Chidera Victoria, and Ayodeji Adegbola. "AI and Machine Learning for Sustainable Energy: Predictive Modelling, Optimization and Socioeconomic Impact In The USA." International Journal of Applied Sciences and Radiation Research 2, no. 1 (2025). https://doi.org/10.22399/ijasrar.19.

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This research explores how Machine Learning and AI can be used to enhance energy efficiency, forecast energy consumption trends, and optimize energy systems in the USA. This research used datasets comprising household energy usage, electric vehicle adoption trends, and smart grid analytics obtained from public sources, databases, and IoT sensor devices. This study applies advanced machine learning techniques such as deep learning, regression models, and ensemble learning to improve forecasting accuracy aimed at achieving efficient resource allocation. Additionally, this study investigates faul

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21

Hu, Guobiao, Hao Luo, Zhenqi Li, and Kun Liu. "Carbon Footprint throughout the Life Cycle of Electric Vehicles and Its Influence on Transport Sector Decarbonization." Journal of Economics & Management Research, February 28, 2025, 1–7. https://doi.org/10.47363/jesmr/2025(6)276.

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To assess the carbon footprint of new energy vehicles, a comprehensive life cycle analysis is essential. This study develops a unified carbon emission calculation model for electric vehicles, addressing all phases from raw material acquisition, component manufacturing, and usage to recycling. Using the BYD E6 (a specific EV car model) as a case study, life cycle carbon emissions were calculated based on the associated data released in 2022. Carbon emissions from material extraction and recycling, component production and assembly, battery production and recycling, as well as energy consumption

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22

Akshay, K. C., G. Hannah Grace, Kanimozhi Gunasekaran, and Ravi Samikannu. "Power consumption prediction for electric vehicle charging stations and forecasting income." Scientific Reports 14, no. 1 (2024). http://dx.doi.org/10.1038/s41598-024-56507-2.

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AbstractElectric vehicles (EVs) are the future of the automobile industry, as they produce zero emissions and address environmental and health concerns caused by traditional fuel-poared vehicles. As more people shift towards EVs, the demand for power consumption forecasting is increasing to manage the charging stations effectively. Predicting power consumption can help optimize operations, prevent grid overloading, and power outages, and assist companies in estimating the number of charging stations required to meet demand. The paper uses three time series models to predict the electricity dem

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23

Kosamia, Ronak Indrasinh. "ML-Based Energy Optimization in Android Infotainment for Electric Vehicles." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 11, no. 3 (2023). https://doi.org/10.37082/ijirmps.v11.i3.232418.

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Electric Vehicles (EVs) increasingly rely on advanced infotainment systems that offer navigation, media play back, connectivity, and occupant-centric applications, often powered by Android or Android Automotive. However, these information units can consume a substantial amount of battery energy, thereby reducing the overall driving range of EVs. This paper proposes a Machine Learning (ML)-based energy optimization framework for Android infotainment. By monitoring user interaction patterns, trip context, and system-level metrics, our ML model forecasts upcoming load demands and dynamically scal

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