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1
Imanaka, Masaki, Hiroyuki Baba, Kazuhiko Ogimoto, and Jun Matsumura. "Study on development of EV charging services coupled with power system conditions using IoT technology." E3S Web of Conferences 396 (2023): 04024. http://dx.doi.org/10.1051/e3sconf/202339604024.
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Deployment of electric vehicles (EVs) has been accelerated in many countries. However, for further deployment of EVs and their contribution to electrical power systems, various new services need to be implemented related to EVs and their charging, called “Place of Use” (PoU) services. The combined menu of the charging services has been proposed which bundles EV charging fees with home electricity bills. Settlement will be needed for such combined menu. This paper proposes the combined menu of EV charging with settlement of electricity. Technical feasibility of the settlement has been tested by EV charging testbed with the IoT-HUB technology. IoT-HUB is the virtual infrastructure which interconnects various connected devices and application by using “drivers” for each device. This paper also proposes a forecast method of EV charging demand for the settlement after the charging starts. The proposed method has reduced the forecast error of total charged energy compared to the simple method, but some of the forecast error has remained because of the variability of the charging time at the final step of state of charge.
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Li, Chenghao. "Count the charging load forecast of the battery remaining life and ownership." Journal of Physics: Conference Series 2588, no. 1 (2023): 012014. http://dx.doi.org/10.1088/1742-6596/2588/1/012014.
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Abstract In order to solve the accuracy of EV charging load prediction, the residual life reduction and the exact value of EV ownership caused by battery aging are taken into consideration. The charging load curve of the EV is simulated based on the Monte Carlo simulation method. Finally, based on the actual data of a certain region, the charging load of electric vehicles within a day is obtained. The case analysis shows that the proposed charging load prediction model of electric vehicles has high accuracy, which also shows that the large-scale grid connection of electric vehicles will bring new challenges to the power system. This method provides data support for the subsequent formulation of electric vehicle charging strategy and load side peak shifting and valley filling.
3
Yi, Xingyu, and Jie Ren. "Ultra-Short-Term Load Forecast of Charging Stations Considering the Travelling and Charging Behaviours of Electric Vehicles." Journal of Physics: Conference Series 2662, no. 1 (2023): 012004. http://dx.doi.org/10.1088/1742-6596/2662/1/012004.
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Abstract The traditional electric vehicle (EV) load forecasting methods are mostly used to predict large areas such as cities, which makes it difficult to serve practical applications. The existing charging station load forecasting methods ignore the traveling and charging behaviors of EVs. To this end, a charging station ultra-short-term load prediction method considering the traveling and charging behaviors of EVs is proposed. Firstly, EV types are classified according to charging behaviors. On this basis, a rolling forecast model of the number of arrivals per unit time of each vehicle type is established, and then the impact of charging randomness on load forecast is reduced by repeated Monte Carlo sampling. The total load of charging stations during the prediction period can be obtained by adding up the load of individual vehicles. The results show that the method enables accurate prediction of ultra-short-term load at charging stations, which has higher accuracy and reference value than the traditional load forecasting methods.
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Aduama, Prince, Zhibo Zhang, and Ameena S. Al-Sumaiti. "Multi-Feature Data Fusion-Based Load Forecasting of Electric Vehicle Charging Stations Using a Deep Learning Model." Energies 16, no. 3 (2023): 1309. http://dx.doi.org/10.3390/en16031309.
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We propose a forecasting technique based on multi-feature data fusion to enhance the accuracy of an electric vehicle (EV) charging station load forecasting deep-learning model. The proposed method uses multi-feature inputs based on observations of historical weather (wind speed, temperature, and humidity) data as multiple inputs to a Long Short-Term Memory (LSTM) model to achieve a robust prediction of charging loads. Weather conditions are significant influencers of the behavior of EV drivers and their driving patterns. These behavioral and driving patterns affect the charging patterns of the drivers. Rather than one prediction (step, model, or variables) made by conventional LSTM models, three charging load (energy demand) predictions of EVs were made depending on different multi-feature inputs. Data fusion was used to combine and optimize the different charging load prediction results. The performance of the final implemented model was evaluated by the mean absolute prediction error of the forecast. The implemented model had a prediction error of 3.29%. This prediction error was lower than initial prediction results by the LSTM model. The numerical results indicate an improvement in the performance of the EV load forecast, indicating that the proposed model could be used to optimize and improve EV load forecasts for electric vehicle charging stations to meet the energy requirements of EVs.
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Jawad, Shafqat, and Junyong Liu. "Electrical Vehicle Charging Load Mobility Analysis Based on a Spatial–Temporal Method in Urban Electrified-Transportation Networks." Energies 16, no. 13 (2023): 5178. http://dx.doi.org/10.3390/en16135178.
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Charging load mobility evaluation becomes one of the main concerns for charging services and power system stability due to the stochastic nature of electrical vehicles (EVs) and is critical for the robust scheduling of economic operations at different intervals. Therefore, the EV spatial–temporal approach for load mobility forecasting is presented in this article. Furthermore, the reliability indicators of large-scale EV distribution network penetration are analyzed. The Markov decision process (MDP) theory and Monte Carlo simulation are applied to efficiently forecast the charging load and stochastic path planning. A spatial–temporal model is established to robustly forecast the load demand, stochastic path planning, traffic conditions, and temperatures under different scenarios to evaluate the charging load mobility and EV drivers’ behavior. In addition, the distribution network performance indicators are explicitly evaluated. A Monte Carlo simulation is adopted to examine system stability considering various charging scenarios. Urban coupled traffic-distribution networks comprising 30-node transportation and 33-bus distribution networks are considered as a test case to illustrate the proposed study. The results analysis reveals that the proposed method can robustly estimate the charging load mobility. Furthermore, significant EV penetrations, weather, and traffic congestion further adversely affect the performance of the power system.
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Rojo-Yepes, Miguel Ángel, Carlos D. Zuluaga-Ríos, Sergio D. Saldarriaga-Zuluaga, Jesús M. López-Lezama, and Nicolas Muñoz-Galeano. "A Novel Neuro-Probabilistic Framework for Energy Demand Forecasting in Electric Vehicle Integration." World Electric Vehicle Journal 15, no. 11 (2024): 493. http://dx.doi.org/10.3390/wevj15110493.
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This paper presents a novel grid-to-vehicle modeling framework that leverages probabilistic methods and neural networks to accurately forecast electric vehicle (EV) charging demand and overall energy consumption. The proposed methodology, tailored to the specific context of Medellin, Colombia, provides valuable insights for optimizing charging infrastructure and grid operations. Based on collected local data, mathematical models are developed and coded to accurately reflect the characteristics of EV charging. Through a rigorous analysis of criteria, indices, and mathematical relationships, the most suitable model for the city is selected. By combining probabilistic modeling with neural networks, this study offers a comprehensive approach to predicting future energy demand as EV penetration increases. The EV charging model effectively captures the charging behavior of various EV types, while the neural network accurately forecasts energy demand. The findings can inform decision-making regarding charging infrastructure planning, investment strategies, and policy development to support the sustainable integration of electric vehicles into the power grid.
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Shang, Renxue, and Yongjun Ma. "Electric Vehicle Charging Load Forecasting Based on K-Means++-GRU-KSVR." World Electric Vehicle Journal 15, no. 12 (2024): 582. https://doi.org/10.3390/wevj15120582.
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The accurate short-term forecasting of an electric vehicle (EV) load is crucial for the reliable operation of a power grid and for effectively reducing energy consumption. Due to the fluctuations in EV charging loads, particularly the significant load variation between commercial and non-commercial areas, global models often suffer from prediction errors when forecasting loads. To address this issue, this paper proposes a regional forecasting method based on K-means++ clustering and deep learning algorithms. First, the K-means++ algorithm was used to partition the data into different regions, and an independent load-forecasting model was established for each region. Then, a combination of kernel support vector regression (KSVR) and gated recurrent unit (GRU) models was used to handle nonlinear features and time-dependent data, where particle swarm optimization (PSO) further optimized the model parameters to improve the forecasting accuracy. Finally, a weighted summation method was used to integrate the forecast results from each region, resulting in a more accurate overall load forecast. The experimental results show that the proposed model provided better prediction performance by capturing the spatiotemporal characteristics of the EV charging load, effectively addressing the challenges posed by regional differences, and outperforming the single-model forecasts.
8
Zhou, Dan, Zhonghao Guo, Yuzhe Xie, et al. "Using Bayesian Deep Learning for Electric Vehicle Charging Station Load Forecasting." Energies 15, no. 17 (2022): 6195. http://dx.doi.org/10.3390/en15176195.
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In recent years, replacing internal combustion engine vehicles with electric vehicles has been a significant option for supporting reducing carbon emissions because of fossil fuel shortage and environmental contamination. However, the rapid growth of electric vehicles (EVs) can bring new and uncertain load conditions to the electric network. Precise load forecasting for EV charging stations becomes vital to reduce the negative influence on the grid. To this end, a novel day-ahead load forecasting method is proposed to forecast loads of EV charging stations with Bayesian deep learning techniques. The proposed methodological framework applies long short-term memory (LSTM) network combined with Bayesian probability theory to capture uncertainty in forecasting. Based on the actual operational data of the EV charging station collected on the Caltech campus, the experiment results show the superior performance of the proposed method compared with other methods, indicating significant potential for practical applications.
9
Aldossary, Mohammad. "Enhancing Urban Electric Vehicle (EV) Fleet Management Efficiency in Smart Cities: A Predictive Hybrid Deep Learning Framework." Smart Cities 7, no. 6 (2024): 3678–704. https://doi.org/10.3390/smartcities7060142.
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Rapid technology advances have made managing charging loads and optimizing routes for electric vehicle (EV) fleets, especially in cities, increasingly important. IoT sensors in EV charging stations and cars enhance prediction and optimization algorithms with real-time data on charging behaviors, traffic, vehicle locations, and environmental factors. These IoT data enable the GNN-ViGNet hybrid deep learning model to anticipate electric vehicle charging needs. Data from 400,000 IoT sensors at charging stations and vehicles in Texas were analyzed to identify EV charging patterns. These IoT sensors capture crucial parameters, including charging habits, traffic conditions, and other environmental elements. Frequency-Aware Dynamic Range Scaling and advanced preparation methods, such as Categorical Encoding, were employed to improve data quality. The GNN-ViGNet model achieved 98.9% accuracy. The Forecast Accuracy Rate (FAR) and Charging Load Variation Index (CLVI) were introduced alongside Root-Mean-Square Error (RMSE) and Mean Square Error (MSE) to assess the model’s predictive power further. This study presents a prediction model and a hybrid Coati–Northern Goshawk Optimization (Coati–NGO) route optimization method. Routes can be real-time adjusted using IoT data, including traffic, vehicle locations, and battery life. The suggested Coati–NGO approach combines the exploratory capabilities of Coati Optimization (COA) with the benefits of Northern Goshawk Optimization (NGO). It was more efficient than Particle Swarm Optimization (919 km) and the Firefly Algorithm (914 km), reducing the journey distance to 511 km. The hybrid strategy converged more quickly and reached optimal results in 100 rounds. This comprehensive EV fleet management solution enhances charging infrastructure efficiency, reduces operational costs, and improves fleet performance using real-time IoT data, offering a scalable and practical solution for urban EV transportation.
10
Lu, Yiqi, Yongpan Li, Da Xie, et al. "The Application of Improved Random Forest Algorithm on the Prediction of Electric Vehicle Charging Load." Energies 11, no. 11 (2018): 3207. http://dx.doi.org/10.3390/en11113207.
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To cope with the increasing charging demand of electric vehicle (EV), this paper presents a forecasting method of EV charging load based on random forest algorithm (RF) and the load data of a single charging station. This method is completed by the classification and regression tree (CART) algorithm to realize short-term forecast for the station. At the same time, the prediction algorithm of the daily charging capacity of charging stations with different scales and locations is proposed. By combining the regression and classification algorithms, the effective learning of a large amount of historical charging data is completed. The characteristic data is divided from different aspects, realizing the establishment of RF and the effective prediction of fluctuate charging load. By analyzing the data of each charging station in Shenzhen from the aspect of time and space, the algorithm is put into practice. The application form of current data in the algorithm is determined, and the accuracy of the prediction algorithm is verified to be reliable and practical. It can provide a reference for both power suppliers and users through the prediction of charging load.
11
Lee, Jaehee, Jinyeong Lee, Young-Min Wi, and Sung-Kwan Joo. "Stochastic Wind Curtailment Scheduling for Mitigation of Short-Term Variations in a Power System with High Wind Power and Electric Vehicle." Applied Sciences 8, no. 9 (2018): 1684. http://dx.doi.org/10.3390/app8091684.
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Occasionally, wind curtailments may be required to avoid an oversupply when wind power, together with the minimum conventional generation, exceed load. By curtailing wind power, the forecast uncertainty and short-term variations in wind power can be mitigated so that a lower spinning reserve is sufficient to maintain the operational security of a power system. Additionally, the electric vehicle (EV) charging load can relieve the oversupply of wind power generation and avoid uneconomical wind power curtailments. This paper presents a stochastic generation scheduling method to ensure the operation security against wind power variation as well as against forecast uncertainty considering the stochastic EV charging load. In the paper, the short-term variations of wind power that are mitigated by the wind curtailment are investigated, and incorporated into a generation scheduling problem as the mixed-integer program (MIP) forms. Numerical results are also presented in order to demonstrate the effectiveness of the proposed method.
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Dukpa, Andu, and Boguslaw Butrylo. "MILP-Based Profit Maximization of Electric Vehicle Charging Station Based on Solar and EV Arrival Forecasts." Energies 15, no. 15 (2022): 5760. http://dx.doi.org/10.3390/en15155760.
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Electric vehicles (EVs) will be dominating the modes of transport in the future. Current limitations discouraging the use of EVs are mainly due to the characteristics of the EV battery and lack of easy access to charging stations. Charging schedules of EVs are usually uncoordinated, whereas coordinated charging offers several advantages, including grid stability. For a solar photovoltaic (PV)-based charging station (CS), optimal utilization of solar power results in an increased revenue and efficient utilization of related equipment. The solar PV and the arrival of EVs for charging are both highly stochastic. This work considers the solar PV forecast and the probability of EV arrival to optimize the operation of an off-grid, solar PV-based commercial CS with a battery energy storage system (BESS) to realize maximum profit. BESS supports the sale of power when the solar PV generation is low and subsequently captures energy from the solar PV when the generation is high. Due to contrasting characteristics of the solar PV and EV charging pattern, strategies to maximize the profit are proposed. One such strategy is to optimally size the BESS to gain maximum profit. A mixed integer linear programming (MILP) method is used to determine the optimal solution.
13
Zhuang, Zhiyuan, Xidong Zheng, Zixing Chen, Tao Jin, and Zengqin Li. "Load Forecast of Electric Vehicle Charging Station Considering Multi-Source Information and User Decision Modification." Energies 15, no. 19 (2022): 7021. http://dx.doi.org/10.3390/en15197021.
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In view of the current multi-source information scenario, this paper proposes a decision-making method for electric vehicle charging stations (EVCSs) based on prospect theory, which considers payment cost, time cost, and route factors, and is used for electric vehicle (EV) owners to make decisions when the vehicle’s electricity is low. Combined with the multi-source information architecture composed of an information layer, algorithm layer, and model layer, the load of EVCSs in the region is forecast. In this paper, the Monte Carlo method is used to test the IEEE-30 model and the traffic network based on it, and the spatial and temporal distribution of charging load in the region is obtained, which verifies the effectiveness of the proposed method. The results show that EVCS load forecasting based on the prospect theory under the influence of multi-source information will have an impact on the space–time distribution of the EVCS load, which is more consistent with the decisions of EV owners in reality.
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Lo Franco, Francesco, Mattia Ricco, Vincenzo Cirimele, Valerio Apicella, Benedetto Carambia, and Gabriele Grandi. "Electric Vehicle Charging Hub Power Forecasting: A Statistical and Machine Learning Based Approach." Energies 16, no. 4 (2023): 2076. http://dx.doi.org/10.3390/en16042076.
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Electric vehicles (EVs) penetration growth is essential to reduce transportation-related local pollutants. Most countries are witnessing a rapid development of the necessary charging infrastructure and a consequent increase in EV energy demand. In this context, power demand forecasting is an essential tool for planning and integrating EV charging as much as possible with the electric grid, renewable sources, storage systems, and their management systems. However, this forecasting is still challenging due to several reasons: the still not statistically significant number of circulating EVs, the different users’ behavior based on the car parking scenario, the strong heterogeneity of both charging infrastructure and EV population, and the uncertainty about the initial state of charge (SOC) distribution at the beginning of the charge. This paper aims to provide a forecasting method that considers all the main factors that may affect each charging event. The users’ behavior in different urban scenarios is predicted through their statistical pattern. A similar approach is used to forecast the EV’s initial SOC. A machine learning approach is adopted to develop a battery-charging behavioral model that takes into account the different EV model charging profiles. The final algorithm combines the different approaches providing a forecasting of the power absorbed by each single charging session and the total power absorbed by charging hubs. The algorithm is applied to different parking scenarios and the results highlight the strong difference in power demand among the different analyzed cases.
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Dong, Ran, and Lijiang Sun. "Short-term Forecast of EV Ownership in Shanghai Based on Metabolic GM(1,1)-Markov Model." Journal of Physics: Conference Series 2351, no. 1 (2022): 012031. http://dx.doi.org/10.1088/1742-6596/2351/1/012031.
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The reasonable forecast of Electric Vehicle (EV) ownership is conducive to grasping the market development and determining the development goals of EVs. In addition, predicting the scale of EVs is conducive to analyzing the impact of EV charging on power grid load and guiding the construction of EV charging stations and other infrastructure. In view of the low amount of EV ownership data over the years in Shanghai, an improved grey model(GM)-Markov prediction method is proposed to predict the ownership of EVs. Firstly, several sets of data at recent time points are selected and the metabolic GM(1,1) replaces the original GM model for data fitting. After obtaining the fitting results, a group of data with the highest fitting accuracy was selected to establish the Markov correction model. Secondly, the Markov correction model is established for the highest fitting accuracy data. The sample mean-standard deviation method is used to divide the state matrix, and the interval partition criterion is dynamically adjusted to fit the relative error distribution probability, which avoids certain subjectivity. Compared with the original model, the relative error of fitting results decreases from 4.780% to 0.764%, and the prediction accuracy of the modified model is further improved. Finally, the ownership of EVs in Shanghai by the Metabolic GM(1,1) -Markov model in the next three years is forecasted, and the ownership of EVs in Shanghai is expected to exceed 1 million in 2023.
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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 existing distribution network. This paper suggests a coordinated method based on the power loss forecast throughout the charging time. The method will prioritise the buses based on power loss impact on the network, which later to determine the suitable numbers of CS operation. The approach considers customer satisfaction to charge the EV at a specific duration fully. Thus, to present the effectiveness of the approach, the analysis conducted using a suitable distribution system with residential block. The results show a positive outcome in enhancing distribution power loss without interrupt customer satisfaction. The method is suitable to deal with many CS that operates simultaneously during off-peak load.
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Cardenas, Alben, Cristina Guzman, and Wilmar Martinez. "EV Overnight Charging Strategy in Residential Sector: Case of Winter Season in Quebec." Vehicles 3, no. 3 (2021): 557–77. http://dx.doi.org/10.3390/vehicles3030034.
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Electric Vehicle (EV) technologies offer a leading-edge solution for clean transportation and have evolved substantially in recent years. The growing market and policies of governments predict EV massive penetration shortly; however, their large deployment faces some resistances such as the high prices compared to Internal Combustion Engine (ICE) cars, the required infrastructure, the liability for novelty and standardisation. During winter periods of cold countries, since the use of heating systems increases, the peak power may produce stress to the grid. This fact, combined with EVs high penetration, during charging periods inside of high consumption hours might overload the network, becoming a threat to its stability. This article presents a framework to evaluate load shifting strategies to reschedule the EV charging to lower grid load periods. The undesirable “rebound” effect of load shifting strategies is confirmed, leading us to our EV local overnight charging strategy (EV-ONCS). Our strategy combines the forecast of residential demand using probabilistic distribution from historical consumption, prediction of the EV expected availability to charge and the charging strategy itself. EV-ONCS avoids demand rebound of classic methods and allows a peak-to-average ratio reduction demonstrating the relief for the grid with very low implementation cost.
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Koohfar, Sahar, Wubeshet Woldemariam, and Amit Kumar. "Performance Comparison of Deep Learning Approaches in Predicting EV Charging Demand." Sustainability 15, no. 5 (2023): 4258. http://dx.doi.org/10.3390/su15054258.
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Electric vehicles (EVs) contribute to reducing fossil fuel dependence and environmental pollution problems. However, due to complex charging behaviors and the high demand for charging, EVs have imposed significant burdens on power systems. By providing reliable forecasts of electric vehicle charging loads to power systems, these issues can be addressed efficiently to dispatch energy. Machine learning techniques have been demonstrated to be effective in forecasting loads. This research applies six machine learning methods to predict the charging demand for EVs: RNN, LSTM, Bi-LSTM, GRU, CNN, and transformers. A dataset containing five years of charging events collected from 25 public charging stations in Boulder, Colorado, USA, is used to validate this approach. Compared to other highly applied machine learning models, the transformer method outperforms others in predicting charging demand, demonstrating its ability for time series forecasting problems.
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Zhao, De, Hua Wang, and Zhiyuan Liu. "Charging-Related State Prediction for Electric Vehicles Using the Deep Learning Model." Journal of Advanced Transportation 2022 (August 8, 2022): 1–12. http://dx.doi.org/10.1155/2022/4372168.
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Electric vehicles (EVs) are becoming the potential contender for the conventional gasoline vehicles in view of the environment-friendly and energy-efficient characteristics. The prediction of EV charging-related states (defined in this study as home charge, outside charge, home stop, outside stop, low-battery travel, and high-battery travel) could help to identify the future charging demand (power consumption) of EV individuals. Specifically, it could guide the operation and management of charging facilities and also provide tailored charger availability information based on users’ real-time locations. This study aims to predict charging-related states of individual EVs using a deep learning approach. We first propose a tangible approach to convert EV trajectory data into state sequences and then develop a bidirectional gated recurrent unit model with attention mechanism (Bi-GRU-Attention) to forecast EV states. A sensitivity analysis is conducted to tune and/or calibrate parameters in the model based on plug-in hybrid EV trajectories dataset collected in Shanghai, China. Experiment results show that (i) the proposed method could achieve an average accuracy of 77.15% with a 1-hour prediction length and it outperforms the baseline models for all tested prediction lengths; (ii) it is also revealed that the prediction accuracy varies dramatically with different states and time periods. Among all states, the proposed model has a higher prediction accuracy on “home stop” (89.0%). As for time periods, the EV states around 08:00 am and 04:00 pm are hard to predict, and a comparatively low prediction accuracy (close to 60%) is obtained; and (iii) the stability and robustness analysis implies that the proposed model is stable and insensitive to SOC noise or season.
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Mazhar, Tehseen, Rizwana Naz Asif, Muhammad Amir Malik, et al. "Electric Vehicle Charging System in the Smart Grid Using Different Machine Learning Methods." Sustainability 15, no. 3 (2023): 2603. http://dx.doi.org/10.3390/su15032603.
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Smart cities require the development of information and communication technology to become a reality (ICT). A “smart city” is built on top of a “smart grid”. The implementation of numerous smart systems that are advantageous to the environment and improve the quality of life for the residents is one of the main goals of the new smart cities. In order to improve the reliability and sustainability of the transportation system, changes are being made to the way electric vehicles (EVs) are used. As EV use has increased, several problems have arisen, including the requirement to build a charging infrastructure, and forecast peak loads. Management must consider how challenging the situation is. There have been many original solutions to these problems. These heavily rely on automata models, machine learning, and the Internet of Things. Over time, there have been more EV drivers. Electric vehicle charging at a large scale negatively impacts the power grid. Transformers may face additional voltage fluctuations, power loss, and heat if already operating at full capacity. Without EV management, these challenges cannot be solved. A machine-learning (ML)-based charge management system considers conventional charging, rapid charging, and vehicle-to-grid (V2G) technologies while guiding electric cars (EVs) to charging stations. This operation reduces the expenses associated with charging, high voltages, load fluctuation, and power loss. The effectiveness of various machine learning (ML) approaches is evaluated and compared. These techniques include Deep Neural Networks (DNN), K-Nearest Neighbors (KNN), Long Short-Term Memory (LSTM), Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT) (DNN). According to the results, LSTM might be used to give EV control in certain circumstances. The LSTM model’s peak voltage, power losses, and voltage stability may all be improved by compressing the load curve. In addition, we keep our billing costs to a minimum, as well.
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Massiani, Jérome, and Jens Weinmann. "Estimating electric car's emissions in Germany: an analysis through a pivotal marginal method and comparison with other methods." ECONOMICS AND POLICY OF ENERGY AND THE ENVIRONMENT, no. 2 (September 2012): 131–55. http://dx.doi.org/10.3280/efe2012-002007.
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In this paper, we estimate the emissions resulting from electric vehicles in Germany. We make use of EMOB, a comprehensive simulation model that provides a forecast and evaluation of the diffusion of alternative fuel vehicles in the next decades. Our method to compute emissions differs from existing ones by a "pivotal marginal" or "hourly marginal" calculation that takes into account the predicted time pattern of EV reloading and can offer a parsimonious alternative to resource intensive micro simulation models. Our approach results in EV emissions of 87 g/km in 2012 and 82 g/km in 2020. These estimates are much higher than those provided by simplified approaches (marginal and average emission) in the short run and get close to marginal emissions after 2035. Co-ordinated charging may reduce the emissions only marginally (usually less than 4 %). Generally, our findings cast doubts on the general claim that electric cars could be fuelled by renewable energy in general, and with fluctuating excess supply of renewables (wind, solar) in particular. This conclusion persists even in the presence of realistic coordination schemes.
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Ayyadi, Soumia, and Mohamed Maaroufi. "Optimal Framework to Maximize the Workplace Charging Station Owner Profit while Compensating Electric Vehicles Users." Mathematical Problems in Engineering 2020 (May 28, 2020): 1–12. http://dx.doi.org/10.1155/2020/7086032.
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Electric vehicles (EVs) are one promising technology for an improved sustainable transportation sector, particularly when they are charged with electricity from renewable energy sources. However, the EV user behaviour uncertainties as well as the fluctuating generation of renewable energy sources make the interaction between these technologies challenging. In this work, a new approach to coordinate the charging process of multiple EVs parked at workplace charging station (WCS) equipped with Photovoltaic panels (PV) is proposed. Considering the PV incremental cost and the day-ahead electricity price (DAEP), an optimal framework is introduced to maximize the WCS owner profit while compensating the EV users for discharging their EVs’ battery. The EV user behaviour uncertainties are modeled by probability distribution functions, and the PV generation is forecasted by the backpropagation neural network model (BPNN). The optimization problem is solved by mixed-integer linear programming (MILP) while the Monte Carlo sampling methods have been applied to handle the EV user behaviour uncertainties. The results show that the proposed method increases the WCS owner profit and the EV user compensation by 54% and 50.7%, respectively, compared to uncoordinated charging. Moreover, the estimated WCS owner profit and the EV user compensation generated by coordinated charging are 1.72% and 1.35%, respectively, higher than the profits based on real user behaviour data.
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Iqbal, Sheeraz, Salman Habib, Muhammad Ali, et al. "The Impact of V2G Charging/Discharging Strategy on the Microgrid Environment Considering Stochastic Methods." Sustainability 14, no. 20 (2022): 13211. http://dx.doi.org/10.3390/su142013211.
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Although electric vehicles (EVs) play a vital role in realizing remarkable features, however, the integration of a huge number of EVs leads to grid congestion as well. As a result, uncontrolled charging might give rise to undervoltage and complex congestion in the electric grid. The reasons for the uncontrolled charging of EVs have been investigated in the recent past to mitigate the effects thereof. It is very challenging to achieve controlled charging due to different constraints at the customer end; therefore, it is better to take the benefits of power prediction schemes for the charging and discharging of EVs. The power prediction scheme is based on a practical power forecast system that exploits the needs of various patterns, and the current research focuses on considering users’ demands. The primary objective of this study is to develop an effective and efficient coordination system for the charging and discharging of EVs by exploiting a smart algorithm that intelligently tackles the possible difficulties to attain optimum power requirements. In this context, a model is proposed based on stochastic methods for analyzing the impact of vehicle-to-grid (V2G) charging and discharging in the microgrid environment. A Markov model is used to simulate the use of EVs. This method works well with the Markov model because of its ability to adjust to random changes. When considering an EV, its erratic travel patterns suggest a string of events that resemble a stochastic process. The proposed model ensures that high power requirements are met during peak hours in a cost-effective manner. In simpler words, the promising features of the proposed scheme are to meet electricity/power demands, monitoring and the efficient forecasting of power. The outcomes revealed an effective power system, EV scheduling, and power supply without compromising the electric vehicle’s presentation of the EV owner’s tour schedule. In terms of comprehensiveness, the developed algorithm exhibits a significant improvement.
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Feng, Jiangpeng, Xiqiang Chang, Yanfang Fan, and Weixiang Luo. "Electric Vehicle Charging Load Prediction Model Considering Traffic Conditions and Temperature." Processes 11, no. 8 (2023): 2256. http://dx.doi.org/10.3390/pr11082256.
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The paper presents a novel charging load prediction model for electric vehicles that takes into account traffic conditions and ambient temperature, which are often overlooked in conventional EV load prediction models. Additionally, the paper investigates the impact of disordered charging on distribution networks. Firstly, the paper creates a traffic road network topology and speed-flow model to accurately simulate the driving status of EVs on real road networks. Next, we calculate the electric vehicle power consumption per unit kilometer by considering the effects of temperature and vehicle speed on electricity consumption. Then, we combine the vehicle’s main parameters to create a single electric vehicle charging model, use the Monte Carlo method to simulate electric vehicle travel behavior and charging, and obtain the spatial and temporal distribution of total charging load. Finally, the actual traffic road network and typical distribution network in northern China are used to analyze charging load forecast estimates for each typical functional area under real vehicle–road circumstances. The results show that the charging load demand in different areas has obvious spatial and temporal distribution characteristics and differences, and traffic conditions and temperature factors have a significant impact on electric vehicle charging load.
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Kim, Chi-Yeon, Chae-Rin Kim, Dong-Keun Kim, and Soo-Hwan Cho. "Analysis of Challenges Due to Changes in Net Load Curve in South Korea by Integrating DERs." Electronics 9, no. 8 (2020): 1310. http://dx.doi.org/10.3390/electronics9081310.
Full textAbstract:
The development of Distributed Energy Resources (DERs) is essential in accordance with the mandatory greenhouse gas (GHG) emission reduction policies, resulting in many DERs being integrated into the power system. Currently, South Korea is also focusing on increasing the penetration of renewable energy sources (RES) and EV by 2030 to reduce GHGs. However, indiscriminate DER development can give a negative impact on the operation of existing power systems. The existing power system operation is optimized for the hourly net load pattern, but the integration of DERs changes it. In addition, since ToU (Time-of-Use) tariff and Demand Response (DR) programs are very sensitive to changes in the net load curve, it is essential to predict the hourly net load pattern accurately for the modification of pricing and demand response programs in the future. However, a long-term demand forecast in South Korea provides only the total amount of annual load (TWh) and the expected peak load level (GW) in summer and winter seasons until 2030. In this study, we use the annual photovoltaic (PV) installed capacity, PV generation, and the number of EV based on the target values for 2030 in South Korea to predict the change in hourly net load curve by year and season. In addition, to predict the EV charging load curve based on Monte Carlo simulation, the EV users’ charging method, charging start time, and State-of-Charge (SoC) were considered. Finally, we analyze the change in hourly net load curve due to the integration of PV and EV to determine the amplification of the duck curve and peak load time by year and season, and present the risks caused by indiscriminate DERs development.
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V., Swarna Rekha, Kiran Kumar G., and E. Vidya Sagar Dr. "Reliability Evaluation of Radial Distribution Feeder Considering Two Load Modelling of Forecasted Electric Vehicle Load." International Journal of Engineering and Advanced Technology (IJEAT) 12, no. 5 (2023): 113–18. https://doi.org/10.35940/ijeat.E4211.0612523.
Full textAbstract:
<strong>Abstract: </strong>The use of an electric vehicle (EV) in place of an internal combustion engine reduces pollution and produces zero emissions. EVs need considerable electrical energy from the grid, and therefore it is necessary to evaluate the performance of the radial distribution system, including the Electrical Vehicle Charging Station (EVCS) load. The future EVCS load is forecasted using Holt's model, and then it is applied uniformly to the distribution system. This increases the magnitudes of currents, which are calculated using the backward and forward sweep method of load flow analysis. The increased magnitude of current moderates the operating temperature of the components and results in an increase in the average failure rate of feeder line sections. The percentage change in the average failure rate is assumed to be directly proportional to the percentage change in current, which in turn affects reliability indices such as SAIDI and ENS. The reliability analysis needs proper modelling of loads on the system and is taken as light and heavy load, considered this as two load model. The existing load without EVs of the distribution system is taken as a light load and the future load including the EV load during the charging period (5hrs) on the distribution system is taken as a heavy load. In this paper, the reliability indices of a radial distribution feeder are calculated for different cases like without EV load, with EV load and for different percentages of faults during EV load duration and the results are compared. This work is validated on IEEE33 standard distribution system.
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S, Abisheak. "AI-Powered Battery Charging System Using Machine Learning Algorithms for EVs." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 3938–48. https://doi.org/10.22214/ijraset.2025.69172.
Full textAbstract:
This paper presents an intelligent charging system powered by AI to boost performance and improve the State of Charge (SoC) and State of Health (SoH) of batteries in Electric Vehicles (EVs). Despite advancements in design and electrical specifications, the significance of effectively charging EV batteries is essential for ensuring safety, efficiency, and longevity. The prototype features a Raspberry Pi as its main controller, along with sensors that monitor key battery metrics including voltage, current, temperature, and pressure. The proposed system employs machine learning algorithms to forecast critical battery metrics such as SoC, SoH, and charging capacity based on the collected sensor data. These metrics are gathered in real-time, allowing the implementation of adaptive charging strategies that maintain consistent battery performance while adjusting to real-time variations in battery conditions to avoid issues like overheating, overcharging, and deterioration. This method not only prolongs battery life but also minimizes safety hazards. The proposed system is scalable, cost-efficient, and superior to traditional charging systems. Furthermore, it can be paired with renewable energy sources to further improve energy efficiency. This paper aims to promote the creation of an environmentally sustainable and smart battery charging system for electric vehicles
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S., Sheik Mohammed, Femin Titus, Sudhakar Babu Thanikanti, Sulaiman S. M., Sanchari Deb, and Nallapaneni Manoj Kumar. "Charge Scheduling Optimization of Plug-In Electric Vehicle in a PV Powered Grid-Connected Charging Station Based on Day-Ahead Solar Energy Forecasting in Australia." Sustainability 14, no. 6 (2022): 3498. http://dx.doi.org/10.3390/su14063498.
Full textAbstract:
Optimal charge scheduling of electric vehicles in solar-powered charging stations based on day-ahead forecasting of solar power generation is proposed in this paper. The proposed algorithm’s major objective is to schedule EV charging based on the availability of solar PV power to minimize the total charging costs. The efficacy of the proposed algorithm is validated for a small-scale system with a capacity of 3.45 kW and a single charging point, and the annual cost analysis is carried out by modelling a 65 kWp solar-powered EV charging station The reliability and cost saving of the proposed optimal scheduling algorithm along with the integration and the solar PV system is validated for a charging station with a 65 kW solar PV system having charging points with different charging powers. A comprehensive comparison of uncontrolled charging, optimal charging without solar PV system, and optimal charging with solar PV system for different vehicles and different time slots are presented and discussed. From the results, it can be realized that the proposed charging algorithm reduces the overall charging cost from 10–20% without a PV system, and while integrating a solar PV system with the proposed charging method, a cost saving of 50–100% can be achieved. Based on the selected location, system size, and charging points, it is realized that the annual charging cost under an uncontrolled approach is AUS $28,131. On the other hand, vehicle charging becomes completely sustainable with net-zero energy consumption from the grid and net annual revenue of AUS $28,134.445 can be generated by the operator. New South Wales (NSW), Australia is selected as the location for the study. For the analysis Time-Of-Use pricing (ToUP) scheme and solar feed-in tariff of New South Wales (NSW), Australia is adopted, and the daily power generation of the PV system is computed using the real-time data on an hourly basis for the selected location. The power forecasting is carried out using an ANN-based forecast model and is developed using MATLAB and trained using the Levenberg–Marquardt algorithm. Overall, a prediction accuracy of 99.61% was achieved using the selected algorithm.
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Kim, Yunsun, and Sahm Kim. "Forecasting Charging Demand of Electric Vehicles Using Time-Series Models." Energies 14, no. 5 (2021): 1487. http://dx.doi.org/10.3390/en14051487.
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This study compared the methods used to forecast increases in power consumption caused by the rising popularity of electric vehicles (EVs). An excellent model for each region was proposed using multiple scaled geographical datasets over two years. EV charging volumes are influenced by various factors, including the condition of a vehicle, the battery’s state-of-charge (SOC), and the distance to the destination. However, power suppliers cannot easily access this information due to privacy issues. Despite a lack of individual information, this study compared various modeling techniques, including trigonometric exponential smoothing state space (i.e., Trigonometric, Box–Cox, Auto-Regressive-Moving-Average (ARMA), Trend, and Seasonality (TBATS)), autoregressive integrated moving average (ARIMA), artificial neural networks (ANN), and long short-term memory (LSTM) modeling, based on past values and exogenous variables. The effect of exogenous variables was evaluated in macro- and micro-scale geographical areas, and the importance of historic data was verified. The basic statistics regarding the number of charging stations and the volume of charging in each region are expected to aid the formulation of a method that can be used by power suppliers.
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Talluri, Grasso, and Chiaramonti. "Is Deployment of Charging Station the Barrier to Electric Vehicle Fleet Development in EU Urban Areas? An Analytical Assessment Model for Large-Scale Municipality-Level EV Charging Infrastructures." Applied Sciences 9, no. 21 (2019): 4704. http://dx.doi.org/10.3390/app9214704.
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This work investigates minimum charging infrastructure size and cost for two typical EU urban areas and given passenger car electric vehicle (EV) fleets. Published forecasts sources were analyzed and compared with actual EU renewal fleet rate, deriving realistic EV growth figures. An analytical model, accounting for battery electric vehicle-plug-in hybrid electric vehicle (BEV-PHEV) fleets and publicly accessible and private residential charging stations (CS) were developed, with a novel data sorting method and EV fleet forecasts. Through a discrete-time Markov chain, the average daily distribution of charging events and related energy demand were estimated. The model was applied to simulated Florence and Bruxelles scenarios between 2020 and 2030, with a 1-year timestep resolution and a multiple scenario approach. EV fleet at 2030 ranged from 2.3% to 17.8% of total fleet for Florence, 4.6% to 16.5% for Bruxelles. Up to 2053 CS could be deployed in Florence and 5537 CS in Bruxelles, at estimated costs of ~8.3 and 21.4 M€ respectively. Maximum energy demand of 130 and 400 MWh was calculated for Florence and Bruxelles (10.3 MW and 31.7 MW respectively). The analysis shows some policy implications, especially as regards the distribution of fast vs. slow/medium CS, and the associated costs. The critical barrier for CS development in the two urban areas is thus likely to become the time needed to install CS in the urban context, rather than the related additional electric power and costs.
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Yao, En Jian, Mei Ying Wang, Yuan Yuan Song, and Ting Zuo. "Estimating the Cruising Range of Electric Vehicle Based on Instantaneous Speed and Acceleration." Applied Mechanics and Materials 361-363 (August 2013): 2104–8. http://dx.doi.org/10.4028/www.scientific.net/amm.361-363.2104.
Full textAbstract:
In contrast to fuel vehicles, the wide-spread use of electric vehicles (EVs) is still hindered by limited battery capacity and cruising range. It is important to forecast the EVs cruising range accurately before departure and reduce the risk of running out of electricity before arriving at the destination or charging station. In this article, through analyzing the variation characteristics of energy consumption rate with instantaneous speed and acceleration, a series of EV energy consumption rate models are established according to different operation modes. Further, a novel cruising range estimation method is proposed based on the presented EV energy consumption rate models, which is characterized by fully considering the impacts of instantaneous speed and acceleration. Finally, using the simulation data of a road in Beijing, the model presented in this article is utilized in conjunction with microscopic traffic simulation technologies to demonstrate its application in estimating the EVs cruising range under different traffic volumes.
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Karandinou, Aikaterini Agapi, and Fotios D. Kanellos. "A Method for the Assessment of Multi-objective Optimal Charging of Plug-in Electric Vehicles at Power System Level." WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL 17 (July 6, 2022): 314–23. http://dx.doi.org/10.37394/23203.2022.17.36.
Full textAbstract:
Nowadays, plug-in electric vehicles (PEVs) have gained popularity because of their operational and environmental advantages. As a result, power systems must deal with new operation challenges from their integration. In this article, a method for the assessment of the effects of multi-objective optimal charging of PEVs at power system level is proposed. The proposed multi-objective optimization method takes into consideration the forecasts of power system load, Renewable Energy Sources (RES) and electricity price. Moreover, it is enhanced by the detailed modeling of the daily EV activity taking into consideration the characteristics of the area they are having activity, the type of the activity, the charging preferences of the driver as well as the technical characteristics of the EV. Moreover, Vehicle to Grid (V2G) operation can be modeled by the proposed method. Real-world data were used and the method was applied to the power system of Crete. The results obtained from the study of indicative application scenarios are presented and finally prove the efficiency of the proposed method.
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NaitMalek, Youssef, Mehdi Najib, Anas Lahlou, Mohamed Bakhouya, Jaafar Gaber, and Mohamed Essaaidi. "A Hybrid Approach for State-of-Charge Forecasting in Battery-Powered Electric Vehicles." Sustainability 14, no. 16 (2022): 9993. http://dx.doi.org/10.3390/su14169993.
Full textAbstract:
Nowadays, electric vehicles (EV) are increasingly penetrating the transportation roads in most countries worldwide. Many efforts are oriented toward the deployment of the EVs infrastructures, including those dedicated to intelligent transportation and electro-mobility as well. For instance, many Moroccan organizations are collaborating to deploy charging stations in mostly all Moroccan cities. Furthermore, in Morocco, EVs are tax-free, and their users can charge for free their vehicles in any station. However, customers are still worried by the driving range of EVs. For instance, a new driving style is needed to increase the driving range of their EV, which is not easy in most cases. Therefore, the need for a companion system that helps in adopting a suitable driving style arise. The driving range depends mainly on the battery’s capacity. Hence, knowing in advance the battery’s state-of-charge (SoC) could help in computing the remaining driving range. In this paper, a battery SoC forecasting method is introduced and tested in a real case scenario on Rabat-Salé-Kénitra urban roads using a Twizy EV. Results show that this method is able to forecast the SoC up to 180 s ahead with minimal errors and low computational overhead, making it more suitable for deployment in in-vehicle embedded systems.
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Rekha, V. Swarna, G. Kiran Kumar, and Dr E. Vidya Sagar. "Reliability Evaluation of Radial Distribution Feeder Considering Two Load Modelling of Forecasted Electric Vehicle Load." International Journal of Engineering and Advanced Technology 12, no. 5 (2023): 113–18. http://dx.doi.org/10.35940/ijeat.e4211.0612523.
Full textAbstract:
The use of an electric vehicle (EV) in place of an internal combustion engine reduces pollution and produces zero emissions. EVs need considerable electrical energy from the grid, and therefore it is necessary to evaluate the performance of the radial distribution system, including the Electrical Vehicle Charging Station (EVCS) load. The future EVCS load is forecasted using Holt's model, and then it is applied uniformly to the distribution system. This increases the magnitudes of currents, which are calculated using the backward and forward sweep method of load flow analysis. The increased magnitude of current moderates the operating temperature of the components and results in an increase in the average failure rate of feeder line sections. The percentage change in the average failure rate is assumed to be directly proportional to the percentage change in current, which in turn affects reliability indices such as SAIDI and ENS. The reliability analysis needs proper modelling of loads on the system and is taken as light and heavy load, considered this as two load model. The existing load without EVs of the distribution system is taken as a light load and the future load including the EV load during the charging period (5hrs) on the distribution system is taken as a heavy load. In this paper, the reliability indices of a radial distribution feeder are calculated for different cases like without EV load, with EV load and for different percentages of faults during EV load duration and the results are compared. This work is validated on IEEE33 standard distribution system.
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Zhang, Cheng, Wei Fang, Banghua Du, Qicheng Zhang, Xiaolan Dai, and Hui Wu. "Day-ahead optimal scheduling method for hydrogen-electric coupling systems considering source-load uncertainty." Journal of Physics: Conference Series 3031, no. 1 (2025): 012001. https://doi.org/10.1088/1742-6596/3031/1/012001.
Full textAbstract:
Abstract In recent years, the concept of microgrids has gained significant traction. However, uncertainties in source-load dynamics within microgrid systems have introduced critical challenges such as scheduling complexities and cumulative operational errors. Furthermore, the increasing integration of new energy vehicles imposes substantial impacts on microgrid stability under high-load conditions, underscoring the imperative for accurate energy prediction and dispatch. To address these challenges, this study proposes a hybrid deep learning-based day-ahead scheduling optimization system for hydrogen-electricity coupled microgrids. First, mathematical models of core internal components are established. On the generation side, a fused TCN with an attention mechanism and GRU is developed to predict renewable energy outputs and pre-optimize scheduling plans. On the demand side, Monte Carlo simulations combined with K-means clustering are employed to classify and forecast EV charging patterns based on historical behavioral data. Finally, an optimization model is formulated with total operational cost minimization as the objective function to generate day-ahead scheduling strategies. Extensive simulation results demonstrate that the proposed hybrid deep learning framework achieves a 34.8% reduction in MAE compared to traditional deep learning benchmarks while reducing scheduling costs by 5% relative to non-prediction-based approaches.
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Desai, Jairaj, Jijo K. Mathew, Nathaniel J. Sturdevant, and Darcy M. Bullock. "Longitudinal Monitoring of Electric Vehicle Travel Trends Using Connected Vehicle Data." World Electric Vehicle Journal 15, no. 12 (2024): 560. https://doi.org/10.3390/wevj15120560.
Full textAbstract:
Historically, practitioners and researchers have used selected count station data and survey-based methods along with demand modeling to forecast vehicle miles traveled (VMT). While these methods may suffer from self-reporting bias or spatial and temporal constraints, the widely available connected vehicle (CV) data at 3 s fidelity, independent of any fixed sensor constraints, present a unique opportunity to complement traditional VMT estimation processes with real-world data in near real-time. This study developed scalable methodologies and analyzed 238 billion records representing 16 months of connected vehicle data from January 2022 through April 2023 for Indiana, classified as internal combustion engine (ICE), hybrid (HVs) or electric vehicles (EVs). Year-over-year comparisons showed a significant increase in EVMT (+156%) with minor growth in ICEVMT (+2%). A route-level analysis enables stakeholders to evaluate the impact of their charging infrastructure investments at the federal, state, and even local level, unbound by jurisdictional constraints. Mean and median EV trip lengths on the six longest interstate corridors showed a 7.1 and 11.5 mile increase, respectively, from April 2022 to April 2023. Although the current CV dataset does not randomly sample the full fleet of ICE, HVs, and EVs, the methodologies and visuals in this study present a framework for future evaluations of the return on charging infrastructure investments on a regular basis using real-world data from electric vehicles traversing U.S. roads. This study presents novel contributions in utilizing CV data to compute performance measures such as VMT and trip lengths by vehicle type—EV, HV, or ICE, unattainable using traditional data collection practices that cannot differentiate among vehicle types due to inherent limitations. We believe the analysis presented in this paper can serve as a framework to support dialogue between agencies and automotive Original Equipment Manufacturers in developing an unbiased framework for deriving anonymized performance measures for agencies to make informed data-driven infrastructure investment decisions to equitably serve ICE, HV, and EV users.
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Farman, Md Khaja, Jarabana Nikhila, A. Bhavya Sreeja, B. Sai Roopa, K. Sahithi, and Devineni Gireesh Kumar. "AI-Enhanced Battery Management Systems for Electric Vehicles: Advancing Safety, Performance, and Longevity." E3S Web of Conferences 591 (2024): 04001. http://dx.doi.org/10.1051/e3sconf/202459104001.
Full textAbstract:
Electric vehicles (EVs) are essential to lowering carbon emissions and solving global environmental issues. The battery powers EVs, making its management crucial to safety and performance. As a self-check system, a Battery Management System (BMS) ensures operating dependability and eliminates catastrophic failures. As batteries age, internal resistance increases and capacity decreases, hence a BMS monitors battery health and performance in real time. EV energy storage systems (ESSs) need a complex BMS algorithm to maintain efficiency. Using battery efficiency calculations that account for charging time, current, and capacity, this approach should reliably forecast the battery's SoC and SoH. As batteries age, internal resistance increases, reducing constant current (CC) charging time. By analyzing these changes, the SoH can be predicted more precisely. Conventional methods for estimating SoC and enhancing BMS performance, such as deep neural networks, are used to minimize error rates. However, as the battery ages, AI approaches have gained prominence for their ability to provide precise diagnostics, fault analysis, and thermal management. These AI-driven techniques significantly enhance safety and reliability during charging and discharging cycles. To further ensure safety, a fault diagnosis algorithm is integrated into the BMS. This algorithm proactively addresses potential issues, thus maintaining the efficiency and safety of the battery. The effectiveness of the proposed BMS algorithms are demonstrated through its successful application in an ESS, validating its capability to manage the battery’s state, enhance performance, and ensure operational sustainability in EVs.
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Chen, Yi-An, Wente Zeng, Adil Khurram, and Jan Kleissl. "Cost-Optimal Aggregated Electric Vehicle Flexibility for Demand Response Market Participation by Workplace Electric Vehicle Charging Aggregators." Energies 17, no. 7 (2024): 1745. http://dx.doi.org/10.3390/en17071745.
Full textAbstract:
In recent years, with the growing number of EV charging stations integrated into the grid, optimizing the aggregated EV load based on individual EV flexibility has drawn aggregators’ attention as a way to regulate the grid and provide grid services, such as day-ahead (DA) demand responses. Due to the forecast uncertainty of EV charging timings and charging energy demands, the actual delivered demand response is usually different from the DA bidding capacity, making it difficult for aggregators to profit from the energy market. This paper presents a two-layer online feedback control algorithm that exploits the EV flexibility with controlled EV charging timings and energy demands. Firstly, the offline model optimizes the EV dispatch considering demand charge management and energy market participation, and secondly, model predictive control is used in the online feedback model, which exploits the aggregated EV flexibility region by reducing the charging energy based on the pre-decided service level for demand response in real time (RT). The proposed algorithm is tested with one year of data for 51 EVs at a workplace charging site. The results show that with a 20% service level reduction in December 2022, the aggregated EV flexibility can be used to compensate for the cost of EV forecast errors and benefit from day-ahead energy market participation by USD 217. The proposed algorithm is proven to be economically practical and profitable.
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Liu, Weidong, Lei Li, Qin Xie, Dan Li, and Jing Zhang. "Forecasting method of electric vehicle load time-space distribution considering traffic distribution." E3S Web of Conferences 194 (2020): 02030. http://dx.doi.org/10.1051/e3sconf/202019402030.
Full textAbstract:
The main work of this paper is to establish an electric vehicle(EV) load forecasting model based on road network traffic distribution for urban and inter-city transportation networks. This paper established a road network model considering the traffic impedance for the EV load forecasting of the urban fast charging network, and studied the prediction method of the time-space distribution of EV charging demand in the fast charging mode .Based on the expressway, the method for predicting the time-space distribution of EV load in the inter-city fast charging network is studied, and a time-space distribution load forecasting model is established. Based on the time-space distribution of traffic flow, combined with EV charging characteristics and travel routes, load simulation is performed. By constructing a prediction method for the time-space distribution of EV charging demand in the fast charging mode, it provides theoretical and methodological support for the research of time-sharing and segmented metering and charging strategies for EV fast charging stations,, and provides an important reference for the development of EV charging facilities operating cost benefits, economic performance indicators and calculation models under fast charging mode, which are of great significance to promote the popularization and application of EV fast charging modes.
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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.
Full textAbstract:
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-month period in Lafayette, Indiana. Over 4000 charging sessions at stations with varying power levels (350 kW, 150 kW, and 50 kW) were monitored, and the median charge time was between 28 and 36 min. A large variation in station utilization was observed at Electrify America charging stations that had a range of stations with 350 kW, 150 kW, and 50 kW available. The highest utilization rates by hour of day on average were observed at 25% at the 150 kW Tesla station. Driver activity during charging influenced dwell times, with the average dwell time of drivers who waited in their vehicles to charge being 10 min shorter than those who would travel to the shops. Rain in the forecast also impacted the number of users per day. Although there are no published metrics for EV utilization and associated driver activities, we believe examining this relationship will produce best practices for planning future investments in EV charging infrastructure as public and private sector partners develop a nationwide charging network.
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Santosh, Kumar Singh, Yang Liangjing, Hao Ma, and G. Voulgaris Petros. "Centralized and Decentralized Schemes for Optimal Scheduling of Electric Vehicles." Indian Journal of Science and Technology 14, no. 19 (2021): 1554–64. https://doi.org/10.17485/IJST/v14i19.502.
Full textAbstract:
Abstract <strong>Objective:</strong> To evaluate the performance of centralized scheduling scheme for charging/discharging of Electric Vehicles (EVs). <strong>Methods:</strong> To achieve optimal scheduling of EV charging and discharging, two schemes such as centralized and decentralized scheduling schemes are proposed and evaluated in this paper. Both schemes are intended to reshape the load profile through scheduled charging/discharging. Proposed schemes are tested for one day scheduling of 200 EVs. <strong>Findings:</strong> Optimal scheduling schemes require perfect information of EV and load so that the actual load can be used in simulation. However, actual load in the future interval is impossible to determine. In decentralized scheduling scheme, forecasted loads are used in the simulation. Hence, this scheme is called practical solution of EV scheduling. <strong>Novelty: </strong>Results demonstrate that, though the centralized scheduling scheme achieved the best results, it is impractical due to its dependency on future data and decentralized scheduling scheme is based on forecasted data which is the practical solution. <strong>Keywords:</strong> Electric Vehicle; Vehicle to Grid; Grid to Vehicle
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Li, Yanbin, Sen Zhang, Feng Yuan, and Menglong Xu. "Evaluation on Chinese and IEC Standards for EV Charging System using fuzzy TOPSIS method." E3S Web of Conferences 236 (2021): 01032. http://dx.doi.org/10.1051/e3sconf/202123601032.
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In recent years, the rapid development of electric vehicle (EV) in China has played an important role in promoting the realization of carbon emission reduction and carbon neutrality in China. The EV charging standard directly affects the domestic and foreign sales of EVs, which is crucial to the sustainable development of the EV industry. This paper compares and analyzes the EV charging system standards in China and International Electrotechnical Commission (IEC), constructs a comprehensive evaluation index system for EV charging system standards, and builds a comprehensive evaluation model for EV charging system standards based on the fuzzy TOPSIS method. The comprehensive evaluation results of China and IEC EV charging system standards show that IEC standards as a whole are better than China 's, but some indicators in China are better than IEC standards. The future development of China's EV charging system standards should be aligned with IEC international standards to further promote the international standardization of China's charging scheme.
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Li, Qiushuo, Yong Xiao, Shuaishuai Zhao, et al. "Performance Status Evaluation of an Electric Vehicle Charging Infrastructure Based on the Fuzzy Comprehensive Evaluation Method." World Electric Vehicle Journal 10, no. 2 (2019): 35. http://dx.doi.org/10.3390/wevj10020035.
Full textAbstract:
Performance status evaluation is essential for the safe running of electric vehicle (EV) charging infrastructure. With the development of the EV industry, the EV charging infrastructure industry has advanced considerably. Safe and reliable operation of the charging infrastructure is important for the development of EVs. As such, we propose a comprehensive evaluation method to assess the performance condition of an EV charging infrastructure. First, based on the analysis of the existing EV charging principles, we established an evaluation index system for EV charging infrastructure. Second, the subjective weight, objective weight, and comprehensive weight of the index system were determined through analytic hierarchy processes (AHP) and the entropy weight method. Then, we used fuzzy comprehensive evaluation to appraise the performance of the charging infrastructure through expert investigation. Finally, based on the actual data from an EV charger, the performance conditions of the EV charging infrastructure were evaluated to demonstrate the feasibility of the method and the reliability of the index system.
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Ghotge, Rishabh, Yitzhak Snow, Samira Farahani, Zofia Lukszo, and Ad van Wijk. "Optimized Scheduling of EV Charging in Solar Parking Lots for Local Peak Reduction under EV Demand Uncertainty." Energies 13, no. 5 (2020): 1275. http://dx.doi.org/10.3390/en13051275.
Full textAbstract:
Scheduled charging offers the potential for electric vehicles (EVs) to use renewable energy more efficiently, lowering costs and improving the stability of the electricity grid. Many studies related to EV charge scheduling found in the literature assume perfect or highly accurate knowledge of energy demand for EVs expected to arrive after the scheduling is performed. However, in practice, there is always a degree of uncertainty related to future EV charging demands. In this work, a Model Predictive Control (MPC) based smart charging strategy is developed, which takes this uncertainty into account, both in terms of the timing of the EV arrival as well as the magnitude of energy demand. The objective of the strategy is to reduce the peak electricity demand at an EV parking lot with PVarrays. The developed strategy is compared with both conventional EV charging as well as smart charging with an assumption of perfect knowledge of uncertain future events. The comparison reveals that the inclusion of a 24 h forecast of EV demand has a considerable effect on the improvement of the performance of the system. Further, strategies that are able to robustly consider uncertainty across many possible forecasts can reduce the peak electricity demand by as much as 39% at an office parking space. The reduction of peak electricity demand can lead to increased flexibility for system design, planning for EV charging facilities, deferral or avoidance of the upgrade of grid capacity as well as its better utilization.
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Wangsupphaphol, Aree, and Surachai Chaitusaney. "Subsidizing Residential Low Priority Smart Charging: A Power Management Strategy for Electric Vehicle in Thailand." Sustainability 14, no. 10 (2022): 6053. http://dx.doi.org/10.3390/su14106053.
Full textAbstract:
Government policies are crucial factors for supporting the growth of the electric vehicle (EV) industry—a growth that can be encouraged, for example, by subsidization designed to reduce the considerable anxiety stemming from the inconvenience of refueling at public charging stations. Subsidizing low priority charging for residential enables cost-effective load management for example controlling of EV charging power for grid reliability at the off-peak rate for 24 h. This solution provides the convenient recharging of EVs at home and prevents an expensive grid upgradation. To advance our understanding of the EV situation, this research used a regression model to forecast the growth rate of the EV market alongside the EV expansion policies in Thailand. The agreement between a policy and forecasting urges the government to prepare power system adequacy for EV loading. The analysis showed that power demand and voltage reduction in a typical low-voltage distribution system that assumes maximum EV loading constitute voltage violations. To address this limitation, this study proposed a rule-based strategy wherein low priority smart EV charging is regulated. The numerical validation of the strategy indicated that the strategy reduced power demand by 25% and 39% compared with that achieved under uncontrolled and time of use (TOU) charging, respectively. The strategy also limited voltage reduction and prolonged battery life. The study presents implications for policymakers and electricity companies with respect to possible technical approaches to stimulating EV penetration.
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Alfraidi, Walied, Mohammad Shalaby, and Fahad Alaql. "Modeling EV Charging Station Loads Considering On-Road Wireless Charging Capabilities." World Electric Vehicle Journal 14, no. 11 (2023): 313. http://dx.doi.org/10.3390/wevj14110313.
Full textAbstract:
Electric vehicle (EV) customers are expected to charge EV batteries at a rapid EV charging station or via on-road wireless EV charging systems when possible, as per their charging needs to successfully complete any remaining trips and reach their destination. When on-road wireless EV charging systems are considered as an alternative charging method for EVs, this can affect the load of a rapid EV charging station in terms of time and magnitude. Hence, this paper presents a probabilistic framework for estimating the arrival rate of EVs at an EV rapid charging station, considering the availability of on-road wireless charging systems as an alternative charging method. The proposed model incorporates an Electric Vehicle Decision Tree that predicts the times when EVs require rapid charging based on realistic transportation data. A Monte Carlo simulation approach is used to capture uncertainties in EV user decisions regarding charging types. A queuing model is then developed to estimate the charging load for multiple EVs at the charging station, with and without the consideration of on-road EV wireless charging systems. A case study and simulation results considering a 32-bus distribution system and the US National Household Travel Survey (NHTS) data are presented and discussed to demonstrate the impact of on-road wireless EV charging on the loads of an rapid EV charging station. It is observed that having on-road wireless EV charging as complementary charging to EV charging stations helps to significantly reduce the peak load of the charging station, which improves the power system capacity and defers the need for system upgrades.
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Wang, Shiqian, Bo Liu, Qiuyan Li, Ding Han, Jianshu Zhou, and Yue Xiang. "EV Charging Behavior Analysis and Load Prediction via Order Data of Charging Stations." Sustainability 17, no. 5 (2025): 1807. https://doi.org/10.3390/su17051807.
Full textAbstract:
To understand the charging behavior of electric vehicle (EV) users and the sustainable use of the flexibility resources of EV, EV charging behavior analysis and load prediction via order data of charging stations was proposed. The user probability distribution model is established from the characteristic dimensions of EV charging initial time, initial state of charge, power level, and charging time. Under the conditions of specific districts, seasons, multiple EV types, and specific weather, the Monte Carlo simulation method is used to predict the EV load distribution at the physical level. The correlation between users’ willingness to charge and the electricity price is analyzed, and the logistic function is used to establish the charging load prediction model on the economic level. Taking a city in Henan Province, China, as an example, the calculation results show that the EV charging load distribution varies with the district, season, weather, and EV type, and the 24 h time-of-use (TOU) electricity price and EV quantity distribution are analyzed. The proposed method can better reflect EV charging behavior and accurately predict EV charging load.
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Tan, Linlin, Wenxuan Zhao, Minghao Ju, Han Liu, and Xueliang Huang. "Research on an EV Dynamic Wireless Charging Control Method Adapting to Speed Change." Energies 12, no. 11 (2019): 2214. http://dx.doi.org/10.3390/en12112214.
Full textAbstract:
In order to solve the problem of the electric vehicle (EV) charging amount fluctuation caused by the variation of driving speed during dynamic wireless charging, this paper proposes an EV dynamic wireless charging control method adapting to speed change. Firstly, a dynamic wireless charging model based on a long-track transmitting coil is established, and the expression of the charging power of each load under multi-load situation is obtained. Secondly, the influence of the EV charging number and maximum driving speed on the range of system parameters is studied. Subsequently, the method for determining the load resistance value according to the driving speed under a multi-EV charging condition is further discussed. Afterwards, a charging power control method adapting to the speed variation by load adjustment is proposed. By adjusting the equivalent load of the variable-speed charging EV, its speed variation range can reach 20~60 km/h, while the remaining EVs’ charging power fluctuation range can be kept within 10%~15%. Finally, the experimental prototype is built to verify the above-mentioned control method.
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Gao, Dexin, Yi Wang, Xiaoyu Zheng, and Qing Yang. "A Fault Warning Method for Electric Vehicle Charging Process Based on Adaptive Deep Belief Network." World Electric Vehicle Journal 12, no. 4 (2021): 265. http://dx.doi.org/10.3390/wevj12040265.
Full textAbstract:
If an accident occurs during charging of an electric vehicle (EV), it will cause serious damage to the car, the person and the charging facility. Therefore, this paper proposes a fault warning method for an EV charging process based on an adaptive deep belief network (ADBN). The method uses Nesterov-accelerated adaptive moment estimation (NAdam) to optimize the training process of a deep belief network (DBN), and uses the historical data of EV charging to construct the ADBN of the normal charging process of an EV model. The real-time data of EV charging is obtained and input into the constructed ADBN model to predict the output, calculate the Pearson coefficient between the predicted output and the actual measured value, and judge whether there is a fault according to the size of the Pearson coefficient to realize the fault warning of the EV charging process. The experimental results show that the method is not only able to accurately warn of a fault in the EV charging process, but also has higher warning accuracy compared with the back propagation neural network (BPNN) and conventional DBN methods.
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Xiao, Jian, and Wei Hou. "Cost Estimation Process of Green Energy Production and Consumption Using Probability Learning Approach." Sustainability 14, no. 12 (2022): 7091. http://dx.doi.org/10.3390/su14127091.
Full textAbstract:
With electric vehicle (EV) charging, green energy production costs could be reduced, and smart grid (SG) reliability improved. Nevertheless, the vast number of EVs could adversely affect the stability of the voltage and cost of operation. The present study designs a new security-based system based on a new EV participation charging method for a decentralized blockchain-enabled SG system. It is aimed at minimizing the level of power alternation in the electrical network and the total charging costs of EVs as mobile systems. In the first step, the power alternation level issue of the SG is formulated based on the capacity of EV batteries, the rate of charging, and EV users’ charging behavior. Next, a new adaptive blockchain-based EV participation (AdBEV) method is proposed, using the Iceberg order execution algorithm for improving EV discharging and charging schedules. Simulated outcomes demonstrate that the suggested method is superior to the genetic algorithm method when it comes to reducing power fluctuation levels and total charging cost.