Electric Vehicle Charging Load Prediction Model Considering Traffic Conditions and Temperature

Feng, Jun; Chang, Xiqiang; Fan, Yanfang; Luo, Wenxin

doi:10.3390/pr11082256

Cited by 11 publications

(3 citation statements)

References 18 publications

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“…The increasing adoption of electric vehicles (EVs) has led to a growing demand for charging infrastructure, particularly electric vehicle charging stations (EVCS). However, the operation and maintenance of EVCS require significant amounts of energy, which can result in high operating costs 1 . To address this issue, accurate prediction of power consumption is necessary to optimize the utilization of charging stations and minimize operational expenses.…”

Section: Introductionmentioning

confidence: 99%

Power consumption prediction for electric vehicle charging stations and forecasting income

Akshay,

Grace,

Gunasekaran

et al. 2024

Sci Rep

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Electric 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 demand for charging stations, and the SARIMA (Seasonal Auto Regressive Integrated Moving Average) model outperforms the ARMA (Auto Regressive Moving Average) and ARIMA (Auto Regressive Integrated Moving Average) models, with the least RMSE (Root Mean Squared Error), MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error) scores in forecasting power demand and revenue. The data used for validation consists of charging activities over a four-year period from public charging outlets in Colorado, six months of charging data from ChargeMOD's public charging terminals in Kerala, India. Power usage is also forecasted based on wheels of vehicles, and finally, a plan subscription data from the same source is utilized to anticipate income, that helps companies develop pricing strategies to maximize profits while remaining competitive. Utility firms and charging networks may use accurate power consumption forecasts for a variety of purposes, such as power scheduling and determining the expected energy requirements for charging stations. Ultimately, precise power consumption forecasting can assist in the effective planning and design of EV charging infrastructure. The main aim of this study is to create a good time series model which can estimate the electric vehicle charging stations usage of power and verify if the firm has a good income along with some accuracy measures. The results show that SARIMA model plays a vital role in providing us with accurate information. According to the data and study here, four wheelers use more power than two and three wheelers. Also, DC charging facility uses more electricity than AC charging stations. These results can be used to determine the cost to operate the EVs and its subscriptions.

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Section: Introductionmentioning

confidence: 99%

Power consumption prediction for electric vehicle charging stations and forecasting income

Akshay,

Grace,

Gunasekaran

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…They also proposed a decision-making method for EV charging stations based on prospect theory. Feng et al [14] proposed a new EV charging load prediction model considering traffic conditions and ambient temperature: a traffic network topology and a speed flow model were established to accurately simulate the driving state of EVs in a real road network, and the power consumption per unit kilometer of an EV was calculated by taking into account the effects of temperature and vehicle speed on power consumption. In order to address the short-term and long-term prediction of EV charging loads, Koohfar et al [15] made the first attempt to use the Transformer model to predict EV charging demand and compare the performance with the RMSE model and the MAE model.…”

Section: Introductionmentioning

confidence: 99%

Bi-Level Planning of Electric Vehicle Charging Stations Considering Spatial–Temporal Distribution Characteristics of Charging Loads in Uncertain Environments

Gan,

Ruan,

Wang

et al. 2024

Energies

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With the increase in the number of distributed energy resources (DERs) and electric vehicles (EVs), it is particularly important to solve the problem of EV charging station siting and capacity determination under the distribution network considering a large proportion of DERs. This paper proposes a bi-level planning model for EV charging stations that takes into account the characteristics of the spatial–temporal distribution of charging loads under an uncertain environment. First, the Origin–Destination (OD) matrix analysis method and the real-time Dijkstra dynamic path search algorithm are introduced and combined with the Larin Hypercube Sampling (LHS) method to establish the EV charging load prediction model considering the spatial and temporal distribution characteristics. Second, the upper objective function with the objective of minimizing the cost of EV charging station planning and user charging behavior is constructed, while the lower objective function with the objective of minimizing the cost of distribution network operation and carbon emission cost considering the uncertainty of wind power and photovoltaics is constructed. The constraints of the lower-layer objective function are transformed into the upper-layer objective function through Karush–Kuhn–Tucker (KKT) conditions, the optimal location and capacity of charging stations are finally determined, and the model of EV charging station siting and capacity determination is established. Finally, the validity of the model was verified by planning the coupled IEEE 33-node distribution network with the traffic road map of a city in southeastern South Dakota, USA.

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“…These systems offer the compelling advantages of reduced manpower, cost savings, and increased economic benefits. In addition, the development of electric vehicles has brought about substantial reductions in carbon emissions and an increased emphasis on environmental friendliness [4][5][6][7]. Small-and medium-sized computers and control boards are the indispensable key components of the automatic driver assistance system.…”

Section: Introductionmentioning

confidence: 99%

Applying a Novel Image Recognition Curve-Fitting Control Strategy Combined with a Cloud Monitoring Technique into an Electric Self-Driving Vehicle (ESDV) to Improve Its Operation Efficiency

Liu,

Lin,

Lai

et al. 2023

Processes

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This study aims to develop an image recognition curve-fitting (IRCF) control strategy integrated with a cloud monitoring technique for application in electric self-driving vehicles (ESDVs) to improve their operation efficiency. The study focuses on an electric vehicle designed to reduce the carbon emissions and promote sustainability. The main camera, combined with the IRCF control strategy, was used to control the ESDV to enhance its operational efficiency. The proposed ESDV employs a pair of cameras to capture images and transmit them to the cloud-based web monitoring platform in real time. This allows the researchers to adjust the control parameters and promptly remove the road obstacles. The ESDV is equipped with a horn, two ultrasonic sensors, and an LED display, which can instantly detect the obstacles ahead of and behind the vehicle. When there are obstacles on the road, the vehicle will automatically stop, and the LED display will provide a visual representation of the obstacles, accompanied by the sounding of the horn as a warning signal. Meanwhile, the secondary camera detects the signal mark and feeds it back to the LED display, thereby informing passengers and other road users about the prevailing driving conditions. The proposed IRCF control strategy was compared with the traditional Hough line detection method on a 110 m ring road. The results revealed that the proposed control strategy outperformed the traditional Hough line detection method in terms of speed, efficiency, and running dexterity. Therefore, integrating the proposed control strategy into the automatic assistance driving system can improve the ESDV’s operation efficiency. Furthermore, the combination of the obstacle detection and signal sign detection functions for the ESDV used in this study can better fulfill the actual ESDV operation requirements on the road.

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Electric Vehicle Charging Load Prediction Model Considering Traffic Conditions and Temperature

Cited by 11 publications

References 18 publications

Power consumption prediction for electric vehicle charging stations and forecasting income

Power consumption prediction for electric vehicle charging stations and forecasting income

Bi-Level Planning of Electric Vehicle Charging Stations Considering Spatial–Temporal Distribution Characteristics of Charging Loads in Uncertain Environments

Applying a Novel Image Recognition Curve-Fitting Control Strategy Combined with a Cloud Monitoring Technique into an Electric Self-Driving Vehicle (ESDV) to Improve Its Operation Efficiency

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