Modeling EV Charging Station Loads Considering On-Road Wireless Charging Capabilities
Abstract
:1. Introduction
- Development of an Electric Vehicle Decision Tree (EVDT) to predict the timing and probability of EVs requiring rapid charging based on realistic transportation data.
- A Monte Carlo simulation (MCS) approach is employed to capture uncertainties in EV user decisions regarding charging type, enabling the robust analysis of different charging scenarios.
- Development of a queuing model to estimate the charging load for multiple EVs served at an EV charging station, with and without on-road wireless charging.
- Investigation of the impact of on-road EV wireless charging systems as an alternative charging method for EVs on the expected loads of a rapid EV charging station.
2. Proposed Framework
2.1. Electric Vehicle Decision Tree and Monte Carlo Simulation Approach for Modeling an EV Charging Station, Considering an On-Road Wireless Charging System
- Point-a: The precise duration between when an EV initiates a fast charging request and its arrival at the EV charging station depends solely on the spatial distance separating the EV’s location when calling for fast charging and the EV charging station. Hence, the hour of the fast charging call is used to estimate the hourly probability of EV arrival, assuming that the EV is expected to arrive at the nearby EV charging station within a designated one-hour timeframe.
- Point-b: It is assumed that one EV charging station can cater to the needs of a few hundred EVs, based on the fact that the United States (US) gasoline fueling facilities numbered nearly 160,000 [27], or about one facility for every 1500 vehicles. Each EVCF is designed to accommodate a portion of the total forecasted vehicles in the distribution system, specifically targeting approximately 20% of the forecasted vehicles.
2.2. Queuing Model
- The time between the arrival of EVs (inter-arrival times) is independent and follows an exponential distribution, meaning the arrival of one EV does not affect the arrival of another, resembling a Poisson process.
- Similarly, the hourly charging rates for EVs at the EV charging station are independent and exponentially distributed, also representing a Poisson process.
- The EV charging station is equipped with c identical fast chargers.
- Charging EVs follows a first-come-first-served rule; upon arrival at the EV charging station, EVs form a single queue. These assumptions enable us to model the process of charging at the EV charging station utilizing an M/M/c queuing model.
3. Input and Simulation Data
- State of Charge (SOC) Window: EVs are assumed to operate within an SOC window of 70%, ranging from 20% to 90%. This range ensures an appropriate balance between battery utilization and availability for charging.
- Home charging: It is assumed that EVs are fully charged at home before starting a trip with no additional charges required before the trip, except for overnight charging at home. This assumption positions fast charging as a complementary method to home charging.
- NHTS data selection: The study utilizes NHTS data, which reportedly includes 1,000,000 trips and 300,000 vehicles. However, to enhance the accuracy of the analysis, the study focuses on specific vehicle types (i.e., automobiles, sports vehicles, vans, and pickup trucks) and excludes any missing data. Consequently, 850,000 trips and 150,000 vehicles are considered for this particular investigation.
- EV battery types: The study primarily considers fully charged EV20, EV40, and EV60 vehicles, representing compact sedans with battery capacities of 6.51 kWh, 10.4 kWh and 15.6 kWh, respectively, enabling ranges of up to 20, 40 and 60 miles on electricity.
- Exclusion of low-mileage vehicles: To optimize computational efficiency, the analysis initially excludes vehicles with total distance covered throughout a day, considering all individual trips made, less than 20 miles as they do not require fast charging. Their inclusion would not significantly impact the charging demand.
4. Results and Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
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Alfraidi, W.; Shalaby, M.; Alaql, F. Modeling EV Charging Station Loads Considering On-Road Wireless Charging Capabilities. World Electr. Veh. J. 2023, 14, 313. https://doi.org/10.3390/wevj14110313
Alfraidi W, Shalaby M, Alaql F. Modeling EV Charging Station Loads Considering On-Road Wireless Charging Capabilities. World Electric Vehicle Journal. 2023; 14(11):313. https://doi.org/10.3390/wevj14110313
Chicago/Turabian StyleAlfraidi, Walied, Mohammad Shalaby, and Fahad Alaql. 2023. "Modeling EV Charging Station Loads Considering On-Road Wireless Charging Capabilities" World Electric Vehicle Journal 14, no. 11: 313. https://doi.org/10.3390/wevj14110313
APA StyleAlfraidi, W., Shalaby, M., & Alaql, F. (2023). Modeling EV Charging Station Loads Considering On-Road Wireless Charging Capabilities. World Electric Vehicle Journal, 14(11), 313. https://doi.org/10.3390/wevj14110313