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• W3119096793 endingPage "100106" @default.
• W3119096793 startingPage "100106" @default.
• W3119096793 abstract "As the electrification of the transportation industry and growing adoption of these electric vehicles continues, the demand for charging infrastructure is increasing concurrently. Properly deploying this infrastructure requires that there exist models which can adequately estimate the performance of these vehicles under real scenarios. While there has been significant work on estimating energy-consumption for realistic drive profiles, there is a need to systematically incorporate the conditions (e.g. wind, elevation) along a route for enabling efficient charger deployment. In this work, we have developed a software package known as INCEPTS, an agent-based simulation software that can predict high-fidelity electric vehicle performance and can capture the dynamics exhibited by the individual vehicles as they travel on their respective routes. The software then uses this to analyze fleets of vehicles which will provide the fleet performance statistics that result from various impacts exhibited by the fleet in the simulated area. These statistics can then be further used to inform decisions such as charger deployment to ensure that the placement of chargers adapts to the local needs of the electric vehicle fleet. Furthermore, this software is developed such that it is capable of simulating different kinds of electric vehicles (e.g. cars, trucks, eVTOLs) and different locations. To demonstrate the capabilities of the software, a variety of simulations are performed using Massachusetts as a target location. The first set of simulations show the importance of accounting for local conditions of individual vehicles. For example, the difference is state-of-charge could differ by up to 30% for the same distance traveled. The second set of simulations looked at the fleet performance within Massachusetts in which a bimodality of energy used per mile is observed, with means at 184 Wh/mi and 244 Wh/mi. Our work demonstrated that this bimodality results from a combination of both local conditions and geometric restrictions caused by the road network. These results are indicative of the unique capabilities of the INCEPTS platform and we plan to demonstrate additional use-cases in follow-up studies." @default.
• W3119096793 created "2021-01-18" @default.
• W3119096793 creator A5011466034 @default.
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• W3119096793 date "2021-02-01" @default.
• W3119096793 modified "2023-10-18" @default.
• W3119096793 title "INCEPTS: Software for high-fidelity electric vehicle en route state of charge estimation, fleet analysis and charger deployment" @default.
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• W3119096793 doi "https://doi.org/10.1016/j.etran.2021.100106" @default.
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