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dc.contributor.advisorTran, Duc Vi
dc.contributor.authorPham, Tuong Van
dc.date.accessioned2025-02-13T08:57:52Z
dc.date.available2025-02-13T08:57:52Z
dc.date.issued2024
dc.identifier.urihttp://keep.hcmiu.edu.vn:8080/handle/123456789/6569
dc.description.abstractThis research addresses the critical challenge of optimizing the placement of charging stations for electric vehicles (EVs), particularly focusing on VinFast electric motorbikes in Ho Chi Minh City (HCMC). The study aims to develop a strategic plan for charging station infrastructure to support the growing adoption of EVs and mitigate range anxiety among potential users. By analyzing typical travel patterns and activity locations of weekday commuters, the research proposes a data-driven approach that balances the trade-off between setup costs and user accessibility. The core of the research lies in developing a mathematical model that considers factors like population density, traffic patterns, and potential future growth to identify ideal locations for charging stations. The model incorporates the concept of "tolerable distance," representing the maximum acceptable distance users are willing to travel from a charging station to their final destination. The research utilizes the Mixed-Integer Linear Programming (MILP) method to solve the optimization problem, ensuring a precise and efficient solution. The results of the study are expected to provide valuable insights for urban planners and policymakers in HCMC, guiding them in making informed decisions regarding charging station infrastructure. By optimizing the placement and capacity of charging stations, this research aims to promote wider EV adoption, reduce emissions, and contribute to a cleaner and more sustainable urban environment in HCMC.en_US
dc.language.isoenen_US
dc.subjectCharging Station Location Problemen_US
dc.subjectMix Integer Linear Programmingen_US
dc.subjectElectric Vehiclesen_US
dc.titleLocation Optimization Of Electric Vehicle Charging Stations: A Case Study Of Vinfasten_US
dc.typeThesisen_US


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