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- W4383669892 abstract "The potential of automation possibility and enhanced user experience for electric vehicle stakeholders is discussed in this paper. Electric vehicles (EV) have acquired development chances in recent years because of the pressures of the energy crisis and environmental pollution and awareness within users. With the large-scale construction of charging stations, exploded EV buying rate, it results in increased power demand on local grids. Further, various direct, indirect, static, and dynamic factors involved in the usage and total charging cost estimation make it difficult for charging station owners, utility, and overall tedious user experience. In the future, EVSE load forecast and associated cost estimation, particularly a short-term forecast, will play a critical role in grid load planning, and the safe functioning of the electric power system while enhancing end user experience. AI-ML-driven and deep learning-based intelligent cost estimation engine proposed here will leverage the IOT connectivity for EV to forecast the load demand at charging station and efficient price estimation for EV charging. EV will provide details like battery state parameters and user historical data, etc. whereas Electric Vehicle Supply Equipment (EVSE) details, base and peak tariff details will be captured from charging station through connected platform. This engine is to provide dynamic and optimized tariff forecast up to next 5 h based on the historical usage of the specific user, past charging station usage, etc. Intelligent price forecast engine can efficiently estimate more accurate total charging cost based on the customer demand. User will have flexibility to choose the best tariff based on the different time slots and this engine will provide the hassle-free estimation." @default.
- W4383669892 created "2023-07-09" @default.
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- W4383669892 date "2023-01-01" @default.
- W4383669892 modified "2023-09-25" @default.
- W4383669892 title "Intelligent Cost Estimation Engine for EV Charging Stations: A Deep Learning-Based Approach" @default.
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- W4383669892 doi "https://doi.org/10.1007/978-981-99-0483-9_13" @default.
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