Cloud based Smart EV Charging Station Recommender

With an increase in Electric Vehicles (EV) that have unique charging requirements, finding suitable Electric Vehicle Supply Equipment (EVSE) for charging EVs is becoming a difficult and challenging task. This paper focuses on developing a cloud-based and customer-oriented EV charging station recommender, which suggests EV charging stations for users based on their location and requirements. The recommender will take the location of the vehicle and other user needs such as type of connector, accessibility, access time, parking lot, station category etc. as inputs to recommend the most convenient charging stations in increasing order of distance from the user's location and facilitate slot booking. From the charging station dataset, the Random Forest Algorithm (RFA) is applied for finding stations that are near the vehicle location; Linear Search Algorithm (LSA) for filtering stations that satisfy the user requirements; and the Haversine formula for calculating distance. Streamlit is used for developing an end-user cloud application that is deployed in Streamlit Cloud. Additional features, such as charging request scheduling, and using live locations to update the station list, can be integrated to the system in the future.