Description

I would recommend on using machine learning software where smart chargers can quickly upgrade an underground garage for a housing complex without major changes. This is achieved by making real-time charging decisions based on various auxiliary data, including driving, environment, pricing, and demand time series, in order to minimize the overall vehicle energy cost. When a charger isn’t in use, it can divert power to other stations that need it. If the battery in a car is almost full–so it’s pulling power in more slowly–the charger can send extra, unused power to another car. Over time, the system can also start to learn when drivers plug in their cars and how much power they’ll need, which also helps the network run more efficiently. IntelliCharge, with algorithm that allows software applications to become more accurate in predicting outcomes also provides a mechanism for reimbursing management for common area electricity usage, as well as power-management technology that allows vehicles to schedule time for charging to maximize the number of vehicles that can charge off of existing electrical infrastructure. In an underground garage that could charge two or three cars in the past, with IntelliCharge system it can be able to support as many as 20 to 50 charging stations. Incorporating the use of IntelliCharge allow tenants, electric vehicle users and building/apartment owner to predict with a fairly high level of accuracy when a charging station is going to be in use. If the system knows that a particular car is likely to stay parked for the next eight hours, and the battery is already 70% full, it can send power to other cars that might need it more quickly, making the charging infrastructure more economic, efficient, and reliable. *Idea inspired by EverCharge (https://evercharge.net) which build charging solution specifically designed for apartments, condominiums, and fleets.

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