Smart Charging Algorithm for Electric Vehicles

With a rising number of electric vehicles (EVs) today, various workplaces, shopping centers, and other similar sites are implementing EV chargers in their parking lots so that owners can charge their EVs when not in their homes. Although electric vehicles are generally eco-friendly, their increasing electricity demand could pose negative impacts to the environment (e.g., if power is being sourced from CO2 emitting plants). Our goal in this project is to develop an algorithm that creates an optimal charging schedule for individual EV units to reduce total electricity costs and prevent negative environmental impacts. First, we investigated SLAC National Accelerator Laboratory parking lot data at San Mateo, analyzed charging patterns across users, and calculated historical electricity costs for the parking lot. Second, we formulated a mixed-integer linear program (MILP) to calculate the optimal charging schedule for the EVs in our dataset. Doing so allowed us to compare the total electricity cost from the standard charging schedules in the data to what the cost could have been if the EVs were optimally scheduled.