A decision support tool for optimal charging scheduling for individual electric vehicle users

The increase in uptake of electric vehicles (EV) is accompanied by an increasingly complex charging service ecosystem characterised by variability in charging tariffs, charging powers, and availability of charging infrastructure in space and time. This increases the necessity of decision support tools (DSTs) that can minimise operating costs by utilizing best charging opportunities, and ensuring all mobility needs are met. We formulate and solve an optimization problem resulting in optimal charging amounts at each stay location based on the users' intended travel itinerary to informtheir decision making. We apply this to a representative sample of London drivers' full day travel itineraries. We analyse the resulting distribution of cost reduction compared to dumb charging. We analyse factors that affect the cost reductions, such as driving mileage, which shows the types of users for whom such tools can be most beneficial, and thus appropriately prioritise its adoption. The preliminary results gave average daily benefits of £0.382 and cost reduction of 43.5% for each user, with the average price of energy being 3 times cheaper in £/kWh. The tool can easedecision making for EV users effectively, by navigating for them the large amount of information. The results showed that even relatively short, uncomplicated travel itineraries could benefit from the DST, with even more benefits for more complicated itineraries. Thus, this optimization could be suitable and beneficial for the majority of BEV users.