Assessment of Prediction Uncertainties in EV Charging Management

The share of electric vehicles (EVs) in the market has been growing rapidly during the past few years. Much research has been conducted in order to enable smart charging, which would fulfil EV user needs while considering technical limitations of the grid and incentives provided by the electricity market. However, the major part of the developed charging methods uses information of EV near-future driving profiles, e.g. departure time and energy need of the next trip, which can be problematic to achieve accurately enough. This kind of information will require either an extra communication link between the EV user and the EV charging controller, or EV tracking. Regardless of how the information is acquired, some uncertainty is likely in the predictions, which can cause undesired results. The aim of this paper is to assess these prediction error-related issues for distributing available charging capacity and to introduce alternative EV charging control methods that eliminate prediction error-related challenges. The results indicate that the EV battery state-based methods can distribute available charging capacity almost as effectively as the EV near future driving profile-based method with perfect predictions.Peer reviewe