Mounir, Nada (ERERA, ENSAM, Mohammed V University, Rabat, Morocco), Ouadi, Hamid (Ismra), Jrhilifa, ismael (ERERA, National High School of Arts and Crafts, Mohammed V Unive), EL BAKALI, Saida (ERERA, ENSAM, Mohammed V University, Rabat, Morocco), GHEOUANY, Saad (ERERA, National School of Arts and Crafts, Mohammed V University)

EV Charging Management : A Forecasting Model Development for Parking Time and Arrival State of Charge

Scheduled for presentation during the Regular Session "Electric vehicle charging" (ThuS2T4), Thursday, July 11, 2024, 12:10−12:30, Session room 4

12th IFAC Symposium on Control of Power & Energy Systems, July 10-12, 2024, Rabat, Morocco

This information is tentative and subject to change. Compiled on January 2, 2025

Abstract

In the context of optimizing electric vehicle (EV) charging and discharging management, this study addresses the critical task of predicting, for the next day, the arrival and departure times of each EV, as well as its state of charge (SoC) when accessing the parking lot. This management challenge requires the application of a robust forecasting approach to ensure accurate forecasts. In this paper, the proposed predictor is developed using the Long Short-Term Memory (LSTM) modeling technique. The model feature selection process, is carried out based on a correlation/autocorrelation matrix integrating data of different natures (weather, EV arrival and departure time, EV state of charge). Performance validation of the proposed forecasting model is carried out using the Mean Absolute Percentage Error (MAPE). The supremacy of the developed LSTM predictor is highlighted by comparing its performances with those of a Convolutional Neural Network (CNN) model.For the next day’s Arrival Time model, the LSTM demonstrates a MAPE of 1.95×10−3, the Departure Time model yields a MAPE of 5.49×10−3,and the SoC at Arrival model exhibits a MAPE of 5 × 10−2 .These findings underscore the significance of the LSTM model, coupled with feature selection strategies, in enhancing the precision of EV charging and discharging management within residential contexts.