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Technical Dialogue

On-board and Off-board Charging Systems

D23.2 - Predicting Efficiency of On-Board and Off-Board EV Charging Systems using Machine Learning

Thursday, March 20, 2025
11:30 AM – 1:30 PM ET
Location: Level One, Hall A3
Prediction of EV charging energy and power profiles is crucial for optimal coordination and scheduling of different EV models. This paper proposes the idea of using advanced machine learning (ML) techniques to predict the operating efficiency of on-board (11 kW) and off-board (50 kW) EV charging systems. An experimental setup is developed to control the charging power, measure and collect datasets under several operating conditions using a real EV model. The collected dataset is used to train, validate, and compare ML techniques such as linear regression (LR), random forest (RF), artificial neural network (ANN) and, conditional generative adversarial network (cGAN). The results demonstrated that the RF and ANN performed better than the LR and cGAN models in both on-board and off-board charging systems.