Electric Vehicle Next Charge Location Prediction

• Published in: IEEE transactions on intelligent transportation systems 2024-10, Vol.25 (10), p.14128-14139
• Main Authors: Marlin, Robert, Jurdak, Raja, Abuadbba, Alsharif, Ruj, Sushmita, Miller, Dimity
• Format: Article
• Language: English
• Subjects: Australia; Batteries; convolutional neural network (CNN); Convolutional neural networks; EV mobility prediction; EV next charge location prediction; Hidden Markov models; Predictive models; Public transportation; Urban areas
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2024.3401703

By 2050, global sales of electric vehicles (EVs) are predicted to account for approximately 70% of all vehicle sales. However, whilst transitioning from combustion engine vehicles to EVs would result in reduced carbon dioxide emissions, it would place significant strain on energy generation, and grid infrastructure. Many EV studies investigated routing or charge station management, while research on predicting energy demand at a specific location was lacking. To address this, our study focused on predicting EV's next charge location. We developed a localised onboard Convolutional Neural Network (CNN) model that achieved accuracies up to 95%. Our proposal used community area Distributed Energy Resource Management Systems (DERMS) to train EV models during charge transactions, while predictions were made onboard each EV. To address the lack of EV mobility charge data, we created a hybrid dataset using empirical Chicago city taxi mobility data adding synthetic EV charging event states. We conducted multiple experiments over various battery charge levels to understand how far ahead in time next charge location could be predicted, achieving reliable predictions up to 3 days before requiring next charge. Finally, this study laid a foundation for future EV mobility research by providing a novel EV mobility charge dataset.