Using Machine Learning to Predict Charge Depletion Patterns in Electric Vehicles
SAE-PP-00217
1/6/2022
- Content
- Electric Vehicles are poised to become a major force in worldwide transportation. The heart of an EV, its lithium battery, is what powers the entire vehicle. Unlike gas cars, which will show a vague scale of how much gas is remaining to power a car, electric vehicles generally show a battery percentage, much like in a mobile device. As more of the worldwide population starts to use electric vehicles, the state of the battery must be assessed and projected for any scenario. In this paper, we leverage machine learning algorithms to predict the battery State of Charge. We use a publicly available dataset describing vehicle parameters and trip details for 70 trips in EV BMW™ i3 (60 ah vehicles and evaluate machine learning algorithms for predicting State of Charge Percentage. We utilized a data processing technique (deltas and staggers to extract different perspectives from each trip record and demonstrate that machine learning techniques can be effectively used to predict battery state of charge for a wide range of driving conditions and trip parameters. Finally, we also demonstrated which parameters most influence the State of Charge Prediction.
- Citation
- Puri, S., "Using Machine Learning to Predict Charge Depletion Patterns in Electric Vehicles," SAE MobilityRxiv™ Preprint, submitted January 6, 2022, https://doi.org/10.47953/SAE-PP-00217.