AI Based Electric Automobile Battery Drain Forecasting System

While electric vehicles (EVs) are gaining appeal as a sustainable mode of transportation, issues like range anxiety and battery depletion prevent them from being widely adopted. To better plan for charging stops, we created a model that takes into account the time of day, the length of the trip, and...

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Bibliographic Details
Published in2023 4th International Conference on Smart Electronics and Communication (ICOSEC) pp. 756 - 761
Main Authors J, Santhosh B, Benvin, Shalet, R, Arvind, V, Aswini, Maranan, Ramya, Thrinath, B.V. Sai
Format Conference Proceeding
LanguageEnglish
Published IEEE 20.09.2023
Subjects
artificial intelligence
battery drain
Electric vehicles
Error analysis
Machine learning
Machine learning algorithms
prediction
Predictive models
Support Vector Machine (SVM)
Support vector machines
Transportation
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Summary:While electric vehicles (EVs) are gaining appeal as a sustainable mode of transportation, issues like range anxiety and battery depletion prevent them from being widely adopted. To better plan for charging stops, we created a model that takes into account the time of day, the length of the trip, and the temperature outside. We constructed an accurate battery drain prediction model using machine learning methods, specifically the Support Vector Machine (SVM) algorithm. With strong correlation and low error rates, our findings show that the SVM algorithm is ideal for this job. Our findings might be useful to EV owners and fleet managers as a resource for reducing wasteful driving and maximizing range. Future work might examine the effect of battery deterioration on electric vehicle performance, leading to more nuanced models for maximizing EV efficiency and reducing transportation emissions.
DOI:10.1109/ICOSEC58147.2023.10276243