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Prediction of Power Consumption of Electric Vehicles: A Deep Learning Approach

Singh, Jatin Rajkumar (2023) Prediction of Power Consumption of Electric Vehicles: A Deep Learning Approach. Masters thesis, Dublin, National College of Ireland.

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Electric vehicles are seen as the next big thing in the automobile industry as conventional fossil-fueled vehicles have major drawbacks which are affecting the environment. Due to its relatively new technology, it is yet to touch every segment of the market and get user approvals. There are many hindrances to it, and one of the major reasons is its unpredictable energy requirements for longer trips as users are anxious about its energy requirement. Therefore this research has put forward a method to predict the power consumption of electric vehicles using realtime data. This method has used deep neural networks to build and train the model to estimate power consumption. The deep neural network achieved RMSE and R2 scores of 0.3153 and 0.8896 respectively and did predictions on real-time data and gave several insights into the data. Also, This study found out that there are some major factors which play an important role in power consumption. Factors such as vehicle speed, the distance of the trip, wind speed, road type and climate etc. are the major components in calculating and predicting consumption. This study can help users to reduce their anxiety about power consumption and can help them in planning their journey.

Item Type: Thesis (Masters)
Horn, Christian
Uncontrolled Keywords: Electric Vehicles; Power Consumption; Prediction; Deep Learning
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
T Technology > TL Motor vehicles. Aeronautics. Astronautics
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Electricity Supply
Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Tamara Malone
Date Deposited: 26 May 2023 13:50
Last Modified: 26 May 2023 13:50

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