Paper Title
AN ENSEMBLE APPROACH FOR THE PREDICTION OF SOC CONSUMPTION IN ELECTRIC VEHICLES
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Registration ID: IJNRD_204616
Published ID: IJNRD2309027
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Keywords
State of Charge (SOC), XGBoost regressor, Random Forest, Stacking, Electric Vehicles
Abstract
The rising acceptance of electric vehicles in today's world has brought a spotlight on how vital it is to precisely compute the state of charge (SOC) for efficient usage of battery capacity and vehicle recharging schedule for lengthy travel. This paper proposed a new stacking ensemble approach of two machine learning algorithms: XGBoost regressor and random forest. This works on the benefits of both algorithms to increase the model’s prediction accuracy. It takes several attributes such as distance, speed, driving condition, and altimetry of the route into account to make accurate SOC estimation. The model consists of two stages, a base model and a meta-model. The base model gives the prediction of the XGBoost regressor and Random Forest individually and the meta-model gives the overall prediction. Finally, the result obtained from the experiment shows that the SOC prediction of the ensemble model is better than the individual models.
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How To Cite (APA)
S.Senthurya & DR.R.Senthamil selvi (September-2023). AN ENSEMBLE APPROACH FOR THE PREDICTION OF SOC CONSUMPTION IN ELECTRIC VEHICLES. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(9), a222-a230. https://ijnrd.org/papers/IJNRD2309027.pdf
Issue
Volume 8 Issue 9, September-2023
Pages : a222-a230
Other Publication Details
Paper Reg. ID: IJNRD_204616
Published Paper Id: IJNRD2309027
Research Area: Computer Science & TechnologyÂ
Author Type: Indian Author
Country: Thanjavur, Tamil Nadu, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2309027.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2309027
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