INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
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.
Keywords:
State of Charge (SOC), XGBoost regressor, Random Forest, Stacking, Electric Vehicles
Cite Article:
"AN ENSEMBLE APPROACH FOR THE PREDICTION OF SOC CONSUMPTION IN ELECTRIC VEHICLES", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 9, page no.a222-a230, September-2023, Available :http://www.ijnrd.org/papers/IJNRD2309027.pdf
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ISSN:
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
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