Artificial intelligence and machine learning-based decision support system for forecasting electric vehicles' power requirement

Sunil Kumar Jauhar, Sunil Sethi, Sachin S. Kamble, Shawn Mathew and Amine Belhadi

Technological Forecasting and Social Change, 2024, vol. 204, issue C

Abstract: Increasing pollution is causing adverse environmental effects, leading to increased interest in combating this issue. There has been a significant interest in minimizing the pollution caused by combustion engine vehicles, with high research and development investments in hybrid and electric vehicle (EV) batteries. The innovations in EVs have a high potential to contribute to an optimized transportation sector while also playing a crucial role in reducing greenhouse gas emissions. This study contributes to the EV industry by precisely predicting the power demand at a particular charging station and identifying the optimal charging station characteristics. We proposed a modified business process based on digital technologies to maximize customer engagement and operational efficiency. Our research has incorporated technologies like artificial intelligence (AI) and machine learning (ML). This study addresses the issues of EV infrastructure facilities, the issues raised by the lack of service features for EVs, and the optimal power requirement for charging stations. The proposed framework has managerial and technological implications, suggesting that the system must promptly receive, store, and analyze substantial volumes of data and demonstrate adaptability in response to environmental factors, such as the availability of EVs and the utilization of renewable energy sources. Despite the challenges, there is potential promise in developing decision assistance systems for electric vehicle power demands based on AI and ML.

Keywords: Artificial intelligence (AI); Machine learning (ML); Electric vehicles (EV); Demand forecasting; Technological implications (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:204:y:2024:i:c:s0040162524001926

DOI: 10.1016/j.techfore.2024.123396

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