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

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

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.

Suggested Citation

  • Jauhar, Sunil Kumar & Sethi, Sunil & Kamble, Sachin S. & Mathew, Shawn & Belhadi, Amine, 2024. "Artificial intelligence and machine learning-based decision support system for forecasting electric vehicles' power requirement," Technological Forecasting and Social Change, Elsevier, vol. 204(C).
    • Handle: RePEc:eee:tefoso:v:204:y:2024:i:c:s0040162524001926
    DOI: 10.1016/j.techfore.2024.123396
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    1. Rachana Vidhi & Prasanna Shrivastava, 2018. "A Review of Electric Vehicle Lifecycle Emissions and Policy Recommendations to Increase EV Penetration in India," Energies, MDPI, vol. 11(3), pages 1-15, February.
    2. Alireza Goli & Amir-Mohammad Golmohammadi & José-Luis Verdegay, 2022. "RETRACTED ARTICLE: Two-echelon electric vehicle routing problem with a developed moth-flame meta-heuristic algorithm," Operations Management Research, Springer, vol. 15(3), pages 891-912, December.
    3. Bag, Surajit & Pretorius, Jan Ham Christiaan & Gupta, Shivam & Dwivedi, Yogesh K., 2021. "Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    4. Gao, Renzhi & Yao, Xiaoyu & Wang, Zhao & Abedin, Mohammad Zoynul, 2024. "Sentiment classification of time-sync comments: A semi-supervised hierarchical deep learning method," European Journal of Operational Research, Elsevier, vol. 314(3), pages 1159-1173.
    5. Bogdan Ovidiu Varga & Arsen Sagoian & Florin Mariasiu, 2019. "Prediction of Electric Vehicle Range: A Comprehensive Review of Current Issues and Challenges," Energies, MDPI, vol. 12(5), pages 1-19, March.
    6. Joris Baars & Teresa Domenech & Raimund Bleischwitz & Hans Eric Melin & Oliver Heidrich, 2021. "Circular economy strategies for electric vehicle batteries reduce reliance on raw materials," Nature Sustainability, Nature, vol. 4(1), pages 71-79, January.
    7. Muhammad, Bashir, 2019. "Energy consumption, CO2 emissions and economic growth in developed, emerging and Middle East and North Africa countries," Energy, Elsevier, vol. 179(C), pages 232-245.
    8. Li, Jing-Ping & Mirza, Nawazish & Rahat, Birjees & Xiong, Deping, 2020. "Machine learning and credit ratings prediction in the age of fourth industrial revolution," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    9. Md Shajalal & Petr Hajek & Mohammad Zoynul Abedin, 2023. "Product backorder prediction using deep neural network on imbalanced data," International Journal of Production Research, Taylor & Francis Journals, vol. 61(1), pages 302-319, January.
    10. Kumar, Rajeev Ranjan & Chakraborty, Abhishek & Mandal, Prasenjit, 2021. "Promoting electric vehicle adoption: Who should invest in charging infrastructure?," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    11. Dong, Xiaoyang & Zhang, Bin & Wang, Bo & Wang, Zhaohua, 2020. "Urban households’ purchase intentions for pure electric vehicles under subsidy contexts in China: Do cost factors matter?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 135(C), pages 183-197.
    12. Jaiswal, Deepak & Kaushal, Vikrant & Kant, Rishi & Kumar Singh, Pankaj, 2021. "Consumer adoption intention for electric vehicles: Insights and evidence from Indian sustainable transportation," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    13. Khuda Bakhsh & Sobia Rose & Muhammad Faisal Ali & Najid Ahmad & Muhammad Shahbaz, 2017. "Economic growth, CO2 emissions, renewable waste and FDI relation in Pakistan: New evidences from 3SLS," Post-Print hal-02000433, HAL.
    14. White, Lee V. & Sintov, Nicole D., 2017. "You are what you drive: Environmentalist and social innovator symbolism drives electric vehicle adoption intentions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 99(C), pages 94-113.
    15. Issa, Helmi & Jabbouri, Rachid & Palmer, Mark, 2022. "An artificial intelligence (AI)-readiness and adoption framework for AgriTech firms," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    16. Trappey, Amy & Trappey, Charles V. & Hsieh, Alex, 2021. "An intelligent patent recommender adopting machine learning approach for natural language processing: A case study for smart machinery technology mining," Technological Forecasting and Social Change, Elsevier, vol. 164(C).
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