IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i8p6684-d1123980.html
   My bibliography  Save this article

Metaheruistic Optimization Based Ensemble Machine Learning Model for Designing Detection Coil with Prediction of Electric Vehicle Charging Time

Author

Listed:
  • Abdulaziz Alshammari

    (Information Systems Department, College of Computer Information and Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia)

  • Rakan C. Chabaan

    (Hyundai American Technical Center, Inc., Superior Township, MI 48198, USA)

Abstract

An efficient charging time forecasting reduces the travel disruption that drivers experience as a result of charging behavior. Despite the machine learning algorithm’s success in forecasting future outcomes in a range of applications (travel industry), estimating the charging time of an electric vehicle (EV) is relatively novel. It can help the end consumer plan their trip based on the estimation data and, hence, reduce the waste of electricity through idle charging. This increases the sustainability factor of the electric charging station. This necessitates further research into the machine learning algorithm’s ability to predict EV charging time. Foreign object recognition is an essential auxiliary function to improve the security and dependability of wireless charging for electric vehicles. A comparable model is used to create the object detection circuit in this instance. Within this research, the ensemble machine learning methods employed to estimate EV charging times included random forest, CatBoost, and XGBoost, with parameters being improved through the metaheuristic Ant Colony Optimization algorithm to obtain higher accuracy and robustness. It was demonstrated that the proposed Ensemble Machine Learning Ant Colony Optimization (EML_ACO) algorithm achieved 20.5% of R 2 , 19.3% of MAE, 21% of RMSE, and 23% of MAPE in the training process. In comparison, it achieves 12.4% of R 2, 13.3% of MAE, 21% of RMSE, and 12.4% of MAPE during testing.

Suggested Citation

  • Abdulaziz Alshammari & Rakan C. Chabaan, 2023. "Metaheruistic Optimization Based Ensemble Machine Learning Model for Designing Detection Coil with Prediction of Electric Vehicle Charging Time," Sustainability, MDPI, vol. 15(8), pages 1-17, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:8:p:6684-:d:1123980
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/8/6684/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/8/6684/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Beibei Yue & Guanghua Sheng & Shengxiang She & Jiaqi Xu, 2020. "Impact of Consumer Environmental Responsibility on Green Consumption Behavior in China: The Role of Environmental Concern and Price Sensitivity," Sustainability, MDPI, vol. 12(5), pages 1-16, March.
    2. Yongyou Nie & Enci Wang & Qinxin Guo & Junyi Shen, 2018. "Examining Shanghai Consumer Preferences for Electric Vehicles and Their Attributes," Sustainability, MDPI, vol. 10(6), pages 1-16, June.
    3. Yueling Xu & Wenyu Zhang & Haijun Bao & Shuai Zhang & Ying Xiang, 2019. "A SEM–Neural Network Approach to Predict Customers’ Intention to Purchase Battery Electric Vehicles in China’s Zhejiang Province," Sustainability, MDPI, vol. 11(11), pages 1-19, June.
    4. Ziwen Ling & Christopher R. Cherry & Yi Wen, 2021. "Determining the Factors That Influence Electric Vehicle Adoption: A Stated Preference Survey Study in Beijing, China," Sustainability, MDPI, vol. 13(21), pages 1-22, October.
    5. Giansoldati, Marco & Monte, Adriana & Scorrano, Mariangela, 2020. "Barriers to the adoption of electric cars: Evidence from an Italian survey," Energy Policy, Elsevier, vol. 146(C).
    6. She, Zhen-Yu & Qing Sun, & Ma, Jia-Jun & Xie, Bai-Chen, 2017. "What are the barriers to widespread adoption of battery electric vehicles? A survey of public perception in Tianjin, China," Transport Policy, Elsevier, vol. 56(C), pages 29-40.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ahmed M. Nassef & Mohammad Ali Abdelkareem & Hussein M. Maghrabie & Ahmad Baroutaji, 2023. "Review of Metaheuristic Optimization Algorithms for Power Systems Problems," Sustainability, MDPI, vol. 15(12), pages 1-27, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ziwen Ling & Christopher R. Cherry & Yi Wen, 2021. "Determining the Factors That Influence Electric Vehicle Adoption: A Stated Preference Survey Study in Beijing, China," Sustainability, MDPI, vol. 13(21), pages 1-22, October.
    2. Christopher Hecht & Jan Figgener & Dirk Uwe Sauer, 2021. "Predicting Electric Vehicle Charging Station Availability Using Ensemble Machine Learning," Energies, MDPI, vol. 14(23), pages 1-24, November.
    3. Iogansen, Xiatian & Wang, Kailai & Bunch, David & Matson, Grant & Circella, Giovanni, 2023. "Deciphering the factors associated with adoption of alternative fuel vehicles in California: An investigation of latent attitudes, socio-demographics, and neighborhood effects," Transportation Research Part A: Policy and Practice, Elsevier, vol. 168(C).
    4. Ye Yang & Zhongfu Tan, 2019. "Investigating the Influence of Consumer Behavior and Governmental Policy on the Diffusion of Electric Vehicles in Beijing, China," Sustainability, MDPI, vol. 11(24), pages 1-20, December.
    5. Dongming Wu & Liukai Yu & Qianqian Zhang & Yangyang Jiao & Yuhe Wu, 2021. "Materialism, Ecological Consciousness and Purchasing Intention of Electric Vehicles: An Empirical Analysis among Chinese Consumers," Sustainability, MDPI, vol. 13(5), pages 1-19, March.
    6. Zhao, Xingrong & Ma, Ye & Shao, Shuai & Ma, Tieju, 2022. "What determines consumers' acceptance of electric vehicles: A survey in Shanghai, China," Energy Economics, Elsevier, vol. 108(C).
    7. Gulnaz Ivanova & António Carrizo Moreira, 2023. "Antecedents of Electric Vehicle Purchase Intention from the Consumer’s Perspective: A Systematic Literature Review," Sustainability, MDPI, vol. 15(4), pages 1-27, February.
    8. Pedro Gerber Machado & Ana Carolina Rodrigues Teixeira & Edmilson Moutinho dos Santos & Dominique Mouette, 2023. "Electric vehicles adoption: A systematic review (2016–2020)," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 12(5), September.
    9. Zhang, Hong & Irfan, Muhammad & Ai, Fengyi & Al-Aiban, Khalid M. & Abbas, Shujaat, 2024. "Analyzing barriers to the adoption and development of electric vehicles: A roadmap towards sustainable transportation system," Renewable Energy, Elsevier, vol. 233(C).
    10. Baresch, Martin & Moser, Simon, 2019. "Allocation of e-car charging: Assessing the utilization of charging infrastructures by location," Transportation Research Part A: Policy and Practice, Elsevier, vol. 124(C), pages 388-395.
    11. Luigi Leclercq-Machado & Aldo Alvarez-Risco & Romina Gómez-Prado & Berdy Briggitte Cuya-Velásquez & Sharon Esquerre-Botton & Flavio Morales-Ríos & Camila Almanza-Cruz & Sarahit Castillo-Benancio & Mar, 2022. "Sustainable Fashion and Consumption Patterns in Peru: An Environmental-Attitude-Intention-Behavior Analysis," Sustainability, MDPI, vol. 14(16), pages 1-18, August.
    12. Elena Higueras-Castillo & Sebastian Molinillo & J. Andres Coca-Stefaniak & Francisco Liébana-Cabanillas, 2020. "Potential Early Adopters of Hybrid and Electric Vehicles in Spain—Towards a Customer Profile," Sustainability, MDPI, vol. 12(11), pages 1-18, May.
    13. Qiaoling Wang & Ziyu Kou & Xiaodan Sun & Shanshan Wang & Xianjuan Wang & Hui Jing & Peiying Lin, 2022. "Predictive Analysis of the Pro-Environmental Behaviour of College Students Using a Decision-Tree Model," IJERPH, MDPI, vol. 19(15), pages 1-14, July.
    14. Ziyuan Tian & Xixiang Sun & Jianguo Wang & Weihuan Su & Gen Li, 2022. "Factors Affecting Green Purchase Intention: A Perspective of Ethical Decision Making," IJERPH, MDPI, vol. 19(18), pages 1-16, September.
    15. Chun Yang & Jui-Che Tu & Qianling Jiang, 2020. "The Influential Factors of Consumers’ Sustainable Consumption: A Case on Electric Vehicles in China," Sustainability, MDPI, vol. 12(8), pages 1-16, April.
    16. Syed Hassan Raza & Umer Zaman & Moneeba Iftikhar & Owais Shafique, 2021. "An Experimental Evidence on Eco-Friendly Advertisement Appeals and Intention to Use Bio-Nanomaterial Plastics: Institutional Collectivism and Performance Orientation as Moderators," IJERPH, MDPI, vol. 18(2), pages 1-16, January.
    17. Özkan, Oktay & Destek, Mehmet Akif & Balsalobre-Lorente, Daniel & Esmaeili, Parisa, 2024. "Unlocking the impact of international financial support to infrastructure, energy efficiency, and ICT on CO2 emissions in India," Energy Policy, Elsevier, vol. 194(C).
    18. Alexander Wahl & Christoph Wellmann & Björn Krautwig & Patrick Manns & Bicheng Chen & Christof Schernus & Jakob Andert, 2022. "Efficiency Increase through Model Predictive Thermal Control of Electric Vehicle Powertrains," Energies, MDPI, vol. 15(4), pages 1-21, February.
    19. Thanh Tung Ha & Thanh Chuong Nguyen & Sy Sua Tu & Minh Hieu Nguyen, 2023. "Investigation of Influential Factors of Intention to Adopt Electric Vehicles for Motorcyclists in Vietnam," Sustainability, MDPI, vol. 15(11), pages 1-16, May.
    20. Shiela Mae C. Ilagan & Ardvin Kester S. Ong & Josephine D. German & Ma. Janice J. Gumasing & Kyla Marie P. Pabalan, 2024. "Holistic Antecedent Analysis of Behavioral Intention among Green Consumers in the Philippines: A Sustainability Theory of the Planned Behavior Approach," Sustainability, MDPI, vol. 16(10), pages 1-23, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:8:p:6684-:d:1123980. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.