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A Fuzzy-Based Product Life Cycle Prediction for Sustainable Development in the Electric Vehicle Industry

Author

Listed:
  • Yung Po Tsang

    (Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China)

  • Wai Chi Wong

    (Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Pokfulam, Hong Kong, China)

  • G. Q. Huang

    (Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Pokfulam, Hong Kong, China)

  • Chun Ho Wu

    (Department of Supply Chain and Information Management, The Hang Seng University of Hong Kong, Shatin, Hong Kong, China
    Big Data Intelligence Centre, The Hang Seng University of Hong Kong, Shatin, Hong Kong, China)

  • Y. H. Kuo

    (Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Pokfulam, Hong Kong, China)

  • King Lun Choy

    (Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China)

Abstract

The development of electric vehicles (EVs) has drawn considerable attention to the establishment of sustainable transport systems to enable improvements in energy optimization and air quality. EVs are now widely used by the public as one of the sustainable transportation measures. Nevertheless, battery charging for EVs create several challenges, for example, lack of charging facilities in urban areas and expensive battery maintenance. Among various components in EVs, the battery pack is one of the core consumables, which requires regular inspection and repair in terms of battery life cycle and stability. The charging efficiency is limited to the power provided by the facilities, and therefore the current business model for EVs is not sustainable. To further improve its sustainability, plug-in electric vehicle battery pack standardization (PEVBPS) is suggested to provide a uniform, standardized and mobile EV battery that is managed by centralized service providers for repair and maintenance tasks. In this paper, a fuzzy-based battery life-cycle prediction framework (FBLPF) is proposed to effectively manage the PEVBPS in the market, which integrates the multi-responses Taguchi method (MRTM) and the adaptive neuro-fuzzy inference system (ANFIS) as a whole for the decision-making process. MRTM is formulated based on selection of the most relevant and critical input variables from domain experts and professionals, while ANFIS takes part in time-series forecasting of the customized product life-cycle for demand and electricity consumption. With the aid of the FPLCPF, the revolution of the EV industry can be revolutionarily boosted towards total sustainable development, resulting in pro-active energy policies in the PEVBPS eco-system.

Suggested Citation

  • Yung Po Tsang & Wai Chi Wong & G. Q. Huang & Chun Ho Wu & Y. H. Kuo & King Lun Choy, 2020. "A Fuzzy-Based Product Life Cycle Prediction for Sustainable Development in the Electric Vehicle Industry," Energies, MDPI, vol. 13(15), pages 1-23, July.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:15:p:3918-:d:392839
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    References listed on IDEAS

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    Cited by:

    1. Rocio de la Torre & Canan G. Corlu & Javier Faulin & Bhakti S. Onggo & Angel A. Juan, 2021. "Simulation, Optimization, and Machine Learning in Sustainable Transportation Systems: Models and Applications," Sustainability, MDPI, vol. 13(3), pages 1-21, February.
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    3. Hadi Jahanshahi & Zahra Alijani & Sanda Florentina Mihalache, 2023. "Towards Sustainable Transportation: A Review of Fuzzy Decision Systems and Supply Chain Serviceability," Mathematics, MDPI, vol. 11(8), pages 1-19, April.

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