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Intelligent SOX Estimation for Automotive Battery Management Systems: State-of-the-Art Deep Learning Approaches, Open Issues, and Future Research Opportunities

Author

Listed:
  • Molla Shahadat Hossain Lipu

    (Department of Electrical and Electronic Engineering, Green University of Bangladesh, Dhaka 1207, Bangladesh)

  • Tahia F. Karim

    (Department of Electrical and Electronic Engineering, Primeasia University, Dhaka 1213, Bangladesh)

  • Shaheer Ansari

    (Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia)

  • Md. Sazal Miah

    (School of Engineering and Technology, Asian Institute of Technology, Pathum Thani 12120, Thailand)

  • Md. Siddikur Rahman

    (Department of Electrical and Electronics Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia)

  • Sheikh T. Meraj

    (Faculty of Science, Engineering and Built Environment, Deakin University, Geelong, VIC 3216, Australia)

  • Rajvikram Madurai Elavarasan

    (School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, QLD 4072, Australia)

  • Raghavendra Rajan Vijayaraghavan

    (Automotive Department, Harman Connected Services India Pvt. Ltd., Bengaluru 560066, India)

Abstract

Real-time battery SOX estimation including the state of charge (SOC), state of energy (SOE), and state of health (SOH) is the crucial evaluation indicator to assess the performance of automotive battery management systems (BMSs). Recently, intelligent models in terms of deep learning (DL) have received massive attention in electric vehicle (EV) BMS applications due to their improved generalization performance and strong computation capability to work under different conditions. However, estimation of accurate and robust SOC, SOH, and SOE in real-time is challenging since they are internal battery parameters and depend on the battery’s materials, chemical reactions, and aging as well as environmental temperature settings. Therefore, the goal of this review is to present a comprehensive explanation of various DL approaches for battery SOX estimation, highlighting features, configurations, datasets, battery chemistries, targets, results, and contributions. Various DL methods are critically discussed, outlining advantages, disadvantages, and research gaps. In addition, various open challenges, issues, and concerns are investigated to identify existing concerns, limitations, and challenges. Finally, future suggestions and guidelines are delivered toward accurate and robust SOX estimation for sustainable operation and management in EV operation.

Suggested Citation

  • Molla Shahadat Hossain Lipu & Tahia F. Karim & Shaheer Ansari & Md. Sazal Miah & Md. Siddikur Rahman & Sheikh T. Meraj & Rajvikram Madurai Elavarasan & Raghavendra Rajan Vijayaraghavan, 2022. "Intelligent SOX Estimation for Automotive Battery Management Systems: State-of-the-Art Deep Learning Approaches, Open Issues, and Future Research Opportunities," Energies, MDPI, vol. 16(1), pages 1-31, December.
    • Handle: RePEc:gam:jeners:v:16:y:2022:i:1:p:23-:d:1009216
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    References listed on IDEAS

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