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Remaining Useful Life Prediction of Super-Capacitors in Electric Vehicles Using Neural Networks

  • Research Article-Electrical Engineering
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Abstract

Batteries for electric vehicles (EVs) have a capacity decay issue as they age. As a result, the use of lithium-ion is becoming more popular with super-capacitors (SCs), particularly in EVs. Over the decrease of carbon dioxide emissions, SC batteries offer a substantial benefit. In EVs, a dependable mechanism that guarantees the SC batteries’ capacity for charging and discharging is crucial. The main obstacle for EVs is the long life of ultra-capacitor battery’s because SCs have a deterioration effect over multiple cycles. Therefore, accurate early prediction of these SC batteries is crucial. The data-based model is more accurate than mechanism-based and model-based methods created for this purpose. The proposed data-driven models, such as machine learning (ML), estimate the electrical parameters for the smooth functioning and working of SCs in addition to considering their operating status. The main factor determining whether electric vehicles can be sustained is an increase in battery cycle life. With a lowest root mean square error of 0.04614 and a mean squared error of 0.002 and an accuracy of 89.6%, ML-based models with various architectures and topologies have been created in this study to reliably estimate the deterioration of SCs capacitance.

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Acknowledgements

The authors are grateful for the support in dataset generation provided by Jinjin Li [44]. The dataset used in this paper can be accessed freely from https://doi.org/10.1016/j.mtener.2020.100537.

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Authors and Affiliations

  1. Institute of Electrical, Electronics and Computer Engineering, University of the Punjab, Lahore, Pakistan

    Syed Wajih-ul-Hassan Gillani, Kamal Shahid & Danish Arif

  2. Department of Control & Instrumentation Engineering and Interdisciplinary Research Center for Sustainable Energy Systems (IRC-SES), King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia

    Muhammad Majid Gulzar

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  1. Syed Wajih-ul-Hassan Gillani
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  2. Kamal Shahid
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  3. Muhammad Majid Gulzar
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  4. Danish Arif
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Corresponding author

Correspondence to Muhammad Majid Gulzar.

Additional information

The research presented in this paper builds upon the foundation laid by the submitted thesis titled: ’Electric Vehicles Super-capacitor’s Remaining Useful Life Using Neural Network’, 2022.

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Gillani, S.WuH., Shahid, K., Gulzar, M.M. et al. Remaining Useful Life Prediction of Super-Capacitors in Electric Vehicles Using Neural Networks. Arab J Sci Eng 49, 7327–7340 (2024). https://doi.org/10.1007/s13369-024-08766-4

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  • DOI: https://doi.org/10.1007/s13369-024-08766-4

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