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Data-Driven RUL Estimation for DC-Link Capacitor in Ultra-Fast EV Charging Systems
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| Author(s) |
Suwaiba MATEEN, Ahteshamul HAQUE, Mohammed Ali KHAN, Thomas EBEL, Shabana MEHFUZ |
| Abstract |
The reliability of ultra-fast electric vehicle charging systems heavily depends on the performance of critical components, notably the DC-link capacitor. This paper introduces a data driven approach for estimating the remaining useful life of DC-link capacitors in such systems. A virtual model of the system, developed as a digital twin, integrates real-time operational data with machine learning algorithms trained on historical degradation patterns. Key health indicators, such as a 30\% reduction in capacitance (from 20,000 µF) and a 100\% increase in Equivalent Series Resistance (from 0.4 mO), define the failure thresholds. Simulation-based validation demonstrates the effectiveness of the proposed methodology in accurately predicting capacitor degradation trends. This approach enhances the reliability of high-power EV charging systems and highlights the role of digital twins in predictive maintenance. |
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| Filename: | 0083-epe2025-full-14263821.pdf |
| Filesize: | 639.6 KB |
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| Type |
Members Only |
| Date |
Last modified 2025-08-31 by System |
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