- Speaker : Prof. Youngsun Hong
- Date : June 17, 2026
- Affiliation : Assistant Professor, Department of IT Convergence Mechatronics Engineering, Jeonbuk National University
- Category : Seminar
PHM Technologies for Future Mobility Ecosystems
Abstract
This seminar, hosted by the KAIST Cho Chun Shik Graduate School of Mobility, introduces data-driven Prognostics and Health Management (PHM) frameworks for electric-vehicle battery and powertrain systems. The shift from internal combustion engines to electric vehicles has replaced engines and fuel tanks with batteries, motors, and power electronics, and as EVs grow heavier and more powerful, the need for robust safety, early fault detection, and optimized control has never been greater. Prof. Youngsun Hong shows how PHM methods can keep future mobility systems safe, reliable, and efficient.
Presentation Overview
This presentation covers the following key topics:
- Battery State-of-Health (SOH) estimation from incremental capacity and real-world driving data, avoiding costly controlled reference performance tests
- Battery pack diagnostics using resistance-based models and scale-down simulators to detect and emulate faults in the interconnection system, wire harnesses, and busbars
- EV fast-charger diagnostics with an embedded multilayer perceptron reaching 97.2% fault-detection accuracy from DC current and internal temperature
- Optimal battery thermal management, holding the coolant near 75°C during fast charging to suppress lithium plating and preserve electrode integrity while improving energy efficiency
- Depth-of-Discharge (DOD) control strategies that balance cycle life and energy efficiency
- Early mechanical fault diagnosis using Spectral Kurtogram and Sub-Band Averaging Enhanced Kurtogram (SAK) methods for robust, early bearing-fault detection
