E-Thesis & Research สถาบันเทคโนโลยีไทย-ญี่ปุ่น

Thesis (MET) 2025

1.	STATE-OF-HEALTH ESTIMATION OF LITHIUM-ION BATTERY THROUGH AI-DRIVEN ELECTROCHEMICAL IMPEDANCE SPECTROSCOPY MODELING. [แสดงบทคัดย่อ] [ซ่อนบทคัดย่อ]
	ผู้แต่ง : CHATCHARAN YOMJINDA
	This thesis develops a hybrid State of Health (SOH) estimation framework for lithium-ion batteries by integrating Electrochemical Impedance Spectroscopy (EIS), Equivalent Circuit Models (ECM), and Long Short-Term Memory (LSTM) networks. To test the diagnostic reliability of AI models under interrupted real-world conditions, NMC pouch cells were cycled for 200 cycles, deliberately separated into Phase 1 (cycles 1-100) and Phase 2 (cycles 101-200) by a 40-day rest period. Four ECM-extracted resistance parameters were fed into the LSTM, and its internal feature-weighting logic was cross-checked against a Pearson correlation benchmark. During continuous operation (Phase 1), the LSTM achieved high predictive accuracy (RMSE 0.99%) and correctly matched the physical benchmarks, identifying Warburg diffusion (Rw) as the primary degradation driver. However, following the 40-day rest (Phase 2), the battery physical degradation regime fundamentally shifted. While the LSTM maintained high prediction accuracy (RMSE 0.75%), it entirely lost its diagnostic fidelity. Instead of adapting to the new physical hierarchy of resistance parameters, the model relied heavily on historical capacity trends to sustain its accuracy. Full Text : Download!

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