Robust SOH Prediction for Lithium-Ion Batteries via ProbSparse Informer Architecture
Date:
Best Paper Award 🏆
Presented research on robust State of Health (SOH) prediction for lithium-ion batteries using the ProbSparse Informer architecture at the 2025 IEEE Global Reliability & PHM Conference in Xi’an, China.
Abstract
This study presents a robust SOH prediction method that effectively captures long-term dependencies in battery degradation patterns using the ProbSparse Informer architecture. The approach demonstrates superior performance in handling time-series data for battery health monitoring.
Key Contributions
- Novel application of ProbSparse Informer architecture for battery SOH prediction
- Improved accuracy in long-term battery degradation forecasting
- Efficient handling of time-series data with reduced computational complexity
Award
This work received the Best Paper Award at PHM-Xi’an 2025.
Authors: Seo Y., Kim T., Barde S.