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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
Pages
Posts
Future Blog Post
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Blog Post number 4
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
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Blog Post number 2
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Blog Post number 1
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portfolio
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Portfolio item number 2
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publications
Robust SOH Prediction for Lithium-Ion Batteries via ProbSparse Informer Architecture
Published in 2025 IEEE Global Reliability & PHM Conference (PHM-Xi'an), 2024
This paper proposes a robust SOH prediction method using ProbSparse Informer architecture for lithium-ion batteries.
Recommended citation: Seo Y., Kim T., & Barde, S. (2025). "Robust SOH Prediction for Lithium-Ion Batteries via ProbSparse Informer Architecture." In 2025 IEEE Global Reliability & PHM Conference (PHM-Xi'an). Xi'an, China.
Download Paper | Download Slides
Hybrid Compression for Accurate End of Life Prediction on Edge Battery Management System
Published in 2025 IEEE Global Reality & PHM Conference (PHM-Xi'an), 2024
This paper develops a hybrid compression method for accurate End of Life prediction on edge Battery Management Systems.
Recommended citation: Kim T., Seo Y., & Barde, S. (2025). "Hybrid Compression for Accurate End of Life Prediction on Edge Battery Management System." In 2025 IEEE Global Reliability & PHM Conference (PHM-Xi'an). Xi'an, China.
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Enhancing Battery SOH Prediction with Physics-Informed Neural Networks in Data-Scarce Environments
Published in Energy, 2025
This paper proposes a Physics-Informed Neural Network approach for battery State of Health prediction in data-scarce environments.
Recommended citation: Seo Y., Kim T., & Barde, S. (2025). "Enhancing Battery SOH Prediction with Physics-Informed Neural Networks in Data-Scarce Environments." Energy. 335, 138316.
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Edge-Compatible SOH Estimation for Li-Ion Batteries via Hybrid Knowledge Distillation and Model Compression
Published in Journal of Energy Storage, 2025
This paper presents an edge-compatible SOH estimation method using hybrid knowledge distillation and model compression techniques.
Recommended citation: Kim T., Seo Y., & Barde, S. (2025). "Edge-Compatible SOH Estimation for Li-Ion Batteries via Hybrid Knowledge Distillation and Model Compression." Journal of Energy Storage. 135, 118275.
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talks
Battery State of Health Prediction Framework Using Physics-Informed Neural Networks
Published:
Presented research on battery State of Health prediction using Physics-Informed Neural Networks at the Korean Society for Reliability conference.
Robust SOH Prediction for Lithium-Ion Batteries via ProbSparse Informer Architecture
Published:
Best Paper Award 🏆
Hybrid Compression for Accurate End of Life Prediction on Edge Battery Management System
Published:
Presented research on hybrid compression techniques for accurate End of Life (EOL) prediction on edge Battery Management Systems at the 2025 IEEE Global Reliability & PHM Conference in Xi’an, China.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.