NRF & TÜBİTAK Funded · 2027–2028
KTBIX
Battery Intelligence
eXchange
Korea–Türkiye joint research on Physics-Informed Variational Information Bottleneck (PI-VIB-ResNet) for lithium-ion battery State-of-Health estimation via Electrochemical Impedance Spectroscopy.
PI-VIB-ResNet Pipeline
Raw EIS Data
Nyquist spectra from T-EV & TOGG EVs
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Wavelet Denoising
Multi-level noise suppression
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Feature Scaling
Normalization & impedance mapping
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VIB Latent Extraction
Physics-informed information bottleneck
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ResNet-BPNN
Residual deep neural regression
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SOH Estimate
RMSE ≤1.2% — BMS ready output
About the Project
Advancing Battery Diagnostics Through AI & Physics
KTBIX (Korea–Türkiye Battery Intelligence eXchange) is a 24-month bilateral mobility project funded by NRF Korea and TÜBİTAK Türkiye. It unites Sejong University (Seoul) and Istanbul Technical University (Istanbul) to tackle a critical challenge in next-generation electric mobility: accurate, robust battery State-of-Health estimation under real-world field conditions.
"We transition from our published AE-BPNN baseline (Scientific Reports, 2025) to a Physics-Informed Variational Information Bottleneck architecture — directly addressing the noise-entanglement and generalizability limitations of purely data-driven EIS models."
The project leverages real EV field data from Turkish T-EV platforms and TOGG vehicles (WP1), combines it with a novel PI-VIB-ResNet model developed at Sejong University's AINTLab (WP2), and validates against internationally recognized Oxford and NASA battery datasets (WP3).
Nyquist Plot — EIS Signature
~1.8%
Baseline RMSE
AE-BPNN (2025)
≤1.2%
Target RMSE
PI-VIB-ResNet
3+
Datasets
Oxford, NASA, T-EV
2+
Chemistries
NMC, LFP, etc.
Research Highlights
Six Pillars of KTBIX
From physics-constrained deep learning to open industrial datasets — KTBIX advances every layer of the battery SOH estimation stack.
Core Innovation
Physics-Informed VIB
The Variational Information Bottleneck disentangles measurement noise from structural EIS features while a Loss_Physics term enforces electrochemical consistency via Randles circuit constraints.
Architecture
ResNet-BPNN Architecture
Residual skip connections prevent gradient vanishing in deep BPNN layers, enabling 15+ layer depth for complex impedance-to-SOH mappings across battery chemistries and aging states.
WP1 — ITU
Industrial Field Data
Unlike lab-only datasets, KTBIX acquires EIS measurements from real Turkish EV fleets (T-EV platforms) and TOGG vehicles, covering diverse duty cycles and climate conditions.
WP3 — Joint
Explainable AI (XAI)
SHAP attributions and gradient saliency maps reveal which impedance features (R_ct, W, CPE) drive SOH predictions — essential for BMS engineers and regulatory validation.
WP3 — Joint
Benchmark Validation
Rigorous comparison against >5 state-of-the-art baselines including SVR, LSTM, Transformer-SOH, and conventional EIS equivalent circuit fitting on Oxford and NASA datasets.
WP4 — Joint
Bilateral Knowledge Exchange
Two international workshops (Istanbul 2027; Seoul 2028), researcher exchange visits, joint PhD co-supervision, and open-source code and dataset releases via GitHub and Zenodo.
Project Structure
Work Packages
Four coordinated work packages over 24 months with measurable milestones, dual-institution leads, and concrete exchange deliverables.
WP1ITU — Istanbul Technical University
Field EIS Data Acquisition
Deploy impedance analyzers on Turkish T-EV platforms and TOGG electric vehicles. Acquire EIS spectra across diverse temperature ranges and aging states. Deliver curated open dataset.
⏱ M1 – M8
WP2SJU — Sejong University · AINTLab
PI-VIB-ResNet Model Design
Design and train the Physics-Informed Variational Information Bottleneck encoder paired with a deep ResNet-BPNN regressor. Ablation studies and hyperparameter optimization.
⏱ M4 – M14
WP3Joint — ITU + SJU
Benchmark Validation & XAI
Cross-validate PI-VIB-ResNet against Oxford, NASA, and T-EV datasets. Compare with 5+ baselines. Apply SHAP and gradient saliency for explainability. Publish ≥2 Q1 papers.
⏱ M10 – M22
WP4Joint — ITU + SJU
Knowledge Exchange
Researcher exchange visits (ITU↔SJU, min. 1 week each direction), two international workshops (Istanbul M10, Seoul M22), PhD co-supervision, open-source code releases.
⏱ M1 – M24
Research Team
Principal Investigators
A cross-continental partnership grounded in a prior exchange visit (Prof. Syafrudin's visit to Türkiye, funded by Sejong University) and a joint 2025 publication.
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Principal Investigator · Korea
Prof. Muhammad Syafrudin
Sejong University (SJU) · AINTLab · Seoul, Korea
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Principal Investigator · Türkiye
Prof. Muhammet Tahir Güneşer
Istanbul Technical University (ITU) · Istanbul, Türkiye
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Foundation Publication (2025)
"AE-BPNN: autoencoder and backpropagation neural network-based model for lithium-ion battery state of health estimation." — Scientific Reports, Nature Publishing Group.
Istanbul Research Visit & ICETAI 2026 Keynote
Collaboration Foundation
Prior to the KTBIX project launch, Prof. Syafrudin visited Istanbul Technical University as an invited keynote speaker at ICETAI 2026, establishing the formal research partnership through a signed Letter of Intent.
ICETAI
2026
3rd Edition
3rd International Conference on Emerging Trends and Applications in Artificial Intelligence
Keynote Talk“Leveraging AI for Inclusive and Sustainable Development in the Digital Era”Through this keynote, Prof. Syafrudin positioned battery State-of-Health estimation as a frontline application of inclusive and sustainable AI — demonstrating how physics-informed deep learning (PI-VIB-ResNet) can democratize reliable EV diagnostics across emerging EV markets in Korea and Türkiye, directly underpinning the KTBIX project’s mission and NRF–TÜBİTAK funding rationale.
📅May 15–16, 2026
📍Istanbul Technical University (ITU), Istanbul, Türkiye
🎤Invited Keynote Speaker — Prof. Muhammad Syafrudin, Sejong University
🏛️Conference Chair: Prof. Muhammet Tahir Güneşer, Istanbul Technical University
📚Proceedings published by Springer · Indexed in Scopus — demonstrating capacity to lead peer-reviewed international conferences
🤝Letter of Intent for KTBIX project signed during the visit
Laboratory Visit · ITU
May 14, 2026
Keynote Presentation · ICETAI 2026
May 15, 2026
Letter of Intent Signing · ITU
May 16, 2026
Laboratory Collaboration
Joint review of EIS data acquisition protocols at ITU's Electrical Engineering laboratory, aligning measurement standards for the forthcoming WP1 field data collection phase.
Invited Keynote Address
Prof. Syafrudin delivered the opening keynote at ICETAI 2026, chaired by Prof. Güneşer (ITU). The conference proceedings are published by Springer and indexed in Scopus — affirming the PIs’ capacity to lead internationally recognized research events and strengthening the credibility of the KTBIX joint proposal.
Letter of Intent Signed
Formal Letter of Intent co-signed by both Principal Investigators, providing the institutional endorsement required for the NRF–TÜBİTAK joint project application.
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Letter of Intent — Signed May 16, 2026
Both institutions formally committed to the KTBIX bilateral research initiative, satisfying the NRF–TÜBİTAK joint call requirement for institutional endorsement prior to full proposal submission. The LOI was co-signed by Prof. Syafrudin (SJU) and Prof. Güneşer (ITU) and is included in the project application package.
✅ Formally Signed
Knowledge Exchange
International Workshops
Two flagship workshops — one on each side of the collaboration — open to the global battery research community. *The exact month and date to be updated (TBU) later.
2027
Istanbul, Türkiye · M10
Integrating Industrial Field Data into Battery Management Systems
Host: Prof. Muhammet Tahir Güneşer · ITU
📍Istanbul Technical University (ITU), Istanbul
🗓️October 2027 (Month 10 of the project)*
👥International participants — Korea, Türkiye, EU partners
📢Open registration via ktbix.org
2028
Seoul, Korea · M22
eXplainable AI (XAI) for Second-Life Battery Prediction
Host: Prof. Muhammad Syafrudin · Sejong University / AINTLab
📍Sejong University (SJU), Seoul, Korea
🗓️October 2028 (Month 22 of the project)*
👥International participants — Korea, Türkiye, ASEAN partners
📢Open registration via ktbix.org
Project Timeline
24-Month Roadmap
Three strategic phases connecting data to discovery to dissemination.
Phase I · 2027 H1
Data Acquisition & Model Foundations
Deploy EIS hardware on T-EV/TOGG platforms. Collect baseline measurements. Begin PI-VIB encoder design. Exchange visit: ITU researchers to SJU (min. 1 week). M3: WP1 Protocol Report.
Phase II · 2027 H2–2028 H1
Model Training, Validation & Istanbul Workshop
Full PI-VIB-ResNet training on T-EV data. Cross-validation on Oxford & NASA datasets. SHAP explainability. Istanbul Workshop (M10). Exchange visit: SJU researchers to ITU. Submit first Q1 paper.
Phase III · 2028 H2
Dissemination, Seoul Workshop & Phase 2 Proposal
Final benchmark comparison (5+ baselines). Seoul Workshop (M22). BMS hardware integration demo. Open-source release on GitHub + Zenodo. Submit second Q1 paper. Prepare Phase 2 grant proposal.
Partners & Funders
Institutions
Sejong University
Seoul, Korea
Istanbul Technical University
Istanbul, Türkiye
NRF Korea
Funder — Korea
TÜBİTAK
Funder — Türkiye
Get in Touch
For collaboration inquiries, dataset access requests, or workshop registration, reach out to the project team.