Abstract
Reliable state‑of‑charge estimation remains difficult in the presence of long‑term ageing, open‑circuit‑voltage hysteresis, and variable operating profiles. We investigate a hybrid estimator that marries a first‑principles equivalent‑circuit model with sequence learners, coupled through a residual‑aware fusion rule. Using a minute‑resolution ageing dataset from a lithium‑titanate (LTO) cell (236,282 samples across 2500 cycles), we identify phase‑aware OCV‑to‑SoC lookup tables for charge and discharge, deploy an extended Kalman filter with soft half‑cycle anchoring, and train LSTM, 1D‑CNN, and Transformer baselines with physics‑informed regularization promoting Coulomb consistency and concordance between OCV and terminal voltage. The phase‑aware EKF attains MAE 0.04535 and RMSE 0.05057 on cycle‑wise averages. A stitched LSTM yields MAE 0.03023 and RMSE 0.05192. Residual‑weighted fusion of EKF and LSTM produces MAE 0.03049 and RMSE 0.03991, which represents a 21% reduction relative to EKF while preserving the LSTM’s low bias. A coarse parameter sweep over the circuit model confirms expected early‑life behavior, namely lower resistance and a longer polarization time constant. At the window level, a physics‑informed LSTM achieves MAE 0.0184. The fusion is model‑agnostic, requires no retraining of the base estimators, and adds negligible computational overhead. We release phase‑aware OCV tables, a trained LSTM, and configuration files for seamless one‑command reproduction.
Keywords
Extended Kalman Filter Hybrid State-of-Charge Estimation Lithium-Ion Battery Aging Physics-Informed Neural Networks Residual-Weighted Fusion.
References
- International Energy Agency, “Global EV Outlook 2024,” 2024. [Online]. Available: https://www.iea.org/reports/global-ev-outlook-2024
- GL. Plett, “Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation,” J Power Sources, vol. 134, no. 2, pp. 277–292, 2004, doi: https://doi.org/ 10.1016/j.jpowsour.2004.02.033.
- H. Fang, Y. Wang, Z. Sahinoglu, T. Wada, and S. Hara, “State of charge estimation for lithium-ion batteries: An adaptive approach,” Control Eng Pract, vol. 25, pp. 45–54, 2014, doi: https://doi.org/10.1016/j.conengprac.2013.12.006.
- S. Yuan, H. Wu, and C. Yin, “State of Charge Estimation Using the Extended Kalman Filter for Battery Management Systems Based on the ARX Battery Model,” Energies (Basel), vol. 6, no. 1, pp. 444–470, 2013, doi: https://doi.org/ 10.3390/en6010444.
- D. Sun, X. Yu, C. Zhang, C. Wang, and R. Huang, “State of charge estimation for lithium-ion battery based on an intelligent adaptive unscented Kalman filter,” Int J Energy Res, vol. 44, no. 14, pp. 11199–11218, 2020, doi: https://doi.org/10.1002/er.5690.
- GL. Plett, “Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 1: Background,” J Power Sources, vol. 134, no. 2, pp. 262–276, 2004.
- IC. Dikmen, N. Yildiran, and T. Karadag, “Machine Learning Approaches for Enhancing the SoH Estimation of LTO Batteries,” International Journal of Automotive Science and Technology (IJASTECH), vol. 9, no. 1, pp. 48–59, 2025, doi: https://doi.org/10.30939/ijastech..1522403
- N. Yildiran, IC. Dikmen, and T. Karadag, “State of Health Estimation of Lithium Titanate Oxide Batteries Through Data-Driven Techniques and Machine Learning,” in 2024 8th International Artificial Intelligence and Data Processing Symposium (IDAP), 2024, 10.1109/IDAP64064.2024.10711165.
- A. Yunusoglu, D. Le, M. Isik, K. Tiwari, IC. Dikmen, and T. Karadag, “Battery State of Health Estimation Using LLM Framework,” in 2025 26th International Symposium on Quality Electronic Design (ISQED), Apr. 2025, pp. 1–8. doi: https://doi.org /10.1109/ISQED65160.2025.11014446.
- E. Chemali, PJ. Kollmeyer, M. Preindl, R. Ahmed, and A. Emadi, “Long short-term memory-networks for accurate state of charge estimation of Li-ion batteries,” IEEE Transactions on Industrial Electronics, vol. 65, no. 8, pp. 6577–6587, 2018.
- HM. Hussein, A. Aghmadi, MS. Abdelrahman, SMSH. Rafin, and O. Mohammed, “A review of battery state of charge estimation and management systems: Models and future prospective,” WIREs Energy and Environment, vol. 13, no. 1, p. e507, 2024, doi: https://doi.org/10.1002/wene.507.
- H. Yu, H. Lu, Z. Zhang, and L. Yang, “A generic fusion framework integrating deep learning and Kalman filter for state of charge estimation of lithium-ion batteries: Analysis and comparison,” J Power Sources, vol. 623, p. 235493, 2024, doi: https://doi.org/10.1016/j.jpowsour.2024.235493.
- F. Wang, Z. Zhai, Z. Zhao, Y. Di, and X. Chen, “Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis,” Nat Commun, vol. 15, no. 1, p. 4332, 2024, doi: https://doi.org/10.1038/s41467-024-48779-z.
- MS. Çetin, MT. Gençoğlu, and A. Dobrzycki, “Investigation of Charging Technologies for Electric Vehicles,” Turkish Journal of Science and Technology, vol. 19, no. 1, pp. 97–106, 2024, doi: https://doi.org/10.55525/tjst.1399120.
- IC. Dikmen, YE. Ekici, T. Karadağ, T. Abasov, and SE. Hamamcı, “Electrification in Urban Transport: A Case Study with Real-time Data,” Balkan Journal of Electrical and Computer Engineering, vol. 9, no. 1, pp. 69–77, 2021, doi: https://doi.org/10.17694/bajece.862931.
- YE. Ekici, IC. Dikmen, M. Nurmuhammed, and T. Karadağ, “A Review on Electric Vehicle Charging Systems and Current Status in Turkey,” International Journal of Automotive Science And Technology, vol. 5, no. 4, pp. 316–330, 2021, doi: https://doi.org/10.30939/ijastech..1011270.
- D. Le, A. Yunusoglu, K. Tiwari, M. Isik, and IC. Dikmen, “Multimodal llm for intelligent transportation systems,” arXiv preprint arXiv:2412.11683, 2024, doi: https://doi.org/10.48550/arXiv.2412.11683.
- IC. Dikmen, T. Karadağ, and A. Arı, “2500 Cycle Single Cell Battery Aging Dataset,” 2025, IEEE. doi: https://doi.org/10.21227/0svj-e338.
- T. Karadağ, T. Abbasov, and M. O. Karadağ, “Measuring, evaluating, and mapping the electromagnetic field levels in Turgut Ozal Medical Center building and environment,” Journal of Turgut Ozal Medical Center, vol. 21, pp. 186–195, 2014, doi: https://doi.org/10.7247/JTOMC.2014.1681.
- YE. Ekici, O. Akdağ, AA. Aydın, and T. Karadağ, “Review and analysis of real-time big data of electric bus consumption data,” in Ankara International Congress on Scientific Research – VII, Ankara, Türkiye, 02 Aralık 2022, ss.1405-1418.
- Nurmuhammed M., Akdağ O., Karadağ T., “Modified Archimedes optimization algorithm for global optimization problems: a comparative study,” Neural Comput. Appl., 36, 8007–8038, 2024. https://doi.org/10.1007/s00521-024-09497-1
Abstract
Uzun vadeli yaşlanma, açık devre voltajı histerezisi ve değişken çalışma profilleri söz konusu olduğunda, güvenilir şarj durumu tahmini yapmak zor olmaya devam etmektedir. Biz, kalıntı farkında füzyon kuralı ile birleştirilen, ilk ilkelere dayalı eşdeğer devre modelini sekans öğrenicilerle birleştiren bir hibrit tahminciyi araştırıyoruz. Lityum titanat (LTO) hücresinden elde edilen dakika çözünürlüklü yaşlanma veri setini (2500 döngüde 236.282 örnek) kullanarak, şarj ve deşarj için faz farkında OCV-SoC arama tabloları belirliyor, yumuşak yarım döngü sabitleme ile genişletilmiş Kalman filtresi uyguluyor ve LSTM, 1D-CNN ve Transformer temel modellerini, OCV ve terminal voltajı arasında Coulomb tutarlılığını ve uyumu destekleyen fizik bilgisine dayalı düzenleme ile eğitiyoruz. Faz farkında EKF, döngü bazında ortalamalarda MAE 0,04535 ve RMSE 0,05057 değerlerine ulaşıyor. Birleştirilmiş LSTM, MAE 0,03023 ve RMSE 0,05192 değerleri veriyor. EKF ve LSTM'nin kalıntı ağırlıklı füzyonu, MAE 0,03049 ve RMSE 0,03991 değerleri verir; bu, LSTM'nin düşük önyargısını korurken EKF'ye göre %21'lik bir azalma anlamına gelir. Devre modeli üzerinde yapılan kaba parametre taraması, beklenen erken yaşam davranışını, yani daha düşük direnç ve daha uzun polarizasyon zaman sabitini doğrulamaktadır. Pencere düzeyinde, fizik bilgisine dayalı LSTM, MAE 0,0184 değerine ulaşmaktadır. Füzyon, modelden bağımsızdır, temel tahmincilerin yeniden eğitilmesini gerektirmez ve ihmal edilebilir düzeyde hesaplama yükü ekler. Kesintisiz tek komutlu yeniden üretim için faz farkında OCV tabloları, eğitilmiş LSTM ve yapılandırma dosyalarını yayınlıyoruz.
Keywords
Genişletilmiş Kalman Filtresi Hibrit Şarj Durumu Tahmini Lityum İyon Pil Yaşlanması Fizik Bilgisine Dayalı Sinir Ağları Artık Ağırlıklı Füzyon.
References
- International Energy Agency, “Global EV Outlook 2024,” 2024. [Online]. Available: https://www.iea.org/reports/global-ev-outlook-2024
- GL. Plett, “Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation,” J Power Sources, vol. 134, no. 2, pp. 277–292, 2004, doi: https://doi.org/ 10.1016/j.jpowsour.2004.02.033.
- H. Fang, Y. Wang, Z. Sahinoglu, T. Wada, and S. Hara, “State of charge estimation for lithium-ion batteries: An adaptive approach,” Control Eng Pract, vol. 25, pp. 45–54, 2014, doi: https://doi.org/10.1016/j.conengprac.2013.12.006.
- S. Yuan, H. Wu, and C. Yin, “State of Charge Estimation Using the Extended Kalman Filter for Battery Management Systems Based on the ARX Battery Model,” Energies (Basel), vol. 6, no. 1, pp. 444–470, 2013, doi: https://doi.org/ 10.3390/en6010444.
- D. Sun, X. Yu, C. Zhang, C. Wang, and R. Huang, “State of charge estimation for lithium-ion battery based on an intelligent adaptive unscented Kalman filter,” Int J Energy Res, vol. 44, no. 14, pp. 11199–11218, 2020, doi: https://doi.org/10.1002/er.5690.
- GL. Plett, “Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 1: Background,” J Power Sources, vol. 134, no. 2, pp. 262–276, 2004.
- IC. Dikmen, N. Yildiran, and T. Karadag, “Machine Learning Approaches for Enhancing the SoH Estimation of LTO Batteries,” International Journal of Automotive Science and Technology (IJASTECH), vol. 9, no. 1, pp. 48–59, 2025, doi: https://doi.org/10.30939/ijastech..1522403
- N. Yildiran, IC. Dikmen, and T. Karadag, “State of Health Estimation of Lithium Titanate Oxide Batteries Through Data-Driven Techniques and Machine Learning,” in 2024 8th International Artificial Intelligence and Data Processing Symposium (IDAP), 2024, 10.1109/IDAP64064.2024.10711165.
- A. Yunusoglu, D. Le, M. Isik, K. Tiwari, IC. Dikmen, and T. Karadag, “Battery State of Health Estimation Using LLM Framework,” in 2025 26th International Symposium on Quality Electronic Design (ISQED), Apr. 2025, pp. 1–8. doi: https://doi.org /10.1109/ISQED65160.2025.11014446.
- E. Chemali, PJ. Kollmeyer, M. Preindl, R. Ahmed, and A. Emadi, “Long short-term memory-networks for accurate state of charge estimation of Li-ion batteries,” IEEE Transactions on Industrial Electronics, vol. 65, no. 8, pp. 6577–6587, 2018.
- HM. Hussein, A. Aghmadi, MS. Abdelrahman, SMSH. Rafin, and O. Mohammed, “A review of battery state of charge estimation and management systems: Models and future prospective,” WIREs Energy and Environment, vol. 13, no. 1, p. e507, 2024, doi: https://doi.org/10.1002/wene.507.
- H. Yu, H. Lu, Z. Zhang, and L. Yang, “A generic fusion framework integrating deep learning and Kalman filter for state of charge estimation of lithium-ion batteries: Analysis and comparison,” J Power Sources, vol. 623, p. 235493, 2024, doi: https://doi.org/10.1016/j.jpowsour.2024.235493.
- F. Wang, Z. Zhai, Z. Zhao, Y. Di, and X. Chen, “Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis,” Nat Commun, vol. 15, no. 1, p. 4332, 2024, doi: https://doi.org/10.1038/s41467-024-48779-z.
- MS. Çetin, MT. Gençoğlu, and A. Dobrzycki, “Investigation of Charging Technologies for Electric Vehicles,” Turkish Journal of Science and Technology, vol. 19, no. 1, pp. 97–106, 2024, doi: https://doi.org/10.55525/tjst.1399120.
- IC. Dikmen, YE. Ekici, T. Karadağ, T. Abasov, and SE. Hamamcı, “Electrification in Urban Transport: A Case Study with Real-time Data,” Balkan Journal of Electrical and Computer Engineering, vol. 9, no. 1, pp. 69–77, 2021, doi: https://doi.org/10.17694/bajece.862931.
- YE. Ekici, IC. Dikmen, M. Nurmuhammed, and T. Karadağ, “A Review on Electric Vehicle Charging Systems and Current Status in Turkey,” International Journal of Automotive Science And Technology, vol. 5, no. 4, pp. 316–330, 2021, doi: https://doi.org/10.30939/ijastech..1011270.
- D. Le, A. Yunusoglu, K. Tiwari, M. Isik, and IC. Dikmen, “Multimodal llm for intelligent transportation systems,” arXiv preprint arXiv:2412.11683, 2024, doi: https://doi.org/10.48550/arXiv.2412.11683.
- IC. Dikmen, T. Karadağ, and A. Arı, “2500 Cycle Single Cell Battery Aging Dataset,” 2025, IEEE. doi: https://doi.org/10.21227/0svj-e338.
- T. Karadağ, T. Abbasov, and M. O. Karadağ, “Measuring, evaluating, and mapping the electromagnetic field levels in Turgut Ozal Medical Center building and environment,” Journal of Turgut Ozal Medical Center, vol. 21, pp. 186–195, 2014, doi: https://doi.org/10.7247/JTOMC.2014.1681.
- YE. Ekici, O. Akdağ, AA. Aydın, and T. Karadağ, “Review and analysis of real-time big data of electric bus consumption data,” in Ankara International Congress on Scientific Research – VII, Ankara, Türkiye, 02 Aralık 2022, ss.1405-1418.
- Nurmuhammed M., Akdağ O., Karadağ T., “Modified Archimedes optimization algorithm for global optimization problems: a comparative study,” Neural Comput. Appl., 36, 8007–8038, 2024. https://doi.org/10.1007/s00521-024-09497-1
Details
| Primary Language | English |
|---|---|
| Subjects | Electrical Energy Storage |
| Journal Section | Research Article |
| Authors | |
| Submission Date | October 29, 2025 |
| Acceptance Date | November 18, 2025 |
| Early Pub Date | February 5, 2026 |
| Publication Date | February 5, 2026 |
| DOI | https://doi.org/10.55525/tjst.1812986 |
| IZ | https://izlik.org/JA69JC97PD |
| Published in Issue | Year 2026 Issue: Advanced Online Publication |
Cite
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The purpose of Turkish Journal of Science & Technology is to follow the developments in science and engineering, to contribute to the national and international development of individuals, to publish original research studies proven by experimental and numerical methods in basic science and engineering. Our journal aims to make highly valuable contributions to the literature, which will serve as an indispensable reference tool in the coming years.
Turkish Journal of Science & Technology is an International Refereed Journal and has been publishing since 2002 with articles in English as 2 issues (September, March) annually. In order to shed light on the knowledge base and technological developments of science and engineering sciences, there are studies of original research articles on experimental and theoretical advances in science and engineering. In order for the scientific content to be delivered correctly and appropriately, all articles are sent to at least two referees who are experts in their fields. Articles that are deemed positive at the end of the evaluation process are put in print order to be published as soon as possible and the article evaluation process is reported to the responsible author.
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Table 1. Print format of articles accepted for publication.
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Explanation
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General
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Articles accepted for publication after the referee evaluation are rearranged by their authors in print format and uploaded to the system. During the printing of the articles, there will be no typesetting operations, except for the format applications of the editor. They must be written in Office 2013 or higher. The text should be written using the Times New Roman Turkish character and single line spacing. |
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In page editing, the top margin should be 35mm, left and right 25mm, and bottom 30mm. Header and footer should be 25mm. The header should be marked differently on odd and even pages and different options on the first page. Headers must be in 8 font size in Times New Roman font. |
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Title
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The title of the article should be written in Times New Roman font, in 11 font size, capitalized only, in capital and bold, and 1 line space should be left before and after the Turkish title. |
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Names of the Authors
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Author names should be written in Times New Roman font, centered, bold and with 10 font size. There should be no space between author names and address. ORCID numbers of the responsible authors and authors should be written in 8 font size as footnotes. |
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Abstract
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“Abstract” should be written in 9 font size, bold, capital letter should be written at the beginning of the first line. Abstract text should be written in 9 font size, single paragraph and not more than 200 words. |
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Keywords
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Keywords should be written in 9 font size, after 1 line space after the abstract text. Minimum 3 and maximum 5 keywords should be written in lowercase. There should be 1 line of space after keywords. |
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Titles
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Titles should be numbered, the first letters of all words should be capitalized in 1st degree titles, only the first letter of the first word should be capitalized in lower titles and all should be written in bold characters. One line space should be left before and after the titles. Titles should be left-justified. |
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Text
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The text should be written in 10 font size with single line spacing, leaving 1 line space (10 font size) after Keywords. The paragraph indent should be 0.7 cm and there should be no space between paragraphs. |
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Tables and figures
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References
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References list should be numbered according to the order of use in the text and written in 9 font size. Journal titles should be abbreviated according to Thomson Reuters Web of Science © abbreviations. (http://www.letpub.com/index.php?page=journalapp, http://woodward.library.ubc.ca/research-help/journal-abbreviations/)
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For Turkish Journal of Science & Technology;
Ethical Principles and Publication Policy
It is obligatory that the articles sent to be published are not published anywhere or can be sent to any journal to be published. In the articles published in our journal or sent for publication, all responsibility for plagiarism and unauthorized data use belongs to the author or authors of the study. In such a case, Turkish Journal of Science & Technology does not accept any responsibility. All authors should contribute academically and scientifically to the submitted article. The authors cannot request that the published publication be prioritized in any way. Articles should be prepared in accordance with internationally accepted code of ethics. Where necessary, a copy of the Ethics Committee Report should be attached with the publication at the time of application.
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Editor in Chief: Assoc. Prof. Ferhat UÇAR
Editor: Prof. Dr. Emrah YILMAZ
Editor: Assoc. Prof. Nida KATI
Language Editor: Instructor Nilay Erdem Ayyıldız
Field Editors
- Prof. Dr. Mediha Kök - Physics
- Assoc. Prof. Kenan Koran - Chemistry
- Assoc. Prof. Fatih Özyurt- Software Engineering
- Prof. Dr. Hasan Çelik - Geological Engineering
- Assoc. Prof. Emine Özpolat - Fishery Product
- Assist Prof. Cengizhan Durucan - Civil Engineering
- Assoc Prof. Turgay Kaya - Electrical and Electronic Engineering
- Assoc. Prof. Muhittin Evren Aydın - Mathematics
- Assoc. Prof. Mehmet Tuzcu - Biology
Advisory Board
- Assoc. Prof. Şengül DOĞAN
- Prof. Dr. Erhan AKIN
- Prof. Dr. Ökkeş YILMAZ
- Prof. Dr. M. Şaban TANYILDIZI
- Prof. Dr. Özge HANAY
- Prof. Dr. Arif GÜLTEN
- Prof. Dr. Hanifi GÜLDEMİR
- Prof. Dr. Mehmet ESEN
- Prof. Dr. Cengiz TATAR
- Prof. Dr. Taner ALATAŞ
- Assist. Prof. Cevdet Emin EKİNCİ
- Prof. Dr. Sinan ÇALIK
- Prof. Dr. Ahmet ŞAŞMAZ
- Prof. Dr. Habibe ÖZMEN
- Prof. Dr. Filiz KAR
- Prof. Dr. İhsan DAĞTEKİN
- Prof. Dr. Vedat SAVAŞ
- Prof. Dr. Vedat ASİL
- Prof. Dr. Ömür AYDOĞMUŞ
- Prof. Dr. Mehtap MURATOĞLU
- Assoc. Prof. Ayça GÜLTEN
- Prof. Dr. Cumali İLKILIÇ
- Prof. Dr. Ayşegül UÇAR
- Prof. Dr. Ayşe Gürel İNANLI
- Prof. Dr. Serap SALER
- Prof. Dr. Kenan KÖPRÜCÜ
- Prof. Dr. Resul DAŞ
Ferhat Uçar, 1983 yılında Adana'da doğdu. Lisans eğitiminin ardından dört yıl süresince Adana, Gaziantep, Mersin ve İskenderun bölgelerinde uluslararası firmalarda yazılım satış mühendisi, otomasyon mühendisi ve proje mühendisi olarak görev yaptı. 2009 yılında akademik kariyere adım atan Uçar, 2018 yılında Fırat Üniversitesi Elektrik Elektronik Mühendisliği Anabilim Dalında makine öğrenmesi üzerine doktorasını tamamladı.
Şu anda Fırat Üniversitesi Teknoloji Fakültesi Yazılım Mühendisliği bölümünde öğretim üyesi olarak görev yapmakta olan Uçar, aynı zamanda Sam Houston State Üniversitesi ile uluslararası ortak lisans anlaşması bulunan programda İngilizce olarak yazılım mühendisliği dersleri vermektedir. Dergipark bünyesinde TR Dizinde taranan "Turkish Journal of Science & Technology", "Fırat Üniversitesi Mühendislik Bilimleri Dergisi" ve yine Dergipark'ta bulunan "Fırat Üniversitesi Fen Bilimleri Dergisi"nin baş editörlüğünü de yürütmektedir. Akademik kariyeri boyunca idari görevler de üstlenen Uçar, Teknoloji Fakültesi Dekan Yardımcılığı görevinin ardından Fırat Üniversitesi Fen Bilimleri Enstitüsü'nde müdür yardımcısı olarak görev yapmaktadır.
Uçar, IEEE, Elsevier, Wiley, Taylor and Francis ve Nature gibi prestijli platformlarda çok sayıda hakemlik görevini üstlenmiş, devam eden TÜBİTAK projelerinde ve bir COST aksiyonu projesinde araştırmacı olarak yer almaktadır. Fırat Üniversitesi'nin en büyük bütçeli projelerinden biri olan Code23 Fırat Yazılım Atölyesi'nin proje yönetim ekibinde de yer alan Uçar, mobil uygulama geliştirme eğitimi mentörü olarak da görev yapmaktadır.
Yapay zeka ve veri bilimi üzerine yoğunlaşan akademik çalışmalarına devam eden Ferhat Uçar, sektör ve akademi arasında köprü oluşturmayı hedefleyen çalışma ve projelerle kariyer yoluna devam etmektedir.
