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Journals / Uludağ Üniversitesi Mühendislik Fakültesi Dergisi / 2026 / Cilt: 30 Sayı: 1

ESTIMATION OF ELECTRIC VEHICLE MULTIPLE PACKAGE LITHIUM-ION BATTERY STATE OF CHARGE USING AN EXTENDED MACHINE LEARNING

Elektrikli Araçların Çoklu Paket Li̇tyum-İyon Batarya Şarj Durumunu Geni̇şleti̇lmi̇ş Bi̇r Maki̇ne Öğrenmesi̇ Kullanarak Tahmi̇ni

Ali Rıza Kaleli(SAMSUN UNIVERSITY)0000-0002-3234-5922
Alptekin Türkkan(Bursa Uludağ Üniversitesi Orhangazi Yeniköy Asil Çelik Meslek Yüksekokulu)0000-0003-1542-0713
Pages
231–244
Publication Date
2025-04-28
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Consequently, the obtained results show that a data-driven ELM approach with a less complex structure can obtain better performance compared with other advanced estimator methods under different operating conditions.

📌 3 Key Takeaways

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Objective & Hypothesis:The estimation of battery state-of-charge (SOC) in electric or hybrid vehicle has vital importance in the designing process of battery management systems.
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Key Findings & Data:The performance of the proposed SOC estimation method is evaluated by using statistical criteria (RMSE, R2, MAPE).
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Conclusion & Impact:Consequently, the obtained results show that a data-driven ELM approach with a less complex structure can obtain better performance compared with other advanced estimator methods under different operating conditions.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Genişletilmiş makine öğrenmesi · Şarj durumu · Tahin

Abstract

The estimation of battery state-of-charge (SOC) in electric or hybrid vehicle has vital importance in the designing process of battery management systems. The state-of-charge estimation is implemented using different modelling approaches, model-based estimators such as Kalman filtering and Luenberger observer and data-driven based modelling techniques like artificial neural network and machine learning methods. This study aimed to develop a battery state-of-charge estimation method and proposed a novel architecture for multiple battery back SOC estimation using an extended learning machine (ELM). The ELM approach is applied considering battery operating conditions using global vehicle driving profiles, New European Driving Cycle and Worldwide harmonized Light vehicles Test Procedure. The performance of the proposed SOC estimation method is evaluated by using statistical criteria (RMSE, R2, MAPE). Consequently, the obtained results show that a data-driven ELM approach with a less complex structure can obtain better performance compared with other advanced estimator methods under different operating conditions.

Özet

Elektrikli veya hibrit araçlarda batarya şarj durumunun (SOC) tahmini, batarya yönetim sistemlerinin tasarım sürecinde hayati öneme sahiptir. Şarj durumu tahmini, farklı modelleme yaklaşımları, Kalman filtreleme ve Luenberger gözlemcisi gibi model tabanlı tahmin ediciler ve yapay sinir ağı ve makine öğrenimi yöntemleri gibi veri odaklı tabanlı modelleme teknikleri kullanılarak gerçekleştirilmektedir. Bu çalışma, bir batarya şarj durumu tahmin yöntemi geliştirmeyi amaçlamış ve genişletilmiş makine öğrenmesi (ELM) kullanarak çoklu batarya geri SOC tahmini için yeni bir mimari önermiştir. ELM yaklaşımı, küresel araç sürüş profilleri, Yeni Avrupa Sürüş Döngüsü ve Dünya Çapında Uyumlaştırılmış Hafif Araçlar Test Prosedürü kullanılarak ve akü çalışma koşulları dikkate alınarak uygulanmıştır. Önerilen SOC tahmin yönteminin performansı istatistiksel kriterler (RMSE, R2, MAPE) kullanılarak değerlendirilmiştir. Çalışma sonunda elde edilen sonuçlar, daha az karmaşık bir yapıya sahip veri güdümlü bir ELM yaklaşımının, farklı çalışma koşulları altında diğer gelişmiş tahmin yöntemlerine kıyasla daha iyi performans elde edebileceğini göstermektedir.

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Cite This Article

Kaleli, A. R., Türkkan, A. (2025). ESTIMATION OF ELECTRIC VEHICLE MULTIPLE PACKAGE LITHIUM-ION BATTERY STATE OF CHARGE USING AN EXTENDED MACHINE LEARNING. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, Cilt: 30 Sayı: 1, 231–244. https://doi.org/10.17482/uumfd.1595646

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