A Reference-Based Feature Representation Approach for Battery Remaining Useful Life Prediction Using K-Nearest Neighbors Regression

Recai Özcan; Ferhat Koca

doi:10.69560/cujast.1904437

EN TR

A Reference-Based Feature Representation Approach for Battery Remaining Useful Life Prediction Using KNN Regression

Öz

This study systematically investigates the effect of feature representation design on performance in Remaining Useful Life (RUL) prediction for lithium-ion batteries. While RUL prediction in existing literature primarily focuses on model architecture and hyperparameter optimization, the role of feature representation design, especially concerning cross-battery generalization, has been addressed to a limited extent. In this context, two different representation approaches, namely absolute feature space-based representation (Absolute Feature Representation (AFR)) and reference cycle-centered representation (Reference-based Feature Representation (RFR)), were developed and evaluated. Experiments were conducted on data from 4 batteries within the NASA PCOE lithium-ion battery dataset, and evaluation was performed using a battery-wise cross-validation strategy. K-Nearest Neighbors (KNN) was used as the regression model, and only the representation effect was analyzed by keeping model parameters constant. The results demonstrate that RFR provides consistent improvements across all performance metrics. In the evaluation metrics, the average MAE value decreased from 0.148 to 0.096, and the RMSE value decreased from 0.173 to 0.120. The R² value increased from 0.637 to 0.825. These improvements correspond to performance increases of approximately 35%, 31%, and 29%, respectively. Correlation analysis revealed that the transformation performed in RFR strengthened the feature-target relationship and made relative degradation dynamics more prominent by suppressing absolute level differences. The diagnostic analyses performed confirm that the error structure is balanced and systematic. The findings indicate that performance improvement in RUL prediction is strongly dependent not only on model selection but also on feature representation design. Especially in cases where initial differences between batteries are high, the RFR approach offers an effective strategy that provides significant and consistent improvements without increasing model complexity.

Anahtar Kelimeler

Remaining Useful Life (RUL) prediction;, Lithium- ion batteries;, Reference- based feature representation;, Distance- based regression;, K- Nearest Neighbors (KNN);, Battery- wise cross- validation

A Reference-Based Feature Representation Approach for Battery Remaining Useful Life Prediction Using K-Nearest Neighbors Regression

Öz

Bu çalışmada, lithium-ion bataryalar için Remaining Useful Life (RUL) tahmininde özellik temsili tasarımının performans üzerindeki etkisini sistematik olarak incelenmiştir. Mevcut literatürde RUL tahmini çoğunlukla model mimarisi ve hiperparametre optimizasyonuna odaklanırken, özellik temsili tasarımının özellikle cross-battery generalization üzerindeki rolü sınırlı biçimde ele alınmıştır. Bu bağlamda mutlak özellik uzayına dayalı temsil (Absolute Feature Representation (AFR)) ve referans döngü merkezlemeli temsil (Reference-based Feature Representation (RFR)) olmak üzere iki farklı temsil yaklaşımı oluşturularak değerlendirilmiştir. Deneyler NASA PCoE lithium-ion battery veri seti üzerinde 4 bataryaya ait veriler üzerinde gerçekleştirilmiş ve değerlendirme a battery-wise cross-validation stratejisi ile yapılmıştır. Regresyon modeli olarak K-Nearest Neighbors (KNN) kullanılmış ve model parametreleri sabit tutularak yalnızca temsil etkisi analiz edilmiştir. Sonuçlar, RFR’nin tüm performans metriklerinde tutarlı iyileşme sağladığını göstermektedir. Değerlendirme metriklerinde ortalama MAE değeri 0.148’den 0.096’ya, RMSE değeri 0.173’ten 0.120’ye düşmüştür. R² değeri ise 0.637’den 0.825’e yükselmiştir. Bu iyileşmeler sırasıyla yaklaşık %35, %31 ve %29 oranında performans artışına karşılık gelmektedir. Korelasyon analizi, RFR’de yapılan transformasyonun feature–target ilişkisini güçlendirdiğini ve mutlak seviye farklarını baskılayarak göreli degradasyon dinamiklerini daha belirgin hale getirdiğini ortaya koymuştur. Gerçekleştirilen tanılayıcı analizler hata yapısının dengeli ve sistematik olduğunu doğrulamaktadır. Elde edilen bulgular, RUL tahmininde performans artışının yalnızca model seçimine değil, özellik temsili tasarımına da güçlü biçimde bağlı olduğunu göstermektedir. Özellikle bataryalar arası başlangıç farklılıklarının yüksek olduğu durumlarda, RFR yaklaşımı model karmaşıklığını artırmadan anlamlı ve tutarlı iyileşmeler sağlayan etkili bir strateji sunmaktadır.

Anahtar Kelimeler

Kalan Kullanım Ömrü (RUL) tahmini;, Lityum iyon bataryalar;, Referans tabanlı özellik temsili;, Mesafe tabanlı regresyon;, K-En Yakın Komşular (KNN);, Batarya bazlı çapraz doğrulama

Kaynakça

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Ayrıntılar

Birincil Dil

Türkçe

Konular

Elektrik Enerjisi Depolama, Elektrik Mühendisliği (Diğer), Enerji Sistemleri Mühendisliği (Diğer)

Bölüm

Araştırma Makalesi

Yazarlar

Recai Özcan ^*
0000-0003-1708-1225
Türkiye

Ferhat Koca
0000-0001-8849-5295
Türkiye

Yayımlanma Tarihi

29 Haziran 2026

Gönderilme Tarihi

6 Mart 2026

Kabul Tarihi

4 Mayıs 2026

Yayımlandığı Sayı

Yıl 2026 Cilt: 5 Sayı: 1

DOI

https://doi.org/10.69560/cujast.1904437

IZ

https://izlik.org/JA54EU46YG

APA

Özcan, R., & Koca, F. (2026). A Reference-Based Feature Representation Approach for Battery Remaining Useful Life Prediction Using K-Nearest Neighbors Regression. Sivas Cumhuriyet Üniversitesi Bilim ve Teknoloji Dergisi, 5(1), 25-39. https://doi.org/10.69560/cujast.1904437

AMA

1.Özcan R, Koca F. A Reference-Based Feature Representation Approach for Battery Remaining Useful Life Prediction Using K-Nearest Neighbors Regression. CUJAST. 2026;5(1):25-39. doi:10.69560/cujast.1904437

Chicago

Özcan, Recai, ve Ferhat Koca. 2026. “A Reference-Based Feature Representation Approach for Battery Remaining Useful Life Prediction Using K-Nearest Neighbors Regression”. Sivas Cumhuriyet Üniversitesi Bilim ve Teknoloji Dergisi 5 (1): 25-39. https://doi.org/10.69560/cujast.1904437.

EndNote

Özcan R, Koca F (01 Haziran 2026) A Reference-Based Feature Representation Approach for Battery Remaining Useful Life Prediction Using K-Nearest Neighbors Regression. Sivas Cumhuriyet Üniversitesi Bilim ve Teknoloji Dergisi 5 1 25–39.

IEEE

[1]R. Özcan ve F. Koca, “A Reference-Based Feature Representation Approach for Battery Remaining Useful Life Prediction Using K-Nearest Neighbors Regression”, CUJAST, c. 5, sy 1, ss. 25–39, Haz. 2026, doi: 10.69560/cujast.1904437.

ISNAD

Özcan, Recai - Koca, Ferhat. “A Reference-Based Feature Representation Approach for Battery Remaining Useful Life Prediction Using K-Nearest Neighbors Regression”. Sivas Cumhuriyet Üniversitesi Bilim ve Teknoloji Dergisi 5/1 (01 Haziran 2026): 25-39. https://doi.org/10.69560/cujast.1904437.

JAMA

1.Özcan R, Koca F. A Reference-Based Feature Representation Approach for Battery Remaining Useful Life Prediction Using K-Nearest Neighbors Regression. CUJAST. 2026;5:25–39.

MLA

Özcan, Recai, ve Ferhat Koca. “A Reference-Based Feature Representation Approach for Battery Remaining Useful Life Prediction Using K-Nearest Neighbors Regression”. Sivas Cumhuriyet Üniversitesi Bilim ve Teknoloji Dergisi, c. 5, sy 1, Haziran 2026, ss. 25-39, doi:10.69560/cujast.1904437.

Vancouver

1.Recai Özcan, Ferhat Koca. A Reference-Based Feature Representation Approach for Battery Remaining Useful Life Prediction Using K-Nearest Neighbors Regression. CUJAST. 01 Haziran 2026;5(1):25-39. doi:10.69560/cujast.1904437

A Reference-Based Feature Representation Approach for Battery Remaining Useful Life Prediction Using KNN Regression

Öz

Anahtar Kelimeler

A Reference-Based Feature Representation Approach for Battery Remaining Useful Life Prediction Using K-Nearest Neighbors Regression

Öz

Anahtar Kelimeler

Kaynakça

Ayrıntılar

Birincil Dil

Konular

Bölüm

Yazarlar

Yayımlanma Tarihi

Gönderilme Tarihi

Kabul Tarihi

Yayımlandığı Sayı

DOI

IZ

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