Lityum iyon batarya için dikkat mekanizmalı geçitli tekrarlayan birim (GRU) sinir ağını temel alan sağlık durumu (SOH) tahmini

Year 2024, , 639 - 647, 15.04.2024

Tuğhan Tunç , Hamit Erdem

https://doi.org/10.28948/ngumuh.1407480

Abstract

Günümüzde lityum iyon bataryalar, verimli bir enerji depolama elemanı olarak üretimin ve yaşamın çeşitli alanlarında yeri doldurulamaz bir rol oynamaktadır. Lityum iyon bataryaların sağlık durumu (SOH), enerji depolama sisteminin güvenli çalışması için kritik öneme sahiptir. Bu çalışmada batarya yönetim sisteminden gelen, yaşlanmaya bağlı olarak değişen gerilim, akım ve sıcaklık profilleri gibi ölçülebilir veriler kullanılmıştır. Bu verilere dayanarak kapasite ile şarj profilleri arasındaki ilişki sinir ağları tarafından öğrenilir. Bu çalışmada ulaşılan deneysel sonuçlar NASA lityum iyon pil veri setine dayanmaktadır. Önerilen dikkat mekanizmalı GRU yöntemi, bataryanın sağlığının tahmininde ortalama mutlak yüzde hata açısından derin öğrenme yöntemlerinden olan LSTM, GRU ve BiLSTM yöntemlerine kıyasla sırasıyla %35, %27 ve %20’ye kadar daha başarılı olduğu görülmüştür. Yapılan benzetim çalışmaları MATLAB ortamında derin öğrenme toolbox’ı kullanılarak gerçekleştirilmiştir. Son yıllarda dikkat mekanizmaları, zaman serisi tahmin modellerinin performansını artırmak için güçlü bir araç olarak ortaya çıkmıştır. Bu çalışmada, zaman serisi problemlerinin çözümlerinde kullanılan LSTM, BiLSTM ve GRU aynı NASA veri setleri üzerinde denenmiş ve bu üç yöntemden daha hızlı ve basit olmasıyla GRU tercih edilmiştir. Bu çalışmada önerilen mekanizma GRU ile Dikkat Mekanizmasını birleştirerek oluşturulmuş SoH öngörüm mekanizmasıdır.

Keywords

Lityum iyon pil, sinir ağı, kalan kullanım ömrü (RUL), kapasite tahmini, pilin sağlık durumu (SoH), LSTM, GRU, Çift Yönlü LSTM, Dikkat Mekanizması

State of health (SOH) estimation based on gated recurrent unit (GRU) neural network with attention mechanism for lithium-on battery

Abstract

Lithium-ion batteries play an irreplaceable role in various areas of production and life as an efficient energy storage element. The state of health (SOH) of lithium-ion batteries is critical to the safe operation of the energy storage system. In this study, measurable data from the battery management system, such as voltage, current and temperature profiles that change due to aging, were used. Based on these data, the relationship between capacity and charging profiles is learned by neural networks. The experimental results achieved in this study are based on the NASA lithium-ion battery data set. It has been observed that compared to the LSTM, GRU and BiLSTM methods, which are deep learning methods, the proposed GRU method with attention mechanism is more successful in estimating the health of the battery in terms of average absolute percentage error by up to 35%, 27% and 20%, respectively. The simulation studies were carried out using the deep learning toolbox in the MATLAB environment. In recent years, attention mechanisms have emerged as a powerful tool to improve the performance of time series forecasting models. In this study, LSTM, BiLSTM and GRU, which are used to solve time series problems, were tested on the same NASA data sets, and GRU was preferred because it is faster and simpler than these three methods. The mechanism proposed in this study is the SoH prediction mechanism created by combining GRU and Attention Mechanism.

Keywords

Lithium-ion battery, neural network, remaining useful life (RUL), capacity estimation, battery state of health (SoH), LSTM, GRU, Bi-directional LSTM, Self-Attention Mechanism

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