Derin Öğrenme Yöntemleri ile Batarya Kalan Kullanım Ömrünün Tahmini

Year 2025, Volume: 3 Issue: 1, 32 - 43, 28.03.2025

Kardelen Kamişli , İclal Çetin Taş

https://doi.org/10.61150/ijonfest.2025030104

Abstract

Yenilenebilir enerji kaynakları ve elektrik şebekelerinin hızla genişlemesi, enerji talep ve arz dengesizliklerine yol açmaktadır. Bu durum, enerji üretimi ve tüketimi arasındaki uyumsuzluklar nedeniyle gerilim ve frekans seviyelerinde dalgalanmalara neden olmakta, enerji sistemlerinin stabilitesini tehdit etmektedir. Özellikle güneş ve rüzgar gibi yenilenebilir enerji kaynaklarının doğası gereği değişken ve öngörülemez olması, bu dalgalanmaları daha da artırmaktadır. Geleneksel enerji üretim sistemlerinin aksine, yenilenebilir enerji sistemleri, talebe anında cevap verme konusunda sınırlı kapasiteye sahiptir. Bu bağlamda, enerji depolama sistemleri, yenilenebilir enerji üretiminin verimli bir şekilde yönetilmesi ve şebeke dengesinin korunması için kritik bir çözüm olarak ön plana çıkmaktadır. Bataryaların kalan kullanım ömrü (Remaining Useful Life, RUL) ve şarj durumu (State of Charge, SoC) üzerine yapılan çalışmalar, batarya güvenilirliği, kullanıcı deneyimi ve çevresel sürdürülebilirlik açısından kritik önemdedir. Bu çalışmalar, enerji verimliliği, mobilite artışı, batarya değişim ihtiyacının azalması ve atık yönetimi avantajları sunmaktadır. Batarya ömrü tahmini, batarya tabanlı sistemlerin etkin yönetimini ve enerji talebine yönelik stratejik planlamayı mümkün kılmaktadır. Derin öğrenme yöntemleri, batarya kapasite ve ömür tahmini alanında önemli ilerlemeler sağlamıştır. Endüstriyel uygulamalarda tercih edilen uzun ömürlü ve yüksek enerji depolama kapasitesine sahip piller, derin öğrenme yöntemleriyle daha iyi analiz edilmektedir. Bu çalışmada da Scaled Conjugate Gradient (SCG) algoritması, batarya kapasite tahmininde kullanılarak elde edilen sonuçlar incelenmektedir. Batarya tabanlı sistemlerin etkin yönetimini kolaylaştırarak enerji depolama teknolojilerinin sürdürülebilirliğini destekleyici yaklaşımlar oluşturmak amaçlanmıştır. Batarya tahmini üzerine yapılan bu çalışmada elde edilen %1.098 MAPE, 0.9823 R2, 0.0019 MSE ve 0.0302 MAE performans metrikleriyle, enerji depolama sistemlerinin verimli yönetimini, enerji kaynaklarının etkin kullanımını ve enerji ihtiyaçlarına yönelik stratejik planlamayı desteklemektedir.

Keywords

batarya ömrü, batarya yönetim sistemi, derin öğrenme, enerji tahmin

Estimation of Battery Remaining Life-time with Deep Learning Methods

Abstract

The swift proliferation of renewable energy sources and electric grids causes discrepancies between energy supply and demand. This scenario causes variations in voltage and frequency levels due to discrepancies between energy generation and consumption, jeopardizing the stability of energy networks. The intrinsically fluctuating and unpredictable characteristics of renewable energy sources, such as the sun and wind, intensify these oscillations. In contrast to conventional have to have energy-producing systems, renewable energy systems have energy-producing systems and a restricted ability to adapt immediately to demand. In this environment, energy storage devices arise as a vital solution for the effective management of renewable energy generation and for sustaining grid stability. Research on Remaining Useful Life (RUL) and State of Charge (SoC) of batteries is essential for battery reliability, user satisfaction, and environmental sustainability. These studies provide benefits in energy efficiency, increased mobility, diminished battery replacement requirements, and superior waste management. Estimating battery longevity facilitates the efficient management of battery-operated equipment and the strategic planning of energy requirements. Deep learning techniques have made substantial progress in estimating battery capacity and longevity. Long-lasting batteries with substantial energy storage capacity, favored in industrial applications, are more efficiently assessed utilizing deep learning methodologies. This study analyzes the outcomes derived from the application of the Scaled Conjugate Gradient (SCG) technique for estimating battery capacity. It seeks to enhance the efficient management of battery systems and devise strategies that promote the sustainability of energy storage technology. This study's performance measures, comprising 1.098% MAPE, 0.9823 R², 0.0019 MSE, and 0.0302 MAE, enhance the effective management of energy storage systems, the optimal use of energy resources, and strategic planning to fulfill energy demands. This study's performance measures, 1.098% MAPE, 0.9823 R2, 0.0019 MSE and 0.0302 MAE obtained in this study on battery estimation, it supports the efficient management of energy storage systems, effective use of energy resources and strategic planning for energy demands.

Keywords

battery, battery management system, deep learning, predictive algorithm, remaining useful life

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