Estimation of Current and Voltage Values Generated from a Thermoelectric Generator Mounted on Automobile Exhaust System by Machine Learning Algorithms: A Comparative Study

Year 2026, Volume: 13 Issue: 1, 17 - 31, 31.01.2026

Ahmet Çelik , Haluk Güneş

Abstract

Electricity is one of the most important sources of energy. Many devices need electrical energy to operate. In addition to the production of electrical energy from renewable sources, the fact that it can be produced from waste heat sources will increase efficiency. As in many systems, it is possible to generate electricity by using thermoelectric generators (TEGs) on the waste heat systems of vehicles using internal combustion engines. Thanks to the electricity obtained from waste heat systems, the load on the alternators and batteries in the vehicles is reduced, thus increasing their service life. In addition, since the charging time of the vehicle battery is reduced, fuel savings can be achieved. Therefore, making electricity generation predictions using machine learning algorithms in internal combustion engines will make a great contribution to the initial project planning phase of the design of automobile systems. Nowadays, research on waste heat energy recovery from automobile exhaust with TEGs using machine learning is a new topic. In this study, a data set containing the attributes of 2692 current and voltage values obtained from a thermoelectric generator on an automobile exhaust system was used. Adaboost and Random Forest machine learning algorithms were used in the estimation process of the designed model. The most successful result was achieved when estimating the current with the Adaboost algorithm. In this study, it has been shown that with the proposed model, electrical energy production estimation can be made over the waste heat sources of different systems.

Keywords

Thermoelectric generator , waste heat source , machine learning , estimation , performance measurement

Otomobil Egzoz Sistemine Monte Edilen Termoelektrik Jeneratörden Üretilen Akım ve Gerilim Değerlerinin Makine Öğrenmesi Algoritmaları ile Tahmini: Karşılaştırmalı Bir Çalışma

Abstract

Elektrik, en önemli enerji kaynaklarından biridir. Birçok cihaz çalışmak için elektrik enerjisine ihtiyaç duyar. Yenilenebilir kaynaklardan elektrik enerjisi üretmenin yanı sıra atık ısı kaynaklarından da elektrik enerjisi üretilebilmesi verimliliği artıracaktır. Birçok sistemde olduğu gibi, içten yanmalı motor kullanan araçların atık ısı sistemlerinde termoelektrik jeneratörler (TEG) kullanarak elektrik üretmek mümkündür. Atık ısı sistemlerinden elde edilen elektrik sayesinde araçlardaki alternatör ve akülerin yükü azaltılarak ömürleri uzatılır. Ayrıca araç aküsünün şarj süresi kısaldığı için yakıt tasarrufu sağlanır. Bu nedenle içten yanmalı motorlarda makine öğrenmesi algoritmaları kullanarak elektrik üretimi tahminleri yapmak, otomobil sistemlerinin tasarımında ilk proje planlama aşamasına büyük katkı sağlayacaktır. Günümüzde, makine öğrenmesi kullanılarak otomobil egzozundan atık ısı enerjisinin TEG’ler ile geri kazanımı üzerine yapılan araştırmalar yeni bir konudur. Bu çalışmada, bir otomobil egzoz sistemi üzerinde bulunan bir termoelektrik jeneratörden elde edilen 2692 akım ve gerilim değerlerinin özniteliklerini içeren bir veri seti kullanılmıştır. Tasarlanan modelin tahmin sürecinde Adaboost ve Rastgele Orman makine öğrenme algoritmaları kullanılmıştır. Akımın tahmininde Adaboost algoritması kullanıldığında en başarılı sonuç elde edilmiştir. Bu çalışmada, önerilen model ile farklı sistemlerin atık ısı kaynakları üzerinden elektrik enerjisi üretim tahmini yapılabildiği gösterilmiştir.

Keywords

Termoelektrik jeneratör , atık ısı kaynağı , makine öğrenmesi , tahmin , performans ölçümü

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