SOC Estimation for Battery of Electric Vehicles

Year 2022, , 161 - 175, 31.12.2022

Büşra Keskin , Efnan Şora Günal , Burak Urazel , Kemal Keskin

https://doi.org/10.51541/nicel.1117756

Cited By: 1

Abstract

Accurate estimation of the state of charge of batteries is critical for the reliable operation of battery packs, not only in electric vehicles, but also in hybrid electric vehicles, unmanned aerial vehicles and smart grid systems. In this study, a model based on the Bagging-Random Forest approach is proposed to predict the value of the state of charge of electric vehicle batteries. With the proposed method, the charge value of the battery is associated with the instantaneous current, voltage and temperature of the battery. In the study, 32067 data obtained from real driving of the battery of the BMW i3 vehicle were used. In order to demonstrate the effectiveness of the proposed method, tests were also carried out using popular machine learning methods including Linear Regression and Support Vector Machine. The experimental results based on the Root Mean Square Error and Mean Absolute Error Metrics revealed that the proposed model is superior to the other methods in the literature.

Keywords

Battery State of Charge Estimation, Electric vehicle, Bagging, Random Forest, Machine Learning

Elektrikli Araç Bataryalarının Şarj Durumu Tahmini İçin Bir Model

Abstract

Bataryaların şarj durumunun doğru tahmini, yalnızca elektrikli araçlarda değil, aynı zamanda hibrit elektrikli araçlarda, insansız hava araçlarında ve akıllı şebeke sistemlerinde yer alan batarya paketlerinin güvenilir çalışması için kritik öneme sahiptir. Bu çalışmada, elektrikli araç bataryalarının şarj durumunun değerini tahmin etmek için Torbalama-Rastgele Orman yaklaşımına dayalı bir model önerilmiştir. Önerilen yöntem ile bataryaya ait şarj değeri, bataryanın anlık akım, gerilim ve sıcaklığı ile ilişkilendirilmiştir. Çalışmada BMW i3 aracının bataryasına ait gerçek sürüşlerden elde edilen 32067 adet veri kullanılmıştır. Önerilen yöntemin etkinliğini göstermek amacıyla, popüler makine öğrenmesi yöntemlerinden Doğrusal Regresyon ve Destek Vektör Makinesi yaklaşımlarıyla da testler gerçekleştirilmiştir. Kök Ortalama Kare Hata ve Ortalama Mutlak Hata metriklerine dayanan deneysel sonuçlar, önerilen modelin literatürdeki diğer yöntemlere göre daha üstün olduğu ortaya koyulmuştur.

Keywords

Batarya Şarj Durum Tahmini, Elektrikli Araç, Torbalama-Rastgele Orman, Makine Öğrenmesi, Battery State of Charge Estimation, Electric vehicle, Bagging, Random Forest, Machine Learning

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