state of healt prediction using support vector machine Agus Mujianto (a), Hery Try Waloyo (a), Muhammad Nizam (b)
a) Universitas Muhammadiyah Kalimantan Timur (UMKT)
b) Universitas Sebelas Maret (UNS)
Abstract
Battery is one of the most important components in electric cars, which today electric cars are growing very rapidly. So that the development of the battery is also very important, both in terms of material and other important parameters. One of the most important parameters for the battery is the state of health (SOH). This parameter is used to determine the health of the battery, so that the battery management system will run well. There are Many methods had been used to calculate this SOH from conventional to using artificial intelligence. In this paper, we will discuss the use of artificial intelligence to predict the SOH of a battery using a support vector machine (SVM). SVM is one type of artificial intelligence that is widely used for prediction and classification. The results of the prediction of the SOH battery will be compared with the results of the experimental data. From the results of using SVM, it can be seen that the prediction has a very small error compared to the experimental data.
