A new study on the prediction of the effects of road gradient and coolant flow on electric vehicle battery power electronics components using machine learning approach
Yükleniyor...
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/embargoedAccess
Özet
Transistor and diode junction temperature is an important factor in battery electric vehicle systems. High
junction temperatures can lead to increased power losses and reduce the efficiency of the power conversion
process. Additionally, high junction temperatures can accelerate the degradation rate of lithium-ion batteries,
leading to capacity loss, increased internal resistance, and reduced overall battery life. In this study, the pa rameters affecting the transistor and diode junction temperatures of the battery group, which is the most
important component of a battery electric vehicle, have been extensively analyzed and estimated by the machine
learning approach. An artificial neural network (ANN) model has been established to estimate the parameters
affecting the transistor and diode junction temperatures. The Bayesian regularization algorithm is preferred as
the training algorithm in the ANN structure with multilayer perceptron architecture. There are 20 neurons in the
hidden layer of the neural network established with a total of 242 data sets obtained from the literature. 80 % of
the data set was employed for the training of the model and 20 % for the testing phase. The ANN model’s
calculated performance parameters were 8.81E-01 for mean squared error (MSE) and 0.91739 for coefficient of
determination (R) value. The proposed ANN model was able to successfully predict the parameters affecting the
transistor and diode junction temperatures with an average proportional deviation of less than 0.038 % from the
actual values. This study can be considered a pioneering study both in terms of cost and time for production
practice and in terms of its contribution to the literature.
Açıklama
Anahtar Kelimeler
Battery electric vehicle, Load gradability, Transistor, Diode, Artificial neural network
Kaynak
Journal of Energy Storage
WoS Q Değeri
Q1
Scopus Q Değeri
N/A
Cilt
70
