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
| dc.contributor.author | Çolak, Andaç Batur | |
| dc.date.accessioned | 2023-11-08T09:25:38Z | |
| dc.date.available | 2023-11-08T09:25:38Z | |
| dc.date.issued | 2023 | en_US |
| dc.department | Rektörlük, Bilişim Teknolojileri Uygulama ve Araştırma Merkezi | en_US |
| dc.description.abstract | 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. | en_US |
| dc.identifier.doi | 10.1016/j.est.2023.108101 | en_US |
| dc.identifier.scopus | 2-s2.0-85162072904 | en_US |
| dc.identifier.scopusquality | N/A | en_US |
| dc.identifier.uri | https://hdl.handle.net/11467/6950 | |
| dc.identifier.uri | https://doi.org/10.1016/j.est.2023.108101 | |
| dc.identifier.volume | 70 | en_US |
| dc.identifier.wos | WOS:001028881100001 | en_US |
| dc.identifier.wosquality | Q1 | en_US |
| dc.indekslendigikaynak | Web of Science | en_US |
| dc.indekslendigikaynak | Scopus | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Elsevier | en_US |
| dc.relation.ispartof | Journal of Energy Storage | en_US |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Başka Kurum Yazarı | en_US |
| dc.rights | info:eu-repo/semantics/embargoedAccess | en_US |
| dc.subject | Battery electric vehicle, Load gradability, Transistor, Diode, Artificial neural network | en_US |
| dc.title | 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 | en_US |
| dc.type | Article | en_US |
