- International Journal of Energy Applications and Technologies
- Vol: 8 Issue: 1
- A multivariate nonlinear regression model for the resistance power of a light rail vehicle
A multivariate nonlinear regression model for the resistance power of a light rail vehicle
Authors : Mine Sertsöz, Mehmet Fidan
Pages : 33-38
Doi:10.31593/ijeat.798799
View : 9 | Download : 4
Publication Date : 2021-03-31
Article Type : Research
Abstract :In Turkey, 20% of energy use caused by transportation. Light rail transportation has developing nowadays. However, it is very important to issue having information about the energy consumption of the light rail vehicle according to different both vehicle and railway situations. In Turkey, approximately 20% of the energy expended is spent on transportation. Light rail transportation technology is still in an evolving process. In this development process, it is crucial to have information about the energy consumption of the light rail vehicle according to the different situations of both the vehicle and the railway. It is necessary to predict the power losses that will occur under different driving conditions sensitively to ensure energy efficiency in light rail systems. The most important of these power losses is the resistance loss caused by contact with the route. Resistance loss is dependent multiple environmental conditions. The most important of these conditions can be listed as the weight of the light rail vehicle, the instantaneous speed of the vehicle, the curve of the route, the ramp slope of the route and the friction force arising from these conditions. Resistance loss is proportional and linearly dependent to some of these variables while others show reverse or nonlinear dependence. Due to these different types of dependencies, it is seen that a single multivariate nonlinear model is needed to explain the loss of resistance in all different conditions. In this study, a new and accurate model for resistance losses has been developed by fitting numerical values obtained from different scenarios to multivariate nonlinear regression model.Keywords : energy efficiency, nonlinear regression, optimization, railway