Effect of outside temperature on energy consumption of electric vehicles: Real-time big data and artificial intelligence-aided seahorse optimization approach
Küçük Resim Yok
Tarih
2025
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Gazi Univ, Fac Engineering Architecture
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
In calculating the energy consumption of electric vehicles (EVs); it is very important to optimize the consumption efficiency and driving range by considering the outdoor temperature. Studies have shown that very low and very high temperatures reduce engine efficiency and significantly increase energy consumption, while affecting regenerative energy recovery. Therefore, in the presented study, the effects of outdoor temperature on range and energy consumption were investigated using real-time big data obtained from Electric Buses (EO). The field application of the study was carried out with 22 24.7-meter EOs. The EO route was divided into 4 different regions and the energy consumption for each region and the analysis of the outdoor temperature corresponding to this consumption were obtained using regression techniques. First, the energy consumption model was created and the driving cycle was calculated for each region. Then, the driving cycle for the entire route was created and the energy consumption on the route was expressed as a mathematical model. Trilayered Neural Network (TNN) gave the best result in the calculations of the entire route. Finally, the mathematical model obtained as a result of TNN was reconsidered using the SeaHorse optimization method. Considering the analysis for the entire route (R), it was calculated that the most efficient consumption is 3.02 kWh/km and this consumption value can be achieved with a temperature of 21.5oC. This study has become a reference study for other electric vehicle manufacturers in determining the range of their vehicles in different climate conditions.
Açıklama
Anahtar Kelimeler
Electric Vehicles, Energy Consumption, Artificial Intelligence, Big Data, Optimization
Kaynak
Journal of the Faculty of Engineering and Architecture of Gazi University
WoS Q Değeri
Q3
Scopus Q Değeri
Q2
Cilt
40
Sayı
4
