Energy consumption model with real-time data for driving range extension of electric buses
Preventing range anxiety in electric vehicles (EVs) requires efficient energy use and an accurate estimation of the battery capacity needed for the desired range. A longer range leads to reduced consumption and extends operational activities. Thus, extended driving range can be achieved, promoting a...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Sustainable Futures |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S266618882500173X |
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| author | Yunus Emre Ekici Ahmet Arif Aydin Teoman Karadağ Ozan Akdağ Abdullah Ateş |
| author_facet | Yunus Emre Ekici Ahmet Arif Aydin Teoman Karadağ Ozan Akdağ Abdullah Ateş |
| author_sort | Yunus Emre Ekici |
| collection | DOAJ |
| description | Preventing range anxiety in electric vehicles (EVs) requires efficient energy use and an accurate estimation of the battery capacity needed for the desired range. A longer range leads to reduced consumption and extends operational activities. Thus, extended driving range can be achieved, promoting a more environmentally sustainable transportation model. This contributes significantly to reducing greenhouse gas emissions and mitigating the environmental impact of transportation. In this study, 250,000 rows of real-world data were collected from electric Trolleybus vehicles for a realistic energy consumption estimation of EVs. First, a mathematical model was obtained from these data using Gaussian Process Regression (GPR) method. To reduce the error rate of this model and increase the accuracy of consumption estimation, it was necessary to re-analyze it with an optimization technique. The accuracy of the consumption prediction model is extremely important for increasing the range of EVs and enabling uninterrupted travels. To solve range anxiety problem, the mathematical model obtained by GPR method is re-optimized by SeaHorse optimization and a new energy consumption prediction model, SHO-EBECM (Seahorse Optimized-Electric Bus Energy Consumption Model), is obtained. The trained SHO-EBECM was applied to 20 real routes of public transportation with internal combustion engine buses in a metropolitan city and the RMSE (Root Mean Square Error) value has been calculated to be between 0.1470 and 0.2920. Based on the achieved error rate, it can be inferred that SHO-EBECM offers a solution with a reduced error rate in comparison to four other optimization techniques. Furthermore, considering global warming, carbon emissions and ecological balance, it is concluded that approximately 12,060 tons/year of CO2, 372.75 tons/year of NO and NO2 gases can be prevented from being emitted to nature by converting internal combustion engine buses on 20 different routes to electric buses (E-Bus) with the help of SHO-EBECM. |
| format | Article |
| id | doaj-art-eaa60708eeb843008db463ac4fee939d |
| institution | Kabale University |
| issn | 2666-1888 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Sustainable Futures |
| spelling | doaj-art-eaa60708eeb843008db463ac4fee939d2025-08-20T03:31:20ZengElsevierSustainable Futures2666-18882025-06-01910060310.1016/j.sftr.2025.100603Energy consumption model with real-time data for driving range extension of electric busesYunus Emre Ekici0Ahmet Arif Aydin1Teoman Karadağ2Ozan Akdağ3Abdullah Ateş4Department of Electric Vehicle Technologies, OIZ Vocational College, Inonu University, 44900 Malatya, TürkiyeDepartment of Computer Engineering, Inonu University, 44000 Malatya, Türkiye; Corresponding author.Department of Electric Electronic Engineering, Inonu University, 44200 Malatya, Türkiye; TEMSA R&D Center, Adana, TürkiyeDepartment of Computer Engineering, Malatya Turgut Özal University, 44210 Malatya, TürkiyeDepartment of Computer Engineering, Inonu University, 44000 Malatya, TürkiyePreventing range anxiety in electric vehicles (EVs) requires efficient energy use and an accurate estimation of the battery capacity needed for the desired range. A longer range leads to reduced consumption and extends operational activities. Thus, extended driving range can be achieved, promoting a more environmentally sustainable transportation model. This contributes significantly to reducing greenhouse gas emissions and mitigating the environmental impact of transportation. In this study, 250,000 rows of real-world data were collected from electric Trolleybus vehicles for a realistic energy consumption estimation of EVs. First, a mathematical model was obtained from these data using Gaussian Process Regression (GPR) method. To reduce the error rate of this model and increase the accuracy of consumption estimation, it was necessary to re-analyze it with an optimization technique. The accuracy of the consumption prediction model is extremely important for increasing the range of EVs and enabling uninterrupted travels. To solve range anxiety problem, the mathematical model obtained by GPR method is re-optimized by SeaHorse optimization and a new energy consumption prediction model, SHO-EBECM (Seahorse Optimized-Electric Bus Energy Consumption Model), is obtained. The trained SHO-EBECM was applied to 20 real routes of public transportation with internal combustion engine buses in a metropolitan city and the RMSE (Root Mean Square Error) value has been calculated to be between 0.1470 and 0.2920. Based on the achieved error rate, it can be inferred that SHO-EBECM offers a solution with a reduced error rate in comparison to four other optimization techniques. Furthermore, considering global warming, carbon emissions and ecological balance, it is concluded that approximately 12,060 tons/year of CO2, 372.75 tons/year of NO and NO2 gases can be prevented from being emitted to nature by converting internal combustion engine buses on 20 different routes to electric buses (E-Bus) with the help of SHO-EBECM.http://www.sciencedirect.com/science/article/pii/S266618882500173XBig dataE-busPredictionSeahorse optimizationRange extensionEnergy consumption |
| spellingShingle | Yunus Emre Ekici Ahmet Arif Aydin Teoman Karadağ Ozan Akdağ Abdullah Ateş Energy consumption model with real-time data for driving range extension of electric buses Sustainable Futures Big data E-bus Prediction Seahorse optimization Range extension Energy consumption |
| title | Energy consumption model with real-time data for driving range extension of electric buses |
| title_full | Energy consumption model with real-time data for driving range extension of electric buses |
| title_fullStr | Energy consumption model with real-time data for driving range extension of electric buses |
| title_full_unstemmed | Energy consumption model with real-time data for driving range extension of electric buses |
| title_short | Energy consumption model with real-time data for driving range extension of electric buses |
| title_sort | energy consumption model with real time data for driving range extension of electric buses |
| topic | Big data E-bus Prediction Seahorse optimization Range extension Energy consumption |
| url | http://www.sciencedirect.com/science/article/pii/S266618882500173X |
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