Dış ortam sıcaklığının elektrikli araçların enerji tüketimine etkisi: Gerçek zamanlı büyük veri ve yapay zekâ destekli seahorse optimizasyon yaklaşımı

Yunus Emre Ekici; Teoman Karadag; Ozan Akdağ; Ahmet Arif Aydın

doi:10.17341/gazimmfd.1623529

Araştırma Makalesi

Yıl 2025, Cilt: 40 Sayı: 4, 2263 - 2280

Yunus Emre Ekici , Teoman Karadag , Ozan Akdağ , Ahmet Arif Aydın

https://doi.org/10.17341/gazimmfd.1623529

Öz

Kaynakça

1. Zou Y., Wei S., Sun F., Hu X. and Shiao Y., Large-scale deployment of electric taxis in Beijing: A real-world analysis, Energy, 100 (2), 25–39, 2016.
2. Zhang X., Zou Y., Fan J. and Guo H., Usage pattern analysis of Beijing private electric vehicles based on real-world data, Energy, 167 (1), 1074–1085, 2019.
3. Mohammadi F. and Saif M., A comprehensive overview of electric vehicle batteries market, e-Prime - Advances in Electrical Engineering, Electronics and Energy, 3 (1), 100127-100140, 2023.
4. Lee G., Song J., Lim Y. and Park J., Energy consumption evaluation of passenger electric vehicle based on ambient temperature under Real-World driving conditions, Energy Conversion and Management, 306 (2024), 110209-110214, 2024.
5. Ekici Y.E., Akdağ O., Aydın A.A. and Karadağ T., Optimization of Proportional-Integral-Derivative Parameters for Speed Control of Squirrel-Cage Motors with Seahorse Optimization, Electrica, 2 (24), 318-326, 2024.
6. Ekici Y.E., Akdağ O., Aydin A.A. and Karadağ T., A novel energy consumption prediction model of electric buses using real-time big data from route , environment , and vehicle parameters, IEEE Access, 11 (1), 104305–104322, 2023.
7. Zhang J., Wang Z., Liu P. and Zhang Z., Energy consumption analysis and prediction of electric vehicles based on real-world driving data, Applied Energy, 275 (5), 115408-115423, 2020.
8. Ji Y., Zhang Y. and. Wang C.-Y, Li-Ion Cell Operation at Low Temperatures, Journal of The Electrochemical Society, 160 (4), 636–649, 2013.
9. Wenjie F., Zhibin Z., Ming D. and Ming R., On-line Estimation Method for Internal Temperature of Lithium-ion Battery Based on Electrochemical Impedance Spectroscopy, 2021 IEEE Electrical Insulation Conference (EIC), Denver-USA, 247–251, 07-28 June, 2021.
10. Sun Y. K., Promising All-Solid-State Batteries for Future Electric Vehicles, ACS Energy Letter, 5 (10), 3221–3223, 2020.
11. Al-Wreikat Y., Serrano C. and Sodré J. R., Driving behaviour and trip condition effects on the energy consumption of an electric vehicle under real-world driving, Applied Energy, 297 (1), 117096-117104, 2021.
12. Ekici Y.E., Akdağ O., Karadağ T. and Aydın A.A., Review And Analysis Of Real Time Big Data Of Electric Bus Consumption Data and Optimum Integration into the Urban Transportation Routes, in Ankara Internatıonal Congress On Scıentıfıc Research-VII, Ankara-Türkiye, 1405-1418, 2-4 December, 2022.
13. Al-Wreikat Y., Serrano C. and Sodré J. R., Effects of ambient temperature and trip characteristics on the energy consumption of an electric vehicle, Energy, 238 (1), 122028-122037, 2022.
14. Song Z., Pan Y., Chen H. and Zhang T., Effects of temperature on the performance of fuel cell hybrid electric vehicles: A review, Applied Energy, 302 (1),117572-117580, 2021.
15. Pesaran A., Addressing the Impact of Temperature Extremes on Large Format Li-Ion Batteries for Vehicle Applications, 30th International Battery Seminary, Colorado-USA, 1-30, 11-14 March, 2013.
16. Deng T., Zhang G., Ran Y. and Liu P., Thermal performance of lithium ion battery pack by using cold plate, Applied Thermal Engineering, 160 (66), 114088-114096, 2019.
17. Shang Z., Qi H., Liu X., Ouyang C. and Wang Y., Structural optimization of lithium-ion battery for improving thermal performance based on a liquid cooling system, International Journal of Heat and Mass Transfer, 130 (1), 33–41, 2019.
18. Ekici Y. E., Dikmen İ. C., Nurmuhammed M. and Karadağ T., Efficiency Analysis of Various Batteries with Real-time Data on a Hybrid Electric Vehicle, International Journal of Automotive Science And Technology, 5 (11), 214–223, 2021.
19. Jaguemont J., Boulon L. and Dubé Y., A comprehensive review of lithium-ion batteries used in hybrid and electric vehicles at cold temperatures, Applied Energy, 164 (1), 99–114, 2016.
20. Lu M., Zhang X., Ji J., Xu X. and Zhang Y., Research progress on power battery cooling technology for electric vehicles, Journal Energy Storage, 27 (1), 101155-101171, 2019.
21. Ji Y. and Wang C. Y., Heating strategies for Li-ion batteries operated from subzero temperatures, Electrochimica Acta, 107 (1), 664–674, 2013.
22. Tran M., Bhatti A., Vrolyk R.,Wong D., Panchal S., Fowler M. And Fraser R., A Review of Range Extenders in Battery Electric Vehicles: Current Progress and Future Perspectives, World Electric Vehicle Journal, 12 (2), 54-70, 2021.
23. Li K., Hongming C., Dingyu X., Hanqi Z., Binlin D., Hua Z., Ni L., Xuejin Z. and Ran T., Assessment method of the integrated thermal management system for electric vehicles with related experimental validation, Energy Conversion and Management, 276 (3), 116571-116584, 2023.
24. Hao X., Wang H., Lin Z. and Ouyang M., Seasonal effects on electric vehicle energy consumption and driving range: A case study on personal, taxi, and ridesharing vehicles, Journal of Cleaner Production, 249 (1), 119403-119416, 2020.
25. Szumska E. M. and Jurecki R. S., Parameters influencing on electric vehicle range, Energies, 14 (16), 4821-4844, 2021.
26. Dikmen İ.C., Ekici Y.E., Karadağ T., Abbasov T. and Hamamcı S.E., Electrification in Urban Transport: A Case Study with Real-time Data, Balkan Journal of Electrical and Computer Engineering, 9 (1), 69–77, 2021.
27. Ramesh A. B, Minovski B. and Sebben S., Thermal encapsulation of large battery packs for electric vehicles operating in cold climate, Applied Thermal Engineering, 212 (2), 118548-118561, 2022.
28. Taggart J., Ambient temperature impacts on real-world electric vehicle efficiency & range, 2017 IEEE Transportation Electrification Conference and Expo (ITEC), Chicago-USA, 186–190, 22-24 June, 2017.
29. Yuksel T. and Michalek J.J., Effects of regional temperature on electric vehicle efficiency, range, and emissions in the united states, Environmental Science and Technology, 49 (6), 3974–3980, 2015.
30. Paffumi E., Otura M., Centurelli M., Casellas R., Brenner A. and Jahn S., Energy Consumption, Driving Range and Cabin Temperature Performances at Different Ambient Conditions in Support to the Design of a User-Centric Efficient Electric Vehicle: the QUIET Project, 14th SDEWES Conference, Dubrovnik - Croatia, 1- 18, 1-6 October, 2019.
31. Ekici Y.E., Dikmen İ.C., Nurmuhammed M. and Karadağ T., A Review on Electric Vehicle Charging Systems and Current Status in Turkey, International Journal of Automotive Science Technology, 5 (4), 316–330, 2021.
32. Varga B. O., Sagoian A. and Mariasiu F., Prediction of electric vehicle range: A comprehensive review of current issues and challenges, Energies, 12 (5), 946-965, 2019.
33. Iora P. and Tribioli L., Effect of ambient temperature on electric vehicles energy consumption and range: Model definition and sensitivity analysis based on Nissan Leaf data, World Electric Vehicle Journal, 10 (1), 1–15, 2019.
34. Ekici Y.E. and Tan N, Investigation Of Charging And Discharging Characteristics Of Different Type Batteries Using Trambus Accelerator Pedal Data On Hybrid Electric Vehicle Model, Internatioal Journal Energy Engineering Science, 3 (3), 55–67, 2018.
35. Steinstraeter M., Heinrich T. and Lienkamp M., Effect of low temperature on electric vehicle range, World Electric Vehicle Journal, 12 (3), 115-141, 2021.
36. Klingler A. L., The effect of electric vehicles and heat pumps on the market potential of PV + battery systems, Energy, 161 (1), 1064–1073, 2018.
37. Ekici Y.E, Karadağ T, Aydın A.A and Akdağ O, Driving Range Problem in Electric Vehicles and Methods to Increase, Article and Reviews in Engineering, Platanus Publishing, Ankara, 253–286, 2024.
38. Xu B. and Arjmandzadeh Z., Parametric study on thermal management system for the range of full (Tesla Model S)/ compact-size (Tesla Model 3) electric vehicles, Energy Conversion and Management, 278 (1), 116753-116766, 2023.
39. Wang J. B., Liu K., Yamamoto T. and Morikawa T., Improving Estimation Accuracy for Electric Vehicle Energy Consumption Considering the Effects of Ambient Temperature, Energy Procedia, 105 (1), 2904–2909, 2017.
40. Liu K., Wang J., Yamamoto T. and Morikawa T., Exploring the interactive effects of ambient temperature and vehicle auxiliary loads on electric vehicle energy consumption, Applied Energy, 227 (2018), 324–331, 2018.
41. Fetene G. M., Kaplan S., Mabit S. L., Jensen A. F. and Prato C. G., Harnessing big data for estimating the energy consumption and driving range of electric vehicles, Transportation Research Part D: Transport and Environment, 54 (1), 1–11, 2017.
42. Mišanović S. M., Glišović J. D., Blagojević I. A. and Taranović D. S., Influencing Factors on Electricity Consumption of Electric Bus in Real Operating Conditions, Thermal Science, 27 (1), 767–784, 2023.
43. Szilassy P. Á. and Földes D., Consumption estimation method for battery-electric buses using general line characteristics and temperature, Energy, 261 (3), 12500-12511, 2022.
44. Doulgeris S., Zafeiriadis A., Athanasopoulos N., Tzivelou N., Michali M.E., Papagianni S. and Samaras Z., Evaluation of energy consumption and electric range of battery electric busses for application to public transportation, Transportation Engineering, 15 (3), 100223-100233, 2024.
45. Motas E-Bus feasibility report, Motaş, https://www.motas.com.tr/ Yayın tarihi Şubat 7, 2024. Erişim tarihi Ocak 10, 2025.
46. Trambüs 24 Meters, Bozankaya, https://www.bozankaya.com.tr/ Yayın tarihi Mart 15, 2015. Erişim tarihi Aralık 22, 2024.
47. Ekici Y.E., Karadag T., Aydin A.A. and Akdag O., Impact of Outside Temperature on Driving Range and Energy Consumption Using Real-Time Big Data for Electric Buses, 8th International Artificial Intelligence and Data Processing Symposium, Malatya-Türkiye, 1-9, 21-22 September, 2024.
48. Ekici Y.E and Tan N., Investigation Of Charging And Discharging Characteristics Of Different Type Batteries On Hybrid Electric Vehicle Model, The International Journal of Energy and Engineering Sciences, 3 (3), 55-68, 2018.
49. Akman T., Yılmaz C. and Sönmez Y., Elektrik Yükü Tahmin Yöntemlerinin Analizi, Gazi Journal Education Science, 4 (3), 29–51, 2018.
50. Zhao L., Ke H. and Huo W., A frequency item mining based energy consumption prediction method for electric bus, Energy, 263 (1), 125915-1259-28, 2023.
51. Liu S., Kong Z., Huang T., Du Y. and Xiang W., An ADMM-LSTM framework for short-term load forecasting, Neural Networks, 173 (2), 106150-106162, 2024.
52. Qin H., Zhao G., Li Y. and Wang H., Hybrid distributed finite-time neurodynamic optimization of electric vehicle charging schemes management in microgrid considering, Neural Networks, 161 (1), 466–475, 2023.
53. Ram J. P., Rajasekar N. and Miyatake M., Design and overview of maximum power point tracking techniques in wind and solar photovoltaic systems: A review, Renewable and Sustainable Energy Reviews, 73 (3), 1138–1159, 2017.
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Dış ortam sıcaklığının elektrikli araçların enerji tüketimine etkisi: Gerçek zamanlı büyük veri ve yapay zekâ destekli seahorse optimizasyon yaklaşımı

Yıl 2025, Cilt: 40 Sayı: 4, 2263 - 2280

Yunus Emre Ekici , Teoman Karadag , Ozan Akdağ , Ahmet Arif Aydın

https://doi.org/10.17341/gazimmfd.1623529

Öz

Elektrikli araçların (EA) enerji tüketimlerinin hesaplanmasında; dış ortam sıcaklığı, göz önünde bulundurularak tüketim verimliliği ve sürüş menzilini optimize etmek çok önemlidir. Araştırmalar, çok düşük ve çok yüksek sıcaklıkların motor verimini düşürmekte ve enerji tüketimini önemli ölçüde artırırken, rejeneratif enerji geri kazanımını etkilediğini göstermiştir. Dolayısıyla, sunulan çalışmada Elektrikli Otobüslerden (EO) elde edilen gerçek zamanlı büyük veriler kullanılarak, dış ortam sıcaklığının menzil ve enerji tüketimi üzerindeki etkileri incelenmiştir. Çalışmanın saha uygulaması, 22 adet 24,7 metrelik EO’lar ile gerçekleştirilmiştir. EO rotası, 4 farklı bölgeye ayrılmış ve her bölge için enerji tüketimi ve bu tüketime karşılık gelen dış ortam sıcaklığının analizi regresyon teknikleri kullanılarak elde edilmiştir. İlk olarak enerji tüketim modeli oluşturularak her bölge için sürüş çevrimi hesaplanmıştır. Daha sonra tüm rota için sürüş çevrimi oluşturulmuş ve rotadaki enerji tüketimi matematiksel model olarak ifade edilmiştir. Rotanın tamamının hesaplamalarında Trilayered Neural Network (TNN) en iyi sonucu vermiştir. Son olarak SeaHorse optimizasyon yöntemi kullanılarak TNN sonucunda elde edilen matematiksel model yeniden ele alınmıştır. Rotanın tamamı (R) için analizler göz önüne alındığında en verimli tüketimin 3,02 kWh/km olduğu ve bu tüketim değerinin 21,5 oC sıcaklık ile elde edilebileceği hesaplanmıştır. Bu çalışma aynı zamanda elektrikli otobüs başta olmak üzere, diğer elektrikli araç üreticilerinin de; araçların farklı iklim şartlarındaki menzillerini belirlemede referans bir çalışma olmuştur.

Anahtar Kelimeler

Elektrikli Araçlar , Enerji Tüketimi , Yapay Zekâ , Büyük Veri , Optimizasyon

Kaynakça

1. Zou Y., Wei S., Sun F., Hu X. and Shiao Y., Large-scale deployment of electric taxis in Beijing: A real-world analysis, Energy, 100 (2), 25–39, 2016.
2. Zhang X., Zou Y., Fan J. and Guo H., Usage pattern analysis of Beijing private electric vehicles based on real-world data, Energy, 167 (1), 1074–1085, 2019.
3. Mohammadi F. and Saif M., A comprehensive overview of electric vehicle batteries market, e-Prime - Advances in Electrical Engineering, Electronics and Energy, 3 (1), 100127-100140, 2023.
4. Lee G., Song J., Lim Y. and Park J., Energy consumption evaluation of passenger electric vehicle based on ambient temperature under Real-World driving conditions, Energy Conversion and Management, 306 (2024), 110209-110214, 2024.
5. Ekici Y.E., Akdağ O., Aydın A.A. and Karadağ T., Optimization of Proportional-Integral-Derivative Parameters for Speed Control of Squirrel-Cage Motors with Seahorse Optimization, Electrica, 2 (24), 318-326, 2024.
6. Ekici Y.E., Akdağ O., Aydin A.A. and Karadağ T., A novel energy consumption prediction model of electric buses using real-time big data from route , environment , and vehicle parameters, IEEE Access, 11 (1), 104305–104322, 2023.
7. Zhang J., Wang Z., Liu P. and Zhang Z., Energy consumption analysis and prediction of electric vehicles based on real-world driving data, Applied Energy, 275 (5), 115408-115423, 2020.
8. Ji Y., Zhang Y. and. Wang C.-Y, Li-Ion Cell Operation at Low Temperatures, Journal of The Electrochemical Society, 160 (4), 636–649, 2013.
9. Wenjie F., Zhibin Z., Ming D. and Ming R., On-line Estimation Method for Internal Temperature of Lithium-ion Battery Based on Electrochemical Impedance Spectroscopy, 2021 IEEE Electrical Insulation Conference (EIC), Denver-USA, 247–251, 07-28 June, 2021.
10. Sun Y. K., Promising All-Solid-State Batteries for Future Electric Vehicles, ACS Energy Letter, 5 (10), 3221–3223, 2020.
11. Al-Wreikat Y., Serrano C. and Sodré J. R., Driving behaviour and trip condition effects on the energy consumption of an electric vehicle under real-world driving, Applied Energy, 297 (1), 117096-117104, 2021.
12. Ekici Y.E., Akdağ O., Karadağ T. and Aydın A.A., Review And Analysis Of Real Time Big Data Of Electric Bus Consumption Data and Optimum Integration into the Urban Transportation Routes, in Ankara Internatıonal Congress On Scıentıfıc Research-VII, Ankara-Türkiye, 1405-1418, 2-4 December, 2022.
13. Al-Wreikat Y., Serrano C. and Sodré J. R., Effects of ambient temperature and trip characteristics on the energy consumption of an electric vehicle, Energy, 238 (1), 122028-122037, 2022.
14. Song Z., Pan Y., Chen H. and Zhang T., Effects of temperature on the performance of fuel cell hybrid electric vehicles: A review, Applied Energy, 302 (1),117572-117580, 2021.
15. Pesaran A., Addressing the Impact of Temperature Extremes on Large Format Li-Ion Batteries for Vehicle Applications, 30th International Battery Seminary, Colorado-USA, 1-30, 11-14 March, 2013.
16. Deng T., Zhang G., Ran Y. and Liu P., Thermal performance of lithium ion battery pack by using cold plate, Applied Thermal Engineering, 160 (66), 114088-114096, 2019.
17. Shang Z., Qi H., Liu X., Ouyang C. and Wang Y., Structural optimization of lithium-ion battery for improving thermal performance based on a liquid cooling system, International Journal of Heat and Mass Transfer, 130 (1), 33–41, 2019.
18. Ekici Y. E., Dikmen İ. C., Nurmuhammed M. and Karadağ T., Efficiency Analysis of Various Batteries with Real-time Data on a Hybrid Electric Vehicle, International Journal of Automotive Science And Technology, 5 (11), 214–223, 2021.
19. Jaguemont J., Boulon L. and Dubé Y., A comprehensive review of lithium-ion batteries used in hybrid and electric vehicles at cold temperatures, Applied Energy, 164 (1), 99–114, 2016.
20. Lu M., Zhang X., Ji J., Xu X. and Zhang Y., Research progress on power battery cooling technology for electric vehicles, Journal Energy Storage, 27 (1), 101155-101171, 2019.
21. Ji Y. and Wang C. Y., Heating strategies for Li-ion batteries operated from subzero temperatures, Electrochimica Acta, 107 (1), 664–674, 2013.
22. Tran M., Bhatti A., Vrolyk R.,Wong D., Panchal S., Fowler M. And Fraser R., A Review of Range Extenders in Battery Electric Vehicles: Current Progress and Future Perspectives, World Electric Vehicle Journal, 12 (2), 54-70, 2021.
23. Li K., Hongming C., Dingyu X., Hanqi Z., Binlin D., Hua Z., Ni L., Xuejin Z. and Ran T., Assessment method of the integrated thermal management system for electric vehicles with related experimental validation, Energy Conversion and Management, 276 (3), 116571-116584, 2023.
24. Hao X., Wang H., Lin Z. and Ouyang M., Seasonal effects on electric vehicle energy consumption and driving range: A case study on personal, taxi, and ridesharing vehicles, Journal of Cleaner Production, 249 (1), 119403-119416, 2020.
25. Szumska E. M. and Jurecki R. S., Parameters influencing on electric vehicle range, Energies, 14 (16), 4821-4844, 2021.
26. Dikmen İ.C., Ekici Y.E., Karadağ T., Abbasov T. and Hamamcı S.E., Electrification in Urban Transport: A Case Study with Real-time Data, Balkan Journal of Electrical and Computer Engineering, 9 (1), 69–77, 2021.
27. Ramesh A. B, Minovski B. and Sebben S., Thermal encapsulation of large battery packs for electric vehicles operating in cold climate, Applied Thermal Engineering, 212 (2), 118548-118561, 2022.
28. Taggart J., Ambient temperature impacts on real-world electric vehicle efficiency & range, 2017 IEEE Transportation Electrification Conference and Expo (ITEC), Chicago-USA, 186–190, 22-24 June, 2017.
29. Yuksel T. and Michalek J.J., Effects of regional temperature on electric vehicle efficiency, range, and emissions in the united states, Environmental Science and Technology, 49 (6), 3974–3980, 2015.
30. Paffumi E., Otura M., Centurelli M., Casellas R., Brenner A. and Jahn S., Energy Consumption, Driving Range and Cabin Temperature Performances at Different Ambient Conditions in Support to the Design of a User-Centric Efficient Electric Vehicle: the QUIET Project, 14th SDEWES Conference, Dubrovnik - Croatia, 1- 18, 1-6 October, 2019.
31. Ekici Y.E., Dikmen İ.C., Nurmuhammed M. and Karadağ T., A Review on Electric Vehicle Charging Systems and Current Status in Turkey, International Journal of Automotive Science Technology, 5 (4), 316–330, 2021.
32. Varga B. O., Sagoian A. and Mariasiu F., Prediction of electric vehicle range: A comprehensive review of current issues and challenges, Energies, 12 (5), 946-965, 2019.
33. Iora P. and Tribioli L., Effect of ambient temperature on electric vehicles energy consumption and range: Model definition and sensitivity analysis based on Nissan Leaf data, World Electric Vehicle Journal, 10 (1), 1–15, 2019.
34. Ekici Y.E. and Tan N, Investigation Of Charging And Discharging Characteristics Of Different Type Batteries Using Trambus Accelerator Pedal Data On Hybrid Electric Vehicle Model, Internatioal Journal Energy Engineering Science, 3 (3), 55–67, 2018.
35. Steinstraeter M., Heinrich T. and Lienkamp M., Effect of low temperature on electric vehicle range, World Electric Vehicle Journal, 12 (3), 115-141, 2021.
36. Klingler A. L., The effect of electric vehicles and heat pumps on the market potential of PV + battery systems, Energy, 161 (1), 1064–1073, 2018.
37. Ekici Y.E, Karadağ T, Aydın A.A and Akdağ O, Driving Range Problem in Electric Vehicles and Methods to Increase, Article and Reviews in Engineering, Platanus Publishing, Ankara, 253–286, 2024.
38. Xu B. and Arjmandzadeh Z., Parametric study on thermal management system for the range of full (Tesla Model S)/ compact-size (Tesla Model 3) electric vehicles, Energy Conversion and Management, 278 (1), 116753-116766, 2023.
39. Wang J. B., Liu K., Yamamoto T. and Morikawa T., Improving Estimation Accuracy for Electric Vehicle Energy Consumption Considering the Effects of Ambient Temperature, Energy Procedia, 105 (1), 2904–2909, 2017.
40. Liu K., Wang J., Yamamoto T. and Morikawa T., Exploring the interactive effects of ambient temperature and vehicle auxiliary loads on electric vehicle energy consumption, Applied Energy, 227 (2018), 324–331, 2018.
41. Fetene G. M., Kaplan S., Mabit S. L., Jensen A. F. and Prato C. G., Harnessing big data for estimating the energy consumption and driving range of electric vehicles, Transportation Research Part D: Transport and Environment, 54 (1), 1–11, 2017.
42. Mišanović S. M., Glišović J. D., Blagojević I. A. and Taranović D. S., Influencing Factors on Electricity Consumption of Electric Bus in Real Operating Conditions, Thermal Science, 27 (1), 767–784, 2023.
43. Szilassy P. Á. and Földes D., Consumption estimation method for battery-electric buses using general line characteristics and temperature, Energy, 261 (3), 12500-12511, 2022.
44. Doulgeris S., Zafeiriadis A., Athanasopoulos N., Tzivelou N., Michali M.E., Papagianni S. and Samaras Z., Evaluation of energy consumption and electric range of battery electric busses for application to public transportation, Transportation Engineering, 15 (3), 100223-100233, 2024.
45. Motas E-Bus feasibility report, Motaş, https://www.motas.com.tr/ Yayın tarihi Şubat 7, 2024. Erişim tarihi Ocak 10, 2025.
46. Trambüs 24 Meters, Bozankaya, https://www.bozankaya.com.tr/ Yayın tarihi Mart 15, 2015. Erişim tarihi Aralık 22, 2024.
47. Ekici Y.E., Karadag T., Aydin A.A. and Akdag O., Impact of Outside Temperature on Driving Range and Energy Consumption Using Real-Time Big Data for Electric Buses, 8th International Artificial Intelligence and Data Processing Symposium, Malatya-Türkiye, 1-9, 21-22 September, 2024.
48. Ekici Y.E and Tan N., Investigation Of Charging And Discharging Characteristics Of Different Type Batteries On Hybrid Electric Vehicle Model, The International Journal of Energy and Engineering Sciences, 3 (3), 55-68, 2018.
49. Akman T., Yılmaz C. and Sönmez Y., Elektrik Yükü Tahmin Yöntemlerinin Analizi, Gazi Journal Education Science, 4 (3), 29–51, 2018.
50. Zhao L., Ke H. and Huo W., A frequency item mining based energy consumption prediction method for electric bus, Energy, 263 (1), 125915-1259-28, 2023.
51. Liu S., Kong Z., Huang T., Du Y. and Xiang W., An ADMM-LSTM framework for short-term load forecasting, Neural Networks, 173 (2), 106150-106162, 2024.
52. Qin H., Zhao G., Li Y. and Wang H., Hybrid distributed finite-time neurodynamic optimization of electric vehicle charging schemes management in microgrid considering, Neural Networks, 161 (1), 466–475, 2023.
53. Ram J. P., Rajasekar N. and Miyatake M., Design and overview of maximum power point tracking techniques in wind and solar photovoltaic systems: A review, Renewable and Sustainable Energy Reviews, 73 (3), 1138–1159, 2017.
54. Boumaaraf H., Talha A. and Bouhali O., A three-phase NPC grid-connected inverter for photovoltaic applications using neural network MPPT, Renewable and Sustainable Energy Reviews, 49 (3), 1171–1179, 2015.
55. Wilbur H. M. and Tilley S. G., Evolutionary Strategies in Lizard Reproduction, Evolution, 24 (1), 55–74, 1970.
56. Kaur S., Awasthi L. K., Sangal A. L. and Dhiman G., Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization, Engineering Applications of Artificial Intelligence, 90 (4), 103541-103570, 2020.
57. Ulucay Ö., Sivrioğlu S., Özkan M., Online optimization of engine control unit to satisfy performance and drivability metrics, Journal of the Faculty of Engineering and Architecture of Gazi University, 39 (4), 2041–2056, 2024.
58. Martin-Smith K. M. and Vincent A. C. J., Exploitation and trade of Australian seahorses, pipehorses, sea dragons and pipefishes (Family Syngnathidae), Oryx, 40 (2), 141–151, 2006.
59. Roos G., Van Wassenbergh S., Herrel A., Adriaens D. and Aerts P., Snout allometry in seahorses: Insights on optimisation of pivot feeding performance during ontogeny, Journal of Experimental Biology, 213 (13), 2184–2193, 2010.
60. Kuiter R. H., Revision of the Australian seahorses of the genus Hippocampus (Syngnathiformes: Syngnathidae) with descriptions of nine new species, Records of the Australian Museum, 53 (3), 293–340, 2001.
61. Kendrick A. J. and Hyndes G. A., Variations in the dietary compositions of morphologically diverse syngnathid fishes, Environmental Biology of Fishes, 72 (4), 415–427, 2005.
62. Maria B., Elena K. and Andrey C., Fast Algorithm for Simulation of Levy Stable Stochastic Self-Similar Processes, 2014 Fifth International Conference on Intelligent Systems, Modelling and Simulation, Langkawi-Malaysia, 225-228, 27-29 January, 2014.
63. Thamarai Chelvi S. K., Yong E. L. and Gong Y., Preparation and evaluation of calix[4]arene-capped β-cyclodextrin-bonded silica particles as chiral stationary phase for high-performance liquid chromatography, Journal of Chromatography A, 1203(1), 54–58, 2008.
64. Derrac J., García S., Molina D. and Herrera F., A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms, Swarm and Evolutionary Computation, 1 (1), 3–18, 2011.
65. Özbay F.A., A modified seahorse optimization algorithm based on chaotic maps for solving global optimization and engineering problems, Engineering Science and Technology, an International Journal, 41 (2), 101408-101434, 2023.
66. Zhao S., Zhang T., Ma S. and Wang M., Sea-horse optimizer: a novel nature-inspired meta-heuristic for global optimization problems, Applied Intelligence, 53 (10), 11833–11860, 2023.

Toplam 66 adet kaynakça vardır.

Ayrıntılar

Birincil Dil	Türkçe
Konular	Memnuniyet ve Optimizasyon
Bölüm	Araştırma Makalesi
Yazarlar	Yunus Emre Ekici 0000-0001-7791-0473 Teoman Karadag 0000-0002-7682-7771 Ozan Akdağ 0000-0001-8163-8898 Ahmet Arif Aydın 0000-0002-4124-7275
Erken Görünüm Tarihi	3 Kasım 2025
Yayımlanma Tarihi	27 Kasım 2025
Gönderilme Tarihi	20 Ocak 2025
Kabul Tarihi	19 Mayıs 2025
Yayımlandığı Sayı	Yıl 2025 Cilt: 40 Sayı: 4

Kaynak Göster

APA	Ekici, Y. E., Karadag, T., Akdağ, O., Aydın, A. A. (2025). Dış ortam sıcaklığının elektrikli araçların enerji tüketimine etkisi: Gerçek zamanlı büyük veri ve yapay zekâ destekli seahorse optimizasyon yaklaşımı. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 40(4), 2263-2280. https://doi.org/10.17341/gazimmfd.1623529
AMA	Ekici YE, Karadag T, Akdağ O, Aydın AA. Dış ortam sıcaklığının elektrikli araçların enerji tüketimine etkisi: Gerçek zamanlı büyük veri ve yapay zekâ destekli seahorse optimizasyon yaklaşımı. GUMMFD. Kasım 2025;40(4):2263-2280. doi:10.17341/gazimmfd.1623529
Chicago	Ekici, Yunus Emre, Teoman Karadag, Ozan Akdağ, ve Ahmet Arif Aydın. “Dış ortam sıcaklığının elektrikli araçların enerji tüketimine etkisi: Gerçek zamanlı büyük veri ve yapay zekâ destekli seahorse optimizasyon yaklaşımı”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 40, sy. 4 (Kasım 2025): 2263-80. https://doi.org/10.17341/gazimmfd.1623529.
EndNote	Ekici YE, Karadag T, Akdağ O, Aydın AA (01 Kasım 2025) Dış ortam sıcaklığının elektrikli araçların enerji tüketimine etkisi: Gerçek zamanlı büyük veri ve yapay zekâ destekli seahorse optimizasyon yaklaşımı. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 40 4 2263–2280.
IEEE	Y. E. Ekici, T. Karadag, O. Akdağ, ve A. A. Aydın, “Dış ortam sıcaklığının elektrikli araçların enerji tüketimine etkisi: Gerçek zamanlı büyük veri ve yapay zekâ destekli seahorse optimizasyon yaklaşımı”, GUMMFD, c. 40, sy. 4, ss. 2263–2280, 2025, doi: 10.17341/gazimmfd.1623529.
ISNAD	Ekici, Yunus Emre vd. “Dış ortam sıcaklığının elektrikli araçların enerji tüketimine etkisi: Gerçek zamanlı büyük veri ve yapay zekâ destekli seahorse optimizasyon yaklaşımı”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 40/4 (Kasım2025), 2263-2280. https://doi.org/10.17341/gazimmfd.1623529.
JAMA	Ekici YE, Karadag T, Akdağ O, Aydın AA. Dış ortam sıcaklığının elektrikli araçların enerji tüketimine etkisi: Gerçek zamanlı büyük veri ve yapay zekâ destekli seahorse optimizasyon yaklaşımı. GUMMFD. 2025;40:2263–2280.
MLA	Ekici, Yunus Emre vd. “Dış ortam sıcaklığının elektrikli araçların enerji tüketimine etkisi: Gerçek zamanlı büyük veri ve yapay zekâ destekli seahorse optimizasyon yaklaşımı”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, c. 40, sy. 4, 2025, ss. 2263-80, doi:10.17341/gazimmfd.1623529.
Vancouver	Ekici YE, Karadag T, Akdağ O, Aydın AA. Dış ortam sıcaklığının elektrikli araçların enerji tüketimine etkisi: Gerçek zamanlı büyük veri ve yapay zekâ destekli seahorse optimizasyon yaklaşımı. GUMMFD. 2025;40(4):2263-80.

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