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Hibrit ve Elektrikli Araçlar için Güç Aktarım Sistemi Bağlantılı Yeşil Rotalama

Year 2021, Volume: 23 Issue: 68, 421 - 433, 24.05.2021
https://doi.org/10.21205/deufmd.2021236807

Abstract

Bu çalışma, gelişmiş araç teknolojileri için aracın güç aktarım unsurlarının durumlarını ve trafik şartlarını göz önünde bulunduran yenilikçi bir yeşil rota optimizasyonu stratejisi önermektedir. Geleneksel navigasyon sistemleri, optimal rotayı belirlerken sürücülere en kısa mesafe, en kısa süre veya en çok tercih edilen yollar gibi seçenekler sunmaktadır. Bu seçenekler doğrultusunda bulunan optimum rota çevreye zararlı olan gaz emisyonlarını minimum seviyede tutmayı garanti etmemektedir. Özellikle hibrit ve elektrikli araçlar gibi gelişmiş araçlar söz konusu olduğunda optimum rotanın belirlenmesine etki eden faktörler değişmektedir. Bu sebeple bu çalışmada, rota optimizasyonuna araç aktarım sistemi dinamikleri, elektrikli ve benzinli motor verimlilikleri, kontrol modları, batarya boyutu, bataryanın enerji seviyesi ve trafik durumu gibi girdilerin entegre edildiği “Araç Güç Aktarım Sistemi Bağlantılı Yeşil Rota Optimizasyonu” olarak adlandırılan bir yöntem önerilmiştir. Rota optimizasyonu probleminin çözümünde yukarıdaki faktörler göz önünde bulundurulmuş, amaç fonksiyonunu en düşük 〖CO〗_2 emisyonu olarak belirlenmiş ve çözüm yöntemi olarak Dijkstra algoritması kullanılmıştır. Ek olarak, önerilen stratejinin, farklı araç teknolojilerinin 〖CO〗_2 emisyon miktarını azaltma oranları ve optimal rotalara etkileri analiz edilmiştir. Sırasıyla geleneksel ve elektrikli araçlar için en kısa mesafe algoritmasına kıyasla bu çalışmada önerilen optimizasyon yöntemi kullanıldığında geleneksel araçların yolculuklarının yaklaşık %80’i ve elektrikli araçla yapılan yolculukların %60'ı yeni yeşil optimal rotalara sahip olmaktadırlar. Ayrıca, önerilen strateji sayesinde, en kısa yol stratejisine kıyasla, sırasıyla geleneksel ve elektrikli araçlar için %60 ve %30'a kadar varan emisyon tasarrufu sağlanmaktadır.

References

  • [1]Belbağ S.,“Yeşil Kapasite Kısıtlı Araç Rotalama Problemi: Bir Literatür Taraması.” Gazi Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 19/1, (2017) sf.345-366.
  • [2] Karabasoglu O., and Michalek J., “Influence of driving patterns on life cycle cost and emissions of hybrid and plug-in electric vehicle powertrains”, Energy Policy 60 (0) (2013) 445 – 461.
  • [3] Zhang C., Vahidi A., Pisu P., Li X., and Tennant K., “Role of terrain preview in energy management of hybrid electric vehicles”, Vehicular Technology, IEEE Transactions on 59 (3) (2010) 1139–1147.
  • [4] Xiao Y., Zhao Q. , Kaku I., and Xu Y., “Development of a fuel consumption optimization model for the capacitated vehicle routing problem”, Computers Operations Research 39 (7) (2012) 1419 – 1431.
  • [5]Tavares G., Zsigraiova Z., Semiao V., Carvalho M., “Optimization of {MSW} collection routes for minimum fuel consumption using 3d {GIS} modelling”, Waste Management 29 (3) (2009) 1176 – 1185.
  • [6] Poonthalir G., and Nadarajan R. (2018). A Fuel Efficient Green Vehicle Routing Problem with varying speed constraint (F-GVRP). Expert Systems with Applications, 100, 131-144. doi:10.1016/j.eswa.2018.01.052.
  • [7] Yi, Zonggen, and Peter H. Bauer. "Optimal Stochastic Eco-Routing Solutions for Electric Vehicles." IEEE transactions on Intelligent Transportation Systems 99 (2018): 1-11.
  • [8] Chenjuan G., "Ecosky: Reducing vehicular environmental impact through eco-routing." Data Engineering (ICDE), 2015 IEEE 31st International Conference on. IEEE, 2015.
  • [9] Huang X., and Huei P., "Eco-Routing based on a Data Driven Fuel Consumption Model." arXiv preprint arXiv:1801.08602 (2018).
  • [10] Behnke M., Kirschstein T., and Bierwirth C., "An Emission-Minimizing Vehicle Routing Problem with Heterogeneous Vehicles and Pathway Selection." Operations Research Proceedings 2016. Springer, Cham, 2018. 285-291.
  • [11] Chang D.J., and Edward K.M., "Vehicle speed profiles to minimize work and fuel consumption." Journal of transportation engineering 131.3 (2005): 173-182.
  • [12] Boriboonsomsin K., "Eco-routing navigation system based on multisource historical and real-time traffic information." IEEE Transactions on Intelligent Transportation Systems 13.4 (2012): 1694-1704.
  • [13] Bottiglione F., "The fuel economy of hybrid buses: the role of ancillaries in real urban driving." Energies 7.7 (2014): 4202-4220.
  • [14] Ericsson E., Larsson H., Brundell-Freij K., “Optimizing route choice for lowest fuel consumption potential effects of a new driver support tool”, Transportation Research Part C: Emerging Technologies14 (6) (2006) 369 – 383.
  • [15] Ahn K. and Rakha H., “The effects of route choice decisions on vehicle energy consumption and emissions”, Transportation Research Part D: Transport and Environment 13 (3) (2008) 151 – 167.
  • [16] Barth M., Boriboonsomsin K., “Traffic congestion and greenhouse gases”, ACCESS Magazine 1 (35).
  • [17] Artmeier A., Haselmayr J., Leucker M. and Sachenbacher M., “The shortest path problem revisited: Optimal routing for electric vehicles” (2010).
  • [18] Zhiqian Q., and Karabasoglu O.,"Vehicle Powertrain Connected Route Optimization for Conventional, Hybrid and Plug-in Electric Vehicles." arXiv preprint arXiv:1612.01243 (2016).
  • [19] Tulpule P., Marano V. and Rizzoni G., Effects of different PHEV control strategies on vehicle performance, American Control Conference, 2009. ACC ‘09., vol., no., pp. 3950–3955, 10–12 June 2009.
  • [20] Sciarretta A., Back M., Guzzella, L., 2004. Optimal control of parallel hybrid electric vehicles. IEEE Transactions on Control Systems Technology 12 (3), 352–363.
  • [21] Sciarretta A., Guzzella L., 2007. Control of hybrid electric vehicles. IEEE Control Systems 27 (2), 60–70.
  • [22] Dijkstra, Edsger W. "A note on two problems in connexion with graphs." Numerische mathematik 1.1 (1959): 269-271.
  • [23] Moura S.J., Fathy H.K., Callaway, D.S., Stein, J.L., 2011. A stochastic optimal control approach for power management in plug-in hybrid electric vehicles. IEEE Transactions on Control Systems Technology 19 (3), 545–555.

Vehicle Powertrain Connected Green Routing for Hybrid and Electrified Vehicles

Year 2021, Volume: 23 Issue: 68, 421 - 433, 24.05.2021
https://doi.org/10.21205/deufmd.2021236807

Abstract

This study proposes a new approach to traditional navigation systems that are now widely used. Conventional navigation systems offer drivers the choice of the shortest distance, the shortest time, or the most preferred routes when determining the optimal route. The optimum route in line with these options does not ensure that the gas emissions that are harmful to the environment are kept to a minimum. Especially, in the case of advanced vehicles such as hybrid and electric vehicles, the factors affecting the determination of the optimum route are subject to change. For this reason, a framework called “Vehicle Powertrain Connected Green Route Optimization” has been proposed to integrate inputs such as vehicle powertrain dynamics, component efficiencies, control modes, initial status and traffic conditions to route optimization. The objective function of route optimization problem is determined as lowest 〖CO〗_2 emission and Dijkstra algorithm is used as solution method. In addition, the proposed strategy reduces the amount of 〖CO〗_2 emissions and the effect on the route change for different vehicle powertrains such as CV, HEV, PHEV and BEV were analyzed. Using the proposed optimization algorithm for conventional and electric vehicles, respectively, compared to the shortest distance algorithm, approximately 80% and 60% of the journeys have new optimal routes. In addition, the proposed strategy saves up to 60% and 30% emissions for CVs and electric vehicles, respectively, compared to the shortest route strategy.

References

  • [1]Belbağ S.,“Yeşil Kapasite Kısıtlı Araç Rotalama Problemi: Bir Literatür Taraması.” Gazi Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 19/1, (2017) sf.345-366.
  • [2] Karabasoglu O., and Michalek J., “Influence of driving patterns on life cycle cost and emissions of hybrid and plug-in electric vehicle powertrains”, Energy Policy 60 (0) (2013) 445 – 461.
  • [3] Zhang C., Vahidi A., Pisu P., Li X., and Tennant K., “Role of terrain preview in energy management of hybrid electric vehicles”, Vehicular Technology, IEEE Transactions on 59 (3) (2010) 1139–1147.
  • [4] Xiao Y., Zhao Q. , Kaku I., and Xu Y., “Development of a fuel consumption optimization model for the capacitated vehicle routing problem”, Computers Operations Research 39 (7) (2012) 1419 – 1431.
  • [5]Tavares G., Zsigraiova Z., Semiao V., Carvalho M., “Optimization of {MSW} collection routes for minimum fuel consumption using 3d {GIS} modelling”, Waste Management 29 (3) (2009) 1176 – 1185.
  • [6] Poonthalir G., and Nadarajan R. (2018). A Fuel Efficient Green Vehicle Routing Problem with varying speed constraint (F-GVRP). Expert Systems with Applications, 100, 131-144. doi:10.1016/j.eswa.2018.01.052.
  • [7] Yi, Zonggen, and Peter H. Bauer. "Optimal Stochastic Eco-Routing Solutions for Electric Vehicles." IEEE transactions on Intelligent Transportation Systems 99 (2018): 1-11.
  • [8] Chenjuan G., "Ecosky: Reducing vehicular environmental impact through eco-routing." Data Engineering (ICDE), 2015 IEEE 31st International Conference on. IEEE, 2015.
  • [9] Huang X., and Huei P., "Eco-Routing based on a Data Driven Fuel Consumption Model." arXiv preprint arXiv:1801.08602 (2018).
  • [10] Behnke M., Kirschstein T., and Bierwirth C., "An Emission-Minimizing Vehicle Routing Problem with Heterogeneous Vehicles and Pathway Selection." Operations Research Proceedings 2016. Springer, Cham, 2018. 285-291.
  • [11] Chang D.J., and Edward K.M., "Vehicle speed profiles to minimize work and fuel consumption." Journal of transportation engineering 131.3 (2005): 173-182.
  • [12] Boriboonsomsin K., "Eco-routing navigation system based on multisource historical and real-time traffic information." IEEE Transactions on Intelligent Transportation Systems 13.4 (2012): 1694-1704.
  • [13] Bottiglione F., "The fuel economy of hybrid buses: the role of ancillaries in real urban driving." Energies 7.7 (2014): 4202-4220.
  • [14] Ericsson E., Larsson H., Brundell-Freij K., “Optimizing route choice for lowest fuel consumption potential effects of a new driver support tool”, Transportation Research Part C: Emerging Technologies14 (6) (2006) 369 – 383.
  • [15] Ahn K. and Rakha H., “The effects of route choice decisions on vehicle energy consumption and emissions”, Transportation Research Part D: Transport and Environment 13 (3) (2008) 151 – 167.
  • [16] Barth M., Boriboonsomsin K., “Traffic congestion and greenhouse gases”, ACCESS Magazine 1 (35).
  • [17] Artmeier A., Haselmayr J., Leucker M. and Sachenbacher M., “The shortest path problem revisited: Optimal routing for electric vehicles” (2010).
  • [18] Zhiqian Q., and Karabasoglu O.,"Vehicle Powertrain Connected Route Optimization for Conventional, Hybrid and Plug-in Electric Vehicles." arXiv preprint arXiv:1612.01243 (2016).
  • [19] Tulpule P., Marano V. and Rizzoni G., Effects of different PHEV control strategies on vehicle performance, American Control Conference, 2009. ACC ‘09., vol., no., pp. 3950–3955, 10–12 June 2009.
  • [20] Sciarretta A., Back M., Guzzella, L., 2004. Optimal control of parallel hybrid electric vehicles. IEEE Transactions on Control Systems Technology 12 (3), 352–363.
  • [21] Sciarretta A., Guzzella L., 2007. Control of hybrid electric vehicles. IEEE Control Systems 27 (2), 60–70.
  • [22] Dijkstra, Edsger W. "A note on two problems in connexion with graphs." Numerische mathematik 1.1 (1959): 269-271.
  • [23] Moura S.J., Fathy H.K., Callaway, D.S., Stein, J.L., 2011. A stochastic optimal control approach for power management in plug-in hybrid electric vehicles. IEEE Transactions on Control Systems Technology 19 (3), 545–555.
There are 23 citations in total.

Details

Primary Language Turkish
Journal Section Research Article
Authors

Orkun Karabasoglu 0000-0001-6804-0904

Publication Date May 24, 2021
Published in Issue Year 2021 Volume: 23 Issue: 68

Cite

APA Karabasoglu, O. (2021). Hibrit ve Elektrikli Araçlar için Güç Aktarım Sistemi Bağlantılı Yeşil Rotalama. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen Ve Mühendislik Dergisi, 23(68), 421-433. https://doi.org/10.21205/deufmd.2021236807
AMA Karabasoglu O. Hibrit ve Elektrikli Araçlar için Güç Aktarım Sistemi Bağlantılı Yeşil Rotalama. DEUFMD. May 2021;23(68):421-433. doi:10.21205/deufmd.2021236807
Chicago Karabasoglu, Orkun. “Hibrit Ve Elektrikli Araçlar için Güç Aktarım Sistemi Bağlantılı Yeşil Rotalama”. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen Ve Mühendislik Dergisi 23, no. 68 (May 2021): 421-33. https://doi.org/10.21205/deufmd.2021236807.
EndNote Karabasoglu O (May 1, 2021) Hibrit ve Elektrikli Araçlar için Güç Aktarım Sistemi Bağlantılı Yeşil Rotalama. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi 23 68 421–433.
IEEE O. Karabasoglu, “Hibrit ve Elektrikli Araçlar için Güç Aktarım Sistemi Bağlantılı Yeşil Rotalama”, DEUFMD, vol. 23, no. 68, pp. 421–433, 2021, doi: 10.21205/deufmd.2021236807.
ISNAD Karabasoglu, Orkun. “Hibrit Ve Elektrikli Araçlar için Güç Aktarım Sistemi Bağlantılı Yeşil Rotalama”. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi 23/68 (May 2021), 421-433. https://doi.org/10.21205/deufmd.2021236807.
JAMA Karabasoglu O. Hibrit ve Elektrikli Araçlar için Güç Aktarım Sistemi Bağlantılı Yeşil Rotalama. DEUFMD. 2021;23:421–433.
MLA Karabasoglu, Orkun. “Hibrit Ve Elektrikli Araçlar için Güç Aktarım Sistemi Bağlantılı Yeşil Rotalama”. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen Ve Mühendislik Dergisi, vol. 23, no. 68, 2021, pp. 421-33, doi:10.21205/deufmd.2021236807.
Vancouver Karabasoglu O. Hibrit ve Elektrikli Araçlar için Güç Aktarım Sistemi Bağlantılı Yeşil Rotalama. DEUFMD. 2021;23(68):421-33.

Dokuz Eylül Üniversitesi, Mühendislik Fakültesi Dekanlığı Tınaztepe Yerleşkesi, Adatepe Mah. Doğuş Cad. No: 207-I / 35390 Buca-İZMİR.