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Predictive equivalent consumption minimization strategy for power-split hybrid electric vehicles using Monte Carlo algorithm

Year 2023, Volume: 38 Issue: 3, 1615 - 1630, 06.01.2023
https://doi.org/10.17341/gazimmfd.1040940

Abstract

This work proposes a predictive equivalent consumption minimization (P-ECMS) strategy for a power-split hybrid electric vehicle (HEV) using predicted driving cycle speed based on Monte Carlo (MC) algorithm. The proposed P-ECMS fully takes advantage of the predicted speed profiles by the MC algorithm to optimally determine the power split among energy sources. In this study, to validate the workings of the MC-based P-ECMS scheme, a series of simulations under a total of seven replicated driving cycles including New European Driving Cycle (NEDC), Worldwide Harmonised Light Vehicles Test Procedure (WLTP), Urban Dynamometer Driving Schedule (UDDS), Highway Fuel Economy Test (HWFET), New York City Cycle (NYCC), California Unified Cycle (LA-92), and a combination of all (ALL-CYC) are conducted. The MC-based P-ECMS strategy is compared with a baseline ECMS in terms of fuel-saving, and fuel economy saving up to 6.01% under NEDC, 9.09% under WLTP, 6.33% under UDDS, 5.14% under HWFET, 1.96% under NYCC, 11.47% under LA-92, and 7.92% under ALL-CYC are achieved. The results in this article put forward that the proposed strategy delivers competitive fuel savings compared to the widely used baseline method.

Project Number

121E260

References

  • Zhang F., Hu X., Langari R., Cao D., Energy management strategies of connected HEVs and PHEVs: Recent progress and outlook, Progress in Energy and Combustion Science, 73, 235-256, 2019.
  • Zhao Z., Tang P., Li, H., Generation, screening, and optimization of powertrain configurations for power-split hybrid electric vehicle: A comprehensive overview, IEEE Transactions on Transportation Electrification, 8(1), 325-344, 2021.
  • Omanovic, A., Zsiga, N., Soltic, P., Onder, C., Optimal degree of hybridization for spark-ignited engines with optional variable valve timings, Energies, 14(23), 8151, 2021.
  • Yazar O, Coskun S, Zhang F, Li L., A comparative study of energy management systems under connected driving: cooperative car-following case, Complex Engineering Systems, 2:7, 2022.
  • Li L., Coskun S., Langari R., Xi J., Incorporated vehicle lateral control strategy for stability and enhanced energy saving in distributed drive hybrid bus, Applied Soft Computing, 111, 107617, 2021.
  • Wirasingha S.G., Emadi A., Classification and review of control strategies for plug-in hybrid electric vehicles, IEEE Transactions on Vehicular Technology, 60(1), 111-122, 2010.
  • Salmasi F.R., Control strategies for hybrid electric vehicles: Evolution, classification, comparison, and future trends, IEEE Transactions on Vehicular Technology, 56(5), 2393-2404, 2007.
  • Riaz M., Hanif A., Masood H., Khan M. A., Afaq K., Kang B. G., Nam, Y., An optimal power flow solution of a system integrated with renewable sources using a hybrid optimizer, Sustainability, 13(23), 13382, 2021.
  • Başlamışlı S. Ç., İnce B., Design of energy management system algorithms for the improvement of fuel economy of intracity hybrid buses and development of an adaptive hybrid algorithm, Journal of the Faculty of Engineering and Architecture of Gazi University, 36 (1), 559-576, 2021.
  • Zhang F., Wang L., Coskun S., Pang H., Cui Y., Xi J., Energy management strategies for hybrid electric vehicles: Review, classification, comparison, and outlook, Energies, 13, 3352, 2020.
  • Chen Z., Liu Y., Zhang Y., Lei Z., Chen Z., Li G., A neural network-based ECMS for optimized energy management of plug-in hybrid electric vehicles, Energy, 243, 122727, 2021.
  • Liu J., Chen Y., Zhan J., Shang F., Heuristic dynamic programming based online energy management strategy for plug-in hybrid electric vehicles, IEEE Transactions on Vehicular Technology, 68(5), 4479–4493, 2019.
  • Chen H., Kessels J.T., Weiland S., Online adaptive approach for a game-theoretic strategy for complete vehicle energy management, In Proceedings of the 2015 European Control Conference (ECC), Linz-Austria, pp. 135–141, 15–17 July, 2015.
  • Kleimaier A., Schroder D., An approach for the online optimized control of a hybrid powertrain, In Proceedings of the 7th International Workshop on Advanced Motion Control. Proceedings (Cat. No.02TH8623), Maribor-Slovenia, pp. 215–220, 3–5 July, 2002.
  • Khodabakhshian M., Feng L., Wikander J., Improving fuel economy and robustness of an improved ECMS method, In Proceedings of the 2013 10th IEEE International Conference on Control and Automation (ICCA), Hangzhou-China, pp. 598–603, 12–14 June, 2013.
  • Hemi H., Ghouili J., Cheriti A., A real time energy management for electrical vehicle using combination of rule-based and ECMS, In Proceedings of the 2013 IEEE Electrical Power & Energy Conference, Halifax, NS, Canada, pp. 1–6, 21–23 August, 2013.
  • Musardo C., Rizzoni G., Staccia B., A-ECMS: An adaptive algorithm for hybrid electric vehicle energy management, In Proceedings of the 2005 44th IEEE Conference on Decision and Control and 2005 European Control Conference (CDC-ECC ’05), Seville-Spain, pp. 1816–1823, 12–15 December, 2005.
  • Xie S., Li H., Xin Z., Liu T., Wei L., A Pontryagin minimum principle-based adaptive equivalent consumption minimum strategy for a plug-in hybrid electric bus on a fixed route, Energies, 10(9), 1379, 2017.
  • Zhang J., Zheng C. Cha S.W., Duan S., Co-state variable determination in Pontryagin’s minimum principle for energy management of hybrid vehicles, International Journal of Precision Engineering and Manufacturing, 17(9), 1215–1222, 2016.
  • Li X., Wang Y., Yang D., Chen Z., Adaptive energy management strategy for fuel cell/battery hybrid vehicles using Pontryagin’s minimal principle. Journal of Power Sources, 440, 227105, 2019.
  • Paganelli, G., Design and control of a parallel hybrid car with electric and thermal powertrain. M. Sc.,1999.
  • Xie P., Tan S., Guerrero J. M., Vasquez J. C., MPC-informed ECMS based real-time power management strategy for hybrid electric ship, Energy Reports, 7, 126-133, 2021.
  • Onori S., Serrao L., On adaptive-ECMS strategies for hybrid electric vehicles, In Proceedings of the International Scientific Conference on Hybrid and Electric Vehicles, Malmaison-France, 6–7 December, 2011.
  • Zeng Y., Cai Y., Kou G., Gao W., Qin D., Energy management for plug-in hybrid electric vehicle based on adaptive simplified-ECMS, Sustainability, 10(6), 2060, 2018.
  • Choi K., Byun J., Lee S., Jang I. G., Adaptive equivalent consumption minimization strategy (A-ECMS) for the HEVs with a near-optimal equivalent factor considering driving conditions, IEEE Transactions on Vehicular Technology, 71(3), 2538-2549, 2021.
  • Zhang F., Xi J., Langari R., Real-time energy management strategy based on velocity forecasts using V2V and V2I communications, IEEE Transactions on Intelligent Transportation Systems, 18(2), 416-430, 2016.
  • Lei Z., Qin D., Liu Y., Peng Z., Lu L., Dynamic energy management for a novel hybrid electric system based on driving pattern recognition, Applied Mathematical Modelling, 45, 940-954, 2017.
  • Wang S., Huang X., López J. M., Xu X., Dong P., Fuzzy adaptive-equivalent consumption minimization strategy for a parallel hybrid electric vehicle, IEEE Access, 7, 133290-133303, 2019.
  • Chen Z., Liu Y., Ye M., Zhang Y., Chen Z., Li G., A survey on key techniques and development perspectives of equivalent consumption minimisation strategy for hybrid electric vehicles, Renewable and Sustainable Energy Reviews, 151, 111607, 2021.
  • Han J., Kum, D., Park Y., Synthesis of predictive equivalent consumption minimization strategy for hybrid electric vehicles based on closed-form solution of optimal equivalence factor, IEEE Transactions on Vehicular Technology, 66(7), 5604–5616, 2017.
  • Bayram A., Almalı M. N., Al-Naqshbandı F. M., Path following of an unmanned ground vehicle with GPS feedback using model predictive control method, Journal of the Faculty of Engineering and Architecture of Gazi University, 38(1), 345-355, 2023.
  • Han J., Shu H., Tang X., Lin X., Liu C., Hu X., Predictive energy management for plug-in hybrid electric vehicles considering electric motor thermal dynamics, Energy Conversion and Management, 251, 115022, 2022.
  • Wang L., Cui Y., Zhang F., Coskun S., Liu K., Li G., Stochastic speed prediction for connected vehicles using improved bayesian networks with back propagation, Science China Technological Sciences, 65(7), 1524–1536, 2022.
  • Kim T., Poplin G., Bollapragada V., Daniel T., Crandall J., Monte Carlo method for estimating whole-body injury metrics from pedestrian impact simulation results, Accident Analysis & Prevention, 147, 105761, 2020.
  • Chen M. H., Shao Q. M., Monte Carlo estimation of Bayesian credible and HPD intervals, Journal of Computational and Graphical Statistics, 8(1), 69-92, 1999.
  • Gill J., Bayesian methods: A social and behavioral sciences approach, 20, CRC press, USA, 2014.
  • Yang Y., Hu X., Pei H., Peng Z., Comparison of power-split and parallel hybrid powertrain architectures with a single electric machine: Dynamic programming approach, Applied Energy, 168, 683–690, 2016.
  • Hu X, Li S, Peng H., A comparative study of equivalent circuit models for Li-ion batteries, Journal of Power Sources, 198, 359–67, 2012.
  • Moura SJ, Chaturvedi NA, Krstic´ M., Adaptive partial differential equation observer for battery state-of-charge/state-of-health estimation via an electrochemical model, Journal of Dynamic Systems, Measurement, and Control, 136(1), 011015, 2014.
  • Rousseau A., Kwon J., Sharer P., Pagerit S., Duoba M., Integrating data, performing quality assurance, and validating the vehicle model for the 2004 Prius using PSAT[R]. SAE Technical Paper, 2006.

Güç paylaşımlı hibrit elektrikli araçlar için Monte Carlo algoritması kullanarak öngörülü eşdeğer tüketim minimizasyon stratejisi

Year 2023, Volume: 38 Issue: 3, 1615 - 1630, 06.01.2023
https://doi.org/10.17341/gazimmfd.1040940

Abstract

Bu çalışma, güç paylaşımlı bir hibrit elektrikli araç (HEA) için, Monte Carlo (MC) algoritmasına dayalı olarak tahmin edilen sürüş çevrimi hızlarını kullanan öngörülü eşdeğer tüketim minimizasyonu stratejisi (Ö-ETMS) önermektedir. Önerilen Ö-ETMS, enerji kaynakları arasındaki güç dağılımını en iyi şekilde belirlemek için MC algoritması tarafından tahmin edilen hız profillerinden tam olarak yararlanmaktadır. Bu çalışmada; MC tabanlı Ö-ETMS metodunu doğrulamak için, New European Driving Cycle (NEDC), Worldwide Harmonised Light Vehicles Test Procedure (WLTP), Urban Dynamometer Driving Schedule (UDDS), Highway Fuel Economy Test (HWFET), New York City Cycle (NYCC), California Unified Cycle (LA-92) ve tüm döngülerin kombinasyonu (ALL-CYC) çevrimleri kullanılmış; toplam yedi tekrarlı sürüş döngüsü altında bir dizi simülasyon çalışması gerçekleştirilmiştir. MC tabanlı Ö-ETMS stratejisi, standart ETMS ile karşılaştırılmıştır. NEDC çevriminde %6,01, WLTP çevriminde %9,09, UDDS çevriminde %6,33, HWFET çevriminde %5,14, NYCC çevriminde %1,96, LA-92 çevriminde %11,47 ve ALL-CYC çevriminde %7,92 oranla yakıt tasarrufu elde edilmiştir. Bu makaledeki sonuçlar, önerilen stratejinin yaygın olarak kullanılan temel yönteme kıyasla, rekabetçi bir yakıt tasarrufu sağladığını ortaya koymaktadır.

Supporting Institution

Türkiye Bilimsel ve Teknolojik Araştırma Kurumu

Project Number

121E260

Thanks

Bu çalışma, Türkiye Bilimsel ve Teknolojik Araştırma Kurumu tarafından 121E260 numaralı proje ile desteklenmektedir

References

  • Zhang F., Hu X., Langari R., Cao D., Energy management strategies of connected HEVs and PHEVs: Recent progress and outlook, Progress in Energy and Combustion Science, 73, 235-256, 2019.
  • Zhao Z., Tang P., Li, H., Generation, screening, and optimization of powertrain configurations for power-split hybrid electric vehicle: A comprehensive overview, IEEE Transactions on Transportation Electrification, 8(1), 325-344, 2021.
  • Omanovic, A., Zsiga, N., Soltic, P., Onder, C., Optimal degree of hybridization for spark-ignited engines with optional variable valve timings, Energies, 14(23), 8151, 2021.
  • Yazar O, Coskun S, Zhang F, Li L., A comparative study of energy management systems under connected driving: cooperative car-following case, Complex Engineering Systems, 2:7, 2022.
  • Li L., Coskun S., Langari R., Xi J., Incorporated vehicle lateral control strategy for stability and enhanced energy saving in distributed drive hybrid bus, Applied Soft Computing, 111, 107617, 2021.
  • Wirasingha S.G., Emadi A., Classification and review of control strategies for plug-in hybrid electric vehicles, IEEE Transactions on Vehicular Technology, 60(1), 111-122, 2010.
  • Salmasi F.R., Control strategies for hybrid electric vehicles: Evolution, classification, comparison, and future trends, IEEE Transactions on Vehicular Technology, 56(5), 2393-2404, 2007.
  • Riaz M., Hanif A., Masood H., Khan M. A., Afaq K., Kang B. G., Nam, Y., An optimal power flow solution of a system integrated with renewable sources using a hybrid optimizer, Sustainability, 13(23), 13382, 2021.
  • Başlamışlı S. Ç., İnce B., Design of energy management system algorithms for the improvement of fuel economy of intracity hybrid buses and development of an adaptive hybrid algorithm, Journal of the Faculty of Engineering and Architecture of Gazi University, 36 (1), 559-576, 2021.
  • Zhang F., Wang L., Coskun S., Pang H., Cui Y., Xi J., Energy management strategies for hybrid electric vehicles: Review, classification, comparison, and outlook, Energies, 13, 3352, 2020.
  • Chen Z., Liu Y., Zhang Y., Lei Z., Chen Z., Li G., A neural network-based ECMS for optimized energy management of plug-in hybrid electric vehicles, Energy, 243, 122727, 2021.
  • Liu J., Chen Y., Zhan J., Shang F., Heuristic dynamic programming based online energy management strategy for plug-in hybrid electric vehicles, IEEE Transactions on Vehicular Technology, 68(5), 4479–4493, 2019.
  • Chen H., Kessels J.T., Weiland S., Online adaptive approach for a game-theoretic strategy for complete vehicle energy management, In Proceedings of the 2015 European Control Conference (ECC), Linz-Austria, pp. 135–141, 15–17 July, 2015.
  • Kleimaier A., Schroder D., An approach for the online optimized control of a hybrid powertrain, In Proceedings of the 7th International Workshop on Advanced Motion Control. Proceedings (Cat. No.02TH8623), Maribor-Slovenia, pp. 215–220, 3–5 July, 2002.
  • Khodabakhshian M., Feng L., Wikander J., Improving fuel economy and robustness of an improved ECMS method, In Proceedings of the 2013 10th IEEE International Conference on Control and Automation (ICCA), Hangzhou-China, pp. 598–603, 12–14 June, 2013.
  • Hemi H., Ghouili J., Cheriti A., A real time energy management for electrical vehicle using combination of rule-based and ECMS, In Proceedings of the 2013 IEEE Electrical Power & Energy Conference, Halifax, NS, Canada, pp. 1–6, 21–23 August, 2013.
  • Musardo C., Rizzoni G., Staccia B., A-ECMS: An adaptive algorithm for hybrid electric vehicle energy management, In Proceedings of the 2005 44th IEEE Conference on Decision and Control and 2005 European Control Conference (CDC-ECC ’05), Seville-Spain, pp. 1816–1823, 12–15 December, 2005.
  • Xie S., Li H., Xin Z., Liu T., Wei L., A Pontryagin minimum principle-based adaptive equivalent consumption minimum strategy for a plug-in hybrid electric bus on a fixed route, Energies, 10(9), 1379, 2017.
  • Zhang J., Zheng C. Cha S.W., Duan S., Co-state variable determination in Pontryagin’s minimum principle for energy management of hybrid vehicles, International Journal of Precision Engineering and Manufacturing, 17(9), 1215–1222, 2016.
  • Li X., Wang Y., Yang D., Chen Z., Adaptive energy management strategy for fuel cell/battery hybrid vehicles using Pontryagin’s minimal principle. Journal of Power Sources, 440, 227105, 2019.
  • Paganelli, G., Design and control of a parallel hybrid car with electric and thermal powertrain. M. Sc.,1999.
  • Xie P., Tan S., Guerrero J. M., Vasquez J. C., MPC-informed ECMS based real-time power management strategy for hybrid electric ship, Energy Reports, 7, 126-133, 2021.
  • Onori S., Serrao L., On adaptive-ECMS strategies for hybrid electric vehicles, In Proceedings of the International Scientific Conference on Hybrid and Electric Vehicles, Malmaison-France, 6–7 December, 2011.
  • Zeng Y., Cai Y., Kou G., Gao W., Qin D., Energy management for plug-in hybrid electric vehicle based on adaptive simplified-ECMS, Sustainability, 10(6), 2060, 2018.
  • Choi K., Byun J., Lee S., Jang I. G., Adaptive equivalent consumption minimization strategy (A-ECMS) for the HEVs with a near-optimal equivalent factor considering driving conditions, IEEE Transactions on Vehicular Technology, 71(3), 2538-2549, 2021.
  • Zhang F., Xi J., Langari R., Real-time energy management strategy based on velocity forecasts using V2V and V2I communications, IEEE Transactions on Intelligent Transportation Systems, 18(2), 416-430, 2016.
  • Lei Z., Qin D., Liu Y., Peng Z., Lu L., Dynamic energy management for a novel hybrid electric system based on driving pattern recognition, Applied Mathematical Modelling, 45, 940-954, 2017.
  • Wang S., Huang X., López J. M., Xu X., Dong P., Fuzzy adaptive-equivalent consumption minimization strategy for a parallel hybrid electric vehicle, IEEE Access, 7, 133290-133303, 2019.
  • Chen Z., Liu Y., Ye M., Zhang Y., Chen Z., Li G., A survey on key techniques and development perspectives of equivalent consumption minimisation strategy for hybrid electric vehicles, Renewable and Sustainable Energy Reviews, 151, 111607, 2021.
  • Han J., Kum, D., Park Y., Synthesis of predictive equivalent consumption minimization strategy for hybrid electric vehicles based on closed-form solution of optimal equivalence factor, IEEE Transactions on Vehicular Technology, 66(7), 5604–5616, 2017.
  • Bayram A., Almalı M. N., Al-Naqshbandı F. M., Path following of an unmanned ground vehicle with GPS feedback using model predictive control method, Journal of the Faculty of Engineering and Architecture of Gazi University, 38(1), 345-355, 2023.
  • Han J., Shu H., Tang X., Lin X., Liu C., Hu X., Predictive energy management for plug-in hybrid electric vehicles considering electric motor thermal dynamics, Energy Conversion and Management, 251, 115022, 2022.
  • Wang L., Cui Y., Zhang F., Coskun S., Liu K., Li G., Stochastic speed prediction for connected vehicles using improved bayesian networks with back propagation, Science China Technological Sciences, 65(7), 1524–1536, 2022.
  • Kim T., Poplin G., Bollapragada V., Daniel T., Crandall J., Monte Carlo method for estimating whole-body injury metrics from pedestrian impact simulation results, Accident Analysis & Prevention, 147, 105761, 2020.
  • Chen M. H., Shao Q. M., Monte Carlo estimation of Bayesian credible and HPD intervals, Journal of Computational and Graphical Statistics, 8(1), 69-92, 1999.
  • Gill J., Bayesian methods: A social and behavioral sciences approach, 20, CRC press, USA, 2014.
  • Yang Y., Hu X., Pei H., Peng Z., Comparison of power-split and parallel hybrid powertrain architectures with a single electric machine: Dynamic programming approach, Applied Energy, 168, 683–690, 2016.
  • Hu X, Li S, Peng H., A comparative study of equivalent circuit models for Li-ion batteries, Journal of Power Sources, 198, 359–67, 2012.
  • Moura SJ, Chaturvedi NA, Krstic´ M., Adaptive partial differential equation observer for battery state-of-charge/state-of-health estimation via an electrochemical model, Journal of Dynamic Systems, Measurement, and Control, 136(1), 011015, 2014.
  • Rousseau A., Kwon J., Sharer P., Pagerit S., Duoba M., Integrating data, performing quality assurance, and validating the vehicle model for the 2004 Prius using PSAT[R]. SAE Technical Paper, 2006.
There are 40 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Makaleler
Authors

Merve Nur Gül This is me 0000-0003-1558-569X

Ozan Yazar 0000-0002-4593-0178

Serdar Coşkun 0000-0002-7080-0340

Fengqi Zhang This is me 0000-0001-9811-2593

Lin Li This is me 0000-0003-1846-8747

İrem Ersöz Kaya 0000-0001-5553-3881

Project Number 121E260
Publication Date January 6, 2023
Submission Date December 24, 2021
Acceptance Date July 24, 2022
Published in Issue Year 2023 Volume: 38 Issue: 3

Cite

APA Gül, M. N., Yazar, O., Coşkun, S., Zhang, F., et al. (2023). Güç paylaşımlı hibrit elektrikli araçlar için Monte Carlo algoritması kullanarak öngörülü eşdeğer tüketim minimizasyon stratejisi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 38(3), 1615-1630. https://doi.org/10.17341/gazimmfd.1040940
AMA Gül MN, Yazar O, Coşkun S, Zhang F, Li L, Ersöz Kaya İ. Güç paylaşımlı hibrit elektrikli araçlar için Monte Carlo algoritması kullanarak öngörülü eşdeğer tüketim minimizasyon stratejisi. GUMMFD. January 2023;38(3):1615-1630. doi:10.17341/gazimmfd.1040940
Chicago Gül, Merve Nur, Ozan Yazar, Serdar Coşkun, Fengqi Zhang, Lin Li, and İrem Ersöz Kaya. “Güç paylaşımlı Hibrit Elektrikli araçlar için Monte Carlo Algoritması Kullanarak öngörülü eşdeğer tüketim Minimizasyon Stratejisi”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 38, no. 3 (January 2023): 1615-30. https://doi.org/10.17341/gazimmfd.1040940.
EndNote Gül MN, Yazar O, Coşkun S, Zhang F, Li L, Ersöz Kaya İ (January 1, 2023) Güç paylaşımlı hibrit elektrikli araçlar için Monte Carlo algoritması kullanarak öngörülü eşdeğer tüketim minimizasyon stratejisi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 38 3 1615–1630.
IEEE M. N. Gül, O. Yazar, S. Coşkun, F. Zhang, L. Li, and İ. Ersöz Kaya, “Güç paylaşımlı hibrit elektrikli araçlar için Monte Carlo algoritması kullanarak öngörülü eşdeğer tüketim minimizasyon stratejisi”, GUMMFD, vol. 38, no. 3, pp. 1615–1630, 2023, doi: 10.17341/gazimmfd.1040940.
ISNAD Gül, Merve Nur et al. “Güç paylaşımlı Hibrit Elektrikli araçlar için Monte Carlo Algoritması Kullanarak öngörülü eşdeğer tüketim Minimizasyon Stratejisi”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 38/3 (January 2023), 1615-1630. https://doi.org/10.17341/gazimmfd.1040940.
JAMA Gül MN, Yazar O, Coşkun S, Zhang F, Li L, Ersöz Kaya İ. Güç paylaşımlı hibrit elektrikli araçlar için Monte Carlo algoritması kullanarak öngörülü eşdeğer tüketim minimizasyon stratejisi. GUMMFD. 2023;38:1615–1630.
MLA Gül, Merve Nur et al. “Güç paylaşımlı Hibrit Elektrikli araçlar için Monte Carlo Algoritması Kullanarak öngörülü eşdeğer tüketim Minimizasyon Stratejisi”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, vol. 38, no. 3, 2023, pp. 1615-30, doi:10.17341/gazimmfd.1040940.
Vancouver Gül MN, Yazar O, Coşkun S, Zhang F, Li L, Ersöz Kaya İ. Güç paylaşımlı hibrit elektrikli araçlar için Monte Carlo algoritması kullanarak öngörülü eşdeğer tüketim minimizasyon stratejisi. GUMMFD. 2023;38(3):1615-30.