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Real-time energy optimization of HEVs under-connected environment: a benchmark problem and receding horizon-based solution

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Real-time energy optimization of HEVs under-connected environment: a benchmark problem and receding horizon-based solution

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dc.contributor.author Xu, Fuguo es_ES
dc.contributor.author Tsunogawa, Hiroki es_ES
dc.contributor.author Kako, Junichi es_ES
dc.contributor.author Hu, Xiaosong es_ES
dc.contributor.author Eben Li, Shengbo es_ES
dc.contributor.author Shen, Tielong es_ES
dc.contributor.author Eriksson, Lars es_ES
dc.contributor.author Guardiola, Carlos es_ES
dc.date.accessioned 2023-09-28T18:02:16Z
dc.date.available 2023-09-28T18:02:16Z
dc.date.issued 2022-05-01 es_ES
dc.identifier.issn 2095-6983 es_ES
dc.identifier.uri http://hdl.handle.net/10251/197322
dc.description.abstract [EN] In this paper, we propose a benchmark problem for the challengers aiming to energy efficiency control of hybrid electric vehicles (HEVs) on a road with slope. Moreover, it is assumed that the targeted HEVs are in the connected environment with the obtainment of real-time information of vehicle-to-everything (V2X), including geographic information, vehicle-to-infrastructure (V2I) information and vehicle-to-vehicle (V2V) information. The provided simulator consists of an industrial-level HEV model and a traffic scenario database obtained through a commercial traffic simulator, where the running route is generated based on real-world data with slope and intersection position. The benchmark problem to be solved is the HEVs powertrain control using traffic information to fulfill fuel economy improvement while satisfying the constraints of driving safety and travel time. To show the HEV powertrain characteristics, a case study is given with the speed planning and energy management strategy. es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof Control Theory and Technology es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Powertrain control es_ES
dc.subject Connected and automated vehicles es_ES
dc.subject Hybrid electric vehicles es_ES
dc.subject Vehicle-to-everything es_ES
dc.subject.classification MAQUINAS Y MOTORES TERMICOS es_ES
dc.title Real-time energy optimization of HEVs under-connected environment: a benchmark problem and receding horizon-based solution es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s11768-022-00086-y es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros Industriales - Escola Tècnica Superior d'Enginyers Industrials es_ES
dc.description.bibliographicCitation Xu, F.; Tsunogawa, H.; Kako, J.; Hu, X.; Eben Li, S.; Shen, T.; Eriksson, L.... (2022). Real-time energy optimization of HEVs under-connected environment: a benchmark problem and receding horizon-based solution. Control Theory and Technology. 20:145-160. https://doi.org/10.1007/s11768-022-00086-y es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1007/s11768-022-00086-y es_ES
dc.description.upvformatpinicio 145 es_ES
dc.description.upvformatpfin 160 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 20 es_ES
dc.relation.pasarela S\483589 es_ES
dc.description.references Zhou, Q., Zhao, D., Shuai, B., Li, Y., Williams, H., & Xu, H. (2021). Knowledge implementation and transfer with an adaptive learning network for real-time power management of the plug-in hybrid vehicle. IEEE Transactions on Neural Networks and Learning Systems, 32(12), 5298–5308. https://doi.org/10.1109/TNNLS.2021.3093429 es_ES
dc.description.references Xu, F., & Shen, T. (2021). Decentralized optimal merging control with optimization of energy consumption for connected hybrid electric vehicles. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2021.3054903 es_ES
dc.description.references Zhuang, W., Li, S., Zhang, X., et al. (2020). A survey of powertrain configuration studies on hybrid electric vehicles. Applied Energy, 262, 114553. es_ES
dc.description.references Wang, S., Chen, K., Zhao, F., & Hao, H. (2019). Technology pathways for complying with corporate average fuel consumption regulations up to 2030: A case study of China. Applied Energy, 241, 257–277. es_ES
dc.description.references Zhang, J., Shen, T., & Kako, J. (2020). Short-term optimal energy management of power-split hybrid electric vehicles under velocity tracking control. IEEE Transactions on Vehicular Technology, 69(1), 182–193. es_ES
dc.description.references Asaei, B. (2010). A fuzzy-genetic algorithm approach for finding a new HEV control strategy idea. 1st Power Electronic and Drive Systems and Technologies Conference, pp. 224 – 229. Tehran, Iran. es_ES
dc.description.references Wu, J., Zhang, C. H., & Cui, N. X. (2008). PSO algorithm-based parameter optimization for HEV powertrain and its control strategy. International Journal of Automotive Technology, 9(1), 53–59. es_ES
dc.description.references Lin, C. C., Peng, H., Grizzle, J. W., & Kang, J.-M. (2003). Power management strategy for a parallel hybrid electric truck. IEEE Transactions on Control Systems Technology, 11(6), 839–849. es_ES
dc.description.references Luján, J. M., Guardiola, C., Pla, B., & Reig, A. (2018). Analytical optimal solution to the energy management problem in series hybrid electric vehicles. IEEE Transactions on Vehicular Technology, 67(8): 6803 – 6813. es_ES
dc.description.references Larsson, V., Johannesson, L., & Egardt, B. (2014). Analytic solutions to the dynamic programming subproblem in hybrid vehicle energy management. IEEE Transactions on Vehicular Technology, 64(4), 1458–1467. es_ES
dc.description.references Serrao, L., Onori, S., & Rizzoni, G. (2009). ECMS as a realization of Pontryagin’s minimum principle for HEV control. American Control Conference, pp. 3964-3969. St. Louis, MO, USA. es_ES
dc.description.references Kim, N., Cha, S., & Peng, H. (2011). Optimal equivalent fuel consumption for hybrid electric vehicles. IEEE Transactions on Control Systems Technology, 20(3), 817–825. es_ES
dc.description.references Rezaei, A., Burl, J. B., Solouk, A., Zhou, B., et al. (2017). Catch energy saving opportunity (CESO), an instantaneous optimal energy management strategy for series hybrid electric vehicles. Applied Energy, 208, 655–665. es_ES
dc.description.references Xie, S., Hu, X., Qi, S., & Lang, K. (2018). An artificial neural network-enhanced energy management strategy for plug-in hybrid electric vehicles. Energy, 163, 837–848. es_ES
dc.description.references Zhang, J., & Shen, T. (2016). Real-time fuel economy optimization with nonlinear MPC for PHEVs. IEEE Transactions on Control Systems Technology, 24(6), 2167–2175. es_ES
dc.description.references Sciarretta, A., Serrao, L., Dewangan, P. C., et al. (2014). A control benchmark on the energy management of a plug-in hybrid electric vehicle. Control Engineering Practice, 29, 287–298. es_ES
dc.description.references Lars, E. (2019). An overview of various control benchmarks with a focus on automotive control. Control Theory and Technology, 17(2), 121–130. es_ES
dc.description.references Moura, S. J., Fathy, H. K., Callaway, D. S., & Stein, J. L. (2010). A stochastic optimal control approach for power management in plug-in hybrid electric vehicles. IEEE Transactions on Control Systems Technology, 19(3), 545–555. es_ES
dc.description.references Sun, C., Hu, X., Moura, S. J., & Sun, F. (2014). Velocity predictors for predictive energy management in hybrid electric vehicles. IEEE Transactions on Control Systems Technology, 23(3), 1197–1204. es_ES
dc.description.references Xiang, C., Ding, F., Wang, W., & He, W. (2017). Energy management of a dual-mode power-split hybrid electric vehicle based on velocity prediction and nonlinear model predictive control. Applied Energy, 189, 640–653. es_ES
dc.description.references Sun, C., Sun, F., & He, H. (2017). Investigating adaptive-ECMS with velocity forecast ability for hybrid electric vehicles. Applied Energy, 185, 1644–1653. es_ES
dc.description.references Zhang, F., Hu, X., Langari, R., & Cao, D. (2019). Energy management strategies of connected HEVs and PHEVs: Recent progress and outlook. Progress in Energy and Combustion Science, 73, 235–256. es_ES
dc.description.references Yang, C., Zha, M., Wang, W., Liu, K., & Xiang, C. (2020). Efficient energy management strategy for hybrid electric vehicles/plug-in hybrid electric vehicles: Review and recent advances under intelligent transportation system. IET Intelligent Transport Systems, 14(7), 702–711. https://doi.org/10.1049/iet-its.2019.0606 es_ES
dc.description.references Zhang, J., Xu, F., Zhang, Y., & Shen, T. (2019). ELM-based driver torque demand prediction and real-time optimal energy management strategy for HEVs. Neural Computing and Applications, 32: 14411C14429. es_ES
dc.description.references Zhang, B., Zhang, J., Xu, F., & Shen, T. (2020). Optimal control of power-split hybrid electric powertrains with minimization of energy consumption. Applied Energy, 266, 114873. es_ES
dc.description.references Zhang, F., Xi, J., & Langari, R. (2016). Real-time energy management strategy based on velocity forecasts using V2V and V2I communications. IEEE Transactions on Intelligent Transportation Systems, 18(2), 416–430. es_ES
dc.description.references Li, J., Zhou, Q., He, Y., et al. (2019). Dual-loop online intelligent programming for driver-oriented predict energy management of plug-in hybrid electric vehicles. Applied Energy, 253, 113617. es_ES
dc.description.references Qi, X., Wu, G., Hao, P., Boriboonsomsin, K., & Barth, M. J. (2017). Integrated-connected eco-driving system for PHEVs with co-optimization of vehicle dynamics and powertrain operations. IEEE Transactions on Vehicular Technology, 2(1), 2–13. es_ES
dc.description.references Uebel, S., Murgovski, N., B$$\ddot{\rm a}$$ker, B., & Sj$$\ddot{\rm o}$$berg, J. (2019). A two-level mpc for energy management including velocity control of hybrid electric vehicles. IEEE Transactions on Vehicular Technology, 68(6): 5494–5505. es_ES
dc.description.references Chen, B., Evangelou, S. A., & Lot, R. (2019). Hybrid electric vehicle two-step fuel efficiency optimization with decoupled energy management and speed control. IEEE Transactions on Vehicular Technology, 68(12), 11492–11504. es_ES
dc.description.references Wang, S., & Lin, X. (2020). Eco-driving control of connected and automated hybrid vehicles in mixed driving scenarios. Applied Energy, 271, 115233. es_ES
dc.description.references Zhang, J., & Xu, F. (2020). Real-time optimization of energy consumption under adaptive cruise control for connected HEVs. Control Theory and Technology, 18(2), 182–192. es_ES
dc.description.references Fu, Q., Xu, F., Shen, T., & Takai, K. (2020). Distributed optimal energy consumption control of HEVs under MFG-based speed consensus. Control Theory and Technology, 18(2), 193–203. es_ES
dc.description.references Chen, B., Evangelou, S. A., & Lot, R. (2019). Series hybrid electric vehicle simultaneous energy management and driving speed optimization. IEEE/ASME Transactions on Mechatronics, 24(6), 2756–2767. es_ES
dc.description.references Hu, Q., Amini, M. R., Feng, Y., Yang, Z., Wang, H., Kolmanovsky, I., & Seeds, J. B. (2020). Engine and aftertreatment co-optimization of connected HEVs via multi-range vehicle speed planning and prediction. SAE Technical Paper, -01-0590. es_ES
dc.description.references Xu, F., & Shen, T. (2020). Look-ahead prediction-based real-time optimal energy management for connected HEVs. IEEE Transactions on Vehicular Technology, 69(3), 2537–2551. es_ES
dc.description.references Xu, F., & Shen, T. (2019). MPC-based optimal control for diesel engine coupled with lean NOx trap system. SICE Journal of Control, Measurement, and System Integration, 12(3), 94–101. es_ES


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