Zusammenfassung
Hybridized powertrains using two or more on-board power sources are getting increasing attention from researchers and automakers due to their ability to achieve equivalent performance to conventional vehicles and minimal harmful emissions. Power management systems (PMS) play a crucial role in hybrid powertrains to achieve high energy efficiency and sustain high drivability. Design requirements to power management include the ability to find optimal power handling decisions, accommodate unscheduled loads, and to be computationally applicable in real-time [1].
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Literatur
M. Ehsani, Y. Gao, S. Longo, and K. Ebrahimi, Modern Electric, Hybrid Electric, and Fuel Cell Vehicles, Third Edition. Boca Raton: CRC Press, Feb. 2018.
S. Onori, L. Serrao, and G. Rizzoni, Hybrid Electric Vehicles: Energy Management Strategies. Springer London, 2016.
J. Wang, Y. Huang, H. Xie, and G. Tian, “Driving pattern recognition and energy management for extended range electric bus,” in 2014 IEEE Vehicle Power and Propulsion Conference (VPPC), Oct 2014, pp. 1–6.
A. M. Ali and D. Söffker, “Towards optimal power management of hybrid electric vehicles in real-time: A review on methods, challenges, and state-of-the-art solutions,” Energies, vol. 11, no. 3, pp. 476–500, February 2018.
M. Özbek and D. Söffker, “About the system design of a fuel-cell / SuperCap hybrid powertrain,” in ASME/IEEE 2009 International Conference on Mechatronic and Embedded Systems and Applications; 20th Reliability, Stress Analysis, and Failure Prevention Conference, 2009.
Ali, M. A.; Shivapurkar, R.; Söffker, D.: Development and improvement of a situation-based power management method for multi-source electric vehicles. IEEE-VPPC-2018 Vehicle Power and Propulsion Conference, Chicago, USA, August 27–30, 2018, pp. 1–6.
B. Moulik and D. Söffker, “Online powermanagement with embedded optimization for a multi-source hybrid with dynamic power sharing between components,” in ASME 2015 Dynamic Systems and Control Conference. Ohio, USA, 2015, pp. V003T41A001.
A. M. Ali and D. Söffker, “Realtime application of progressive optimal search and adaptive dynamic programming in multi-source HEVs,” in Volume 2: ASME DSCC-2017 Dynamic Systems and Control Conference, Virginia, USA, Oct 2017.
H. He, C. Sun, and X. Zhang, “A method for identification of driving patterns in hybrid electric vehicles based on a lvq neural network,” Energies, vol. 5, no. 9, pp. 3363–3380, 12.
M. Staackmann, B. Y. Liaw, and D. Y. Y.Yun, “Dynamic driving cycle analyses using electric vehicle time-series data,” in IECEC-97 Proceedings of the Thirty-Second Intersociety Energy Conversion Engineering Conference, vol. 3, 1997, pp. 2014–2018.
Y. L. Murphey, Z. Chen, L. Kiliaris, J. Park, M. Kuang, A. Masrur, and A. Phillips, “Neural learning of driving environment prediction for vehicle power management,” in IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 2008, pp. 3755–3761.
I. Fomunung, S. Washington, and R. Guensler, “A statistical model for estimating oxides of nitrogen emissions from light duty motor vehicles,” Transportation Research Part D: Transport and Environment, vol. 4, no. 5, pp. 333–352, 1999.
X. Huang, Y. Tan, and X. He, “An intelligent multifeature statistical approach for the discrimination of driving conditions of a hybrid electric vehicle,” IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 2, pp. 453–465, 2011.
Ali, A. M.; Shivapurkar, R.; Söffker, D.: Optimal situation-based power management and application to state predictive models for multi-source electric vehicles. IEEE Transactions on Vehicular Technology, 2019, submitted.
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M. Ali, A., Moulik, B., Söffker, D. (2020). Optimizing the fuel efficiency of fuel cell-based hybrid electric vehicles considering real implications. In: Proff, H. (eds) Neue Dimensionen der Mobilität. Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-29746-6_17
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DOI: https://doi.org/10.1007/978-3-658-29746-6_17
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