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Convex Optimization for the Energy Management of Hybrid Electric Vehicles Considering Engine Start and Gearshift Costs

Author

Listed:
  • Tobias Nüesch

    (Institute for Dynamic Systems and Control, ETH Zurich, Sonneggstrasse 3, Zurich 8092, Switzerland)

  • Philipp Elbert

    (Institute for Dynamic Systems and Control, ETH Zurich, Sonneggstrasse 3, Zurich 8092, Switzerland)

  • Michael Flankl

    (Institute for Dynamic Systems and Control, ETH Zurich, Sonneggstrasse 3, Zurich 8092, Switzerland)

  • Christopher Onder

    (Institute for Dynamic Systems and Control, ETH Zurich, Sonneggstrasse 3, Zurich 8092, Switzerland)

  • Lino Guzzella

    (Institute for Dynamic Systems and Control, ETH Zurich, Sonneggstrasse 3, Zurich 8092, Switzerland)

Abstract

This paper presents a novel method to solve the energy management problem for hybrid electric vehicles (HEVs) with engine start and gearshift costs. The method is based on a combination of deterministic dynamic programming (DP) and convex optimization. As demonstrated in a case study, the method yields globally optimal results while returning the solution in much less time than the conventional DP method. In addition, the proposed method handles state constraints, which allows for the application to scenarios where the battery state of charge (SOC) reaches its boundaries.

Suggested Citation

  • Tobias Nüesch & Philipp Elbert & Michael Flankl & Christopher Onder & Lino Guzzella, 2014. "Convex Optimization for the Energy Management of Hybrid Electric Vehicles Considering Engine Start and Gearshift Costs," Energies, MDPI, vol. 7(2), pages 1-23, February.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:2:p:834-856:d:33072
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    References listed on IDEAS

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    1. Hu, Xiaosong & Murgovski, Nikolce & Johannesson, Lars & Egardt, Bo, 2013. "Energy efficiency analysis of a series plug-in hybrid electric bus with different energy management strategies and battery sizes," Applied Energy, Elsevier, vol. 111(C), pages 1001-1009.
    2. Jeongbin Lee & Jaeshin Yi & Chee Burm Shin & Seung Ho Yu & Won Il Cho, 2013. "Modeling the Effects of the Cathode Composition of a Lithium Iron Phosphate Battery on the Discharge Behavior," Energies, MDPI, vol. 6(11), pages 1-12, October.
    3. Pérez, Laura V. & Bossio, Guillermo R. & Moitre, Diego & García, Guillermo O., 2006. "Optimization of power management in an hybrid electric vehicle using dynamic programming," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 73(1), pages 244-254.
    4. Yuan Zou & Fengchun Sun & Xiaosong Hu & Lino Guzzella & Huei Peng, 2012. "Combined Optimal Sizing and Control for a Hybrid Tracked Vehicle," Energies, MDPI, vol. 5(11), pages 1-14, November.
    5. Zou Yuan & Liu Teng & Sun Fengchun & Huei Peng, 2013. "Comparative Study of Dynamic Programming and Pontryagin’s Minimum Principle on Energy Management for a Parallel Hybrid Electric Vehicle," Energies, MDPI, vol. 6(4), pages 1-14, April.
    6. Ravi Shankar & James Marco & Francis Assadian, 2012. "The Novel Application of Optimization and Charge Blended Energy Management Control for Component Downsizing within a Plug-in Hybrid Electric Vehicle," Energies, MDPI, vol. 5(12), pages 1-32, November.
    7. Tobias Ott & Christopher Onder & Lino Guzzella, 2013. "Hybrid-Electric Vehicle with Natural Gas-Diesel Engine," Energies, MDPI, vol. 6(7), pages 1-22, July.
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    Cited by:

    1. Liu, Teng & Tan, Wenhao & Tang, Xiaolin & Zhang, Jinwei & Xing, Yang & Cao, Dongpu, 2021. "Driving conditions-driven energy management strategies for hybrid electric vehicles: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    2. Ximing Wang & Hongwen He & Fengchun Sun & Jieli Zhang, 2015. "Application Study on the Dynamic Programming Algorithm for Energy Management of Plug-in Hybrid Electric Vehicles," Energies, MDPI, vol. 8(4), pages 1-20, April.
    3. Felipe Jiménez & Wilmar Cabrera-Montiel, 2014. "System for Road Vehicle Energy Optimization Using Real Time Road and Traffic Information," Energies, MDPI, vol. 7(6), pages 1-23, June.
    4. Jixiang Fan & Jiangyan Zhang & Tielong Shen, 2015. "Map-Based Power-Split Strategy Design with Predictive Performance Optimization for Parallel Hybrid Electric Vehicles," Energies, MDPI, vol. 8(9), pages 1-23, September.
    5. Md Ragib Ahssan & Mehran Ektesabi & Saman Gorji, 2023. "Evaluation of a Three-Parameter Gearshift Strategy for a Two-Speed Transmission System in Electric Vehicles," Energies, MDPI, vol. 16(5), pages 1-28, March.
    6. Tengda Hu & Yunwu Li & Zhi Zhang & Ying Zhao & Dexiong Liu, 2021. "Energy Management Strategy of Hybrid Energy Storage System Based on Road Slope Information," Energies, MDPI, vol. 14(9), pages 1-18, April.
    7. Jinlong Hong & Liangchun Zhao & Yulong Lei & Bingzhao Gao, 2018. "Architecture Optimization of Hybrid Electric Vehicles with Future High-Efficiency Engine," Energies, MDPI, vol. 11(5), pages 1-23, May.
    8. Fengqi Zhang & Lihua Wang & Serdar Coskun & Hui Pang & Yahui Cui & Junqiang Xi, 2020. "Energy Management Strategies for Hybrid Electric Vehicles: Review, Classification, Comparison, and Outlook," Energies, MDPI, vol. 13(13), pages 1-35, June.
    9. Huijun Yue & Jinyu Lin & Peng Dong & Zhinan Chen & Xiangyang Xu, 2023. "Configurations and Control Strategies of Hybrid Powertrain Systems," Energies, MDPI, vol. 16(2), pages 1-18, January.
    10. Zhang, Hailong & Peng, Jiankun & Tan, Huachun & Dong, Hanxuan & Ding, Fan & Ran, Bin, 2020. "Tackling SOC long-term dynamic for energy management of hybrid electric buses via adaptive policy optimization," Applied Energy, Elsevier, vol. 269(C).
    11. Zhang, Hailong & Peng, Jiankun & Dong, Hanxuan & Tan, Huachun & Ding, Fan, 2023. "Hierarchical reinforcement learning based energy management strategy of plug-in hybrid electric vehicle for ecological car-following process," Applied Energy, Elsevier, vol. 333(C).
    12. Shabbir, Wassif & Evangelou, Simos A., 2014. "Real-time control strategy to maximize hybrid electric vehicle powertrain efficiency," Applied Energy, Elsevier, vol. 135(C), pages 512-522.
    13. Duhr, Pol & Christodoulou, Grigorios & Balerna, Camillo & Salazar, Mauro & Cerofolini, Alberto & Onder, Christopher H., 2021. "Time-optimal gearshift and energy management strategies for a hybrid electric race car," Applied Energy, Elsevier, vol. 282(PA).
    14. Anwar, Hamza & Vishwanath, Aashrith & Ahmed, Qadeer & Chunodkar, Apurva, 2023. "Comprehensive energy footprint benchmarking of commercial electrified powertrains," Applied Energy, Elsevier, vol. 345(C).
    15. Weichao Zhuang & Xiaowu Zhang & Huei Peng & Liangmo Wang, 2016. "Simultaneous Optimization of Topology and Component Sizes for Double Planetary Gear Hybrid Powertrains," Energies, MDPI, vol. 9(6), pages 1-17, May.
    16. Zhang, Pei & Yan, Fuwu & Du, Changqing, 2015. "A comprehensive analysis of energy management strategies for hybrid electric vehicles based on bibliometrics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 88-104.
    17. Caiyang Wei & Theo Hofman & Esin Ilhan Caarls & Rokus van Iperen, 2020. "A Review of the Integrated Design and Control of Electrified Vehicles," Energies, MDPI, vol. 13(20), pages 1-31, October.
    18. Yongjian Zhou & Rong Yang & Song Zhang & Kejun Lan & Wei Huang, 2023. "Optimization of Power-System Parameters and Energy-Management Strategy Research on Hybrid Heavy-Duty Trucks," Energies, MDPI, vol. 16(17), pages 1-21, August.
    19. Zhuang, Weichao & Zhang, Xiaowu & Ding, Yang & Wang, Liangmo & Hu, Xiaosong, 2016. "Comparison of multi-mode hybrid powertrains with multiple planetary gears," Applied Energy, Elsevier, vol. 178(C), pages 624-632.
    20. Camillo Balerna & Marc-Philippe Neumann & Nicolò Robuschi & Pol Duhr & Alberto Cerofolini & Vittorio Ravaglioli & Christopher Onder, 2020. "Time-Optimal Low-Level Control and Gearshift Strategies for the Formula 1 Hybrid Electric Powertrain," Energies, MDPI, vol. 14(1), pages 1-30, December.
    21. Teng Liu & Yuan Zou & Dexing Liu & Fengchun Sun, 2015. "Reinforcement Learning–Based Energy Management Strategy for a Hybrid Electric Tracked Vehicle," Energies, MDPI, vol. 8(7), pages 1-18, July.
    22. Balerna, Camillo & Lanzetti, Nicolas & Salazar, Mauro & Cerofolini, Alberto & Onder, Christopher, 2020. "Optimal low-level control strategies for a high-performance hybrid electric power unit," Applied Energy, Elsevier, vol. 276(C).
    23. Zhou, Xingyu & Qin, Datong & Hu, Jianjun, 2017. "Multi-objective optimization design and performance evaluation for plug-in hybrid electric vehicle powertrains," Applied Energy, Elsevier, vol. 208(C), pages 1608-1625.
    24. Chen, Zheng & Xia, Bing & You, Chenwen & Mi, Chunting Chris, 2015. "A novel energy management method for series plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 145(C), pages 172-179.
    25. Li, Yapeng & Tang, Xiaolin & Lin, Xianke & Grzesiak, Lech & Hu, Xiaosong, 2022. "The role and application of convex modeling and optimization in electrified vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).

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