IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v115y2014icp174-189.html
   My bibliography  Save this article

Approximate Pontryagin’s minimum principle applied to the energy management of plug-in hybrid electric vehicles

Author

Listed:
  • Hou, Cong
  • Ouyang, Minggao
  • Xu, Liangfei
  • Wang, Hewu

Abstract

This paper proposes an optimal energy management strategy based on the approximate Pontryagin’s Minimum Principle (A-PMP) algorithm for parallel plug-in hybrid electric vehicles (HEVs). When the driving cycles are known in advance, the Pontryagin’s Minimum Principle (PMP) can help to achieve the best fuel economy, but real-time control has been unavailable due to the massive computational load required by instantaneous Hamiltonian optimization. After observing some regular patterns in numeric PMP results, we were inspired to apply a novel piecewise linear approximation strategy by specifying the turning point of the engine fuel rate for the Hamiltonian optimization. As a result, the instantaneous Hamiltonian optimization becomes convex. Considering the engine state, there are only five candidate solutions for the optimization. For the engine off state, only one of the available torque split ratios (TSR) is one of these five candidates. The other four TSR candidates are for the engine on state, including the TSR when the engine operates at the best efficiency point for the current speed, the TSR when the engine delivers all the required torque and two terminal TSRs. The optimal TSR is the one with the smallest Hamiltonian of the current engine state. The engine state with the smallest Hamiltonian will be requested for the next time step. The results show that the A-PMP strategy reduced fuel consumption by 6.96% compared with the conventional “All-Electric, Charge-Sustaining” (AE–CS) strategy. In addition, the A-PMP shortened the simulation time from 6h to only 4min, when compared with the numeric PMP method. Unlike other approximation methods, the proposed novel piecewise linear approximation caused no severe distortion to the engine map model. The engine state switching frequency is also reduced by 43.40% via both the filter and the corresponding engine on/off optimal control strategy.

Suggested Citation

  • Hou, Cong & Ouyang, Minggao & Xu, Liangfei & Wang, Hewu, 2014. "Approximate Pontryagin’s minimum principle applied to the energy management of plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 115(C), pages 174-189.
    • Handle: RePEc:eee:appene:v:115:y:2014:i:c:p:174-189
    DOI: 10.1016/j.apenergy.2013.11.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261913008945
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2013.11.002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Cipek, Mihael & Pavković, Danijel & Petrić, Joško, 2013. "A control-oriented simulation model of a power-split hybrid electric vehicle," Applied Energy, Elsevier, vol. 101(C), pages 121-133.
    2. Doucette, Reed T. & McCulloch, Malcolm D., 2011. "Modeling the prospects of plug-in hybrid electric vehicles to reduce CO2 emissions," Applied Energy, Elsevier, vol. 88(7), pages 2315-2323, July.
    3. Trovão, João P. & Pereirinha, Paulo G. & Jorge, Humberto M. & Antunes, Carlos Henggeler, 2013. "A multi-level energy management system for multi-source electric vehicles – An integrated rule-based meta-heuristic approach," Applied Energy, Elsevier, vol. 105(C), pages 304-318.
    4. 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.
    5. Tzeng, Sheng-Chung & David Huang, K. & Chen, Chia-Chang, 2005. "Optimization of the dual energy-integration mechanism in a parallel-type hybrid vehicle," Applied Energy, Elsevier, vol. 80(3), pages 225-245, March.
    6. Al-Alawi, Baha M. & Bradley, Thomas H., 2013. "Total cost of ownership, payback, and consumer preference modeling of plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 103(C), pages 488-506.
    7. Bizon, Nicu, 2013. "Energy efficiency for the multiport power converters architectures of series and parallel hybrid power source type used in plug-in/V2G fuel cell vehicles," Applied Energy, Elsevier, vol. 102(C), pages 726-734.
    8. Jimenez-Espadafor, Francisco José & Marín, Juan José Ruiz & Becerra Villanueva, José A. & García, Miguel Torres & Trujillo, Elisa Carvajal & Ojeda, Francisco José Florencio, 2011. "Infantry mobility hybrid electric vehicle performance analysis and design," Applied Energy, Elsevier, vol. 88(8), pages 2641-2652, August.
    9. Hung, Yi-Hsuan & Wu, Chien-Hsun, 2012. "An integrated optimization approach for a hybrid energy system in electric vehicles," Applied Energy, Elsevier, vol. 98(C), pages 479-490.
    10. Xu, Liangfei & Ouyang, Minggao & Li, Jianqiu & Yang, Fuyuan & Lu, Languang & Hua, Jianfeng, 2013. "Optimal sizing of plug-in fuel cell electric vehicles using models of vehicle performance and system cost," Applied Energy, Elsevier, vol. 103(C), pages 477-487.
    11. Wu, Xiaolan & Cao, Binggang & Li, Xueyan & Xu, Jun & Ren, Xiaolong, 2011. "Component sizing optimization of plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 88(3), pages 799-804, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Du, Jiuyu & Chen, Jingfu & Song, Ziyou & Gao, Mingming & Ouyang, Minggao, 2017. "Design method of a power management strategy for variable battery capacities range-extended electric vehicles to improve energy efficiency and cost-effectiveness," Energy, Elsevier, vol. 121(C), pages 32-42.
    3. Feroldi, Diego & Carignano, Mauro, 2016. "Sizing for fuel cell/supercapacitor hybrid vehicles based on stochastic driving cycles," Applied Energy, Elsevier, vol. 183(C), pages 645-658.
    4. Finesso, Roberto & Spessa, Ezio & Venditti, Mattia, 2016. "Cost-optimized design of a dual-mode diesel parallel hybrid electric vehicle for several driving missions and market scenarios," Applied Energy, Elsevier, vol. 177(C), pages 366-383.
    5. Jiajun Liu & Huachao Dong & Tianxu Jin & Li Liu & Babak Manouchehrinia & Zuomin Dong, 2018. "Optimization of Hybrid Energy Storage Systems for Vehicles with Dynamic On-Off Power Loads Using a Nested Formulation," Energies, MDPI, vol. 11(10), pages 1-25, October.
    6. Onori, Simona & Tribioli, Laura, 2015. "Adaptive Pontryagin’s Minimum Principle supervisory controller design for the plug-in hybrid GM Chevrolet Volt," Applied Energy, Elsevier, vol. 147(C), pages 224-234.
    7. Song, Ziyou & Hofmann, Heath & Li, Jianqiu & Han, Xuebing & Ouyang, Minggao, 2015. "Optimization for a hybrid energy storage system in electric vehicles using dynamic programing approach," Applied Energy, Elsevier, vol. 139(C), pages 151-162.
    8. Cordiner, Stefano & Galeotti, Matteo & Mulone, Vincenzo & Nobile, Matteo & Rocco, Vittorio, 2016. "Trip-based SOC management for a plugin hybrid electric vehicle," Applied Energy, Elsevier, vol. 164(C), pages 891-905.
    9. De Filippo, Giovanni & Marano, Vincenzo & Sioshansi, Ramteen, 2014. "Simulation of an electric transportation system at The Ohio State University," Applied Energy, Elsevier, vol. 113(C), pages 1686-1691.
    10. Song, Ziyou & Li, Jianqiu & Han, Xuebing & Xu, Liangfei & Lu, Languang & Ouyang, Minggao & Hofmann, Heath, 2014. "Multi-objective optimization of a semi-active battery/supercapacitor energy storage system for electric vehicles," Applied Energy, Elsevier, vol. 135(C), pages 212-224.
    11. Xu, Liangfei & Mueller, Clemens David & Li, Jianqiu & Ouyang, Minggao & Hu, Zunyan, 2015. "Multi-objective component sizing based on optimal energy management strategy of fuel cell electric vehicles," Applied Energy, Elsevier, vol. 157(C), pages 664-674.
    12. Song, Ziyou & Hofmann, Heath & Li, Jianqiu & Hou, Jun & Han, Xuebing & Ouyang, Minggao, 2014. "Energy management strategies comparison for electric vehicles with hybrid energy storage system," Applied Energy, Elsevier, vol. 134(C), pages 321-331.
    13. Torres, J.L. & Gonzalez, R. & Gimenez, A. & Lopez, J., 2014. "Energy management strategy for plug-in hybrid electric vehicles. A comparative study," Applied Energy, Elsevier, vol. 113(C), pages 816-824.
    14. Qin, Zhaobo & Luo, Yugong & Zhuang, Weichao & Pan, Ziheng & Li, Keqiang & Peng, Huei, 2018. "Simultaneous optimization of topology, control and size for multi-mode hybrid tracked vehicles," Applied Energy, Elsevier, vol. 212(C), pages 1627-1641.
    15. Hung, Yi-Hsuan & Wu, Chien-Hsun, 2012. "An integrated optimization approach for a hybrid energy system in electric vehicles," Applied Energy, Elsevier, vol. 98(C), pages 479-490.
    16. Sanfélix, Javier & Messagie, Maarten & Omar, Noshin & Van Mierlo, Joeri & Hennige, Volker, 2015. "Environmental performance of advanced hybrid energy storage systems for electric vehicle applications," Applied Energy, Elsevier, vol. 137(C), pages 925-930.
    17. Jiajun Liu & Tianxu Jin & Li Liu & Yajue Chen & Kun Yuan, 2017. "Multi-Objective Optimization of a Hybrid ESS Based on Optimal Energy Management Strategy for LHDs," Sustainability, MDPI, vol. 9(10), pages 1-18, October.
    18. Verma, Anoop & Asadi, Ali & Yang, Kai & Tyagi, Satish, 2015. "A data-driven approach to identify households with plug-in electrical vehicles (PEVs)," Applied Energy, Elsevier, vol. 160(C), pages 71-79.
    19. Hung, Yi-Hsuan & Wu, Chien-Hsun, 2015. "A combined optimal sizing and energy management approach for hybrid in-wheel motors of EVs," Applied Energy, Elsevier, vol. 139(C), pages 260-271.
    20. Mayyas, Abdel Ra'ouf & Kumar, Sushil & Pisu, Pierluigi & Rios, Jacqueline & Jethani, Puneet, 2017. "Model-based design validation for advanced energy management strategies for electrified hybrid power trains using innovative vehicle hardware in the loop (VHIL) approach," Applied Energy, Elsevier, vol. 204(C), pages 287-302.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:115:y:2014:i:c:p:174-189. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.