IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v251y2022ics0360544222007289.html
   My bibliography  Save this article

Determination of vehicle working modes for global optimization energy management and evaluation of the economic performance for a certain control strategy

Author

Listed:
  • Xu, Nan
  • Kong, Yan
  • Zhang, Yuanjian
  • Yue, Fenglai
  • Sui, Yan
  • Li, Xiaohan
  • Liu, Heng
  • Xu, Zhe

Abstract

As the physical subject, determining vehicle operating modes is a prerequisite for implementing global optimization energy management. To avoid the case study of different vehicle configurations, a “kinetic/potential energy & onboard energy” conservation framework is proposed to determine vehicle working modes. Firstly, typical topologies and existing work modes for hybrid vehicles with different architectures are summarized. As a numerical method, the state space is meshed, which is restricted by introducing trip information. Then, a “kinetic/potential energy & onboard energy” conservation framework is established to determine the work mode between any reachable state points. By combining external factors, internal factors and additional factors reasonably and feasibly, various trigger conditions are generated to realize the one-to-one mapping between work mode and driving condition, which standardizes the DP optimizing process. Correspondingly, the stage cost and control are determined to achieve the optimal energy distribution. Finally, regarding DP strategy as a benchmark, multiple evaluation indexes are proposed to evaluate the utilization ratio of a control strategy to global trip information. An example is given to evaluate the optimal rule-based strategy. The higher the index is, the higher the similarity with the DP strategy is, and the higher the economic performance of the vehicle is.

Suggested Citation

  • Xu, Nan & Kong, Yan & Zhang, Yuanjian & Yue, Fenglai & Sui, Yan & Li, Xiaohan & Liu, Heng & Xu, Zhe, 2022. "Determination of vehicle working modes for global optimization energy management and evaluation of the economic performance for a certain control strategy," Energy, Elsevier, vol. 251(C).
  • Handle: RePEc:eee:energy:v:251:y:2022:i:c:s0360544222007289
    DOI: 10.1016/j.energy.2022.123825
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222007289
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2022.123825?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. Dian Wang & Yun Bao & Jianjun Shi, 2017. "Online Lithium-Ion Battery Internal Resistance Measurement Application in State-of-Charge Estimation Using the Extended Kalman Filter," Energies, MDPI, vol. 10(9), pages 1-11, August.
    2. Yang, Yalian & Pei, Huanxin & Hu, Xiaosong & Liu, Yonggang & Hou, Cong & Cao, Dongpu, 2019. "Fuel economy optimization of power split hybrid vehicles: A rapid dynamic programming approach," Energy, Elsevier, vol. 166(C), pages 929-938.
    3. Sun, Chao & Sun, Fengchun & He, Hongwen, 2017. "Investigating adaptive-ECMS with velocity forecast ability for hybrid electric vehicles," Applied Energy, Elsevier, vol. 185(P2), pages 1644-1653.
    4. Kong, Yan & Xu, Nan & Zhang, Yuanjian & Sui, Yan & Ju, Hao & Liu, Heng & Xu, Zhe, 2021. "Acquisition of full-factor trip information for global optimization energy management in multi-energy source vehicles and the measure of the amount of information to be transmitted," Energy, Elsevier, vol. 236(C).
    5. Peng, Jiankun & He, Hongwen & Xiong, Rui, 2017. "Rule based energy management strategy for a series–parallel plug-in hybrid electric bus optimized by dynamic programming," Applied Energy, Elsevier, vol. 185(P2), pages 1633-1643.
    6. Zhang, Shuo & Xiong, Rui & Zhang, Chengning, 2015. "Pontryagin’s Minimum Principle-based power management of a dual-motor-driven electric bus," Applied Energy, Elsevier, vol. 159(C), pages 370-380.
    7. Xu, Nan & Kong, Yan & Yan, Jinyue & Zhang, Yuanjian & Sui, Yan & Ju, Hao & Liu, Heng & Xu, Zhe, 2022. "Global optimization energy management for multi-energy source vehicles based on “Information layer - Physical layer - Energy layer - Dynamic programming” (IPE-DP)," Applied Energy, Elsevier, vol. 312(C).
    8. 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.
    9. Lei, Zhenzhen & Qin, Datong & Hou, Liliang & Peng, Jingyu & Liu, Yonggang & Chen, Zheng, 2020. "An adaptive equivalent consumption minimization strategy for plug-in hybrid electric vehicles based on traffic information," Energy, Elsevier, vol. 190(C).
    10. 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.
    11. Wang, Hong & Huang, Yanjun & Khajepour, Amir & Song, Qiang, 2016. "Model predictive control-based energy management strategy for a series hybrid electric tracked vehicle," Applied Energy, Elsevier, vol. 182(C), pages 105-114.
    12. Yang, Yalian & Hu, Xiaosong & Pei, Huanxin & Peng, Zhiyuan, 2016. "Comparison of power-split and parallel hybrid powertrain architectures with a single electric machine: Dynamic programming approach," Applied Energy, Elsevier, vol. 168(C), pages 683-690.
    13. Richard Bellman, 1957. "On a Dynamic Programming Approach to the Caterer Problem--I," Management Science, INFORMS, vol. 3(3), pages 270-278, April.
    14. He, Hongwen & Zhang, Xiaowei & Xiong, Rui & Xu, Yongli & Guo, Hongqiang, 2012. "Online model-based estimation of state-of-charge and open-circuit voltage of lithium-ion batteries in electric vehicles," Energy, Elsevier, vol. 39(1), pages 310-318.
    15. Li, Yuecheng & He, Hongwen & Khajepour, Amir & Wang, Hong & Peng, Jiankun, 2019. "Energy management for a power-split hybrid electric bus via deep reinforcement learning with terrain information," Applied Energy, Elsevier, vol. 255(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yu, Xiao & Lin, Cheng & Xie, Peng & Liang, Sheng, 2022. "A novel real-time energy management strategy based on Monte Carlo Tree Search for coupled powertrain platform via vehicle-to-cloud connectivity," Energy, Elsevier, vol. 256(C).
    2. Kong, Yan & Xu, Nan & Liu, Qiao & Sui, Yan & Yue, Fenglai, 2023. "A data-driven energy management method for parallel PHEVs based on action dependent heuristic dynamic programming (ADHDP) model," Energy, Elsevier, vol. 265(C).

    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. Xu, Nan & Kong, Yan & Yan, Jinyue & Zhang, Yuanjian & Sui, Yan & Ju, Hao & Liu, Heng & Xu, Zhe, 2022. "Global optimization energy management for multi-energy source vehicles based on “Information layer - Physical layer - Energy layer - Dynamic programming” (IPE-DP)," Applied Energy, Elsevier, vol. 312(C).
    2. Zhuang, Weichao & Li (Eben), Shengbo & Zhang, Xiaowu & Kum, Dongsuk & Song, Ziyou & Yin, Guodong & Ju, Fei, 2020. "A survey of powertrain configuration studies on hybrid electric vehicles," Applied Energy, Elsevier, vol. 262(C).
    3. Geng, Wenran & Lou, Diming & Wang, Chen & Zhang, Tong, 2020. "A cascaded energy management optimization method of multimode power-split hybrid electric vehicles," Energy, Elsevier, vol. 199(C).
    4. Kong, Yan & Xu, Nan & Liu, Qiao & Sui, Yan & Jia, Yifan, 2024. "Variable horizon-based predictive energy management strategy for plug-in hybrid electric vehicles and determination of a suitable predictive horizon," Energy, Elsevier, vol. 294(C).
    5. Anselma, Pier Giuseppe, 2022. "Computationally efficient evaluation of fuel and electrical energy economy of plug-in hybrid electric vehicles with smooth driving constraints," Applied Energy, Elsevier, vol. 307(C).
    6. 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.
    7. Jen-Chiun Guan & Bo-Chiuan Chen & Yuh-Yih Wu, 2019. "Design of an Adaptive Power Management Strategy for Range Extended Electric Vehicles," Energies, MDPI, vol. 12(9), pages 1-24, April.
    8. Wei, Changyin & Chen, Yong & Li, Xiaoyu & Lin, Xiaozhe, 2022. "Integrating intelligent driving pattern recognition with adaptive energy management strategy for extender range electric logistics vehicle," Energy, Elsevier, vol. 247(C).
    9. Liu, Hanwu & Lei, Yulong & Fu, Yao & Li, Xingzhong, 2022. "A novel hybrid-point-line energy management strategy based on multi-objective optimization for range-extended electric vehicle," Energy, Elsevier, vol. 247(C).
    10. Zhuang, Weichao & Zhang, Xiaowu & Li, Daofei & Wang, Liangmo & Yin, Guodong, 2017. "Mode shift map design and integrated energy management control of a multi-mode hybrid electric vehicle," Applied Energy, Elsevier, vol. 204(C), pages 476-488.
    11. Wang, Yue & Zeng, Xiaohua & Song, Dafeng & Yang, Nannan, 2019. "Optimal rule design methodology for energy management strategy of a power-split hybrid electric bus," Energy, Elsevier, vol. 185(C), pages 1086-1099.
    12. 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.
    13. Bizon, Nicu, 2019. "Real-time optimization strategies of Fuel Cell Hybrid Power Systems based on Load-following control: A new strategy, and a comparative study of topologies and fuel economy obtained," Applied Energy, Elsevier, vol. 241(C), pages 444-460.
    14. Chen, Z. & Liu, Y. & Ye, M. & Zhang, Y. & Chen, Z. & Li, G., 2021. "A survey on key techniques and development perspectives of equivalent consumption minimisation strategy for hybrid electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    15. Wei, Changyin & Sun, Xiuxiu & Chen, Yong & Zang, Libin & Bai, Shujie, 2021. "Comparison of architecture and adaptive energy management strategy for plug-in hybrid electric logistics vehicle," Energy, Elsevier, vol. 230(C).
    16. Wang, Yue & Zeng, Xiaohua & Song, Dafeng, 2020. "Hierarchical optimal intelligent energy management strategy for a power-split hybrid electric bus based on driving information," Energy, Elsevier, vol. 199(C).
    17. Sánchez, Marcelino & Delprat, Sébastien & Hofman, Theo, 2020. "Energy management of hybrid vehicles with state constraints: A penalty and implicit Hamiltonian minimization approach," Applied Energy, Elsevier, vol. 260(C).
    18. Nicu Bizon & Mihai Oproescu, 2018. "Experimental Comparison of Three Real-Time Optimization Strategies Applied to Renewable/FC-Based Hybrid Power Systems Based on Load-Following Control," Energies, MDPI, vol. 11(12), pages 1-32, December.
    19. Dong, Peng & Zhao, Junwei & Liu, Xuewu & Wu, Jian & Xu, Xiangyang & Liu, Yanfang & Wang, Shuhan & Guo, Wei, 2022. "Practical application of energy management strategy for hybrid electric vehicles based on intelligent and connected technologies: Development stages, challenges, and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    20. Bizon, Nicu, 2017. "Energy optimization of fuel cell system by using global extremum seeking algorithm," Applied Energy, Elsevier, vol. 206(C), pages 458-474.

    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:energy:v:251:y:2022:i:c:s0360544222007289. 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.journals.elsevier.com/energy .

    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.