IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i3p1497-d1056041.html
   My bibliography  Save this article

Comparative Assessment of Supervisory Control Algorithms for a Plug-In Hybrid Electric Vehicle

Author

Listed:
  • Nikolaos Aletras

    (Mechanical Engineering Department, Aristotle University of Thessaloniki, GR 54124 Thessaloniki, Greece)

  • Stylianos Doulgeris

    (Mechanical Engineering Department, Aristotle University of Thessaloniki, GR 54124 Thessaloniki, Greece)

  • Zissis Samaras

    (Mechanical Engineering Department, Aristotle University of Thessaloniki, GR 54124 Thessaloniki, Greece)

  • Leonidas Ntziachristos

    (Mechanical Engineering Department, Aristotle University of Thessaloniki, GR 54124 Thessaloniki, Greece)

Abstract

The study examines alternative on-board energy management system (EMS) supervisory control algorithms for plug-in hybrid electric vehicles. The optimum fuel consumption was sought between an equivalent consumption minimization strategy (ECMS) algorithm and a back-engineered commercial rule-based (RB) one, under different operating conditions. The RB algorithm was first validated with experimental data. A method to assess different algorithms under identical states of charge variations, vehicle distance travelled, and wheel power demand criteria is first demonstrated. Implementing this method to evaluate the two algorithms leads to fuel consumption corrections of up to 8%, compared to applying no correction. We argue that such a correction should always be used in relevant studies. Overall, results show that the ECMS algorithm leads to lower fuel consumption than the RB one in most driving conditions. The difference maximizes at low average speeds (<40 km/h), where the RB leads to more frequent low load engine operation. The two algorithms lead to fuel consumption differences of 3.4% over the WLTC, while the maximum difference of 24.2% was observed for a driving cycle with low average speed (18.4 km/h). Further to fuel consumption performance optimization, the ECMS algorithm also appears superior in terms of adaptability to different driving cycles.

Suggested Citation

  • Nikolaos Aletras & Stylianos Doulgeris & Zissis Samaras & Leonidas Ntziachristos, 2023. "Comparative Assessment of Supervisory Control Algorithms for a Plug-In Hybrid Electric Vehicle," Energies, MDPI, vol. 16(3), pages 1-17, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1497-:d:1056041
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/3/1497/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/3/1497/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Woong Lee & Tacksu Kim & Jongryeol Jeong & Jaewoo Chung & Deokjin Kim & Beomho Lee & Namwook Kim, 2020. "Control Analysis of a Real-World P2 Hybrid Electric Vehicle Based on Test Data," Energies, MDPI, vol. 13(16), pages 1-15, August.
    2. 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.
    3. 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.
    4. Fontaras, Georgios & Valverde, Víctor & Arcidiacono, Vincenzo & Tsiakmakis, Stefanos & Anagnostopoulos, Konstantinos & Komnos, Dimitrios & Pavlovic, Jelica & Ciuffo, Biagio, 2018. "The development and validation of a vehicle simulator for the introduction of Worldwide Harmonized test protocol in the European light duty vehicle CO2 certification process," Applied Energy, Elsevier, vol. 226(C), pages 784-796.
    5. Küng, Lukas & Bütler, Thomas & Georges, Gil & Boulouchos, Konstantinos, 2019. "How much energy does a car need on the road?," Applied Energy, Elsevier, vol. 256(C).
    6. 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).
    7. Hsiu-Ying Hwang & Jia-Shiun Chen, 2020. "Optimized Fuel Economy Control of Power-Split Hybrid Electric Vehicle with Particle Swarm Optimization," Energies, MDPI, vol. 13(9), pages 1-18, May.
    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. Bảo-Huy Nguyễn & João Pedro F. Trovão & Ronan German & Alain Bouscayrol, 2020. "Real-Time Energy Management of Parallel Hybrid Electric Vehicles Using Linear Quadratic Regulation," Energies, MDPI, vol. 13(21), pages 1-19, October.
    2. Barouch Giechaskiel & Dimitrios Komnos & Georgios Fontaras, 2021. "Impacts of Extreme Ambient Temperatures and Road Gradient on Energy Consumption and CO 2 Emissions of a Euro 6d-Temp Gasoline Vehicle," Energies, MDPI, vol. 14(19), pages 1-20, September.
    3. Penghui Qiang & Peng Wu & Tao Pan & Huaiquan Zang, 2021. "Real-Time Approximate Equivalent Consumption Minimization Strategy Based on the Single-Shaft Parallel Hybrid Powertrain," Energies, MDPI, vol. 14(23), pages 1-22, November.
    4. 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.
    5. 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.
    6. 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).
    7. 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).
    8. Yang, Dongpo & Liu, Tong & Song, Dafeng & Zhang, Xuanming & Zeng, Xiaohua, 2023. "A real time multi-objective optimization Guided-MPC strategy for power-split hybrid electric bus based on velocity prediction," Energy, Elsevier, vol. 276(C).
    9. Xie, Shaobo & Hu, Xiaosong & Xin, Zongke & Brighton, James, 2019. "Pontryagin’s Minimum Principle based model predictive control of energy management for a plug-in hybrid electric bus," Applied Energy, Elsevier, vol. 236(C), pages 893-905.
    10. 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).
    11. Yang, Chao & Du, Xuelong & Wang, Weida & Yuan, Lijuan & Yang, Liuquan, 2024. "Variable optimization domain-based cooperative energy management strategy for connected plug-in hybrid electric vehicles," Energy, Elsevier, vol. 290(C).
    12. 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.
    13. 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).
    14. 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).
    15. Ahmed M. Ali & Dirk Söffker, 2018. "Towards Optimal Power Management of Hybrid Electric Vehicles in Real-Time: A Review on Methods, Challenges, and State-Of-The-Art Solutions," Energies, MDPI, vol. 11(3), pages 1-24, February.
    16. Yang, Chao & Du, Siyu & Li, Liang & You, Sixong & Yang, Yiyong & Zhao, Yue, 2017. "Adaptive real-time optimal energy management strategy based on equivalent factors optimization for plug-in hybrid electric vehicle," Applied Energy, Elsevier, vol. 203(C), pages 883-896.
    17. 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).
    18. Yuan, Jingni & Yang, Lin, 2019. "Predictive energy management strategy for connected 48V hybrid electric vehicles," Energy, Elsevier, vol. 187(C).
    19. Serrano, José Ramón & Piqueras, Pedro & De la Morena, Joaquín & Gómez-Vilanova, Alejandro & Guilain, Stéphane, 2021. "Methodological analysis of variable geometry turbine technology impact on the performance of highly downsized spark-ignition engines," Energy, Elsevier, vol. 215(PB).
    20. Yongbing Xiang & Xiaomin Yang, 2021. "An ECMS for Multi-Objective Energy Management Strategy of Parallel Diesel Electric Hybrid Ship Based on Ant Colony Optimization Algorithm," Energies, MDPI, vol. 14(4), pages 1-21, February.

    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:gam:jeners:v:16:y:2023:i:3:p:1497-:d:1056041. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.