IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i20p7467-d938927.html
   My bibliography  Save this article

A Robust Design of the Model-Free-Adaptive-Control-Based Energy Management for Plug-In Hybrid Electric Vehicle

Author

Listed:
  • Xiaodong Liu

    (School of Mechanical & Automotive Engineering, Liaocheng University, Liaocheng 252000, China)

  • Hongqiang Guo

    (School of Mechanical & Automotive Engineering, Liaocheng University, Liaocheng 252000, China)

  • Xingqun Cheng

    (School of Mechanical & Automotive Engineering, Liaocheng University, Liaocheng 252000, China)

  • Juan Du

    (School of Mechanical & Automotive Engineering, Liaocheng University, Liaocheng 252000, China)

  • Jian Ma

    (School of Automobile, Chang’an University, Xi’an 710064, China)

Abstract

This paper proposes a robust design approach based on the Design for Six Sigma (DFSS), to promote the robustness of our previous model-free-adaptive-control-based (MFAC-based) energy management strategy (EMS) for the plug-in hybrid electric vehicles (PHEVs) in real-time application. First, the multi-island genetic algorithm (MIGA) is employed for a deterministic design of the MFAC-based EMS, and the Monte Carlo simulation (MCS) is utilized to evaluate the sigma level of the strategy with the deterministic design results. Second, a DFSS framework is formulated to reinforce the robustness of the MFAC-based EMS, in which the velocity and the vehicle mass are considered external disturbances whilst the terminal state of charge (SOC) of the battery and the fuel consumption (FC) are conducted as responses. In addition, real-time SOC constraints are incorporated into Pontryagin’s minimum principle (PMP) to confine the fluctuation of battery SOC in MFAC-based EMS to make it closer to the solution of the dynamic programming (DP). Finally, the effectiveness of the robust design results is assessed by contrasting with other strategies for various combined driving cycles (including velocity, vehicle mass, and road slope). The comparisons demonstrate the remarkable promotion of the robust design in terms of the energy-saving potential and the performance against external disturbance. The average improvement of the FCs can reach up to a considerable 19.66% and 9.79% in contrast to the charge-depleting and charge-sustaining (CD-CS) strategy as well as the deterministic design of MFAC-based EMS. In particular, the energy-saving performance is comparable to DP, where there is only a gap of −1.68%.

Suggested Citation

  • Xiaodong Liu & Hongqiang Guo & Xingqun Cheng & Juan Du & Jian Ma, 2022. "A Robust Design of the Model-Free-Adaptive-Control-Based Energy Management for Plug-In Hybrid Electric Vehicle," Energies, MDPI, vol. 15(20), pages 1-24, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:20:p:7467-:d:938927
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/20/7467/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/20/7467/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ganesh, Akhil Hannegudda & Xu, Bin, 2022. "A review of reinforcement learning based energy management systems for electrified powertrains: Progress, challenge, and potential solution," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    2. 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.
    3. 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.
    4. Girade, Piyush & Shah, Harsh & Kaushik, Karan & Patheria, Akil & Xu, Bin, 2021. "Comparative analysis of state of charge based adaptive supervisory control strategies of plug-in Hybrid Electric Vehicles," Energy, Elsevier, vol. 230(C).
    5. Xie, Shanshan & He, Hongwen & Peng, Jiankun, 2017. "An energy management strategy based on stochastic model predictive control for plug-in hybrid electric buses," Applied Energy, Elsevier, vol. 196(C), pages 279-288.
    6. Guo, Hongqiang & Sun, Qun & Wang, Chong & Wang, Qinpu & Lu, Silong, 2018. "A systematic design and optimization method of transmission system and power management for a plug-in hybrid electric vehicle," Energy, Elsevier, vol. 148(C), pages 1006-1017.
    7. He, Hongwen & Wang, Yunlong & Han, Ruoyan & Han, Mo & Bai, Yunfei & Liu, Qingwu, 2021. "An improved MPC-based energy management strategy for hybrid vehicles using V2V and V2I communications," Energy, Elsevier, vol. 225(C).
    8. Xie, Shaobo & Hu, Xiaosong & Qi, Shanwei & Lang, Kun, 2018. "An artificial neural network-enhanced energy management strategy for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 163(C), pages 837-848.
    9. Qi, Chunyang & Song, Chuanxue & Xiao, Feng & Song, Shixin, 2022. "Generalization ability of hybrid electric vehicle energy management strategy based on reinforcement learning method," Energy, Elsevier, vol. 250(C).
    10. Qi, Chunyang & Zhu, Yiwen & Song, Chuanxue & Yan, Guangfu & Xiao, Feng & Da wang, & Zhang, Xu & Cao, Jingwei & Song, Shixin, 2022. "Hierarchical reinforcement learning based energy management strategy for hybrid electric vehicle," Energy, Elsevier, vol. 238(PA).
    11. 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.
    12. Chen, Zhihang & Liu, Yonggang & Zhang, Yuanjian & Lei, Zhenzhen & Chen, Zheng & Li, Guang, 2022. "A neural network-based ECMS for optimized energy management of plug-in hybrid electric vehicles," Energy, Elsevier, vol. 243(C).
    13. Zhang, Feitie & Yang, Fuyuan & Xue, Dianlun & Cai, Yuanchun, 2019. "Optimization of compound power split configurations in PHEV bus for fuel consumption and battery degradation decreasing," Energy, Elsevier, vol. 169(C), pages 937-957.
    14. Huang, Ruchen & He, Hongwen & Zhao, Xuyang & Wang, Yunlong & Li, Menglin, 2022. "Battery health-aware and naturalistic data-driven energy management for hybrid electric bus based on TD3 deep reinforcement learning algorithm," Applied Energy, Elsevier, vol. 321(C).
    15. 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.
    16. 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. Hyo Chan Lee & Hyeoncheol Lee & Jae Kwang Lee & Hyun Duck Choi & Kyunghwan Choi & Yonghun Kim & Seok-Kyoon Kim, 2022. "Output-Feedback Multi-Loop Positioning Technique via Dual Motor Synchronization Approach for Elevator System Applications," Energies, MDPI, vol. 15(23), pages 1-20, December.
    2. Mahmoud Khaled & Mostafa Mortada & Jalal Faraj & Khaled Chahine & Thierry Lemenand & Haitham S. Ramadan, 2022. "Effect of Airflow Non-Uniformities on the Thermal Performance of Water–Air Heat Exchangers—Experimental Study and Analysis," Energies, MDPI, vol. 15(21), pages 1-14, October.

    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. 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).
    2. 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.
    3. Wu, Yitao & Zhang, Yuanjian & Li, Guang & Shen, Jiangwei & Chen, Zheng & Liu, Yonggang, 2020. "A predictive energy management strategy for multi-mode plug-in hybrid electric vehicles based on multi neural networks," Energy, Elsevier, vol. 208(C).
    4. Huang, Ruchen & He, Hongwen & Zhao, Xuyang & Wang, Yunlong & Li, Menglin, 2022. "Battery health-aware and naturalistic data-driven energy management for hybrid electric bus based on TD3 deep reinforcement learning algorithm," Applied Energy, Elsevier, vol. 321(C).
    5. Chen, Zheng & Gu, Hongji & Shen, Shiquan & Shen, Jiangwei, 2022. "Energy management strategy for power-split plug-in hybrid electric vehicle based on MPC and double Q-learning," Energy, Elsevier, vol. 245(C).
    6. Yaqian Wang & Xiaohong Jiao, 2022. "Dual Heuristic Dynamic Programming Based Energy Management Control for Hybrid Electric Vehicles," Energies, MDPI, vol. 15(9), pages 1-19, April.
    7. 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.
    8. 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).
    9. Li, Yapeng & Wang, Feng & Tang, Xiaolin & Hu, Xiaosong & Lin, Xianke, 2022. "Convex optimization-based predictive and bi-level energy management for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 257(C).
    10. Manzolli, Jônatas Augusto & Trovão, João Pedro & Antunes, Carlos Henggeler, 2022. "A review of electric bus vehicles research topics – Methods and trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    11. Shaobo Xie & Xiaosong Hu & Kun Lang & Shanwei Qi & Tong Liu, 2018. "Powering Mode-Integrated Energy Management Strategy for a Plug-In Hybrid Electric Truck with an Automatic Mechanical Transmission Based on Pontryagin’s Minimum Principle," Sustainability, MDPI, vol. 10(10), pages 1-23, October.
    12. 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).
    13. 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).
    14. Chen, Jiaxin & Shu, Hong & Tang, Xiaolin & Liu, Teng & Wang, Weida, 2022. "Deep reinforcement learning-based multi-objective control of hybrid power system combined with road recognition under time-varying environment," Energy, Elsevier, vol. 239(PC).
    15. Zhou, Jianhao & Xue, Yuan & Xu, Da & Li, Chaoxiong & Zhao, Wanzhong, 2022. "Self-learning energy management strategy for hybrid electric vehicle via curiosity-inspired asynchronous deep reinforcement learning," Energy, Elsevier, vol. 242(C).
    16. Tang, Wenbin & Wang, Yaqian & Jiao, Xiaohong & Ren, Lina, 2023. "Hierarchical energy management strategy based on adaptive dynamic programming for hybrid electric vehicles in car-following scenarios," Energy, Elsevier, vol. 265(C).
    17. Guo, Xiaokai & Yan, Xianguo & Chen, Zhi & Meng, Zhiyu, 2022. "Research on energy management strategy of heavy-duty fuel cell hybrid vehicles based on dueling-double-deep Q-network," Energy, Elsevier, vol. 260(C).
    18. Guo, Ningyuan & Zhang, Xudong & Zou, Yuan & Guo, Lingxiong & Du, Guodong, 2021. "Real-time predictive energy management of plug-in hybrid electric vehicles for coordination of fuel economy and battery degradation," Energy, Elsevier, vol. 214(C).
    19. Marouane Adnane & Ahmed Khoumsi & João Pedro F. Trovão, 2023. "Efficient Management of Energy Consumption of Electric Vehicles Using Machine Learning—A Systematic and Comprehensive Survey," Energies, MDPI, vol. 16(13), pages 1-39, June.
    20. Cui, Wei & Cui, Naxin & Li, Tao & Cui, Zhongrui & Du, Yi & Zhang, Chenghui, 2022. "An efficient multi-objective hierarchical energy management strategy for plug-in hybrid electric vehicle in connected scenario," Energy, Elsevier, vol. 257(C).

    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:15:y:2022:i:20:p:7467-:d:938927. 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.