IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i6p1537-d152244.html
   My bibliography  Save this article

Jerk Analysis of a Power-Split Hybrid Electric Vehicle Based on a Data-Driven Vehicle Dynamics Model

Author

Listed:
  • Xiaohua Zeng

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China)

  • Haoyong Cui

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China)

  • Dafeng Song

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China)

  • Nannan Yang

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China)

  • Tong Liu

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China)

  • Huiyong Chen

    (Zhengzhou Yutong Bus Co., Ltd., Zhengzhou 450016, China)

  • Yinshu Wang

    (Zhengzhou Yutong Bus Co., Ltd., Zhengzhou 450016, China)

  • Yulong Lei

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China)

Abstract

Given its highly coupled multi-power sources with diverse dynamic response characteristics, the mode transition process of a power-split Hybrid Electric Vehicle (HEV) can easily lead to unanticipated passenger-felt jerks. Moreover, difficulties in parameter estimation, especially power-source dynamic torque estimation, result in new challenges for jerk reduction. These two aspects entangle with each other and constitute a complicated coupling problem which obstructs the realization of a valid anti-jerk method. In this study, a vehicle dynamics model with reference to a data-driven modeling method is first established, integrating a full-time artificial neural network engine dynamic model that can accurately predict engine dynamic torque. Then the essential reason for the occurrence of vehicle jerks in real driving conditions is analyzed. Finally, to smooth the mode transition process, a more practical anti-jerk strategy based on power-source torque changing rate limitation (TCRL) is proposed. Verification studies indicate that the data-driven vehicle dynamics model has enough accuracy to reflect the vehicle dynamic characteristics, and the proposed TCRL strategy could reduce the vehicle jerk by up to 85.8%, without any sacrifice of vehicle performance. This research provides a feasible method for precise modeling of vehicle dynamics and a reference for improving the riding comfort of hybrid electric vehicles.

Suggested Citation

  • Xiaohua Zeng & Haoyong Cui & Dafeng Song & Nannan Yang & Tong Liu & Huiyong Chen & Yinshu Wang & Yulong Lei, 2018. "Jerk Analysis of a Power-Split Hybrid Electric Vehicle Based on a Data-Driven Vehicle Dynamics Model," Energies, MDPI, vol. 11(6), pages 1-20, June.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:6:p:1537-:d:152244
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/6/1537/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/6/1537/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yang Zhang & Bo Guo, 2015. "Online Capacity Estimation of Lithium-Ion Batteries Based on Novel Feature Extraction and Adaptive Multi-Kernel Relevance Vector Machine," Energies, MDPI, vol. 8(11), pages 1-19, November.
    2. Hutchinson, Tim & Burgess, Stuart & Herrmann, Guido, 2014. "Current hybrid-electric powertrain architectures: Applying empirical design data to life cycle assessment and whole-life cost analysis," Applied Energy, Elsevier, vol. 119(C), pages 314-329.
    3. Jing Sun & Guojing Xing & Chenghui Zhang, 2017. "Data-Driven Predictive Torque Coordination Control during Mode Transition Process of Hybrid Electric Vehicles," Energies, MDPI, vol. 10(4), pages 1-21, April.
    4. Rezaei, Javad & Shahbakhti, Mahdi & Bahri, Bahram & Aziz, Azhar Abdul, 2015. "Performance prediction of HCCI engines with oxygenated fuels using artificial neural networks," Applied Energy, Elsevier, vol. 138(C), pages 460-473.
    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. Andrzej Łebkowski, 2018. "Steam and Oxyhydrogen Addition Influence on Energy Usage by Range Extender—Battery Electric Vehicles," Energies, MDPI, vol. 11(9), pages 1-20, September.
    2. Xu, Yueru & Zheng, Yuan & Yang, Ying, 2021. "On the movement simulations of electric vehicles: A behavioral model-based approach," Applied Energy, Elsevier, vol. 283(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. Robert J.R. Elliott & Viet Nguyen-Tien & Eric Strobl & Chengyu Zhang, 2024. "Estimating the longevity of electric vehicles: What do 300 million MOT test results tell us?," CEP Discussion Papers dp1972, Centre for Economic Performance, LSE.
    2. Du, Jiuyu & Ouyang, Danhua, 2017. "Progress of Chinese electric vehicles industrialization in 2015: A review," Applied Energy, Elsevier, vol. 188(C), pages 529-546.
    3. Bahri, Bahram & Shahbakhti, Mahdi & Aziz, Azhar Abdul, 2017. "Real-time modeling of ringing in HCCI engines using artificial neural networks," Energy, Elsevier, vol. 125(C), pages 509-518.
    4. Guoqing Jin & Lan Li & Yidan Xu & Minghui Hu & Chunyun Fu & Datong Qin, 2020. "Comparison of SOC Estimation between the Integer-Order Model and Fractional-Order Model Under Different Operating Conditions," Energies, MDPI, vol. 13(7), pages 1-17, April.
    5. Desantes, J.M. & García-Oliver, J.M. & Vera-Tudela, W. & López-Pintor, D. & Schneider, B. & Boulouchos, K., 2016. "Study of the auto-ignition phenomenon of PRFs under HCCI conditions in a RCEM by means of spectroscopy," Applied Energy, Elsevier, vol. 179(C), pages 389-400.
    6. Han, Zhiqiang & Li, Bolun & Tian, Wei & Xia, Qi & Leng, Songpeng, 2019. "Influence of coupling action of oxygenated fuel and gas circuit oxygen on hydrocarbons formation in diesel engine," Energy, Elsevier, vol. 173(C), pages 196-206.
    7. Hung, Yi-Hsuan & Tung, Yu-Ming & Chang, Chun-Hsin, 2016. "Optimal control of integrated energy management/mode switch timing in a three-power-source hybrid powertrain," Applied Energy, Elsevier, vol. 173(C), pages 184-196.
    8. Wang, Jinqiu & Bedei, Julian & Deng, Jun & Andert, Jakob & Zhu, Denghao & Li, Liguang, 2021. "Detection of transient low-temperature combustion characteristics by ion current – The missing link for homogeneous charge compression ignition control?," Applied Energy, Elsevier, vol. 283(C).
    9. Zhou, Quan & Zhang, Wei & Cash, Scott & Olatunbosun, Oluremi & Xu, Hongming & Lu, Guoxiang, 2017. "Intelligent sizing of a series hybrid electric power-train system based on Chaos-enhanced accelerated particle swarm optimization," Applied Energy, Elsevier, vol. 189(C), pages 588-601.
    10. Cocco Mariani, Viviana & Hennings Och, Stephan & dos Santos Coelho, Leandro & Domingues, Eric, 2019. "Pressure prediction of a spark ignition single cylinder engine using optimized extreme learning machine models," Applied Energy, Elsevier, vol. 249(C), pages 204-221.
    11. Pei, Huanxin & Hu, Xiaosong & Yang, Yalian & Tang, Xiaolin & Hou, Cong & Cao, Dongpu, 2018. "Configuration optimization for improving fuel efficiency of power split hybrid powertrains with a single planetary gear," Applied Energy, Elsevier, vol. 214(C), pages 103-116.
    12. Li, Yaopeng & Jia, Ming & Han, Xu & Bai, Xue-Song, 2021. "Towards a comprehensive optimization of engine efficiency and emissions by coupling artificial neural network (ANN) with genetic algorithm (GA)," Energy, Elsevier, vol. 225(C).
    13. Thangarasu, Vinoth & M, Angkayarkan Vinayakaselvi & Ramanathan, Anand, 2021. "Artificial neural network approach for parametric investigation of biodiesel synthesis using biocatalyst and engine characteristics of diesel engine fuelled with Aegle Marmelos Correa biodiesel," Energy, Elsevier, vol. 230(C).
    14. Ye Yang & Youtong Zhang & Si Zhang & Jingyi Tian & Shaoyi Hu, 2019. "Control Strategy of Mode Transition with Engine Start in a Plug-in Hybrid Electric Bus," Energies, MDPI, vol. 12(15), pages 1-20, August.
    15. Berzi, Lorenzo & Delogu, Massimo & Pierini, Marco & Romoli, Filippo, 2016. "Evaluation of the end-of-life performance of a hybrid scooter with the application of recyclability and recoverability assessment methods," Resources, Conservation & Recycling, Elsevier, vol. 108(C), pages 140-155.
    16. Wang, Zengkai & Zeng, Shengkui & Guo, Jianbin & Qin, Taichun, 2019. "State of health estimation of lithium-ion batteries based on the constant voltage charging curve," Energy, Elsevier, vol. 167(C), pages 661-669.
    17. Kshirsagar, Charudatta M. & Anand, Ramanathan, 2017. "Artificial neural network applied forecast on a parametric study of Calophyllum inophyllum methyl ester-diesel engine out responses," Applied Energy, Elsevier, vol. 189(C), pages 555-567.
    18. Palmer, Kate & Tate, James E. & Wadud, Zia & Nellthorp, John, 2018. "Total cost of ownership and market share for hybrid and electric vehicles in the UK, US and Japan," Applied Energy, Elsevier, vol. 209(C), pages 108-119.
    19. Charalambides, A.G. & Sahu, S. & Hardalupas, Y. & Taylor, A.M.K.P. & Urata, Y., 2018. "Evaluation of Homogeneous Charge Compression Ignition (HCCI) autoignition development through chemiluminescence imaging and Proper Orthogonal Decomposition," Applied Energy, Elsevier, vol. 210(C), pages 288-302.
    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:gam:jeners:v:11:y:2018:i:6:p:1537-:d:152244. 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.