Skip to main content

Advertisement

SpringerLink
Log in

Composite braking AMT shift strategy for extended-range heavy commercial electric vehicle based on LHMM/ANFIS braking intention identification

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

In order to improve the safety and braking energy recovery rate of the composite braking system for extended-range heavy commercial electric vehicle, AMT shift control strategy is studied based on Layering Hidden Markov Model/Adaptive Neuro-fuzzy Inference System (LHMM/ANFIS) braking intention identification model. Firstly, according to the requirement of the AMT shift strategy in braking process, the braking intentions were classified into normal braking condition and emergency braking condition. Then combined with the composite braking force distribution, the motor braking power generation characteristics and the critical condition of dangerous working state, the AMT shift strategy was analyzed and established under two braking conditions. Finally, to verify the control effect, the verification test was carried out with the initial braking speed of 60 km h−1 under the normal braking condition and the emergency braking condition separately on the hardware in the loop simulation platform based on A&D 5435 and the testing vehicle. Meanwhile, simulation study was completed in Matlab/Simulink under NEDC_90 cycle condition. The experiment and simulation results show that the developed AMT shift control strategy can accurately identify the braking intention, and the transmission shifts correctly according to corresponding conditions, which can also make the motor operating points closer to the high efficiency area. Therefore, the AMT shift control strategy proposed in this paper can effectively improve the braking energy recovery rate, and ensure the braking safety and stability.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

References

  1. Guo, L., Gao, B., Liu, Q., et al.: On-line optimal control of the gearshift command for multispeed electric vehicles. IEEE/ASME Trans. Mechatron. 22(4), 1519–1530 (2017). https://doi.org/10.1109/TMECH.2017.2716340

    Article  Google Scholar 

  2. Qin, D.-T., Yao, M.-Y., Chen, S.-J., et al.: Shifting process control for two-speed automated mechanical transmission of pure electric vehicles. Int. J. Precis. Eng. Manuf. 17(5), 623–629 (2016). https://doi.org/10.1007/s12541-016-0075-z

    Article  Google Scholar 

  3. Shen, W., Yu, H., Hu, Y., et al.: Optimization of shift schedule for hybrid electric vehicle with automated manual transmission. Energies 9(3), 220–230 (2016). https://doi.org/10.3390/en9030220

    Article  Google Scholar 

  4. Guo, L., Gao, B., Chen, H.: Online shift schedule optimization of 2-speed electric vehicle using moving horizon strategy. IEEE/ASME Trans. Mechatron. 21(6), 2858–2869 (2016). https://doi.org/10.1109/TMECH.2016.2586503

    Article  Google Scholar 

  5. Li, L., Wang, X., Xiong, R., et al.: AMT downshifting strategy design of HEV during regenerative braking process for energy conservation. Appl. Energy 183, 914–925 (2016). https://doi.org/10.1016/j.apenergy.2016.09.031

    Article  Google Scholar 

  6. Ko, J., Ko, S., Son, H., et al.: Development of brake system and regenerative braking cooperative control algorithm for automatic-transmission-based hybrid electric vehicles. IEEE Trans. Veh. Technol. 64(2), 431–440 (2015). https://doi.org/10.1109/TVT.2014.2325056

    Article  Google Scholar 

  7. Ma, B., Lin, M., Chen, Y., et al.: Investigation of energy efficiency for electro-hydraulic composite braking system which is based on the regenerated energy. Adv. Mech. Eng. 8(9), 1–13 (2016). https://doi.org/10.1177/1687814016666449

    Article  Google Scholar 

  8. Ruan, J., Walker, P.D., Watterson, P.A., et al.: The dynamic performance and economic benefit of a blended braking system in a multi-speed battery electric vehicle. Appl. Energy 183, 2140–2158 (2016). https://doi.org/10.1016/j.apenergy.2016.09.057

    Article  Google Scholar 

  9. Midgley, W.J.B., Cebon, D.: Control of a hydraulic regenerative braking system for a heavy goods vehicle. Proc. Inst. Mech. Eng. D 230(10), 1338–1350 (2016). https://doi.org/10.1177/0954407015607780

    Article  Google Scholar 

  10. Zeng, X., Lia, G., Yin, G., et al.: Model predictive control-based dynamic coordinate strategy for hydraulic hub-motor auxiliary system of a heavy commercial vehicle. Mech. Syst. Signal Process. 101, 97–120 (2018). https://doi.org/10.1016/j.ymssp.2017.08.029

    Article  Google Scholar 

  11. Fang, S., Song, J., Song, H., et al.: Design and control of a novel two-speed uninterrupted mechanical transmission for electric vehicles. Mech. Syst. Signal Process. 75(15), 473–493 (2016). https://doi.org/10.1016/j.ymssp.2015.07.006

    Article  Google Scholar 

  12. Zhang, Z., Wang, L., Zhang, J., et al.: Study on requirements for load emulation of the vehicle with an electric braking system. IEEE Trans. Veh. Technol. 66(11), 9638–9653 (2017). https://doi.org/10.1109/TVT.2017.2739425

    Article  Google Scholar 

  13. Zheng, H., Ma, S.: Development and test of braking intention recognition strategies for commercial vehicle. In: SAE 2015 Commercial Vehicle Engineering Congress, pp. 2841–2850 (2015). https://doi.org/10.4271/2015-01-2841

  14. Pongsathorn, R., Takuya, M., Masao, N.: Direct yaw moment control system based on driver behavior recognition. Veh. Syst. Dyn. 46, 911–921 (2008). https://doi.org/10.1080/00423110802037156

    Article  Google Scholar 

  15. Berndt, H., Emmert, J., Dietmayer, K.: Continuous driver intention recognition with hidden markov models. In: 11th International IEEE Intelligent Transportation Systems Conference, pp. 9–17 (2008). https://doi.org/10.1109/itsc.2008.4732630

  16. Suntharalingam, P., Economou, J.T., Knowles, K.: Kinetic energy storage using a dual-braking system for an unmanned parallel hybrid electric vehicle. Proc. Inst. Mech. Eng. D 231(10), 1353–1373 (2017). https://doi.org/10.1177/0954407016672591

    Article  Google Scholar 

  17. ECE: Regulation No. 13, Uniform provisions concerning the approval of vehicles of categories M, N and O with regard of braking (version 5) (2011). http://www.unece.org/fileadmin/DAM/trans/main/wp29/wp29regs/r013r7e.pdf

Download references

Acknowledgements

This work was supported by Natural Science Foundation of China (Grant: 51507013), China Postdoctoral Science Foundation (Grant: 2017M613034), Postdoctoral Science Foundation of Shaanxi Province (Grant: 2017BSHEDZZ36), Natural Science Foundation of Shaanxi Province (Grant: 2016JQ5012), Science and Technology Research Project of Shaanxi Province (Grant: 2016GY-043).

Author information

Authors and Affiliations

  1. School of Automobile, Chang’an University, Xi’an, China

    Xuan Zhao, Shiwei Xu, Yiming Ye & Man Yu

  2. School of Electronic and Control Engineering, Chang’an University, Xi’an, China

    Guiping Wang

Authors
  1. Xuan Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shiwei Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yiming Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Man Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Guiping Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xuan Zhao.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, X., Xu, S., Ye, Y. et al. Composite braking AMT shift strategy for extended-range heavy commercial electric vehicle based on LHMM/ANFIS braking intention identification. Cluster Comput 22 (Suppl 4), 8513–8528 (2019). https://doi.org/10.1007/s10586-018-1888-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-018-1888-6

Keywords

Search

Navigation