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An adaptive model predictive controller for a novel battery-powered anti-idling system of service vehicles

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  • Huang, Yanjun
  • Fard, Soheil Mohagheghi
  • Khazraee, Milad
  • Wang, Hong
  • Khajepour, Amir

Abstract

This paper presents an anti-idling regenerative auxiliary power system for service vehicles. The energy storage system in the regenerative auxiliary power system is able to electrify the auxiliary systems so as to achieve anti-idling. Service vehicles (e.g. delivery trucks or public buses) generally have predetermined routes, thus it is feasible and profitable to utilize a model predictive control strategy to improve the fuel economy of the new powertrain. However, the mass/load of such service vehicles is time-varying during a drive cycle. Therefore, an adaptive model predictive controller should be designed to account for this variation. Although the drive cycle is preset, it would experience uncertainties or disturbances caused by traffic or weather conditions in real situations. To deal with this problem, a large step size prediction method is used in the adaptive model predictive algorithm to enhance its robustness. The proposed algorithm is compared to a prescient model predictive controller in different scenarios to demonstrate its applicability and optimality (more than 7% fuel savings). The proposed approach is independent of the powertrain topology such that it is able to be directly extended to other types of hybrid electric vehicles.

Suggested Citation

  • Huang, Yanjun & Fard, Soheil Mohagheghi & Khazraee, Milad & Wang, Hong & Khajepour, Amir, 2017. "An adaptive model predictive controller for a novel battery-powered anti-idling system of service vehicles," Energy, Elsevier, vol. 127(C), pages 318-327.
    • Handle: RePEc:eee:energy:v:127:y:2017:i:c:p:318-327
    DOI: 10.1016/j.energy.2017.03.119
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    References listed on IDEAS

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    1. Li, Liang & You, Sixiong & Yang, Chao & Yan, Bingjie & Song, Jian & Chen, Zheng, 2016. "Driving-behavior-aware stochastic model predictive control for plug-in hybrid electric buses," Applied Energy, Elsevier, vol. 162(C), pages 868-879.
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    Cited by:

    1. Kim, Sangjo, 2021. "A new performance adaptation method for aero gas turbine engines based on large amounts of measured data," Energy, Elsevier, vol. 221(C).
    2. Fard, Soheil Mohagheghi & Huang, Yanjun & Khazraee, Milad & Khajepour, Amir, 2017. "A novel anti-idling system for service vehicles," Energy, Elsevier, vol. 127(C), pages 650-659.
    3. Nitin S. Solke & Pritesh Shah & Ravi Sekhar & T. P. Singh, 2022. "Machine Learning-Based Predictive Modeling and Control of Lean Manufacturing in Automotive Parts Manufacturing Industry," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 23(1), pages 89-112, March.

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