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Optimal control of natural gas compression engine hybrid electric mining trucks for balanced fuel efficiency and overall emission improvement

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  • Feng, Yanbiao
  • Dong, Zuomin

Abstract

Hybrid electric propulsion has served as an advantageous powertrain solution for improving the performance, fuel efficiency and emissions of passenger vehicles using well-established fueling infrastructure of gasoline and diesel, and with low conversion cost. Natural gas (NG) presents as a promising alternative fuel to replace diesel for heavy-duty vehicles with prominent potentials of fuel cost and emissions reduction, but its wide adoption still faces a number of technical challenges, including weaker engine performance, and sharply increased hydrocarbon and carbon monoxide emissions at certain engine operation states. This work introduces a new approach to integrate the NG-diesel dual-fuel compression ignited (CI) engine, hybrid electric powertrain system, and optimal powertrain control and energy management to consider vehicle performance, fuel efficiency and CO2/HC/CO/NOx emissions simultaneously to address these issues. The mining truck’s hybrid powertrain system and component models, including NG engine fuel efficiency and emission models, have been introduced to support the work. An advanced surrogate model based global optimization method is extended to solve the computational intensive, black-box optimization problem for optimal power control and energy management. Implementation of the optimization results into control rules for real-time operation, and HIL simulation results to demonstrate the benefits of the newly introduced method are presented using a case study on a representative mining truck and its operations. The study formed the foundation for further research in this area.

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  • Feng, Yanbiao & Dong, Zuomin, 2019. "Optimal control of natural gas compression engine hybrid electric mining trucks for balanced fuel efficiency and overall emission improvement," Energy, Elsevier, vol. 189(C).
    • Handle: RePEc:eee:energy:v:189:y:2019:i:c:s0360544219319711
    DOI: 10.1016/j.energy.2019.116276
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    Cited by:

    1. Juraj Karlušić & Mihael Cipek & Danijel Pavković & Željko Šitum & Juraj Benić & Marijan Šušnjar, 2020. "Benefit Assessment of Skidder Powertrain Hybridization Utilizing a Novel Cascade Optimization Algorithm," Sustainability, MDPI, vol. 12(24), pages 1-15, December.
    2. Majid Astaneh & Jelena Andric & Lennart Löfdahl & Dario Maggiolo & Peter Stopp & Mazyar Moghaddam & Michel Chapuis & Henrik Ström, 2020. "Calibration Optimization Methodology for Lithium-Ion Battery Pack Model for Electric Vehicles in Mining Applications," Energies, MDPI, vol. 13(14), pages 1-27, July.

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