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Improving the efficiency of hybrid hydraulic excavators with a novel powertrain and energy management system

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  • Nguyen, Van Hien
  • Do, Tri Cuong
  • Dang, Tri Dung
  • Ahn, Kyoung Kwan

Abstract

The urgent issue of the global energy crisis and environmental pollution underscores the need for more efficient, eco-friendly heavy machinery, particularly hybrid hydraulic excavators. Although widely used, conventional excavators consume substantial amounts of fossil fuels and emit significant pollutants. This paper addresses these challenges by proposing a novel powertrain and energy management strategy (EMS) that improves fuel efficiency and captures potential energy from the boom system. The proposed design incorporates a hydrostatic transmission into the hybrid powertrain, enabling real-time dynamic optimization of the internal combustion engine's operating conditions. Additionally, an electric energy regeneration subsystem is utilized during boom-down operations. Extremum seeking control allocates power among sources during boom-up phases, while a map search algorithm optimizes the generator's torque in boom-down mode. Experimental results from a laboratory-scale test bench demonstrate that the new powertrain and EMS reduce overall energy consumption by 9.44 % and improve energy regeneration by up to 10.51 %. These results highlight the system's potential to lower fuel consumption and emissions, paving the way for sustainable excavator solutions. Future research will extend this approach to multiple actuators and refine the control strategy for safe and efficient operation across diverse conditions.

Suggested Citation

  • Nguyen, Van Hien & Do, Tri Cuong & Dang, Tri Dung & Ahn, Kyoung Kwan, 2025. "Improving the efficiency of hybrid hydraulic excavators with a novel powertrain and energy management system," Energy, Elsevier, vol. 323(C).
  • Handle: RePEc:eee:energy:v:323:y:2025:i:c:s0360544225014082
    DOI: 10.1016/j.energy.2025.135766
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    References listed on IDEAS

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    1. Sun, Chao & Sun, Fengchun & He, Hongwen, 2017. "Investigating adaptive-ECMS with velocity forecast ability for hybrid electric vehicles," Applied Energy, Elsevier, vol. 185(P2), pages 1644-1653.
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    Full references (including those not matched with items on IDEAS)

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