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Optimal design for improving operation performance of electric construction machinery collaborative system: Method and application

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  • Huang, Xiaohui
  • Huang, Qi
  • Cao, Huajun
  • Yan, Wanbin
  • Cao, Le
  • Zhang, Qiongzhi

Abstract

Electrification of construction machinery is an important measure to reduce carbon emissions from the transportation industry, and collaborative work is an essential feature of construction machinery. Current research on vehicle mechanical component optimization lacks consideration of the collaborative system's overall performance and a unified standard for optimizing component selection. So, it is uncertain to optimize single-machine components to improve system performance. This paper proposes an optimization design method for collaborative system. Firstly, based on the meta-action theory and analytic hierarchy process, the meta-action unit of the optimal design is determined. Then, a dual-layer hierarchical optimization framework is used to optimize the meta-action unit. This method selects the gearbox of typical collaborative system as the optimization design objective. And the multi-objective marine predator algorithm is used to solve it. The results show that the motor efficiency of wheel loader and dump truck increased to 87.13% and 85.64% respectively. The energy consumption of a single operating cycle was reduced by 1.55% and 5.69% respectively, battery cycle life increased by 1.32% and 4.69%. Furthermore, the daily operation cost of the collaborative system is reduced by 2.61%. The proposed optimization design method can be broadly applied in the optimization design of collaborative systems.

Suggested Citation

  • Huang, Xiaohui & Huang, Qi & Cao, Huajun & Yan, Wanbin & Cao, Le & Zhang, Qiongzhi, 2023. "Optimal design for improving operation performance of electric construction machinery collaborative system: Method and application," Energy, Elsevier, vol. 263(PA).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pa:s0360544222025154
    DOI: 10.1016/j.energy.2022.125629
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    1. Huang, Xiaohui & Huang, Qi & Cao, Huajun & Wang, Qianyue & Yan, Wanbin & Cao, Le, 2023. "Battery capacity selection for electric construction machinery considering variable operating conditions and multiple interest claims," Energy, Elsevier, vol. 275(C).
    2. Do, Tri Cuong & Dinh, Truong Quang & Yu, Yingxiao & Ahn, Kyoung Kwan, 2023. "Innovative powertrain and advanced energy management strategy for hybrid hydraulic excavators," Energy, Elsevier, vol. 282(C).

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