A Multi-Objective Collaborative Optimization Method for the Excavator Working Device to Support Energy Consumption Reduction

Zhe Lu, Shuwen Lin (), Jianxiong Chen, Tianqi Gu and Yu Xie
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Zhe Lu: School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
Shuwen Lin: School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
Jianxiong Chen: School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
Tianqi Gu: School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
Yu Xie: School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China

Energies, 2023, vol. 16, issue 20, 1-18

Abstract: To address the limitation of existing excavator optimization methods, which primarily focus on the force performance while neglecting energy consumption and fail to realize environmentally friendly and low-carbon designs, this paper proposes a new multi-objective collaborative optimization method for an excavator to reduce energy consumption during the working process while maintaining optimal performance. By formulating a mathematical model that quantifies the energy consumption during the working process, this paper optimizes the working conditions by analyzing the energy consumption characteristics under typical working conditions. To overcome the limitation of existing linear weighting methods in multi-objective optimization, such as imbalanced optimization quality among sub-objectives, this paper proposes a new modeling approach based on the loss degree of sub-objectives. A multi-objective collaborative optimization model for the excavator with reduced energy consumption is established, and a corresponding multi-objective collaborative optimization algorithm is developed and applied to achieve optimal solutions for sub-objectives. The optimization results demonstrate that applying the new multi-objective collaborative optimization method to the excavator achieves better optimization quality than traditional methods. It also provides a more balanced improvement in the optimization values of each sub-objective, resulting in a significant reduction in the energy consumption of the excavator during operation.

Keywords: excavator; energy consumption modeling; energy-saving; excavator working performance; multiobjective optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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