Development of a Comprehensive Driving Cycle for Construction Machinery Used for Energy Recovery System Evaluation: A Case Study of Medium Hydraulic Excavators

Author:

Hu Peng12ORCID,Zhu Jianxin12,Gong Jun3,Zhang Daqing2,Liu Changsheng2,Zhao Yuming2,Guo Yong1

Affiliation:

1. State Key Laboratory of High Performance and Complex Manufacturing, Central South University, Changsha, China

2. The National Enterprise Research and Develop Center, Sunward Intelligence Equipment Co.,Ltd., Changsha, China

3. Engineering Research Center of Advanced Mining Equipment Ministry of Education, Hunan University of Science and Technology, Xiangtan, China

Abstract

Energy recovery and hybrid power technology are new directions in construction machinery energy-saving research. Thus far, however, no uniform standard method exists to evaluate the efficiency of novel energy-saving concept systems in early stages of development. Efficiency assessment is valuable and credible only by relying on driving cycles that are consistent with actual data. As representative products of construction machinery, hydraulic excavators are multifunctional, object-uncertain, and heavily influenced by operator habits. It is therefore challenging to develop standard excavator driving cycles. Aiming at the energy efficiency evaluation of new energy-saving products characterized by energy recovery and power system optimization, taking medium-sized hydraulic excavator as an example, this paper proposes an evaluation method of energy-saving efficiency and a classification construction method of comprehensive driving cycle of hydraulic excavator based on actual operating data with load demand power and boom recoverable power as combined variables. First, based on the analysis of general driving cycle variables for excavators with different energy-saving schemes, a load demand power model and boom recoverable power model based on machine sensing data are established. Second, 10 excavators were selected at different locations in southern China, and 30 days of real-world working data were recorded. Third, according to the periodic characteristics of working data, a method for dividing microcycle operation structure is proposed. The microcycle sample space based on the data conversion of working data is constructed and classified via the clustering algorithm. Finally, a comprehensive excavator driving cycle based on the classification results is constructed through the Markov method. Results show that the energy-saving efficiency of the three classification driving cycles was 17.45%, 13.60%, and 11.88%, respectively; the comprehensive energy-saving efficiency was 15.76%. The deviations in maximum load demand power and maximum boom recoverable power between the constructed comprehensive cycle and the sample space were 7.81% and 8.61%; the average deviation of characteristic parameters was 4.26%. The comprehensive driving cycles can fairly reflect the general characteristics of real-world working conditions. The proposed construction method of comprehensive driving cycles based on sample space is therefore reliable and holds great promise for evaluating the energy-saving efficiency of new energy-saving concept systems.

Funder

Natural Science Foundation of Hunan Province

Publisher

Hindawi Limited

Subject

General Engineering,General Mathematics

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1. Experimental analysis of slewing energy recovery potential of large hydraulic excavator;Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science;2024-03-03

2. Novel control strategy for the energy recovery system of a hydraulic excavator;International Journal of Agricultural and Biological Engineering;2024

3. Boom Potential Energy Regeneration Method for Hybrid Hydraulic Excavators;IEEE Access;2024

4. Challenges and solutions for designing Energy-Efficient and Low-Pollutant Machines in Off-Road hydraulics;Energy Conversion and Management: X;2024-01

5. A Reinforcement Learning-Based Automatic Video Editing Method Using Pre-trained Vision-Language Model;Proceedings of the 31st ACM International Conference on Multimedia;2023-10-26

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