Developing a data-driven hydraulic excavator fuel consumption prediction system based on deep learning

Song, Haoju; Li, Guiqin; Li, Xihang; Xiong, Xingchuang; Qu, Qiang; Mitrouchev, Peter

doi:10.1016/j.aei.2023.102063

Cited by 3 publications

(1 citation statement)

References 51 publications

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“…Using ML and inertial sensors, [24] developed a model to predict emission level of construction equipment. In another study [25] employed data driven prediction model to anticipate excavator fuel consumption and emission during operation. Table 1 summarized the evaluated research work on reducing CO 2 emission using various optimization algorithms for onsite vehicles and equipment.…”

Section: Results and Interpretationsmentioning

confidence: 99%

A Review on Machine Learning for Sustainable Construction Equipment

Khan,

Afzal

2023

Preprint

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The construction sector is producing enormous amount of emission due to usage of heavy machinery. To address global climate concerns, mitigating these greenhouse gas (GHG) emissions is important. The latest technological advancement offers an opportunity to improve the sustainability of equipment operation, often deployed in large scale construction projects. Thus, this research evaluates machine learning algorithms to decrease equipment emission and encourages construction practitioners to adopt innovative tools to replace existing practices. A systematic review was conducted upon a collection of 15 publications related to the subject. Future research direction was given for improvement meeting real-world cases.

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Section: Results and Interpretationsmentioning

confidence: 99%

A Review on Machine Learning for Sustainable Construction Equipment

Khan,

Afzal

2023

Preprint

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A Study on the Parameters Matching of Dynamics System Electric Bulldozer

Tian,

Tan,

Wang

et al. 2024

Lecture Notes in Mechanical Engineering

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In this paper, the parameter matching of dynamics system electric bulldozer was proposed. Firstly, Structure and parameters of the electric bulldozer were introduced. Secondly, the speed and power of the motor were calculated. Finally, power battery parameter was calculated. The results show the peak torque of the drive motor is 800 N·m, the peak power is 100 kW, the maximum rotational speed is 3000 rpm, the rated rotational speed is 1200 rpm and discharge power of the power battery are 235kw.

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