Dynamics Modeling, Control, and Eco-Driving of Heavy Equipment & Machinery for Eco-Friendly Environments
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".
Deadline for manuscript submissions: closed (15 September 2023) | Viewed by 2257
Special Issue Editors
Interests: multibody system dynamics; vehicle system dynamics; batteries; data-driven modeling and control
Special Issues, Collections and Topics in MDPI journals
Interests: CAD/CAE/CAx; virtual design and manufacture; design for MC; design optimization; product innovation; R&D management
Interests: sustainable renewables; solar energy
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Heavy equipment, or heavy machinery, refers to heavy-duty vehicles. These vehicles are frequently used for construction, lifting, landscaping, digging, road paving, forestry, etc., and consume extensive amounts of energy. Research on the dynamics modeling, control and environmental friendliness of heavy machinery is thus critical to reduce their energy consumption and develop a more ecofriendly, less energy-intensive vehicle model. Recently, a large number of methods, including machine learning methods, have been applied for the dynamics modeling, control and development of heavy equipment and machinery for ecofriendly environments.
In this Special Issue, we will analyze how an accurate dynamics model can be developed for ecofriendly safety and control applications to improve heavy equipment’ overall efficiency, aiming to present high-quality research detailing recent progress in the development of safe, energy-saving control technologies.
We welcome original papers on topics including, but not limited to, the following:
- Machine learning methods for modeling and control;
- Machine learning methods for eco-driving and energy saving applications;
- Multibody system methods for dynamics modeling and analysis;
- Other effective methods for modeling, control and energy saving applications;
- Health monitoring and management of heavy equipment and machinery;
- Fault diagnosis and prognosis of heavy equipment and machinery;
- Comfort and performance of heavy equipment and machinery;
- Passive and active safety control of heavy equipment and machinery.
Dr. Yongjun Pan
Prof. Dr. Liang Hou
Prof. Dr. Zhixiong Li
Guest Editors
Manuscript Submission Information
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- machine learning methods for modeling and control
- eco-driving
- energy saving
- heavy equipment and machinery
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