Special Issue "Advances in Automation, Industrial and Power Engineering"

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Energy and Power Engineering".

Deadline for manuscript submissions: 31 December 2022 | Viewed by 619

Special Issue Editors

Prof. Dr. Andrey A. Radionov
E-Mail Website
Guest Editor
Department of Mechatronics and Automation, South Ural State University, 454080 Chelyabinsk, Russia
Interests: industrial and power electronics; power engineering; industrial mechatronic systems; automation and control systems
Special Issues, Collections and Topics in MDPI journals
Dr. Vadim R. Gasiyarov
E-Mail Website
Guest Editor
Mechatronics and Automation Department, South Ural State University, 454080 Chelyabinsk, Russia
Interests: power engineering; industrial mechatronic systems; automation and control systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will accept contributions describing innovative research and developments in “Automatic Control”, “Industrial Engineering” and “Power Engineering”. The overall objective is to cover a wide range of disciplines, including mechanical engineering, materials engineering and technologies for production and processing, energy, electrical engineering and automation engineering. Emphasis is given to methods and findings aimed at determining the prospects for the development of smart industry technologies and the creation of promising technologies for the digital transformation of industries.

Suitable topics for this Special Issue include, but are not limited to:

  • Machinery and mechanism design;
  • Dynamics of machines and working processes;
  • Friction, wear and lubrication in machines;
  • Design and manufacturing engineering of industrial facilities;
  • Transport and technological machines;
  • Mechanical treatment of materials;
  • Industrial hydraulic systems;
  • Steels and alloys, metallurgical and metalworking technologies;
  • Surface engineering and coatings;
  • Processing and controlling technologies.
  • Control theory;
  • Machine learning, big data and the Internet of things;
  • Flexible manufacturing systems;
  • Industrial robotics and mechatronic systems;
  • Computer vision;
  • Industrial automation systems cybersecurity;
  • Industrial processes control and automation;
  • Diagnostics and reliability of automatic control systems.
  • Modeling and computer technologies for industrial applications;
  • Power systems and smart grids;
  • Renewable energy;
  • Electromagnetic compatibility, power and voltage quality;
  • Electrical power systems: optimization and modeling;
  • Power electronics converters, electrical machines and industrial drives.

We invite you to contribute your high-quality research in the form of research or review articles.

Prof. Dr. Andrey A. Radionov
Dr. Vadim R. Gasiyarov
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 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.

Published Papers (1 paper)

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Research

Article
Optimization and Realization of the Coordination Control Strategy for Extended Range Electric Vehicle
Machines 2022, 10(5), 297; https://doi.org/10.3390/machines10050297 - 22 Apr 2022
Viewed by 328
Abstract
This paper designed a fuzzy adaptive proportional integral differential (PID) control algorithm to optimize the overshoot of speed and torque, fuel consumption and exhaust emissions of the traditional PID control strategy in the process of working condition switching of an extended range electric [...] Read more.
This paper designed a fuzzy adaptive proportional integral differential (PID) control algorithm to optimize the overshoot of speed and torque, fuel consumption and exhaust emissions of the traditional PID control strategy in the process of working condition switching of an extended range electric vehicle. The simulation was carried out in Matlab/Simulink, and the optimization of the control strategy was verified by a bench test. The results show that the fuzzy adaptive PID control strategy effectively reduced the speed overshoot in the process of working condition switching compared with the traditional PID control strategy. The bench test proved that the fuzzy adaptive PID control strategy could effectively optimize the switching process, especially in the speed and torque reduction switching process, and the speed overshoot rate of the fuzzy PID control was greatly reduced to 0.7%, far less than that of the traditional PID control with 6.6%, while the torque overshoot rate was within 0.8%. Additionally, the fuzzy adaptive PID control could effectively reduce the fuel consumption, especially in the switching process of increasing the speed and torque, where the fuel consumption of the fuzzy adaptive PID control was 2.1% and 0.5% lower than that of the traditional PID control, respectively. Additionally, the fuzzy adaptive PID control could also reduce the particulate emissions, especially in the process of increasing the speed and torque, where the number of particles of the fuzzy PID control was 11% and 19% less than that of the traditional PID control, respectively. However, the NOx emissions based on the fuzzy PID control were slightly higher than those of the traditional PID control due to the smooth operation and improved combustion. Full article
(This article belongs to the Special Issue Advances in Automation, Industrial and Power Engineering)
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