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Special Issue for the 64th International Conference of Scandinavian Simulation...
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Special Issue for the 64th International Conference of Scandinavian Simulation Society, SIMS 2023

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 507

Special Issue Editors

Prof. Dr. Konstantinos Kyprianidis

E-Mail Website
Guest Editor
School of Business Society and Engineering, Division of Automation in Energy and Environmental Engineering, Mälardalen University, 72123 Vasteras, Sweden
Interests: energy and environment; process control; system analysis; design optimization; mechanical and gas turbine engineering; aerospace; defense
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Erik Dahlquist

E-Mail Website
Guest Editor
Business, society and engineering, Future Energy Center, Mälardalen University, 72123 Vasteras, Sweden
Interests: process automation; soft-sensors; learning systems; energy efficient buildings; novel process development; pulp and paper; steel
Special Issues, Collections and Topics in MDPI journals
Dr. Ioanna Aslanidou

E-Mail Website
Guest Editor
School of Innovation, Design and Engineering , Division of Product Realisation, Mälardalen University, 72123 Vasteras, Sweden
Interests: industrial AI systems; renewable energy; resource efficiency with focus on energy efficiency, low emissions and nutrient and material recovery; digitalisation of future energy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will include a selection of papers deemed as journal quality from the 64th International Conference of Scandinavian Simulation Society, SIMS 2023.

The SIMS2023 conference will cover broad aspects of recent research and development work in modeling, simulation and optimization in engineering applications. The scientific program will include technical sessions with submitted papers and a poster session. Ph.D. students are especially encouraged to contribute papers according to the conference themes.

Conference Themes:
  • Modeling and simulation for design, planning, optimization, control and monitoring;
  • Tools for modeling and simulation, numerical methods for simulation and novel techniques;
  • Visualization of modeling and simulation results;
  • Practical case studies of industrial automation.
Application areas include: 
  • Renewable energy systems: bioenergy and biofuels, geothermal, hydro, solar, thermal, wave, tidal and wind energy;
  • Hydrogen technologies: production, storage and transportation and hydrogen value chain;
  • Energy systems: electric power, energy storage, fuel cells, heat pumps, industrial plants, energy use in buildings and power plants;
  • Transportation: automotive, hybrid and electrical vehicles, marine and infrastructure;
  • Industrial processes including carbon capture and storage, chemical processing, hydrogen production, oil and gas and water treatment;
  • Cyber-physical systems;
  • Biosystems and medical systems.

We are looking forward to your submissions

Prof. Dr. Konstantinos Kyprianidis
Prof. Dr. Erik Dahlquist
Dr. Ioanna Aslanidou
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 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.

Published Papers (1 paper)

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Research

19 pages, 1841 KiB  
Article
Synergizing Transfer Learning and Multi-Agent Systems for Thermal Parametrization in Induction Traction Motors
by Fozia Mehboob, Anas Fattouh and Smruti Sahoo
Appl. Sci. 2024, 14(11), 4455; https://doi.org/10.3390/app14114455 - 23 May 2024
Viewed by 210
Abstract
Maintaining optimal temperatures in the critical parts of an induction traction motor is crucial for railway propulsion systems. A reduced-order lumped-parameter thermal network (LPTN) model enables computably inexpensive, accurate temperature estimation; however, it requires empirically based parameter estimation exercises. The calibration process is [...] Read more.
Maintaining optimal temperatures in the critical parts of an induction traction motor is crucial for railway propulsion systems. A reduced-order lumped-parameter thermal network (LPTN) model enables computably inexpensive, accurate temperature estimation; however, it requires empirically based parameter estimation exercises. The calibration process is typically performed in labs in a controlled experimental setting, which is associated with a lot of supervised human efforts. However, the exploration of machine learning (ML) techniques in varied domains has enabled the model parameterization in the drive system outside the laboratory settings. This paper presents an innovative use of a multi-agent reinforcement learning (MARL) approach for the parametrization of an LPTN model. First, a set of reinforcement learning agents are trained to estimate the optimized thermal parameters using the simulated data in several driving cycles (DCs). The selection of a reinforcement learning agent and the level of neurons in the RL model is made based on variability of the driving cycle data. Furthermore, transfer learning is performed on a new driving cycle data collected on the measurement setup. Statistical analysis and clustering techniques are proposed for the selection of an RL agent that has been pre-trained on the historical data. It is established that by synergizing within reinforcement learning techniques, it is possible to refine and adjust the RL learning models to effectively capture the complexities of thermal dynamics. The proposed MARL framework shows its capability to accurately reflect the motor’s thermal behavior under various driving conditions. The transfer learning usage in the proposed approach could yield significant improvement in the accuracy of temperature prediction in the new driving cycles data. This approach is proposed with the aim of developing more adaptive and efficient thermal management strategies for railway propulsion systems. Full article
(This article belongs to the Special Issue Special Issue for the 64th International Conference of Scandinavian Simulation Society, SIMS 2023)
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