Innovation and Optimization of Production Processes in Industry 4.0

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 1710

Special Issue Editor

Special Issue Information

Dear Colleagues,

Industry 4.0 is transforming our lives in various aspects. The innovation and optimization of production processes are especially actively taking place in the field of production. In this situation, more in-depth research is needed on how the innovation and optimization of production processes of products or services are achieved. Through such discussions, the types of innovations suitable for Industry 4.0 and the optimization techniques for smart factories may be uncovered.

Industry 4.0 aims to automate and optimize production processes by using advanced technologies such as artificial intelligence, big data, internet of things, cloud computing, etc. To do this, it is necessary to perform tasks such as the real-time monitoring of demand and supply of products or services, establishing optimal production plans and resource allocation, monitoring and predicting the performance and condition of equipment, and performing predictive maintenance. These tasks can have effects such as improving the quality and safety of products or services, reducing costs and time, considering environmental issues and social responsibility, etc.

This special Issue seeks high-quality works focusing on related areas and topics include, but are not limited to:

  • Innovation efficiency measurement;
  • Optimized production system;
  • Case study in Industry 4.0;
  • Smart factory and its application;
  • System dynamics approach in Industry 4.0.

Dr. Changhee Kim
Guest Editor

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

  • innovation efficiency
  • optimized production system
  • production process performance estimation
  • optimization in Industry 4.0
  • innovation in production processes

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Published Papers (1 paper)

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Research

15 pages, 2343 KiB  
Article
Extended Kalman Filter Algorithm for Accurate State-of-Charge Estimation in Lithium Batteries
by Gen Li, Qian Mao and Fan Yang
Processes 2024, 12(8), 1560; https://doi.org/10.3390/pr12081560 - 25 Jul 2024
Viewed by 1217
Abstract
With the continuous development of the industrial and energy industries, the development of new energy vehicles is entering a period of rapid development and is one of the hot research directions today. Due to the needs of different working environments, the demand for [...] Read more.
With the continuous development of the industrial and energy industries, the development of new energy vehicles is entering a period of rapid development and is one of the hot research directions today. Due to the needs of different working environments, the demand for mobile power sources in automobiles is increasing, which means that battery design and battery system management (BMS) determine their work efficiency. How to enable users to accurately and in real-time understand the usage status of their electric vehicle batteries is a very important thing, and it is also an important challenge faced in the development process of electric vehicles. This article proposes a battery state-of-charge (SOC) estimation method based on the extended Kalman filter algorithm (EKF) for one of the core areas of the BMS–battery state-of-charge (SOC). According to the guidance and direction of Industry 4.0 in Germany, we hope to address some of the aforementioned challenges for users of automotive and robotics products while developing our industry. Therefore, we made some innovative explorations in this direction. In this study, it was found that the algorithm can adjust parameters in real-time to achieve better convergence. The final estimation results indicate that the algorithm had high accuracy and robustness and can meet the current needs of battery estimation for new energy vehicles, providing an important means for the safety control of automotive BMS. In the long run, this will change the current situation of battery monitoring using mobile power sources. At the same time, it provided an effective and practical implementation method and template for current production estimation, which has a certain heuristic effect on the future process of Industry 4.0 and production estimation. Full article
(This article belongs to the Special Issue Innovation and Optimization of Production Processes in Industry 4.0)
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