Advanced Technologies and Artificial Intelligence for Sustainable and Intelligent Transportation Systems

A special issue of Inventions (ISSN 2411-5134). This special issue belongs to the section "Inventions and Innovation in Electrical Engineering/Energy/Communications".

Deadline for manuscript submissions: 15 December 2024 | Viewed by 2201

Special Issue Editors

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, Asia University, Taichung 41354, Taiwan
Interests: information security; cyber physical systems; cloud computing; blockchain technologies; intrusion detection; artificial intelligence; social media and networking
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Intelligent transportation systems (ITS) have been important elements in today’s era. Apart from the basic requirement of safety, affordability, and accessibility, a leading vision, namely, sustainability is desired. In the literature, we have witnessed the successful research and development of intelligent transportation systems. However, with the ever-growing transport network, it may require enhancement and upgrade of existing systems with advanced technologies and artificial intelligence. The vision is specified in many proposals, such as United Nations Sustainable Development Goals and European Commission Mobility and Transport. This Special Issue is intended to report high-quality research on recent advances in technologies and artificial intelligence in transportation systems, more specifically to the state-of-the-art theories, methodologies, and systems for the design, development, deployment, and innovative use of those convergence technologies for providing insights into the theoretical and technological advancement in transportation science and engineering. Real-world and pilot case studies are also welcome. The topics of interest include but are not limited to the following:

  • Large-scale data collection, storage, and processing for ITS
  • Deep learning for ITS
  • Transfer learning for ITS
  • Big data technologies for ITS
  • Autonomous and semi-autonomous vehicles for ITS
  • Edge, fog, and cloud computing for ITS
  • Cybersecurity for ITS
  • Video, image, and signal processing techniques for ITS.

Dr. Kwok Tai Chui
Prof. Dr. Brij B. Gupta
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at 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. Inventions 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 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.


  • artificial intelligence
  • big data
  • computational intelligence
  • deep learning
  • intelligent transportation systems
  • internet of things

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:


30 pages, 12148 KiB  
Investigations on Using Intelligent Learning Techniques for Anomaly Detection and Diagnosis in Sensors Signals in Li-Ion Battery—Case Study
by Nicolae Tudoroiu, Mohammed Zaheeruddin, Roxana-Elena Tudoroiu, Mihai Sorin Radu and Hana Chammas
Inventions 2023, 8(3), 74; - 22 May 2023
Cited by 5 | Viewed by 1669
This research paper aims to design and implement an intelligent least short time memory (LSTM) deep learning classification technique to detect possible anomalies in measurements dataset within a particular Li-ion battery type. For the state of charge (SOC) and battery faults estimation, a [...] Read more.
This research paper aims to design and implement an intelligent least short time memory (LSTM) deep learning classification technique to detect possible anomalies in measurements dataset within a particular Li-ion battery type. For the state of charge (SOC) and battery faults estimation, a Joint State and Parameter Extended Kalman Filter (JEKF) estimator is developed. The SOC accuracy performance is excellent, with less than 0.5% error during steady-state, compared to the 2% error reported in the literature. For the design and implementation of JEKF SOC and parameter estimation is chosen a preset Li-ion battery Simulink Simscape generic model. It is also helpful to generate the healthy and faulty measurement dataset to design and implement the proposed intelligent LSTM classifier deep learning technique. The generic Li-ion battery model is wisely selected for the “proof concept” purpose, model validation, and algorithms’ robustness, accuracy, and effectiveness. Compared to the traditional EKF fault diagnosis and isolation (FDI), a model-based estimation strategy, the proposed classification LSTM technique is an intelligent data-driven-based deep learning algorithm of high accuracy (around 80%) and loss performance close to zero. Therefore, this feature makes data collection of dataset measurements directly from Li-ion battery sensors possible, which is beneficial for generating online fault scenarios. Additionally, the LSTM deep learning technique can remarkably classify all detected anomalies with high accuracy, independent of battery model accuracy, uncertainties, and unmodeled dynamics. Also, high-performance accuracy root mean square error (RMSE) of 0.0588 (voltage fault), approximately 5.5×107 (healthy) and 8.87 × 106 (current fault) for deep learning shallow neural network (DLSNN) reveals an obvious superiority of both compared to the traditional FDI estimation strategies. Full article
Show Figures

Figure 1

Back to TopTop