Intelligent Energy Vehicle Control Technology
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "E: Electric Vehicles".
Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 2549
Special Issue Editors
Interests: hybrid electric vehicle; nonlinear dynamics; bifurcation mechanism
Special Issues, Collections and Topics in MDPI journals
Interests: key technologies of new energy vehicles; energy management and control
Special Issues, Collections and Topics in MDPI journals
Interests: artificial intelligence technology and application; vehicle control and intelligence; integrated energy system
Interests: powertrain topology; design and optimization of electric vehicles; energy storage system configuration; sizing and energy management strategy optimization
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid advancement of artificial intelligence technology has paved the way for intelligent control in new energy vehicles. Machine learning techniques, including deep learning, reinforcement learning, and transfer learning, have emerged as vital tools for enhancing the safety and overall capabilities of vehicle systems. However, the precise application of machine learning to achieve intelligent control of vehicle systems remains a bottleneck that hinders the progress of intelligent energy vehicle control technology.
In light of this, we propose a Special Issue entitled "Intelligent Energy Vehicle Control Technology" to showcase the latest original achievements, foster the exchange of cutting-edge perspectives, and promote interdisciplinary research in this field. This Special Issue aims to explore the potential of machine learning-based solutions in addressing the complex and dynamic challenges faced by vehicle systems, including high performance and low energy consumption requirements under various driving conditions.
The topics to be covered in this Special Issue include, but are not limited to, the following:
- Machine learning-based decision making for self-driving vehicles;
- Energy management of new energy vehicles using machine learning;
- Battery life prediction for electric vehicles based on machine learning;
- Machine learning approaches for battery health status management in electric vehicles;
- Power control methods and mechanisms of vehicle power systems utilizing machine learning;
- Machine learning-based power prediction and tracking of vehicle powertrains;
- Fault diagnosis and evaluation of electric drive systems using machine learning techniques;
- Intelligent thermal management integrated within vehicles employing machine learning;
- Multi-sensor fusion for intelligent fault diagnosis of on-board hydrogen systems;
- Fuel cell hydrogen remaining estimation based on machine learning algorithms.
We invite researchers from academia and industry to contribute their original research, methodologies, and perspectives to this Special Issue. By bringing together these diverse contributions, we aim to accelerate the development and application of intelligent energy vehicle control technology.
We look forward to receiving your valuable contributions and sharing ground-breaking advancements in the field of intelligent energy vehicle control.
Dr. Donghai Hu
Prof. Dr. Fengyan Yi
Prof. Dr. Xizheng Zhang
Prof. Dr. Jiageng Ruan
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. Energies 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 2600 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
- new energy vehicles
- machine learning
- vehicle autonomous driving
- powertrain power control
- vehicle energy management
- vehicle integrated thermal management
