Advances in Energy and Power Management of the Electrified Vehicles
A special issue of Vehicles (ISSN 2624-8921).
Deadline for manuscript submissions: closed (30 December 2020) | Viewed by 332
Special Issue Editor
Interests: control and optimisation; energy-efficient control strategies; nonlinear model-predictive control (NMPC); connected and autonomous vehicles; distributed optimisation, control and estimation; multidisciplinary modelling and simulation; reinforcement learning for control
Special Issue Information
Dear colleagues,
It has already been demonstrated that electrified powertrains (hybrid and fully electric) shape the portfolio of technologies, enhancing the energy consumption and emission of transportation systems. Electrified powertrains are overactuated systems with multiple drivetrains and storages which are optimally operating due to management strategies. The growing concerns regarding the energy security and environmental impact of transportation are driving further interest in more effective electrified vehicles with advanced management strategies. New methodologies like model-predictive controllers, deep learning from large datasets and reinforcement learning are promising developments to address these new interests. Furthermore, recent research has illustrated that the cyberphysical co-design of both the powertrain and the corresponding management strategy improves the performance of electrified vehicles compared to the present sequential design approach where management strategies are developed for pre-existing powertrains.
This Special Issue of Vehicles aims to provide a dedicated space to report original contributions within the area of energy and power management strategies for electrified vehicles. Topics include but are not limited to:
- Optimisation-based management strategies and their real-time implementation;
- Hardware-in-the-loop and experimental validation of advanced energy/power management strategies;
- Energy/power management strategies considering lifespan of batteries;
- Cyberphysical co-design of the electrified powertrains and associated management strategies;
- Reinforcement learning for energy/power management of the electrified powertrains;
- How large datasets can improve the performance of the electrified powertrains?
- Energy-efficient torque distribution and energy management strategies for offroad vehicles.
Dr. Arash M. Dizqah
Guest Editor
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. Vehicles is an international peer-reviewed open access quarterly 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 1600 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
- energy-efficient powertrains
- energy management strategies
- optimisation and control
- machine learning for vehicles
- cyberphysical co-design
- energy-efficient torque distribution
- hybrid storage systems
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.
Further information on MDPI's Special Issue polices can be found here.
