Special Journal Issue on Optimal Energy Management and Control in CAVs
Special Journal Issue on Optimal Energy Management and Control in CAVs
The ASME Journal of Dynamic Systems, Measurement, and Control is currently accepting manuscripts for a special issue focusing on the topic “Optimal Energy Management and Control in Connected and Automated Vehicles (CAVs).” Authors who are interested in having their manuscripts included in the special issue, to be published in September 2021, should submit their manuscripts by March 30, 2021.
The automotive industry is undergoing major changes, with substantial growth in the areas of autonomy and connectivity. In this context, several studies have highlighted the potential offered by Connected and Automated Vehicle (CAV) technologies to reduce traffic congestion, expand and improve mobility options, and increase safety. Recent advancements in driver assistance systems and increased market penetration of connectivity options have created the opportunity to improve the energy efficiency of individual vehicles and fleets in new ways. Vehicle Dynamic and Powertrain Control (VD&PT) technologies, implemented on a single vehicle basis or across a fleet of communicating and cooperating vehicles, could significantly improve individual vehicle and, ultimately, fleet energy efficiency.
On the other hand, the deployment and commercial success of future CAV technologies requires an unprecedented effort in advancing and applying physics-based and data-driven modeling, optimization and control methods to manage the information available from the expanded sensing and the capabilities provided by the different levels and forms of automation. Further challenges arise from the presence of multiple layers of interactions among the surrounding vehicles and transportation systems at large, in a context where Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication is becoming mainstream technology.
The proposed special issue collects and presents original research papers dealing with innovations in the use of connectivity and automation, which aims at co-optimizing vehicle operation and powertrain controls with the ultimate goal of reducing energy consumption and emissions.
Manuscripts to be included in the special issue should concentrate on a range of topics including vehicle and powertrain dynamic models for control and energy management of CAVs; optimization and learning methods for single and multi-vehicle planning, coordination, and controls; application of machine learning and artificial intelligence in the energy efficient operation and controls of CAVs; cooperative automation with Vehicle to Everything (V2X) connectivity for energy efficiency; model-based and predictive control methods for energy management of individual vehicles and powertrains; incorporation of information, data and enhanced sensing in optimization and control algorithms; data-driven and physics-based methods for VD&PT optimization and control; modeling and simulation environments for virtual verification of Driver-in-the-Loop and Driver Assistance Systems; estimation of subsystem, system, and vehicle states under nominal and fault conditions; and concepts and technologies supported by experimental studies.
Manuscripts should be submitted electronically to the journal by March 30, 2021, via Journals Connect at journaltool.asme.org. Authors who have an account should log in and select “Submit Paper” at the bottom of the page. Authors without an account should select “Submissions” and follow the steps. At the Paper Submittal page, authors should select the “ASME Journal of Dynamic Systems, Measurement, and Control” and then select the special issue “Optimal Energy Management and Control in Connected and Automated Vehicles (CAVs).” Papers received after the deadline or papers not selected for inclusion in the special issue may be accepted for publication in a regular issue.
The special issue guest editors are Marcello Canova, Ph.D., Department of Mechanical and Aerospace Engineering, The Ohio State University, U.S., canova.1@osu.edu; Mahdi Shahbakhti, Ph.D., Department of Mechanical Engineering, University of Alberta, Canada, mahdi@ualberta.ca; Hoseinali Borhan, Ph.D., M.B.A., Cummins Inc., U.S., hoseinali.borhan@cummins.com; and Scott Hotz, P.E., Southwest Research Institute, U.S., scott.hotz@swri.org.
For more information on the ASME Journal of Dynamic Systems, Measurement, and Control, visit https://asmedigitalcollection.asme.org/dynamicsystems. To learn more about the ASME Journal Program, visit www.asme.org/publications-submissions/journals/information-for-authors.
The automotive industry is undergoing major changes, with substantial growth in the areas of autonomy and connectivity. In this context, several studies have highlighted the potential offered by Connected and Automated Vehicle (CAV) technologies to reduce traffic congestion, expand and improve mobility options, and increase safety. Recent advancements in driver assistance systems and increased market penetration of connectivity options have created the opportunity to improve the energy efficiency of individual vehicles and fleets in new ways. Vehicle Dynamic and Powertrain Control (VD&PT) technologies, implemented on a single vehicle basis or across a fleet of communicating and cooperating vehicles, could significantly improve individual vehicle and, ultimately, fleet energy efficiency.
On the other hand, the deployment and commercial success of future CAV technologies requires an unprecedented effort in advancing and applying physics-based and data-driven modeling, optimization and control methods to manage the information available from the expanded sensing and the capabilities provided by the different levels and forms of automation. Further challenges arise from the presence of multiple layers of interactions among the surrounding vehicles and transportation systems at large, in a context where Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication is becoming mainstream technology.
The proposed special issue collects and presents original research papers dealing with innovations in the use of connectivity and automation, which aims at co-optimizing vehicle operation and powertrain controls with the ultimate goal of reducing energy consumption and emissions.
Manuscripts to be included in the special issue should concentrate on a range of topics including vehicle and powertrain dynamic models for control and energy management of CAVs; optimization and learning methods for single and multi-vehicle planning, coordination, and controls; application of machine learning and artificial intelligence in the energy efficient operation and controls of CAVs; cooperative automation with Vehicle to Everything (V2X) connectivity for energy efficiency; model-based and predictive control methods for energy management of individual vehicles and powertrains; incorporation of information, data and enhanced sensing in optimization and control algorithms; data-driven and physics-based methods for VD&PT optimization and control; modeling and simulation environments for virtual verification of Driver-in-the-Loop and Driver Assistance Systems; estimation of subsystem, system, and vehicle states under nominal and fault conditions; and concepts and technologies supported by experimental studies.
Manuscripts should be submitted electronically to the journal by March 30, 2021, via Journals Connect at journaltool.asme.org. Authors who have an account should log in and select “Submit Paper” at the bottom of the page. Authors without an account should select “Submissions” and follow the steps. At the Paper Submittal page, authors should select the “ASME Journal of Dynamic Systems, Measurement, and Control” and then select the special issue “Optimal Energy Management and Control in Connected and Automated Vehicles (CAVs).” Papers received after the deadline or papers not selected for inclusion in the special issue may be accepted for publication in a regular issue.
The special issue guest editors are Marcello Canova, Ph.D., Department of Mechanical and Aerospace Engineering, The Ohio State University, U.S., canova.1@osu.edu; Mahdi Shahbakhti, Ph.D., Department of Mechanical Engineering, University of Alberta, Canada, mahdi@ualberta.ca; Hoseinali Borhan, Ph.D., M.B.A., Cummins Inc., U.S., hoseinali.borhan@cummins.com; and Scott Hotz, P.E., Southwest Research Institute, U.S., scott.hotz@swri.org.
For more information on the ASME Journal of Dynamic Systems, Measurement, and Control, visit https://asmedigitalcollection.asme.org/dynamicsystems. To learn more about the ASME Journal Program, visit www.asme.org/publications-submissions/journals/information-for-authors.
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