Eco-Safe Intelligent Mobility Development and Application

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: 15 April 2026 | Viewed by 399

Special Issue Editors

Department of Electrical Engineering, The University of Texas at Arlington, Arlington, TX 76010, USA
Interests: control and optimization; artificial intelligence; automotive; robotics

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Guest Editor
School of Mechanical Engineering, Southeast University, Nanjing 210018, China
Interests: connected and automated vehicles; energy-efficient driving; vehicle dynamics control

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Guest Editor
School of Vehicle and Mobility, Tsinghua University, Beijing, China
Interests: behavioral sciences; connected vehicles; vehicle dynamics; cloud computing; control systems

Special Issue Information

Dear Colleagues,

The development of eco-safe intelligent mobility aims to maximize safety, efficiency, and sustainability for diverse future transport modes, including carbon-free autonomous vehicles, urban air mobility, and mobile robots. Central to this effort is the design of generalized and scalable modeling and control methodologies for intelligent driving and propulsion systems. The scope spans from fundamental theory to system-level applications, with the potential to support various propulsion types and scalable control frameworks across diverse mobility platforms. It covers intelligent propulsion system design, eco-safe control strategies, renewable energy integration, and cooperative decision-making for heterogeneous mobile agents in mixed mobility scenarios. This Special Issue seeks to present the latest research and practical outcomes, inviting submissions that propose novel methodologies, theoretical frameworks, practical implementations, or critical reviews, thereby enriching the scholarly discourse.

The aim is to consolidate advances in universal mobility system design and control into a coherent body of knowledge. Although the prior literature on autonomous driving and powertrain control has been explored, generalized and scalable frameworks for modeling, optimization, and control remain scarce in the context of emerging, diverse mobility systems. This Special Issue aims to bridge this gap by fostering cross-disciplinary dialogue and presenting integrated solutions that connect artificial intelligence, automotive engineering, robotics, and energy systems. It is meaningful to build upon, extend, and innovate existing methods to inspire a new theoretical framework for the design of next-generation mobility systems.

In this Special Issue, original research articles and reviews are welcome, with an emphasis on scalability, application efficiency, interoperability, and measurable improvements in the safety and sustainability of emerging mobility platforms. Both simulation-based research and real-world deployment studies are welcome, and topics of interest include, but are not limited to, the following:

  • Generalized, scalable, and interoperable foundational models for intelligent mobility devices;
  • Safety and eco-efficiency assessment for autonomous and multi-modal mobility systems;
  • Propulsion system topology optimization and component sizing of future mobility platforms;
  • Digital twin-based modeling and remaining driving range prediction for mobility devices;
  • Intelligent decision-making for connected vehicles using large models and game theory;
  • Risk perception and prediction models for complex mixed-traffic or multi-agent scenarios;
  • Path/trajectory/motion planning and control of multi-modal mobility devices;
  • Strategies for cooperative driving and multi-agent coordination in mixed mobility scenarios;
  • Human–machine interaction in multi-modal mobility systems;
  • Learning-based energy management of electrified mobility devices in dynamic environments;
  • Eco-driving/flying strategies for electrified mobility platforms;
  • Learning-based mobility-to-grid interaction strategies for electric mobility platforms;
  • Risk evaluation of decision-making policy in mobility devices;
  • Digital twin and simulation platforms for intelligent mobility testing and validation;
  • Real-world deployment case studies in urban, aerial, and off-road environments.

Dr. Hao Zhang
Dr. Bingbing Li
Dr. Chaoyi Chen
Guest Editors

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Keywords

  • multi-modal mobility systems
  • carbon-free autonomous vehicles
  • urban air mobility
  • mobile robots
  • propulsion system design
  • eco-safe path planning
  • eco-driving and energy management
  • multi-agent coordination

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Published Papers (1 paper)

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Research

25 pages, 22171 KB  
Article
Physics-Informed Co-Optimization of Fuel-CellFlying Vehicle Propulsion and Control Systems with Onboard Catalysis
by Yifei Bao, Chaoyi Chen, Hao Zhang and Nuo Lei
Electronics 2025, 14(21), 4150; https://doi.org/10.3390/electronics14214150 - 23 Oct 2025
Viewed by 263
Abstract
Fuel-cell flying vehicles suffer from limited endurance, while ammonia, decomposed onboard to supply hydrogen, offers a carbon-free, high-density solution to extend flight missions. However, the system’s performance is governed by a multi-scale coupling between propulsion and control systems. To this end, this paper [...] Read more.
Fuel-cell flying vehicles suffer from limited endurance, while ammonia, decomposed onboard to supply hydrogen, offers a carbon-free, high-density solution to extend flight missions. However, the system’s performance is governed by a multi-scale coupling between propulsion and control systems. To this end, this paper introduces a novel optimization paradigm, termed physics-informed gradient-enhanced multi-objective optimization (PI-GEMO), to simultaneously optimize the ammonia decomposition unit (ADU) catalyst composition, powertrain sizing, and flight control parameters. The PI-GEMO framework leverages a physics-informed neural network (PINN) as a differentiable surrogate model, which is trained not only on sparse simulation data but also on the governing differential equations of the system. This enables the use of analytical gradient information extracted from the trained PINN via automatic differentiation to intelligently guide the evolutionary search process. A comprehensive case study on a flying vehicle demonstrates that the PI-GEMO framework not only discovers a superior set of Pareto-optimal solutions compared to traditional methods but also critically ensures the physical plausibility of the results. Full article
(This article belongs to the Special Issue Eco-Safe Intelligent Mobility Development and Application)
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