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Rapid urbanisation and increased fossil-fuel consumption causing excessive greenhouse gas (GHG) emissions and climate change globally pose significant threats to the cities. Smart cities strive for increased efficiency, reduced GHG emissions and improved quality of life by leveraging information and ...

Rapid urbanisation and increased fossil-fuel consumption causing excessive greenhouse gas (GHG) emissions and climate change globally pose significant threats to the cities. Smart cities strive for increased efficiency, reduced GHG emissions and improved quality of life by leveraging information and communication technologies (ICT) to deliver scalable, affordable and reliable solutions. As energy and mobility are considered as indispensable components for such transformations, the convergence of energy and mobility is essential. Prospect of energy and mobility is growing rapidly as electric vehicles proliferate, and gradually autonomous vehicles penetrate to urban transportation in the future. New forms of mobility are emerging along with the required ICT infrastructure to accommodate this growth. These advancements correspond with the evolution towards cleaner, decentralized and digitized energy systems and services, and electrification in the transportation systems. As the future of energy would be electrified, digitized and decentralized, and the future of mobility would be connected, autonomous, electric and shared, new ways of diffusion of sustainable energy and sustainable transportation would lead to sustainable smart cities. Since electro-mobility (e-mobility) and shared mobility are key constituents of a sustainable transportation, Connected and Autonomous Electric Vehicles (CAEVs) are expected to cause fundamental transformations to the urban transportation with potential benefits including improved fuel efficiency, improved safety, reduced emissions, and reduced traffic congestion. Similarly, decentralized energy resources along ICT technologies would be enabler for a low-carbon smarter electric grid. Furthermore, Artificial Intelligence (AI) and Machine Learning will play vital role in CAEV Applications.

Innovation challenges related to sustainable energy and sustainable transportation require multistakeholder approach to produce a large set of emergent technologies to technical maturity. This Research Topic is a platform for researchers, developers and practitioners to disseminate their latest research, development, results, and innovative ideas in the areas of sustainable energy and sustainable transportation for the smart cities. Such a topic solicits novel solutions and techniques in term of concepts, state-of-the-art, implementations, test-beds, and industrial case studies. Contributions, which can be in the form of Original Research papers, Perspective, Review articles, and technical notes, should focus on following topics of interests (but are not limited to):

• Visions and strategies for convergence of mobility and energy: Electromobility (e-mobility); EV charging infrastructure; EV charging/discharging strategies; low-carbon smart grid and vehicle-to-grid (V2G); cloud and fog computing based applications; cybersecurity; reliability, resilience and sustainability; innovative business models.

• CAEV for sustainable transportation: Smart urban mobility; mobility on demand; mobility as a service; multimodal route planning; cooperative mobility; eco-driving; fuel efficiency and emissions control; CAEV support systems; smart demand management.

• Data analytics: Big data and analytics for sustainable smart e-mobility solutions; big data for CAEV analytics; big data for travel behavior analytics; context-aware mobility related analytics; visualisation approaches; self-learning (pattern discovery, prediction, auto-configuration); optimization and metaheuristics; data mining.

• Sustainable smart city and society: Sustainable smart city policy making; socio-economic and environmental impacts of urban e-mobility; institutional and legal aspects of urban e-mobility; new concepts and case studies of urban e-mobility.

• AI and Machine Learning: AI and smart urban mobility; machine learning, reinforcement learning, deep learning in urban e-mobility; transfer learning and federated learning for CAEV applications in smart cities.

Keywords: Sustainable smart city, sustainable transportation, sustainable energy, connected and autonomous electric vehicles (CAEVs), sustainable smart e-mobility, smart urban mobility, AI and Machine Learning


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