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Advanced Technology for Low-Emission Mobility

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Dear Colleagues,

transport is the backbone of the economy, a contributor for growth and jobs, essential for the functioning of the internal market and the free movement of goods and people. Market integration, economic growth and transport are closely related. The global transition to a low carbon economy has begun, supported by the Paris Agreement. Transport will have an important role to play in this transition. The transition to a low-carbon economy also presents an opportunity for jobs and growth in the transport sector as low-carbon mobility markets grow globally. This transition is supported by disruptive trends such as digitization and new technologies. Transport is increasingly becoming an on-demand service as consumer needs and perceptions of mobility solutions change. Taken together, these trends also present important competitiveness challenges, and significant efforts will be required from businesses and regulators to convert them into growth and employment opportunities for technologically advanced countries around the world. Researchers from universities, research institutes and industry are cordially invited to send original articles on this topic.

Dr. Pavel Kučera
Guest Editor

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Keywords

means of low-emission land transport
low-emission maritime transport
means of low-emission air transport
powertrains for low-emission mobile devices
economic aspects of low-emission transport

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Research

29 pages, 18313 KiB

Open AccessArticle

Project and Development of a Reinforcement Learning Based Control Algorithm for Hybrid Electric Vehicles

by Claudio Maino, Antonio Mastropietro, Luca Sorrentino, Enrico Busto, Daniela Misul and Ezio Spessa

Appl. Sci. 2022, 12(2), 812; https://doi.org/10.3390/app12020812 - 13 Jan 2022

Cited by 4 | Viewed by 2077

Abstract

Hybrid electric vehicles are, nowadays, considered as one of the most promising technologies for reducing on-road greenhouse gases and pollutant emissions. Such a goal can be accomplished by developing an intelligent energy management system which could lead the powertrain to exploit its maximum energetic performances under real-world driving conditions. According to the latest research in the field of control algorithms for hybrid electric vehicles, Reinforcement Learning has emerged between several Artificial Intelligence approaches as it has proved to retain the capability of producing near-optimal solutions to the control problem even in real-time conditions. Nevertheless, an accurate design of both agent and environment is needed for this class of algorithms. Within this paper, a detailed plan for the complete project and development of an energy management system based on Q-learning for hybrid powertrains is discussed. An integrated modular software framework for co-simulation has been developed and it is thoroughly described. Finally, results have been presented about a massive testing of the agent aimed at assessing for the change in its performance when different training parameters are considered. Full article

(This article belongs to the Special Issue Advanced Technology for Low-Emission Mobility)

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14 pages, 1457 KiB

Open AccessArticle

Closed-Loop Modeling to Evaluate the Performance of a Scaled-Up Lithium–Sulfur Battery in Electric Vehicle Applications

by Qingxin Zeng, Zhuo Zou, Jie Chen, Yali Jiang, Lingzhi Zeng and Changming Li

Appl. Sci. 2021, 11(20), 9593; https://doi.org/10.3390/app11209593 - 14 Oct 2021

Cited by 1 | Viewed by 1748

Abstract

A closed-loop modeling method was established here to evaluate the performance of new battery technology from lab research to scaled-up developed electric vehicle (EV) applications. As an emerging energy-storage device, the lithium–sulfur battery (LSB) is a very promising candidate for the next generation of rechargeable batteries. However, it has been difficult to commercialize the LSB up to now. In this work, we designed and built a battery, EV, and driver system loop model to study the key performance parameters of LSB operation in EVs, in which the tested data from the lab were introduced into the model followed by simulating driving cycles and fast charging. A comparison with the lithium-ion batteries used in real vehicles verified the high reliability of the model. Meanwhile, the simulation results showed that the LSB needs more improvements for EV application; in particular, developments are still highly needed to overcome the high power and energy loss and sharp voltage vibration for practical applications. The novelty of this work relies on the created closed-loop modeling method to simulate lab research results for evaluating new battery technology in scaled-up EV applications in order to not only vividly predict EV operation performance and commercialization feasibility, but also thoughtfully guide researchers and developers for further optimization and problem solutions. Therefore, this method holds great promise as a powerful tool for both lab research and the industrial development of new batteries for EV applications. Full article

(This article belongs to the Special Issue Advanced Technology for Low-Emission Mobility)

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