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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Research in Progress (RIP)</title>
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      <title>Green Mobility in Texas: Comparative Environmental Impacts and Lifecycle Cost Analysis of Hybrid, Electric, and Hydrogen Fuel Cell Cars</title>
      <link>https://rip.trb.org/View/1948653</link>
      <description><![CDATA[Compared to gasoline/diesel cars and battery electric cars (BECs), hydrogen fuel cell cars (HFCCs) are relatively new. Two main advantages of HFCCs over electric vehicles are no special requirements for heavy battery and long charging time. There is no HFCC in Texas; thus, exploring the use of electricity or hydrogen produced from renewable energies for personal cars and mitigating vehicle emissions is critical for sustaining a long-term decarburization strategy for megacities like Houston and Dallas under different energy scenarios. The proposed project would address this critical gap and develop environmental impacts and cost assessments for hybrid cars, BECs, and HFCCs in Texas under several possible energy scenarios from now to 2040 using some tools of lifecycle assessment (LCA) and lifecycle cost analysis (LCCA). The carbon footprint of BECs and HFCCs will be determined with respect to the transport, logistics, and supply chain sectors. A new lifecycle cost model for BECs and HFCCs will be designed with the consideration of some uncertainties of renewable resources and vehicle demands. Faculty working on this research will integrate LCA as an important focus area for all senior and graduate-level civil and environmental engineering courses. These students will also be introduced to the techniques of well-to-wheel analysis and production cost evaluation for renewable energy-powered vehicles.]]></description>
      <pubDate>Mon, 09 May 2022 05:57:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/1948653</guid>
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    <item>
      <title>Preparing Future Workforce for Electric Vehicle Infrastructure Deployment</title>
      <link>https://rip.trb.org/View/1874588</link>
      <description><![CDATA[Transportation is at cusp of a revolutionary transformation with the confluence of the rapid deployment of automation, connectivity, and electric mobility solutions in the near future. Accompanying this swift transformation is the need to inform, educate, and prepare the workforce to support and enable the deployment of these technologies in a fast and effective approach. Specifically, electrification of the transportation sector is the most imminent and requires professionals in disparate domains to be educated and trained about the interdisciplinary aspects of electric vehicles (EVs) and the concomitant infrastructure and policy requirements. The objective of this proposal is to develop a course addressing the needs of working professionals in public and private transportation infrastructure organizations (at the local, state and federal level) , utilities, EV manufacturers, grid operators and others. The course will be structured into modules that address different aspects so that the course can be customized to audience requirements. The course can be delivered in person or in a virtual setting.]]></description>
      <pubDate>Wed, 25 Aug 2021 17:17:01 GMT</pubDate>
      <guid>https://rip.trb.org/View/1874588</guid>
    </item>
    <item>
      <title>A multi-AI-agent framework for vehicle-infrastructure integration and electric vehicle robust charging</title>
      <link>https://rip.trb.org/View/1751186</link>
      <description><![CDATA[Transportation systems and in particular fossil fuels utilized in transportation are responsible for approximately 27% of greenhouse gases (such as Co2 emissions) in the U.S. in 2015. Under the Clean Air Act, more and more states are adopting California’s Zero Emission Vehicle (ZEV) regulations. This results in an increase in the number of electric vehicles (EVs). It is expected that EVs will comprise 30% of all cars globally by 2030. In addition to the pollution caused by the fossil fuels, the transportation system itself has the potential to greatly reduce emissions production and energy consumption through reducing congestion. Traffic congestion not just cause travel delays but also increases fuel consumption and emissions production. One of the major reasons for congestion in urban areas is traffic accidents. These crashes are the leading cause of accidental death in the United States with the major factor in over 90 percent of all fatal crashes being human error. Currently, traffic cameras and video surveillance are one of the ways used to monitor the traffic. However, these methods are capital demanding, and don’t provide real-time trip information to the travelers. New technologies, such as vehicle to infrastructure (V2I) and vehicle to vehicle (V2V) communication, may be able to greatly reduce congestion. This communication allows real-time detection of congestion, which can result in immediate distributing of traffic affected by the congestion and therefore result in a more efficient transportation network. Advances in wireless communication technology, for instance advanced 5G communication networks will enable this interconnection and will allow users to make better decisions regarding the use of the transportation system. In the foreseen transportation infrastructure, vehicles will communicate with other vehicles, traffic control units and traffic management centers, to make more efficient trip decisions. In addition to the communication reducing traffic congestion, it will also help in fast EVs charging, utility and capital cost management. Developing an effective management scheme that maximize the use of limited EV charge stations currently available is essential for transportation infrastructure agents, and utility companies to deliver high-quality service to travelers (availability of chargers, prices, reduced capital investment in new chargers, etc). This project proposes to develop a novel multi-agent artificial intelligence (AI) communication and charging system that will focus on (1) reducing traffic congestion and (2) smart EV charging. In this proposed management system traffic control units, traffic management centers, EV charging stations, utility companies and EVs are AI-powered agents capable of making smart decisions based on real-time traffic conditions, and EV charging demands and prices.]]></description>
      <pubDate>Wed, 11 Nov 2020 09:53:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/1751186</guid>
    </item>
    <item>
      <title>Preparing the Next Generation of Undergraduate and Graduate Engineers
in Autonomous Robotic System for Damage Detection
</title>
      <link>https://rip.trb.org/View/1688008</link>
      <description><![CDATA[Given the growing importance of science and engineering research in
meeting national goals, US research needs to remain at world frontiers if
the United States is to boost economic productivity and competitiveness.
Morgan State University (MSU) recognizes the value of continued
diversification and growth of Maryland’s and U.S. economy, and it
continues to develop its research capabilities to ensure the next generation
of STEM graduates is multidisciplinary, collaborative, and working in an
environment that fosters their most creative ideas. As part of CIAMTIS
outreach activities, we will engage in educational and research outreach to
undergraduate and graduate students in the transportation engineering
field. Our particular interest is to train the next generation of transportation
engineers in supporting the Maryland Department of Transportation (MDDOT) Sustainable Mobility Initiative of automation technologies in
electric vehicle (EV) operations and meeting transportation energy
demands. Maryland is currently home to just over 12,000 EVs and aiming
to put 300,000 EVs on the road by 2025 to boost EV adoption. With the
evolutionary wireless EV technologies for sustainable transport network,
their implementation in densely populated cities in MD can provide
continuous vehicle charging, thus, eliminating the EV range issues and
need for large battery capacities. However, one of the teething problems
to be envisaged in wireless electric vehicle charging implementation in
MD is maintaining the road infrastructure to support this innovative
technology for economic viability. Current approaches for damage
detection of roads such as visual inspection are time consuming, as
difficulty in scanning larger surfaces of square miles, need for a spatially referenced grid, and skilled operators are required]]></description>
      <pubDate>Thu, 20 Feb 2020 16:25:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/1688008</guid>
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    <item>
      <title>On-Board Prediction of Remaining Useful Life of Lithium-Ion Battery
</title>
      <link>https://rip.trb.org/View/1403070</link>
      <description><![CDATA[This project was intended to create an intelligent prognostics platform for lithium-ion (Li-ion) batteries, which would equip existing battery management systems with the capability to perform predictive maintenance/control for failure prevention. The platform developed in this project consisted of two modules: 
•	Deep feature learning, which automatically learns the features of (capacity) fade from large volumes of voltage and current measurement data during partial charge cycles and estimates the real-time state of health (SOH) of a battery cell in operation
•	Ensemble prognostics, which leverage the current and past SOH estimates in Module 1 to achieve robust prediction of the cell’s remaining useful life
Robust prediction of remaining useful life was achieved by ensemble learning-based prognostics, which synthesized the generalization strengths of multiple prognostic algorithms to ensure high prediction accuracy for an expanded range of battery applications and their operating conditions. The two modules aimed to learn features of fade from partial charge data, assess real-time health of individual battery cells, and predict when and how the cells are likely to fail. A case study involving implantable-grade Li-ion cells was conducted to demonstrate a deep learning approach to online capacity estimation, developed for Module 1.
]]></description>
      <pubDate>Tue, 05 Apr 2016 12:26:07 GMT</pubDate>
      <guid>https://rip.trb.org/View/1403070</guid>
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