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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
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    <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>Information Strategies in the Electric Vehicles (EV) Battery Reverse Supply Chain with Blockchain Technology</title>
      <link>https://rip.trb.org/View/2402272</link>
      <description><![CDATA[The average lifespan of lithium batteries is relatively short and a huge amount of electric vehicle (EV) batteries will be retired in the next decade, leading to considerable environmental concerns and sparking an influx of recycling enterprises into the battery industry. However, unauthorized recyclers often follow recycling processes that are not compliant with the established regulations, which may cause serious harm to the natural environment and humans, resulting in the idle capacity of the authorized recyclers who are unable to achieve economies of scale, and pose challenges such as lack of information sharing and product untraceability. This proposed project will examine the use of blockchain in the EV battery supply chain by answering the following questions: (1) What is the status quo of blockchain implementation in EV battery recycling? (2) How does unauthorized recycling affect the battery recovery supply chain? (3) What is the impact of blockchain technology on closed-loop supply chains (CLSC)? (4) What factors can motivate supply chain participants to adopt blockchain technology to facilitate battery recycling?]]></description>
      <pubDate>Fri, 12 Jul 2024 11:00:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/2402272</guid>
    </item>
    <item>
      <title>Transforming Transportation Planning and Policy for Safety</title>
      <link>https://rip.trb.org/View/2292643</link>
      <description><![CDATA[Transportation policy studies and improved planning are essential for furthering goals of the University Transportation Centers (UTCs) and the US Department of Transportation (USDOT).  This project is intended to build upon long-standing and successful activities in these areas.  Three tasks are envisioned.  First, a database design and tracking process for Intelligent Transportation Systems (ITS) progress in the United States will be devised.  This activity will be undertaken in partnership with ITS America.  Second, research on safety policy improvements for battery electric vehicles will be initiated with the goal of producing a new policy brief on this topic.  Audiences will be USDOT, U.S. Department of Energy (USDOE) and State officials.  Safety concerns of interest include battery fires, vehicle sound, and vehicle weight.   Third, project participants will continue to work with Regional Industrial Development Corporation (RIDC) in the planning for Pennsylvania Safety Transportation and Research Track (PennStart).  PennStart will be a $ 22M state funded transportation technology testing and traffic incident management training center in the Pittsburgh region.  Connecting Pittsburgh and PennStart is a 'Smart Corridor' that will also be used for testing in the real world. PennStart capital expenditures will be used for project match.  Carnegie Mellon researchers will include Chris Hendrickson (Hamerschlag University Professor of Engineering Emeritus), Corey Harper (Assistant Professor of Civil and Environmental Engineering), and Heather Cain (Research Administrator Civil and Environmental Engineering).  The new Executive Director of Safety21 Center will be asked to participate as well.  This project builds upon successful synthesis and policy projects undertaken with Mobility21 UTC funding that have resulted in six policy briefs, professional papers and numerous public presentations.]]></description>
      <pubDate>Mon, 20 Nov 2023 19:48:05 GMT</pubDate>
      <guid>https://rip.trb.org/View/2292643</guid>
    </item>
    <item>
      <title>Honeycomb-encapsulated phase change materials composites for battery transportation safety</title>
      <link>https://rip.trb.org/View/2067998</link>
      <description><![CDATA[Virginia Commonwealth University proposes to manufacture composites that can prevent thermal runaway. The composites are based on inorganic aerogels, which are excellent, porous thermal insulators.]]></description>
      <pubDate>Mon, 21 Nov 2022 16:26:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2067998</guid>
    </item>
    <item>
      <title>Sodium Ion Battery Testing</title>
      <link>https://rip.trb.org/View/2067994</link>
      <description><![CDATA[Research will focus on SOC and determine the effect of foreign body intrusion on the NaB.]]></description>
      <pubDate>Mon, 21 Nov 2022 16:26:28 GMT</pubDate>
      <guid>https://rip.trb.org/View/2067994</guid>
    </item>
    <item>
      <title>Multi-stage Planning for Electrifying Transit Bus Systems with Multiformat Charging Facilities</title>
      <link>https://rip.trb.org/View/1675095</link>
      <description><![CDATA[Although electric transit bus systems (ETBS) present significant benefits, it is challenging for a public transit authority to plan the process of electrifying its bus fleet and continue to operate its mixed fleet cost-effectively. This IDEA project developed a decision support tool for public transit authorities for facilitating the process of electrifying their transit buses. Mathematical models of dynamic wireless charging facilities (DWCF) locations for full electrification and partial electrification (multi-objective optimization), and models for route selection and DWCF locations were developed (multi-stage optimization). Solutions algorithms were exploited to solve large scale multi-stage multi-objective optimization problems. Specifically, given the periodic budget and transit network and features, the tool would provide outcomes at different stages, including (1) which routes the acquired electric buses should serve, (2) where to deploy charging facilities (both plug-in at stations and dynamic wireless charging facilities (DWCF) embedded in road pavement), and (3) what should be the right size of the onboard battery for a specific route. The proposed method was applied to a local transit network, HART, in the Tampa Bay area. For a given scenario, i.e., the transit authority needs to choose five routes to electrify in the first stage and another five in the second stage, the tool identified the best routes to electrify and optimal locations of DWCF in both stages. Following the solutions, the transit authority will bear the minimal total cost of constructing DWCF and energy consumptions in a defined planning horizon. Furthermore, the research team developed a Graphic User Interface (GUI) on a Linux system that consolidated data structure design, solution algorithm implementation, economic analysis, and design result visualization. A user manual was produced to help potential users to understand and become familiar with the tool. Users can perform scenario analysis with this tool by changing setting parameters, such as number of routes to be electrified (or budget constraint), cost of DWCF, price of electricity, etc. 

The final report is available.]]></description>
      <pubDate>Thu, 26 Dec 2019 19:27:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/1675095</guid>
    </item>
    <item>
      <title>LIDAR, Electric Bikes, and Transportation Safety – Phase II</title>
      <link>https://rip.trb.org/View/1582010</link>
      <description><![CDATA[Mobile light detection and ranging (LIDAR) technology offers a significant opportunity to increase transportation safety and efficiency. Moreover, electric bikes (e-bikes) provide a potentially important avenue to facilitate large reductions in greenhouse gases and hazardous emissions while promoting the usage of public transportation. However, little research exists at the crossroads of these two technologies. Hence, the second year of this project will expand the prior e-bike LIDAR testing efforts to obtain quantitative and qualitative data on its operation under low, medium, and heavy traffic scenarios on campus. Data will include e-bike battery and motor parameters while also capturing the interaction of this e-bike with the surrounding pedestrians, other cyclists, and motor vehicles. This will result in the development of e-bike models for driving simulator, gap acceptance, and emissions reduction studies. Furthermore, a re-envisioning of this inexpensive mobile LIDAR system will occur in order to facilitate extensions to other transportation-based safety outcomes, such as pavement quality monitoring.]]></description>
      <pubDate>Mon, 04 Feb 2019 16:52:08 GMT</pubDate>
      <guid>https://rip.trb.org/View/1582010</guid>
    </item>
    <item>
      <title>Integrated Strategic and Operational Planning for a Fast-Charging Battery Electric Bus System</title>
      <link>https://rip.trb.org/View/1564667</link>
      <description><![CDATA[As an integral part of a multimodal transportation ecosystem, the public bus system provides an economical and sustainable travel mode that plays a key role in reducing traffic congestion and exhaust emissions. Conventional bus fleets, however, are mainly powered by diesel engines characterized by low energy efficiency, exhaust emissions, and oil dependence. Compared to diesel buses, battery electric buses (BEBs) have several advantages, including higher energy efficiency, zero tailpipe emissions, improved reliability, a lower maintenance burden, and the capability for using renewable energy sources, such as wind, solar, and water energies. Moreover, BEBs are easier to deploy and more flexible in their operation than trolley buses.

Although BEBs have many advantages and have been adopted by a number of transit agencies, due to limitations in battery technology, they are disadvantaged by cumbersome and costly on-board batteries. Moreover, it takes very long time to recharge BEBs using either standard or slow-charging methods. The emerging fast-charging technology promises the potential to offset these drawbacks. With fast-charging technology, a BEB with a modest battery capacity can utilize the dwelling times between trips to quickly recharge its battery and maintain continuous operation. Fast-charging technology has been adopted by many BEB demonstration projects, and promising results have been report.

In this project, the research team simultaneously considers and optimizes the battery and charger configurations of a fast-charging BEB system, as well as its recharging scheduling during operation. The team also explicitly consider the demand charges of high-power recharging activities of BEBs.]]></description>
      <pubDate>Wed, 24 Oct 2018 13:24:07 GMT</pubDate>
      <guid>https://rip.trb.org/View/1564667</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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