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    <title>Research in Progress (RIP)</title>
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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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      <link>https://rip.trb.org/</link>
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    <item>
      <title>Identifying Targets for Electric Vehicle Industry Improvement</title>
      <link>https://rip.trb.org/View/2519188</link>
      <description><![CDATA[Electric vehicle (EV) sales have increased dramatically over the last several years. While Tesla has a growing network of Supercharging stations, owners of the newer, more luxurious EVs cannot necessarily use these charging facilities and are only able to consistently access public charging stations. In general, the public perceives the Supercharger network as more reliable and consistent than most other networks. Users of public charging stations site issues with charger maintenance and rank overall charging satisfaction lower. Though, in March 2023, Tesla announced that it was planning on opening up a portion of Superchargers to the public to qualify for federal funds.

Many perceive the availability of charging facilities as inadequate and forecasts of electrical energy availability for charging may not be adequate to support a complete conversion of  internal combustion engines (ICE) cars to EV status. In order to sustain demand for electricity, one would have to upgrade the electrical grid. However, how those costs would be covered is also unclear. There are also questions about whether enough lithium is available on this planet to produce all the batteries that would be required for conversion of all ICE vehicles to electric.

Without significant improvements to features, batteries, and support infrastructure one might wonder whether EVs will boom and then drop in popularity like bikes did in the late 19th century.  This research will examine the complete spectrum of the EV industry to identify all the issues that should be identified as targets for improvement. Problem identification will be done from several different perspectives including: potential EV buyers, EV owners, EV makers, public agencies (State DOT, City, and MPO), and engineering researchers.  One of the largest EV manufacturing facilities (Tesla) in the world is located in Austin, TX so the research team will work closely with Tesla on this part of the study. A combination of surveys and expert panels will be used to gather perceptions.  Potential solutions to improvement targets will be identified and evaluated. Evaluation will include benefit-cost analyses and alternative funding mechanisms.]]></description>
      <pubDate>Fri, 07 Mar 2025 17:01:46 GMT</pubDate>
      <guid>https://rip.trb.org/View/2519188</guid>
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    <item>
      <title>Emergency Management Strategies for Electric Vehicles</title>
      <link>https://rip.trb.org/View/2445135</link>
      <description><![CDATA[Electric vehicle (EV) sales have steadily increased in recent years, representing 16.3% of new vehicle sales. The vast majority of EVs use high-voltage lithium-ion batteries. When these batteries are damaged in crashes, they are susceptible to thermal runaway where uncontrolled increases in temperature and pressure can lead to fires and reignition hours or days later. Conventional fire suppression techniques are often ineffective as cathode materials inside lithium-ion batteries release heat and oxygen when decomposing at high temperatures, creating instances where electric vehicles can burn even when submerged in water. This has led to electric vehicle fires which can burn at the roadside for several hours and reignite during and after transport from the scene, creating additional congestion and safety risks for responders. The unique characteristics of lithium-ion battery fires create new risks for emergency responders, and require new strategies and coordination among police, fire, tow companies, and Virginia Department of Transportation (VDOT). This project aims to assess the presence and risk of EV fires, review the state of the art and practice in EV fire suppression, coordinate with firefighters in Virginia and nationally, and identify methods for VDOT to support EV fire suppression.]]></description>
      <pubDate>Fri, 25 Oct 2024 08:33:02 GMT</pubDate>
      <guid>https://rip.trb.org/View/2445135</guid>
    </item>
    <item>
      <title>Implementation of Backup Power for Traffic Cabinets During Extended Power Loss</title>
      <link>https://rip.trb.org/View/2362133</link>
      <description><![CDATA[Objectives and associated tasks of the project are as follows: 1. Expand the literature review conducted in project BE703 to include additional backup technologies that became available in the last two years. 2. Perform additional research on the topic of battery life cycle, especially with newer chemistries like lithium-ion or lithium polymer batteries. 3. Research how many solar panels are adequate enough to charge a battery and if they can be installed at a signalized intersection and provide solutions. 4. Conduct pilot deployment of a selected backup system from a selected technology with a local agency and monitor degradation of the charge provided by the product including the solar panels. 5. Conduct a benefit-cost analysis for a selected backup system from a selected technology.]]></description>
      <pubDate>Mon, 03 Jun 2024 14:46:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2362133</guid>
    </item>
    <item>
      <title>Strategies to De-energize Damaged/Defective and End-of-Life Lithium-ion Batteries for Safe Shipment</title>
      <link>https://rip.trb.org/View/2071554</link>
      <description><![CDATA[The research project will identify essential attributes of fire-resistant packaging technologies and develop a novel prototype system to enable the safe shipment of aged/defective lithium-ion batteries via highway, railway and maritime vessel.]]></description>
      <pubDate>Mon, 28 Nov 2022 14:19:59 GMT</pubDate>
      <guid>https://rip.trb.org/View/2071554</guid>
    </item>
    <item>
      <title>First responder and EV crash investigation safety</title>
      <link>https://rip.trb.org/View/2050268</link>
      <description><![CDATA[New research to augment and update existing first responder safety protocol recommendations including toxicity of Li-ion fires will be pursued. This will include expanding internal capabilities with charging and discharging equipment purchases as well as personal protective equipment to safely supports more electric vehicle (EV) crash investigations.]]></description>
      <pubDate>Tue, 25 Oct 2022 10:24:32 GMT</pubDate>
      <guid>https://rip.trb.org/View/2050268</guid>
    </item>
    <item>
      <title>Intelligent Incipient Fault Detection System for Electric Vehicle Battery: Fault Isolation Schemes and Prototype Development</title>
      <link>https://rip.trb.org/View/1904907</link>
      <description><![CDATA[Lithium-ion (Li-ion) batteries are the primary power source for electric vehicles (EVs) due to their
high energy and power density, and long life-cycle. The recent variants of the high-end plug-in
EVs, with Li-ion battery pack, offer a range of approximately 300 miles on a single full charge
close to their gasoline counterparts. Further, to bridge the gap between the fueling time of the
gas-powered vehicles and the charging time of EVs, high power chargers have also been
introduced, reducing the charging time to less than 30 minutes. The Li-ion battery packs operate
at maximum limits to deliver the required power to achieve these optimal performances.
The extreme operating conditions and abusive operations may lead to internal and
external faults, such as short circuits, cell internal temperature rise, lithium plating and loss of
lithium, and mechanical failure due to vibration. These internal faults have a cumulative effect on
the battery’s health, aggravating the vulnerability to thermal runaway. Although various external
safety technologies are employed in the battery monitoring system and battery management
system (BMS) to protect the battery from external fault conditions, it is still challenging to detect
the internal faults from the available measurements (e.g., voltage, current, and surface
temperature). The state-of-the-art internal fault detection approaches use Li-ion battery models
with constant parameters to develop model-based fault detection algorithms, which may lead to
inaccurate results since these parameters change with health degradation. In a companion
research project funded by Tran-SET in Cycle-4, the research team proposed developing a real-time learning-based
fault detection scheme. However, the scheme proposed in the TranSET Cycle-4 project
requires significant improvements from the perspective of hardware implementation. Therefore,
the development of an intelligent incipient fault detection system, which addresses the challenges
of the computational complexity of real-time machine learning using neural networks for the
embedded-hardware implementation, is critical to exploit the advantages of the real-time learning
schemes in the field of Li-ion battery management for EVs.
The proposed research project’s overall objective is to develop, implement, and validate
an intelligent fault detection scheme capable of detecting a Li-ion battery’s internal faults in its
incipient stage. This involves significant intellectual challenges related to root-cause analysis for
determining the interrelation between internal parameters and type of fault and develop a
computationally efficient neural network algorithm for hardware implementation. The team will address
these challenges by (1) developing failure mode analysis schemes to identify the root-causes, (2)
developing computationally efficient fault detection algorithms using real-time machine learning,
(3) developing Field Programmable Gate Array (FPGA)-based hardware architecture to implement
fault detection scheme, and (4) validating the prototype experimentally. The success of the project
will lead to a significant improvement in the safety of EVs. The research aligns with the vision of
TranSET of overcoming transportation challenges in Region-6 by using innovative approaches.
The proposed research is highly relevant to multiple disciplines, such as control systems,
machine learning, and EVs. This project’s success will provide necessary validation results and
a prototype of the fault detection system for the safe operation of the Li-ion battery fostering its
adoption by stakeholders. The project will also educate students and working professionals on
this innovative multidisciplinary research and technology development. The team will also integrate
the research results into mechatronics courses and reach out to secondary school students
hosting lab visits and workshops to motivate them to seek STEM as a career.]]></description>
      <pubDate>Thu, 20 Jan 2022 16:25:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/1904907</guid>
    </item>
    <item>
      <title>Lithium-Ion Battery Transit Bus Fire Prevention and Risk Management</title>
      <link>https://rip.trb.org/View/1893354</link>
      <description><![CDATA[The risk of lithium-ion battery fires is a concern for transit agencies that are considering whether to electrify their bus fleets. The transit industry has largely addressed lithium-ion battery fire risk by incorporating rigorous early detection and protection protocols in battery management systems that prevent thermal runaway when the battery pack is physically compromised through improper use or external impact. At current zero-emission bus (ZEB) fleet scales, the magnitude of these risks is relatively small; there is, however, no widespread understanding of how lithium-ion battery fire risks will be magnified when fleet size increases. Damaged cells in a lithium-ion battery can lead to thermal runaway, a phenomenon in which a failure in the architecture of a battery cell (e.g., a short) causes the heat of the battery to rapidly increase, releasing flammable gas which then ignites, triggering similar events in adjacent cells. The ensuing fires are difficult to extinguish and must be addressed with significant quantities of specialized fire suppressants. There are also documented instances of stranded energy remaining after a lithium-ion battery fire is extinguished, causing batteries to reignite after the fires have been initially suppressed by first responders. 

The objective of this research is to develop a guide to lithium-ion battery transit bus fire prevention and risk management with recommended practices for original equipment manufacturers, battery companies, transit agency facilities, and vehicle maintenance.

The focus should be on zero-emission transit bus fire prevention and risk management. A parallel project is addressing power generation, distribution, and charging infrastructure; institutional relations; and operations.

At a minimum, the research team shall (1) review the potential root causes of ZEB lithium-ion battery fires, including an analysis of the potential of such fires to spread to other vehicles or reignite after suppression; (2) evaluate risk mitigation options; (3) identify, evaluate, and summarize effective practices for fire risk mitigation and suppression, focusing on agencies that store and charge their buses in indoor facilities; (4) identify quantitative and qualitative metrics that can be used to evaluate vehicle and battery performance as they relate to fire and life safety; and (5) address the technical, economic, and institutional barriers to implementing identified solutions.]]></description>
      <pubDate>Mon, 22 Nov 2021 22:05:03 GMT</pubDate>
      <guid>https://rip.trb.org/View/1893354</guid>
    </item>
    <item>
      <title>Smart Battery Management System for Electric Vehicles: Self-learning Algorithms for Simultaneous State and Parameter Estimation, and Stress Detection</title>
      <link>https://rip.trb.org/View/1751178</link>
      <description><![CDATA[Electric vehicles (EVs) are the future means of transportation systems due to their cost-effective and environment-friendly nature. The rapid advancement in energy storage technologies such as lithium-ion (Li-ion) battery with high energy density has accelerated the acceptance of EVs in recent years. Efficient and safe operation of Li-ion batteries in EVs requires an intelligent and smart battery management system (BMS) capable of learning the health degradation for accurately estimating the state-of-charge (SOC) and the state-of-health (SOH). This will add autonomy to the BMS in health-conscious decision making such as fast charging, discharging, cell balancing, and optimal power and energy management. The design of smart BMS requires the development of 1) enhanced SOC and SOH dependent parameter-varying dynamical model of Li-ion battery and 2) real-time learning algorithms to learn the parameter-varying model. The enhanced electric circuit model (ECM) of the Li-ion battery, by incorporating the SOH indicators such as capacity loss and power loss, both under normal and accelerated degradation conditions, can also be used to detect internal faults and stress. The existing BMSs use the constant parameter electro-chemical or electric circuit model of the battery for the estimation of SOC and SOH. These estimation approaches require the complete knowledge of the model, found experimentally or estimated adaptively a-priori. The use of constant parameters model leads to inaccuracy in the long run since the model parameters vary with both SOC and SOH. Further, the parameter variations are accelerated under degradation conditions, such as extreme weather, internal faults and stress, and aggressive driving profiles. The proposed research will investigate the effects of both normal and accelerated degradation on the battery health to develop a SOC and SOH dependent parameter-varying electric circuit model of Li-ion battery and learning algorithms to learn the developed model. The proposed model will integrate the capacity and health dynamics into the model to facilitate simultaneous and real-time estimation of the internal parameters along with SOC and SOH. The self-learning algorithms will learn the battery health in real-time to provide the EV drivers with continuously updated range information based on current health, which will help in reducing the range anxiety. Moreover, the proposed novel technical approach for the smart BMS offers a number of advantages over the existing ones including a realistic SOC and SOH-dependent model, which can further be used to optimize the Li-ion battery charging, power and energy management functions, and computationally efficient real-time machine learning algorithms for implementing on hardware. The intellectual merit of the proposed research is the development of smart BMS with human brain-like complex learning for accurate estimation of the health and remaining life of the Li-ion battery. This involves significant intellectual challenges related to the development of the SOC and SOH dependent model and real-time learning of highly nonlinear and time-varying dynamics. The proposed research points towards a unified design and will lead to a significant increase in the safety, capabilities, and autonomy of the BMS for Li-ion batteries. The success of this project will provide the necessary tools for smart BMS design leading to an efficient and safe operation of EVs. The research aligns with the vision of TranSET of overcoming transportation challenges in Region-6 by using innovative approaches.]]></description>
      <pubDate>Wed, 11 Nov 2020 09:35:09 GMT</pubDate>
      <guid>https://rip.trb.org/View/1751178</guid>
    </item>
    <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>
    </item>
    <item>
      <title>RFID Hazard Assessment &amp; Lithium Battery Packaging Performance</title>
      <link>https://rip.trb.org/View/1361057</link>
      <description><![CDATA[No summary provided.]]></description>
      <pubDate>Thu, 16 Jul 2015 01:00:28 GMT</pubDate>
      <guid>https://rip.trb.org/View/1361057</guid>
    </item>
    <item>
      <title>Dual Variable Output Fuel Cell Hybrid Bus Validation Testing and Demonstration (Proterra Bus) Project</title>
      <link>https://rip.trb.org/View/1255660</link>
      <description><![CDATA[Under the National Fuel Cell Bus Program, the Center for Transportation and the Environment is conducting the Dual Variable Output Fuel Cell Hybrid Bus Validation Testing and Demonstration (Proterra Bus) project.  This project is demonstrating an innovative fuel cell system, using two 16kW fuel cell stacks packaged into a single 32kW operating system.  The dual variable output configuration operates the two cell stacks in parallel during times of high load demand, and individually when less power is required, automatically alternating stack usage under normal conditions.  The expected result could extend combined fuel cell stack life up to 75 percent.  The battery dominant hybrid-electric bus is an innovative composite design developed by Proterra, LLC (formerly Mobile Energy Solutions).  This design adopts modular elements allowing bus components to be updated quickly and easily.  The bus uses advanced lithium-titanate batteries.]]></description>
      <pubDate>Wed, 10 Jul 2013 01:01:04 GMT</pubDate>
      <guid>https://rip.trb.org/View/1255660</guid>
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