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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>Detecting and Mitigating Low-Level DC Leakage and Fault Currents In Transit Systems



</title>
      <link>https://rip.trb.org/View/2487299</link>
      <description><![CDATA[Low-level electrical fault currents are phenomena found in direct current (DC) traction systems used in public transit systems and electrified rail systems worldwide. These low-level currents are typically caused by small and sporadic failures of insulation within the electrification system, which often make them difficult to locate, measure, and control. The apparent effects of these faults can go unnoticed for long periods of time as a result of their slow and progressive nature; however, if these faults are left undetected, evidence exists to show that extensive damage to infrastructure of transit systems and infrastructure of adjacent private/public utilities may result. Recently, a transit system suffered damage to its electrification system because of low-level faults in the central core area. The failure resulted in damage valued at more than a million dollars that impacted rush hour revenue service at the time of occurrence. The failure further necessitated service reductions for several days in the central core transit system area while emergency repairs were performed. Similar problems have occurred at other transit agencies.
 
Low-level DC leakage and fault currents may also create safety hazards to transit employees, patrons, and the general public as contact to any metallic structure (such as fences, light poles, and handrails) is potentially lethal because structures may become energized to dangerous voltages. At present, awareness of such hazards is dependent on acute conditions observed (e.g., boom, flame, smoke, steaming or glowing poles, steaming manholes, smoking insulators; train doors that do not open) or felt (e.g., sluggish train operation; shock or tingle on contact; hot water in cable hole), as well as chronic conditions observed (e.g., rail deterioration, rail web entirely destroyed, burnt surge arresters).
 
Currently, there are no known technologies available to easily detect low-level DC leakage and fault currents. To detect low-level DC leakage and fault currents (at the agency level), it is necessary to conduct extensive field research, which is costly, labor intensive, and difficult to accomplish, particularly in areas remote from traction power substations. With current operating budget restrictions prevalent throughout the industry, this type of testing is not feasible. Research is needed to identify possible workable solutions; develop prototypes for detection and monitoring systems; and, produce a guide to mitigating low-level DC leakage and fault currents.
 
The objectives of this research are to develop (a) one or more prototype methods, tools, or techniques for detecting/monitoring low-level DC leakage and fault currents (i.e., magnitude of current and location of fault) in electrified transit systems and (b) a guide to detecting and mitigating low-level DC leakage and fault currents in transit systems. Electrical faults of interest include, but are not limited to, those originating from subsurface conductors as well as third rail and overhead contact systems.
 
]]></description>
      <pubDate>Tue, 07 Jan 2025 18:09:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/2487299</guid>
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      <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>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>Detecting and Mitigating Low-Level DC Fault Currents in Transit Systems (Phase II TCRP Project D-17)



</title>
      <link>https://rip.trb.org/View/1747284</link>
      <description><![CDATA[Low-level electrical fault currents are a phenomenon found in DC traction systems used in public transit systems worldwide. These low-level currents are typically caused by small and sporadic failures of insulation within the electrification system, which make them difficult to locate, measure, and control. The effects of these faults go unnoticed for long periods of time because of their slow and progressive nature; however, evidence shows that if these faults go undetected, they can cause extensive damage to infrastructure of transit systems and that of adjacent private/public utilities. The main concern of private/public utilities is the significant corrosion of subsurface utilities caused by the compounding effects of low-level faults. They may also create a safety hazard to transit patrons and the general public as contact with any metallic structures, such as fences, is potentially lethal because they become energized to dangerous voltages. Furthermore, smoke generated by burning cable insulation in tunnels and confined areas creates safety hazards for transit patrons.
 
Under TCRP Project D-17, a lab breadboard model was developed and testing was conducted at the Greater Cleveland Regional Transit Authority (GCRTA). This research showed positive results in detecting low-level faults in DC transit systems.
Moreover, work is needed to detect low-level fault conditions. To detect these conditions, it will be necessary to conduct extensive testing.
The objective of this research is to develop a prototype system that can detect low-level faults in electrified transit systems powered by third rails.]]></description>
      <pubDate>Mon, 26 Oct 2020 19:36:52 GMT</pubDate>
      <guid>https://rip.trb.org/View/1747284</guid>
    </item>
    <item>
      <title>Progressive Fault Identification and Prognosis of Railway Tracks Based on Intelligent Inference (1.6)</title>
      <link>https://rip.trb.org/View/1590558</link>
      <description><![CDATA[The objectives of this project are to synthesize novel sensors integrated with physics-informed data analytics to monitor the railway track for enhanced reliability and durability.  New active sensing mechanisms will be explored, to enable autonomous detection and identification.  New physics-informed statistical inference algorithms will be formulated, to realize highly accurate fault diagnosis and prognosis.  Direct collaboration with industry partner will be carried out.]]></description>
      <pubDate>Tue, 05 Mar 2019 10:20:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/1590558</guid>
    </item>
    <item>
      <title>SEISMIC: Bridge Design for Earthquake Fault Crossings: Synthesis of Design Issues and Strategies</title>
      <link>https://rip.trb.org/View/1234317</link>
      <description><![CDATA[This proposed research plan is aimed at translating previously completed research into products that can be easily implemented by bridge engineers. Specific products that will be developed during this project are: (a) Report on independent verification of the reliability of the three previously developed procedures; (b) Examples for the use of the three procedures for different types of bridges, different fault types, and different orientation between the bridge and fault; and (c) Procedures that are clear and can be put into Caltrans Memo to Designers and Seismic Design Criteria. The proposed research plan consists of seven tasks: (1) Identification of Bridge Examples; (2) Selection of Ground Motion Histories; (3) Development of Computer Models; (4) Evaluation of Previous Procedures; (5) Review of Analysis Approaches for Practical Use; (6) Development of Example Problems; and (7) Development of Recommendations for Bridges in Fault-Rupture Zones. This research will use realistic bridge models for independent evaluation of the three previously developed procedures, investigate impediments to their practical application and suggest possible solutions, develop simple to follow example problems for different scenarios, and provide recommendations to Caltrans that can be offered for adoption in the next Seismic Design Criteria and Memo to Designers.]]></description>
      <pubDate>Thu, 03 Jan 2013 15:10:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/1234317</guid>
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