<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
    <description></description>
    <language>en-us</language>
    <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>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
    </image>
    <item>
      <title>Accelerated Materials Discovery Through the Power of Artificial Intelligence for Energy Storage</title>
      <link>https://trid.trb.org/View/2651999</link>
      <description><![CDATA[Half a century of concerted basic science and engineering research has led to today’s lithium-ion battery technology. About 50% of materials cost is from the cathode, and basic science research in the 1980s offered pathways to lower the cost and ease supply chain—from cobalt oxide to manganese oxide to iron polyanion oxide cathodes. As the battery market expands to large-scale applications such as electric vehicles (EVs) and grid storage, cost and supply-chain challenges are poised to become increasingly critical. Efforts must focus on eliminating scarce critical metals such as cobalt and nickel, while advancing battery chemistries based on earth-abundant elements like iron, manganese, sulfur, and sodium, as well as organic materials. Replacing liquid electrolytes with solid-state alternatives offers the potential to improve safety and increase energy density. With the vast troves of experimental data available in the literature and industry, artificial intelligence (AI) can help accelerate the discovery of cost-effective, supply-chain-resilient chemistries and materials. Appropriately combining machine intelligence with human ingenuity could enable rapid, transformative advances in energy storage.]]></description>
      <pubDate>Tue, 13 Jan 2026 09:21:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2651999</guid>
    </item>
    <item>
      <title>A State of Charge Analysis of Power Banks (38.5 Wh) Shipped by Air</title>
      <link>https://trid.trb.org/View/2604091</link>
      <description><![CDATA[UN 3480, lithium-ion batteries (batteries not packed with or contained in equipment) are forbidden on passenger carrying aircraft and cannot exceed a 30% state of charge (SoC) when transported on cargo aircraft. Lithium-ion power banks are included in this requirement. In December 2024, an undeclared package containing three lithium-ion power banks experienced a thermal event. The package had been previously shipped from Florida to Kentucky via aircraft. The package was visibly smoking and had a burn hole in the side of the package. The fire eventually self-extinguished. Members from the Federal Aviation Administration's (FAA’s) Office of Hazardous Materials Safety (AXH) contacted the FAA’s Fire Safety Branch to aid in analysis of the power banks. Five lithium-ion power banks from packages offered by the same shipper were delivered to the FAA’s Tech Center, where testing was conducted to determine the as-delivered SoC. Test findings determined that all the power banks that were shipped had a SoC exceeding 90%.]]></description>
      <pubDate>Mon, 13 Oct 2025 16:31:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2604091</guid>
    </item>
    <item>
      <title>Synergistic optimization of thermal and electrical energy storage for zero-emission electric buses</title>
      <link>https://trid.trb.org/View/2589237</link>
      <description><![CDATA[In winter, the operation of cabin heating systems in battery electric vehicles could significantly decrease battery lifespan and driving range, due to extended cabin warm-up time and substantial energy consumption increase associated with maintaining a comfortable cabin temperature. These pose a notable challenge to the overall performance and practicality of battery electric vehicles in cold climates. To address this challenge, thermal energy storage, particularly, the integration of a metallic phase change material-based thermal energy storage device is proposed to extend the driving range and reduce the cabin warm-up time during cold start. An energy storage system sizing framework based on a detailed battery electric bus simulation model incorporating this approach was developed. Based on real-world driving data, an optimal energy storage system configuration was obtained as 318.8 kWh of battery and 86.5 kWh of thermal energy storage. Using these devices for heating, the cabin warm-up time was found to be reduced by up to 68.3% and the battery service life could be extended by 13.8%, leading to an annual operating cost reduction by 7.8%. This study demonstrates the significant improvements of electrical bus performance through the integration of thermal energy storage with battery electric buses.]]></description>
      <pubDate>Fri, 26 Sep 2025 09:07:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2589237</guid>
    </item>
    <item>
      <title>A Practical Analysis of Data Driven Lithium - Ion Battery Health Estimation</title>
      <link>https://trid.trb.org/View/2584876</link>
      <description><![CDATA[Lithium-Ion (Li-Ion) batteries are used as the main source of power for various applications, such as electric vehicles and portable devices. Monitoring the health of these batteries is crucial for ensuring safety, longevity, and optimal performance. Central to this monitoring is the concept of State of Health (SOH), which quantifies a battery’s remaining capacity in relation to its maximum when the battery was new, as well as battery functionality in energy storage and delivery. This work aims at accurate SOH estimation, which is critical for safe battery operation, preventing catastrophic failures, and minimizing safety risks. A practical analysis of various data-centric techniques like machine learning (ML) and Artificial Neural Network (ANN) and their effectiveness in estimating the maximum capacity available of Li-Ion batteries is discussed in this paper. The data-driven models’ performance is validated by the following error metrics: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R²). The study utilizes distinct Li-Ion battery datasets, and a validation dataset created by real-time Li-Ion battery parameters. It was found that supervised ML models were better at accurately estimating SOH for the existing dataset, but ANN did better when real-time Li-Ion battery parameters were used to test it. This proposed approach establishes a foundation for accurate and robust battery health monitoring, mitigating the challenges posed by uncertainty factors and further promoting the widespread adoption of Li-Ion batteries in diverse industries.]]></description>
      <pubDate>Mon, 15 Sep 2025 17:05:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2584876</guid>
    </item>
    <item>
      <title>Marine Heat-Driven Ejector Refrigeration Machine for Air-Conditioning System with Thermal Energy Storage Unit</title>
      <link>https://trid.trb.org/View/2592186</link>
      <description><![CDATA[A novel heat-driven ejector refrigeration machine for an onboard air-conditioning system of a merchant ship was conceptualized and analyzed. The source of waste heat is low-pressure steam from the exhaust gas boiler (0.5-0.7 MPa). However, the steam from auxiliary boilers is used in port. It was proposed to include a thermal energy storage unit in the ejector refrigeration machine design to reduce fuel consumption by auxiliary boilers in the port. A phase change material with a melting point of 142 °C was proposed for use as thermal energy storage material. It was admitted for the TES unit to replace half of the generator load for 1 day of operation. For such conditions the volume and mass of the thermal energy storage unit were 16.5 tons and 125 m3 at an ejector refrigeration machine cooling capacity of 174 kW. It was shown that ejector refrigeration machine with thermal energy storage unit inherent the lower fuel consumption for its operation compared to the vapor-compression refrigeration machine: 53916 vs. 80567 kg of fuel per year. It is advisable to use the proposed ejector refrigeration machine with a thermal energy storage unit on ships that have short voyages and frequent stays in port. The problems in the TES unit design that need to be studied further have been noted in the paper.]]></description>
      <pubDate>Mon, 25 Aug 2025 09:04:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2592186</guid>
    </item>
    <item>
      <title>lgarIntegrated Bidirectional Electric Vehicle Battery Network for Sustainable Communities: A Planning Framework</title>
      <link>https://trid.trb.org/View/2569707</link>
      <description><![CDATA[The transition towards sustainable and net-zero energy communities has become imperative in addressing the challenges of climate change and ensuring a resilient energy future. This work proposes an innovative planning framework through the development of an integrated bidirectional electric vehicle (EV) battery storage network for net-zero communities. The proposed framework is intended for neighborhood planning and integrates a bidirectional charging infrastructure that allows EV batteries to seamlessly contribute to the grid during periods of high demand or store excess renewable energy during off-peak hours. To analyze various EV microgrid integration scenarios, a combined Matlab-Simulink and EnergyPlus simulation environment is proposed to simulate EV battery networks in neighborhood settings. This study examines state of charge (SoC), and energy exchange characteristics based on specific user behaviors and charging scenarios. A neighborhood archetype of 48 single-family detached houses is considered along with five EV use profiles (EVPs) for the demonstration of the proposed method. For the considered neighborhood, in winter, EVPs have eliminated the peak loads during early morning hours (1 am - 6 am) by discharging stored energy. In spring, loads exceeding the base load are observed from 1 am to 10 am, with all EVPs discharging energy until 9 am and then recharging during off-peak hours. Summer required strategic charging management, with EVPs supporting peak loads from 7 am to 6 pm. In the fall, EVPs discharged energy from 12:01 am to 6 am and recharged from 10 am to 6 pm. The study introduces the EVP peak support index facilitating real-time charging adjustments and incentivizing greater participation. By leveraging this index, smart charging systems can develop algorithms to control charging times based on grid needs, ensuring efficient energy distribution and enhanced grid stability. This framework offers a robust approach to scenario generation for energy and urban planners during the neighborhood planning stages predicting energy performance and management.]]></description>
      <pubDate>Tue, 22 Jul 2025 14:39:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2569707</guid>
    </item>
    <item>
      <title>Optimizing fast charger location for hybrid electric bus transit networks</title>
      <link>https://trid.trb.org/View/2562551</link>
      <description><![CDATA[The electrification of buses running on urban transit networks is one of the many weapons in the battle to limit greenhouse gas emissions. Existing diesel buses can be replaced by new fully electric buses or retrofitted to become hybrid. The latter is an interesting alternative in markets where electrification budgets are limited. Hybrid buses can run both on diesel and electric drive modes. They are typically equipped with low-capacity but fast-charging energy storage devices. As a result, their electric range is limited, but they can quickly charge en route while executing their tasks. In this paper, the authors devise a mixed integer programming model and two versions of a branch-and-check algorithm to locate chargers on multi-line hybrid bus transit networks. More specifically, the authors' methods decide how many chargers to install at each candidate location and what should be the drive mode on each segment of each line in the network. The objective is to maximize the total distance driven using the electric mode. One novelty of the authors' approaches is that they allow for charger sharing between lines. The latter allows for more cost-effective electrification of the network but makes the problem more difficult to solve as line service level and timetabling feasibility constraints become intertwined. The authors discuss extensive computational experiments on a set of 210 instances based on the transit network of the city of Tours (France). The authors provide managerial insights into the operational and economic benefits of allowing charger sharing and the trade-offs between increasing the budget and achieving greater electrification.]]></description>
      <pubDate>Mon, 30 Jun 2025 16:37:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2562551</guid>
    </item>
    <item>
      <title>Energy-Efficient Train Control Considering Energy Storage Devices and Traction Power Network Using a Model Predictive Control Framework</title>
      <link>https://trid.trb.org/View/2512155</link>
      <description><![CDATA[The optimization of the train speed trajectory and the traction power supply system (TPSS) with hybrid energy storage devices (HESDs) has significant potential to reduce electrical energy consumption (EEC). However, some existing studies have focused predominantly on optimizing these components independently and have ignored the goal of achieving systematic optimality from the standpoint of both electric systems and train control. This article aims to establish a comprehensive coupled model integrating the train control, dc traction power supply, and stationary HESDs to reach the minimum EEC within the integrated system. The original nonconvex and time-varying model is initially relaxed and reformulated as a convex program that can be solved quickly. On this basis, a model predictive control (MPC) framework is proposed to derive specifications in the space-domain-based model and overcome the drawbacks of the time-domain-based model. The designed controller solves the optimization problem for the remaining journey through time sampling, guaranteeing real-time and closed-loop performance. The numerical experiments present five case studies based on the real-world scenario, i.e., Guangzhou Metro Line No. 7. The results demonstrate that the proposed integrated convex model without stationary HESDs can reduce the accumulated EEC by up to 27.99% compared to the existing field test results. In addition, compared to the mixed integer linear programming (MILP) method, the convex program proposed in this work obtains the highest energy savings rate (48.71%) and significant computational efficiency, ranging from milliseconds (0.03 s) to seconds (4.20 s) in the TPSS with stationary HESDs. In addition, the convex model features satisfactory modeling accuracy by invoking the nonlinear solver to simulate the power flow of the integrated system and recalculate the EEC.]]></description>
      <pubDate>Thu, 12 Jun 2025 13:46:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2512155</guid>
    </item>
    <item>
      <title>Multi-objective electric vehicle charge scheduling for photovoltaic and battery energy storage based electric vehicle charging stations in distribution network</title>
      <link>https://trid.trb.org/View/2529924</link>
      <description><![CDATA[Recently, with the increasing demand of the electric vehicle (EV) in transportation, the power grid faces critical challenges in meeting the extra power demand. Companies are focusing on expanding EV charging infrastructure to meet customer requirements. Ensuring power supply security, reliability, and economics for EV charging stations remains a challenge, despite efforts to align photovoltaic (PV) and battery energy storage system (BESS) based designs with distribution system requirements. A criteria weight ranking mechanism has been designed to accept charging requests for EVs depending on the criteria weights specified by the EV owner. This paper uses a multi-objective remora optimization algorithm (MOROA) to determine the optimal location of two electric vehicle charging stations (EVCS) in the distribution system, and capacity of PV & BESS units in two EVCS for optimizing three conflicting objective functions, such as (1) minimizing total power loss, (2) minimizing annual substation power cost, and annual capital, operation & maintenance cost of the PV and BESS, and (3) minimizing emission from upstream grid. Moreover, the EVs are also scheduled optimally at each charging station. The effectiveness of these methodologies has been demonstrated through four case studies using IEEE 33 bus radial distribution system (RDS). Furthermore, the smart EV charge scheduling reduces the overall load burden on the grid network and the benefit of EVCS operators and EV owners.]]></description>
      <pubDate>Thu, 29 May 2025 14:02:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2529924</guid>
    </item>
    <item>
      <title>Configuration and Parameter Design of Electrified Propulsion Systems for Three-Dimensional Transportation: A Comprehensive Review</title>
      <link>https://trid.trb.org/View/2526557</link>
      <description><![CDATA[The design of electrified propulsion systems, including their configuration and parameter layers, is crucial to overall vehicle performance. However, with the electrification of three-dimensional transportation (TDT) (air, ground, and sea) and net-zero emission requirements, new challenges and opportunities arise. The design of propulsion systems for aircraft, marines, and trains is inherently more challenging than that of ground vehicles, due to the significantly larger and more complex propulsions. Therefore, this paper provides a comprehensive review and discussion of the configuration design and parameter optimization methods for electrified propulsion systems. Firstly, four development periods of propulsion design are summarized, and the potential topological design space is analyzed from five aspects: energy storage devices, coupling devices, output types, actuators, and power elements. Then, for the first time, six design stages—configuration representation, comparison and modification, generation, physical layer screening, control layer screening, and configuration-parameter co-optimization—are systematically reviewed and evaluated. Then, the strengths and limitations of each stage are analyzed in detail, and the challenges for the propulsion design for TDT in each stage are discussed. Finally, research challenges and trends are discussed, providing useful inspiration and guidance for advancing the configuration design and sizing optimization of electrified propulsion systems for TDT.]]></description>
      <pubDate>Thu, 29 May 2025 14:02:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2526557</guid>
    </item>
    <item>
      <title>Enhanced Voltage Stability for DC Microgrids Integrating Hybrid Electric Vehicles with Virtual Inertia and Damping Control</title>
      <link>https://trid.trb.org/View/2528703</link>
      <description><![CDATA[DC microgrids (MGs) have experienced swift growth, driven by the expanding integration of energy storage devices (ESD), renewable energy sources (RESs), and localized loads. However, it suffers from insufficient inertia owing to less rotating mass sources, which can lead to poor voltage stability. This paper introduces a control method that emulates both inertia and damping to mitigate fluctuations in DC voltage, enhance system stability, and address the low inertia concern. The proposed virtual inertia and damping (VID) control system is adopted through using hybrid electric vehicle (HEV). The suggested HEV comprises three power sources: a battery, a fuel cell (FC), and a supercapacitor (SC). In this setup, both the battery and the FC are employed to supply virtual damping owing to their high-energy density, and the SC is utilized to support virtual inertial characteristics due to its high-power density. Through this approach, enhancements in the DC voltage stability of an islanded DC MG can be achieved. Simulations demonstrate that the proposed control system significantly outperforms existing works utilizing virtual inertia (VI) based on an EV’s battery only, as well as over systems lacking VI. The proposed control system effectively reduces voltage fluctuations and improves system stability, specifically in terms of the rate of change of voltage (ROCOV) and accelerated settling time.]]></description>
      <pubDate>Fri, 16 May 2025 09:33:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2528703</guid>
    </item>
    <item>
      <title>Multiphysics Fields Analysis and Optimization Design of a Novel Saucer-Shaped Magnetic Suspension Flywheel Battery</title>
      <link>https://trid.trb.org/View/2511774</link>
      <description><![CDATA[Existing flywheel batteries are still difficult to balance in terms of space layout, decoupling control, and anti-interference ability. In addition, because the flywheel battery system involves multiple physical fields in the design process, if it is not comprehensively analyzed and optimized, the design quality of the flywheel battery system will be significantly reduced. In this study, a novel saucer-shaped vehicle-mounted flywheel battery is proposed, which is characterized by its unique gravity bearing mode, compact structure arrangement, and decoupled magnetic circuit design. First, the structural characteristics and working principle of the new flywheel battery are introduced. Then, when considering the structural design of the flywheel battery, the comprehensive influence of the design parameters on multiple physical fields such as electromagnetic field, structural field, and temperature field is further creatively added to make it have good performance in each physical field to obtain the best design. Finally, performance tests are carried out, and the good experimental results verify the rationality of the prototype and the correctness of the multi-field analysis.]]></description>
      <pubDate>Mon, 21 Apr 2025 12:12:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2511774</guid>
    </item>
    <item>
      <title>Analytical Models for Solid and Litz Wire AC Winding Loss in Toroidal Inductors</title>
      <link>https://trid.trb.org/View/2511705</link>
      <description><![CDATA[Inductors are energy storage devices that serve as key components in power conversion technology. The advent of wide bandgap (WBG) semiconductor devices, and the promise of ultra-WBG, has led to high-frequency operation of switching converters at higher voltage and current levels. In the kHz–MHz range, additional sources of loss in magnetic components interfacing with these WBG semiconductors are introduced due to high-frequency electromagnetic phenomena. A finite element analysis (FEA) can capture these effects to a high degree of accuracy but requires extensive computation resources precluding application in robust optimization and design schemes. There exist several analytical models for winding loss in literature that can evaluate multiple designs with minimal computation cost. In this work, popular models are reviewed, investigated, and expanded on in context of various toroidal winding geometries. The goal of this work is to analyze well-known winding loss models and demonstrate accuracies at high frequency as they pertain to toroidal inductors while maintaining low computation cost required for integration into optimization and design schemes. Furthermore, Litz wire’s role in high-frequency magnetics is discussed and a novel approach to predicting the winding loss for Litz wire toroidal inductors is laid out and benchmarked against another preexisting model.]]></description>
      <pubDate>Mon, 14 Apr 2025 09:35:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2511705</guid>
    </item>
    <item>
      <title>A novel reconfigurable supercapacitor system with equalization and surge current suppression to improve energy-utilization in supercapacitor urban transit systems</title>
      <link>https://trid.trb.org/View/2519465</link>
      <description><![CDATA[Supercapacitors (SCs), with their high energy density, long cycle life, and excellent low-temperature performance, have emerged as a highly suitable energy storage devices for the electrification of urban transit systems (UTSs), especially for the fast-charging applications. However, the wide voltage range of SCs results in low energy utilization for the SC UTSs. Reconfigurable SC systems (RSSs) are considered a promising solution to significantly improve energy utilization rate. However, the existing RSSs are unable to maximize energy utilization and lack effective equalization capability. Moreover, the surge phenomenon is common in the RSSs, which will lead to the power device failure. To tackle these challenges, a novel RSS is proposed, integrating series-parallel reconfiguration, reconfiguration equalization, and surge current suppression. This innovative approach enables achieving ultra-high energy utilization of the SCs while significantly improving voltage consistency. Furthermore, the surge current during the mode-switching process can be effectively suppressed. Experimental results demonstrate that the RSS achieves an impressive energy utilization rate of 93.7 %, which is 137.8 % higher than the fixed-connected supercapacitor pack, and 8.8 %–16.7 % higher than the existing RSSs. The voltage difference is reduced from 493 mV to a mere 13 mV, resulting in a further 10.9 % increase in energy utilization. The surge suppression method limits the surge current from 42.3 C rate to nearly zero. The RSS proposed can remarkably enhance the driving range of electric urban rail trains and electric buses, concurrently reducing the cost and volume of the SESSs.]]></description>
      <pubDate>Fri, 21 Mar 2025 09:03:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2519465</guid>
    </item>
    <item>
      <title>Industrialization challenges for sulfide-based all solid state battery</title>
      <link>https://trid.trb.org/View/2447556</link>
      <description><![CDATA[All-solid-state battery(ASSB) is the most promising solution for next-generation energy-storage device due to its high energy density, fast charging capability, enhanced safety, wide operating temperature range and long cycle life. Although great efforts and breakthroughs have been made in recent years, many challenges still exist for its industrialization. This perspective aims to summarize the most critical challenges in mass production of ASSB to fully release its potential and facilitate the arrival of a more sustainable future.]]></description>
      <pubDate>Mon, 18 Nov 2024 14:21:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2447556</guid>
    </item>
  </channel>
</rss>