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    <title>Transport Research International Documentation (TRID)</title>
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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>Transport Research International Documentation (TRID)</title>
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      <title>Thermal analysis and efficiency optimization of diesel- stirling combined cycle with CI engine exhaust heat recovery</title>
      <link>https://trid.trb.org/View/2685063</link>
      <description><![CDATA[Nowadays, because of their widespread applications in thermal efficiency enhancement, combined power cycles have attracted the attention and interest of the researchers. This research is devoted to provide a comprehensive modeling and thermal analysis of a new arrangement of combined cycle based on a compression ignition (CI) engine and α-type Stirling engine. Furthermore, the influences of the diesel exhaust gas temperature and Stirling working pressure on Stirling and combined engines power and efficiency are examined considering various scenarios. The Stirling engine cycle is combined with a CI engine cycle to recover the CI engine exhaust gas waste heat. OM355 experimental results have been considered for generated power, thermal balance and exhaust gas temperature analysis. According to thermal analysis and the obtained results, it is revealed that about 34% of input energy wastes by the exhaust gas. The simulation of α-type Stirling engine is also performed and the Solo V161 experimental results were employed for validation. Furthermore, Stirling engine heater is suggested for installation on the exhaust pipe in order to analyze the new proposed combined cycle properties. Thermodynamic analysis of combined cycle is implemented and thermal efficiency and net power are obtained for Stirling engine, diesel engine and combined cycle for various Stirling engine and diesel exhaust temperatures. The results indicate that, by installing a Stirling engine heater on the exhaust pipe of the CI engine, about 9.3 kW of the wasted heat could be recovered. Compared to the ordinary engine, coupled engines heat balance reveals higher thermal efficiency and combined cycle power which increase by 7.3% and 5.6%, respectively.]]></description>
      <pubDate>Wed, 15 Apr 2026 10:30:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685063</guid>
    </item>
    <item>
      <title>Detuned Parameter Design for LCC-S Compensated Inductively Coupled Power Transfer with Wide Coupling Variation</title>
      <link>https://trid.trb.org/View/2113867</link>
      <description><![CDATA[The output performance of inductively coupled power transfer (ICPT) system can be influenced by the variation of coupling conditions evidently. In order to keep the constant voltage output (CVO) characteristic under the wide coupling variation condition, a detuned parameter design method is proposed and analyzed in this paper. The principle of this parameter designing process is only adjusting the compensated capacitors in transmitting side, without the change of switching frequency and the design value of other resonant elements. Then, the effectiveness of the proposed approach is verified based on a simulation model of 50 kW operating at zero current switching (ZCS) condition in MATLAB. With different combinations of detuned compensation capacitors, the output voltage can be stable under different coupling coefficients, and the amplitude fluctuation of it can be restricted into 4.76%.]]></description>
      <pubDate>Wed, 15 Apr 2026 08:31:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2113867</guid>
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    <item>
      <title>Novel Air System for 300 kW Heavy Duty Fuel Cell</title>
      <link>https://trid.trb.org/View/2692249</link>
      <description><![CDATA[Hydrogen fuel cell powered vehicles for heavy duty trucking are a promising path for reducing future vehicle emissions due to their reduced mass for storage and faster refueling compared to battery electric trucks. These benefits come at the cost of increased system complexity stemming from the fact that fuel cells generate electricity through a chemical reaction which must be tightly controlled. The air handling system delivers the proper amount of air (oxygen) to react with fuel (hydrogen) in the fuel cell to produce power. Air delivery requires significant power and is the largest parasitic loss for a 300 kW fuel cell. Today’s systems use an electric motor driving an air compressor to supply pressurized air to the fuel cell stack. By operating at elevated pressure levels, fuel cells can achieve higher power density, which is important for vehicle powertrains. In addition to parasitic power loss, hydrogen fuel cell systems often have reliability issues associated with the air handling system. Reliability is of significant concern for heavy duty applications (especially long-haul applications). This project aims to improve both the electrical power consumption and reliability of hydrogen fuel cell air handling systems to meet the needs of heavy duty on-highway vehicle applications. The air handling is provided by a twin vortices series (TVS) compressor in addition to adding a TVS expander to recover waste heat energy back into the compressor. The final configuration includes a 600 V, 39 kW motor connected with a single shaft to the compressor and expander. This configuration reduced the total electrical power consumption from 48.6 kW to 37 kW at full load, 13.1 kW to 9 kW at half load and 0.44 kW to 0.22 kW at idle. The response time requirement was to be less than 2 sec while the final demonstration yielded 0.62 sec. Additional design changes, including water dosing into the compressor, addition of a recuperator, and elimination of the intercooler, were made to increase the energy efficiency of the air system.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692249</guid>
    </item>
    <item>
      <title>Identifying Pressure Drop and Heat Transfer Correlations for a Liquid-Cooled Lithium-Ion Battery Pack Model of Electric Vehicles under Hot-Cold Driving</title>
      <link>https://trid.trb.org/View/2692189</link>
      <description><![CDATA[The performance of a full battery pack with its effective thermal management system (BTMS) depends on coolant flow and heat transfer characteristics inside the pack. To develop a full BTMS using model-based design (MBD), the model must capture the coolant pressure drop ∆?? and heat-exchange performance from the cell to ambient air via the coolant, cooling flow channels, air gaps, and pack cases. Predicting battery pack responses (i.e., voltage, SOC, temperature) under all weather conditions is a challenge, as a complete pack contains several hundred to thousands of cells, coolant lines, coolant line bends, and coolant channels. This work presents a detailed approach to identifying heat transfer and ∆P correlations that can capture the real-time thermal-electrical performance of a mass-produced LIB pack under constant speed (in winter) and transient driving (in summer). A vehicle test is conducted using a Tesla Model Y, 2-motor model equipped with a 75-kWh LIB pack. The LIB pack's thermal and electrical performance is recorded at 60 km/h under cold conditions and during transient driving in summer. The pack is based on the 2RC equivalent circuit model, reduced from the P2D-based NCA/Gr-SiOx Li-ion cell, to accelerate simulation times at the pack and vehicle levels. The approach to identifying ∆P and heat transfer correlations are discussed, with pack model validations under coolant temperatures ranging from 0 to 40 °C and coolant flow rates of 4 to 14 L/min. The thermal and electrical performances (voltage, SOC, ∆P, and temperatures of the coolant, bricks, and modules) of the high-fidelity battery pack model are validated against vehicle test data at 60 km/h driving (ambient temperature Ta = -10 °C) and repeated FTP+HWFET cycle (Ta = 30°C). The whole pack model achieves an average accuracy of 90%, and this work can serve as a guideline for designing battery packs with their BTMS using MBD.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692189</guid>
    </item>
    <item>
      <title>Thermal Characterization of an Automotive High Voltage Junction Box</title>
      <link>https://trid.trb.org/View/2692186</link>
      <description><![CDATA[The high voltage battery junction box (HVJB) controls and protects the high voltage connections of the battery pack to the traction, auxiliary, and charging systems. HVJBs are composed of busbars, contactors, fuses, and other protection systems. The health of the HVJB is paramount to ensure performance of electric vehicles. However, sensing and monitoring in the HVJB are often lacking due to packaging cost, causing limited capability of the vehicle controller to estimate the status and health of the unit. This publication focuses on the experimentation of an automotive HVJB to characterize the operation and build the foundation for the development of prognostic algorithms for HVJB. A production HVJB has been acquired and heavily instrumented. Extensive testings are performed in adiabatic and in ambient conditions at various current levels for various durations of operation. The testing setup was calibrated and iterated based on preliminary results, and the testing conditions were adjusted to increase the accuracy of the data. These results were analyzed to identify patterns in the behavior of the heat generation for each individual component and the heat exchange between them. The analysis of these results allows for the calibration of an electrothermal model of the HVJB using MATLAB Simulink. Upon finalizing model calibration, the model will be able to accurately predict the electrothermal behavior of the HVJB, allowing for critical feedback data that can be used by production engineers to assist in reducing overall pack failures.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692186</guid>
    </item>
    <item>
      <title>Simplified Thermal Runaway Propagation Modeling to Design a Mass Optimized Battery Pack</title>
      <link>https://trid.trb.org/View/2692181</link>
      <description><![CDATA[This paper presents a simplified approach to model thermal runaway propagation in a multi-cell battery pack, with the goal of designing a safe and lightweight pack for mass-sensitive applications. The key parameters which characterize single-cell thermal runaway, including heat release profile, apparent cell emissivity and mass loss, were extracted from empirical nail penetration tests. This characterization was used to drive a three-dimensional thermal model of a 19-cell hexagonal sub-pack with a center trigger cell. To enable rapid design exploration, a symmetry-based computationally simplified domain was used for a full-factorial Design of Experiments (DOE) varying cell spacing, epoxy thickness, heat spreader thickness, and cup geometry. The DOE results were used to identify dominant heat-transfer mechanisms, capture main and interaction effects, and determine mass-efficient design levers governing peak-neighbor cell temperature during propagation. Insights from the DOE study informed the design of a physical prototype and the placement of thermocouples for model validation. Measured temperature data showed good agreement with model predictions across multiple initiator locations, with 4–7 °C error in peak temperature and 3–5 s error in time to reach peak temperature. However, accurate reproduction of the observed trends required increasing epoxy thermal conductivity on the initiator cell to represent epoxy carbonization observed during post-test teardown. This simplified modeling approach, paired with targeted testing, can provide practical design guidance, reduce overall testing cost, and enable fast development of mass-optimized, propagation-resistant battery packs.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692181</guid>
    </item>
    <item>
      <title>Development of a Virtual Test Rig to Evaluate Thermal Management of a Battery-Electric Vehicle under Battery Heating-Cooling Modes</title>
      <link>https://trid.trb.org/View/2692174</link>
      <description><![CDATA[A battery-electric vehicle (BEV) has multiple powertrain components (battery, inverter, e-motor), a thermal management system (compressor, heat exchanger, cabin heating, ventilation, and air-conditioning), and a vehicle body, among others. Vehicle testing is time-consuming, and changing powertrain components during the testing and design process is costly. Simulation models (aka virtual or simulation test rig) have been widely used for efficient vehicle design. This work presents a systematic approach to developing a virtual test rig to evaluate the thermal performance of battery-electric vehicles. A Tesla Model Y is tested in a chassis dynamometer, and the measured vehicle performance data are used as boundary conditions for the complete vehicle model. The detailed lithium-ion battery (LIB) pack model, including its cooling system, was developed and calibrated using various transient driving cycle data. The HVAC model uses a simplified controller to maintain the cabin temperature at 25 °C in both battery heating and cooling modes. The predicted thermal and electrical performance of the BEV is well validated by test data. Then, the complete vehicle model is used to compare the thermal performances of the BEV under cabin heating and cooling modes for various transient driving cycles. The simulated results show that using an external cabin air circulation model can reduce the battery energy consumption and dissipated heat by 9.9% and 2.4%, respectively. This calibrated virtual test rig can be used to evaluate a new HVAC system.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692174</guid>
    </item>
    <item>
      <title>Numerical Framework for Predicting Power Loss and Oil Flow Dynamics in Dip-Lubricated Gear Systems</title>
      <link>https://trid.trb.org/View/2692166</link>
      <description><![CDATA[Oil churning and windage power losses in dip-lubricated gearboxes can significantly affect overall transmission efficiency, particularly at high rotational speeds. As modern gearbox systems are pushed toward higher efficiency and reliability, understanding and predicting these losses becomes increasingly important. In addition to energy dissipation, the associated multiphase flow phenomena—such as oil splashing, thin film formation along gear surfaces, and aeration of the sump—strongly influence lubrication effectiveness, heat transfer, and component durability. Capturing these effects requires a robust numerical strategy that can resolve both power loss mechanisms and multiphase flow dynamics with sufficient accuracy. In this study, a single spur gear is numerically analyzed under varying oil depths and rotational speeds to quantify total power loss and investigate oil flow patterns. The computational approach employs a volume-of-fluid multiphase framework, and the predictions are systematically validated against experimental data from the OSU Lab. Validation is carried out in two stages: first, by comparing the simulated oil free-surface shapes with experimental flow visualizations for various operating conditions; and second, by comparing total power loss across a range of rotational speeds and immersion depths. The findings confirm that qualitative comparisons of oil behavior show good agreement with experimental observations including splash generation, oil streak formation, and gear surface wetting. Furthermore, predicted power loss trends align with experiments, exhibiting exponential growth with RPM and a transition toward quadratic scaling as oil depth increases. Overall, this work highlights the capability of the numerical framework to predict both churning losses and multiphase flow behavior in gear lubrication systems, providing a foundation for future gearbox design and optimization.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692166</guid>
    </item>
    <item>
      <title>Advancing Safe Battery Design through Experimental Cell Characterization of Thermal, Electrical and Mechanical Properties</title>
      <link>https://trid.trb.org/View/2692162</link>
      <description><![CDATA[Lithium-ion batteries are critical to Electric Vehicles (EV) and grid-scale energy storage. Safe design of battery systems relies on accurate simulation of thermal runaway under electrical, thermal, and mechanical abuse. A predictive battery simulation requires characterization of electrical, thermal, and mechanical properties at the full cell and cell-component levels. In this study, a commercial cell from an EV was disassembled, and tested to support both homogenized and detailed computational models. At the cell level, electrical properties were characterized using Hybrid Pulse Power Characterization (HPPC) testing to assess the cell’s power capability. Full cell compression tests were conducted to characterize mechanical behavior under deformation and used to develop a multi-physics homogenized cell model. On the other hand, detailed cell modeling that includes different component layers could help users understand localized cell integrity under mechanical deformation. At the component level, cathode and anode electrodes, separator, and cell pouch laminate were tested for their thermal properties, including heat capacity, thermal conductivity, and melting points. This data is essential to modeling heat generation and dissipation in the detailed battery cell model. Mechanical behavior of these component materials was tested to understand structural integrity and failure modes. Electrical conductivity of cell component materials was also characterized. These experimentally measured properties and derived parameters may be integrated into a representative multi-physics battery cell model. By providing detailed characterization of a commercial lithium-ion EV cell, this research provides an experimental framework for developing both macro and detailed cell computational models needed for safety design assessments of EV battery systems.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692162</guid>
    </item>
    <item>
      <title>Leveraging Large Language Models for Natural Language-Driven CAD Automation and Multi-Objective Optimization in Thermal Component Design</title>
      <link>https://trid.trb.org/View/2692110</link>
      <description><![CDATA[The design of thermal components (such as automotive heat exchangers) requires balancing multiple competing objectives—thermal performance, aerodynamic efficiency, structural integrity, and manufacturability. Traditional design workflows rely on manual Computer Aided Design (CAD) modeling and iterative simulations, which are both labor-intensive and time-consuming. Recent advances in Large Language Models (LLMs) present untapped potential for automating parametric CAD generation. However, current LLM-based approaches primarily handle simple, isolated geometric primitives rather than complex multi-component assemblies. This work introduces a progressive framework that leverages fine-tuned LLMs (Qwen2.5-3B-SFT) integrated with the CadQuery CAD kernel to automatically generate parametric geometries from natural language descriptions. As a foundational study, this work focuses on Step 1 of the framework: generating and optimizing isolated geometric primitives (cylinders, pipes, etc.) that form the building blocks of complex assemblies. The generated models are automatically exported to standard CAD formats and seamlessly integrated within a multi-objective Bayesian optimization pipeline using Gaussian Process regression. By decoupling natural language-driven CAD code generation from traditional manual scripting, this work demonstrates how LLMs can accelerate design space exploration while eliminating the need for engineers to write geometry-specific Python scripts. Case studies on parametric pipe optimization demonstrate the framework's efficiency gains and establish a foundation for future steps: handling constraints, multi-component assemblies, and full thermal component designs. This work contributes to next-generation Artificial Intelligence (AI) assisted engineering design by demonstrating LLM-powered automation as a practical pathway toward fully automated design-to-optimization workflows.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692110</guid>
    </item>
    <item>
      <title>Modular Smart Corner Systems for Next-Generation Electric Vehicle Architecture</title>
      <link>https://trid.trb.org/View/2692031</link>
      <description><![CDATA[The transition to software-defined vehicles (SDVs) necessitates a paradigm shift in both control strategies and vehicle architecture. The EU-funded R&D project SmartCorners addresses this challenge by developing integrated, modular, and scalable smart corner systems (SCS) that combine in-wheel motor (IWM)-based propulsion, brake blending, active suspension system, and steer-by-wire functionality in one module. These SCS can be retrofit or smoothly integrated into the highly adaptable skateboard chassis architecture of modern electric vehicles (EVs), enabling scalable deployment across diverse vehicle types. The central approach of this paper is the utilization of artificial intelligence (AI) and machine learning (ML) to implement multi-layer, data-driven control strategies, facilitating real-time actuation, fault mitigation, and user-centric EV architecture. The SmartCorners project strives to demonstrate significant enhancements, including improved real-world driving range due to enhanced energy-efficiency, reduced component and system costs, and a cut-down in development time of EVs, enabled by digital-twin-based design methodologies. Beyond these performance gains, SmartCorners establishes the foundational principles of modularity, adaptability, and software integration that underpin the evolution toward SDVs. The role of thermal and cabin comfort control is completely different for EVs and internal combustion engine vehicles, with the latter using waste heat from the combustion of fossil fuels for cabin heating, ventilation, and cooling (HVAC). In EVs the required energy is directly taken from the traction battery and precise thermal and cabin comfort control affecting essential components of the vehicle but also the user-perceived driving experience. These project achievements highlight a critical bridge between innovation and electrification on component-level, and the holistic software-defined mobility systems of the future.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692031</guid>
    </item>
    <item>
      <title>AirCARE: Assessing the Impact of an Integrated Air Purification System on Vehicle Thermal Management</title>
      <link>https://trid.trb.org/View/2691990</link>
      <description><![CDATA[The increasing concentration of atmospheric pollutants in urban environments necessitates innovative solutions to mitigate their impact on public health and the environment. This work presents the AirCARE project, which investigates the integration of a catalytic converter and a particulate filter with a vehicle's radiator to create an active air purification system. The primary objective is to evaluate the feasibility and performance implications of this integrated system on the vehicle's thermal management. A comprehensive methodology combining computational modeling and experimental testing was employed. A 1D longitudinal vehicle model was developed to simulate the powertrain's heat generation and the cooling system's performance under various representative driving conditions. This model allows for a parametric study of the radiator, assessing the impact of the additional components on its heat exchange efficiency. Concurrently, experimental tests were conducted on a radiator to measure the pressure drop across the integrated filter and to validate the heat exchange performance predicted by the simulations. This paper focuses on the results from the vehicle and component-level simulations and the corresponding experimental validation of the radiator's fluid-dynamic and thermal behavior. The results provide a quantitative analysis of the trade-offs between the potential for pollutant abatement and the constraints imposed on the vehicle's cooling system. The study identifies key design parameters and operating conditions that influence system performance, offering insights for optimizing the integration. The findings demonstrate the technical considerations required to implement such a system without compromising vehicle safety and performance, establishing a foundation for the future development of vehicles as mobile air purification platforms.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691990</guid>
    </item>
    <item>
      <title>Role of Advanced Polishing Techniques in Enhancing Al-Cu Diffusion Bonding for EV Application</title>
      <link>https://trid.trb.org/View/2691966</link>
      <description><![CDATA[The demand for lightweight, high-efficiency components in electric vehicles (EVs) highlights the critical need for reliable Al-Cu joints with superior electrical and thermal conductivity. While diffusion bonding has emerged as a promising approach, interfacial impurities and voids often degrade joint quality and conductivity. Conventional manual polishing was initially employed to prepare Cu and Al surfaces; however, this method proved insufficient in consistently removing oxides and contaminants, leading to non-uniform bonding. In addition, the larger surface area of the samples made traditional polishing impractical, further motivating the use of electropolishing. To overcome these limitations, we introduce electropolishing pretreatment to achieve cleaner, void-free interfaces. Electropolishing effectively dissolves surface asperities and contaminants, enabling intimate atomic contact during bonding and minimizing the formation of brittle intermetallic phases. A systematic investigation of bonding parameters was conducted using a custom-designed graphite clamping system. Microstructural analyses reveal that advanced polishing plays a pivotal role in producing uniform, impurity-free interfaces, resulting in reduced intermetallic thickness, improved bonding strength, and enhanced current-carrying capability. This study demonstrates the clear advantages of electropolishing over conventional polishing and establishes a scalable pathway to manufacture high-performance conductive joints for next-generation EV motor and power distribution systems.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691966</guid>
    </item>
    <item>
      <title>Thermal Management Simulation of Liquid-Cooled Energy Storage Batteries Using a Reduced-Order Model</title>
      <link>https://trid.trb.org/View/2691936</link>
      <description><![CDATA[Linear time-invariant (LTI) reduced-order models (ROMs) have been widely used in battery thermal management simulations due to their low hardware requirements, high computational efficiency, and good accuracy. However, the inherent assumption of LTI behavior limits their applicability in scenarios with varying coolant flow rates, where this assumption is no longer valid. To address this limitation, a novel ROM is developed by decomposing the entire battery thermal system into two subsystems. All solid components are modeled as a traditional LTI ROM, while the coolant channel is represented using Newton’s cooling law. The two subsystems are then coupled through the exchange of heat transfer rate and temperature at the fluid–solid interface between the coolant and the cold plate. Model fidelity is further enhanced by introducing a spatially distributed heat flux during the generation of the LTI ROM for solid components. Validation is performed against CFD simulations at both module and pack levels, under constant and varying flow rates. The results demonstrate that the proposed ROM achieves high accuracy while requiring several orders of magnitude less computational time than the corresponding CFD models.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691936</guid>
    </item>
    <item>
      <title>Predictive Modeling and Sensitivity Analysis of Thermal Runaway in 800V Lithium-Ion Battery Module Using DFSS Methodology</title>
      <link>https://trid.trb.org/View/2691911</link>
      <description><![CDATA[Thermal runaway in high-voltage lithium-ion battery modules should focus on critical safety and design challenges in electric vehicle applications, which need predictive methods that enhance passenger safety and support regulatory compliance. The primary purpose of a lithium-ion battery in an electric vehicle is to provide reliable energy storage while maintaining safe operation under different operating conditions. This study proposes a Design for Six Sigma (DFSS) methodology to virtually predict and correlate thermal runaway and its propagation in an 800V high-power lithium-ion battery pack module. Conventional propagation analysis relies heavily on physical testing, whereas the DFSS-based virtual framework enables cost-effective evaluation at early design stages. Input factors included are heat transfer pathways, which are sensitive to the temperature changes, as well as thermal propagation time. Control factors are the design or process parameters that engineers use to establish the functional performance of a system. The noise factors capture material variability and manufacturing tolerances affecting thermal properties. Output responses included the maximum cell temperature Versus time, thermal propagation time to adjacent cells, and total propagation duration across the module, measured in minutes. The validated 1D GT-SUITE model shows strong correlation with experimental data, confirming its reliability to predict thermal propagation time and supporting safer, thermally optimized battery pack designs. The validated model can be integrated into system (battery pack) level 1D thermal simulations, offering a calibrated model for future pack level propagation studies and supporting the development of safer, thermally optimized battery architectures.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691911</guid>
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