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    <title>Transport Research International Documentation (TRID)</title>
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    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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    <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>
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    <item>
      <title>Distributed Operation of Hydrogen Integrated Microgrids and Transportation System Considering Energy Sharing and Ancillary Service Market</title>
      <link>https://trid.trb.org/View/2735067</link>
      <description><![CDATA[The widespread adoption of electric vehicles (EVs) and hydrogen fuel cell electric vehicles (HVs) is tightening the interdependence between power and transportation systems (TCs), calling for better coordination between them. To address this challenge, this article proposed a distributed coordination method for the hydrogen-integrated microgrids (MGs) and transportation system. First, we introduce energy sharing among MGs, which reduces the overall system cost by 16.2%, and analyze how it improves the traffic flow. Additionally, we develop bidding models for MGs participating in joint energy and ancillary service markets, maximizing flexible resources utilization and increasing revenue by 147%. A mixed vehicle flow TS model is then established, including EVs, HVs, and gasoline vehicles (GVs). To coordinate the two individual systems efficiently, a distributed algorithm is proposed, incorporating a filtering mechanism that reduces the communication burden by 63% during the iterative process. Uncertainties and nonlinearities are handled using distributionally robust method and linearization techniques. Finally, case studies validate the effectiveness of the proposed method and highlight the mutual impact between the two systems.]]></description>
      <pubDate>Fri, 14 Aug 2026 15:05:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2735067</guid>
    </item>
    <item>
      <title>Adoption of BEV and FCEV in Transport: Evaluating progress toward emission reduction</title>
      <link>https://trid.trb.org/View/2692498</link>
      <description><![CDATA[Road transport accounts for around one quarter of the total greenhouse gas emissions in the European Union, remaining a dominant source and making the pace of electric vehicle (EV) diffusion a central determinant of climate-policy feasibility. This study evaluates recent adoption patterns of battery electric vehicles (BEVs) and fuel cell electric vehicles (FCEVs) in the EU27 and quantifies the gap between observed diffusion and policy-relevant trajectories. The methodology combines a short-term exponential growth model to capture near-term diffusion dynamics (2025–2030) and a mid-term logistic growth model to represent market saturation effects (2030–2040), complemented by a segmented techno-economic comparison (range, purchase cost and energy cost) across cars, vans, buses, and trucks. Results indicate sustained BEV dominance in light-duty segments and increasing penetration in selected heavy-duty applications, whereas FCEV uptake remains contingent on hydrogen costs and refuelling infrastructure deployment. This interdisciplinary framework offers a robust tool for policymakers and industry stakeholders to assess and correct the trajectory of road transport emissions.]]></description>
      <pubDate>Mon, 10 Aug 2026 16:51:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692498</guid>
    </item>
    <item>
      <title>A Hybrid Deep Reinforcement-Supervised Learning Framework for Energy Management of Fuel Cell-Battery Hybrid Vehicles</title>
      <link>https://trid.trb.org/View/2735072</link>
      <description><![CDATA[Hydrogen fuel cell vehicles (FCVs) typically integrate energy storage components like lithium batteries to enhance dynamic response and support regenerative braking. The energy management strategy (EMS) plays a crucial role in optimizing power distribution among power components. Although deep reinforcement learning (DRL) offers strong adaptability and optimization capabilities for EMS, its training process often demands extensive data, computational resources, and careful hyperparameter tuning, posing challenges in stability and safety. To address these limitations, this article proposes a hierarchically structured knowledge-transfer framework that synergistically fuses offline expert guidance with online reinforcement autonomy. First, a supervised behavior cloning procedure extracts optimal power distribution policies from dynamic programming (DP) trajectories, achieving hydrogen consumption minimization through direct policy imitation. A deep neural network (DNN) is then trained to replicate these rules, providing a pretrained model for online EMSs. Subsequently, a twin-delayed deep deterministic policy gradient (TD3)-based EMS is developed for real-time power allocation, incorporating hydrogen consumption, fuel cell degradation, and battery state-of-charge (SOC) fluctuations into the reward function. Finally, the pretrained DNN initializes the TD3 actor network, effectively improving training efficiency and strategy security. The results demonstrate that the proposed framework accelerates convergence, improves system performance, and ensures safer control strategies compared to conventional DRL-based methods.]]></description>
      <pubDate>Mon, 10 Aug 2026 11:16:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2735072</guid>
    </item>
    <item>
      <title>Adaptive Health-Conscious Operation of PEMFC System for Hydrogen Locomotives in Plateau Environment</title>
      <link>https://trid.trb.org/View/2735078</link>
      <description><![CDATA[To decelerate the degradation of the proton exchange membrane fuel cell (PEMFC) system for hydrogen locomotives during long-term operation in plateau environments, this article proposes an adaptive health-conscious operation strategy. First, a degradation mechanism model based on membrane electrode assembly (MEA) decay is established to accurately simulate the degradation of the PEMFC system for hydrogen locomotives in a plateau environment. The electrochemical surface area (ECSA) and membrane thickness are then selected as the key degradation indices to extract the health state of the stack. Combined with the net power of the system, a comprehensive evaluation model is constructed to capture the tradeoff relationship between the state of health (SOH) and performance output. By analyzing the operation characteristics under different altitudes and load currents, an optimal health-conscious operation region is identified. A decoupling sliding mode control (SMC) approach based on a disturbance observer is designed to reliably track this optimal operation region. Comparative experiments on a hardware-in-the-loop (HIL) platform demonstrate that the proposed strategy effectively extends the service life while maintaining high net power output of the PEMFC system for hydrogen locomotives under plateau conditions.]]></description>
      <pubDate>Fri, 07 Aug 2026 09:21:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2735078</guid>
    </item>
    <item>
      <title>Salinity and volatile organic compounds in engine room air of a sea-going vessel</title>
      <link>https://trid.trb.org/View/2701368</link>
      <description><![CDATA[Introducing polymer electrolyte membrane fuel cells (PEMFCs) in vessels is a viable way to accomplish zero-emission shipping. However, PEMFC performance can degrade due to intrusion of airborne contaminants via the cathode inlet. This study focuses on salt and the VOCs benzene, toluene and naphthalene specifically. Little to no data is available on their concentration inside engine rooms of sea-going vessels because on board measurements have not been conducted or reported. Especially for air salinity, experimental contamination concentrations might be significantly higher than the salt in sea air. Therefore, field-measurements were conducted on board of a ship in various weather conditions and at different locations on Western European sea routes. The average saline concentration was 4.3×10⁻³ μg/L with a maximum of 2.0×10⁻² μg/L inside the ship. The highest measured value is 4.2×10⁵ times lower than the average concentration applied in experimental literature. This suggests further degradation studies are needed to clarify the impact of lower, representative amounts of salt on PEMFCs performance. Benzene, toluene and naphthalene remained at least one order of magnitude below harmful concentrations and are therefore not expected to cause degradation in maritime fuel cells.]]></description>
      <pubDate>Wed, 05 Aug 2026 09:14:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2701368</guid>
    </item>
    <item>
      <title>Research on Reliability Test Methods for Electric Drive Axle
                    Differentials</title>
      <link>https://trid.trb.org/View/2742652</link>
      <description><![CDATA[With the rapid development of the global economy, issues such as the energy                     crisis and environmental pollution have become increasingly severe. Owing to                     their environmental friendliness, structural simplicity, and high energy                     efficiency, electric vehicles have attracted widespread attention. Electric                     drive technology serves as the most promising and versatile propulsion solution                     for battery electric vehicles, hybrid electric vehicles, and fuel cell vehicles.                     As an advanced mechatronic transmission system, the electric drive axle offers                     high transmission efficiency, flexible packaging, and ease of digital and active                     chassis control integration, and has thus been increasingly adopted in modern                     vehicle architectures. The differential is a key component within the electric                     drive axle, responsible for regulating the rotational speed difference between                     the left and right wheels and ensuring balanced torque distribution. It plays a                     decisive role in vehicle stability and traction performance. This study focuses                     on the reliability testing methodology for differentials in electric drive                     axles, primarily including the extraction of reliability test conditions and the                     feasibility analysis of the proposed testing scheme. Specifically, based on the                     parameters of a given electric vehicle, a Simulink model of the motor and                     differential is established, and a complete four-wheel-drive vehicle model is                     constructed. Through simulation under typical driving conditions, operational                     data of the rear-drive axle differential are obtained. The collected data are                     then preprocessed and subjected to dimensionality reduction using Principal                     Component Analysis. The selected principal components are further analyzed using                     K-means clustering to construct representative differential reliability test                     conditions. The limitations of existing testing methods are analyzed based on                     the simulated results and relevant literature. Finally, a reinforced fatigue                     testing method for the differential is designed according to the extracted test                     conditions, and the feasibility of the corresponding test bench is                     evaluated.]]></description>
      <pubDate>Mon, 03 Aug 2026 15:43:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2742652</guid>
    </item>
    <item>
      <title>Vehicle energy type preferences with hydrogen fuel cell vehicles as an alternative in electrified mobility</title>
      <link>https://trid.trb.org/View/2725266</link>
      <description><![CDATA[Hydrogen fuel cell vehicles (HFCVs) have emerged as a potential pathway for extending mobility electrification. In Beijing, where battery electric vehicles (BEVs) have expanded rapidly under long-standing acquisition and usage restrictions on internal combustion engine vehicles, HFCVs are being introduced into a mobility system shaped by widespread BEV adoption. Using stated preference data and a hybrid choice modelling framework, this study examines how vehicle ownership and related attitudes influence consumers’ future vehicle energy type choices. The results show that BEV ownership is positively associated with expectations towards HFCVs and represents an important source of behavioural heterogeneity. Scenario simulation results indicate that price-related factors, particularly purchase subsidies, increase HFCV uptake primarily through substitution from BEVs, whereas exemptions from acquisition and usage restrictions increase HFCV shares mainly by enhancing their competitiveness relative to conventional vehicles. Improved refuelling-related conditions further strengthen the competitiveness of HFCVs, particularly under the assumed phase-out scenarios for internal combustion engine vehicles.]]></description>
      <pubDate>Fri, 31 Jul 2026 15:16:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2725266</guid>
    </item>
    <item>
      <title>A Health-Driven Integrated Optimization of Energy Management and Component Sizing for Fuel Cell Hybrid Electric Vehicles</title>
      <link>https://trid.trb.org/View/2731473</link>
      <description><![CDATA[Optimized algorithms for component sizing and energy management are pivotal in reducing energy consumption, operating cost and extending fuel cell (FC) lifespan in FC hybrid electric vehicles (FCHEVs). These interdependent challenges are of significant research interest. Therefore, this paper proposes an integrated approach to optimize energy management control and system design for an FCHEV, featuring a battery-supercapacitor hybrid energy storage system (HESS), by employing the nondominated sorting genetic algorithm III (NSGA-III). The optimization process targets maximizing driving range and FC lifespan, while concurrently minimizing hydrogen consumption, HESS size, and overall lifetime costs. The health-driven fuzzy logic-based energy management strategy (HFBEMS) is proposed to allocates power demand between the FC and HESS effectively by considering FCHEV operating cost optimization and the thermal constraints for battery safety. The optimized FCHEV configuration achieved a hydrogen consumption rate of 0.58 kg/100 km and driving range of 411.4 km for US06 test driving conditions, while maintaining an optimal trade-off among all objectives. Furthermore, this optimized HFBEMS proved resilient under real-world driving conditions like US06 and UDDS, while ensures compliance with operational constraints, including FC power ramp rate limits, SOC limits, and thermal safety constraints, minimizing FC and battery degradation. A comparative analysis of various EMS revealed that the proposed HFBEMS exhibit notable superiority in minimizing hydrogen fuel consumption, achieving reductions of approximately 38.30% and 25.46%, when compared to the conventional PI control and frequency-decoupling fuzzy-based control, respectively. Consequently, an optimized HFBEMS, due to its rule-based structure and minimal execution complexity, is suitable for real-time integration within a vehicle, and outperforms conventional methods by optimally balancing key performance criteria.  All rights reserved.]]></description>
      <pubDate>Thu, 30 Jul 2026 10:07:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2731473</guid>
    </item>
    <item>
      <title>Hybrid thermal point mass network approximated/electrochemical cell simulation model approach for simulation aided testing on thermal management cell level of BEV, FCEV and hybrid ICE architectures</title>
      <link>https://trid.trb.org/View/2727344</link>
      <description><![CDATA[This paper deals with the design of a thermal network for simulating the behaviour of individual cells, both autonomously and in combination. It concludes an evaluation of a suitable overall simulation methodology at the electrochemical-thermal level and is based on a series of findings and results from previous in-depth research in the field of cell electrochemistry. Another focus is on the development of a cycle environment that makes it possible to simulate real cell behaviour on the test bench. This article will address the abstraction of the individual cell layers into a volume represented by thermal masses, as well as its parametrization and structure within the simulation methodology. The greatest effort in creating and parametrizing the cell as a thermal network result from the need to make it completely variable in order to meet user requirements. At this stage of the simulation setup, the aim was to move beyond the stand-alone cell level and consider subunits in the form of module or pack arrangements. Accordingly, in addition to electrochemical simulation using the electrochemical model (ECM), thermal network simulation using the thermal network model (TNM) is also adapted. One challenge was the parametrization of the transition layer between the individual cell shells and the (cooling) environment within the module or pack arrangements. As part of the overall simulation methodology, validation was performed at the single-cell level to compare the results of the surface temperature distribution as well as the current and voltage levels occurring during operation with measurements of corresponding cells on the test bench.]]></description>
      <pubDate>Wed, 29 Jul 2026 09:14:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2727344</guid>
    </item>
    <item>
      <title>A Model-Based, Iterative Framework for Developing Reconfigurable Validation Environments for Proton Exchange Membrane Fuel Cell (PEMFC) Systems in Mobile Applications</title>
      <link>https://trid.trb.org/View/2724732</link>
      <description><![CDATA[Fuel cell electric vehicles are described on cell, stack and system levels. In driving operation, multi-physics coupling across subsystems (reactant supply, humidification, thermal management, etc.) reshapes cell- and stack-level boundary conditions, impacting performance and degradation mechanisms. Isolated single-topic approaches on one specific level may have limited transferability, as cross-level interdependencies under changing operating conditions can negate improvements or shift limiting factors. This underscores the development of validation environments (VEs) that represent cross-level interactions and evolve as experimental evidence redirects research questions.Models such as the V-Model provide phase-oriented logic for developing VEs when validation scope, boundary conditions and acceptance criteria can be specified upfront and remain stable. However, in PEMFC VE development, experimental conclusions frequently reshape hypotheses, operating conditions and research topics across successive cycles. Consequently, existing approaches often provide limited methodological support for a traceable and repeatable evolution of VEs where iterative reconfiguration is essential.To address this need, we developed the Development Model of the Validation Environment (eMVU, German for Entwicklungsmodell der Validierungsumgebung) for PEMFC technology to enable a structured, model-based and iterative evolution of VEs. Embedded in the system triple of product engineering, the eMVU guides the iterative transformation of objectives into validation configurations (VCs) through model-based derivation of boundary conditions, test requirements and extension measures. It structures each development cycle into the five phases design, specification, implementation and commissioning, experiments and results processing, as well as derivation of measures with feedback of the resulting insights into the objectives of the subsequent cycle.The framework is demonstrated by realizing a fully functional baseline VE and deriving an additional VC enabling semi-automated operation across eMVU cycles. Their implementation and operation provide experimental evidence that both the eMVU and the resulting VE enable traceable, repeatable and targeted assessment of cross-level interdependencies with measurable impact on cell and stack behavior.]]></description>
      <pubDate>Tue, 28 Jul 2026 12:27:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2724732</guid>
    </item>
    <item>
      <title>Analysis of Running Properties of a Rail Multiple-Unit with a Diesel and a Hydrogen Powertrain</title>
      <link>https://trid.trb.org/View/2579414</link>
      <description><![CDATA[This research is focused on the assessment of running properties of a rail multiple unit (MU), which an original diesel powertrain has been changed by a hydrogen powertrain. There are analysed the output quantities in a wheel/rail contact and their influence to running safety. A rail MU is evaluated, which is produced by a commercial manufactured and with newly designed powertrain includes hydrogen fuel cells. This modification of the solved MU means a significant change of its structure. The vehicle itself consists of three articles. The changes mainly related with the position of a centre of gravity of individual articles, their masses as well as moments of inertia. The research has been conducted in a commercial multibody software Simpack. A multibody model of the solved MU consists of rigid bodies and other modelling elements. A real railway track section in Slovakia has been chosen for simulation computations. Based on the findings and obtained results, the modified MU equipped with the hydrogen powertrain operation is within the valid safety criteria. However, the reached axleload of this vehicle does not meet the required values.]]></description>
      <pubDate>Mon, 27 Jul 2026 11:16:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579414</guid>
    </item>
    <item>
      <title>Design of Finite Time Sliding Mode Control for Stable DC Bus and Current Tracking in a Multisource Hybrid Electric Vehicle</title>
      <link>https://trid.trb.org/View/2714089</link>
      <description><![CDATA[The increasing demand for clean and efficient transportation has shifted research focus towards hybrid electric vehicles. This study focuses on designing a nonlinear controller for a multisource hybrid electric vehicle comprising a fuel cell, battery, supercapacitor, and photovoltaic panels. DC converters act as an interface between sources and the DC bus. An FTSMC controller is designed and developed to ensure accurate current tracking for all sources and effective regulation of the DC bus voltage. The stability of the proposed controller is verified using Lyapunov stability analysis, and its performance is assessed using MATLAB/Simulink. The EUDC cycle has been used as a load profile for the proposed HEV. The results demonstrate fast and robust control performance under varying operating conditions, confirming the controller’s effectiveness for multisource HEV applications.]]></description>
      <pubDate>Mon, 27 Jul 2026 11:16:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2714089</guid>
    </item>
    <item>
      <title>High-Performance DC-Link Voltage Regulation in Fuel-Cell Hybrid Systems Using Adaptive Observer-Based Control</title>
      <link>https://trid.trb.org/View/2714075</link>
      <description><![CDATA[This paper presents a robust converter-level control strategy for a fuel-cell hybrid electric vehicle (FCEV) integrating a fuel cell, battery, and ultracapacitor through a common DC-link. An Adaptive Disturbance–Observer–Based Sliding Mode Controller (ADOB–SMC) is proposed to achieve precise DC-bus voltage regulation and fuel-cell current tracking under multiple source-side uncertainties. The controller combines real-time disturbance estimation with a continuous super-twisting sliding-mode law, effectively suppressing chattering while ensuring fast and stable convergence. A comprehensive nonlinear model of the hybrid powertrain was developed, and the control law was designed based on Lyapunov stability theory to guarantee global bounded-ness and robustness. Simulation studies were performed in MATLAB/Simulink under varying voltage disturbances of the battery, ultracapacitor, and fuel cell. The results confirm that the proposed controller maintains the DC-link voltage at 400 V with negligible steady-state error and rapid error convergence, even under combined source-voltage variations. The fuel-cell current adapts smoothly to load dynamics, ensuring coordinated energy sharing among all sources. Overall, the ADOB–SMC provides a highly effective and computationally feasible solution for stable and efficient operation of next-generation hybrid fuel-cell powertrains.]]></description>
      <pubDate>Mon, 27 Jul 2026 11:16:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2714075</guid>
    </item>
    <item>
      <title>Transformer Enhanced Multi Agent Reinforcement Learning for Joint EV and Hydrogen Charging Infrastructure Planning</title>
      <link>https://trid.trb.org/View/2713953</link>
      <description><![CDATA[The rapid expansion of electric and hydrogen powered vehicles is reshaping urban mobility, but it introduces new challenges for charging and refueling infrastructure planning, grid stability, and low carbon energy utilization. This paper proposes a transformer enhanced multi agent reinforcement learning framework to coordinate heterogeneous actors, including electric vehicles, hydrogen trucks, charging stations, and grid operators, under a centralized training and decentralized execution paradigm. Agents learn policies through a joint objective that balances grid stress mitigation, equitable access, and emission reduction while respecting station and feeder constraints. A proof of concept simulation is evaluated on a smart city setting with 10,000 EVs, 2,000 hydrogen trucks, and 150 stations under peak demand surges, renewable intermittency, and station outage conditions. Compared to a MILP scheduler and a greedy decentralized scheduling baseline, the proposed approach reduces peak to average grid load ratio by 23% and 37%, respectively. It also lowers average waiting time by 18% versus MILP and 41% versus greedy scheduling, and improves fairness by 26%. When aligned with renewable availability windows, the framework achieves a 15% reduction in CO₂ emissions. Under outages affecting 10% of stations, it restores stable operation in 15 steps, compared to 34 for MILP and more than 50 for greedy scheduling. These results indicate a scalable and adaptive solution for sustainable smart city charging ecosystems.]]></description>
      <pubDate>Mon, 27 Jul 2026 11:16:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2713953</guid>
    </item>
    <item>
      <title>Exploring the Potential of Renewable Energy for Sustainable Mobility: A Simulation- Based study of Hydrogen Vehicle Penetration in Oman’s Road Network</title>
      <link>https://trid.trb.org/View/2713813</link>
      <description><![CDATA[Hydrogen fuel is gaining attention as a promising zero-emission energy source, aligning with global sustainability goals and supporting the transition to zero carbon emissions. This study examines the potential of using hydrogen as an alternative fuel for sustainable mobility in Muscat, Oman. We developed an integrated modelling framework that combines microscopic traffic simulation, energy demand modeling, refueling infrastructure station’ estimation, and well-to-wheel (WTW) emissions evaluation. A microscopic simulation software (SUMO) was applied to evaluate the penetration rate of hydrogen-powered vehicles (0%, 20%, 40%, 60%) with different hydrogen production pathways. Results indicate that with a 60% penetration of green hydrogen, total greenhouse gas (GHE) emissions can be reduced by nearly 95%. Blue hydrogen achieved intermediate benefits by about 60% reduction, whereas grey hydrogen increased total emissions due to fossil- based feedstock. Moreover, cumulative hydrogen consumption increases nonlinearly with the hydrogen vehicle penetration level. The required number of refuelling stations increases exponentially due to quantity and temporal demand. These findings provide decision-makers with quantitative perception into hydrogen adoption, infrastructure requirements, and emissions mitigation pathways.]]></description>
      <pubDate>Mon, 27 Jul 2026 11:16:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2713813</guid>
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