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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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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Mathematical Modeling and Genetic Algorithm-Based Energy Management in Hydrogen PEM Fuel Cell Electric Vehicles</title>
      <link>https://trid.trb.org/View/2712084</link>
      <description><![CDATA[Hydrogen Fuel Cell Electric Vehicles (FCEVs) represent a significant trajectory in vehicular decarbonization, harnessing the inherently high energy density of diatomic hydrogen within electrochemical conversion systems. When sourced via renewable pathways, such hydrogen facilitates propulsion architectures characterized by zero tailpipe emissions, enhanced energy efficiency, and extended operational range profiles. Realizing peak systemic efficacy necessitates the synergistic orchestration of high-fidelity fuel cell stack design, resilient compressed gas storage modalities, and nuanced energy governance protocols. To reduce transient stressors and guarantee long-term electrochemical stability, employing multi-scale modeling and predictive simulation, combined with constraint-aware architectural synthesis, is crucial in handling stochastic driving conditions spectra.This study develops a high-fidelity mathematical plant model of a hydrogen Proton Exchange Membrane (PEM) fuel cell vehicle and implements advanced Energy Management Strategies (EMS). The FCEV plant model is developed with the forward approach method, taking into account the power limitations of the power plant. A PEM fuel cell system is accurately and in detail modeled, representing voltage loss mechanisms. The performance of the mathematical model was calibrated with the experimental results with an error margin of 8-10%. Whereas, a permanent magnet synchronous motor is modeled mathematically along with a Field-Oriented Controller (FoC) for ensuring precise torque regulation.Energy Management Strategies (EMS) optimize fuel cell and battery coordination to boost vehicle performance and efficiency. Online EMS adapts control using real-time data, while offline EMS applies machine learning to past driving patterns for predictive energy allocation. In this study, a Genetic Algorithm (GA)-based EMS, which is one of the types of offline EMS, is implemented to enhance fuel economy, dynamic performance, and component-level energy usage. Compared to non-optimized operation, the GA approach offers improved power split efficiency, 9-12% improvement in hydrogen consumption, resulting in lower energy consumption and enhanced overall vehicle performance.This work improves PEM FCEV technology through better design, simulation, and optimization methods, laying a solid foundation for future advancements in sustainable and efficient transportation.]]></description>
      <pubDate>Wed, 10 Jun 2026 17:05:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2712084</guid>
    </item>
    <item>
      <title>Electrical and Copper Compatibility Studies of Transmission
     Lubricants for Advanced Electric Vehicles</title>
      <link>https://trid.trb.org/View/2712075</link>
      <description><![CDATA[Improved energy efficiency and lower CO2 emissions are the two major                     drivers for the emergence of E-mobility. Growth of electric vehicles (EVs) has                     sustained ever since their introduction till 2020 and has substantially                     increased thereafter. EVs require specialized lubricants, which are different                     from conventional lubricants mainly due to the addition of new hardware                     technology including e-motor, inverter, battery, and new materials (copper                     windings, elastomers, plastic, and other materials). Lubricant when used in an                     advanced powertrain electric vehicle specifically in E-powertrains may encounter                     the e-motor and must deliver unique performance attributes such as optimal                     electrical properties, thermal management, and material compatibility apart from                     the traditional features including extreme pressure, friction performance,                     oxidation, and wear control. In the current study, we have investigated                     conventional GL5, manual transmission fluid (MTF), automatic transmission fluid                     (ATF), and dedicated e-fluids to understand additive and viscosity effects on                     aforesaid performance traits. Our study emphasized that additive chemistry plays                     a significant role on key properties such as electrical properties, corrosion                     resistance, oxidation resistance, and tribological performance.]]></description>
      <pubDate>Wed, 10 Jun 2026 17:04:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2712075</guid>
    </item>
    <item>
      <title>Adaptive HVAC Strategies for Enhanced Cabin Thermal Comfort, Air Quality and Energy Efficiency</title>
      <link>https://trid.trb.org/View/2712089</link>
      <description><![CDATA[Passenger comfort within vehicles and aerospace cabins relies on finely tuned management of temperature, air quality, and energy use. This paper proposes an integrated HVAC framework that combines zonal climate control, intelligent airflow distribution, and real-time sensor data to maintain thermal balance across different cabin zones. Leveraging predictive thermal load modelling and machine learning, the system anticipates environmental changes—such as sudden shifts in external temperature or passenger load—and proactively adjusts heating and cooling outputs. Simultaneously, air quality is enhanced through a multistage filtration system, active air purification technologies, and dynamic CO₂ concentration monitoring. Comfort assessment integrates PMV (Predicted Mean Vote) and PPD (Predicted Percentage Dissatisfied) indices to adapting environmental conditions. Simulations and early-stage prototypes improve energy savings and improve occupant comfort and air quality. The proposed HVAC approach is a promising avenue for enhancing passenger experience and operational efficiency in both ground and air mobility platforms.]]></description>
      <pubDate>Wed, 10 Jun 2026 13:20:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2712089</guid>
    </item>
    <item>
      <title>Transitioning Towards Green Port Operations: A State-of-Practice Approach on Emerging “Green” Technologies</title>
      <link>https://trid.trb.org/View/2581597</link>
      <description><![CDATA[This paper aims at providing a comprehensive review of the latest technologies developed within the context of EU funded projects, for the purpose of contributing to zero emission and carbon-neutral port operations. To identify the latest developments in (a) technologies that enhance educated decision making; (b) platforms ensuring seamless communication; (c) technologies dealing with physical processes automation and (d) technologies addressing energy management & greenhouse gas (GHG) emissions reduction, the present review and evaluation incorporates a critical up-to-date analysis of the current leading-edge products and services in port operations. These advancements are identified in recent and upcoming EU funded projects and are described in trade journals and port-related industry sources of information. The impact of the developed technologies and solutions on the greening of operations and broadly on the port sector are demonstrated in an effort to highlight the rising technological tendencies. The present review also attempts to align these findings with the implementation of the key “green” technologies in ports, offering a state-of-practice approach.]]></description>
      <pubDate>Fri, 05 Jun 2026 16:39:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2581597</guid>
    </item>
    <item>
      <title>Development and Implementation of (More) Sustainable and Resilient Electric Vehicle Charging Infrastructure in Public Buildings</title>
      <link>https://trid.trb.org/View/2581591</link>
      <description><![CDATA[The PROBONO project has as main objective to produce validated solutions for the design, construction and operation of zero-emission and positive-energy buildings in sustainable green neighbourhoods through targeted interventions in six different Living Labs (LLs) (Madrid, Dublin, Porto, Brussels, Aarhus, and Prague). In the Dublin LL, energy efficiency is addressed from multiple perspectives, being one of them to deploy a sustainable mobility infrastructure perfectly integrated with the buildings’ power grid that is able to optimize demand and supply of energy in order to maximize renewable energy use and reduce overall energy consumption. To this end, several technologies will be put in place: bi-directional chargers with vehicle-to-grid (V2G) capabilities to allow the electric vehicle (EV) fleet of the LL to be charged in the most flexible way possible, deployment of alternative charging solutions such as battery swapping for the e-bike fleet or inductive charging for the vehicles, and second life battery banks to help minimize demand peaks during the day. All these features of the infrastructure will be managed by a secure software platform that enables optimal energy use while reducing the total cost of ownership of the charging infrastructure.]]></description>
      <pubDate>Fri, 05 Jun 2026 16:39:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2581591</guid>
    </item>
    <item>
      <title>Does Eco-Routing Even Work? Some Experimental Findings</title>
      <link>https://trid.trb.org/View/2581580</link>
      <description><![CDATA[We live in an ever-changing world. The increasing urbanization rate has resulted, among others, in an increase in private vehicles ownership and usage. The research focuses on eco-driving and its potential to reduce greenhouse gas emissions in urban areas with high private vehicle usage. The proposed framework, called FERPS (Fuel Efficient Route Planning System) uses unsupervised machine learning techniques to categorize driving behavior into three trip-based profiles. A fuel consumption model is then employed using Gradient Boosting Decision Trees algorithm to estimate fuel consumption for upcoming trips based on dynamic driving profiles, vehicle data, and trip characteristics. FERPS is then implemented in in the inner-ring urban transport network of Athens, Greece, using the SUMO microscopic simulator. Eleven scenarios, including a business-as-usual (BAU) scenario and various FERPS penetration rates, are simulated during the morning peak hour. Emissions-related KPIs are measured for comparison and the results indicate that higher FERPS penetration rates lead to reduced emissions, highlighting the potential benefits of eco-driving in urban transport networks. By providing personalized eco-routing information, FERPS aims to promote environmentally friendly driving behavior and contribute to overall emission reduction efforts.]]></description>
      <pubDate>Fri, 05 Jun 2026 16:39:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2581580</guid>
    </item>
    <item>
      <title>Performance Assessment of an Advanced Hybrid System Between SOFC and ICE to be Applied Onboard a Short-Distance Ferry</title>
      <link>https://trid.trb.org/View/2581578</link>
      <description><![CDATA[It is imperative to research innovative energy systems to maximize energy efficiency and decarbonize the maritime sector because the International Maritime Organization has set two milestones in 2030 and 2040 to reduce greenhouse gas emissions with an ultimate decarbonization target of zero emissions by 2050. This paper investigates an advanced integration between a solid oxide fuel cell (SOFC) and an internal combustion engine (ICE) targeting a passenger ferry operating on short-sea navigation as a case study with a rated power of 750 kW. The paper aims to model the hybrid system by using dedicated in-house software developed by the authors’ research group to assess the system performance by exploring the system efficiency, fuel consumption, and carbon dioxide (CO₂) emissions and conducting a sensitivity analysis for operating parameters. The results show that an efficiency improvement of 12% over the marine gas engine, with 32.4% fuel savings, and 29.7% CO₂ emissions savings, is possible by maintaining the current density at 5000 A/m2, fuel utilization at 80% and using a 50–50 power split between SOFC and ICE.]]></description>
      <pubDate>Fri, 05 Jun 2026 16:39:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2581578</guid>
    </item>
    <item>
      <title>Development and Validation of a Self-Propelled Trailer: A Standards-Based Approach to Enhancing Compact Vehicle Towing</title>
      <link>https://trid.trb.org/View/2691854</link>
      <description><![CDATA[This paper presents a structured test plan for the development and validation of a Self-Propelled Trailer (SPT), an emerging concept designed to enhance the towing capacity of compact, fuel-efficient vehicles. Unlike conventional trailers, the proposed system integrates electric propulsion and autonomous sensing to actively assist the towing vehicle, reducing engine load and improving both safety and fuel economy. The methodology employs a Design Failure Mode and Effects Analysis (DFMEA) to systematically identify potential risks, while incorporating Society of Automotive Engineers (SAE) standards to guide environmental durability testing (dust, water ingress, gravel impact) and dynamic performance evaluations (gradeability, braking, and stability). A comprehensive set of test procedures is outlined to validate system reliability, robustness, and compliance with established towing requirements. The study demonstrates how powered trailer technology can extend the practical use of compact vehicles for heavier load applications without compromising efficiency or emissions targets. This work contributes to the advancement of autonomous trailer systems and provides a foundation for future prototype development, testing, and eventual deployment.]]></description>
      <pubDate>Wed, 03 Jun 2026 09:07:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691854</guid>
    </item>
    <item>
      <title>Research on Performance Prediction Methods for Centrifugal Refrigeration Compressors in Data Centers</title>
      <link>https://trid.trb.org/View/2706249</link>
      <description><![CDATA[As the “digital brain” and core foundational support for the development of intelligent transportation and connected vehicles, the performance of data centers directly determines the operational capability of intelligent transportation systems. In the process of advancing the vehicle-road-cloud collaborative architecture, the demand for high-performance computing power in data centers has experienced explosive growth. The substantial increase in computing tasks has posed severe challenges to thermal management, making efficient and reliable cooling systems an indispensable core component. Centrifugal compressor water-cooling units are the mainstream cooling solution for large-capacity scenarios, and their design optimization is crucial for improving the energy efficiency and performance of the entire cooling system. This paper proposes a one-dimensional performance prediction method for centrifugal compressors based on an empirical loss model, and realizes the iterative calculation of parameters in the entire flow path from the impeller inlet to the diffuser outlet through Python programming. A systematic impact assessment was carried out for major loss mechanisms such as surface friction, tip clearance, and wake mixing under standard operating conditions and critical operating conditions. The results show that the original model has high prediction accuracy under standard operating conditions, with isentropic efficiency error not exceeding 5%; however, under critical operating conditions, the efficiency prediction deviation reaches 7.54% due to the neglect of coupling effects between various losses. To address this issue, this paper introduces deviation correction factors related to flow rate, rotational speed, and density, which significantly improve the model’s prediction capability under extreme operating conditions: the efficiency error under critical operating conditions is reduced to 1.54%, and only 0.3% under rated operating conditions. This model provides a reliable tool for compressor performance prediction and extreme operating boundary identification, and has high application value in engineering practice.]]></description>
      <pubDate>Tue, 02 Jun 2026 11:12:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2706249</guid>
    </item>
    <item>
      <title>An Energy-Saving Coupling-Decoupling Optimization Strategy for Electric Modular Buses Considering Vehicle to Vehicle Charging</title>
      <link>https://trid.trb.org/View/2706244</link>
      <description><![CDATA[As an emerging innovative mode of public transportation, electric modular buses (EMBs) offer a novel solution to the problems of existing public transportation systems, due to the coupling-decoupling processes. In this paper, we study the energy consumption characteristics of EMBs by joining vehicle-to-vehicle (V2V) charging and reduction in aerodynamic drag due to coupling. For the pursuit of energy economy, ride comfort, and operational efficiency, we constructed an optimization scheme based on the simulated annealing (SA) algorithm to facilitate the coupling-decoupling process. The simulation results show that EMBs can meet 82.5 % of service requests compared with 61.8 % for the benchmark group, and V2V presents a significant contribution to energy efficiency, especially at low battery state of charge (SOC). Additionally, sensitivity analysis is conducted to study the impact of initial SOC, operation interval, and route type. The results provide insights for optimizing EMBs’ operations and emphasize the potential role of EMBs in supporting low-carbon and sustainable urban mobility systems.]]></description>
      <pubDate>Tue, 02 Jun 2026 11:12:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2706244</guid>
    </item>
    <item>
      <title>Research on Waste Heat Recovery Characteristics of Electric Drive System in Hybrid Electric Light-Duty Trucks</title>
      <link>https://trid.trb.org/View/2706212</link>
      <description><![CDATA[Aimed at the high energy consumption for battery heating of a light hybrid truck in low-temperature winter, this paper proposes an optimized battery thermal management scheme based on motor waste heat and PTC cooperation. Then it verifies its energy-saving performance based on multi-condition simulation and testing. Taking the constant-speed condition at -5°C as an example, firstly, the accuracy of the battery thermal management model is verified by comparative simulation and test. Then, based on the verified model, the battery thermal management model is simulated under typical winter conditions at 0°C and 5°C. The analysis results show that, when the battery temperature is raised from the initial state to a certain target, the energy consumption of the motor waste heat-assisted PTC heating scheme is obviously less than that of PTC heating. The energy saving rates are 33.137% at -5°C, 32.45% at 0°C, and 32.56% at 5°C, respectively. The research results have proved that the effective utilization of motor waste heat can reduce PTC energy consumption.]]></description>
      <pubDate>Tue, 02 Jun 2026 11:12:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2706212</guid>
    </item>
    <item>
      <title>Energy Saving and Personalized Thermal Comfort Control Based on
          Reinforcement Learning and Decision Tree</title>
      <link>https://trid.trb.org/View/2706245</link>
      <description><![CDATA[Indoor thermal comfort is closely related to people’s health and work efficiency.                     Control systems typically consume a large amount of energy to maintain a                     comfortable thermal environment. Currently, reinforcement learning is widely                     applied to optimize thermal comfort control systems. However, existing research                     mainly adopts universal thermal comfort evaluation models that aim to satisfy                     the majority of people, which makes it difficult to quickly and accurately                     reflect the specific thermal comfort needs of individuals. As a result, the hot                     environment is neither comfortable nor energy-efficient in practical use.                     Therefore, this paper proposes an energy-saving personalized thermal comfort                     control method based on decision trees and reinforcement learning. First,                     decision tree learning is used to obtain an individual thermal comfort                     evaluation model from a small amount of historical data. Then, this individual                     comfort model is combined with energy consumption to form a reward function,                     which is used in reinforcement learning to derive personalized thermal comfort                     control strategies. The experiments show that, compared to traditional methods,                     this approach can improve user thermal comfort by 43.8% and achieve an                     energy-saving effect of 30.7%.]]></description>
      <pubDate>Tue, 02 Jun 2026 11:09:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2706245</guid>
    </item>
    <item>
      <title>Decarbonizing Mobility in Minor Cities in Finland</title>
      <link>https://trid.trb.org/View/2580099</link>
      <description><![CDATA[Smaller cities struggle to achieve carbon-neutrality goals. In Finland, the goal is to reach carbon neutrality by 2035, which means that by and large the smaller cities need to address this target. Several measures to cut carbon emissions have been analyzed in different cities, but it seems that in most cases the reductions in the mobility sector are far behind the ambitious goals, and hence jeopardize the reaching of the 2035 milestone. However, many other improvements are possible regarding accessibility, equity, health, and affordability of urban mobility in smaller cities that have traditionally relied very heavily on private car use. It seems that one of the keys is the electrification of mobility while shifting energy production from fossil energy to cleaner forms, such as wind, hydro, and solar power in the future but more effective land/use policy in the long term.]]></description>
      <pubDate>Fri, 29 May 2026 15:36:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2580099</guid>
    </item>
    <item>
      <title>Application of Topology Optimization to Production-Ready Passenger Seat Components Design</title>
      <link>https://trid.trb.org/View/2692117</link>
      <description><![CDATA[Lightweighting of components has become a key challenge in the development of modern transportation systems. In the automotive and aerospace industries, the overall mass of a vehicle has a significant impact on its fuel efficiency and manufacturing cost. Therefore, the lightweight design of vehicle components is crucial in the industrial field. Topology optimization (TO) is a computational design approach aimed at achieving lightweight designs. However, most existing studies focus on simplified academic models, with limited demonstration in real-world applications. This paper presents a revised TO workflow to obtain production-ready design and a practical implementation of TO in the design of three structural components in the aerospace industry: seatback frame, seat fuselage mount, and seat spreader. The revised TO workflow incorporates the practical demands of industry, including enhanced manufacturability and cost efficiency through TO design. The resulting designs are evaluated to ensure all regulatory requirements are satisfied. Comparative results show that the designs produced by the presented TO-based design method achieve a significant weight reduction of 50% for the seatback frame. For the seat fuselage mount and seat spreader, the proposed method produced designs with a weight comparable to the baseline while satisfying stricter crashworthiness requirements. These components also ensure manufacturability, efficient fabrication cost, and compatibility with family parts. These findings demonstrate that TO can deliver production-ready solutions without compromising structural performance. The study highlights the potential of integrating TO into a revised design workflow to support performance-driven development of complex, production-ready industrial components.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692117</guid>
    </item>
    <item>
      <title>Optimization Method for Vehicle Shape to Drastically Reduce Aerodynamic Drag in Heavy-Duty Vehicles</title>
      <link>https://trid.trb.org/View/2692051</link>
      <description><![CDATA[Since air drag is proportional to the square of the speed, it is expected that reducing air drag will significantly improve fuel efficiency for on-highway trucks and buses, which are often driven at high speeds. Therefore, the purpose of this study is to propose an optimization method for vehicle shape to drastically reduce aerodynamic drag in heavy-duty vehicles. Using NSGA-II, one of a genetic algorithm, the overall vehicle shape was optimized with drag coefficient (CD) and lift coefficient (CL) values as objective functions and design variables as parameters in a total of 13 locations. Among the Pareto solutions, an 86% reduction in CD was achieved compared to the base shape when the CD value was the lowest. Since the CL value remains low with this shape, it can be seen that driving stability does not deteriorate. Among the design variables in optimization, it was confirmed that the corner radius of the vehicle side was particularly effective in reducing the CD value. In addition, when optimizing only the cab shape, the optimal value for the front virtual angle was 30 deg., but in relation to the corner radius, the value for this optimized shape was around 40 deg. The CD value of a 1/20 model of the optimized shape was measured in a wind tunnel test and compared with the optimization results from the aforementioned numerical analysis. As a result, the CD value reduction effect of the optimization shape was confirmed in the wind tunnel test as well, demonstrating the validity of the optimization results described above. In addition, an investigation into the yaw angle dependency of the optimized shape revealed that adding a yaw angle provided a sailing effect that reduces CD. A program for calculating fuel consumption rates for heavy-duty vehicles was used to compare fuel efficiency when using the base shape and the optimization shape. The weighted average fuel economy in urban driving (JE05) and interurban driving (highway) modes was improved by approximately 21% using the optimization shape.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692051</guid>
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