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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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    <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>Temperature Effects on Electric and Hybrid Vehicle Efficiency</title>
      <link>https://trid.trb.org/View/2701102</link>
      <description><![CDATA[This research evaluates the powertrain efficiency of select battery electric vehicles (BEVs) and hybrid electric vehicles (HEVs) tested at ambient temperatures of 20°F, 75°F, and 95°F using an AVL emissions test cell chassis dynamometer. BEV test vehicles include the Chevrolet Equinox EV, Ford Mustang Mach-E, and Tesla Model Y. HEV test vehicles include the Toyota Prius, Honda CR-V Hybrid, and Hyundai Tucson Hybrid. The objective of this study is to quantify temperature-related efficiency changes and assess whether hybrid powertrains mitigate efficiency losses more effectively compared to fully electric vehicles under hot and cold environmental conditions. The results indicate that hot and cold ambient temperatures—most notably cold conditions—substantially increase energy demand due to reduced battery discharge efficiency and elevated thermal management and cabin conditioning loads. The findings underscore the importance of incorporating seasonal and Heating, Ventilation, and Air Conditioning (HVAC)-related energy impacts into range planning and performance assessments, as real-world operating range can deviate significantly from Environmental Protection Agency (EPA)-rated values under non-ideal environmental conditions. The results of this study are intended to provide consumers, policymakers, and automotive stakeholders with objective data regarding electrified vehicle performance in cold and hot weather operation. The cost comparison between operating a BEV and an HEV provides a quantitative basis for informing consumers about which powertrain configuration may be most appropriate for their specific usage patterns and operating conditions.]]></description>
      <pubDate>Thu, 28 May 2026 16:15:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2701102</guid>
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    <item>
      <title>Assessment of an Integrated Cooling/HVAC Circuit for Electric Heavy Quadricycles</title>
      <link>https://trid.trb.org/View/2581595</link>
      <description><![CDATA[Within the last decades, an always increasing attention has been addressed to the development and market diffusion of alternative powertrains, either hybrid or fully electric. Especially for electric powertrains some open points are nowadays still present with respect to thermal management and cabin comfort, which are intended to be addressed in the present study. This is the reason why the European Commission is striving the research towards the development of innovative and efficient electric powertrains. Within this framework, the REFLECTIVE project aims at developing an electric heavy quadricycle equipped with a HVAC (heating ventilation air conditioning) module integrated with the powertrain/charging cooling system, with the aim of reusing part of the heat generated at the powertrain during driving conditions to heat up the cabin, with the consequence of reducing the thermal power requested at the electric cabin heater to fulfil this task. Although an additional heat exchanger is required, it is possible to guarantee a certain amount of heat preventing the use of the battery for the electrical heater activation. Moreover, the battery thermal management as well can be done also using hot/fresh air generated by the HVAC sub-system. By this way, several thermal loads can be managed through the same integrated circuit with apparent benefit in terms of energetic efficiency, despite some complexity is introduced. The aim of this paper is to assess the effectiveness of this solution on the vehicle range and battery state of charge, through the integration of a 1-D model of the cooling circuit with a 0-D model of the entire vehicle. Different driving conditions, namely summer and winter scenarios and different speed profiles, will be considered. Results show that the HVAC and cooling systems have a huge effect on range reduction with respect to the range estimated, but at the same time, but the benefit of the recuperator can be also assessed.]]></description>
      <pubDate>Mon, 18 May 2026 11:01:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2581595</guid>
    </item>
    <item>
      <title>A BIM-Based Approach for Smart HVAC Design in Cruise Shipbuilding: Automation and Simulation Insights</title>
      <link>https://trid.trb.org/View/2693037</link>
      <description><![CDATA[The increasing complexity of shipboard systems and the growing demand for digital integration in marine design have accelerated the need for innovative, model-based engineering solutions. This paper presents a novel methodology for the detailed design of Heating, Ventilation and Air Conditioning systems in cruise vessels, leveraging Building Information Modelling and parametric modelling within Autodesk® Revit MEP. The approach addresses key limitations of traditional manual workflows by enabling automated duct weight estimation and equipment list generation directly from the model environment. A case study involving two representative shipboard zones—a cabin deck and a galley—was used to compare the proposed methodology with conventional practices. The BIM-based approach demonstrated substantial improvements in efficiency, accuracy, and data reliability. By embedding engineering logic and functional metadata in a 3D parametric model, the methodology supports real-time updates, parallel task execution, and alignment with the project’s Work Breakdown Structure, enhancing information flow between engineering and production. The system is structured to interface with simulation platforms and immersive technologies, facilitating virtual prototyping and laying groundwork for future digital twin integration. The results highlight the method’s potential to drive digital transformation in marine HVAC design.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2693037</guid>
    </item>
    <item>
      <title>Efficient Air Conditioning of Battery-Electric Multiple Units (BEMU): Modeling and Optimization</title>
      <link>https://trid.trb.org/View/2580004</link>
      <description><![CDATA[The power required for heating, ventilation and air-conditioning (HVAC) of the passenger compartment in battery-electric multiple units (BEMU) strongly affects the capacity and lifetime of the battery. Accordingly, 11 different measures for reducing the energy demand of HVAC are presented in this paper. The measures are investigated using Dymola models of a thermal car body to determine the energy saving potential as well as the effects on the vehicle. Reducing the amount of fresh air while complying with CO₂ limits and using a heat pump are efficient measures to reduce the annual energy demand for the HVAC system by 62% or 48%, respectively. In addition, a control-based, catenary dependent HVAC operating strategy is being developed that decisively reduces the load on the battery during dynamic operation. This approach is particularly suitable for straightforward implementation since no design changes to the vehicle are necessary.]]></description>
      <pubDate>Thu, 23 Apr 2026 09:11:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2580004</guid>
    </item>
    <item>
      <title>Building Energy Simulation for Dynamic Control of HVAC in an Airport Terminal Integrating Agent-Based Model</title>
      <link>https://trid.trb.org/View/2529873</link>
      <description><![CDATA[As important infrastructure and transition spaces, airport terminals have a significant impact on the sustainable development of cities. In terminals, heating, ventilation, and air conditioning (HVAC) systems are usually constant and uniformly distributed, ignoring the dynamic changes of passenger flow, thereby resulting in wasteful energy consumption and thermal discomfort. Therefore, this paper adopted an integrated agent-based model approach to building energy modeling to dynamically partition thermal zones and regulate HVAC systems based on the thermal demand and occupancy status of the terminal. The occupancy status simulated by Anylogic was inputted into Energyplus, using the thermal zone size, the time interval for adjustment, and the temperature setpoint as variables that were analyzed in terms of energy-saving benefits and thermal comfort. The findings of this study identified the significant energy-saving potential of regulating HVAC systems based on the changes in occupant status, guiding design, and optimization of HVAC systems in airport terminals.]]></description>
      <pubDate>Fri, 09 Jan 2026 16:22:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2529873</guid>
    </item>
    <item>
      <title>Control-Oriented Model for Thermal Energy Management of Battery Electric Vehicles</title>
      <link>https://trid.trb.org/View/2593867</link>
      <description><![CDATA[This paper presents a control-oriented thermal model for a novel heating, ventilation, and air conditioning (HVAC) system with a heat pump designed specifically for battery electric vehicles (BEVs). The model is validated through high-fidelity simulations from GT-SUITE, achieving a root mean square error of 1.54 % for cabin air temperature and 0.70 % for battery temperature. The model is used for exploring energy reduction strategies. Optimal results demonstrate a 5.7 % decrease in energy consumption during a cool-down scenario in high ambient temperatures. The usage of the model is showcased in a cabin thermal pre-conditioning problem where the vehicle is not occupied for an interval of 600 s between two drives. Results show that the ability to predict occupancy and optimally manage cabin temperature could reduce energy usage by 12.9 %, compared to maintaining cabin temperature at the same level. Another example examines the impact of predicted data uncertainty and the result shows that it is more energy-efficient to maintain cabin thermal comfort instead of turning off the HVAC system when the likelihood of passenger arrival is uncertain and exceeds approximately 60 % of the predicted non-occupied interval.]]></description>
      <pubDate>Wed, 24 Sep 2025 15:31:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2593867</guid>
    </item>
    <item>
      <title>Learning to Learn HVAC Failures: Layering ML Experiments in the Absence of Ground Truth</title>
      <link>https://trid.trb.org/View/2407644</link>
      <description><![CDATA[Passenger comfort systems such as Heating, Ventilation, and Air-Conditioning units (HVACs) usually lack the data monitoring quality enjoyed by mission-critical systems in trains. But climate change, in addition to the high ventilation standards enforced by authorities due to the COVID pandemic, have increased the importance of HVACs worldwide. We propose a machine learning (ML) approach to the challenge of failure detection from incomplete data, consisting of two steps: 1. human-annotation bootstrapping, on a fraction of temperature data, to detect ongoing functional loss and build an artificial ground truth (AGT); 2. failure prediction from digital-data, using the AGT to train an ML model based on failure diagnose codes to foretell functional loss. We exercise our approach in trains of Dutch Railways, showing its implementation, ML-predictive capabilities (the ML model for the AGT can detect HVAC malfunctions online), limitations (we could not foretell failures from our digital data), and discussing its application to other assets.]]></description>
      <pubDate>Tue, 19 Aug 2025 15:55:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2407644</guid>
    </item>
    <item>
      <title>Multidimensional Data Fusion and Performance Prediction for Health Monitoring of Electric Locomotive Ventilation and Cooling Systems</title>
      <link>https://trid.trb.org/View/2526361</link>
      <description><![CDATA[The ventilation and cooling system constitutes a vital component of electric locomotives, significantly influencing the operational integrity of the locomotive’s traction system. This study is grounded in acquired health monitoring data from the electric locomotive’s ventilation and cooling system. The data encompass parameters such as the front and rear bearing temperatures and bearing vibration speeds of the traction cooling fans, cooling tower fans, and auxiliary converter cooling fans. Furthermore, it encompasses details regarding the internal wind speeds within various ducts of the ventilation and cooling system. In addition, operational data including locomotive speed, traction force, motor temperature, fan control frequency, longitude, latitude, and altitude data of Beidou positioning are integrated. The paper undertakes a thorough correlation analysis between the health monitoring data of the ventilation and cooling system and the locomotive’s operational data. It delves into the intricate coupling relationships and dynamic patterns between these datasets. Employing correlation analysis and machine learning methodologies, the research investigates predictive aspects of the ventilation and cooling system’s performance, such as the condition of fan bearings and the presence of duct blockages. The objective of this study is to facilitate informed assessments of the operational state of the ventilation and cooling system, thereby furnishing a solid theoretical foundation for the formulation of maintenance and servicing strategies.]]></description>
      <pubDate>Wed, 30 Jul 2025 09:57:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2526361</guid>
    </item>
    <item>
      <title>A Critical Review and Future Prospects of Control-Oriented HVAC Modeling Strategies in Electric Vehicles</title>
      <link>https://trid.trb.org/View/2511906</link>
      <description><![CDATA[Electric vehicles (EVs) have emerged as a promising solution in the transportation industry, but their adoption is hindered by range anxiety due to uncertainty in driving range. Specifically, severe weather conditions can result in a high requirement for the use of heating, ventilation, and air conditioning (HVAC) to regulate the cabin’s thermal comfort, leading to significant demand for battery power. To address this, understanding real-time HVAC power usage can help precise range prediction and control. Furthermore, achieving real-time capability involves exploring simplified control-oriented models for EV HVAC systems. Therefore, this research aims to address the gap between current and previous HVAC modeling research for EVs by providing a detailed discussion of three modeling techniques: physics-based, data-driven, and hybrid models. Later, various evaluation metrics, such as modeling level capability, accuracy, complexity, generalization, adaptability, cost, and required effort, are defined and used to compare these models. The potential of using control-oriented models for design optimization, synthetic data generation, fault detection, diagnosis, prognosis, and failure mode effect analysis (FMEA) is also discussed, and the need for further research in this area is noted. Overall, this article provides a comprehensive overview of control-oriented HVAC modeling for EVs and offers insights for researchers in this field.]]></description>
      <pubDate>Fri, 23 May 2025 15:34:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2511906</guid>
    </item>
    <item>
      <title>Prediction of Air Induced Noise Levels for Automotive HVAC Systems</title>
      <link>https://trid.trb.org/View/2437280</link>
      <description><![CDATA[With the advent of electric vehicles and advance trends in power train there is an increasing demand in improving the overall cabin comfort level. This is especially true when it comes to electric vehicles as the major source of sound other than the external flows would be from the heating, ventilation, and air conditioning (HVAC) systems for the vehicle. The noise source can be further divided on structural noise and flow induced noise; the paper would be focusing on prediction of flow induced noise levels. Flow induced noise by the HVAC’s would be critical since there are significant advancements in the cabin insulations/materials from externally generated noise sources. Automotive HVAC system consist of complex flow paths, blower, flaps, ducts, and vents which are the main source of noise generation in HVAC systems. With packaging space beneath the IP been premium, changes proposed to improve the noise levels are expensive and are understood at later level/phase of product development cycle. This paper puts forward an approach to evaluate the air induced noise levels for HVAC systems/components which can be used in early phase of development cycle. Computational Aeroacoustics (CAA) is proposed to be used to predict the sound generation and propagation level for the given microphone positions. The noise source can be identified and improved in early phase of development cycle which if done in later phase would require an expensive change. The process for prediction of noise has been validated and tested with good correlations between the two approaches.]]></description>
      <pubDate>Fri, 25 Oct 2024 14:46:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2437280</guid>
    </item>
    <item>
      <title>HVAC Blower: A Steady State RANS Noise Prediction Method</title>
      <link>https://trid.trb.org/View/2397782</link>
      <description><![CDATA[In an ever-transforming sector such as that of private road transport, major changes in the propulsion systems entail a change in the perception of the noise sources and the annoyance they cause. As compared to the scenario encountered in vehicles equipped with an internal combustion engine (ICE), in electrically propelled vehicles the heating, ventilation, and air conditioning (HVAC) system represents a more prominent source of noise affecting a car’s passenger cabin.By virtue of the quick turnaround, steady state Reynolds-averaged Navier Stokes (RANS)- based noise source models are a handy tool to predict the acoustic power generated by passenger car HVAC blowers. The study shows that the most eminent noise source type is the dipole source associated with fluctuating pressures on solid surfaces. A noise map is generated from the noise source models data, giving indications of how changes in operating conditions affect the acoustic output of the machine throughout its operating range. The capability to predict power spectra with steady state RANS is investigated, and the overall sound power level of several operating points is validated against experimental data, showing good match.The study aims at establishing steady state RANS noise source models as a valuable tool in preliminary acoustic analyses of HVAC blower designs, in particular in the early stage of new design studies, thus helping the industry to better target quieter operation and enhanced passenger comfort.]]></description>
      <pubDate>Tue, 13 Aug 2024 14:58:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2397782</guid>
    </item>
    <item>
      <title>Investigating the effect of lower body local radiant warming on occupant thermal comfort in battery electric vehicles during cold conditions</title>
      <link>https://trid.trb.org/View/2391303</link>
      <description><![CDATA[The wintertime decrease in the driving range of battery electric vehicles (BEVs), partially attributed to cabin heating with improper setpoints, necessitates the improvement of occupant thermal comfort with reduced energy consumption. While local warming shows promise, understanding its impact on occupant comfort with thermal transients from cabin heating, ventilation, and air conditioning (HVAC) is limited. To address this gap, the authors investigated thermal perception in twenty-five participants under three distinct warming modes within BEVs operating through typical winter conditions: HVAC alone, HVAC with continuous lower body local radiant warming (LRW), and HVAC with periodic lower body LRW. Results show that the rapid change in skin temperature from lower body local warming does not immediately improve comfort. While a significant improvement in comfort (p < 0.05) is observed with lower body warming, it becomes evident only when the cabin transients subside. Consequently, the authors propose a framework that advocates for reducing HVAC setpoint and control of LRW of the lower body based on occupant preferences. The feasibility study demonstrates a 45.3 % reduction in energy consumption by reducing the HVAC setpoint from 25 to 18 °C. These findings enhance the prospects for efficient thermal comfort management in BEVs.]]></description>
      <pubDate>Thu, 11 Jul 2024 13:53:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2391303</guid>
    </item>
    <item>
      <title>Optimization of air quality and energy consumption in the cabin of electric vehicles using system simulation</title>
      <link>https://trid.trb.org/View/2365608</link>
      <description><![CDATA[In electric vehicles, the Heating, Ventilation and Air-Conditioning (HVAC) function is often performed by a heat pump. Heating and cooling the cabin air drains energy directly from the vehicle's battery. In addition, these vehicles may operate in environments with high level of air pollution. In the cabin, passengers are confined to a small space where particles and harmful gases can accumulate. In addition, the ventilation system must also handle the air which does not enter the cabin through blower operation. This “infiltration” is a function of the vehicle speed and allows pollution to enter the cabin without being filtered or thermally treated. The objective of the study is to optimize the competing goals of the HVAC system: achieving the best air quality while maintaining good thermal comfort, at minimum energy costs. A system simulation tool is calibrated to represent the heating and cooling of an electric car. With this model, the influence of key factors is evaluated. Depending on ambient conditions and other parameters (number of occupants, vehicle speed, etc.), the blower flow rate and recirculation ratio can be adjusted to reach the objectives. The management of the proportion of fresh and recirculated air allows to regulate the humidity and carbon dioxide levels. Optimum controls are proposed as good trade-offs to reduce the power consumption, while maintaining a safe and comfortable environment for occupants. Compared to the full fresh air mode, the driving range gains are estimated in cold (−15 °C) and hot (30 °C) scenarios at 9 and 26 km respectively.]]></description>
      <pubDate>Fri, 07 Jun 2024 10:13:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2365608</guid>
    </item>
    <item>
      <title>An investigation of the cooling, heating and power systems integration with carbon capture and storage for LNG carriers</title>
      <link>https://trid.trb.org/View/2377895</link>
      <description><![CDATA[This study investigates the cooling, heating and power (CHP) systems integration with the carbon capture and storage (CCS) for different LNG carriers. Different integration levels of conventional waste heat recovery, boil-off natural gas (BO-NG) reliquefaction, CO2 capture and liquefaction, and ORC systems are modelled and simulated using Aspen HYSYS. Thermoeconomic optimisation is also carried out using Genetic algorithm method. Results demonstrate that using CCS system is more cost-effective than paying European Union carbon tax for LNG carriers. Integrating the BO-NG and CO2 liquefaction cycles could reduce the total life cycle cost of the CCS system up to 17%. Optimised results show that selecting working pressure is very critical for the liquefaction of BO-NG and CO2. CHP system using ORC reduces the energy penalty of the CCS system, however, it increases the total life cycle cost of the system due to its initial cost and relatively low exergy efficiency.]]></description>
      <pubDate>Thu, 16 May 2024 16:38:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2377895</guid>
    </item>
    <item>
      <title>Energy consumption of mobile air-conditioning systems in electrified vehicles under different ambient temperatures</title>
      <link>https://trid.trb.org/View/2326020</link>
      <description><![CDATA[In 2019, passenger car CO₂ emissions peaked at 3.2 billion metric tons globally. Despite efforts to curb vehicle CO₂ emissions and the ambitious targets adopted for greenhouse gas mitigation in the European Union (EU), emissions from road vehicles increased steadily over the past decade. Electrified vehicles have gained significant market share in the last years and are an essential technical option to reduce CO₂ emissions. Range anxiety and insufficient charging infrastructure limit electrified vehicles’ customer acceptance and market attractiveness. The use of auxiliary systems under certain circumstances may reduce vehicle range. In this regard, energy management improvements lead to better vehicle range results. As well-considered in numerous studies, the most consuming auxiliary system is the vehicle’s heating, ventilation and air-conditioning (HVAC) system, also known as Mobile Air-Conditioning (MAC). The present work explores the influence of different parameters on the energy consumption of the MAC system in plug-in hybrid vehicles (PHEV) and battery electric vehicles (BEV). For this purpose, one PHEV and one BEV were tested in laboratory conditions at different cell temperatures of −7°C (19.4°F), 22°C (71.6°F) and 35°C (95°F), over the Worldwide Harmonised Light vehicle Test Cycle (WLTC). Laboratory tests with the same conditions were repeated with MAC on and off for each temperature. For the reference 23°C (73.4°F) condition, additional factors affecting energy consumption were analysed, such as the impact of depleting/sustaining modes on the MAC performance in PHEV, or the effect of warm and cold start in PHEV and BEV. Results suggest that the electric energy required to heat the cabin at low temperature (−7°C) could be 4–10 times higher than the energy needed to cool down the cabin in hot conditions (35°C). Compared to the vehicle energy required at the wheels during a WLTC, the MAC impact at −7°C ranges from 35% to 45% while at 35°C goes from 15% to 18%.]]></description>
      <pubDate>Tue, 19 Mar 2024 15:19:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2326020</guid>
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