<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
    <description></description>
    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
    </image>
    <item>
      <title>Understanding the charging behavior of electric vehicle drivers on long-distance trips – The roles of range regulation and human-automation cooperation</title>
      <link>https://trid.trb.org/View/2697880</link>
      <description><![CDATA[Advances in battery technology and charging infrastructure have improved long-distance electric vehicle (EV) travel. However, effective trip planning can be challenging. EV trip planners (EVTs) can support drivers in range regulation, yet their effectiveness and acceptance depend on how drivers experience the interaction. In this context, the authors introduce Driver Electric Vehicle Trip Planner Interaction Style (DEVTIS), which captures individual differences in reliance on or modification of system-generated recommendations. DEVTIS is assumed to be shaped by driver characteristics, experience, and perceived system cooperativity. The objective of the present study was to examine EV drivers' interaction with EVTs for range regulation on long distances and its underlying psychological dynamics. Through a correlational online study with 133 EV drivers, the authors assessed affinity for technology interaction and subjective range competence as personal characteristics variables, perceived cooperativity and DEVTIS as core interaction-related variables, trust and system acceptance as evaluative user experience measures and range stress and range utilization as EV range-regulation-related variables. Findings revealed that perceived cooperativity negatively predicted DEVTIS, indicating that higher perceived cooperativity was associated with less frequent modification of system recommendations. Moreover, perceived cooperativity positively predicted trust and system acceptance, while DEVTIS negatively predicted both. Subjective range competence showed a slight negative association with range stress. These findings underscore the importance of understanding human-automation cooperation in EVT usage. Considering perceived cooperativity and individual interaction tendencies supports the design of adaptive, driver-centered EVTs, facilitating a seamless transition to EV-based mobility. Promoting perceived cooperativity may enhance trust and system acceptance, while excessive modification of EVT recommendations could undermine these factors.]]></description>
      <pubDate>Tue, 19 May 2026 15:12:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2697880</guid>
    </item>
    <item>
      <title>Charging ahead unfairly: An examination of temporal shifts in electric vehicle supply equipment accessibility across California’s communities</title>
      <link>https://trid.trb.org/View/2694626</link>
      <description><![CDATA[With the expansion of electric vehicle (EV) use, it has become increasingly important to ensure that public electric vehicle supply equipment (EVSE) meets growing demand and is accessible to everyone in need. Yet a meaningful assessment of how communities have benefited from EVSE expansion requires a lens that goes beyond static snapshots of EVSE accessibility. In this study, the authors conduct a longitudinal analysis over the past decade (2014–2024) to uncover how public charging infrastructure has evolved and how accessibility has shifted over time in the State of California. The authors examine EVSE accessibility across communities using a mixture of descriptive and statistical methods. A core feature of the analysis is that the authors examine changes in accessibility over time using a generalized additive model (GAM) with time-varying effects. The findings suggest that accessibility patterns across California’s communities have varied according to three distinct stages in EVSE deployment: early deployment and planning, accelerated expansion, and coordinated growth. The GAM analysis identifies a clear temporal shift in relative EVSE accessibility levels across income groups: lower-income locations exhibited relative advantages prior to 2017, but higher-income locations became increasingly advantaged during the accelerated expansion phase in later years. Majority Black locations maintained above-average accessibility but experiencing a gradual decline over time, while majority Hispanic locations remained consistently below average throughout the study period. These time-varying findings enhance understanding of how disparities unfold across deployment stages and motivate more adaptive, stage-aware EVSE policy design.]]></description>
      <pubDate>Tue, 19 May 2026 15:12:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2694626</guid>
    </item>
    <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>New Models and Solutions to Vehicle Routing with Cardinality and Distance Constraints</title>
      <link>https://trid.trb.org/View/2703788</link>
      <description><![CDATA[Many emerging transportation and logistics operations are constrained by both the maximum distance a vehicle can travel and the number of customers it can serve before requiring replenishment, recharging, or maintenance. These operational realities motivate the need for new routing optimization models that explicitly integrate distance and cardinality constraints. This project proposes the first comprehensive study of a novel Black-and-White Vehicle Routing Problem (BWVRP), where customer nodes and replenishment nodes are jointly routed across a fleet of vehicles, with replenishment nodes allowed to be visited multiple times. The project will develop new mixed-integer linear programming models and exact branch-and-cut methods to obtain optimal solutions for small and medium-sized instances. To address large-scale instances, efficient heuristic and metaheuristic algorithms will be designed and implemented. In addition to methodological advances, the project will develop a data-driven optimization decision-support tool integrating models, algorithms, and user-friendly interface. 
]]></description>
      <pubDate>Sat, 16 May 2026 11:45:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2703788</guid>
    </item>
    <item>
      <title>A Comprehensive Leakage-Free Forecasting Pipeline for Segmented Time Series: Application to Cross-Trip State-of-Charge Prediction in Automated Electric Vehicles</title>
      <link>https://trid.trb.org/View/2659151</link>
      <description><![CDATA[The rapid adoption of Electric Vehicles (EVs) in the global pursuit of energy efficiency and carbon neutrality necessitates effective strategies to mitigate their carbon footprint and enhance operational stability. Similarly, in order to achieve Sustainability Development Goals, a promising solution toward green mobility, which is gaining ground nowadays, constitutes Automated Vehicles (AVs), which are EVs having the capability to move autonomously, without the need for a driver. One of the most critical factors regarding energy efficiency is the optimal management of energy consumption of AVs. This research study explores the application of machine learning (ML) models for State-of-Charge (SoC) forecasting in AVs, crucial for addressing challenges such as range anxiety and grid overloading. Leveraging real-life EV data from automated minibuses in Gothenburg, Sweeden, a comprehensive pipeline is proposed for data pre-processing, feature selection, and model training. With a focus on predicting SoC several minutes ahead, various ML techniques, including linear regression, ridge regression, lasso regression, and elastic-net regression are embedded in a pipeline specifically developed to overcome the challenge of training time-series models on discontinuous data segments, corresponding to discharge cycles. This pipeline is called Cross-Segment-Leakage-Free (CSLF). The results demonstrate the efficacy of CSLF, with the best-performing model achieving a Mean Absolute Error (MAE) of 0.92 in a forecasting horizon of 30 minutes, representing a significant improvement over baseline models. The study underscores the importance of meaningful pre-processing and model selection in SoC consumption forecasting for AVs, offering insights into future research directions and deployment strategies for enhancing EV efficiency and grid stability.]]></description>
      <pubDate>Wed, 29 Apr 2026 09:10:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659151</guid>
    </item>
    <item>
      <title>Assessment of the factors affecting electric vehicle charging stations demand prediction</title>
      <link>https://trid.trb.org/View/2682141</link>
      <description><![CDATA[Electric vehicles (EVs) represent a pivotal sustainable mobility solution for urban decarbonization. This work investigates how proximity to main urban attraction points, such as restaurants, supermarkets, tourist sites, schools, and hospitals, influences EV charging demand within a fixed service range. Given the critical role of charging accessibility in mitigating EV range limitations, the study proposes a stepwise linear regression to quantify the relationship between charging station utilization and driving distances to nearby points of interest (POIs). Our analysis identifies statistically significant demand predictors, providing practical insights into strategic infrastructure planning. Applied to the Lombardy region, Italy, findings can support data-driven optimization of charging station placement, balancing urban accessibility with equitable spatial distribution. The results can contribute to sustainable urban mobility frameworks by integrating POI-based demand modelling into EV infrastructure expansion strategies.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2682141</guid>
    </item>
    <item>
      <title>The flight range estimation of commercial aircraft – Boeing 737 max 800 considering the transition from internal combustion to SOFC (solid oxide fuel cell) engines</title>
      <link>https://trid.trb.org/View/2625517</link>
      <description><![CDATA[The range of the aircraft is the primary concern when operating commercial airliners. In transitioning from conventional-engine commercial airliners to carbonless ones, the flight range has been investigated in this paper to provide design modification metrics for future electrified aircraft designs and processes. The total weight of both cases is fixed to compare the performance of two different engines directly. Due to the use of ammonia for fuel cell systems, such as SOFC with a turbogenerator (TG), the engines are heavier and have a lower fuel capacity than jet engines. The lower range associated with SOFC-TG propulsion highlights the need to significantly enhance aerodynamic efficiency to achieve parity or improve operational capabilities. Given the pivotal role of aerodynamic design in mitigating drag forces and optimizing fuel efficiency, innovative strategies and advancements in airframe design have become imperative. By prioritizing aerodynamic enhancements tailored to the unique requirements of SOFC-TG propulsion systems, such as optimizing weight distribution and minimizing drag coefficients, it is conceivable to bridge the range disparity between conventional and alternative fuel-powered aircraft variants. This paper presents a critical parameterization analysis for considering a conversion of an existing commercial aircraft. With fixed operational conditions and weight and volumetric specifications, a crucial aerodynamic performance, flight range, is revealed as a function of the adopted propulsion system. This effort will extend to any “conversion” or design and operation modification studies for future aircraft.]]></description>
      <pubDate>Mon, 27 Apr 2026 14:59:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2625517</guid>
    </item>
    <item>
      <title>Enhancement of driving range for automotive thermal management systems based on immersion liquid cooling technology</title>
      <link>https://trid.trb.org/View/2691741</link>
      <description><![CDATA[The application of immersion liquid cooling (ILC) technology in electric vehicle (EV) thermal management systems (TMS) achieves improved heat exchange performance, significantly enhancing the temperature consistency and control responsiveness of both the battery and passenger cabin. A new ILC system for EV TMS is proposed to explore its feasibility and energy economics. Firstly, the mathematical model incorporating a direct ILC system is developed using AMESim® software, with key components parameter-matched and partially validated. Then, the set temperatures of the battery are optimized to maximize the driving range, based on the designed startup strategy under the China Light-duty Vehicle Test Cycle (CLTC) conditions. Finally, simulation studies were conducted to investigate the effect of ILC on improving heat transfer performance during the cold start process, resulting in a relative reduction in cold start times and enhanced passenger compartment comfort. The conclusions indicate that the driving range follows an inverse U-shaped relationship with the set temperature of the battery, with an optimal value of 321.61 km at 5°C. Additionally, the passenger compartment can consistently maintain a temperature between 21°C and 23°C relatively quickly under various operating conditions.]]></description>
      <pubDate>Thu, 23 Apr 2026 09:39:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691741</guid>
    </item>
    <item>
      <title>Charging while driving lanes: A boon to electric vehicle owners or a disruption to traffic flow</title>
      <link>https://trid.trb.org/View/2645481</link>
      <description><![CDATA[Large-scale adoption of commercial and personal Electric Vehicles (EVs) is expected to significantly affect traffic flow dynamics, emissions, and energy consumption in the transportation sector. Range anxiety and challenges associated with charging EVs are among the key issues that reduce the adoption rate of EVs and, in turn, limit their system-level impacts. A promising solution to address these challenges is the introduction of charging while driving (CWD) lanes, either by appropriating an existing lane or augmenting an EV reserved lane to the highway segment. Although technological advancements have made it possible to charge vehicles wirelessly while driving, introducing such lanes to the traffic stream can potentially disturb traffic flow and result in new congestion patterns. This study puts forward a microscopic simulation framework to investigate the effects of CWD lanes on traffic flow dynamics at the segment level. It takes into account different market penetration rates (MPRs) of both personal and commercial EVs in the forms of Automated Vehicles (AVs) and Electric drayage Trucks (ETs), respectively. Different policies have been investigated to suggest the best design for CWD lanes. Results indicate that introducing CWD lanes can decrease overall traffic throughput and increase congestion due to additional lane-changing maneuvers by electric vehicles aiming to utilize the CWD lane. Although higher MPRs of EVs help stabilize traffic flow and reduce the number of shockwaves, speed disruption tends to increase in the CWD lane and propagate to adjacent lanes. Emission analyses show significant reductions (up to 63 %) in pollution levels with increasing MPRs of personal and commercial EVs. Our analysis shows that while CWD lanes can facilitate the adoption of EVs, they can deteriorate traffic efficiency, emphasizing the importance of careful design and policy considerations.]]></description>
      <pubDate>Wed, 22 Apr 2026 16:15:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2645481</guid>
    </item>
    <item>
      <title>Stated car choices in Norway and Italy: a comparison based on the integrated choice and latent variable model</title>
      <link>https://trid.trb.org/View/2647718</link>
      <description><![CDATA[The study investigates whether the large difference in battery electric vehicle (BEV) uptake between Norway and Italy could be explained by differences in car buyers' preference structures, either in terms of their evaluation of the vehicles' characteristics or in terms of their perceptions\attitudes towards BEVs. Based on stated preference data collected in the two countries, we find that car drivers evaluate vehicle attributes very similarly. Norwegians value BEV driving range slightly more and are more sensitive to fuel\electricity costs. Ceteris paribus, Italian respondents, in contrast to Norwegian ones, still prefer petrol cars to BEVs. The results of the integrated choice and latent variable (ICLV) model indicate that respondents’ perceptions\attitudes influence car choice in both countries. In Norway, BEVs are preferred by those who view them as economically, environmentally, technically, and morally superior. In Italy, the evidence is similar but for the environmental aspects, which are not decisive for BEV choice. Such perceptions\attitudes are correlated with age, sex, and BEV density.]]></description>
      <pubDate>Wed, 22 Apr 2026 16:15:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2647718</guid>
    </item>
    <item>
      <title>Vehicle design responses to attribute-based regulation with capped tradable credits</title>
      <link>https://trid.trb.org/View/2689463</link>
      <description><![CDATA[This paper studies how battery electric vehicle automakers responded to the 2021 revision of China’s Dual-Credit policy. Using a difference-in-differences design and model-level data from 2018 to 2024, we show that the revised credit rules led automakers to increase vehicle curb weight through larger batteries and higher horsepower, without corresponding improvements in driving range. These design changes increased electricity consumption and partially offset the policy’s intended environmental benefits. The findings suggest that combining attribute-based regulation with tradable credits can distort automakers’ design choices and undermine energy efficiency.]]></description>
      <pubDate>Wed, 22 Apr 2026 16:15:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2689463</guid>
    </item>
    <item>
      <title>Developing Sulfide Based Solid State Battery with High Energy Density for Automotive Applications</title>
      <link>https://trid.trb.org/View/2579984</link>
      <description><![CDATA[The overall aim of the SUBLIME (Solid state sUlfide Based LI-MEtal batteries for electric vehicle (EV) applications) project is to respond to the further battery development challenges for EVs and produce next-generation solid-state batteries (SSB) with extreme high energy density of up to 450 Wh/kg as compared to 250–280 Wh/kg for conventional cells to double the driving range of electrical vehicles. The SUBLIME cell consists of a sulfide solid electrolyte (SE), Li metal anode and high nickel content cathode (NMC based). Up to now, we have overcome several challenges of this technology. The sulfide SE has been produced in kilogram scale with high ionic conductivity of 2.5 mS/cm at 25 ℃ and specific cathode and Li metal anode were developed for SSB application. The quality of the developed materials was confirmed in coin cell format, delivering a capacity of 195 mAh/g at 25 ℃. Next, we have been focusing on producing mono and multilayer pouch cells based on scalable process and optimizing the interfacial resistances between the cell components. For this purpose, coatings are applied on Li metal anode and cathode active material. The initial testing results of the pouch cells demonstrate the potential of this technology.]]></description>
      <pubDate>Tue, 21 Apr 2026 16:23:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579984</guid>
    </item>
    <item>
      <title>Empirical evaluation of battery swapping technology adoption in urban electric taxi operations</title>
      <link>https://trid.trb.org/View/2692648</link>
      <description><![CDATA[Battery swapping is an efficient recharging method for electric vehicles (EVs). In taxi fleets, its shorter recharging time reduces drivers’ concerns about remaining battery energy and shortens downtime during services. In this study, we analyse real-world operational data from 872 electric taxis to assess the effectiveness of adopting battery swapping technology (relative to plug-in charging) on reducing range anxiety and improving operational efficiency. Our findings show that battery swapping technology significantly mitigates range anxiety among electric taxi drivers. This effect is largely driven by its pronounced impact under low- and moderate temperature conditions, with non-significant effects observed in high-temperature environments. Additionally, its impact on operational efficiency exhibits a similar pattern—battery swapping significantly improves efficiency in low- and moderate temperature settings. These findings offer new empirical evidence on the operational implications of battery swapping, particularly under varying temperature conditions.]]></description>
      <pubDate>Mon, 20 Apr 2026 09:25:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692648</guid>
    </item>
    <item>
      <title>Joint Influence of driving range and charging infrastructure on electric vehicle utilization</title>
      <link>https://trid.trb.org/View/2686379</link>
      <description><![CDATA[Driving range and charging have been widely studied to understand battery electric vehicle (BEV) adoption patterns, yet evidence remains limited on how they jointly relate to BEV utilization. This study examines the relationship between driving range, charging infrastructure, and BEV mileage, using data from the 2019 and 2024 waves of the California Vehicle Survey. We estimate multivariate linear regression models to assess how self-reported availability of home and workplace charging options—by charger level (Level 1, Level 2, and DCDC chargers)—affects annual BEV mileage, considering vehicle driving range. Our key results indicate that range and charging options near the work location act as substitutes influencing the demand for driving. Availability of charging opportunities at workplace is associated with higher mileage primarily among shorter- and mid-range BEVs, and this association diminishes as driving range increases. In contrast, we find no significant interaction between range and charging options at home. These results highlight the importance of jointly considering vehicle range and charging infrastructure in planning discussions to optimize BEV adoption and usage patterns.]]></description>
      <pubDate>Tue, 14 Apr 2026 10:09:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686379</guid>
    </item>
    <item>
      <title>EVs@Scale: NextGen Profiles: Station Impact Analysis 2025</title>
      <link>https://trid.trb.org/View/2685487</link>
      <description><![CDATA[As part of the U.S. Department of Energy (DOE) EVs@Scale consortium, the NextGen Profiles (NGP) project presents analysis and results from the study of High Power Charging Electric Vehicles (EVs) and Battery Charging Infrastructure. High Power Charging equipment is capable of recharging electric vehicle traction batteries at power levels of 200KW and above. The intent of the project is to further understand the most recent technological capabilities of the electric mobility industry related to charging performance. The project aims to develop Electric Vehicle, Electric Vehicle Supply Equipment (EVSE), and fleet characterization testing practices and comprehensive analysis with inputs from key industry stakeholders. The results published in this NextGen Profiles project discuss how EV specifications and initial charge conditions, State of Charge (SOC) bounding limits, temperature considerations and Direct Current Fast Charging (DCFC) station topologies impact DCFC charging performance. Examination of the SOC bounding limit scenario finds that charging to 100% SOC can significantly increase the length of a charging session for only marginal range gains. Examination of temperature considerations finds that the impacts of extreme temperatures can be mitigated by maximizing the use of battery preconditioning functions of the vehicle and by taking weather forecasts into account during trip planning. Examination of DCFC station topologies finds that DCFC stations with power sharing topologies and high utilization rates can limit charging speeds but also enable more effective utilization of installed DC charging infrastructure. Additional high-power charging results are anticipated in future publications in support of the U.S. DOE EVs@Scale consortium NextGen Profiles project.]]></description>
      <pubDate>Tue, 07 Apr 2026 17:08:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685487</guid>
    </item>
  </channel>
</rss>