<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>Shaping Future Sustainable Eco-Cities: Profit-Driven and Cost-Effective Optimization of Energy Systems Integrated with Public Transport Fleets and CHP for Near Net-Zero Emissions</title>
      <link>https://trid.trb.org/View/2681819</link>
      <description><![CDATA[This study develops an integrated and cost-effective energy management framework combining Public Transport Fleets (PTFs), Renewable Energy Sources (RES), Combined Heat and Power (CHP) systems, Battery Energy Storage Systems (BESS), and Vehicle-to-Grid (V2G) technologies to achieve near net-zero emissions in urban power distribution networks. The proposed framework not only ensures energy reliability but also optimises operational costs and reduces carbon emissions, contributing to the development of sustainable eco-cities. The numerical simulations reveal substantial improvements in economic and environmental performance. In the most advanced configuration (Test Case 4), incorporating PTFs, V2G, and BESS, daily operational costs were reduced by 47.20%, from £31,820 to £16,801.67, while CO₂ emissions costs dropped by 47.90%, from £2,898 to £1,509.8, compared to the baseline case (Test Case 1). The inclusion of harmonised CHP and RES systems (Test Case 3) led to a 44.49% reduction in operational costs and a 47.13% decrease in emissions costs, highlighting the synergies of integrating solar, wind, and CHP technologies. Moreover, the integration of PTFs into the energy framework improved system efficiency, especially during peak demand hours, with operational cost reductions of up to 58.17% and emission cost savings of 54.87% in critical periods. This study highlights the critical role of PTFs in enhancing energy efficiency and reducing carbon footprints in urban networks. The findings provide policymakers and urban planners with actionable strategies to accelerate the transition toward low-carbon, cost-effective, and resilient energy systems, paving the way for sustainable urban mobility and energy ecosystems.]]></description>
      <pubDate>Wed, 17 Jun 2026 16:13:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2681819</guid>
    </item>
    <item>
      <title>Did transportation electrification help to reduce transportation sector CO₂ emissions? A study considering the dynamic electricity carbon emission factor</title>
      <link>https://trid.trb.org/View/2647717</link>
      <description><![CDATA[Transportation electrification is a key strategy for achieving carbon neutrality goals. However, the contributions of transportation electrification to carbon emissions (CRE) depend on the degree of power grid decarbonization. This study employed the Kaya Identity, the Logarithmic Mean Divisia Index (LMDI) model, and Moran's Index to examine CRE and its spatial clustering characteristics across China's seven power grid regions from 2004 to 2022, covering 30 provinces. Results indicated that the transportation electrification rate (EE) remained stable in the early years but increased gradually after 2016, reaching 12.35 % by 2022. Transportation electrification primarily impacts carbon emissions through three aspects: electricity substitution, electricity decarbonization, and energy efficiency improvement. Transportation electrification resulted in an additional 9.94 Mt of carbon emissions from 2004 to 2022, with the electricity substitution effect being the primary contributing factor. Most provinces have not achieved carbon reductions through transportation electrification. This outcome is mainly because these provinces have placed greater emphasis on the quantity of transportation electrification (rapid EE increase) rather than its quality (lagging grid decarbonization). Furthermore, regarding spatial distribution, CRE exhibits a clear pattern of “high in the north and low in the south”, indicating spatial clustering. This study provides valuable insights for advancing transportation electrification strategies.]]></description>
      <pubDate>Wed, 22 Apr 2026 16:15:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2647717</guid>
    </item>
    <item>
      <title>Communications Reliability for Vehicle Grid Integration</title>
      <link>https://trid.trb.org/View/2685474</link>
      <description><![CDATA[Electric Vehicles (EVs) adoption rate has been steadily increasing in the US leading to a growing number of charging stations including faster DC (Direct Current) chargers and slower Level 1 and Level 2 AC (Alternating Current) chargers. This increase in demand for electricity is further exacerbated by recent developments in Artificial Intelligence (AI) technology, advanced manufacturing, and digitization. These factors will require electric utilities to upgrade their infrastructure to keep up with the increasing electrical demand (especially during peak hours). An easy way to counteract the need for these upgrades is to shift a major chunk of active charge sessions (durations where there is energy transfer from charger to EV's propulsion battery) to off-peak hours thereby flattening the load curve and making the infrastructure more resilient. This concept is known as Smart Charge Management (SCM). EV owners also benefit from SCM since it lowers their charging costs and consequently their transportation costs by prioritizing charging during off-peak hours. SCM takes advantage of EV's capability to act as a controllable load or DER (Distributed Energy Resource). This report summarizes the reliability analysis performed on the communication required for two of these SCM use-cases. This analysis only focuses on SCM strategies for unidirectional charging (energy transfer from EVSE to EV or V1G) and not bidirectional charging.]]></description>
      <pubDate>Mon, 06 Apr 2026 16:59:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685474</guid>
    </item>
    <item>
      <title>Aligning vehicle electrification with power sector transitions: life cycle insights across diverse grids</title>
      <link>https://trid.trb.org/View/2665418</link>
      <description><![CDATA[Battery electric vehicles (BEVs) are central to transport decarbonization, but their climate performance depends on the grid’s carbon intensity, which varies regionally and evolves over time. This study applies a temporal Life Cycle Assessment framework to quantify the greenhouse gas emissions of BEVs relative to hybrid and combustion vehicles across five regions through 2035. Results show large regional variation: BEV emissions range from ~198 gCO₂e/km in the EU to ~351 gCO₂e/km in India. Breakeven distances range from 32,000 to 124,000 km, with high utilization accelerating payback. We highlight three contributions: (i) quantifying conditional BEV performance under regional decarbonization pathways, (ii) incorporating carbon-intensive regions like the Middle East and North Africa, and (iii) linking findings to policy triggers including grid-indexed incentives, utilization targeting, and technology-neutral procurement. These insights support context-sensitive deployment and challenge the assumption that e-mobility is uniformly green, particularly when vehicle use, power-sector transitions, and regional readiness are misaligned.]]></description>
      <pubDate>Fri, 06 Feb 2026 08:47:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2665418</guid>
    </item>
    <item>
      <title>Privacy-preserving energy management in local energy communities with EVs – An enhanced benders-like solution strategy</title>
      <link>https://trid.trb.org/View/2608883</link>
      <description><![CDATA[Local Energy Communities (LECs) are collectives of prosumers collaborating to reach common goals, such as the reduction of energy procurement costs and the provision of ancillary services to the network operator. They use the flexibility of modern residential installations, including rooftop photovoltaic (PV) systems and controllable loads, such as the charging stations of electric vehicles (EVs). A central unit, called community manager, usually coordinates the actions of prosumers. However, the need for large information exchange in this multi-agent framework is a problem for the widespread adoption of such models. Data privacy concerns between prosumers and the manager may deter participation. This paper presents a novel energy management strategy for LECs with EV charging stations that protects privacy. The proposed approach only shares dual variables with the community manager, while all primal variables, such as power schedules, remain private. The method uses Benders decomposition to solve the day-ahead energy management problem, which has a separable structure. To speed up the process, a Benders-bundle algorithm has been developed, which is faster than the basic Benders method. The method also makes it easy to include network constraints, so the results can be implemented in the network without congestions or voltage problems. The method is tested on a case of a community with 14 prosumers connected to a 15-bus radial low voltage distribution network. Results show that the new proposal performs as well as a centralized approach and is characterized by a good balance between solution accuracy and privacy preservation compared to other distributed and decentralized methods.]]></description>
      <pubDate>Thu, 18 Dec 2025 10:56:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2608883</guid>
    </item>
    <item>
      <title>Modular Open System Approach to High Voltage Power Architectures Enabled by the Universal High Voltage Converter</title>
      <link>https://trid.trb.org/View/2604431</link>
      <description><![CDATA[Increased power density is essential to improving the capabilities of ground vehicles. High voltage systems allow for more efficient power generation and distribution than legacy low voltage systems and can accomplish this through a variety of methods, including HV generation, HV batteries, and HV conversion from the already present LV batteries. GVSC has defined three high voltage architectures that use a Modular Open System Approach (MOSA) to encompass varying levels of power demand: High Power, Mild Hybrid, and Full Hybrid. The Universal High Voltage Converter (UHVC) is a critical enabling technology for the Hybrid architectures, allowing for bidirectional power conversion from 600 Vdc to a variable 270-600 Vdc bus. The first UHVC was received and tested in FY24 for compliance with its performance specification. The safety interlocks, efficiency, step-load performance, and operational control priorities were tested, and their results are presented in this paper.]]></description>
      <pubDate>Mon, 24 Nov 2025 10:24:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2604431</guid>
    </item>
    <item>
      <title>Joint price and power optimization experiment for workplace charging stations</title>
      <link>https://trid.trb.org/View/2602061</link>
      <description><![CDATA[Workplace electric vehicle (EV) charging infrastructure is a key enabler of sustainable urban transitions—by facilitating daytime charging aligned with renewable energy and expanding access for drivers without home-charging options. However, financial sustainability remains challenging as these services are often provided for free or at flat rates. This paper demonstrates the effectiveness of joint price and power optimization in increasing revenue and shifting load at workplace charging stations. We integrate empirically estimated behavioral models to influence user decisions through price signals that: (i) enable smart charging and reduce operational costs, (ii) increase charging service revenue, and (iii) maintain adequate utilization. Our framework considers the trade-offs between high utilization and the first two objectives. We achieve high utilization and smart charging outcomes by incentivizing delayed charging only when cost savings are available. We achieve high utilization and high gross revenue by modeling the choice of not charging as an increasing function of the charging tariff. We demonstrate our approach through a 33-day pilot at the University of California, Berkeley, achieving a 28.9% increase in net revenue, 18.4% reduction in utility costs, and a 17% load shift to low-cost periods.]]></description>
      <pubDate>Thu, 20 Nov 2025 17:07:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2602061</guid>
    </item>
    <item>
      <title>Efficient Adaptive Power Coordination Control for Heavy-Duty Series Hybrid Electric Vehicles With Model and Weight Transfer Awareness</title>
      <link>https://trid.trb.org/View/2591404</link>
      <description><![CDATA[To fully explore the potential of dynamic and economic performance in series hybrid electric vehicles (SHEVs), efficient coordinated control of power flow is essential. On the one hand, the variability in vehicle characteristics and driving modes complicates the stability of power output in hybrid electric powertrains. On the other hand, improving fuel economy while ensuring sufficient power output remains a significant challenge. To address this issue, this article proposes an efficient adaptive power coordination approach for SHEVs. To effectively capture changes in vehicle status, an enhanced least-squares parameter estimator is implemented to facilitate the adaptation of control-oriented model parameter. Moreover, a fuzzy adaptive weight method is proposed to enhance the interpretability of the cost function by adjusting the target weight based on inferred driving behavior. Furthermore, a modified continuation/generalized minimal residual (MC/GMRES) algorithm is developed to alleviate the significant computational burden of online nonlinear model predictive control (NMPC) controller, thereby enhancing real-time control performance. Finally, simulation and hardware-in-the-loop (HIL) test results demonstrate that the proposed control strategy can effectively optimize fuel economy while maintaining dynamic performance under complex driving conditions. Compared to the benchmark strategy, the proposed strategy achieves the fuel savings of 4.75% and 5.81% under two test driving cycles.]]></description>
      <pubDate>Wed, 15 Oct 2025 15:42:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2591404</guid>
    </item>
    <item>
      <title>Coordinated Dispatch of Power and Transportation Systems Considering Hydrogen Storage Based on Heterogeneous Decomposition</title>
      <link>https://trid.trb.org/View/2511928</link>
      <description><![CDATA[With the increasing penetration of electric vehicles (EVs) and the widespread utilization of dynamic wireless charging technology, it is imperative to coordinate the dispatch of electric power systems and electrified transportation networks. In this article, a coordinated power and transportation dispatch model is developed to achieve the maximal social welfare of the power and transportation networks. Specifically, the hydrogen energy storage (HES) system is incorporated into the coordinated power and transportation dispatch model. This allows for more cost-effective power supply and energy storage by enabling the bidirectional conversion of hydrogen and electricity. In addition, the semidynamic traffic assignment (SDTA) model is established to capture the dynamics of the traffic flows. Inspired by the heterogeneity of the power and transportation networks, the heterogeneous decomposition (HGD) algorithm is introduced to solve the coordinated power and transportation dispatch problem. To cope with the numerical oscillation issues, an improved HGD (I-HGD) algorithm is developed, and its convergence and optimality are analyzed theoretically. Numerical simulations are conducted in two test systems to demonstrate the optimal utilization of the HES system in reducing energy costs and maintaining power balance. The efficiency and robustness of the I-HGD algorithm are validated through numerical results.]]></description>
      <pubDate>Fri, 23 May 2025 15:34:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2511928</guid>
    </item>
    <item>
      <title>Vehicle-to-grid response to a frequency contingency in a national grid</title>
      <link>https://trid.trb.org/View/2437373</link>
      <description><![CDATA[Vehicle-to-grid technology enables electric vehicles to contribute their large, high-power batteries to power systems reserves. Here the authors report the first demonstration of a fleet of vehicles discharging to support system security after a frequency contingency in a national grid. The authors' results highlight the potential of vehicle-to-grid, with vehicles discharging within 6 s of the contingency event, and shortcomings, with vehicles recommencing charging before the power system had fully recovered.]]></description>
      <pubDate>Mon, 27 Jan 2025 08:55:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2437373</guid>
    </item>
    <item>
      <title>Unveiling sectoral coupling for resilient electrification of the transportation sector</title>
      <link>https://trid.trb.org/View/2491066</link>
      <description><![CDATA[Electrifying the transportation sector is crucial for reducing greenhouse gas emissions and offers numerous benefits including increased energy efficiency, lower total ownership costs, enhanced national energy security, and improved air quality. Despite the availability of necessary technologies, fully integrating the transportation and electricity sectors presents challenges in understanding all benefits and risks. Previous studies have not highlighted the role of coupling between these sectors. To better understand this coupling, this work reviews the structure of the current fossil-fuel-based transportation sector (including its dependence on the electricity sector) and case studies of its vulnerabilities to key risks. By adopting a systemic perspective, the authors uncover the indispensable interplay between the transportation and electricity sectors, shedding light on previously neglected dynamics. Leveraging the principles of grid architecture (GA), the authors introduce a hierarchical approach to assess vulnerabilities within the prevailing fuel-based transportation system and elucidate pathways for enhancement through electrification.]]></description>
      <pubDate>Mon, 27 Jan 2025 08:55:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2491066</guid>
    </item>
    <item>
      <title>Designing tariff for charging electric vehicles at home with equity in mind – The tripartite tariff</title>
      <link>https://trid.trb.org/View/2471642</link>
      <description><![CDATA[Extant electricity tariffs model an industrial age when electricity predominantly came from centralised conventional generators, and they still model the pre-pandemic years when virtually everyone shared similar work pattern of working from dawn to dusk. The extant home electricity tariffs offer off-peak electricity mainly during night hours. The tripartite tariff – a home Electric Vehicle (EV) charging tariff that offers off-peak EV charging opportunities during daytime and night hours – is presented. The objective is to assess how access to a tripartite tariff impacts an individual worker's ability to charge their EV at home using off-peak electricity and implications in cognizance of a democratised next generation energy system desirable in an heterogenous society. Using 15 user profiles that represent low-income, middle-income, and high-income earners, working at different times of the day within four successive weeks, the tripartite tariff is designed for inclusive EV charging. With a traditional tariff regime – which represents existing off-peak electricity tariffs – the low-income earners who would typically need off-peak EV charging the most tend to have the least access to it. The tripartite tariff offers inclusive EV charging opportunity at lower off-peak rates for every worker category: night-time, daytime, and mix daytime-and-nighttime workers.]]></description>
      <pubDate>Mon, 30 Dec 2024 09:57:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2471642</guid>
    </item>
    <item>
      <title>Electric mobility investment in the power and transport sector coupling context: Lessons from Argentina, the Philippines, Poland and Romania</title>
      <link>https://trid.trb.org/View/2447377</link>
      <description><![CDATA[Many developing countries are at a crucial juncture in road transport electrification with electric mobility because they have limited economic capacity to implement government policy support and financial mechanisms that have spurred the capital-intensive electric mobility growth in developed countries. Attracting private sector investments remains a viable option for developing countries. While investors have identified opportunities to invest in developing countries, it remains to be seen which countries to prioritize, considering the complexities involved in investment decision-making despite the availability of myriad investment appraisal tools. This paper contributes to this academic and policy debate. With a power and transport sector coupling viewpoint, the authors explain the interaction of the power and transport sectors in the context of decarbonization and digitalization to identify developing countries that could be considered for private sector investment in electric mobility. Then, the authors apply the framework to case studies of the Philippines, Argentina, Romania, and Poland. The authors argue that countries with wholesale power markets and wholesale and retail power markets could attract electric mobility investment. The authors offer policy recommendations to stakeholders interested in electric mobility investment issues in developing countries.]]></description>
      <pubDate>Fri, 15 Nov 2024 11:01:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2447377</guid>
    </item>
    <item>
      <title>Performance improvement of SWH system using fuzzy-grey relational and POA</title>
      <link>https://trid.trb.org/View/2427766</link>
      <description><![CDATA[This manuscript proposes a hybrid method for modelling solar water heating systems (SWHS) for the residential sector. The proposed hybrid method is the combination of fuzzy grey relational analysis (F-GRA) and pelican optimization algorithm (POA), together called as F-GRA/POA method. The main aim of the proposed method is to increase the solar thermal energy generation and reduce the system cost. The operation of heating system is depending on solar thermal collectors, heat-only boiler, heat pumps, electric heaters, and thermal energy storage units. The F-GRA/POA method is utilized to calculate the maximal solar thermal energy generation with minimal possible net cost of the system under various constraints. Also, the proposed method considers the reduction of heat demand because of the thermal insulation of buildings, the amount based on the lowest net heat cost is analyzed. Then, the performance of the proposed method is implemented in MATLAB site and compared with existing methods.]]></description>
      <pubDate>Wed, 23 Oct 2024 11:46:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2427766</guid>
    </item>
    <item>
      <title>Eco-environmental regret-aware optimization of networked multi-energy microgrids with fully carbon elimination and electric vehicles' promotion</title>
      <link>https://trid.trb.org/View/2432006</link>
      <description><![CDATA[With the escalating concern of global warming propelled by the rise in Earth's temperature, the need for effective CO₂ management has become crucial. This paper presents an innovative CO₂ elimination approach, wherein a multiple integrated system of energies (MISEs) incorporating sustainable resources, including renewable resources (RENs), plug-in electric vehicles (PEVs), and demand response programs, is optimized. The proposed carbon elimination framework begins by modeling the onsite carbon capturing and recycling within each MISE. To effectively utilize the onsite carbon recycling facilities and achieve carbon neutrality, the proposed model also incorporates carbon transfer capability between MISEs, thereby enhancing the efficiency of overall carbon recycling. Furthermore, a stochastic p-robust optimization technique is proposed to effectively manage uncertainties by combining the advantages of stochastic programming and robust optimization. This uncertainty modeling approach promotes greater utilization of sustainable resources like PEVs and RENs due to their lower operational regrets from economic and environmental perspectives. Based on the simulation results, implementing the p-robust-based regret assessment technique led to the total operation cost increasing by only 2.75 %, while achieving a significant 44.5 % reduction in maximum relative regret. These results underscore the effectiveness of the proposed framework in enhancing both the economic and environmental performance of MISEs.]]></description>
      <pubDate>Mon, 21 Oct 2024 17:02:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2432006</guid>
    </item>
  </channel>
</rss>