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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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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>Enhancing Cybersecurity Protocols for Secure Communication between Electric Vehicles and the Electric Vehicle Supply Equipment</title>
      <link>https://trid.trb.org/View/2761917</link>
      <description><![CDATA[The increasing adoption of electric vehicles (EVs) has raised the importance of secure communication between EVs and Electric Vehicle Supply Equipment (EVSE). As EV infrastructure rapidly evolves, cybersecurity threats targeting the vehicle-charger interface pose major risks to user safety, data integrity, and operational continuity. This paper presents an overview of existing EV-EVSE communication standards and explores their associated vulnerabilities. We identify potential cyber threats, including man-in-the-middle attacks, replay attacks, and protocol spoofing, that could compromise the security of EV charging systems. The study proposes an enhanced cybersecurity framework incorporating session authentication, and anomaly detection techniques to fortify EV-EVSE communication. The proposed mitigation strategies aim to ensure secure, reliable, and resilient charging infrastructure essential for the widespread adoption of electric mobility.]]></description>
      <pubDate>Thu, 08 Oct 2026 17:03:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2761917</guid>
    </item>
    <item>
      <title>Identifying and prioritizing key factors influencing electric vehicle fast-charging infrastructure planning in China: A Delphi study</title>
      <link>https://trid.trb.org/View/2786143</link>
      <description><![CDATA[The rapid expansion of China's new energy vehicle market has intensified demand for a coordinated, equitable fast-charging infrastructure system. Despite substantial growth, regional disparities, technical incompatibilities, and fragmented governance arrangements continue to constrain the efficiency and long-term sustainability of network deployment. This study systematically identifies and prioritizes the key determinants shaping the planning of fast-charging infrastructure in China. A two-round Delphi process was conducted with 30 experts drawn from government, industry, and academia to establish consensus on critical influencing factors. Anchored in diffusion of innovation and institutional theory, the analysis integrates technological, economic, spatial, social, environmental, and governance dimensions into a coherent evaluative framework. Six principal factors emerged: governance and institutional coordination; economic and financial viability; technological integration and standardization; social equity and user-oriented considerations; spatial and urban planning; and environmental sustainability. Governance and institutional coordination ranked highest in importance, with strong consensus among participants (Kendall's W = 0.696, p < 0.001). The findings underscore the central role of policy coherence, sustainable financial models, and interoperability in accelerating the diffusion of infrastructure and reducing regional inequality. The study advances a theoretically informed prioritization framework to support strategic infrastructure planning in rapidly electrifying economies.]]></description>
      <pubDate>Tue, 06 Oct 2026 15:52:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2786143</guid>
    </item>
    <item>
      <title>The paradox of cost relief and network strain: a distributional analysis of progressive pricing strategies in spatially targeted electric vehicle charging infrastructure</title>
      <link>https://trid.trb.org/View/2704398</link>
      <description><![CDATA[As electric vehicle (EV) adoption accelerates, policymakers have increasingly turned to geographically targeted subsidies to improve affordability and expand access to charging infrastructure. This research interrogates a growing paradox: spatially targeted policies designed to relieve economic distress may inadvertently strain the physical network, thereby shifting burden rather than eliminating it. While well-intentioned, such interventions can overlook behavioral responses and operational constraints that intensify as systems mature. Despite widespread use, few studies rigorously assess how these pricing schemes perform when demand surges or infrastructure saturates. This study addresses this gap through a simulation-based case study of a 100% EV mature-demand stress-test scenario, evaluating progressively targeted tariff relief within a coordinated pricing-and-siting support framework using a state-of-the-art agent-based model. Varying levels of spatially targeted charging subsidies are tested across the Chicago metropolitan region to examine how user behavior, system performance, and network outcomes shift under different intensity thresholds. A comprehensive Distributional Performance Measures (DPM) framework is operationalized to assess economic, geographic, and service impacts, while Lorenz curves and Gini coefficients quantify distributional change across demographic groups. Contrary to prevailing assumptions, results show that increasing subsidy generosity does not linearly improve distributional outcomes. Instead, deeper discounts can measurably worsen service performance by increasing wait times, diffusing benefits to non-targeted users, and producing regressive distributional effects once subsidy intensity exceeds a critical threshold. These findings underscore the importance of calibrating subsidy design to network conditions, not only to economic targeting. The study advances a performance-based framework to support adaptive, efficiency-oriented EV infrastructure policy under high-demand conditions.]]></description>
      <pubDate>Tue, 06 Oct 2026 10:14:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2704398</guid>
    </item>
    <item>
      <title>Magnetic Coupling Resonance-Based Wireless Energy Transmission System for Electric Vehicles Multicombination Shielding Design</title>
      <link>https://trid.trb.org/View/2781016</link>
      <description><![CDATA[In the traditional electric vehicle wireless power transfer (WPT) system, the loosely coupled power transmission method increases the leakage magnetic field, leading to safety problems. Additionally, the lower coupling coefficient reduces the system’s transmission efficiency. For this reason, this article proposes an integrated shielding scheme based on a magnetically coupled resonant wireless energy transmission system for a 6.6 kW electric vehicle WPT system. In this article, an improved integrated shielding system for electric vehicles is proposed, which incorporates special surface morphology aluminum plates and ferrite materials, and corresponding optimization algorithms are proposed for different components. It improves the safety and transmission performance of the charging system and has good robustness in the case of misaligned electric vehicle charging coils. The parameter sets of various possible shielding schemes are compared and optimized using FEA, and a 6.6 kW experimental prototype is constructed to verify the final optimized shielding scheme. The obtained prototype has a transmission efficiency of 94.44% in the positive alignment case, with a maximum leakage magnetic field of 1.45 µT in the horizontal direction and a maximum leakage magnetic field of 23.07 µT in the vertical direction.]]></description>
      <pubDate>Mon, 05 Oct 2026 11:01:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2781016</guid>
    </item>
    <item>
      <title>Environmental impacts of bidirectional charging for electric school buses: a cross-regional assessment</title>
      <link>https://trid.trb.org/View/2785524</link>
      <description><![CDATA[Electric school buses can interact bidirectionally with the grid during overnight idle periods, but their environmental consequences are poorly understood. This study develops a multi-objective overnight scheduling framework for electric school bus fleets that jointly optimizes charging costs, grid export, and age-dependent battery degradation under discrete charger-assignment constraints. A cross-regional assessment across six US grid subregions shows the net CO₂ effect is region-dependent, yielding reductions where the peak-to-off-peak marginal emission factor ratio is high and increases where it is low; coal-dominated grids produce net increases under current generation mixes. Benchmarking against smart unidirectional charging isolates what export itself contributes: deeper CO₂ and NOₓ reductions, but higher SO₂ and a reversal of the PM₂.₅ benefit that unidirectional charging delivers. These outcomes are robust across five fleet scales, six utility tariffs, and five climate zones, though their magnitude depends on regional grid composition, arguing against blanket environmental credits and in favor of region-specific policy design.]]></description>
      <pubDate>Mon, 05 Oct 2026 09:07:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2785524</guid>
    </item>
    <item>
      <title>Recent Advances in Hybrid, Plug-in, Battery, and Fuel Cell EVs: Control Schemes, Modes, Converter Topologies, and Pros and Cons</title>
      <link>https://trid.trb.org/View/2735008</link>
      <description><![CDATA[Direct current (DC)–DC converters are the backbone of electric vehicle (EV) power trains, enabling efficient and bidirectional energy flow between the battery, high-voltage (HV) DC link, and auxiliary rails under tight isolation, gain, ripple, and electromagnetic interference (EMI) constraints. This review catalogs 29 EV DC–DC converter families and classifies them by isolation requirement, gain window, bidirectionality, and ripple tolerance. On the implementation side, we survey modern control-digital voltage/current-mode (VMC/CMC), model-predictive/model-free, and artificial intelligence (AI)–assisted scheduling-together with protections (device level desaturation/soft turn-off through contactor policy) and thermal/EMI codesign (hot-loop minimization, spread spectrum, common-/differential-mode filtering). Normalized benchmarks at three representative operating points-auxiliary 12–48 V, mid-rail 48–800 V, and traction 350–1000 V show dual active bridge (DAB) and inductor–inductor–capacitor/capacitor–inductor–inductor–capacitor (LLC/CLLC) at 96%–98% for isolated HV links and on-board chargers (OBCs), interleaved buck–boost and multidevice interleaved bidirectional converters (MDIBCs) at 97%–98% for nonisolated bus ties, and multi-input power electronic converters/boost stages (PECs/boost) at 92%–96% with superior source decoupling. We provide a selection matrix mapping battery EV/hybrid EV/plug-in hybrid EV/fuel cell EV (BEV/HEV/PIHEV/FCEV) functions to topologies, a grid-to-vehicle ↔ vehicle-to-grid (G2V↔V2G) mode transition procedure, and 2020–2025 updates (silicon-carbide/gallium-nitride [SiC/GaN] devices, single-/triple-phase shift (SPS/TPS) and variable-frequency scheduling, and predictive/digital control). No single converter is universally optimal; choices balance isolation and gain against ripple limits, control complexity, EMI/thermal budgets, and cost.]]></description>
      <pubDate>Thu, 01 Oct 2026 09:25:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2735008</guid>
    </item>
    <item>
      <title>UAV charging dock location and patrol path planning under epistemic uncertainty using distributed reinforcement learning</title>
      <link>https://trid.trb.org/View/2704544</link>
      <description><![CDATA[This paper tackles the integrated problem of UAV charging dock placement and patrol-routing under uncertainty. Signal base stations are adopted to provide continuous control links for UAVs, and only tower-based stations can host charging docks. We propose a two-stage framework that combines a Distributed Multi-Agent Reinforcement Learning (DMARL) scheme with a time–space network model. In Stage 1, each tower-based station is represented by an independent agent that learns, with only local observations, whether to install a dock; the collective decisions converge to a stable, scalable deployment plan. In Stage 2, the resulting dock configuration is fed into a time–space network formulation that optimizes UAV travel, patrol and charging schedules, guaranteeing uninterrupted operation. To balance efficiency and equity, we introduce an Adaptive Weighted Round-Robin Strategy (AWRRS) that dynamically adjusts patrol frequencies. Extensive experiments on real city-wide signal-station data show that DMARL attains near-optimal utility without prior information, outperforming centralized RL. Different AWRRS variants demonstrate controllable trade-offs between efficiency and equity, validating the framework’s adaptability to diverse mission requirements.]]></description>
      <pubDate>Wed, 30 Sep 2026 16:23:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2704544</guid>
    </item>
    <item>
      <title>Rooftop solar PV for green campus and EV charging hub</title>
      <link>https://trid.trb.org/View/2704148</link>
      <description><![CDATA[Global warming demands sustainable energy solutions, with solar photovoltaic (PV) systems emerging as a key technology for developing green campuses. Simultaneously, electric vehicles (EVs) are transforming transportation and supporting Nepal's target of net-zero CO₂ emissions by 2045. This study assesses the rooftop solar PV potential at Suryanarayan Satyanarayan Morbaita Yadav Multiple (SSMYM) Campus, Siraha. The campus load is only 40 kWh/day, while the proposed system generates 875.83 kWh/day. The surplus energy of 835.83 kWh/day will power an EV charging hub for the city with 60 kW and 10 kW chargers, serving 29 heavy vehicles or 85 light vehicles. Using PVsyst, the 3E analysis shows 310.7 MWh of energy generation per year, along with economic benefits of USD 330,678 and a reduction of 753.5 tCO₂ over the 25-year system lifetime. The study also offers practical guidance for grid-tied solar PV design and EVCS planning. It covers the current EVCS landscape, site-suitability criteria, optimal site selection, key performance indicators, planning strategies, and implementation requirements. A proposed single-line diagram for fast charging is also included. The framework provides actionable insights for policymakers, campus administrators, and investors. It can be adapted across Nepal and other developing countries to promote sustainable energy use and e-mobility adoption.]]></description>
      <pubDate>Wed, 30 Sep 2026 15:57:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2704148</guid>
    </item>
    <item>
      <title>Electric Vehicle Technologies in the Smart Grid Era: A Comprehensive Review</title>
      <link>https://trid.trb.org/View/2735123</link>
      <description><![CDATA[Electric vehicles (EVs) present an efficient solution for reducing greenhouse gas (GHG) emissions and enhancing grid power quality. They offer multiple advantages over traditional internal combustion engines (ICEs), including lower emissions, reduced dependence on oil, higher energy efficiency, quieter operation, zero emissions, and improved air quality by minimizing the release of toxic chemicals into the atmosphere. However, there is a lack of literature that comprehensively reviews the factors that can facilitate the assimilation of EV technology. Therefore, this paper provides a comprehensive review of EV technologies, focusing on the growth of global EV adoption and the various types of EVs, including all-EVs and hybrid EVs (HEVs). The comparative analysis of different HEV technologies is presented, covering full HEVs, mild HEVs, and plug-in HEVs (PHEVs). The paper also discusses the different classifications of HEVs based on electrification level and energy source, along with a comparative analysis of their configurations. Furthermore, the EV architecture is examined, with a specific focus on electric motors, battery management systems (BMSs), batteries, and charging technologies, including conductive and wireless charging systems. The challenges in EV charging and the associated charging standards are also addressed. The paper concludes by highlighting the need for the advancement of EV technologies and infrastructure to overcome the significant barriers to rapid EV adoption, while demonstrating how smart grid technologies enhance EV charging efficiency, grid resilience, and energy sustainability.]]></description>
      <pubDate>Wed, 30 Sep 2026 09:29:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2735123</guid>
    </item>
    <item>
      <title>Cost and Emission Aware Strategic Optimization: A Fleet-Level Tool to Support Scenario-Oriented Design</title>
      <link>https://trid.trb.org/View/2784441</link>
      <description><![CDATA[The transition toward low-emission transport systems requires not only technologically optimized Battery Electric Vehicles (BEVs) but also integrated methodologies capable of supporting industrial stakeholders throughout the deployment phase. In particular, for logistics operators, fleet sizing and charging infrastructure planning are tightly coupled with vehicle configuration and mission scheduling. Therefore, decision-support tools are required to minimize total operational costs and environmental impact while ensuring service continuity. Building upon a previously developed two-level BEV design framework, this work introduces a higher-level optimization tool aimed at extending powertrain design outcomes toward fleet-level decision-making, providing an integrated methodology capable of determining not only the optimal vehicle configuration but also the optimal number of vehicles and charging stations required to satisfy operational scheduling constraints. The proposed tool performs fleet charging management optimization under customizable objective functions. Two BEV configurations, equipped respectively with 7 and 10 battery packs, are selected as candidate solutions from the upstream two-level design framework. Starting from these configurations, the tool simultaneously optimizes fleet size, charging infrastructure dimensioning, and charging scheduling strategy. In the first case study, the objective is the minimization of fleet operational costs, primarily associated with charging energy, while introducing a tunable penalty factor on mission time-shifting for schedule flexibility. In the second case study, a CO2-based term is incorporated into the objective function through an equivalent emission cost. By varying its weighting factor, the analysis quantifies how environmental prioritization influences the optimal fleet and infrastructure configuration. Across all the examined scenarios, the optimal fleet size consistently converges to 3 vehicles with a single 50 kW DC charging station. The key difference between cost-driven and environmentally-oriented optimization lies in the battery configuration: in the cost-driven scenario, the 10-packs configuration achieves the lowest Total Cost of Ownership (1134 EUR/week), as its larger energy buffer reduces weekly grid energy demand and thus charging costs. Conversely, under CO₂-prioritized optimization, the optimal configuration shifts to the 7-packs, yielding a lower TCO of 1008 EUR/week and a 15% reduction in CO₂ emissions (127 vs 149 kgCO2/week). The proposed fleet-level optimization framework represents a scalable extension of the vehicle design methodology, enabling logistics companies to support electrification strategies through data-driven, application-specific, and sustainability-oriented decision-making.]]></description>
      <pubDate>Tue, 29 Sep 2026 13:23:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2784441</guid>
    </item>
    <item>
      <title>Characterisation of Brake Wear PM2.5 and PN10 Emissions Across Pad/Disc Technologies, Light-Duty Vehicle Types, and Braking Behaviours Under Real World Conditions</title>
      <link>https://trid.trb.org/View/2784423</link>
      <description><![CDATA[Targeted brake emissions investigations undertaken within the Department for Transport’s Non-Exhaust Emissions programme are described in this paper. The non-exhaust emissions study aimed to improve understanding of particulate mass and particle number emissions from friction braking, and to quantify the influence of component selection, vehicle technology, operating conditions, and emissions control measures. A brake enclosure and sampling methodology, developed in an earlier project phase, was refined to improve airflow control, reduce leakage, and minimise artefacts. The updated system incorporated MPEC (hot and cold), APC10, DMS500, and eFilter instruments, enabling simultaneous measurement of volatile and non-volatile PN10, plus PM2.5.Nine brake pad formulations and two disc types were evaluated using a common C-segment platform during chassis dynamometer and on-road drive cycles, and under specific controlled braking events. Speed, deceleration and temperature effects on PM2.5 and PN10 emissions were investigated. The common platform testing included ICE, PHEV, and EV variants to capture test mass and regenerative braking influences, together with assessments of aged components.Results showed clear, repeatable differences between pad formulations, with low dust/ceramic pads yielding the lowest PM2.5 and PN10 emissions. Disc type effects were minimal, while component ageing/conditioning reduced emissions and improved repeatability. Brake temperature and energy input dominated emissions behaviour: dynamic braking produced the highest emissions, these increasing with road speed and disc temperature. Regenerative braking reduced EV and PHEV PM2.5 versus ICE, but PN10 remained comparable due to the dominance of non-volatile PN emissions during friction braking events. Increased vehicle mass led to proportionally higher emissions.]]></description>
      <pubDate>Tue, 29 Sep 2026 13:23:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2784423</guid>
    </item>
    <item>
      <title>Experimental Comparison Between Real and Simulated Battery Pack Efficiency in Light Quadricycles Using a Custom Inertia Dynamometer</title>
      <link>https://trid.trb.org/View/2784410</link>
      <description><![CDATA[This study compares the energy efficiency of a real battery pack and a simulated battery pack using a hardware-in-the-loop battery emulator, through experimental testing on a dedicated inertia dynamometer for light quadricycles. The investigated LiFePo4 battery pack has a nominal voltage of 48 V and a nominal capacity of 100 Ah. Initial characterization identified a reduced State of Health based on capacity (SOHC), with a measured usable capacity of 48 Ah (from the nominal 100 Ah) and a corresponding reduction in charge acceptance capability. The emulator was configured to replicate the degraded battery characteristics, including the open-circuit voltage (OCV)-SOC relationship, internal resistance, and current limitations, enabling a direct comparison between simulated and experimental dynamic behavior. The experimental setup was designed to overcome the limitations of conventional chassis dynamometers for low-mass, independent four-wheel-drive quadricycles operating without mechanical friction braking systems. Multiple driving cycles were reproduced using a PLC-based closed-loop control system, with data acquisition performed via CAN communication. The comparative analysis highlights significant differences during regenerative braking events. While the emulator accurately reproduces baseline electrical behavior under mild operating conditions, the aged battery exhibits strong limitations during both high-power acceleration and severe deceleration phases. In particular, increased internal resistance and transient electrochemical polarization lead to premature saturation of charge acceptance, resulting in rejection of high-frequency current transients. Consequently, the experimentally observed energy recovery is significantly lower than the theoretical values predicted by the emulator. In addition, due to the absence of mechanical braking, the reduced regenerative capability directly leads to speed tracking deviations, as the required braking torque cannot be fully achieved. These results identify battery degradation as a key physical constraint affecting both energy efficiency and dynamic braking performance, highlighting the importance of improved electro-thermal and aging-aware model calibration for realistic system-level simulations.]]></description>
      <pubDate>Tue, 29 Sep 2026 13:23:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2784410</guid>
    </item>
    <item>
      <title>Research on charging patterns of electric taxis based on high-dimensional cluster analysis: a case study of Hangzhou, China</title>
      <link>https://trid.trb.org/View/2780366</link>
      <description><![CDATA[The promotion of electric vehicles (EVs) poses challenges to the power grid due to the large-scale and disordered charging behaviors. While previous studies have investigated the charging patterns of EVs, little attention has been paid to electric taxis (ETs). To address this gap, this study proposes a novel combinatorial clustering model to investigate the charging patterns of ETs. This model employs Principle Component Analysis (PCA) for dimensionality reduction, an Canopy + to determine the optimal number of clusters, and concludes with K-means for rapid clustering. It exploits the rich information from the high-dimensional features, such as battery status, time, driving range, and environmental conditions, and enables fast and accurate analysis of large-scale ET charging behavior. The model analyzed a year of charging data from 164 ETs in Hangzhou, identifying six typical patterns. The impact of 20,000 ET charging loads on the power grid was further simulated. The results indicate that increasing the proportion of three types of fast-charging patterns can alleviate the peak and standard deviation of the power load of the grid. This study contributes to a better understanding of the charging behaviors of ETs and provides insights for managing the power demand in the context of urban transportation.]]></description>
      <pubDate>Mon, 28 Sep 2026 16:46:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2780366</guid>
    </item>
    <item>
      <title>Co₂ Regulation, Expected Number of ZEVs (BEV and H₂), and Actual Infrastructure Demand
Sustainable Transport Forum: Deliverable 2</title>
      <link>https://trid.trb.org/View/2724802</link>
      <description><![CDATA[This document constitutes the final deliverable of Task Force 2 within the Sustainable Transport Forum (STF) subgroup on heavy-duty vehicles. The primary objective of this deliverable is to identify and quantify the actual demand for recharging and refuelling infrastructure required to support the deployment of zero-emission heavy-duty vehicles (ZEVs), including battery-electric vehicles (BEVs) and hydrogen-powered trucks (H2PTs). The report has been developed collaboratively by a diverse group of stakeholders participating in the task force and reflects the consolidated outcome of 12 months of discussions (November 2024 - November 2025), analyses, and collective reflections on this critical topic.]]></description>
      <pubDate>Mon, 28 Sep 2026 16:45:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2724802</guid>
    </item>
    <item>
      <title>Optimal Traffic-Informed Design and Operation of EV Charging Stations in Power Distribution Grids</title>
      <link>https://trid.trb.org/View/2734863</link>
      <description><![CDATA[This article introduces a novel electric vehicle charging station (EVCS) siting framework that simultaneously optimizes power distribution and transportation network (TN) performance. Addressing the limitations of traditional approaches, which often neglect the dynamic demand of EVCS, our framework employs a multiobjective optimization strategy that jointly determines EVCS location and capacity, alongside battery energy storage system (BESS) deployment. By integrating data-driven techniques, we optimize for grid stability, traffic congestion, and infrastructure costs. Real-world simulations, utilizing actual energy consumption data, demonstrate the effectiveness of our framework. Notably, the optimized BESS effectively mitigates voltage fluctuations and peak load challenges, while the EVCS placement minimizes travel costs and queuing delays, ensuring compliance with both power and traffic constraints.]]></description>
      <pubDate>Mon, 28 Sep 2026 16:45:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2734863</guid>
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