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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>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
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    <item>
      <title>Optimizing BSS Charging Management Considering Renewable Energy by Dynamic Multi-Step Deep RL Approach for Urban Governance</title>
      <link>https://trid.trb.org/View/2610663</link>
      <description><![CDATA[The integration of battery swapping station (BSS) with renewable energy further promotes the electric vehicle (EV) industry in urban governance. However, the complex uncertainty of the environment and limited information hinders the optimization of multi-action battery charging management in BSS. The current research also lacks consideration for renewable energy procurement actions. To address this problem, a dynamic simulation environment incorporating battery charging characteristics and transportation processes for BSS multi-action charging management is introduced. On this basis, a multi-step dynamic battery charging management model based on Proximal Policy Optimization (PPO) is proposed. The goal includes cost reduction considering both price and carbon emissions, minimizing queue lengths and avoiding wastage of charging bays. First, a prediction module estimates the real-time energy price. Then, two decision modules sequentially determine the optimal reservation number of charging bays and the procurement of renewable energy. Unlike traditional methods, the model optimizes the charging management process across multiple actions, particularly in analyzing the impact of renewable energy. Additionally, equivalent energy price coefficients are proposed as a straightforward measure to assess cost disparities between renewable energy and grid energy. The proposed model enhances decision-making efficiency compared to classical methods and demonstrates stability and performance across various scales in experiments. The simulation data of proposed model is available at https://github.com/MyGitHubCAT/GRU-MSPPO]]></description>
      <pubDate>Thu, 26 Mar 2026 17:02:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2610663</guid>
    </item>
    <item>
      <title>Electricity Demand Assessment and Charging Infrastructure Planning for Long-Haul Electric Vehicle Operations in Ontario, Canada</title>
      <link>https://trid.trb.org/View/2643981</link>
      <description><![CDATA[As Canada advances toward its target of zero-emission heavy commercial vehicle sales by 2040, the success of long-haul electric vehicle (LHEV) adoption will depend on the availability of well-placed, high-capacity public charging infrastructure. This study evaluates how two critical real-world constraints, namely daily utilization time and site space capacity, affect the optimal design of Ontario’s future on-route charging network by simulating a total of 63 scenarios. The results show that modest utilization thresholds (e.g., 8 hours per day) can reduce the number of required stations by up to 20% with minimal impact on service coverage. In contrast, restricted space capacity leads to steep declines in the number of supported trips unless more locations are added. When both constraints are applied together, their impacts are largely additive, increasing the need for infrastructure expansion while shifting grid demand across space and time. The study highlights the need to align transportation and electricity infrastructure planning, prioritize high-demand freight corridors, and support regulatory frameworks that promote efficient, high-utilization, and grid-resilient charging solutions.]]></description>
      <pubDate>Wed, 07 Jan 2026 09:27:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643981</guid>
    </item>
    <item>
      <title>Evaluating spatial disparities in public EV charging infrastructure across the United States</title>
      <link>https://trid.trb.org/View/2636401</link>
      <description><![CDATA[Growing environmental concerns and rising energy demand have driven efforts to promote electric vehicles (EVs). However, geographic disparities in public EV charging station (EVCS) distribution continue to hinder progress toward a cleaner transportation future. While previous studies have largely focused on local or regional patterns, this study provides a comprehensive national analysis of public EVCS accessibility across the contiguous United States. Integrating spatial statistics (Bivariate Local Moran's I, colocation analysis) with machine learning (XGBoost) and explainable GeoAI (GeoShapley), we assess how EVCS distribution relates to socioeconomic, demographic, built environment, policy, and environmental factors. We find that EVCS accessibility is positively associated with population density, income, and the driving-age population, but negatively associated with road length per capita in many regions, including Florida, California, and parts of the Northeast. Spatial mismatches persist: EVCSs colocate with restaurants in urban areas and with hotels in rural areas, while proximity to gas stations emerges as a stronger predictor of EVCS accessibility; EV-friendly warm regions in the South show a lack of EVCS. Racial and ethnic disparities are evident, disproportionately affecting Black communities in the Deep South, Hispanic populations in the Southwest, and White populations in the central Midwest. These findings underscore the need for equitable, region-specific planning and targeted policy interventions. Addressing geographic and social disparities in EV infrastructure is critical to ensuring a just energy transition and broad-based access to clean mobility.]]></description>
      <pubDate>Mon, 05 Jan 2026 09:53:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2636401</guid>
    </item>
    <item>
      <title>Improving last-mile delivery operations of electric vehicles using on-demand portable chargers</title>
      <link>https://trid.trb.org/View/2572605</link>
      <description><![CDATA[We investigate the utilization of portable chargers (PCs) for recharging delivery electric vehicles (EVs) en route. While EVs deliver customer orders within predefined time windows, PC delivery vans (PCDVs) are dispatched to supply PCs to EVs at designated customer locations during their visits. We refer to the arising problem as the Electric Vehicle Routing Problem with Time Windows and On-Demand Portable Chargers (EVRPTW-PC). The objective is to minimize overall operational costs by optimizing the fleet size. This study builds upon the Electric Vehicle Routing Problem with Time Windows and Mobile Charging Stations (EVRPTW-MCS) recently proposed in the literature, which primarily focused on recharging EVs at specific locations via Mobile Charging Stations (MCSs). While an MCS should stay parked while recharging an EV, PCDV can deliver the PC and continue its tour to serve other EVs, which improves its overall utilization. In addition, parking restrictions may limit the recharging service of MCSs. Initially, we present the EVRPTW-PC mathematical model of. Then, we propose a matheuristic approach that combines Variable Neighborhood Search alongside an exact method. Finally, we perform numerical experiments to compare the solutions obtained by using PCs and MCSs, and elaborate on the potential benefits of employing PCs.]]></description>
      <pubDate>Wed, 31 Dec 2025 10:55:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2572605</guid>
    </item>
    <item>
      <title>Strategic XFC Charging Station Placement in Equilibrium Traffic Networks</title>
      <link>https://trid.trb.org/View/2553334</link>
      <description><![CDATA[Electric vehicles have become a trend as a replacement to gasoline-powered vehicles, and been promoted by worldwide policy makers as a solution to combat environmental problems and stimulate economy, whereas the lack of extreme fast charging infrastructure has become one main obstacle to broad adoption of electric vehicles. To promote the commercial success of electric vehicles, effective placement of electric vehicle (EV) charging stations is pivotal. While numerous studies address EV charging station placement, the integration of transportation network traffic, specifically equilibrium traffic assignment, where flows stabilize as drivers seek routes to minimize travel time, has been relatively limited. This research investigates equilibrium traffic assignment with the inclusion of extreme fast charging (XFC) stations and introduces an algorithmic solution. We assess diverse charging station placement strategies, including node-based and network-based approaches, weighing their respective advantages and drawbacks. Extensive experiments on real transportation networks of varying scales validate our algorithm and evaluate different charging station placement strategies. Many interesting findings are drawn from the study. For instance, increasing the number of XFC charging stations may not always result in reduced traffic time; the added value of extra stations beyond a certain threshold can be quite limited. The findings offer valuable insights for strategically deploying EV charging infrastructure, thus promoting electric vehicle adoption.]]></description>
      <pubDate>Mon, 22 Dec 2025 17:03:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2553334</guid>
    </item>
    <item>
      <title>Dynamic Spatio-Temporal Planning Strategy of EV Charging Stations and DGs Using GCNN-Based Predicted Power Demand</title>
      <link>https://trid.trb.org/View/2553309</link>
      <description><![CDATA[As a sustainable participant in the modernization of transportation systems, electric vehicles (EVs) call for a well-planned charging infrastructure. To meet the ever-increasing charging demands of EVs, an efficient dynamic spatio-temporal allocation strategy of charging stations (CSs) is necessary. With newly allocated CSs, additional distributed generators (DGs) are required to compensate for the load increase. Given a budget to be allocated over a certain time horizon, we formulate the joint spatio-temporal CSs and DGs planning problem as a multi-objective optimization problem. During each planning period, the allocation strategy aims at minimizing the total power generation costs and CSs/DGs installation costs while satisfying budgetary and power constraints and ensuring a minimum level for the charging requests satisfaction rate. In this regard, we first predict the future power demand of EVs using a graph convolutional neural network (GCNN). Then, using the power demand forecast, we obtain the optimal number and locations of CSs and DGs at each time stage using reinforcement learning. A case study of the proposed allocation strategy over 6 time stages for the 2000-bus power grid of Texas coupled with 720 initially existing CSs is presented to illustrate the performance of the planning strategy.]]></description>
      <pubDate>Mon, 22 Dec 2025 17:03:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2553309</guid>
    </item>
    <item>
      <title>Connected Charging Stations to Govern Individual Mobility: The Contribution of Digitalized Electric Mobility Networks to Sustainable Mobility Policies in France</title>
      <link>https://trid.trb.org/View/2613370</link>
      <description><![CDATA[Since the 1990s, the management of individual mobility has emerged as a significant domain of public policy in France. With the intention of influencing travel behavior, public authorities have invested in public transport networks, active mobility, and shared transport solutions. More recently, aligned with greenhouse gas reduction targets, these public interventions have expanded to include the promotion of electric mobility through substantial investments in open-access charging infrastructures for electric vehicles (EVs). As with other urban infrastructures, charging stations are now undergoing a process of digital transformation. This change encompasses not only technological advancements but also social factors, including personal and professional relationships, cultural practices, and legal frameworks. Key challenges also persist around data governance: ownership, collection, and the negotiation capacity of charging station public operators in interactions with information communication and technology (ICT) firms. Drawing on a qualitative study of 45 interviews with local stakeholders in the Hauts-de-France region, this article argues that the effectiveness of charging infrastructure as a tool of public policy hinges on complex socio-technical conditions. For charging stations to reach their potential for providing real-time data to inform mobility governance, there is a need to address coordination between actors, to clarify data ownership, and to develop shared frameworks for data collection and exchange.]]></description>
      <pubDate>Mon, 22 Dec 2025 17:03:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613370</guid>
    </item>
    <item>
      <title>Barriers to convenient electric vehicle charging: Evidence from China's first-tier cities</title>
      <link>https://trid.trb.org/View/2633873</link>
      <description><![CDATA[The Chinese government has established a charging infrastructure framework that prioritizes private community-based slow charging, supplemented by public fast charging facilities. However, the deployment of private charging points in communities has faced opposition from multiple stakeholders. Based on a micro-level survey, this study analyzes the factors underlying such resistance. Key findings include: a) Property management companies often obstruct charging infrastructure development, and their opposition also influences other residents negatively. b) Increased EV adoption does not directly raise residents' support, indicating a need for further intervention to overcome barriers. c) Residents prefer grid companies to take a more active role in the installation process to reduce complexity and resolve difficulties. Accordingly, we recommend that the government clarify the responsibilities of property management companies, charging infrastructure providers, and grid companies through policy implementation, emphasizing the coordinating role of grid companies. Additionally, property management practices should be more strictly regulated to ensure cooperation with EV owners in installing charging facilities.]]></description>
      <pubDate>Mon, 22 Dec 2025 17:03:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633873</guid>
    </item>
    <item>
      <title>Innovative business model for private charging pile sharing operation and its vehicle-to-grid energy management</title>
      <link>https://trid.trb.org/View/2630646</link>
      <description><![CDATA[The optimization of private charging pile sharing mode operation and the intelligent interaction between electric vehicles and the energy system are crucial. Its social benefits include improving resource utilization and complementing existing charging networks. However, several practical challenges constrain the efficient operation of this innovative business model, namely the private charging pile sharing mode. This paper focuses on the operation of private charging pile sharing mode and its vehicle-to-grid energy management, which encompasses multiple dimensions of the characteristics of innovative business models, the integration of emerging technologies and the complex interactions. Moreover, this paper constructs a multi-objective optimization model that incorporates key elements of uncertain information, decarbonization management, and demand response strategies, and analyzes their potential impacts. Meanwhile, intelligent dispatching and matching mechanisms are used to improve the utilization of private charging resources, balance supply and demand, and effectively manage energy flows. The numerical analysis using real data demonstrates the feasibility and potential of the operational optimization of innovative business models, and verifies the enhancement of multiple economic and environmental gains, such as alleviating the shortage of public charging facilities, reducing charging costs, and improving decarbonization management capabilities.]]></description>
      <pubDate>Mon, 22 Dec 2025 17:03:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2630646</guid>
    </item>
    <item>
      <title>A hierarchical decision framework for dynamic operation of mobile charging stations</title>
      <link>https://trid.trb.org/View/2630647</link>
      <description><![CDATA[The growing adoption of electric vehicles (EVs) has led to increasing demand for flexible and responsive charging services in urban areas. To address spatiotemporal imbalance between charging supply and EV demand, this study investigates the real-time operation of mobile charging stations (MCSs), a promising solution that leverages portable chargers to meet spatiotemporally distributed charging demand. We propose a two-layer online decision framework that integrates reinforcement learning (RL) with exact optimization to jointly address upper-level deployment of the MCS and lower-level charging service scheduling decisions. In the upper layer, a deep Q-network (DQN) learns an adaptive dispatching policy to determine the next deployment location of the MCS based on real-time system states. In the lower layer, an exact optimization solver computes the optimal charging schedule by jointly selecting which EVs to charge, assigning them to ports, and determining the service duration at each stop. The proposed framework follows an event-driven paradigm and enables closed-loop coordination between hierarchical decision layers. Numerical experiments on both synthetic and realistic settings validate the proposed framework. In small-scale tests, the RL policy achieves 24.5 % higher cumulative rewards than the strongest greedy baseline, with greater improvements over queue-based, static, and random heuristics. In a realistic case study with 20 candidate facilities and spatiotemporally heterogeneous demand, it improves cumulative rewards by 11.2 % over the strongest baseline under practical operational constraints. The findings are robust to variations in the number of ports, dwell time, and demand scale; moreover, in a multi-MCS setting the RL policy also outperforms the strongest baseline. These results highlight the effectiveness, scalability, and practical value of the proposed hierarchical framework for dynamic EV charging operations.]]></description>
      <pubDate>Mon, 22 Dec 2025 17:03:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2630647</guid>
    </item>
    <item>
      <title>Magnetically Coupled Interleaved Buck Integrated On-Board Charger for Light Electric Vehicles</title>
      <link>https://trid.trb.org/View/2604072</link>
      <description><![CDATA[This article presents a novel onboard charger that integrates the electric vehicle (EV) motor into its charging system. One of the novelties of this invention is the use of a permanent magnet synchronous motor (PMSM) as a coupled inductor in place of a conventional inductor. Additionally, the inverter used to drive the vehicle’s motor is used in conjunction with the PMSM to establish an interleaved buck converter configuration for battery charging. This is made possible because the special built-in features of the PMSM allow the motor to remain still without any torque generation and decrease the current ripples in the charging mode of operation. The minimal current ripple content and the zero-torque generation have been proven mathematically, and the different charging operational modes of the model have been studied. The proposed charger’s efficiency has also been analyzed, tested, and evaluated experimentally on a 960-W charger to confirm the robustness of this novel system in this article. The research findings prove that the system performs very well, with an efficiency of 94%, providing a reliable charging solution for light EVs.]]></description>
      <pubDate>Mon, 22 Dec 2025 13:19:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2604072</guid>
    </item>
    <item>
      <title>A Synergistic Anode Strategy for Fast and Durable Lithium-Ion Battery Capacitors</title>
      <link>https://trid.trb.org/View/2628376</link>
      <description><![CDATA[Lithium-ion battery capacitors (LIBCs) have gained attention as promising energy storage systems that narrow the performance disparity between lithium-ion batteries and supercapacitors. However, conventional anode materials such as graphite (Gr), suffer from sluggish lithium-ion intercalation kinetics and safety risks at high rates, while soft carbon (SC) faces limitations including low capacity and wide voltage window. To address these challenges, the authors design a Gr/SC hybrid anode that synergistically combines the advantages of both materials. Hybrid anodes with varying mass ratios were fabricated, and their structural features and electrochemical behaviors were systematically investigated. The findings display that the hybrid anodes effectively combine the beneficial characteristics of Gr and SC, offering enhanced conductivity, improved rate capability, and superior cycling stability. Current distribution test was applied for the first time to explore the synergistic effect between two components in hybrid anode, indicating its suitability for fast and durable devices. Moreover, the distribution of relaxation times (DRT) and galvanostatic intermittent titration technique (GITT) results reveal favorable lithium-ion transport dynamics. The hybrid anode LIBC with a Gr to SC ratio of 1:1 achieves 318.4 Wh kg−1 at 0.09 kW kg−1 and 56.3 Wh kg−1 at 15.6 kW kg−1, with 87.9% capacity after 2,000 cycles, demonstrating excellent electrochemical performance. Anode potential analysis further confirms suppressed lithium dendrite growth and electrolyte degradation, contributing to enhanced operational safety. Overall, this work demonstrates that the Gr/SC hybrid anode effectively resolves the trade-off between power performance and safety in LIBCs, offering a practical approach for next-generation fast-charging batteries.]]></description>
      <pubDate>Fri, 05 Dec 2025 17:12:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2628376</guid>
    </item>
    <item>
      <title>Wireless Charging Systems for Electric Vehicles: Review</title>
      <link>https://trid.trb.org/View/2625577</link>
      <description><![CDATA[The gasoline internal combustion engine is a significant source of greenhouse gas emissions. Electric vehicles (EVs) can promote and encourage ecologically sustainable transportation. The growing widespread interest in EVs is driven by the need for more efficient and dependable battery charging methods. Wireless Power Transfer (WPT) is a popular charging method for EVs due to its safety, cheap maintenance, convenience, and dependability. This technology replaces the conductive charging mechanism while preserving the same power rating and efficiency. This paper covers the three leading wireless charging technologies, inductive, capacitive, and magnetic gear, in terms of operating principles, architectures, topologies, benefits, and problems. Considering both stationary and dynamic modes. Furthermore, the Magnetic gear wireless power transmission technique uses synchronous permanent magnets to deliver power wirelessly. This article discusses the developments, alternatives, fundamentals, and benefits of different WPT charging technologies. The economic sustainability of stationary and dynamic wireless charging for EVs is also investigated. Health standards and techniques such as electromagnetic field (EMF) shielding are proposed to deal with safety issues related to human exposure to EMF. To make wireless charging EVs more widely adopted and help developers determine the best design technique to improve the WPT system.]]></description>
      <pubDate>Fri, 05 Dec 2025 17:12:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2625577</guid>
    </item>
    <item>
      <title>Real-World Vehicle Charging Duration Prediction Based on Transfer Learning with SENet-CNN-Transformer Model</title>
      <link>https://trid.trb.org/View/2625576</link>
      <description><![CDATA[Charging duration, defined as the duration required to charge from current to target state of charge, critically determines user experience enhancement. Accurate charging duration prediction faces significant technical challenges, primarily stemming from two issues: the scarcity of real-world charging data and battery systems’ inherent deeply nested nonlinear features. To address these issues, the authors propose a charging duration prediction method based on the Squeeze-and-Excitation Network (SENet)-Convolutional Neural Network (CNN)-Transformer network. First, data-enhancement techniques are employed to improve the richness of the data set. Then, the SENet module is utilized to dynamically adjust feature weights through a channel attention mechanism. The CNN-Transformer model, combining the local feature extraction capabilities of CNN and the global modeling capacity of the Transformer, is employed to overcome the limitations of single networks in feature extraction and dependency modeling. The proposed model is pre-trained on laboratory data and then fine-tuned on real-world data using transfer learning techniques, significantly reducing model training time. Experiments on real-world battery data suggest that the SENet-CNN-Transformer model outperforms CNN-Transformer, Transformer, and Long Short-Term Memory models, with mean absolute error reductions of 56%, 65%, and 75%, respectively. With the transfer learning technique, the training time can be reduced by 4.5 times without sacrificing prediction accuracy. This approach boosts accuracy and cuts training time.]]></description>
      <pubDate>Fri, 05 Dec 2025 17:12:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2625576</guid>
    </item>
    <item>
      <title>Heating–Charging Synergistic Control Method for Low-Temperature Lithium-Ion Batteries Based on Deep Reinforcement Learning</title>
      <link>https://trid.trb.org/View/2603980</link>
      <description><![CDATA[Lithium-ion batteries (LIBs) are subject to very slow charging speed and capacity degradation in low-temperature environments and are prone to lithium precipitation. Herein, a heating–charging synergistic control (HCSC) method for low-temperature LIBs based on deep reinforcement learning (DRL) is proposed, which can achieve constant temperature heating of LIBs in low-temperature environments as well as safe and fast charging without lithium precipitation. This method uses a deep deterministic policy gradient (DDPG) algorithm and combines with the electrical–thermal coupled model of LIBs to optimize alternating current (ac) for heating and direct current (dc) for charging, according to the real-time state of batteries and environmental conditions using the lithium precipitation boundary and the cutoff voltage of the batteries as constraints to obtain the optimal current superposition sequence. The proposed HCSC method is used after the battery is preheated; heating and charging processes are carried out simultaneously and mutually promoted. The experimental results show that compared with the traditional constant-current and constant-voltage (CC-CV) charging method after preheating, the proposed HCSC method not only maintains the battery temperature at a constant level but also increases the charging speed by 79.2% and the charging capacity by 16%, which greatly improves the charging performance of the battery in a low-temperature environment.]]></description>
      <pubDate>Tue, 02 Dec 2025 16:09:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2603980</guid>
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