<?xml version="1.0" encoding="utf-8"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>TRB Publications Index</title><link>http://pubsindex.trb.org/</link><atom:link href="http://pubsindex.trb.org/common/TRIS Suite/feeds/rss.aspx?tc=NN%3AQtpfb%2A" rel="self" type="application/rss+xml" /><description></description><language>en-us</language><copyright>Copyright © 2015. 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>TRB Publications Index</title><url>http://pubsindex.trb.org/Images/PageHeader-wTitle.png</url><link>http://pubsindex.trb.org/</link></image><item><title>Separation and Rendezvous Control With Batteries Replacement for the UAV-USV Ecosystem: A Finite-Time Bipartite Method Under the MPC Structure</title><link>http://pubsindex.trb.org/view/2553287</link><description><![CDATA[In most of the recent literature about model predictive control (MPC) on vehicles, the finite-time and bipartite control are rarely considered. Although existing MPC methods can be applied to address the separation and rendezvous problem, they may suffer from time wasting and consistency conflicts. The finite-time control facilitates the motion planning by pre-calculating the coverage time horizon, which can avoid the unnecessary time-wasting. The bipartite control can realize the collision avoidance for the multi-vehicle system unless the reference state is zero. Therefore, a finite-time bipartite (FTB) control method for the UAV-USV system based on MPC is proposed. However, in most of the existing UAV-USV systems, the sustainable operating mechanism is not considered as usual. In this paper, to realize the sustainable operating mechanism, an UAV-USV ecosystem is introduced and further improved. Specifically, a battery replacement process is proposed and realized by the MPC method with switching topologies. Considering an UAV-USV system, the UAVs on the USV can help the USV replace the rechargeable batteries. And this process can be realized automatically by the proposed MPC method with switching topologies in a finite time. The recursive feasibility and asymptotic stability of the proposed method are proven. In addition, a series of simulations are given to demonstrate the effectiveness and advantages of the proposed method.]]></description><pubDate>Mon, 26 Jan 2026 14:17:10 GMT</pubDate><guid>http://pubsindex.trb.org/view/2553287</guid></item><item><title>Adaptive Battery Swapping for Autonomous Delivery Vehicles Using Dueling Double Deep Q Network</title><link>http://pubsindex.trb.org/view/2652214</link><description><![CDATA[Faced with the rapid growth in demand for instant delivery, traditional logistics delivery modes have struggled to meet these needs effectively because of capacity constraints. Autonomous delivery vehicles (ADVs) can compensate for a shortage of human labor. ADVs, which rely on batteries for propulsion, occasionally need to return to battery-swapping stations to maintain the state of charge of their batteries during delivery. In the context of applying ADVs for instant delivery, we employ agent-based modeling to set the behavioral rules of customers, the ADVs, and the distribution center; therefore, an instant delivery scheduling simulation environment is created. A vehicle routing problem with time windows mathematical model is established and solved to optimize the delivery scheduling by the adaptive large neighborhood search heuristic algorithm. Given the dynamically changing environmental conditions, we utilize the Dueling Double Deep Q Network deep reinforcement learning algorithm, which adapts to these changes, to train ADVs on autonomous battery swapping decisions. The performance of the proposed model is compared with several benchmark policies, including threshold-based strategies, alternative reinforcement learning algorithms, and a fixed strategy in which the ADV swaps its battery on each return to the distribution center. Simulation experiments, based on real-world cases, demonstrate that the proposed model achieves better results. Specifically, it reduces the delay time by approximately 17.55% compared with the average delays of all other benchmark policies and decreases the number of battery swaps by approximately 49.06%. Furthermore, the model exhibits strong adaptability to the dynamically changing simulation environment.]]></description><pubDate>Tue, 20 Jan 2026 10:11:05 GMT</pubDate><guid>http://pubsindex.trb.org/view/2652214</guid></item><item><title>Coordinated Battery Charging and Swapping Scheduling of EVs Based on Multilevel Deep Reinforcement Learning for Urban Governance</title><link>http://pubsindex.trb.org/view/2553259</link><description><![CDATA[Intelligent and efficient energy supply management lays an essential foundation for urban governance and electric vehicle (EV) industry. Specifically, battery swapping is a novel mode of power supply for EVs. However, the new way of energy supply complicates the action policies of EVs, especially when the number of power supply facilities is limited. To address this issue, this paper proposes a multilevel deep reinforcement learning (DRL) method to coordinate the action of EVs within the battery charging and swapping station (BCSS) environment. Firstly, an action-driven simulation framework is developed to simulate the BCSS environment and obtain the EVs’ attributes. Then the multilevel algorithm is proposed to drive the EVs to obtain charging strategies. In the multilevel algorithm, the initial decision for EVs is provided by a DRL-based model. Then the advantage value function is utilized to adjust the initial decision of EVs to meet the constraints of limited charging and swapping equipment. Besides, unlike traditional DRL-based methods, the proposed model is driven by the rewards obtained from EV actions. Finally, extensive experiments have shown that the proposed multilevel DRL-based method has superior performance over existing methods in resolving coordinated battery charging and swapping actions. In particular, the proposed model can provide a suggested and reasonable price range for the practical battery swapping mode operation.]]></description><pubDate>Wed, 10 Dec 2025 08:24:57 GMT</pubDate><guid>http://pubsindex.trb.org/view/2553259</guid></item><item><title>Research on Joint Distribution Path Planning of Electric Logistics Vehicles with Different Recharge Modes</title><link>http://pubsindex.trb.org/view/2414255</link><description><![CDATA[To address the route planning issues under the community group purchase model for joint delivery, this study thoroughly considers electric logistics vehicles with different recharging methods. The objective is to minimize the sum of operating costs, recharging costs, time window penalty costs, and carbon emission costs. Separate multi-objective optimization models for route planning are constructed for both charging and battery-swapping logistics vehicles. An improved seagull optimization algorithm, guided by the golden sine strategy of the Lévy flight guidance mechanism, is employed to avoid local optima and enhance the solution efficiency. The feasibility of the models and the algorithm is verified through simulation examples. Experimental results show that, at the current stage, battery-swapping logistics vehicles display significant advantages over charging electric logistics vehicles. Although battery-swapping logistics vehicles extend delivery time, they can reduce the total delivery costs to a certain extent. Therefore, the future development prospects of battery-swapping logistics vehicles will be even broader.]]></description><pubDate>Fri, 09 Aug 2024 08:40:49 GMT</pubDate><guid>http://pubsindex.trb.org/view/2414255</guid></item><item><title>Insights into User Acceptance of Battery Swapping Services for Sustainable Micromobility using the Unified Theory of Acceptance and Use of Technology 2 Model</title><link>http://pubsindex.trb.org/view/2414244</link><description><![CDATA[Intelligent Battery Swapping Services (BSSs) present an innovative solution to the challenges of charging, safety hazards, and disorganized battery management encountered by electric micromobility vehicles (EMVs). Although BSSs have gained traction in the business-to-business domain, their acceptance in the business-to-customer sector remains uncertain. This study leverages the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) combined with a structural equation modeling framework to discern the adoption intentions toward BSS among individual drivers. Drawing from a survey of 434 EMV users in Jiangsu Province, China, we analyzed the relationship between latent variables, while also evaluating the moderating effects of sociodemographic and transport-related factors via multi-group analysis. The findings revealed a model with impressive explanatory power, accounting for approximately 54.8% of the variance in intention. Notably, social influence emerged as the most potent influencer on intention, trailed by effortlessness expectancy, price sensitivity, and performance expectancy. Intriguingly, both price sensitivity and technology anxiety exhibited a negative correlation with intention. Furthermore, variables such as gender, age, income, riding purpose, and riding frequency were found to significantly shape users’ intentions to embrace BSS. This research offers valuable insights for policy makers aiming to promote BSS adoption among EMV users and encourage the EMV market’s growth and sustainability.]]></description><pubDate>Fri, 09 Aug 2024 08:40:49 GMT</pubDate><guid>http://pubsindex.trb.org/view/2414244</guid></item><item><title>Intermittent Electrification with Battery Locomotives and the Post-Diesel Future of North American Freight Railroads</title><link>http://pubsindex.trb.org/view/2408331</link><description><![CDATA[Changing attitudes, regulations, public policy, and international treaties concerning fossil fuels are likely to lead freight railroads toward carbon-neutral technologies, yet only electric traction matches the performance of diesel-electric locomotives. The tremendous power requirements of freight trains make efforts to reduce greenhouse gas (GHG) emissions challenging, but the prospect of 7.2?megawatt-hour battery-electric locomotives (BELs) offers promise, and intermittent electrification can facilitate battery charging. Train performance calculations under simulated real-world conditions show four 7.2?megawatt-hour BELs can power 8,000-ton trains up to 230?mainline miles unassisted, with average energy consumption of 12.5?watt-hours/ton-mile. This permits discontinuous electrification of major freight lines, leaving “gaps” of up to 200?mi to reduce capital costs, especially in rugged terrain or where the power grid is sparse. Massive onboard battery arrays and intermittent access to the electrical grid for traction power and recharging provide great energy savings by recycling energy now lost when traveling downhill or braking. A case study of a hypothetical Class I railroad found intermittent electrification with BELs more than 60% more cost-effective than contiguous electric districts, and dramatically reduced engine changes. To reduce railroad GHG emissions, governments must support technical development, show that electrification works in various North American settings, and develop institutional-financial frameworks to incentivize intermittent electrification with BELs, all in the context of massive environmental and capacity upgrades to electrical networks. Given proper assistance and incentives, early 21st century railroads may find discontinuous overhead-wire electrification offers great promise in operating terms.]]></description><pubDate>Sat, 03 Aug 2024 16:24:50 GMT</pubDate><guid>http://pubsindex.trb.org/view/2408331</guid></item><item><title>Future of Global Electric Vehicle Supply Chain: Exploring the Impact of Global Trade on Electric Vehicle Production and Battery Requirements</title><link>http://pubsindex.trb.org/view/2386155</link><description><![CDATA[Electric vehicle (EV) adoption is a key action toward reducing global greenhouse gas emissions from the transport sector, enabling a shift from a fossil fuel intensive on-road sector to a material intensive one, especially for critical minerals used in lithium-ion batteries: lithium, cobalt, and nickel. Recent literature has manly focused on forecasting future EV demand and subsequent global battery and critical mineral requirements without modeling which countries will produce the EVs. The Model for International Electric Vehicle Trade (MONET) is a policy-scenario model that combines up-to-date EV demand forecasts, light-duty vehicle global trade flows under different scenarios, and battery characterization to estimate future EV production and battery requirements per country. Results indicate that future EV global trade will be characterized by trade within regional blocks, with contrasting results: North America will be a big producer that will still require imports to meet their demand, Europe will be a big trade region within itself, Japan and South Korea will be big exporters of EVs, and China’s production could go almost entirely to satisfy their domestic supply. Different scenarios show variability according to changes in global trade flows, which are affected by economic and geopolitical events. A major insight reflected in MONET is that an increase in demand for EVs in one country does not translate to a proportional increase in production in the same country. MONET helps to inform which countries will be major producers of EVs by vehicle size, and the amount of battery capacity they will need to secure. Future expansion of MONET includes critical materials estimations based on future battery chemistry development.]]></description><pubDate>Tue, 11 Jun 2024 17:00:43 GMT</pubDate><guid>http://pubsindex.trb.org/view/2386155</guid></item><item><title>Emerging Opportunities for Battery Swapping in the Electric Two-Wheeler Segment in India</title><link>http://pubsindex.trb.org/view/2196825</link><description><![CDATA[With greenhouse gas (GHG) emissions and mobility being inextricably linked, the Indian government has introduced a slew of policies to decarbonize transportation and transition toward electric mobility. Though electric vehicle penetration is currently at a nascent stage, India offers the world’s largest untapped market, especially in the two-wheeler (2W) segment. However, high upfront purchase costs, scarcity of charging-enabled parking spaces, and longer charging times have been the major challenges in accelerating electric two-wheeler (e-2W) adoption in India. Addressing these issues, battery swapping is an alternative fast refueling option that eliminates wait time for charging, makes better use of land, and promises increased available run time. This paper analyzes the status of e-2W adoption, their total cost of ownership (TCO), and the growth trajectory of e-2Ws in three different scenarios of sales penetration to estimate the battery capacity requirements for battery swapping by 2030. The TCO analysis suggests that e-2Ws are more economical for commercial than private usage because of their higher daily utilization; however, battery swapping makes e-2Ws economical even for private usage. By 2030, the cumulative number of e-2Ws is estimated to increase from 17.4?million with a 20% sales penetration of e-2Ws (pessimistic scenario) to 54.4?million with an 80% sales penetration (optimistic scenario). Noting the additional battery pack requirements for the battery-swapping option, India is estimated to require a staggering figure of 133–291?GWh, 75–162?GWh, and 42–91?GWh of battery capacities under the optimistic, realistic, and pessimistic sales scenarios respectively.]]></description><pubDate>Fri, 16 Jun 2023 16:43:50 GMT</pubDate><guid>http://pubsindex.trb.org/view/2196825</guid></item><item><title>Battery Electric Vehicles Network Equilibrium With Flow-Dependent Energy Consumption</title><link>http://pubsindex.trb.org/view/2063696</link><description><![CDATA[Recent studies show that energy consumption of battery electric vehicles (BEVs) increases in traffic congestion. Therefore, it is important to consider the effect of link flow on BEV energy consumption. The flow-dependent energy consumption changes the route choice and user equilibrium conditions. In this paper, some shortcomings of available BEV flow-dependent energy consumption user equilibrium models are shown first. Then, “sufficient” as well as “sufficient and necessary” user equilibrium based on the generalized travel time of each path and sub-path penalties are defined and modeled for flow-dependent energy consumption. While it is difficult to solve the sufficient and necessary model, the sufficient model can be solved directly with commercial solvers for small to medium-sized networks by generating all paths. An iterative algorithm is also presented to generate paths as required to solve the problem for larger networks. Numerical examples demonstrate the model and proposed algorithm, and analyze the impact of flow-dependent energy consumption on equilibrium conditions.]]></description><pubDate>Wed, 23 Nov 2022 12:27:19 GMT</pubDate><guid>http://pubsindex.trb.org/view/2063696</guid></item><item><title>Point of View: Decarbonizing the U.S. Power Grid: Are Battery Electric Vehicles Part of the Solution?</title><link>http://pubsindex.trb.org/view/2055568</link><description><![CDATA[Net energy storage within battery electric vehicles (EVs)—powered solely by an electric battery with zero tailpipe emissions—will quickly eclipse grid-scale storage projects. Assuming battery electric vehicles are limited to smaller 30 kilowatt-hour batteries, their combined storage capacity is on track to double net grid storage capacity in 2022. What if all of this mobile energy storage could be deployed as power available to the grid while an electric vehicle is otherwise not in use? The author of this article suggests that the onboard storage of electric vehicles should be viewed as a means to help decarbonize the power system. Topics discussed include: benefits of coordination of EV battery energy storage; U.S. transmission system operators and the concept of virtual power plants; regulatory challenges; and the issue of battery lifespan.]]></description><pubDate>Tue, 15 Nov 2022 10:50:50 GMT</pubDate><guid>http://pubsindex.trb.org/view/2055568</guid></item><item><title>Design of Battery Electric Bus System Considering Waiting Time Limitations</title><link>http://pubsindex.trb.org/view/2014945</link><description><![CDATA[Battery electric bus (BEB) is getting increasing consideration from transit agencies as a sustainable public transportation alternative. Although BEBs' limited driving range and longer charging duration cause concern for bus operators, fast-charging technology is a potential remedy for extending the daily driving range of BEBs while simultaneously reducing the charging duration and battery size. However, increased queue length at the terminals for fast charging can also cause a problem in adhering to BEB schedules. In this context, a comprehensive analysis is required to plan and design the BEB system considering waiting time limitations, and finding the right trade-off between battery sizes and charging infrastructure. In this study, we developed a base optimization model, as well as its stochastic version, accounting for varying energy demand scenarios under different operating conditions. Our models determined the effects of waiting time limitations over the BEB battery sizes and charger design variables (location, size, and capacity) while minimizing the total cost of the BEB system. The sample average across all the scenarios is used to estimate the stochastic optimization model objective, which is then solved using the Lagrangian relaxation method. We implemented our model over a public bus subnetwork proposed for electrification in the city of New Delhi, India. The results suggest that considering waiting time limitations at a fast-charging station can affect the system design and will help bus operators abide by the service level agreements covering trip delay, frequency, daily mileage, and so forth, made with the transit agency.]]></description><pubDate>Fri, 02 Sep 2022 15:50:59 GMT</pubDate><guid>http://pubsindex.trb.org/view/2014945</guid></item><item><title>Are Electric Vehicle Targets Enough? The Decarbonization Benefits of Managed Charging and Second-Life Battery Uses</title><link>http://pubsindex.trb.org/view/1930161</link><description><![CDATA[Vehicle electrification delivers fast decarbonization benefits by significantly improving vehicle efficiency and relying on less carbon-intense feedstocks. As power grids transition away from carbon-intensive generation and battery energy density improves, the transportation sector’s greenhouse gas savings may deliver upwards of a 75% reduction in current carbon footprint for many nations. Actual savings depend on many variables, like power grid feedstocks, charging rates and schedules, driver behavior, and weather. A special synergy between power and transportation sectors comes via managed charging and second-life battery uses for energy storage systems. This paper reviews the added carbon and energy savings that can come from these two strategies. If charging stations are widely available at one’s destination, utility-controlled managed charging could reduce emissions from electric vehicle charging by one-third. Downcycling electric vehicle batteries for energy storage can also lower peaker power plant use, avoid curtailment of renewable feedstocks, and lessen households’ power-based carbon footprints by half—or contribute up to 5% of grid power capacity.]]></description><pubDate>Wed, 23 Mar 2022 10:53:57 GMT</pubDate><guid>http://pubsindex.trb.org/view/1930161</guid></item><item><title>Improving the Quality and Cost Effectiveness of Multimodal Travel Behavior Data Collection: A Case Study</title><link>http://pubsindex.trb.org/view/1759697</link><description><![CDATA[Multimodal transportation such as transit, bike, walk, ride-hailing (e.g., Uber, Lyft), carshare, and bikeshare are vital to supporting livable communities. However, current multimodal travel behavior data collection techniques, including travel behavior survey apps, have limitations (e.g., negative impact on battery life, user acquisition). This paper describes a case study of software developed to collect multimodal travel behavior data on an ongoing basis from users of an existing open-source mobile app for multimodal information, OneBusAway. To overcome battery life challenges, the research team used the Android Activity Transition API, which leverages hardware advancements in modern mobile phones. An update to the app was released to 676 users. Over 10 weeks, 74 users opted into the study without any incentive and contributed 65,582 trips. Key concerns for data collection when conserving battery life are the timeliness and accuracy of data. Location data was collected for 86% of all origins and destinations. Most delays in location acquisition when starting or ending activities were under a few minutes (e.g., 90th percentile of delay at origins was 3.2 minutes, 68th percentile was 14 seconds). The locations for origins and destinations were building-level accuracy or better (95th percentile of estimated accuracy was 48 meters). The primary cause of low activity classification confidence values seems to be uncertainty for walking vs. standing still. The software deployed in this project is a promising new tool with a tradeoff of reduced data density for the ability to collect data from many users for longitudinal studies with little incentives required.]]></description><pubDate>Thu, 04 Feb 2021 10:57:40 GMT</pubDate><guid>http://pubsindex.trb.org/view/1759697</guid></item><item><title>Operational Feasibility Assessment of Battery Electric Construction Equipment Based on In-Use Activity Data</title><link>http://pubsindex.trb.org/view/1758999</link><description><![CDATA[Despite the significant progress in on-road vehicle electrification, the majority of construction equipment types are still using conventional diesel engines. Though there has been a steady flow of studies in this field, not all equipment types have yet been evaluated. This paper contributes to filling that data gap by analyzing real-world second-by-second activity data from 17 off-road vehicles across six equipment types to investigate their electrification potential. The collected data are used to determine real-world power and torque demands—which are then used to select currently available electric motors suited for electrification of these types of equipment. Required battery sizes for battery electric operation are also calculated considering recorded energy demands, and battery sizes are standardized across equipment types for realistic implementation. The resulting battery electric systems are simulated to determine their effectiveness in fulfilling real-world activity demands. The results show that four of the six types can be electrified to a significant extent using battery electric powertrains with a single-motor set-up, while the remaining two types are more suitable for hybridization because of their high energy needs.]]></description><pubDate>Thu, 04 Feb 2021 10:54:23 GMT</pubDate><guid>http://pubsindex.trb.org/view/1758999</guid></item><item><title>Electric Vehicle Charger Placement Optimization in Michigan Considering Monthly Traffic Demand and Battery Performance Variations</title><link>http://pubsindex.trb.org/view/1762433</link><description><![CDATA[Limited charging infrastructure for electric vehicles (EVs) is one of the main barriers to adoption of these vehicles. In conjunction with limited battery range, the lack of charging infrastructure leads to range-anxiety, which may discourage many potential users. This problem is especially important for long-distance or intercity trips. Monthly traffic patterns and battery performance variations are two main contributing factors in defining the infrastructure needs of EV users, particularly in states with adverse weather conditions. Knowing this, the current study focuses on Michigan and its future needs to support the intercity trips of EVs across the state in two target years of 2020 and 2030, considering monthly traffic demand and battery performance variations. This study incorporates a recently developed modeling framework to suggest the optimal locations of fast EV chargers to be implemented in Michigan. Considering demand and battery performance variations is the major contribution of the current study to the proposed modeling framework by the same authors in the literature. Furthermore, many stakeholders in Michigan are engaged to estimate the input parameters. Therefore, the research study can be used by authorities as an applied model for optimal allocation of resources to place EV fast chargers. The results show that for charger placement, the reduced battery performance in cold weather is a more critical factor than the increased demand in warm seasons. To support foreseeable annual EV trips in Michigan in 2030, this study suggests 36 charging stations with 490 chargers and an investment cost of $23 million.]]></description><pubDate>Tue, 19 Jan 2021 13:03:07 GMT</pubDate><guid>http://pubsindex.trb.org/view/1762433</guid></item></channel></rss>