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    <title>Research in Progress (RIP)</title>
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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>
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      <title>Research in Progress (RIP)</title>
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      <link>https://rip.trb.org/</link>
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    <item>
      <title>Assessing Electric Vehicle Benefits in a Rural, Cold, and Mountainous Region</title>
      <link>https://rip.trb.org/View/2425460</link>
      <description><![CDATA[There is a pressing need to develop a place-specific understanding of the factors that affect the greenhouse gas (GHG) emissions impacts of vehicle electrification. Prior research indicates that the GHG benefits of vehicle electrification depend on the composition of vehicles in a household, vehicle attributes, how they are used, how they are charged, the share of miles that are electric (utility factor), and the efficiency of vehicles. While limited evidence suggests that electric vehicle use, charging, and GHG benefits may differ in rural contexts, cold climates, and mountainous regions, little is known about how their use and performance differs in these contexts, or how to modify vehicle electrification policies and programs to ensure greater GHG benefits of vehicle electrification. This study will collect
real-world driving data in the mountainous and largely rural northern state of Vermont to determine how plug-in electric vehicle (PEV) use and performance differ across these contexts and for different vehicle types. A household survey and on-board Global Positioning System (GPS) monitoring devices will be used to evaluate vehicle use, vehicle and household utility factors, electric range, and efficiency, as well as implications for use and utility factors of future electric vehicle adopters. Findings from this research are critical to informing vehicle incentive programs and public charging investments to ensure that PEV adoption reduces GHGs in a broader range of contexts.]]></description>
      <pubDate>Sat, 07 Sep 2024 11:56:22 GMT</pubDate>
      <guid>https://rip.trb.org/View/2425460</guid>
    </item>
    <item>
      <title>Use of Carbon Dots to Boost Energy Content of Biodiesel to Enable Next-Generation Hybrid Heavy Vehicles for Ground Transportation While Improving Safety</title>
      <link>https://rip.trb.org/View/2342031</link>
      <description><![CDATA[Increasing energy demands due to rapid industrialization and urbanization, stringent emission limits, and depleting sources of conventional fossil fuels urges the scientific community in search of renewable, reliable, cost-effective, and environmentally friendly alternative and sustainable options. In the transportation sector, this has translated as both electrification and increased adoption of biofuel. The electrification seems sufficient for light duty vehicles but for heavy duty vehicles, hybrid model will be the way forward during technology transition. Thus, biofuel, particularly biodiesel, has become a center of research initiatives as a replacement or a supplement to conventional petroleum-based fossil fuels [1-6]. Biodiesel was the second most produced and consumed biofuel in the United States in 2021 and accounted for about 11% and 12% of total U.S. biofuels production and consumption respectively [7]. Also, 1.64 billion gallons of biodiesel were produced in 2021 of which Soybean oil-based biodiesel contributes the most to this production (around 68%). Biodiesel can be blended and used in many different concentrations, including B100 (pure biodiesel), B20 (20% biodiesel, 80% petroleum diesel), B5 (5% biodiesel, 95% petroleum diesel), and B2 (2% biodiesel, 98% petroleum diesel). B20 is a common biodiesel blend in the United States.
     
Biodiesel advantages include low or no sulfur content, no aromatics content, high flash point, inherent lubricity, biodegradability, reduction of most regulated exhaust emissions, miscibility with petro-diesel in all blend ratios and compatibility with the existing fuel distribution infrastructure [4-6]. Technical challenges associated with biodiesel include reduction of NOx exhaust emissions, improvement in specific energy density and improvement of oxidative stability and cold flow properties. Achieving the same energy content as petro-diesel is a major challenge which will enable the widespread adoption of biodiesel for heavy duty diesel vehicles as biodiesel typically have ~10% lower energy content compared to their Petro-diesel counterpart. Carbon nanoparticles have emerged as a unique and potential addition to current fuel additives used in biodiesel and diesel fuels, resulting in lower emissions and improved engine performance [5-6]. Carbon nanoparticles offer unique features (such as greater surface area/volume ratio, higher combustion rate, increased energy density and so on) that make them ideal for various engineering purposes. In addition, nanometric materials may achieve the necessary chemical and thermal properties standard. Combining different nanoparticles with biodiesel provided evidence of enhancement in engine performance and reduce pollution. In addition, Carbon nanoparticles’ prospects as fire safety additives has been explored in the previous works [8-15]. However, the inclusion of Carbon nanoparticles is limited by their adverse effects on environment and health. Thus, biocompatible and bio-degradable carbon nanoparticles come into play for commercial use of nanoadditives for biodiesel. 
    
Hence, the focus of this project will be evaluating the biocompatible and bio-degradable carbon nanoparticles (e.g.: Carbon dots) as fuel additive for biodiesel particularly enhancing the energy content of biodiesel without compromising the positive benefits associated of using biodiesel. The work will also include technology transfer issues like testing, tuning, and validating the fuel additive mixture performance when combined with existing fuel additives, low and high temperature storage and operation, and other performance specifications as needed for commercial introduction.
]]></description>
      <pubDate>Mon, 19 Feb 2024 17:07:51 GMT</pubDate>
      <guid>https://rip.trb.org/View/2342031</guid>
    </item>
    <item>
      <title>Selecting Revenue Models for Electric Vehicle Charging</title>
      <link>https://rip.trb.org/View/1892170</link>
      <description><![CDATA[No abstract provided.
]]></description>
      <pubDate>Mon, 15 Nov 2021 18:51:35 GMT</pubDate>
      <guid>https://rip.trb.org/View/1892170</guid>
    </item>
    <item>
      <title>Optimal Deployment of Dynamic Charging Lanes for Plug-in Hybrid Trucks</title>
      <link>https://rip.trb.org/View/1493885</link>
      <description><![CDATA[To effectively implement charging-while-driving (CWD) technology in trucking freight transportation, charging lanes need to be strategically deployed in the road network connecting logistics centers, such as ports, terminals, and distribution centers. The charging lane deployment problem is twofold. First, it is necessary to determine the optimal location for the construction of charging lanes. Second, one must consider the influence of deployed charging lanes on the route choice behaviors of drivers, especially drivers of  plug-in hybrid electric trucks (PHETs). The behaviors of drivers in a transportation network are usually described with a user equilibrium (UE) assignment model. Although a number of studies have formulated UE models considering electric vehicles (e.g., Jiang et al., 2012, 2014; He et al., 2014, 2015, 2016; Chen et al., 2016), none of them are capable of describing the behaviors of PHET drivers in a network with charging lanes. An electric motor has much higher energy efficiency than an internal combustion engine (ICE), and as a result, PHET drivers can significantly reduce fuel costs by consuming electricity instead of petroleum fuel (Granovskii et al., 2006; Nanaki and Koroneos, 2013; USDOE, 2017). Therefore, PHET drivers may simultaneously consider travel time and fuel costs when traveling from their origin to their destination and may prefer routes with charging lanes. These two problems should be treated simultaneously in a network setting.]]></description>
      <pubDate>Wed, 03 Jan 2018 10:58:28 GMT</pubDate>
      <guid>https://rip.trb.org/View/1493885</guid>
    </item>
    <item>
      <title>Instructional Design Guides for Inspecting Electric and Hybrid-Electric Commercial Motor Vehicles</title>
      <link>https://rip.trb.org/View/1400191</link>
      <description><![CDATA[This project was predicated on the findings and recommendations of another project, “Electric Drive Vehicle Systems: Suggested Changes to Large Truck and Motorcoach Regulations and Inspection Procedures.” An online training course was developed to instruct inspectors how to safely inspect high-voltage electric-drive vehicles.]]></description>
      <pubDate>Thu, 03 Mar 2016 10:36:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/1400191</guid>
    </item>
    <item>
      <title>Emissions &amp; Performance of Alternative Vehicles in Northern Climates</title>
      <link>https://rip.trb.org/View/1359769</link>
      <description><![CDATA[The focus of this project is to quantify "real-world" emissions from hybrid versus non-hybrid vehicles. State-of-the-art micro-simulation models can replicate vehicle activity, fuel economy and emissions. Unfortunately, the factors used by such models are often based on data from laboratory tests conducted under ideal conditions.  Transportation planning models are the basis for decision-making related to new infrastructure, congestion mitigation and safety. These models are also used to evaluate the air quality impacts of transportation projects under Federal "Conformity" legislation requirements (CFR, 2006; FHWA, 2006). State-of-the-art micro simulation models, such as TRANSIMS, model second-by-second vehicle activity (speed and acceleration rate), fuel economy, and emissions (LANL, 2005). Unfortunately, the emissions algorithms used by such models are often based on look-up tables of data from laboratory dynamometer tests conducted under ideal conditions (i.e. new vehicles, 70 degrees F) that do not capture actual "real-world", on-road emissions accurately and do not account for real-world factors such as road grade, temperature or other non-ideal factors. Furthermore, due to their recent introduction, the emissions benefits of alternative technologies like hybrid-electric vehicles and alternative fuels (biofuels) are not incorporated due to a lack of emissions and performance data.  The project's engineers will focus on quantifying real-world emissions from alternative vehicles; the project's behavioral scientists will focus on public understanding of vehicle emissions - how citizens understand things they cannot see -- and the effect that their understanding has on their behavior related to emissions. Therefore, in addition to developing a new alternative vehicle/fuels emissions database, this unique research collaboration will explore ways to improve the communication of the research results to the public as well as to transportation planners and policy makers.  The objectives of the project are to:  1. Quantify real-world emissions and performance of hybrid passenger car vehicles operating in cold weather and hilly, rural terrain. 2. Quantify emissions from diesel vehicles and engines operating on biodiesel fuels. 3. Develop and evaluate low-cost sensors to facilitate widespread real-world vehicle testing. 4. Evaluate disaggregate hybrid vehicle performance for micro simulation models. 5. Develop modal emissions and activity models for hybrid and biodiesel vehicles. 6. Establish a baseline for public knowledge of and behaviors affecting vehicle emissions. 7. Utilize the public knowledge baseline from Objective 6 to develop educational materials that maximize information internalization and affect subsequent emissions-related behavior, including information dissemination through social networks.]]></description>
      <pubDate>Thu, 02 Jul 2015 01:01:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/1359769</guid>
    </item>
    <item>
      <title>Multi-Scale Model of the U.S. Transportation Energy Market for Policy Assessment - Part 3</title>
      <link>https://rip.trb.org/View/1359748</link>
      <description><![CDATA[Our current regulatory scheme for energy may not be viable as we turn to new energy sources including plug-in hybrid vehicles. By understanding the multitude of factors influencing market forces, this project will help assure that the regulatory actions by federal, state and local governments play a positive role in influencing the transportation energy market. Achievement of federal targets for alternative energy use will require large-scale infrastructure investment in: (1) New types of vehicles (3) Facilities for extraction, refining, and transportation of different alternative fuels (4) Biofuel feedstock production and transportation facilities (5) Fuel storage facilities (6) Fuel supply stations (7) Research and research facilities necessary to provide technological advances required for alternative fuels to be feasible. The exact type of investment, and the utility and life-span of the resulting infrastructure, is sensitive to a wide variety of dynamic factors, including supply and usage of different alternative fuels, weather fluctuations, production of biofuel feedstocks, prices and supply of traditional fossil fuels, and public perception of environmental concerns. All of these various factors are closely integrated and are ultimately regulated by market forces. Regulatory actions by federal, state and local governments can play a critical role in influencing the transportation energy market, but because of the high degree of interdependency between the various factors that govern this market, it is difficult to predict the market consequences and sensitivity to any given regulatory change. The proposed research will develop an agent-based complex systems model for transportation energy usage. This model is intended to be used for development of optimal regulatory approaches for control of alternative energy usage and infrastructure investment. Two scales of modeling are considered - a city scale, in which the actions of agents represent choices made by individual users, and a national scale, in which agents represent the aggregate population of a town or city and the agent choices are made subject to a probability distribution representative of the choices of the city population.]]></description>
      <pubDate>Thu, 02 Jul 2015 01:01:01 GMT</pubDate>
      <guid>https://rip.trb.org/View/1359748</guid>
    </item>
    <item>
      <title>Emissions and Performance of Alternative Vehicles in Northern Climates</title>
      <link>https://rip.trb.org/View/1359741</link>
      <description><![CDATA[The focus of this project is to quantify "real-world" emissions from hybrid versus non-hybrid vehicles. State-of-the-art micro-simulation models can replicate vehicle activity, fuel economy and emissions. Unfortunately, the factors used by such models are often based on data from laboratory tests conducted under ideal conditions. Transportation planning models are the basis for decision-making related to new infrastructure, congestion mitigation and safety. These models are also used to evaluate the air quality impacts of transportation projects under Federal "Conformity" legislation requirements (CFR, 2006; FHWA, 2006). State-of-the-art micro simulation models, such as TRANSIMS, model second-by-second vehicle activity (speed and acceleration rate), fuel economy, and emissions (LANL, 2005). Unfortunately, the emissions algorithms used by such models are often based on look-up tables of data from laboratory dynamometer tests conducted under ideal conditions (i.e. new vehicles, 70 degrees F) that do not capture actual "real-world", on-road emissions accurately and do not account for real-world factors such as road grade, temperature or other non-ideal factors. Furthermore, due to their recent introduction, the emissions benefits of alternative technologies like hybrid-electric vehicles and alternative fuels (biofuels) are not incorporated due to a lack of emissions and performance data.]]></description>
      <pubDate>Thu, 02 Jul 2015 01:00:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/1359741</guid>
    </item>
    <item>
      <title>Multi-Scale Model of the U.S. Transportation Energy Market for Policy Assessment - Part 2</title>
      <link>https://rip.trb.org/View/1359738</link>
      <description><![CDATA[Our current regulatory scheme for energy may not be viable as we turn to new energy sources including plug-in hybrid vehicles. By understanding the multitude of factors influencing market forces, this project will help assure that the regulatory actions by federal, state and local governments play a positive role in influencing the transportation energy market. Achievement of federal targets for alternative energy use will require large-scale infrastructure investment in: New types of vehicles Facilities for extraction, refining, and transportation of different alternative fuels Biofuel feedstock production and transportation facilities Fuel storage facilities Fuel supply stations Research and research facilities necessary to provide technological advances required for alternative fuels to be feasible The exact type of investment, and the utility and life-span of the resulting infrastructure, is sensitive to a wide variety of dynamic factors, including supply and usage of different alternative fuels, weather fluctuations, production of biofuel feedstocks, prices and supply of traditional fossil fuels, and public perception of environmental concerns. All of these various factors are closely integrated and are ultimately regulated by market forces. Regulatory actions by federal, state and local governments can play a critical role in influencing the transportation energy market, but because of the high degree of interdependency between the various factors that govern this market, it is difficult to predict the market consequences and sensitivity to any given regulatory change. The proposed research will develop an agent-based complex systems model for transportation energy usage. This model is intended to be used for development of optimal regulatory approaches for control of alternative energy usage and infrastructure investment. Two scales of modeling are considered - a city scale, in which the actions of agents represent choices made by individual users, and a national scale, in which agents represent the aggregate population of a town or city and the agent choices are made subject to a probability distribution representative of the choices of the city population.]]></description>
      <pubDate>Thu, 02 Jul 2015 01:00:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/1359738</guid>
    </item>
    <item>
      <title>Emissions and Performance of Alternative Vehicles in Northern Climates</title>
      <link>https://rip.trb.org/View/1359724</link>
      <description><![CDATA[The focus of this project is to quantify "real-world" emissions from hybrid versus non-hybrid vehicles. State-of-the-art micro-simulation models can replicate vehicle activity, fuel economy and emissions. Unfortunately, the factors used by such models are often based on data from laboratory tests conducted under ideal conditions. Transportation planning models are the basis for decision-making related to new infrastructure, congestion mitigation and safety. These models are also used to evaluate the air quality impacts of transportation projects under Federal "Conformity" legislation requirements (CFR, 2006; FHWA, 2006). State-of-the-art micro simulation models, such as TRANSIMS, model second-by-second vehicle activity (speed and acceleration rate), fuel economy, and emissions (LANL, 2005). Unfortunately, the emissions algorithms used by such models are often based on look-up tables of data from laboratory dynamometer tests conducted under ideal conditions (i.e. new vehicles, 70 degrees F) that do not capture actual "real-world", on-road emissions accurately and do not account for real-world factors such as road grade, temperature or other non-ideal factors. Furthermore, due to their recent introduction, the emissions benefits of alternative technologies like hybrid-electric vehicles and alternative fuels (biofuels) are not incorporated due to a lack of emissions and performance data.]]></description>
      <pubDate>Thu, 02 Jul 2015 01:00:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/1359724</guid>
    </item>
    <item>
      <title>Modeling Plug-In Hybrid Electric Vehicle Impacts</title>
      <link>https://rip.trb.org/View/1357589</link>
      <description><![CDATA[In this study, funded by the US Department of Transportation and Vermont utilities, volunteer drivers will use the PHEV for their regular daily travel, and from these trips, data will be collected about carbon emissions, electricity use, local variations in the electrical supply, and performance over differing distances and driving styles. The research also includes an on-going effort to determine the capacity of Vermont's electric grid to handle 50,000, 100,000 or 200,000 plug-in hybrids.]]></description>
      <pubDate>Tue, 16 Jun 2015 01:00:46 GMT</pubDate>
      <guid>https://rip.trb.org/View/1357589</guid>
    </item>
    <item>
      <title>Mechanical and Economic Performance of an Electric Car Utilizing the Zebra Battery Technology in Vermont</title>
      <link>https://rip.trb.org/View/1357588</link>
      <description><![CDATA[Due to its hilly terrain and cold climate, Vermont offers a unique environment for testing the performance of electric and plug-in hybrid electric vehicles. In this study, researchers evaluated the performance of a battery electric vehicle. Vermont converted a 2005 Toyota Echo from an internal combustion engine automobile to a battery powered electric vehicle. The researchers examined the overall performance of this vehicle in daily use. In particular, they investigated the influence of air temperature and internal battery temperature on vehicle performance. Additionally, Dr. Varhue considered the economic cost of operating this vehicle. Data was collected over a period of nine months and 260 trips totaling over 5,500 miles traveled. The yearly range of the vehicle in this study was found to be 67 miles, with an estimated energy cost of 7.7 cents per mile.]]></description>
      <pubDate>Tue, 16 Jun 2015 01:00:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/1357588</guid>
    </item>
    <item>
      <title>Electric Vehicles and their Impact on the Electric Power Delivery System</title>
      <link>https://rip.trb.org/View/1357369</link>
      <description><![CDATA[The overall goal of this project is to eliminate technical barriers to electrified transportation by developing empirically validated methods to quantify and mitigate the impacts of plug-in electric vehicle (PEV) charging on the electric power distribution infrastructure. This goal is divided into four objectives. The first two objectives are to develop data-calibrated methods to quantify the impact of PEV charging on the expected life of underground distribution cables and transformers in the low and medium voltage distribution systems. The third objective is to develop methods to mitigate potential damage to infrastructure by managing electric vehicle charging. The fourth objective is to integrate these components into a model of the total impact of high PEV penetration on the distribution infrastructure in a neighborhood. In order to perform the proposed calibration, the team will obtain data from a neighborhood distribution system in the Green Mountain Power territory which has existing Smart Grid infrastructure. These data will allow us to calibrate the transformer and cable models, which will be subsequently combined with existing work by the PIs to develop a tool for estimating the total impact of PEV charging on a given distribution system with known characteristics. This one-year project will lay the foundation for future work, which we expect to fund through externally funded research grants.]]></description>
      <pubDate>Fri, 12 Jun 2015 01:01:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/1357369</guid>
    </item>
    <item>
      <title>Emissions and Performance of Alternative Vehicles in Northern Climates</title>
      <link>https://rip.trb.org/View/1357357</link>
      <description><![CDATA[The focus of this project is to quantify "real-world" emissions from hybrid versus non-hybrid vehicles. State-of-the-art micro-simulation models can replicate vehicle activity, fuel economy and emissions. Unfortunately, the factors used by such models are often based on data from laboratory tests conducted under ideal conditions. Transportation planning models are the basis for decision-making related to new infrastructure, congestion mitigation and safety. These models are also used to evaluate the air quality impacts of transportation projects under Federal "Conformity" legislation requirements (CFR, 2006; FHWA, 2006). State-of-the-art micro simulation models, such as TRANSIMS, model second-by-second vehicle activity (speed and acceleration rate), fuel economy, and emissions (LANL, 2005). Unfortunately, the emissions algorithms used by such models are often based on look-up tables of data from laboratory dynamometer tests conducted under ideal conditions (i.e. new vehicles, 70 degrees F) that do not capture actual "real-world", on-road emissions accurately and do not account for real-world factors such as road grade, temperature or other non-ideal factors. Furthermore, due to their recent introduction, the emissions benefits of alternative technologies like hybrid-electric vehicles and alternative fuels (biofuels) are not incorporated due to a lack of emissions and performance data.]]></description>
      <pubDate>Fri, 12 Jun 2015 01:01:02 GMT</pubDate>
      <guid>https://rip.trb.org/View/1357357</guid>
    </item>
    <item>
      <title>Environmental and Safety Attributes of Electric Vehicle Ownership and Commuting Behavior: Public Policy and Equity Considerations</title>
      <link>https://rip.trb.org/View/1357221</link>
      <description><![CDATA[Electric vehicles (EVs) are expected to reduce climate-changing greenhouse gas emissions. Maryland promotes EVs by subsidizing purchases of them, invests in charging facilities at rail transit stations, and assists in funding such facilities for local jurisdictions.  This research would discern whether charging facilities at rail transit stations enhance market penetration of EVs and affect commuting behavior and mode choice. This research posits that there is a nested automobile choice between using plug-in hybrid electric (HE) or battery electric (BE) cars and using conventional cars and that owners of EVs have a lower propensity to choose rail transit for commute trips than owners of conventional cars. Research would also discern attitudes toward safety and demand for in-vehicle safety technologies.  This research will survey current registered HE and BE owners in Maryland regarding attitudes toward environmental and safety considerations, demand for safety technologies, commuting behavior and use of rail transit before and after purchase. The survey will also query a random sample of non-EV owners about their attitudes, demand for safety technologies, commuting behavior, and propensity to buy EVs. According to the Maryland Motor Vehicle Administration (MVA), as of November 1, 2013 there were 2,793 EVs registered (C. Burke, MVA, personal communication, Feb. 6, 2014).  MVA will identify EV owners by county and develop a random, geographically stratified sample of a similar number of non-EV owners. MVA will then notify the owners and non-owners by letter of the survey objectives and a web link that would take owners to the on-line survey. Morgan State University (MSU)  will reach a privacy agreement between the university and MVA. All information regarding participation in this survey will be confidential.  Mode choice behavior will be incorporated into state-wide traffic models and charging infrastructure plans. The social equity issues resulting from public charging facility investment would be addressed through policy recommendations for EV promotion by income group.  The survey results and statistical analyses of data will lead to policy recommendations for EV promotion, funding and placement of public charging facilities, and traffic safety considerations.  The survey analyses could be incorporated into state and local transportation planning processes and calibrations for travel demand modeling.]]></description>
      <pubDate>Thu, 11 Jun 2015 01:01:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/1357221</guid>
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