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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

R&D Insights for Extreme Fast Charging of Medium- and Heavy-Duty Vehicles: Insights from the NREL Commercial Vehicles and Extreme Fast Charging Research Needs Workshop, August 27-28, 2019

As battery costs have declined and battery performance has improved, the applicability of vehicle electrification has expanded beyond passenger cars to the commercial vehicle sector. However, due to the larger batteries that would be needed for the medium- and heavy-duty (MDHD) sector, the electric charging capabilities to serve these larger commercial vehicles will need to be substantially more powerful than light-duty chargers. More specifically, such 'extreme fast charging' (XFC) will likely need to reach the megawatt scale to provide a full charge in less than 30 minutes in some applications. In addition, the combined cost of electrified vehicles and charging must be competitive with the costs of petroleum-based technologies and other alternatives to encourage widespread adoption of battery electric vehicles (BEVs) among MDHD fleets. Most of these fleets have a commercial mission and demand low total cost of ownership (TCO) (which motivates minimal refueling times) and high performance from their vehicles.

25 ENERGY STORAGE↗
DOE OSTI2023

Commercial Vehicle Electrification Evaluation Toolkit (CVEET)

The CVEET can identify and characterize charging and battery capacity challenges for class 3-8 commercial vehicle electrification, including battery electric and plug in hybrid vehicles for various trucks, vans and buses, over a wide range of vehicle operation. The tool can help build the understanding of the impact of commercial vehicle electrification on energy consumption, payload penalty, and GHG (i.e., CO2) emissions. The tool employs a quantitative approach to estimate electric vehicle energy consumption, characterize component performance and determine appropriate charging assumptions to conduct a comprehensive priority analysis of MD/HD vehicle electrification using public domain data.

Gao, Zhiming↗
DOE OSTI2022

Developing Fuel Cell Electric Powertrain Architectures for Commercial Vehicles

Here, this article addresses the architecture development for a commercial vehicle fuel cell electric powertrain by establishing a clear multi-step formalized workflow that employs a unique technoeconomic solution for architecture selection. The power capability of the fuel cell, the energy capacity and chemistry of the electrical energy storage (battery), the DC-DC converter (including the input current rating and isolation resistance requirements), the traction drive solution, the on-board hydrogen storage solution, and the real-time power-split management of the fuel cell and the battery are all considered and developed in this effort. The methods were used to select architecture for Class 8 urban, regional, and line haul applications. When compared to traditional load-following power-split controllers, an energy management power-split controller can increase system energy efficiency by up to 19.5%. The energy-efficient power-split controller may increase the required battery capacity for an equivalent life by up to 2.6 times. The impact on the total cost of ownership (TCO) for a variety of financial cases demonstrates that high C-rate capable batteries have the potential to provide better TCO solutions over a six-year vehicle life than low C-rate capable batteries. To achieve TCO parity with the 600 A non-isolated DC-DC converter case, the specific choice of the fuel cell DC-DC converter to achieve a target power output based on current levels (from 500 A to 2400 A) shows that efficiency decreases and cost increases due to the higher current, requiring fuel cell prices to decrease by $50–$100/kW, $60–$110/kW, and $100–$220/kW for urban, regional, and line haul applications, respectively. Key recommendations for powertrain system architectures are provided, with specifics based on vehicle dynamics, mission and application characteristics, end customer use-case profile, critical powertrain component costs, and architecture selection cost function. This study rigorously demonstrates the interplay of the above parameters, with a focus on TCO, and provides application decision-makers with a mechanism and well-defined set of impact factors to consider as part of their architecture selection process.

25 ENERGY STORAGE↗
DOE OSTI2023

Spatio-temporal and weather characterization of road loads of electrified heavy-duty commercial vehicles across U.S. interstate roads

Adoption of battery electric vehicles (BEV) in heavy duty (HD) commercial freight transportation is difficult due to technological and economic hurdles. Beyond safety and compliance, fleet and operational logistics necessitate both high uptime and parity with diesel system productivity/Total Cost of Ownership to support widespread deployment of electric powertrains. However, relatively high energy storage costs, along with the higher weight of BEV systems, limit the viability of HD commercial freight transport to shorter-range applications where smaller batteries will serve for mission energy requirements (single operational shift). Knowing the energy consumption and operating variations of these commercial vehicle systems is crucial for effectively sizing the energy storage systems. This paper is the first in a series of studies to understand the regional specific operating design domain variations of commercial Class 8 HD trucks and the associated impact to their energy requirements. In particular, the local weather conditions are shown to influence the total vehicle energy usage. Further, the impact of temperature, pressure, and humidity changes are shown to impact the local air density. This is needed to calculate aerodynamic drag on vehicles and has been found to play a significant role in the overall performance of a vehicle. Although the methodology of calculating air density varies only slightly throughout the literature, the application to transportation has still been limited. This study provides a means by which air density can be estimated for the contiguous U.S. using the NOAA MADIS dataset. These air density estimates were then used to determine vehicle performance in varying regions of the country to highlight the importance of consideration of weather variables when monitoring vehicle performance, but also to provide recommendations based on these locales upon fleet conversion to BEVs.

Moore, Amy↗
DOE OSTI2021

Characterization of commercial vehicles’ start-up operations from in-use data

Diesel engines produce disproportionate levels of emissions when the engine and after-treatment systems are operating at low temperatures. This situation arises most commonly when the vehicle is first started after overnight. To quantify emissions attributable to vehicle starts, a sizable collection of on-road commercial vehicle operating data is analyzed to identify start-up events and inspect associated emissions. Data was obtained from the National Renewable Energy Laboratory’s (NREL’s) Fleet DNA and from the Center for Environmental Research & Technology (CE-CERT). Included are 500 + diesel vehicles with more than 42,000 recorded days, drawn from 25 vocational categories across the United States. Analysis shows that vehicle behavior, in terms of engine cold- and warm-operation, starts per day, soak time, and warm-up duration, differs significantly between vehicle vocations. Also, weighting factors for cold- and hot-starts currently used in the U.S. Environmental Protection Agency’s Federal Test Procedure (FTP) for heavy-duty emissions certification accurately represent real-world operations. Although the FTP includes a comparable fraction of cold operation, the hot fraction is much shorter than real-world operation due to limited test duration. The investigation also revealed that real-world engines operate for a significant amount of time when the engine coolant is in the “hot-stabilized” region, but the selective catalytic reduction (SCR) temperature is below its effective operating temperature of 200 °C. Of the vehicles under investigation, almost 20% of their operational time is within this condition. Therefore, novel approaches to raise and maintain SCR temperature are highly required to further reduce engine emissions.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

A Machine Learning–Based Tire Life Prediction Framework for Increasing Life of Commercial Vehicle Tires

In the commercial freight industry, tire retreading decisions are often conservative due to limited knowledge of a tire’s remaining service life. This practice leads to increased costs and material waste. This paper proposes a machine learning–based approach for estimating tire casing life and retreadability, focusing on usage data rather than wear information. This approach could extend the tire’s lifespan and reduce landfill waste. Data integration from diverse tire casing measurement sources presents challenges, including imbalanced removal data. Our methodology addresses these challenges by using historical inspection, telematics, and finite element modeling (FEM) datasets. We introduce “Tire Casing Energy” as a comprehensive usage input and apply a Variance-Reduction Synthetic Minority Oversampling Technique (VR-SMOTE) for data imbalance rectification. A random forest model is used to estimate the state of the tire casing and the casing removal probability, with Bayesian optimization applied for hyperparameter tuning, enhancing model accuracy. Here, the proposed prediction framework is able to differentiate different truck fleets and tire locations based on their usage parameters. With the aid of this machine learning model, the importance and sensitivity of different tire usage parameters can be obtained, which is beneficial to maximize tire life.

Data balancing↗
DOE OSTI2021

Commercial Vehicle Driver Behaviors and Decision Making: Lessons Learned from Urban Ridealongs

As e-commerce and urban deliveries spike, cities grapple with managing urban freight more actively. In order to effectively manage urban deliveries, city planners and policy makers need to better understand driver behaviors and the challenges they experience in performing deliveries. In this study, we collected data on commercial vehicle (CV) driver behaviors by performing ridealongs with various logistics carriers. Ridealongs were performed in Seattle, Washington, covering a range of vehicles (cars, vans, and trucks), goods (parcels, mail, beverages, and printed materials), and customer types (residential, office, large and small retail). Observers collected qualitative observations and quantitative data on trip and dwell times, while also tracking vehicles through GPS devices. The results showed that, on average, urban CVs spent 80 percent of their daily operating time parked. The study also found that, unlike the common belief, drivers (especially those operating heavier vehicles) parked in authorized parking locations, with only less than 5 percent of stops occurring in the travel lane. Dwell times associated with authorized parking locations were significantly longer than those of other parking locations, and mail and heavy goods deliveries generally had longer dwell times. We also identified three main criteria CV drivers used for choosing a parking location: avoiding unsafe maneuvers, minimizing conflicts with other users of the road, and coopetition with other commercial drivers. Here, the results provide estimates for trip times, dwell times, and parking choice types, as well as insights into why those decisions are made and the factors affecting driver choices.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Code Artifact for: Clustering Analysis of Commercial Vehicles Using Automatically Extracted Features from Time Series Data [SWR-21-96]

This repository contains data ingestion, feature extraction, and analysis code used in NREL Technical report "Clustering Analysis of Commercial Vehicles Using Automatically Extracted Features from Time Series Data." The code is written in Python. The ETL and feature extraction code must be run in a Spark context. The analysis code can be run without Spark, provided you have pre-computed features in a CSV file. Analysis code related to the NREL Technical Report NREL/TP-2C00-74212. Includes PySpark functions to perform trip segmentation and feature extraction over big time series data in Apache Spark. Includes "domain specific" features such as Aerodynamic Speed (ft/s), Characteristic Acceleration (ft/s2), Percent Below 55 (%), Percent Zero (%), Stops Per Mile, Average Speed (mph), Maximum Speed (mph), and Speed Standard Deviation (mph). Includes Pyspark UDF to compute "domain agnostic" features using the TSFresh library. This software record also includes the analysis notebooks and code to generate the results in the previously mentioned technical report.

Perr-Sauer, Jordan↗
DOE OSTI2020

Real-Time Dynamic Brake Assessment for Heavy Commercial Vehicle Safety

This paper summarizes initial results and findings of a model developed to determine the braking performance of commercial motor vehicles in motion regardless of brake type or gross weight. Real-world data collected by Oak Ridge National Laboratory for a U.S. Department of Energy study was used to validate the model. Expanding on previous proof-of-concept research showing the linear relationship of brake application pressure and deceleration additional parameters such as elevation were added to the model. Outputs from the model consist of coefficients calculated for every constant pressure braking event from a vehicle that can be used to calculate a deceleration and thus compute a stopping distance for a given scenario. Using brake application pressure profiles derived from the dataset, stopping distances for light and heavy loads of the same vehicle were compared for various speed and road grades. For a constant brake application pressure profile (i.e. the pressure is relatively constant throughout the entire stop) it was shown that the lighter vehicle was able to stop in a shorter distance than the heavier vehicle in most scenarios other than large uphill grades. Significant changes in weight can be observed through the fuel efficiency of the vehicle to determine if the model needs to be calibrated with new or additional braking events. The model was initially developed for tracking brake performance to inform routine maintenance and the ordering of vehicles in a platoon but can be expanded to other connected and automated research areas.

Siekmann, Adam↗
DOE OSTI2021

Development of in-use engine speed/torque heat maps across multiple heavy-duty commercial vehicle vocations

The U.S. Department of Energy (DOE) established the SuperTruck program with the goal of achieving brake thermal efficiency (BTE) greater than or equal to 55% as demonstrated in an operational heavy-duty (HD) diesel engine at a 65-miles-per-hour (mph) cruise point. Beyond the line-haul application, HD engines operate in a wide range of speed and torque conditions that are unlikely to yield the same efficiency under real-world operation. Thereby, the in-use engine heat maps described in this paper are a valuable tool to illustrate whether the engine-efficiency “sweet spot” matches the most frequent operating conditions. In this study, NREL developed engine heat maps to quantify the important operating points for various vocations using our Fleet DNA database of commercial fleet vehicle operations data. These heat maps clearly show that high-frequency operating points vary significantly according to vehicle vocation, while only a few of them match the sweet spot. Beyond the illustration, engine in-use heat maps can also be leveraged to build up reduced-order engine-efficiency models, needed by many rapid powertrain simulations. As case studies, nine reduced-order models – including line-haul truck, transfer truck, transit bus, transit bus with compressed natural gas (CNG) engine, drayage, refuse pickup, local delivery, utility truck, and school bus with CNG engine – using a trust-region reflective algorithm to fit the on-road data extracted based on the engine in-use heat maps.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Achieving Diesel Powertrain Ownership Parity in Battery Electric Heavy Duty Commercial Vehicles Using a Rapid Recurrent Recharging Architecture

Battery electric vehicles (BEV) in heavy duty (HD) commercial freight transport face challenging technoeconomic barriers to adoption. Specifically, beyond safety and compliance, fleet and operational logistics require both high up-time and parity with diesel system productivity/Total Cost of Ownership (TCO) to enable strong adoption of electrified powertrains. At present, relatively high energy storage prices coupled with the increased weight of BEV systems limit the practicality of HD commercial freight transport to shorter range applications, where smaller batteries will suffice for the mission energy requirements (single operational shift). This paper presents an approach to extend the feasibility of BEV HD trucking for a broad range of applications. The concept is based on the leveraging rapid and recurrent recharging of a BEV HD truck that may either already make frequent stops due to shipment drop-offs/reloading or be required to make frequent stops along with longer missions for recharging. While the challenges of the latter are well appreciated, the concept proposed explores making minimal impact to overall mission time by targeting high C-rate charging while optimizing the frequency (miles) through which these events must occur. The concept optimizes battery size and chemistry, such that the expected life (years and total energy throughput) is balanced with the depth of discharge between recharging events, thus making complete use of the energy available through the life of the battery system. The solution is constrained to minimize the impact on payload capacity. The paper analyzes critical levers in achieving diesel price parity (based on a simplified vehicle TCO), achieved through different purchase options (including lease versus buy) and operational models (half-life swap out). Finally, the paper identifies the application design domain where these solutions are viable with limited impact on fleet operations.

Sujan, Vivek↗
DOE OSTI2020

Clustering Analysis of Commercial Vehicles Using Automatically Extracted Features from Time Series Data

Standard of practice approaches to time series cluster analysis involve careful feature engineering, often utilizing expert input to tune and select features by hand. In many cases, expert input may not be readily available, or there may not yet exist a community consensus on the ideal features for a given application. This paper compares the results of several cluster analysis methods, using both hand selected features and those extracted automatically, when applied to large geospatial time series telematics data from commercial trucking fleets. The impacts of feature selection, dimensionality reduction, and choice of clustering algorithm on the quality of clustering results are explored. Results from this analysis confirm prior results that domain agnostic features are competitive with the hand engineered features with respect to clustering quality metrics. These results also provide new insight into the most successful strategies for identifying structure in large unstructured vehicle telematics data, and suggest that time series clustering using automatic feature extraction can be an effective approach to extract structure from large scale geospatial time series data in cases when hand selected features are not available.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Heavy Duty Commercial Vehicle Platooning Energy Benefits for Conventional and Electrified Powertrains

Heavy duty vehicle platooning under highway operating conditions, has been projected to provide significant fuel economy gains based on aerodynamic drag improvements of the platooning vehicles. Realizing these benefits under real-world operating conditions presents several challenges. This paper (the first as part of series) provides a framework to quantify the fuel economy benefits of both conventional and electrified powertrains operating across the U.S. Interstate highway system. In addition to the powertrain, key interactions that are explored include the vehicle separation distance, baseline vehicle aerodynamic properties, vehicle weight, number of vehicles in the platoon, excursions from road speed limits, and particular traffic interactions. While the approach makes use of a limited fidelity vehicle model for longitudinal dynamics, the narrative provides application decision personnel with a mechanism and well-defined set of fuel economy impact factors to consider as part of their architecture selection process.

Sujan, Vivek↗
DOE OSTI2020

Development and Demonstration of a Class 6 Range-Extended Electric Vehicle for Commercial Pickup and Delivery Operation: Preprint

Range-extended hybrids are an attractive option for medium- and heavy-duty commercial vehicle fleets because they offer the efficiency of an electrified powertrain with the driving range of a conventional diesel powertrain. The vehicle essentially operates as if it was purely electric for most trips, while ensuring that all commercial routes can be completed in any weather conditions or geographic terrain. Fuel use and point-source emissions can be significantly reduced, and in some cases eliminated, as many shorter routes can be fully electrified with this architecture. Under a U.S. Department of Energy (DOE)-funded project for Medium- and Heavy-Duty Vehicle Powertrain Electrification, Cummins has developed a plug-in hybrid electric Class 6 truck with a range-extending engine designed for pickup and delivery application. The National Renewable Energy Laboratory (NREL) assisted by developing representative workday drive cycles for Class 6 operation and an adapted cycle to enable vehicle track testing. A novel, automated driving system was utilized by Southwest Research Institute (SwRI) to improve the repeatability of the track testing conducted to quantify vehicle energy consumption. Cummins used a drivetrain model to design the hybrid control system for increased fuel savings. The control system functionality and fuel savings objective of 50% or more were confirmed by track testing described in this paper.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Medium- and Heavy-Duty Vehicle Electrification: An Assessment of Technology and Knowledge Gaps

The objective of this assessment was to leverage current medium- and heavy-duty vehicle (MHDV) data and information to evaluate the state of commercial vehicle electrification technologies, including the following: 1. Assessment and inventory of current MHDV electrification architectures; 2. Identification and assessment of MHDV component technologies; 3. Assessment of potential performance and cost drivers; and, 4. Identification of R&D gaps where appropriate R&D and technology would accelerate commercial vehicle electrification. These gaps include shortfalls in technology, gaps in data, and inadequate knowledge and understanding. To inform this study, the National Renewable Energy Laboratory–Oak Ridge National Laboratory team examined the open literature; conducted workshops; assessed and analyzed data on more than 175 electrified MHDVs, including vehicle and powertrain specifications; and conducted one-on-one meetings with industry representatives. This systems-level information was concatenated and parsed with respect to key characteristics such as battery energy, power, and range compared across vehicle classes. Operational data of many conventional vehicles were also examined to help determine the requirements on electrified vehicle attributes such as range. Previous studies on the total cost of ownership of electrified MHDVs were reviewed to determine where improvements in technologies (efficiency, cost, maintenance) were needed to accelerate adoption. The team also assessed the state of the art in electric drivetrains for component specifications and challenges for the commercial vehicle sector, including electric machines, power electronics, and off-board high-power charging systems (extreme fast charging and beyond). The goal of this analysis was to identify the barriers to widespread adoption of commercial vehicle electrification technologies and prioritize the research and development gaps that need to be overcome to accelerate significant market penetration of these technologies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗