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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Charging Needs for Battery Electric Semi-Trucks

Battery-electric vehicles provide a pathway to decarbonize heavy-duty trucking, but the market for heavy-duty battery-electric semi-trailer trucks is nascent, and specific charging requirements remain uncertain. We leverage large-scale vehicle telematics data (>205 million miles of driving) to estimate the charging behaviors and infrastructure requirements for U.S. battery-electric semi-trailer trucks within three operating segments: local, regional, and long-haul. We model two types of charging; mid-shift (fast) and off-shift (slow), and show that off-shift charging at speeds compatible with current light-duty charging infrastructure (i.e., =350 kW) can supply 35 to 77% of total energy demand for local and regional trucks with =300-mile range. Megawatt-level speeds are required for mid-shift charging, which make up 44 to 57% of energy demand for long-haul trucks with =500-mile range. However, demand shifts from mid-shift to off-shift charging as the range for battery-electric trucks increases and when off-shift charging is widely available. Finally, we observe geographic trends in charging demand, finding that local trucks have greater demand within urban areas, whereas long-haul trucks have more demand along rural interstate corridors. As the range for battery-electric trucks increases, we show that charging demand shifts from rural to urban locations due to observed vehicle dwell tendencies.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2021

Impact of Lateral Alignment for Cooling Airflow during Heavy-Truck Platooning

A truck platooning system was tested using two heavy-duty tractor-trailer trucks on a closed test track to investigate the thermal control/heat rejection system sensitivity to intentional lateral offsets over a range of intervehicle spacings. Previous studies have shown the following vehicle can experience elevated temperatures and reduced airflow through the cooling package as a result of close-formation platooning. Four anemometers positioned across the grille of the following trucks as well as aligned and multiple offset positions are used to evaluate the sensitivity of the impact. Straight sections of the track are isolated for the most accurate airflow impact measurements and to be most representative of on-highway driving. An intentional lateral offset in truck platooning is considered as a controls approach to mitigate reduced cooling efficacy at close following scenarios where the highest platoon savings are achieved. The truck platooning system was tested at four gap distances from 9 to 23 meters and the following truck was either aligned with the lead or offset with a 25% width (0.65 m) or 50% width (1.3 m) lateral distance. The experimental results clearly show that offset driving is effective at bringing cooling airflow from 56% with an aligned platoon back up to 75%-80% with minimal impact to fuel savings. In addition, the offset driving can also reduce the trailing truck’s engine coolant temperature by 2°C and the corresponding underhood temperature rise by 10°C. The test outcomes indicate a plausible controls strategy to mitigate reduced cooling flow during hot-weather truck platooning while still enabling the highest-energy-saving, close-following scenarios.

27 ARPA - Advanced Research Projects Agency-Energy↗
DOE OSTI2015

Design and Validation Testing of TruckScan to Assay Large Sacks of Fukushima Radioactive Debris on a Truck

As a result of the March 2011 earthquake and resulting tsunami in Japan, there was a serious accident at the Fukushima Dai-ichi Nuclear Power Plant. This accident has contaminated soil and vegetation in a wide area around the plant. Decontamination projects over the last 4 years have resulted in large numbers of 1 cubic meter canvas bags of debris, commonly called Super Sacks [SS]. These are currently stored nearby where they were generated, but starting in 2015, they will be moved to various Interim Storage Facilities [ISF]. Trucks will typically carry 8-20 of these SSs. When the trucks arrive at the ISF they need to be rapidly sorted into groups according to radioactivity level, for efficient subsequent processing. Canberra Industries, Inc. [CI] has designed a new truck monitoring system 'TruckScan' for use at these ISFs. The TruckScan system must measure the entire truck loaded with multiple closely packed SSs, and generate a nuclide specific assay report showing the radioactivity in each individual SS. The Canberra-Japan office, along with Obayashi Corporation have performed validation testing to demonstrate to the regulatory authorities that the proposed technique was sufficiently accurate. These validation tests were conducted at a temporary storage area in Fukushima prefecture. Decontaminated waste of various representative types and of various levels of radioactivity was gathered and mixed to create homogeneous volumes. These volumes were sampled multiple times and assayed with laboratory HPGe detectors to determine the reference concentration of each pile. Multiple SSs were loaded from each pile. Some of the SSs were filled 50% full, others 75% full, and others 100% full, to represent the typical loading configuration of the existing SSs in the field. The content of the SSs are either sand, soil, or vegetation with densities ranging from 0.3 g/cc - 1.6 g/cc. These SSs with known concentrations of Cs-134 and Cs-137 were then loaded onto trucks in a variety of configurations, typical of how they might be on the real trucks. A partial system was installed at the site and used to assay these trucks with the various loading configurations. Whereas the full system will have 8 collimated 3 x 3'' NaI detectors, the test system only had two detectors; therefore the truck was moved and counted 4 times. The data were acquired and analyzed with the Canberra Genie software to determine the peak counts for both Cesium nuclides. That data was then analyzed with a prototype version of a Maximum Entropy algorithm, to determine the individual SS activity. The goal of the validation tests was to demonstrate that the system could detect 1000 Bq/kg in 15 seconds, and to determine how accurately it could quantify individual SSs. The validation tests demonstrated that the product would perform as predicted. The TruckScan results were consistent with the sample assay results [y = 1.0029 x, R{sup 2} = 0.914]. The Total Propagated Uncertainty, including both uncertainties from these tests and others that were estimated but not tested was 16.6% percent. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Exploring the Effects of Population and Employment Characteristics on Truck Flows: An Analysis of NextGen NHTS Origin-Destination Data

Truck transportation remains the dominant mode of US freight transportation because of its advantages, such as the flexibility of accessing pickup and drop-off points and faster delivery. Because of the massive freight volume transported by trucks, understanding the effects of population and employment characteristics on truck flows is critical for better transportation planning and investment decisions. The US Federal Highway Administration published a truck travel origin-destination data set as part of the Next Generation National Household Travel Survey program. This data set contains the total number of truck trips in 2020 within and between 583 predefined zones encompassing metropolitan and nonmetropolitan statistical areas within each state and Washington, DC. In this study, origin-destination-level truck trip flow data was augmented to include zone-level population and employment characteristics from the US Census Bureau. Census population and County Business Patterns data were included. The final data set was used to train a machine learning algorithm-based model, Extreme Gradient Boosting (XGBoost), where the target variable is the number of total truck trips. Shapley Additive ExPlanation (SHAP) was adopted to explain the model results. Results showed that the distance between the zones was the most important variable and had a nonlinear relationship with truck flows.

Uddin, Majbah↗
DOE OSTI2020

Injury severity analysis of truck-involved crashes under different weather conditions

This paper investigates truck-involved crashes to determine the statistically significant factors that contribute to injury severity under different weather conditions. The analysis uses crash data from the state of Ohio between 2011 and 2015 available from the Highway Safety Information System. To determine if weather conditions should be considered separately for truck safety analyses, parameter transferability tests are conducted; the results suggest that weather conditions should be modeled separately with a high level of statistical confidence. To this end, three separate mixed logit models are estimated for three different weather conditions: normal, rain and snow. The estimated models identify a variety of statistically significant factors influencing the injury severity. Different weather conditions are found to have different contributing effects on injury severity in truck-involved crashes. Rural, rear-end and sideswipe crash parameters were found to have significantly different levels of impact on injury severity. Based on the findings of this study, several countermeasures are suggested: 1) safety and enforcement programs should focus on female truck drivers, 2) a variable speed limit sign should be used to lower speeds of trucks during rainy condition, and 3) trucks should be restricted or prohibited on non-interstates during rainy and snowy conditions. These countermeasures could reduce the number and severity of truck-involved crashes under different weather conditions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Experimental Fuel Consumption Results from a Heterogeneous Four-Truck Platoon

Platooning has the potential to reduce greenhouse gas missions of heavy-duty vehicles. Prior platooning studies have chiefly focused on the fuel economy characteristics and three-truck platoons, and most have investigated aerodynamically homogeneous platoons with trucks of the same trim. For real world application and accurate return on investment for potential adopters, non-uniform platoons and the impacts of grade and disturbances on a platoon’s fuel economy must also be characterized. This study investigates the fuel economy of a heterogeneous four-truck platoon on a closed test track. Tests were run for one hour at a speed of 45 mph. The trucks used for this study are two 2015 Peterbilt 579’s with a Cummins ISX15 and a Paccar MX-13, and two 2009 Freightliner M915A5’s, one armored and the other unarmored. Many analysis methodologies were leveraged to describe and compare the fuel data, including lap-wise and track-segment analysis. The methodology for dividing the data into laps is described in detail. The influence of other factors beyond the aerodynamics of platooning is discussed. CAN fuel rate analysis showed excellent agreement with previous experimental trends for two and three-truck platoons. In general, the indicated fuel economy benefits in this study were 5-11% for following vehicles and 0-4% for the lead vehicle in platoon relative to their baseline fuel consumption. On a cumulative basis, all platoons saved fuel, ranging from 6% to 8% versus the sum of the standalone trucks’ fuel consumption. The practical implications of the fuel economy results are discussed, as well as avenues for future research. Introduction and Motivation platooning is controlled coordination of two or more vehicles in a convoy, sometimes called CACC (Coordinated or Cooperative Adaptive Cruise Control). The distance between vehicles in a platoon can be controlled tightly at no additional fatigue to the driver. Platooning vehicles can also respond quickly to braking events of the leader, much more quickly than the typical human driver’s reaction time of 1-1.5 s. Therefore, vehicles in a platoon can follow each other much more closely than would usually be considered a safe following distance under human operation. It is implied that under very close following conditions, platooning technology must be extremely robust before it is safe for wide-scale implementation. Platooning is under investigation as a fuel-saving technology. Close-following significantly reduces aerodynamic drag for both leading and trailing vehicles. According to the NRC in 2010, aerodynamic drag represents roughly half of a Class-8 truck’s on-highway fuel usage, meaning a 20% reduction in drag roughly equals a 10% reduction in fuel usage, if all other sources of energy loss remain equal (i.e. accessory, rolling resistance, drivetrain, braking). It is by aerodynamics that platooning saves fuel. At risk of oversimplifying the aerodynamics, following (or trailing) vehicles experience reduced wind velocity due to shielding, and the leading vehicles experience an increased aft pressure, especially at distances closer than 75’ (23 m).

greenhouse gas emissions, truck platoon, heavy-dut↗
DOE OSTI2018

Influences on Energy Savings of Heavy Trucks Using Cooperative Adaptive Cruise Control

An integrated adaptive cruise control (ACC) and cooperative ACC (CACC) was implemented and tested on three heavy-duty tractor-trailer trucks on a closed test track. The first truck was always in ACC mode, and the followers were in CACC mode using wireless vehicle-vehicle communication to augment their radar sensor data to enable safe and accurate vehicle following at short gaps. The fuel consumption for each truck in the CACC string was measured using the SAE J1321 procedure while travelling at 65 mph and loaded to a gross weight of 65,000 lb, demonstrating the effects of: inter-vehicle gaps (ranging from 3.0 s or 87 m to 0.14 s or 4 m, covering a much wider range than previously reported tests), cut-in and cut-out maneuvers by other vehicles, speed variations, the use of mismatched vehicles (standard trailers mixed with aerodynamic trailers with boat tails and side skirts), and the presence of a passenger vehicle ahead of the platoon. The results showed that energy savings generally increased in a non-linear fashion as the gap was reduced. The middle truck saved the most fuel at gaps shorter than 12 m and the trailing truck saved the most at longer gaps, while lead truck saved the least at all gaps. The cut-in and cut-out maneuvers had only a marginal effect on fuel consumption even when repeated every two miles. The presence of passenger-vehicle traffic had a measurable impact. The fuel-consumption savings on the curves was less than on the straight sections.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2014

Comparative Study of Hybrid Powertrains on Fuel Saving, Emissions, and Component Energy Loss in HD Trucks

We compared parallel and series hybrid powertrains on fuel economy, component energy loss, and emissions control in Class 8 trucks over both city and highway driving. A comprehensive set of component models describing battery energy, engine fuel efficiency, emissions control, and power demand interactions for heavy duty (HD) hybrids has been integrated with parallel and series hybrid Class 8 trucks in order to identify the technical barriers of these hybrid powertrain technologies. The results show that series hybrid is absolutely negative for fuel economy benefit of long-haul trucks due to an efficiency penalty associated with the dual-step conversions of energy (i.e. mechanical to electric to mechanical). The current parallel hybrid technology combined with 50% auxiliary load reduction could elevate 5-7% fuel economy of long-haul trucks, but a profound improvement of long-haul truck fuel economy requires additional innovative technologies for reducing aerodynamic drag and rolling resistance losses. The simulated emissions control indicates that hybrid trucks reduce more CO and HC emissions than conventional trucks. The simulated results further indicate that the catalyzed DPF played an important role in CO oxidations. Limited NH 3 emissions could be slipped from the Urea SCR, but the average NH 3 emissions are below 20 ppm. Meanwhile our estimations show 1.5-1.9% of equivalent fuel-cost penalty due to urea consumption in the simulated SCR cases.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Charging Needs for Battery Electric Semi Trucks

Battery-electric vehicles provide a pathway to decarbonize heavy-duty trucking, but the market for electric trucks is nascent, and specific charging requirements remain uncertain. This paper summarizes methods and findings from Charging Needs for Electric Semi-Trailer Trucks [1] wherein we leverage large-scale vehicle telematics data (>205 million miles of driving) to estimate the charging behaviors and infrastructure requirements for U.S. battery-electric semi-trailer trucks within three operating segments: local, regional, and long-haul. We model two types of charging - mid-shift (fast en-route charging) and off-shift (slow depot charging) - and show that off-shift charging at speeds compatible with current light-duty charging infrastructure (i.e., =350 kW) can supply 35% to 77% of total energy demand for local and regional trucks with =300-mile range. Megawatt-level speeds are required for mid-shift charging, which make up 44% to 57% of energy demand for long-haul trucks with =500-mile range. However, the role of off-shift charging increases as the range for battery-electric trucks increases and when off-shift charging is widely available.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2014

Fuel assembly shaker and truck test simulation

This study continues the modeling support of the SNL shaker table task from 2013 and includes analysis of the SNL 2014 truck test campaign. Detailed finite element models of the fuel assembly surrogate used by SNL during testing form the basis of the modeling effort. Additional analysis was performed to characterize and filter the accelerometer data collected during the SNL testing. The detailed fuel assembly finite element model was modified to improve the performance and accuracy of the original surrogate fuel assembly model in an attempt to achieve a closer agreement with the low strains measured during testing. The revised model was used to recalculate the shaker table load response from the 2013 test campaign. As it happened, the results remained comparable to the values calculated with the original fuel assembly model. From this it is concluded that the original model was suitable for the task and the improvements to the model were not able to bring the calculated strain values down to the extremely low level recorded during testing. The model needs more precision to calculate strains that are so close to zero. The truck test load case had an even lower magnitude than the shaker table case. Strain gage data from the test was compared directly to locations on the model. Truck test strains were lower than the shaker table case, but the model achieved a better relative agreement of 100-200 microstrains (or 0.0001-0.0002 mm/mm). The truck test data included a number of accelerometers at various locations on the truck bed, surrogate basket, and surrogate fuel assembly. This set of accelerometers allowed an evaluation of the dynamics of the conveyance system used in testing. It was discovered that the dynamic load transference through the conveyance has a strong frequency-range dependency. This suggests that different conveyance configurations could behave differently and transmit different magnitudes of loads to the fuel even when traveling down the same road at the same speed. It is recommended that the SNL conveyance system used in testing be characterized through modal analysis and frequency response analysis to provide context and assist in the interpretation of the strain data that was collected during the truck test campaign.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Correlation between Sensor Performance, Autonomy Performance and Fuel-Efficiency in Semi-Truck Platoons

Semi-trucks, specifically class-8 trucks, have recently become a platform of interest for autonomy systems. Platooning involves multiple trucks following each n close proximity, with only the lead truck being manually driven and the rest being controlled autonomously. This approach to semi-truck autonomy is easily integrated on existing platforms, reduces delivery times, and reduces green- house gas emissions via fuel economy benefits. Level 1 SAE fuel studies were performed on class-8 trucks operating with the Auburn Cooperative Adaptive Cruise Control (CACC) system, and fuel savings up to 10-12% were seen. Enabling platooning autonomy required the use of radar, global positioning systems (GPS), and wireless vehicle-to-vehicle (V2V) communication. Poor measurements and state estimates can lead to incorrect or missing positioning data, which can lead to unnecessary dynamics and finally wasted fuel. This is especially an issue if deceleration is applied in response to a bad measurement. In this study, a faulty radar was shown to cause a greater than 5% increase in fuel consumption. The mechanism of this fuel consumption increase is investigated and applied to other types of sensor failures to indicate their potential effects on fuel economy. This analysis indicates that poor GPS signals over short time can be largely filtered out, with no real gain or loss of fuel economy. V2V communications were intentionally limited by causing interference, which resulted in dropped communication packets over a small physical area, but not an appreciable impact on fuel economy.

vehicle platooning, sensor performance, semi-auton↗
DOE OSTI2022

Control Oriented Model of Cabin-HVAC System in a Long-Haul Trucks for Energy Management Applications

Super Truck II is a 48V mild hybrid class 8 truck with an all auxiliary loads powered purely by the battery pack. Electric Heating Ventilation and Air Conditioning (HVAC) load is the most prominent battery load during the hotel period, when the truck driver is resting inside the sleeper. For the PACCAR Super Truck II (ST-II) project a 48 V battery system provides the required power during the hotel period. A cabin-HVAC model estimates the electric load on the 48V battery system, allowing the control system to implement an efficient energy management strategy that avoids engine idling during the hotel period. The thermal model accounts for the sun load due to the time of day and the geographic location of the truck during the hotel period. The cabin-HVAC model has two parts. First, a grey box model with two heat exchangers (Condenser and Evaporator) working in unison with refrigerant mass flow rate as an input and HVAC load as an output. Second, a two-node cabin model formulated to estimate the cabin temperature as a function of the Global Horizontal Irradiance (GHI), HVAC load and ambient temperature. The models are calibrated using experimental cabin-HVAC system data as for long-haul class 8 truck (e.g. ST-II). Here, the model simulations show that the overall Root Mean Square Error (RMSE) value of 0.4°C between the experimental and simulated cabin temperature.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

National Energy Impacts of Heavy Electric Truck Adoption For Freight

Freight trucks are projected to account for nearly 30% of transportation sector energy use in 2050 in the United States. Potentially some of the freight energy use and associated emissions can be reduced by electrified vehicle technologies, smart technologies (e.g. platooning, driverless truck), and possible mode shifts. Several U.S. truck makers, new startups, and independent engine manufacturers made announcements of taking commericialized medium- and heavy-duty trucks into production for these emerging technologies. By analyzing the freight shipment by commodity and freight zones, the study objectives are 1) idenfiy long-haul freight miles that could be electrified without on-route charging infrastructure; and 2) quantify future energy consumption impacts. This paper utilized Argonne’s NEAT and VISION models to estimate the potential energy impacts of electric truck adoption by 2050 in the United States. Analysis results shows that either a Class 7 and 8 electric truck with 500-mile electric range could potentially reduce the petroleum consumption by 1.61 quad in 2050, while the electricity consumption increases by 0.99 quad.

Electric heavy duty truck, freight↗
DOE OSTI2020

Market Segmentation Analysis of Medium and Heavy Duty Trucks with a Fuel Cell Emphasis

The medium- and heavy-duty transportation sector is experiencing rapid changes in powertrain technology innovation with recent announcements of battery electric and fuel cell electric trucks being offered. However, the economics of these alternative powertrain vehicles are uncertain and difficult to compare directly. This analysis seeks to bridge the gap of techno-economic analyses for these alternative powertrain vehicles by comparing all of them within the same analytic framework. Specifically, this report evaluates the total cost of ownership (TCO) of six different truck powertrain technologies (diesel, diesel hybrid-electric, plug-in hybrid electric, compressed natural gas, battery electric, and fuel cell electric) for three different truck vocations (Class 8 long haul [750 mile range], Class 8 short haul [300 mile range], and Class 4 parcel delivery), for different Department of Energy technology statuses (2018, 2025, and Ultimate). The TCO framework includes direct costs (purchase price, fuel, operating and maintenance), indirect costs (dwell time costs due to refueling/recharging and payload opportunity costs from forgone revenue due to the truck being weight-limited), but excludes general operation costs (driver wages and benefits, insurance, tire replacements, permits, tolls) that are assumed to be the same across powertrains. The TCO was evaluated for four scenarios that reflect typical business operating conditions. The TCO analysis results highlight that each powertrain technology may have an economic advantage on a TCO basis in certain business operating conditions and depending on fuel price realized. For Class 8 long haul trucks when payload opportunity costs are not incurred, battery electric and fuel cell electric powertrains could be cost competitive with diesel if the 2025 targets are achieved and fuel prices are low. If payload opportunity costs are incurred, battery electric powertrains are not estimated to reach TCO parity with diesel even if Ultimate targets and low electricity prices are realized, indicating a need for significant vehicle lightweighting. In Class 8 short haul commercial applications when payload opportunity costs are not incurred, battery electric, fuel cell electric, and compressed natural gas vehicle powertrains have very competitive TCOs with diesel if the 2025 targets are achieved. In Class 8 short haul commercial applications when payload opportunity costs are incurred, the battery electric vehicle powertrains can achieve TCO parity with diesel only if the Ultimate battery prices are met ($80/kWh). In Class 4 parcel delivery truck operating scenarios where there are no dwell time costs incurred, the plug-in hybrid electric vehicle, battery electric vehicle, and fuel cell electric vehicle could be cost-competitive with diesel and compressed natural gas with current (2018) technology performance and costs. In general, the payload opportunity costs can be a significant driver to TCO for the Class 8 long and short haul commercial vehicle applications while the dwell time costs could be a major TCO cost driver for the Class 4 parcel delivery vehicle if the business scenario realizes those costs.

advanced powertrains↗
DOE OSTI2023

Development and Demonstration of Medium-Heavy Duty PHEV Work Trucks

The heavy-duty vehicle market (Class 6-8) has been a difficult segment for the introduction of plug-in vehicles due to the large energy storage requirement (with corresponding cost), challenging duty cycles, and the diversity of vehicle configurations. The Work Truck market represents a significant opportunity for Heavy-Duty PHEV adoption. (1) The usage cycle includes driving and stationary/worksite power requirements, ensuring full daily usage of the grid-charged battery (battery size: 15-30kWhr). Though daily driving can often be short (an average of 26 miles per day), worksite power includes substantial demand (hydraulics, exportable 110/220V power, 12V support, HVAC). (2) Worksite power demands for conventional vehicles require continuous loaded engine operation, resulting in significant emissions, fuel consumption and noise impacts. (3) These trucks serve an industry that is highly diverse in final vehicle duty cycle, configuration, and jobsite power demands resulting in the need for a modular, configurable hybrid solution. Through this program, Odyne has developed and demonstrated a medium/heavy duty plug-in hybrid solution capable of meeting the needs of the work truck market while delivering fuel and emissions reductions of 50% or greater when evaluated against the full-day work truck duty cycle. The Odyne system developed in this project was released for commercial sale as the G2V7 Odyne Plug-in Hybrid and ePTO systems. The testing and field demonstration proved that the Odyne hybrid system is capable of reducing work truck fuel use and emissions by over 50% while subsequent commercial sales demonstrate the flexibility of the modular design and the capability to support electric systems of 12 – 30 kW and hydraulic based systems of 60 kW (80 HP) or greater. Odyne is continuing to work with suppliers on reducing component costs and working with supporting agencies to initiate projects to increase the driving and full day fuel and emissions savings in order to continue to improve the customer value and return on investment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Optimal management of electric hotel loads in mild hybrid heavy duty truck

Here, the problem of engine idling for heavy-duty trucks has been under study for decades with Auxiliary Power Units (APUs) and Truck Stop Electrification (TSE) as the most compelling solutions. With the electrification of trucks approaching feasibility in terms of cost-effective technology, hybridization offers another “degree of freedom” to tackle the problem. Here, this work aims at exploiting a battery pack of a 48 V mild-hybrid heavy-duty truck to store sufficient onboard energy for powering the auxiliary loads during the hoteling. This problem is not trivial, as the battery packs typically cannot recover the entire energy required through regeneration alone; hence an optimal energy management strategy needs to be employed to charge the battery through the engine during drive operation. This strategy optimizes powertrain performances among the four modes: (i) Engine of Coasting (EOC), (ii) Regeneration by braking, (iii) Regeneration by engine, and (iv) engine idling. This paper presents the development of a Dynamic Programming (DP) framework that employs a multi-objective cost function to minimize the fuel consumption and maximize the regeneration using the above-mentioned four modes. A typical heavy-duty truck drive cycle is used to represent the drive phase, with mandatory hoteling stops as per regulations. A comprehensive powertrain model is developed using validated components’ model. The DP employs two state variables: battery State-of-Charge (SOC) and engine mode, and three control inputs: (i) the engine ON–OFF state, (ii) clutch engagement state, and (iii) power request at the Electric Machine (EM) for calculating optimal SOC trajectory. The framework also tackles rapid engine ON–OFF scenarios to avoid the challenges associated with DP and the compromises in fuel cost with those approaches. Finally, the effectiveness of the proposed framework is tested for potential fuel savings on two different battery packs by performing the full cycle simulations. The results show 6.47% of fuel consumption reduction as compared to traditional APU-based heavy-duty truck.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2018

Hydrogen Fuel-Cell Electric Hybrid Truck Demonstration (Final Technical Report)

The purpose of the project was to demonstrate three (3) fully developed, operational, advanced vehicle technology prototypes for ON-ROAD goods movement applications. The major vehicle OEM for this demonstration was Navistar International Trucks, with the fuel cell and power control hybrid technology provided and integrated by US Hybrid. CEM was to provide independent data collection and analysis, while EDF would provide education, outreach, and communication for the project. GTI managed the partnership and arranged for hydrogen fuel for the demonstration. The drayage truck is used to transfer the cargo container from/to ports in a warehouse or logistic yard. For port facilities in the Port of Houston Area, as well as virtually all other ports, drayage trucks often queue for long periods, inching along at slow speeds to await loading and unloading. During this process, the trucks are essentially idle, with conventional diesel engines continuously running and producing accompanying emissions. The ZECT would essentially power off during idle periods, or at least utilize minimal electric energy. As such, range limitation by using a gaseous fuel would be mitigated by the fact that the vehicle is not using fuel during much of its duty cycle. The project team expected the vehicle range to be about 200 miles which is well within the duty cycle requirements of the original fleet partner, Richardson Trucking. The project was intended to demonstrate commercialization and production potential, and to clearly illustrate the selected vehicle technology’s potential to reduce GHG emissions and petroleum use if implemented widely. To achieve this, the project team proposed a two-year demonstration project in the Port of Houston area to assess and track vehicle performance, and evaluate fuel savings, petroleum reduction, and emissions reduction benefits of the prototype technology. Additional time at the front end of the project was required for vehicle and support infrastructure preparation. In June 2017 the remaining project partners, which now included Air Liquide, agreed that the project would be unlikely to move forward with the vehicle demonstration, primarily due to the lack of a viable cargo truck fleet partner in the Port of Houston area willing to make a sufficient commitment of resources to operate the vehicles for the demonstration.

08 HYDROGEN↗
DOE OSTI2019

CFD Estimation of Truck Induced Wind Gusts on Variable Message Signs

Cantilevered highway Variable Message Signs (VMS, also referred to as CMS, Changeable Message Signs, in latest design specifications) are potentially vulnerable to vibration caused by gusts from wind or that from truck-induced air flow. An example of such a vibration scenario was a VMS on State Route 17 in northern New Jersey, which was reported to experience large amplitude vertical displacements at the end of the cantilever. Reported vibration displacements ranged from the neighborhood of one to two feet to approximately four feet at the tip of the cantilever. This prompted the New Jersey Department of Transportation to take down the VMS and to support the structure to protect the safety of motorists. At the time of the New Jersey State Route 17 VMS vibration episodes, the AASHTO Standard Specifications for Structural Supports for Highway Signs, Luminaires, and Traffic Signals (referred to as Standard Specifications hereinafter) did not offer much guidance in the design for fatigue. It was also limited in instructions for VMS supporting structures, which might have produced some faulty designs. The sixth edition of the Standard Specifications indicated that the vibration problem is commonly associated with a large projected area in the horizontal plane and a vertical gust pressure created by truck passage under the VMS. An equivalent static gust pressure from trucks, PTG, is given for design purpose. A drag coefficient of 1.7 associated with the truck velocity VT is used in the calculation of PTG with an indication that it is an interim value before more research is completed to obtain a better coefficient. The study presented in this report was done to gain a better understanding of the aerodynamic properties of VMS and supporting structures, the dynamic characteristics of truck-induced gust pressure, and the potential for wind-truck-structure interaction that may produce significant vibration episodes for the VMS structures.

42 ENGINEERING↗