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The current focus in the ongoing development of autonomous driving systems (ADS) for heavy duty vehicles is that of vehicle operational safety. To this end, developers and researchers alike are working towards a complete understanding of the operating environments and conditions that autonomous vehicles are subject to during their mission. This understanding is critical to the testing and validation phases of the development of autonomous vehicles and allows for the identification of both the nominal and edge case scenarios encountered by these systems. Previous work by the authors saw the development of a comprehensive scenario generation framework to identify an operating domain specification (ODS), or external and internal conditions an autonomous driving system can expect to encounter on its mission to form critical scenario groups for autonomous vehicle testing and validating using statistical patterns, clustering, and correlation.

Highway safety remains a significant concern, especially in mixed traffic scenarios involving heavy-duty vehicles (HDV) and smaller passenger cars. The vulnerability of HDVs following closely behind smaller cars is evident in incidents involving the lead vehicle, potentially leading to catastrophic rear-end collisions. This paper explores how automatic speed enforcement systems, using speed cameras, can mitigate risks for HDVs in such critical situations. While historical crash data consistently demonstrates the reduction of accidents near speed cameras, this paper goes beyond the conventional notion of crash occurrence reduction. Instead, it investigates the profound impact of driver behavior changes within desired travel speed distribution, especially around speed cameras, and their contribution to the safety of trailing vehicles, with a specific focus on heavy-duty trucks in accident-prone scenarios.

Adoption of fuel cell electric vehicles (FCEV) or battery electric vehicles (BEV) in heavy-duty (HD) commercial freight transportation is hampered by difficult technoeconomic obstacles. To enable widespread deployment of electrified powertrains, fleet and operational logistics need high uptime and parity with diesel system productivity/total cost of ownership (TCO), while meeting safety compliance. Due to a mix of comparatively high powerplant and energy storage costs, high energy costs (more so for FCEV), greater weight (more so for BEV), slow refueling / recharging durations, and limited supporting infrastructure, FCEV and BEV powertrains have not seen significant uptake in the HD freight transport market. The use of dynamic wireless power transfer (DWPT) systems, consisting of inductive electrical coils on the vehicle and power source transmitting coils embedded in the roadways, may address several of these challenges.

Traditional autonomous vehicle perception subsystems that use onboard sensors have the drawbacks of high computational load and data duplication. Infrastructure-based sensors, which can provide high quality information without the computational burden and data duplication, are an alternative to traditional autonomous vehicle perception subsystems. However, these technologies are still in the early stages of development and have not been extensively evaluated for lane detection system performance. Therefore, there is a lack of quantitative data on their performance relative to traditional perception methods, especially during hazardous scenarios, such as lane line occlusion, sensor failure, and environmental obstructions.

Lane detection plays a critical role in autonomous vehicles for safe and reliable navigation. Lane detection is traditionally accomplished using a camera sensor and computer vision processing. The downside of this traditional technique is that it can be computationally intensive when high quality images at a fast frame rate are used and has reliability issues from occlusion such as, glare, shadows, active road construction, and more. This study addresses these issues by exploring alternative methods for lane detection in specific scenarios caused from road construction-induced lane shift and sun glare. Specifically, a U-Net, a convolutional network used for image segmentation, camera-based lane detection method is compared with a radar-based approach using a new type of sensor previously unused in the autonomous vehicle space: radar retro-reflectors.

The phenomena of global warming and climate change are encouraging more and more countries, local communities, and companies to establish carbon neutrality targets, which has very significant implications for the US trucking industry. Truck electrification helps fleets to achieve zero tailpipe emissions and macro-scale decarbonization while allowing continued business growth in response to the rapid expansion of e-commerce and shipping related to increased globalization. This paper presents an analysis of Class 8 long-haul truck electrification using a commercial vehicle electrification evaluation tool and Fleet DNA drive data. The study provides new insight into the impacts of streamlined chassis, battery energy density, and superfast charging on battery capacity needs as well as implications for payload, energy consumption, and greenhouse gas emissions for electric long-haul trucks. The study also identifies a pathway for achieving optimal long-haul truck electrification.

Reducing criteria pollutants while reducing greenhouse gases is an active area of research for commercial on-road vehicles as well as for off-road machines. The heavy duty on-road sector has moved to reducing NOx by 82.5% compared to 2010 regulations while increasing the engine useful life from 435,000 to 650,000 miles by 2027 in the United States (US). An additional certification cycle, the Low Load Cycle (LLC), has been added focusing on part load operation having tight NOx emissions levels. In addition to NOx, the total CO2 emissions from the vehicle will also be reduced for various model years. The off-road market is following with a 90% NOx reduction target compared to Tier 4 Final for 130-560 kW engines along with greenhouse gas targets that are still being established. The off-road market will also need to certify with a Low Load Application Cycle (LLAC), a version of which was proposed for evaluation in 2021.

The present study was motivated by a need to expand information for consumers offered through the FuelEconomy.Gov website. To that end, a power-based modeling approach has been used to examine the effect of steady-speed driving on estimated range for model year 2020 – 2023 battery electric vehicles (BEVs). This approach allowed rapid study of a broader range of BEV models than could be accomplished through vehicle tests. Publicly accessible certification test results and other data were used to perform a regression between cycle-average tractive power requirements and the resulting electrical power. This regression enabled estimation of electric power and energy use over a range of steady highway speeds. These analyses in turn allowed projection of vehicle range at differing speeds. The projections agree within 6% with available 65 MPH manufacturer test data.

The proposed Euro-7 regulations are expected to build on the significant emissions reductions that have already been achieved using advanced Euro VI compliant after treatment systems (ATS). The introduction of in-service conformity (ISC) requirements during Euro VI paved the way for enabling compliance during real-world driving conditions. The diverse range of applications and resulting operating conditions greatly impact ATS design and the ability of the diesel particulate filter (DPF) to maintain performance under the most challenging boundary conditions including cold starts, partial/complete regenerations, and high passive soot burn operation. The current study attempts to map the particle number (PN) filtration performance of different DPF technologies under a variety of in-use cycles developed based on field-data from heavy duty Class-8 / N3 vehicles.

With more stringent CO2 emission regulation in the world, Plug-in Hybrid Electric Vehicle (PHEV, also known as off-vehicle charging hybrid electric vehicle, OVC-HEV) plays a more important role in the current modern market, such as in China. At the same time, Real Driving Emission (RDE) was introduced in both Euro 6d and China 6b regulation, which covers more factors in the real driving practice including altitude, environment temperature, fuel quality, driving behaviors, and so on, which could potentially impact the pollutant emissions. Besides above mentioned, for PHEV, the state of charge (SOC) of the battery is also considered as one important factor, which could impact the engine load and emissions.

The gasoline particulate filter (GPF) represents a practical solution for particulate emissions control in light-duty gasoline-fueled vehicles. It is also seen as an essential technology in North America to meet the upcoming US EPA tailpipe emission regulation, as proposed in the “Multi-pollutant Rule for Model Year 2027”. The goal of this study was to introduce advanced, uncoated GPF products and measure their particulate mass (PM) reduction performance within the existing US EPA FTP vehicle testing procedures, as detailed in Code of Federal Regulations (CFR) part 1066. Various state-of-the-art GPF products were characterized for their microstructure properties with lab-bench checks for pressure drop and filtration efficiency, then pre-conditioned with an EPA-recommended 1500 mile on-road break-in, and finally were tested on an AWD vehicle chassis-dyno emissions test cell at both 25°C and -7°C ambient conditions.

Gasoline particulate filters (GPF) have become a standard aftertreatment component in Europe, China, and since recently, India, where particulate emissions are based on a particle number (PN) standard. The anticipated evolution of regulations in these regions towards future EU7, CN7, and BS7 standards further enhances the needs with respect to the filtration capabilities of the GPFs used. Emission performance has to be met over a broader range in particle size, counting particles down to 10nm, and over a broader range of boundary conditions. The requirements with respect to pressure drop, aiming for as low as possible, and durability remain similar or are also enhanced further. To address these future needs new filter technologies have been developed. New technologies for uncatalyzed GPF applications have been introduced in our previous publications.

The ever-tightening regulation norms across the world emphasize the magnitude of the air pollution problem. The decision to leapfrog from BS4 to BS6 – with further reduction in emission limits -showed India’s commitment to clean up its atmosphere. The overall cycle emissions were reduced significantly to meet BS6 targets [1]. However, the introduction of RDE norms in BS6.2 [1] demanded further reduction in emissions under real time operating conditions – start-stop, hard acceleration, idling, cold start – which was possible only through strategies that demanded a cost effective yet robust solutions. The first few seconds of the engine operation after start contribute significantly to the cycle gaseous emissions. This is because the thermal inertia of the catalytic converter restricts the rate at which temperature of the catalyst increases and achieves the desired “light-off” temperature.

Accurate perception of the driving environment and a highly accurate position of the vehicle are paramount to safe Autonomous Vehicle (AV) operation. AVs gather data about the environment using various sensors. For a robust perception and localization system, incoming data from multiple sensors is usually fused together using advanced computational algorithms, which historically requires a high-compute load. To reduce AV compute load and its negative effects on vehicle energy efficiency, we propose a new infrastructure information source (IIS) to provide environmental data to the AV. The new energy–efficient IIS, chip–enabled raised pavement markers are mounted along road lane lines and are able to communicate a unique identifier and their global navigation satellite system position to the AV. This new IIS is incorporated into an energy efficient sensor fusion strategy that combines its information with that from traditional sensor.

The recently concluded partnership for advancing combustion engines (PACE) was a US Department of Energy consortium involving multiple national laboratories focused on addressing key efficiency and emission barriers in light-duty engines. Generation of detailed experimental data and modeling capabilities to understand and predict cold-start behavior was a major pillar in this program. Cold-start, as defined by the time between first engine crank and three-way catalyst light-off, is responsible for a large percentage of NOx, unburned hydrocarbon, and particulate matter emissions in light-duty engines. Minimizing emissions during cold-start is a trade-off between achieving faster three-way catalyst light-off, and engine out emissions during that period. In this study, engine performance, emissions, and catalyst warmup potential were monitored while the engine was operated using a single direct injection (baseline case) as well as a two-way-equal-split direct injection strategy.

With increasingly stringent regulations mandating the improvement of vehicle fuel economy, automotive manufacturers face growing pressure to develop and implement technologies that improve overall system efficiency. One such technology is an automatic (auto) stop-start feature. Auto stop-start reduces idle time and reduces fuel use by temporarily shutting the engine off when the vehicle comes to a stop and automatically re-starting it when the brake is released, or the accelerator is pressed. As mandated by the U.S. Congress, the U.S. Environmental Protection Agency (EPA) is required to keep the public informed about fuel saving practices. This is done, in partnership with the U.S. Department of Energy (DOE), through the fueleconomy.gov website. The “Fuel-Saving Technologies” and “Gas Mileage Tips” sections of the website are focused on helping the public make informed purchasing decisions and encouraging fuel-saving driving habits.

The development of a future hydrogen energy economy will require the development of several hydrogen market and industry segments including a hydrogen based commercial freight transportation ecosystem. For a sustainable freight transportation ecosystem, the supporting fueling infrastructure and the associated vehicle powertrains making use of hydrogen fuel will need to be co-established. This paper develops a long-term plan for refueling infrastructure deployment using the OR-AGENT (Optimal Regional Architecture Generation for Electrified National Transportation) tool developed at the Oak Ridge National Laboratory, which has been used to optimize the hydrogen refueling infrastructure requirements on the I-75 corridor for heavy duty (HD) fuel cell electric commercial vehicles (FCEV).

To achieve carbon neutrality by 2060, the Chinese government has put effort into decarbonizing the transportation sector. Consequently, China elaborated a new energy vehicle strategy promoting the production of electric vehicles and expanding into hydrogen (H2) vehicle technologies including fuel cell electric vehicles and H2 internal combustion engine vehicles. The Transportation Energy Analysis Model (TEAM) projects the market penetration as well as energy demand and greenhouse gas emissions in China up to 2050. By integrating the Monte Carlo simulation, this study tests the robustness of TEAM and investigates the key parameters that will shape passenger vehicle sales and emissions in the future. The results show that fuel cell cost, H2 price, and battery cost are the most sensitive parameters for H2 vehicle technologies.

China's plug-in electric vehicle (PEV) market with stocks at 7.8 million is the world's largest in 2021, and it accounts for half of the global PEV growth in 2021. The PEV market in China has dramatically evolved since the pandemic in 2020: over 20% of all new PEV sales are from China by mid-2022. Recent features of PEV market dynamics, consumer acceptance, policies, and infrastructure have important implications for both the global energy market and manufacturing stakeholders. From the perspective of demand pull-supply push, this study analyzes China's PEV industry with a market dynamics framework by reviewing sales, product and brand, infrastructure, and government policies from the last few years and outlooking the development of the new government’s 14th Five-Year Plan (2021-2025).

As the official proposal for emission regulation Euro 7 has been released by European Commission, PN above 10nm is taken into consideration for the ultrafine particulate emissions control. The challenges of GPF filtration efficiency emerge for the light-duty manufactures to meet the future emission standards. In the present study, a China 6 compliant vehicle was tested to reveal its performance over the China 6 standards and potential to meet the upcoming Euro 7. Three GPF product types (Gen 1, Gen 2, and concept Gen 3) were mounted to the tested vehicle. WLTC tests were conducted on chassis dynamometer in laboratory as well as a self-designed aggressive cycle (“Base Cycle”) tests. To explore the GPFs performance for PN emissions above 10nm against the proposed limit 6.0E11 #/km, PN emission above 10nm were measured in our laboratory tests for both engine out and tailpipe as well as the PN emission above 23nm.