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The global transportation industry, and road freight in particular, faces formidable challenges in reducing Greenhouse Gas (GHG) emissions; both Europe and the US have already enabled legislation with CO2 / GHG reduction targets. In Europe, targets are set on a fleet level basis: a CO2 baseline has already been established using Heavy Duty Vehicle (HDV) data collected and analyzed by the European Environment Agency (EEA) in 2019/2020. This baseline data has been published as the reference for the required CO2 reductions. More recently, the EU has proposed a Zero Emissions Vehicle definition of 3g CO2/t-km. The Zero Emissions Vehicle (ZEV) designation is expected to be key to a number of market instruments that improve the economics and practicality of hydrogen trucks. This paper assesses the permissible amount of carbon-based fuel in hydrogen fueled vehicles – the Pilot Energy Ratio (PER) – for each regulated subgroup of HDVs in the baseline data set.

In response to global climate change, there is a widespread push to reduce carbon emissions in the transportation sector. For the difficult to decarbonize heavy-duty (HD) vehicle sector, lower carbon intensity fuels can offer a low-cost, near-term solution for CO2 reduction. The use of natural gas can provide such an alternative for HD vehicles while the increasing availability of renewable natural gas affords the opportunity for much deeper reductions in net-CO2 emissions. With this in consideration, the US National Renewable Energy Laboratory launched the Natural Gas Vehicle Research and Development Project to stimulate advancements in technology and availability of natural gas vehicles. As part of this program, Southwest Research Institute developed a hybrid-electric medium-HD vehicle (class 6) to demonstrate a substantial CO2 reduction over the baseline diesel vehicle and ultra-low NOx emissions.

Low temperature combustion (LTC) modes are among the advanced combustion technologies which offer thermal efficiencies comparable to conventional diesel combustion and produce ultra-low NOx and particulate matter (PM) emissions. However, combustion timing control, excessive pressure rise rate and high cyclic variations are the common challenges encountered by the LTC modes. These challenges can be addressed by developing model-based control framework for the LTC engine. In the current study, in-cylinder pressure data for dual-fuel LTC engine operation is analyzed for 636 different operating conditions and the heat release rate (HRR) traces are classified into three distinct classes based on their distinct shapes. These classes are named as Type-1, Type-2 and Type-3, respectively.

A numerical investigation of a six-stroke direct injection compression ignition engine operation in a low temperature combustion (LTC) regime is presented. The fuel employed is a gasoline-like oxygenated fuel consisting of 90% isobutanol and 10% diethyl ether (DEE) by volume to match the reactivity of conventional gasoline with octane number 87. The computational simulations of the in-cylinder processes were performed using a high-fidelity multidimensional in-house 3D CFD code (MTU-MRNT) with improved spray-sub models and CHEMKIN library. The combustion chemistry was described using a two-component (isobutanol and DEE) fuel model whose oxidation pathways were given by a reaction mechanism with 177 species and 796 reactions.

The US EPA and the California Air Resources Board (CARB) require electric vehicle range to be determined according to the Society of Automotive Engineers (SAE) surface vehicle recommended practice J1634 - Battery Electric Vehicle Energy Consumption and Range Test Procedure. In the 2021 revision of the SAE J1634, the Short Multi-Cycle Test (SMCT) was introduced. The proposed testing protocol eases the chassis dynamometer test burden by performing a 2.1-hour drive cycle on the dynamometer, followed by discharging the remaining battery energy into a battery cycler to determine the Useable Battery Energy (UBE). Opting for a cycler-based discharge is financially advantageous due to the extended operating time required to fully deplete a 70-100kWh battery commonly found in Battery Electric Vehicles (BEVs).

Pouch cells are increasingly popular form factors for the construction of energy storage systems in electric vehicles of all classes. Knowledge of the stress generated by these higher capacity pouch cells is critical to properly design battery modules and packs for both normal and abnormal operation. Existing literature predominantly offers data on smaller pouch cells with capacities of less than 10 Ah, leaving a gap in our understanding of the behavior of these larger cells. This experimental study aimed to bridge this knowledge gap by measuring loads and stresses in constrained 65 Ah pouch cells under both cycling and abuse conditions. To capture the desired responses, a load cell was located within a robust fixture to measure cell stress in real time after the application of a preload of approximately 30 kilograms or 294 N, equivalent to a pressure of 0.063 bar, with a fixed displacement.

Eco Approach and Departure (Eco-AnD) is a Connected Automated Vehicle (CAV) technology aiming to reduce energy consumption for crossing a signalized intersection or set of intersections in a corridor that features vehicle-to-infrastructure (V2I) communication capability. This research focuses on developing a Dynamic Programming (DP) based algorithm for a PHEV operating in Charge Depleting mode. The algorithm used the Reduced Order Energy Model (ROM) to capture the vehicle powertrain characteristics and road grade to capture the road dynamics. The simulation results are presented for a real-world intersection and 20-25% energy benefits are shown by comparing against a simulated human driver speed profile. The vehicle-level validation of the developed algorithm is carried out by performing closed-course track testing of the optimized speed solutions on a real CAV vehicle.

It is widely recognized that internal combustion engines (ICE) are needed for transport worldwide for years to come, however, demands on ICE fuel efficiency, emissions, cost, and performance are extremely challenging. Gasoline compression ignition (GCI) is one approach to achieve demanding efficiency and emissions targets. At Aramco Research Center-Detroit, an advanced, multi-cylinder GCI engine was designed and built using the latest combustion system, engine controls, and lean aftertreatment. The combustion system uses Aramco’s PPCI-diffusion process for ultra-low NOx and smoke. A P2 48V mild hybrid system was integrated on the engine for braking energy recovery and improved cold starts. For robust low-load operation, a 2-step valvetrain system was used for exhaust rebreathing. Test data showed that part-load fuel consumption was reduced 7 to 10 percent relative to a competitive 2.0L European diesel engine.

The AutoDrive Challenge competition sponsored by General Motors and SAE gives undergraduate and graduate students an opportunity to get hands-on experience with autonomous vehicle technology and development as they work towards their degree. Michigan Technological University has participated in the AutoDrive Challenge since its inception in 2017 with students participating through MTU’s Robotic System Enterprise. The MathWorks Simulation Challenge has been a component of the competition since its second year, tasking students with the development of perception, control and testing algorithms using MathWorks software products. This paper presents the pedagogical approach graduate student mentors used to enable students to build their understanding of autonomous vehicle concepts using familiar tools. This approach gives undergraduate students a productive experience with these systems that they may not have encountered in coursework within their academic program.

Vehicle-to-everything (V2X) communication, primarily designed for communication between vehicles and other entities for safety applications, is now being studied for its potential to improve vehicle energy efficiency. In previous work, a 20% reduction in energy consumption was demonstrated on a 2017 Prius Prime using V2X-enabled algorithms. A subsequent phase of the work is targeting an ambitious 30% reduction in energy consumption compared to a baseline. In this paper, we present the Eco-routing algorithm, which is key to achieving these savings. The algorithm identifies the most energy-efficient route between an Origin-Destination (O-D) pair by leveraging information accessible through commercially available Application Programming Interfaces (APIs). This algorithm is evaluated both virtually and experimentally through simulations and dynamometer tests, respectively, and is shown to reduce vehicle energy consumption by 10-15% compared to the baseline over real-world routes.

This research investigates the energy savings achieved through eco-driving controls in connected and automated vehicles (CAVs), with a specific focus on the influence of powertrain characteristics. Eco-driving strategies have emerged as a promising approach to enhance efficiency and reduce environmental impact in CAVs. However, uncertainty remains about how the optimal strategy developed for a specific CAV applies to CAVs with different powertrain technologies, particularly concerning energy aspects. To address this gap, on-track demonstrations were conducted using a Chrysler Pacifica CAV equipped with an internal combustion engine (ICE), advanced sensors, and vehicle-to-infrastructure (V2I) communication systems, compared with another CAV, a previously studied Chevrolet Bolt electric vehicle (EV) equipped with an electric motor and battery.

This paper presents a methodology to optimize the blending of charge-depleting (CD) and charge-sustaining (CS) modes in a multi-mode plug-in hybrid electric vehicle (PHEV). The objective of the optimization is to best utilize onboard energy for minimum overall energy consumption based on speed and elevation profile. The optimization reduces overall energy consumption when the selected route cannot be completely driven in all-electric mode. The optimization method splits drive cycles into constant distance segments and then uses a reduced-order model to sort the segments by the best use of battery energy vs. fuel energy. The PHEV used in this investigation is the Stellantis Pacifica. Results support energy savings up to 20% which depend on the route and initial battery State of Charge (SOC). Initial optimization takes 1 second for 38 km and 3 seconds for 154 km.

Engine knock is one of the limiting factors in determining the compression ratio and engine efficiency for spark ignited engines. Using the Southwest Research Institute Knock-CoV test method, it was previously shown that the knock limited load versus combustion phasing (CA50) has a constant slope. All of the knock mitigation strategies tested provided a shift to these knock limited loads but also increased the slope. That is, for the same CA50 retard the knock limited load could be increased more. Our hypothesis was that due to fuel sensitivity, or the difference between the RON and MON, the reactions that lead to knock will behave differently as the pressure-temperature history changes with engine speeds and loads. The fuel affects on the knock and CoV limits were studied by testing fuels with various sensitivities including methanol, E85 (85% ethanol) and Iso-octane.

Since the early 1990’s, commercial vehicles have suffered from repeated vulnerability exploitations that resulted in a need for improved automotive cybersecurity. This paper outlines the strategies and challenges of implementing an automotive Zero Trust Architecture (ZTA) to secure intra-vehicle networks. Zero Trust (ZT) originated as an Information Technology (IT) principle of “never trust, always verify”; it is the concept that a network must never assume assets can be trusted regardless of their ownership or network location. This research focused on drastically improving security of the cyber-physical vehicle network, with minimal performance impact measured as timing, bandwidth, and processing power. The automotive ZTA was tested using a software-in-the-loop vehicle simulation paired with resource constrained hardware that closely emulated a production vehicle network.

Off-road diesel engines remain one of the most significant contributors to the overall oxides of nitrogen (NOX) inventory and the California Air Resources Board (CARB) has indicated that reductions of up to 90% from current standards may be necessary to achieve its air quality goals. In recognition of this, CARB has funded a program aimed at demonstrating emission control technologies for off-road engines. This program builds on previous efforts to demonstrate Low NOX technologies for on-road engines. The objective was to demonstrate technologies to reduce tailpipe NOX and particulate matter (PM) emissions by 90 and 75%, respectively, from the current Tier 4 Final standards. In addition, the emission reductions were to be achieved while also demonstrating a 5 to 8.6% carbon dioxide (CO2) reduction and remaining Greenhouse Gas (GHG) neutral with respect to nitrous oxide (N2O) and methane (CH4).

Biodiesel (i.e., mono-alkyl esters of long chain fatty acids derived from vegetable oils and animal fats) is a renewable diesel fuel providing life-cycle greenhouse gas emission reductions relative to petroleum-derived diesel. With the expectation that there would be widespread use of biodiesel as a substitute for ultra-low sulfur diesel (ULSD), there have been many studies looking into the effects of biodiesel on engine and aftertreatment, particularly its compatibility to the current aftertreatment technologies. The objective of this study was to generate experimental data to measure the effectiveness of a current technology diesel oxidation catalysts (DOC) to oxidize soy-based biodiesel at various blend levels with ULSD. Biodiesel blends from 0 to 100% were evaluated on an engine using a conventional DOC.

According to the Annual Energy Outlook 2022 (AEO2022) report, almost 30% of the transport sector will still use internal combustion engines (ICE) until 2050. The transportation sector has been actively seeking different methods to reduce the CO2 emissions footprint of fossil fuels. The use of lower carbon-intensity fuels such as Renewable Diesel (RD) can enable a pathway to decarbonize the transport industry. This suggests the need for experimental or advanced numerical studies of RD to gain an understanding of its combustion and emissions performance. This work presents a numerical modeling approach to study the combustion and emissions of RD. The numerical model utilized the development of a reduced chemical kinetic mechanism for RD’s fuel chemistry. The final reduced mechanism for RD consists of 139 species and 721 reactions, which significantly shortened the computational time from using the detailed mechanism.

1Increasing adoption of downsized, boosted, spark-ignition engines has improved vehicle fuel economy, and continued improvement is desirable to reduce carbon emissions in the near-term. However, this strategy is limited by damaging preignition events which can cause hardware failure. Research to date has shed light on various contributing factors related to fuel and lubricant properties as well as calibration strategies, but the causal factors behind an individual preignition cycle remain elusive. If actionable precursors could be identified, mitigation through active control strategies would be possible. This paper uses artificial neural networks to search for identifiable precursors in the cylinder pressure data from a large real-world data set containing many preignition cycles. It is found that while follow-up preignition cycles in clusters can be readily predicted, the initial preignition cycle is not predictable based on features of the cylinder pressure.

Conventional diesel combustion (CDC) is known to provide high efficiency and reliable engine performance, but often associated with high particulate matter (PM) and nitrogen oxides (NOX) emissions. Combustion of fossil diesel fuel also produces carbon dioxide (CO2), which acts as a harmful greenhouse gas (GHG). Renewable and low-carbon fuels such as renewable diesel (RD) and methanol can play an important role in reducing harmful criteria and CO2 emissions into the atmosphere. This paper details an experimental study using a single-cylinder research engine operated under dual-fuel combustion using methanol and RD. Various engine operating strategies were used to achieve diesel-like fuel efficiency. Measurements of engine-out emissions and in-cylinder pressure were taken at test conditions including low-load and high-load operating points.

Low carbon emissions policies for the transportation sector have recently driven more interest in using low net-carbon fuels, including biodiesel. An internal combustion engine (ICE) can operate effectively using biodiesel while achieving lower engine-out emissions, such as soot, mostly thanks to oxygenate content in biodiesel. This study selected a heavy-duty (HD) single-cylinder engine (SCE) platform to test biodiesel fuel blends with 20% and 100% biodiesel content by volume, referred to as B20, and B100. Test conditions include a parametric study of exhaust gas recirculating (EGR), and the start of injection (SOI) performed at low and high engine load operating points. In-cylinder pressure and engine-out emissions (NOX and soot) measurements were collected to compare diesel and biodiesel fuels.