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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.

This work for the Coordinating Research Council (CRC) explores dependencies on the opportunity for fuel to impinge on internal engine surfaces (i.e., fuel–wall impingement) as a function of fuel properties and engine operating conditions and correlates these data with measurements of stochastic preignition (SPI) propensity. SPI rates are directly coupled with laser–induced florescence measurements of dye-doped fuel dilution measurements of the engine lubricant, which provides a surrogate for fuel–wall impingement. Literature suggests that SPI may have several dependencies, one being fuel–wall impingement. However, it remains unknown if fuel-wall impingement is a fundamental predictor and source of SPI or is simply a causational factor of SPI. In this study, these relationships on SPI and fuel-wall impingement are explored using 4 fuels at 8 operating conditions per fuel, for 32 total test points.

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).

The use of wireless power transfer systems, consisting of inductive electrical coils on the vehicle and the power source may be designed for dynamic operations where the vehicle will absorb energy at highway speeds from transmitting coils in the road. This has the potential to reduce the onboard energy storage requirements for vehicles while enabling significantly longer missions. This paper presents an approach to architecting a dynamic wireless power transfer corridor for heavy duty battery electric commercial freight vehicles. By considering the interplay of roadway power capacity, roadway and vehicle coil coverage, seasonal road traffic loading, freight vehicle class and weight, vehicle mobility energy requirements, on-board battery chemistry, non-electrified roadway vehicle range requirements, grid capacity, substation locations, and variations in electricity costs, we minimize the vehicle TCO by architecting the electrified roadway and the vehicle battery simultaneously.

G-Equation models represent propagating flame fronts with an implicit two-dimensional surface representation (level-set). Level-set methods are fast, as transport source terms for the implicit surface can be solved with finite-volume operators on the finite-volume domain, without having to build the actual surface. However, they include approximations whose practical effects are not properly understood. In this study, we improved the numerics of the FRESCO CFD code’s G-Equation solver and developed a new method to simulate kernel growth using signed distance functions and the analytical sphere-mesh overlap. We analyzed their role for simulating propane/air flames, using three well-established constant-volume configurations: a one-dimensional, freely propagating laminar flame; a disc-shaped, constant-volume swirl combustor; and torch-jet flame development through an orifice from a two-chamber device.

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.

We examine the effects on materials temperatures and engine efficiency via simulations of engines operating at temperatures which exceed the thermal limits of today’s materials. Potential focus areas include high-speed, high-load operation (in the fuel-enrichment zone) as well as conditions of selective cooling at lower speeds and loads. We focus on a light-duty DISI and a heavy-duty CI engine using GT-Power. Temperature distributions within the head, block, piston, and valves were obtained from 3D FEA simulations coupled with 1D GT-Power representations of the engine’s gas flow and combustion regions.

This work employs a novel laser induced fluorescence (LIF) diagnostic to measure fuel dilution in a running single cylinder research engine operated at stochastic pre ignition (SPI) and non-SPI prone conditions. Measurements of LIF based fuel dilution are quantified over a range of engine loads and fuel injection timings for two fuels. The in situ LIF measurements of fuel/lubricant interactions illustrate regions of increased fuel dilution from fuel-wall interactions and is believed to be a fundamental underpinning to generating top ring zone liquid conditions conducive to SPI. A novel level of dye doped in the fuel, between 50 to 500 ppm was used as the fluorescence source, at engine operating speed of 2000r/min from 5 to 18 bar of IMEPg injection timings was swept for two fuels of varying volatility.

Exhaust gas recirculation (EGR) has been shown to enable efficiency improvements in SI engines through multiple different mechanisms, including decreasing the knock propensity at high load, which allows higher compression ratio. While many of the benefits of EGR are applicable to both low and high power density engines, including reductions in pumping work and improved specific heat ratio, the knock benefits and corresponding compression ratio increases have been limited to low power density naturally aspirated engines primarily intended for hybrid vehicle architectures. An earlier study [1] indicated that there may be a kinetic limitation for the ability of EGR to mitigate knock under these conditions, but that study only considered a small number of conditions. This investigation expands on that study while also providing data for model validation for the new light-duty combustion consortium from the U.S. Department of Energy: Partnership for Advancing Combustion Engines (PACE).

The widespread adoption of boosted, downsized SI engines has brought pre-ignition phenomena into greater focus, as the knock events resulting from pre-ignitions can cause significant hardware damage. Much attention has been given to understanding the causes of pre-ignition and identify lubricant or fuel properties and engine design and calibration considerations that impact its frequency. This helps to shift the pre-ignition limit to higher specific loads and allow further downsizing but does not fundamentally eliminate the problem. Real-time detection and mitigation of pre-ignition would thus be desirable to allow safe engine operation in pre-ignition-prone conditions. This study focuses on advancing the time of detection of pre-ignition in an engine cycle where it occurs.

Today’s transportation is quickly transforming with the nascent advent of connectivity, automation, shared-mobility, and electrification. These technologies will not only affect our safety and mobility, but also our energy consumption, and environment. As a result, it is of unprecedented importance to understand the overall system impacts due to the introduction of these emerging technologies and concepts. Existing modeling tools are not able to effectively capture the implications of these technologies, not to mention accurately and reliably evaluating their effectiveness with a reasonable scope. To address these gaps, a dynamometer-in-the-loop (DiL) development and testing approach is proposed which integrates test vehicle(s), chassis dynamometer, and high fidelity traffic simulation tools, in order to achieve a balance between the model accuracy and scalability of environmental analysis for the next generation of transportation systems.

Eco-Approach and Departure (EAD) has been considered as a promising eco-driving strategy for vehicles traveling in an urban environment, where information such as signal phase and timing (SPaT) and geometric intersection description is well utilized to guide vehicles passing through intersections in the most energy-efficient manner. Previous studies formulated the optimal trajectory planning problem as finding the shortest path on a graphical model. While this method is effective in terms of energy saving, its computation efficiency can be further enhanced by adopting machine learning techniques. In this paper, we propose an innovative deep learning-based queue-aware eco-approach and departure (DLQ-EAD) system for a plug-in hybrid electric bus (PHEB), which is able to provide an online optimal trajectory for the vehicle considering both the downstream traffic condition (i.e. traffic lights, queues) and the vehicle powertrain efficiency.

The current energy climate has created a push toward reducing consumption of fossil fuels and lowering emissions output in power generation applications. Combined with the desire for a more distributed energy grid, there is currently a need for small displacement, high efficiency engines for use in stationary power generation. An enabling technology for achieving high efficiencies with spark ignited engines for such applications is the use of jet ignition which enables ultra-lean (λ > ~1.6) combustion via air dilution. This paper provides a comprehensive review of the development of a 390cc, high efficiency single cylinder natural gas-fueled jet ignition engine operating ultra-lean. The engine was developed as part of the Department of Energy’s Advanced Research Projects Agency–Energy (DOE ARPA-E) GENSETS program. Design choices for minimizing friction are highlighted as well as test results showing further friction reduction through downspeeding.

This work explores the impact of the interaction of lubricant and fuel properties on the propensity for stochastic pre-ignition (SPI). Findings are based on statistically significant changes in SPI tendency and magnitude, as determined by measurements of cylinder pressure. Specifically, lubricant detergents, lubricant volatility, fuel volatility, fuel chemical composition, fuel-wall impingement, and engine load were varied to study the physical and chemical effects of fuel-lubricant interactions on SPI tendency. The work illustrates that at low loads, with fuels susceptible to SPI events, lubricant detergent package effects on SPI were non-significant. However, with changes to fuel distillation, fuel-wall impingement, and most importantly engine load, lubricant detergent effects could be observed even at reduced loads This suggests that there is a thermal effect associated with the higher load operation.

Increasing the strength of materials is effective in reducing weight and boosting structural part performance, but there are cases in where the residual strain generated during the process of manufacturing of high-strength materials results in a decline of durability. It is therefore important to understand how the residual strain in a manufactured component changes due to processing conditions. In the case of a connecting rod, because the strain load on the connecting rod rib sections is high, it is necessary to clearly understand the distribution of strain in the ribs. However, because residual strain is generally measured by using X-ray diffractometers or strain gauges, measurements are limited to the surface layer of the parts. Neutron beams, however, have a higher penetration depth than X-rays, allowing for strain measurement in the bulk material.

It is widely recognized in the automotive industry that, in very cold climatic conditions, the driving range of an Electric Vehicle (EV) can be reduced by 50% or more. In an effort to minimize the EV range penalty, a novel thermal energy storage system has been designed to provide cabin heating in EVs and Plug-in Hybrid Electric Vehicles (PHEVs) by using an advanced phase change material (PCM). This system is known as the Electrical PCM-based Thermal Heating System (ePATHS) [1, 2]. When the EV is connected to the electric grid to charge its traction battery, the ePATHS system is also “charged” with thermal energy. The stored heat is subsequently deployed for cabin comfort heating during driving, for example during commuting to and from work. The ePATHS system, especially the PCM heat exchanger component, has gone through substantial redesign in order to meet functionality and commercialization requirements.

This work explores the dependence of fuel ignition delay on stochastic pre-ignition (SPI). Findings are based on bulk gas thermodynamic state, where the effects of kinetically controlled bulk gas pre-spark heat release (PSHR) are correlated to SPI tendency and magnitude. Specifically, residual gas and low temperature PSHR chemistry effects and observations are explored, which are found to be indicative of bulk gas conditions required for strong SPI events. Analyzed events range from non-knocking SPI to knocking SPI and even detonation SPI events in excess of 325 bar peak cylinder pressure. The work illustrates that singular SPI event count and magnitude are found to be proportional to PSHR of the bulk gas mixture and residual gas fraction. Cycle-to-cycle variability in trapped residual mass and temperature are found to impose variability in singular SPI event count and magnitude.

Deposit formation within turbocharger compressor housings can lead to compressor efficiency degradation. This loss of turbo efficiency may degrade fuel economy and increase CO2 and NOx emissions. To understand the role that engine oil composition and formulation play in deposit formation, five different lubricants were run in a fired engine test while monitoring turbocharger compressor efficiency over time. Base stock group, additive package, and viscosity modifier treat rate were varied in the lubricants tested. After each test was completed the turbocharger compressor cover and back plate deposits were characterized. A laboratory oil mist coking rig has also been constructed, which generated deposits having the same characteristics as those from the engine tests. By analyzing results from both lab and engine tests, correlations between deposit characteristics and their effect on compressor efficiency were observed.

Modeling and simulation of batteries, in conjunction with theory and experiment, are important research tools that offer opportunities for advancement of technologies that are critical to electric motors. The development of data from the application of these tools can provide the basis for managerial and technical decision-making. Together, these will continue to transform batteries for electric vehicles.

Engine acoustics measured by microphones near the engine have been used in controlled laboratory settings for combustion feedback and even combustion phasing control, but the use of these techniques in a vehicle where many other noise sources exist is problematic. In this study, surface-mounted acoustic emissions sensors are embedded in the block of a 2.0L turbocharged GDI engine, and the signal is analyzed to identify useful feedback features. The use of acoustic emissions sensors, which have a very high frequency response and are commonly used for detecting material failures for health monitoring, including detecting gear pitting and ring scuffing on test stands, enables detection of acoustics both within the range of human hearing and in the ultrasonic spectrum. The high-speed acoustic time-domain data are synchronized with the crank-angle-domain combustion data to investigate the acoustic emissions response caused by various engine events.