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The global push towards reducing green-house gas and criteria pollutant emissions is leading to tighter emission standards for heavy-duty engines. Among the most stringent of these standards are the California Air Resource Board (CARB) 2024+ HD Omnibus regulations adopted by the agency in August 2020. The CARB 2024+ HD Omnibus regulations require up to 90% reduction in NOx emissions along with updated compliance testing methods for on-road heavy-duty engines. Subsequently, the agency announced development of new Tier 5 standards for off-road engines in November 2021. The Tier 5 standards aim to reduce NOx/PM emissions by 90%/75% respectively from Tier 4 final levels, along with introduction of greenhouse gas emission standards for CO2/CH4/N2O/NH3. Furthermore, CARB is also considering similar updates on compliance testing as those implemented in 2024+ HD Omnibus regulations including, low-load cycle, idle emissions and 3-bin moving average in-use testing.

Vehicle OEM’s for MHD applications are facing significant challenges in meeting the stringent 2027 low-NOx and GHG emissions regulations. To meet such challenges, advanced engine and aftertreatment technologies along with powertrain electrification are being applied to achieve robust solutions. FEV has previously conducted model-based assessments to show the potential of 48V engine and aftertreatment technologies to simultaneously meet GHG and low NOx emission standards. This study focuses on evaluating the full potential of 48V electrification technology through addition of 48V P3 hybrid system to the previously developed 48V advanced engine and aftertreatment technology package. Previously, a model-based approach was utilized for selection and sizing of a 48V system-enabled engine and aftertreatment package for class 6-7 MHD application.

The Environmental Protection Agency (EPA) and California Air Resources Board (CARB) have recently announced rulemakings focused on tighter emission limits for oxides of nitrogen (NOx) from heavy-duty trucks. As part of the new rulemaking CARB has proposed a Low Load Cycle (LLC) to specifically evaluate NOx emission performance over real-world urban and vocational operation typically characterized by low engine loads, thereby demanding the implementation of continuous active thermal management of the engine and aftertreatment system. This significant drop in NOx levels along with continued reduction in the Green House Gas (GHG) limits poses a more significant challenge for the engine developer as the conventional emission reduction approaches for one species will likely result in an undesirable increase in the other species.

The automotive industry continues to develop new powertrain and vehicle technologies aimed at reducing overall vehicle-level fuel consumption. Specifically, the use of electrified propulsion systems, including electrified and electric drive units (EDU), is expected to play a significant role in helping OEMs meet fleet CO2 reduction targets for 2025 and beyond. The change to vehicles propelled by electrified powertrains leads to a reduction in vehicle noise levels. Despite the overall noise levels being low, the NVH behavior of such vehicles can be objectionable due to the presence of tonal noise coming from electric machines and geartrain components. In order to ensure customer acceptance of electrically propelled vehicles, it is imperative that these NVH challenges are understood and solved. Specifically, this paper discusses the EDU NVH development process. This includes considerations for CAE/test-based development and validation processes to ensure optimal NVH development.

Stochastic Preignition (SPI) is an abnormal combustion event that occurs in a turbocharged engine and can lead to the loss in fuel economy and engine hardware damage, and in turn result in customer dissatisfaction. It is a significant limiting factor on the use and continued downsizing of turbocharged spark ignited direct injection (SIDI) gasoline engines. Understanding and mitigating all the factors that cause and influence the rate and severity of SPI occurrence are of critical importance to the engine’s continued use and fuel economy improvements for future designs. Previous studies have shown that the heavy molecular weight components of the fuel formulations are one factor that influences the rate of SPI from a turbocharged SIDI gasoline engine. All the previous studies have involved analyzing the fuel’s petroleum hydrocarbon chemistry, but not specifically the additives that are put in the fuel to protect and clean the internal components over the life of the engine.

Electrification of heavy-duty trucks has received significant attention in the past year as a result of future regulations in some states. For example, California will require a certain percentage of tractor trailers, delivery trucks and vans sold to be zero emission by 2035. However, the relatively low energy density of batteries in comparison to diesel fuel, as well as the operating profiles of heavy-duty trucks, make the application of electrified powertrain in these applications more challenging. Heavy-duty vehicles can be broadly classified into two main categories; long-haul tractors and vocational vehicles. Long-haul tractors offer limited benefit from electrification due to the majority of operation occurring at constant cruise speeds, long range requirements and the high efficiency provided by the diesel engine.

Analysis-driven pre-calibration of a modern automotive engine is extremely valuable in significantly reducing hardware investments and accelerating engine designs compliant with stricter emission regulations. Advanced modelling tools, such as a Virtual Engine Model (VEM) using Computational Fluid Dynamics (CFD), are often used within the framework of a Design of Experiments for Powertrain Engineering (DEPE) with the goal of streamlining significant portions of the calibration process. The success of the methodology largely relies on the accuracy of analytical predictions, especially engine-out emissions. Results show excellent agreements in engine performance parameters (with R2 > 98%) and good agreements in NOx and combustion noise (with R2 > 87%), while the Carbon Monoxide (CO), Unburned Hydrocarbons (HC) and Smoke emissions predictions remain a challenge even with a large n-heptane mechanism consisting of 144 species and 900 reactions and refined mesh resolution.

Electrification of heavy-duty trucks has received significant attention in the past year as a result of future regulations in some states. For example, California will require a certain percentage of tractor trailers, delivery trucks and vans sold to be zero emission by 2035. However, the relatively low energy density of batteries in comparison to diesel fuel, as well as the operating profiles of heavy-duty trucks, make the application of electrified powertrain in these applications more challenging. Heavy-duty vehicles can be broadly classified into two main categories; long-haul tractors and vocational vehicles. Long-haul tractors offer limited benefit from electrification due to the majority of operation occurring at constant cruise speeds, long range requirements and the high efficiency provided by the diesel engine.

Stochastic Pre-Ignition (SPI) or Low Speed Pre-Ignition (LSPI) is an abnormal combustion event that can occur during the operation of modern, highly boosted direct-injection gasoline engines. This abnormal combustion event is characterized by an undesired and early start of combustion that is not initiated by the spark plug. Early SPI events can subsequently lead to violent auto-ignitions that are referred to as Mega- or Super-Knock in literature and have the potential to severely damage engines in the field. Numerous studies to analyze impact factors on SPI occurrence and severity have been conducted in recent years. While initial studies have focused strongly on engine oil formulation, calibration and engine design and their respective impact on SPI initiation, the impact of physical and chemical properties of the fuel have also become of interest in recent years.

Past experimental studies conducted by the current authors on a 13 liter 16.7:1 compression ratio heavy-duty diesel engine have shown that diesel-Compressed Natural Gas (CNG) Reactivity Controlled Compression Ignition (RCCI) combustion targeting low NOx emissions becomes progressively difficult to control as the engine load is increased. This is mainly due to difficulty in controlling reactivity levels at higher loads. For the current study, CFD investigations were conducted in CONVERGE using the SAGE combustion solver with the application of the Rahimi mechanism. Studies were conducted at a load of 5 bar BMEP to validate the simulation results against RCCI experimental data. In the low load study, it was found that the Rahimi mechanism was not able to predict the RCCI combustion behavior for diesel injection timings advanced beyond 30 degCA bTDC. This poor prediction was found at multiple engine speed and load points.

Exhaust systems traditionally require a specific amount of muffler volume to reduce sound levels appropriately. However, as hybridization evolves, the packaging area becomes smaller, reducing available muffler space and requiring alternative solutions to attenuate exhaust sound with less volume. Passive exhaust valves are a key solution, leveraging the physics of the exhaust (flow, temperature, and pressure) to cycle the valve. Passive exhaust valves typically operate in a closed position under low-flow conditions (low engine speeds and loads), which helps to reduce low-frequency boom, moderately increasing backpressure when it is not detrimental to engine efficiency. Conversely, under higher engine speed and load operating conditions, when exhaust flow increases and backpressure is critical to achieve desired power output, the passive valve opens to reduce its impact.

A Machine Learning (ML) approach is presented to correlate in-cylinder images of early flame kernel development within a spark-ignited (SI) gasoline engine to early-, mid-, and late-stage flame propagation. The objective of this study was to train machine learning models to analyze the relevance of flame surface features on subsequent burn rates. Ultimately, an approach of this nature can be generalized to flame images from a variety of sources. The prediction of combustion phasing was formulated as a regression problem to train predictive models to supplement observations of early flame kernel growth. High-speed images were captured from an optically accessible SI engine for 357 cycles under pre-mixed operation. A subset of these images was used to train three models: a linear regression model, a deep Convolutional Neural Network (CNN) based on the InceptionV3 architecture and a CNN built with assisted learning on the VGG19 architecture.

While significant progress has been made in recent years to develop hybrid and battery electric vehicles for passenger car and light-duty applications to meet future fuel economy targets, the application of hybrid powertrains to heavy-duty truck applications has been very limited. The relatively lower energy and power density of batteries in comparison to diesel fuel and the operating profiles of most heavy-duty trucks, combine to make the application of hybrid powertrain for these applications more challenging. The high torque and power requirements of heavy-duty trucks over a long operating range, the majority of which is at constant cruise point, along with a high payback period, complexity, cost, weight and range anxiety, make the hybrid and battery electric solution less attractive than a conventional powertrain.

As greenhouse gas regulations continue to tighten, more opportunities to improve engine efficiency emerge, including exhaust gas heat recovery. Upon cold starts, engine exhaust gases downstream of the catalysts are redirected with a bypass valve into a heat exchanger, transferring its heat to the coolant to accelerate engine warm-up. This has several advantages, including reduced fuel consumption, as the engine’s efficiency improves with temperature. Furthermore, this accelerates readiness to defrost the windshield, improving both safety as well as comfort, with greater benefits in colder climates, particularly when combined with hybridization’s need for engine on-time solely for cabin heating. Such products have been in the market now for several years; however they are bulky, heavy and expensive, yielding opportunities for competitive alternatives.

A novel Spatially Optimized Diffusion Alloy (SODA) material has been developed and applied to exhaust systems, which are an aggressive environment subject to high temperatures and loads, as well as excessive corrosion. Traditional stainless steels disperse chromium homogeneously throughout the material, with varying amounts ranging from 10% to 20% dependent upon its grade (e.g. 409, 436, 439, 441, and 304). SODA steels, however, offer layered concentrations of chromium, enabling an increased amount along the outer surface for much needed corrosion resistance and aesthetics. This outer layer, typically about 70μm thick, exceeds 20% of chromium concentration locally, but is less than 3% in bulk, offering selective placement of the chromium to minimize its overall usage. Since this layer is metallurgically bonded, it cannot delaminate or separate from its core, enabling durable protection throughout manufacturing processes and full useful life.

The automotive industry continues to develop new powertrain and vehicle technologies aimed at reducing overall vehicle-level fuel consumption. Specifically, the use of electrified propulsion systems is expected to play an increasingly important role in helping OEM’s meet fleet CO2 reduction targets for 2025 and beyond. This will also include a strong growth in the global demand for electric drive units (EDUs). The change from conventional vehicles to vehicles propelled by EDUs leads to a reduction in overall vehicle exterior and interior noise levels, especially during low-speed vehicle operation. Despite the overall noise levels being low, the NVH behavior of such vehicles can be objectionable due to the presence of tonal noise coming from electric machines and geartrain components as well as relatively high shares of road/wind noise. In order to ensure customer acceptance of electrically propelled vehicles, it is imperative that these NVH challenges are understood and solved.

Sector mesh modeling is the dominant computational approach for combustion system design optimization. The aim of this work is to quantify the errors descending from the sector mesh approach through three geometric modeling approaches to an optical diesel engine. A full engine geometry mesh is created, including valves and intake and exhaust ports and runners, and a full-cycle flow simulation is performed until fired TDC. Next, an axisymmetric sector cylinder mesh is initialized with homogeneous bulk in-cylinder initial conditions initialized from the full-cycle simulation. Finally, a 360-degree azimuthal mesh of the cylinder is initialized with flow and thermodynamics fields at IVC mapped from the full engine geometry using a conservative interpolation approach. A study of the in-cylinder flow features until TDC showed that the geometric features on the cylinder head (valve tilt and protrusion into the combustion chamber, valve recesses) have a large impact on flow complexity.

A novel approach to the Integration of Turbocompounding/WHR, Electrification and Supercharging technologies (ITES) to reduce fuel consumption in a medium heavy-duty diesel engine was previously published by FEV. This paper describes a modified approach to ITES to reduce fuel consumption on a heavy-duty diesel engine applied in a Class 8 tractor. The original implementation of the ITES incorporated a turbocompound turbine as the means for waste heat recovery. In this new approach, the turbocompound unit connected to the sun gear of the planetary gear set has been replaced by an organic Rankine cycle (ORC) turbine expander. The secondary compressor and the electric motor-generator are connected to the ring gear and the carrier gear respectively. The ITES unit is equipped with dry clutch and band brake allowing flexibility in mechanical and electrical integration of the ORC expander, secondary compressor and electric motor-generator to the engine.

The occurrence of abnormal combustion events leading to high peak pressures and severe knock can be considered to be one of the main challenges for modern turbocharged, direct-injected gasoline engines. These abnormal combustion events have been referred to as Stochastic Pre-Ignition (SPI) or Low-Speed Pre-Ignition (LSPI). The events are characterized by an undesired, early start of combustion of the cylinder charge which occurs before or in parallel to the intended flame kernel development from the spark plug. Early SPI events can subsequently lead to violent auto-ignitions that are often referred to as Mega- or Super-Knock. These heavy knock events lead to strong pressure oscillations which can destroy production engines within a few occurrences. SPI occurs mainly at low engine speed and high engine load, thus limiting the engine operating area that is in particular important to achieve good drivability in downsized engines.

Powertrain electrification could be a key enabler for compliance with future exhaust emission standards and carbon dioxide (CO2) emissions limits or a customer facing product differentiator. The main objective of this study was to assess the potential of electrified propulsion systems in achieving a substantial reduction in CO2 emissions when applied to a representative full-size heavy-duty (HD) truck compared to the baseline configuration. A representative full-size HD four-wheel drive (4WD) truck of adjusted loaded vehicle weight (ALVW) 4082 kg or 9000 lbs with a 6.6 L diesel engine was simulated with various electrified drive configurations over the combined US FTP-72 (Federal Test Procedure) cycle and the Highway Fuel Economy Test (HWFET). Every hybrid vehicle configuration used in the study was designed using representative battery pack and electric drive components.