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The authors have published SAE paper 2008-01-0088 on the analytical comparison between 4 and 8 counterweight crankshafts for an I4 gasoline engine. This paper showed that for a particular design of a 4 counterweight crankshaft, the differences in bearing force and oil film thickness were very small and the only major difference in terms of bearing shaft tilt angle occurred at mains 2 and 4 (increase of ∼20% compared with 8 counterweight version). The 4 counterweight crankshaft has a significant mass advantage as it was 1.42kg lighter than the 8 counterweight crankshaft. This new paper addresses the testing performed to validate the analysis results in bearing durability by subjecting the engine to a mixture of high speed and general durability cycles. A comparison was made on the bearing conditions after running a total of 100 hours through prescribed durability cycles on a gasoline engine with both 4 and 8 counterweight crankshafts.

This paper describes the Lean Boost System, a gasoline engine concept for improved fuel economy. The system combines direct injection, lean operation and pressure charging, and allows significant reduction in swept volume, or ‘downsizing’. Engine tests have been undertaken which demonstrate the validity of the combustion concept. The strategy a typical manufacturer might adopt in order to meet future European requirements for CO2 emissions is proposed. Vehicle simulation results for typical North American and European vehicles are presented. Using the exhaust gas emission levels from engine tests and drive cycle simulation, aftertreatment requirements and configurations are considered.

A new global NOx emissions formation model, formulated by a single analytically derived algebraic equation, is developed with relevance to post-flame gases. The model originates from subsets of detailed kinetic schemes for thermal and N2O pathway NO formation, needs no calibration and is quick to implement and run. Due to its simplicity, the model can be readily used in both 1D and 3D-CFD simulation codes, as well as for direct post-processing of engine test data. Characteristic timescales that describe the kinetic nature of the involved NO formation routes, when they evolve in the post-flame gases independently the one from another, are introduced incorporating kinetic information from all relevant elementary reactions.

This paper uses a one-dimensional (1-D) simulation based approach to compare the steady state and transient performance of a Split Cycle Clean Combustion (SCCC) diesel engine to a similarly sized conventional diesel engine. Caterpillar Inc's one-dimensional modeling tool “Dynasty” is used to convert the simulation model of Caterpillar's current production turbocharged diesel engine Cat® C4.4 (used in their Hydraulic Excavator 316) to operate on the SCCC cycle. Steady state and transient engine performance is compared between the two engine variants. This study is focused only on the performance aspects of engine and relies on the other independently published papers for emissions prediction. This paper also demonstrates the use of Caterpillar's proprietary modeling software Dynasty to replicate the two cylinder SCCC engine model presented by University of Pisa in their paper [2].

Reducing emissions and the carbon footprint of our society have become imperatives requiring the automotive industry to adapt and develop technologies to strive for a cleaner sustainable transport system and for sustainable economic prosperity. Electrified hybrid electric vehicles (HEV), plug-in hybrid electric vehicles (PHEV) and range extender powertrains provide potential solutions for reducing emissions, but they present challenges in terms of thermal management. A key requirement for meeting these challenges is accurately to predict the thermal loading and temperatures of an internal combustion engine (ICE) quickly under multiple full-load and part-load conditions. Computational Fluid Dynamics (CFD) and thermal survey database methods are used to derive thermal loading of the engine structure and are well understood but typically only used at full-load conditions.

The high-frequency whining noise produced by motors in modern electric vehicles causes a significant issue, leading to annoyance among passengers. This noise becomes even more noticeable due to the quiet nature of electric vehicles, which lack other noises to mask the high-frequency whining noise. To improve the noise caused by motors, it is essential to optimize various motor design parameters. However, this task requires expert knowledge and a considerable time investment. In this study, we explored the application of artificial intelligence to optimize the NVH performance of motors during the design phase. Firstly, we selected and modeled three benchmark motor types using Motor-CAD. Machine learning models were trained using Design of Experiment methods to simulate batch runs of Motor-CAD inputs and outputs.

The inherent fuel economy benefits of diesel powertrains over gasoline offer significant potential for reduced energy consumption and provide vehicle manufacturers with a viable method of improving Corporate Average Fuel Economy (CAFE). However, achieving the stringent NOx emission levels of upcoming LEV II and Tier 2 standards, while retaining low fuel consumption and satisfying consumer demand for higher performance, presents significant technical challenges. For diesel powertrains to succeed in the US light duty vehicle market, manufacturers must apply engine and exhaust aftertreatment technologies which meet the technical demands within a viable business case. This paper outlines the trends in advanced electronics and controls for diesel powertrains and the role of these technologies in enabling diesel as a viable fuel efficient option for the US light duty vehicle market.

For optimum efficiency, the direct injection (DI) gasoline engine requires two operating modes to cover the full load/speed map. For lower loads and speeds, stratified charge operation can be used, while homogeneous charge is required for high loads and speeds. This paper has focused its attention on the latter of these modes, where the performance is highly dependent on the quality of the fuel spray, evaporation and the air-fuel mixture preparation. Results of quantitative and qualitative Laser Induced Fluorescence (LIF) measurements are presented, together with shadow-graph spray imaging, made within an optically accessed DI gasoline engine. These are compared with previously acquired air flow measurements, at various injection timings, and with engine performance and emissions data obtained in a fired single cylinder non-optical engine, having an identical cylinder head and piston crown geometry.

Modern control strategies for internal combustion engines use increasingly complex networks of sensors and actuators to measure different physical parameters. Often indirect measurements and estimation of variables, based off sensor data, are used in the closed loop control of the engine and its subsystems. Thus, sensor fusion techniques and virtual instrumentation have become more significant to the control strategy. With the large volumes of data produced by the increasing number of sensors, the analysis of sensor networks has become more important. Understanding the value of the information they contain and how well it is extracted through uncertainty quantification will also become essential to the development of control architecture. This paper proposes a methodology to quantify how valuable a sensor is relative to the architecture. By modelling the sensor network as a Bayesian network, Bayesian analysis and control metrics were used to assess the value of the sensor.

The need to reduce fuel consumption and CO2 emissions while meeting future emission legislation has lead to the investigation of alternative engines, transmissions, aftertreatment and control strategies. The evaluation of alternative configurations at the concept stage requires vehicle drive cycle simulation tools, which include the following features: Fast run-time Alternative transmission models Cold start effects on fuel consumption and emissions modeled After-treatment models. This paper describes the development of a MATLAB/SIMULINKTM - based drive cycle simulation model meeting these requirements. The paper includes validation data comparing fuel consumption, engine-out and tailpipe emissions for a direct injection gasoline vehicle with a stoichiometric/lean switching strategy and lean NOx catalyst.

High-speed planar laser Mie scattering and Laser Induced Fluorescence (PLIF) were employed for the determination of Sauter Mean Diameter (SMD) distribution in non-evaporating diesel sprays. The effect of rail pressure, distillation profile, and consequent fuel viscosity on the drop size distribution developing during primary and secondary atomization was investigated. Samples of conventional crude-oil derived middle-distillate diesel and light distillate kerosene were delivered into an optically accessible mini-sac injector, using a customized high-pressure common rail diesel fuel injection system. Two optical channels were employed to capture images of elastic Mie and inelastic LIF scattering simultaneously on a high-speed video camera at 10 kHz. Results are presented for sprays obtained at maximum needle lift during the injection. These reveal that the emergent sprays exhibit axial asymmetry and vorticity.

For electric vehicles (EVs), driving range is one of the major concerns for wider customer acceptance and the cabin climate system represents the most significant auxiliary load for battery consumption. Unlike internally combustion engine (ICE) vehicles, EVs cannot utilize the waste heat from an engine to heat the cabin through the heating, ventilation and air conditioning (HVAC) system. Instead, EVs use battery energy for cabin heating, this reduces the driving range. To mitigate this situation, one of the most promising solutions is to optimize the recirculation of cabin air, to minimize the energy consumed by heating the cold ambient air through the HVAC system, whilst maintaining the same level of cabin comfort. However, the development of this controller is challenging, due to the coupled, nonlinear and multi-input multi-output nature of the HVAC and thermal systems.

If fuel cell vehicles are to compete in the same marketplace as conventional vehicles, then they must provide the consumer with the same, or improved, levels of convenience, comfort, refinement and performance at the same, or lower, price. In 2003/4, several vehicle manufacturers are planning to launch their first commercial fuel cell vehicles onto the market. In this remarkably short timeframe, many systems must be integrated into a vehicle including the fuel cell system plus thermal and water management, cabin heating, ventilation and air-conditioning, control and on-board diagnostics, power electronics, electric motor and gearbox, suspension, steering, braking, refinement and crash protection. This paper presents a range of modelling techniques which allow the user to design and develop key systems, including the power management system, compressed air supply, thermal management and control algorithms.

Internal combustion (IC) engines that meet Tier 4 Final emissions standards comprise of multiple engine operation and control parameters that are essential to achieve the low levels of NOx and soot emissions. Given the numerous degrees of freedom and the tight cost/time constraints related to the test bench, application of virtual engineering to IC engine development and emissions reduction programmes is increasingly gaining interest. In particular, system level simulations that account for multiple cycle simulations, incylinder turbulence, and chemical kinetics enable the analysis of combustion characteristics and emissions, i.e. beyond the conventional scope of focusing on engine performance only. Such a physico-chemical model can then be used to develop Electronic Control Unit in order to optimise the powertrain control strategy and/or the engine design parameters.

Hydrogen has been identified as a promising decarbonization fuel in internal combustion engine (ICE) applications in many areas including heavy-duty on- and off-road, power-generation, marine, etc. Hydrogen ICEs can achieve high power density and very low tailpipe emissions. However, there are challenges; designing systems for a gaseous fuel with its own specific mixing, burn rate and combustion control needs, which can differ from legacy products. The primary pollutant of concern for Hydrogen ICEs is NOx which can be addressed by running the engine at very lean equivalence ratios and the use of Exhaust Gas Recirculation (EGR). Computation Fluid Dynamics (CFD) is a valuable tool to model the combustion characteristics under different conditions, as presented in SAE-2023-01-0197 [1], which can also be used to predict thermal loading.

The ability to accurately predict exhaust system acoustics, including transmission loss (TL) and tailpipe noise, based on CAD geometry has long been a requirement of most OEM’s and Tier 1 exhaust suppliers. Correlation to measurement data has been problematic under various operating conditions, including flow. This study was undertaken to develop robust modelling technique, ensuring sensible correlation between the 1-D models and test data. Ford use Ricardo WAVE as one of their 1-D NVH tools, which was chosen for the purpose of this benchmark study. The most commonly used metrics for evaluating the acoustical performance of mufflers are insertion loss (IL), TL, and noise reduction (NR). TL is often the first step of analysis, since it represents the inherent capability of the muffler to attenuate sound if both the source and termination are assumed to be anechoic. It can also be reliably measured and numerically simulated without having to connect to an engine.

In this paper, a mathematical model for simulating the 3D dynamic response of a valve spring is described. The 3D model employs a ‘geometrically exact’ 3D beam connected between each mass of the discretised mass-elastic system. Shear deformations within the beam are also considered, which makes it a Timoshenko type finite element. Results from the 3D model are compared with results from a more conventional 1D model. To validate the results further, some results are compared with real test data that was gathered during a technical consulting project. In this project, a prototype valvetrain that was originally giving acceptable durability began to wear the spring seats when a new batch of springs were procured and tested. 1D and 3D simulation results were used to help understand the cause of the failure and to make recommendations to resolve the issue. Results showed that the 3D model was able to predict the spring seat loads with greater precision than the 1D spring could.

A new total fatigue life methodology was utilized to make fatigue life predictions, where total fatigue life is defined as crack initiation and subsequent crack propagation to a crack of known size or the component’s inability to carry load. Fatigue life predictions of an A36 steel T-joint geometry were calculated using the same total fatigue life methodology for both welded and machined test specimens that have the same geometry. The only significant difference between the two analyses was the inclusion of the measured weld residual stresses in the welded specimen life predictions. Constant amplitude tests at several load levels and R ratios were analyzed along with block cycle and variable amplitude loading tests. The accuracy of the life predictions relative to experimental test lives was excellent, with most within a factor of +/- two.

Driven by the desire to implement low-cost, high-efficiency NOx aftertreatment systems, such as Three Way Catalysts (TWC) or Lean NOx Traps (LNT), a novel 6-Stroke engine cycle was explored to determine the feasibility of implementing such a cycle on a compression ignition engine while continuing to deliver fuel efficiency. Fundamental questions regarding the abilities and trade-offs of a 6-stroke engine cycle were investigated for near-stoichiometric and lean operation. Experiments were performed on a single-cylinder 15-liter (equivalent) research engine equipped with flexible valvetrain and fuel injection systems to allow direct comparison between 4-stroke and 6-stroke performance across multiple hardware configurations. 1-D engine simulations with predictive combustion models were used to support, iterate on, and explore the 6-stroke operation in conjunction with the experiments.

The Ethanol-Boosted Direct Injection (EBDI) demonstrator engine is a collaborative project led by Ricardo targeted at reducing the fuel consumption of a spark-ignited engine. This paper describes the design challenges to upgrade an existing engine architecture and the synergistic use of a combination of technologies that allows a significant reduction in fuel consumption and CO₂ emissions. Features include an extremely reduced displacement for the target vehicle, 180 bar cylinder pressure capability, cooled exhaust gas recirculation, advanced boosting concepts and direct injection. Precise harmonization of these individual technologies and control algorithms provide optimized operation on gasoline of varying octane and ethanol content.