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Proportional integral derivative (PID) control technique is the most common control algorithm applied in various engineering applications. Also, particle swarm optimization (PSO) is extensively applied in various optimization problems. This paper introduces an investigation into the use of a PSO algorithm to tune the PID controller for a semi-active vehicle suspension system incorporating magnetorheological (MR) damper to improve the ride comfort and vehicle stability. The proposed suspension system consists of a system controller that determine the desired damping force using a PID controller tuned using PSO, and a continuous state damper controller that estimate the command voltage that is required to track the desired damping force. The PSO technique is applied to solve the nonlinear optimization problem to find the PID controller gains by identifying the optimal problem solution through cooperation and competition among the individuals of a swarm.

The paper will present the design of shift fork for automotive manual gearbox dedicated for the manufacturing with powder metal technology. Based on limitations and requirements of new production technology of shifting sleeve, the new design of shifting sleeve and shifting fork are created. The prototypes of shift fork and sleeve were built, but for cost reasons, were manufactured from steel. The functionality of several forks with different tolerances was tested. Prototypes were put through functional and durability tests on testing bench in real automotive gearbox. The tests were accomplished on the inertia test stand. Drawings and CAD models of tested prototypes, custom tools and designed shift forks are described in detail in this article.

The physical 1-D model of a radial turbine consists of a set of gas ducts featuring total pressure and/or temperature changes and losses. Therefore, the wave propagation and filling/emptying plays a significant role if a turbine is subjected to unsteady gas flow. The results of unsteady turbine simulation using the basic modules of generalized 1-D manifold solver in GT Power are demonstrated. The turbine model calibration parameters can be identified by means of 1-D steady model used in optimization code loop. The examples of model results are compared to steady flow map predictions of turbine efficiency and engine pumping loop work. The model may be used for prediction of turbine data in out-of-design points as presented in the paper. The other important role of a model, however, is an accurate evaluation of turbine parameters from pressure and speed measurements at an engine in operation.

A previously developed CFD-based optimization tool is utilized to find optimal engine operating conditions with respect to fuel consumption and emissions. The optimization algorithm employed is based on the steepest descent method where an adaptive cost function is minimized along each line search using an effective backtracking strategy. The adaptive cost function is based on the penalty method, where the penalty coefficient is increased after every line search. The parameter space is normalized and, thus, the optimization occurs over the unit cube in higher-dimensional space. The application of this optimization tool is demonstrated for the Sulzer S20, a central-injection, non-road DI diesel engine. The optimization parameters are the start of injection of the two pulses of a split injection system, the duration of each pulse, the exhaust gas recirculation rate, the boost pressure and the compression ratio.

The aim of the current contribution is to develop a tool for the improvement of accuracy of turbocharger turbine simulation during matching of a turbocharger to an engine. The paper demonstrates the possibility of unsteady turbine simulation in pulsating flow caused by an internal combustion engine using the basic modules of generalized 1-D manifold solver with entities (pipes, channels) under centrifugal acceleration in general direction and under non-uniform angular speed, which has not yet been explored. The developed model extrapolates steady operation turbine maps by this way. It uses 1-D model parameters identified from steady flow experiments. Unlike the lumped-parameter standard models of turbocharger turbines, the model takes into account complete 1-D features of a turbine flow path including arbitrary shape of turbine impeller vanes.

Increasingly, the exchanges of data in complex ECM (Electronic Control Module) systems rely on multiple communication networks across various physical and network layers. This has greatly increased system flexibility and provided an excellent medium to create well-defined exchangeable interfaces between components; however this added flexibility comes with increased network complexity. A system-level approach allows for the optimization of data exchange and network configuration as well as the development of a comprehensive network failure strategy. Many current ECM systems utilize complex multi-network communication strategies to exchange and control data to components. Recently, Caterpillar implemented an HIL (Hardware-In-the-Loop) test system that provides an approach for developing and testing a comprehensive ECM network strategy.

Faced with competitive environments, pressure to lower development costs and aggressive timelines engineers are not only increasingly adopting numerical simulation techniques but are also embracing design optimization schemes to augment their efforts. These techniques not only provide more understanding of the trade-offs but are also capable of proactively guiding the decision making process. However, design optimization and exploration tools have struggled to find complete acceptance and are typically underutilized in many applications; especially in situations where the algorithms have to compete with existing swift decision making processes. In this paper we demonstrate how the type of setup and algorithmic choice can have an influence and make optimization more lucrative in a new product development atmosphere. We also present some results from a design exploration activity, involving linkage and structural development, of an earth moving machine application.

Today's engine and combustion process development is closely related to the intake port layout. Combustion, performance and emissions are coupled to the intensity of turbulence, the quality of mixture formation and the distribution of residual gas, all of which depend on the in-cylinder charge motion, which is mainly determined by the intake port and cylinder head design. Additionally, an increasing level of volumetric efficiency is demanded for a high power output. Most optimization efforts on typical homogeneous charge spark ignition (HCSI) engines have been at low loads because that is all that is required for a vehicle to make it through the FTP cycle. However, due to pumping losses, this is where such engines are least efficient, so it would be good to find strategies to allow the engine to operate at higher loads.

The NOx reduction performance under lean conditions over γ-alumina was evaluated using a micro-reactor system and a non-thermal plasma-equipped bench test system. Various alumina samples were obtained from alumina manufacturers to assess commercial alumina materials. In addition, γ-alumina samples were synthesized at Caterpillar with a sol-gel technique in order to control alumina properties. The deNOx performances of the alumina samples were compared. The alumina samples were characterized with analytical techniques such as inductively coupled plasma (ICP) emission spectroscopy, temperature programmed desorption (TPD) and surface area measurements (BET) to understand physical and chemical properties. The information derived from these techniques was correlated with the NOx reduction performance to identify key parameters of γ-alumina for optimizing materials for lean-NOx and plasma assisted catalysis.

Most of the history of systems engineering has been focused on processes for engineering a single complex system. However, most large enterprises design, manufacture, operate, sell, or support not one product but multiple product lines of related but varying systems. They seek to optimize time to market, costs of development and production, leverage of intellectual assets, best use of talented human resources, overall competitiveness, overall profitability and productivity. Optimizing globally across multiple product lines does not follow from treating each system family member as an independently engineered system or product. Traditional systems engineering principles can be generalized to apply to families. This article includes a multi-year case study of the actual use of a generic model-based systems engineering methodology for families, Systematica™, across the embedded electronic systems products of one of the world's largest manufacturers of heavy equipment.

A simulation framework with a validated system model capable of estimating fuel consumption is a valuable tool in analysis and design of the hybrid vehicles. In particular, the framework can be used for (1) benchmarking the fuel economy achievable from alternate hybrid powertrain technologies, (2) investigating sensitivity of fuel savings with respect to design parameters (for example, component sizing), and (3) evaluating the performance of various supervisory control algorithms for energy management. This paper describes such a simulation framework that can be used to predict fuel economy of series hydraulic hybrid vehicle for any specified driver demand schedule (drive cycle), developed in MATLAB/Simulink. The key components of the series hydraulic hybrid vehicle are modeled using a combination of first principles and empirical data. A simplified driver model is included to follow the specified drive cycle.

The paper proposes the way of using scavenging curves, i.e., dependence of residual gas fraction in exhaust port or valve on residual fraction in a cylinder, found by CFD simulations. In the general case, exhaust gas recirculation outside of a cylinder (EGR) or internal gas recirculation caused by variable values of burned gas backflow to inlet system may influence in-cylinder residual gas fraction. These deviations may take place during engine optimization, done by 1D models. The determination of scavenging curves via 3D CFD simulations is a time consuming process, which cannot be repeated for every 1D case.

The effects of downspeeding and supercharging a passenger car diesel engine were studied through laboratory investigation and vehicle simulation. Changes in the engine operating range, transmission gearing, and shift schedule resulted in improved fuel consumption relative to the baseline turbocharged vehicle while maintaining performance and drivability metrics. A shift schedule optimization technique resulted in fuel economy gains of up to 12% along with a corresponding reduction in transmission shift frequency of up to 55% relative to the baseline turbocharged configuration. First gear acceleration, top gear passing, and 0-60 mph acceleration of the baseline turbocharged vehicle were retained for the downsped supercharged configuration.

The objective of this study is the development of a computationally efficient CFD-based tool for finding optimal engine operating conditions with respect to fuel consumption and emissions. The optimization algorithm employed is based on the steepest descent method where an adaptive cost function is minimized along each line search using an effective backtracking strategy. The adaptive cost function is based on the penalty method, where the penalty coefficient is increased after every line search. The parameter space is normalized and, thus, the optimization occurs over the unit cube in higher-dimensional space. The application of this optimization tool is demonstrated for the Sulzer S20, a central-injection, non-road DI diesel engine. The optimization parameters are the start of injection of the two pulses, the duration of each pulse, the duration of the dwell, the exhaust gas recirculation rate and the boost pressure.

With stringent emission regulations, aftertreatment systems with a Diesel Particulate Filter (DPF) and a Selective Catalytic Reduction (SCR) are required for diesel engines to meet PM and NOx emissions. The adoption of aftertreatment increases the back pressure on a typical diesel engine and makes engine calibration a complicated process, requiring thousands of steady state testing points to optimize engine performance. When configuring an engine to meet Tier IV final emission regulations in the USA or corresponding Stage IV emission regulations in Europe, this high back pressure dramatically impacts transient performance. The peak NOx, smoke and exhaust temperature during a diesel engine transient cycle, such as the Non-Road Transient Cycle (NRTC) defined by the US Environmental Protection Agency (EPA), will in turn affect the performance of the aftertreatment system and the tailpipe emissions level.

With the rapid development of artificial intelligence, autonomous driving technology will finally reshape an automotive industry. Although fully autonomous cars are not commercially available to common consumers at this stage, partially autonomous vehicles, which are defined as level 2 and level 3 autonomous vehicles by SAE J3016 standard, are widely tested by automakers and researchers. A typical Human-Machine-Interface (HMI) for a vehicle takes a form to support a human domination role. Although modern driving assistance systems allow vehicles to take over control at certain scenarios, the typical human-machine-interface has not changed dramatically for a long time. With deep learning neural network technologies penetrating into automotive applications, multi-modal communications between a driver and a vehicle can be enabled by a cost-effective solution.

Caterpillar uses heat treatment to enhance the properties of a significant number of parts. Traditional heat treat process optimization is both time consuming and expensive when done by empirical methods. This paper describes a computer simulation of the heat treatment process, developed by Caterpillar, based upon finite element analysis. This approach combines thermal, microstructural, and stress analysis to accurately model material transformation during quenching. Examples are presented to illustrate the program.

Computer based analytical design techniques are transforming the engine design process. Analytical tools allow faster and more accurate design optimization. The design process is also shortened because the electronic transfer of files permits the design to be worked concurrently by engineers working with different analysis packages or on various parts of the design. Prototype parts and tooling can be made directly from the Computer-Aided Design (CAD) by various rapid prototyping methods. The analytical design techniques can also permit a highly optimized design with less possibility of corrections being necessary in the development stages. This paper reviews these new design techniques and examines how they can be used to improve the design technique. The following design tools are discussed.

The design of a pressure compensated hydraulic valve is optimized using CFD analysis. The valve is used in a hydraulic system to control implement movement. High flow rates through the valve resulted in unacceptably high pressure drops, leading to an effort to optimize the valve design. Redesign of the valve had to be achieved under the constraint of minimal manufacturing cost. The flow path of hydraulic oil through the valve, the spool design, and various components of the valve that caused the high pressure drops were targeted in this analysis. A commercially available CFD package was used for the 3D analysis. The hydraulic oil flow was assumed to be turbulent, isothermal and incompressible. The steady-state results were validated by comparison with experimental data.

The purpose of this project was to modify existing small off-road engines to meet ARB's originally proposed 1999 emissions standards. A particular point was to show that compliance could be attained without the need to redesign the base engines. Four high-sales volume, ARB-certified 1997 model engines were selected from the following categories: 1) handheld two-stroke engine, 2) handheld four-stroke engine, 3) non-handheld side-valve engine, and 4) a non-handheld overhead-valve engine. Engines were selected, procured, and baseline emission tested using applicable ARB test procedures. Appropriate emission control strategies were then selected and applied to the four engines. Emission reduction strategies used included air/fuel ratio optimization, and catalytic aftertreatment. Following the development of the four emission-controlled engines, final, certification-quality emissions tests were performed. All four engines met ARB's original 1999 Tier 2 emission standards after development.