Composite Adaptive Internal Model Control: Theory and Applications to Engine Control

To meet customer demands for vehicle performance and to satisfy increasingly stringent emission standard, powertrain control strategies have become more complex and sophisticated. As a result, controller development and calibration have presented a time-consuming and costly challenge to the automotive industry. This thesis aims to develop new control methodologies with reduced calibration effort. Internal model control (IMC) lends itself to automotive applications for its intuitive control structure with simple tuning philosophy. A few applications of IMC to the boost-pressure control problem have been reported, however, none offered an implementable and easy-to-calibrate solution. Motivated by the need to develop robust and easily calibratable control technologies for boost-pressure control of turbocharged gasoline engines, this thesis developed new control design methodologies in the IMC framework. Two directions are pursued: adaptive IMC (AIMC) and nonlinear IMC. A plant model and a plant inverse are explicit components of IMC. In the presence of plant-model uncertainty, combining the IMC structure with parameter identification through the certainty equivalence principle leads to adaptive IMC (AIMC), where the plant model is identified and the plant inverse is derived by inverting the model. We propose the composite AIMC (CAIMC), which identifies the model and the inverse in parallel, and reduces the tracking error through the online identification. ``Composite" refers to the simultaneous identifications. The constraint imposed by the stability of an n-th order model is nonconvex, and it is re-parameterized as a linear matrix inequality. The parameter identification problem with the stability constraint is reformulated as a convex programming problem. Stability proof and asymptotic performance are established for CAIMC of a general n-th order plant. CAIMC is applied to the boost-pressure control problem of a turbocharged gasoline engine. It is first validated on a physics-based high-order and nonlinear proprietary turbocharged gasoline engine Simulink model, and then validated on a turbocharged 2L four-cylinder gasoline engine on a Ford Explorer EcoBoost. Both simulations and experiments show that CAIMC is not only effective, but also drastically reduces the calibration effort compared to the traditional PI controller with feedforward. Nonlinear IMC is presented in the context of the boost-pressure control of a turbocharged gasoline engine. To leverage the available tools for linear IMC design, the quasi-linear parameter varying (quasi-LPV) models are explored. A new approach for nonlinear inversion, referred to as the structured quasi-LPV model inverse, is developed and validated. A fourth-order nonlinear model which sufficiently describes the dynamic behavior of the turbocharged engine is used as the design model, and the IMC controller is derived based on the structured quasi-LPV model inverse. The nonlinear IMC is applicable when the nonlinear system has a special structural property and has not been generalized yet. Simulations on a high-fidelity turbocharged engine model are carried out to show the feasibility of the proposed nonlinear IMC.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/136978/1/connieqz_1.pd

Compound cycle engine for helicopter application

The compound cycle engine (CCE) is a highly turbocharged, power-compounded, ultra-high-power-density, lightweight diesel engine. The turbomachinery is similar to a moderate-pressure-ratio, free-power-turbine gas turbine engine and the diesel core is high speed and a low compression ratio. This engine is considered a potential candidate for future military helicopter applications. Cycle thermodynamic specific fuel consumption (SFC) and engine weight analyses performed to establish general engine operating parameters and configurations are presented. An extensive performance and weight analysis based on a typical 2-hour helicopter (+30 minute reserve) mission determined final conceptual engine design. With this mission, CCE performance was compared to that of a contemporary gas turbine engine. The CCE had a 31 percent lower-fuel consumption and resulted in a 16 percent reduction in engine plus fuel and fuel tank weight. Design SFC of the CCE is 0.33 lb/hp-hr and installed wet weight is 0.43 lb/hp. The major technology development areas required for the CCE are identified and briefly discussed

The Incorruptible Integrator: A Streamlined Approach to IMC-PID Controller Tuning

In automakers\u27 never-ending quest to reduce emissions and improve performance, the turbocharger represents a major step in advancing these goals. By repurposing waste exhaust and compressing the air intake, they are able to increase overall power. One critical control loop in the turbocharger is control of boost pressure via the wastegate. This is a highly nonlinear process and experimental data has shown that a gain-scheduled PID (proportional integral derivative) controller developed with IMC (internal model control) tuning methodology is an effective means to control boost pressure. Motivated by this successful implementation of IMC-PID tuning in the automotive world, this work hopes to extend and analyze that framework. Traditionally, the success of an IMC controller depends on the accuracy of the plant model. This research challenges this view and investigates using IMC with a gain-integrator-delay (GID) model identified at a critical frequency, regardless of the actual plant. The GID model is useful because of its simplicity to characterize and its ability to be translated to the ubiquitous PID controller easily. Three design techniques are developed: (1) design for post-hoc tuning, (2) design for closed loop bandwidth, and (3) design for phase margin. In addition, these techniques are investigated via a Monte Carlo simulation to determine efficacy for when there exists plant/model mismatch. Finally, the three techniques are applied to control the speed of an inertia disk on the Quanser Servo 2 device

Accelerometer-based SOC estimation methodology for combustion control applied to Gasoline Compression Ignition

The European Community's recent decision to suspend the marketing of cars with conventional fossil-fueled internal combustion engines from 2035 requires new solutions, based on carbon-neutral technologies, that ensure equivalent performances in terms of reliability, trip autonomy, refueling times and end-of-life disposal of components compared to those of current gasoline or diesel cars. The use of bio-fuels and hydrogen, which can be obtained by renewable energy sources, coupled with high-efficiency combustion methodologies might allow to reach the carbon neutrality of transports (net-zero carbon dioxide emissions) even using the well-known internal combustion engine technology. Bearing this in mind, experiments were carried out on compression ignited engines running on gasoline (GCI) with a high thermal efficiency which, in the future, could be easily adapted to run on a bio-fuel. Despite the well-reported benefits of GCI engines in terms of efficiency and pollutant emissions, combustion instability hinders the diffusion of these engines for industrial applications. A possible solution to stabilize GCI combustion is the use of multiple injections strategies, typically composed by 2 early injected fuel jests followed by the main injection. The heat released by the combustion of the earlier fuel jets allows to reduce the ignition delay of the main injection, directly affecting both delivered torque and center of combustion. As a result, to properly manage GCI engines, a stable and reliable combustion of the pre-injections is mandatory. In this paper, an estimation methodology of the start of combustion (SOC) position, based on the analysis of the signal coming from an accelerometer sensor mounted on the engine block, is presented (the optimal sensor positioning is also discussed). A strong correlation between the SOC calculated from the accelerometer and that obtained from the analysis of the rate of heat release (RoHR) was identified. As a result, the estimated SOC could be used to feedback an adaptive closed-loop combustion control algorithm, suitable to improve the stability of the whole combustion process

Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

Design and simulation of high-performance hybrid electric vehicle powertrains

The intent of this study was the design, modeling, and simulation of several high-performance light-duty hybrid electric vehicle powertrains. The design requirements of each proposed configuration are to meet or exceed a set of performance baselines based on a composite set of particular high-performance conventional vehicles presently available, while demonstrating increased fuel efficiency over regulated government cycles.;Several hybrid powertrain configurations were studied; however, the most promising and feasible for production designs were selected for further modeling. All of the proposed designs are post-transmission parallel hybrids for primarily performance reasons, with the auxiliary motive power coming after the transmission, utilizing a modeled spark-ignited, Variable Valve Timing (VVT) equipped internal combustion engine. A control strategy has been developed for the operation of these powertrains for virtually any driving condition---the strategy was not optimized for any particular government regulated cycle. Computer simulations were performed to simulate both the performance and the fuel economy of the proposed vehicle designs.;The simulation results show that the fuel economy of the modeled hybrid vehicles exceeds that of the comparable conventional vehicles, as well as meeting or exceeding the performance requirements of the baseline vehicles by 12--23%. In addition the exhaust gas emissions may be reduced, compared to a conventional vehicle due to hybridization. All modeled components were selected from available off-the-shelf applications, and the selected designs were chosen to be readily mass-produced

Multi-objective and multi-model shape optimization of turbocharger turbines over real-world drive cycles for low carbon vehicles

Turbocharging is the established method for downsizing internal combustion (IC) engines to lower CO2 emissions and fuel consumption while meeting the desired performance. Turbochargers for automotive engines commonly utilize radial turbines for exhaust energy extraction. However, the design of a turbocharger turbine is subject to conflicting requirements. A crucial consideration when matching a turbocharger to an engine is the ability to meet the specified low-end torque target while minimizing the turbine inlet pressure (particularly at high engine speed) to reduce the engine pumping work. Conventionally, the matching procedure used in the industry relies on experimentally measured compressor and turbine performance maps to model turbocharger operation within engine cycle simulation software. In this way, the compressor and turbine configuration that best meets the specified customer requirements is down-selected. Thus, only existing turbine geometries can be evaluated during the conventional matching process. This makes it a passive process as the turbine aerodynamic performance and inertia cannot be modified during the matching evaluations. Ideally, what is needed is a framework that physically models both the turbine and engine with sufficient accuracy and allows turbine geometric changes to be accounted for. To this end, the objective of this work is to establish a novel and fast-running framework that allows turbine shape optimization based on engine-level objectives and constraints, and understand from a fluid dynamic perspective why a given turbine design is better for the engine. An in-house reduced-order model (meanline code) to estimate aerodynamic performance and a neural network-based inertia prediction tool for radial turbines are developed. These are integrated in a validated engine model to provide a framework for modelling the engine-turbine interaction using a numerically inexpensive technique. It allows the effect of turbine geometric changes on inertia and aerodynamic performance to be reflected in the exhaust boundary conditions and thereby in the overall performance of the engine. A genetic algorithm is employed within the framework, providing an opportunity for single-objective (for example, weighted cycle-average BSFC) or multi-objective (for example, weighted cycle-averaged BSFC and engine transient response) shape optimization of turbine meridional geometry. The framework has been applied to a Renault 1.2L turbocharged gasoline engine to minimize the fuel consumption and therefore CO2 emissions, while meeting a sensible transient response constraint. Turbine shape optimization was carried out over a cluster of weighted part-load operating points that represent the World harmonized Light vehicles Test Cycle (WLTC). The design candidates lying on the Pareto front present improvements of up to 0.4% in the weighted cycle-averaged fuel consumption, and up to 8% in transient response. Dynamic vehicle simulations over the WLTC are used to confirm the improvement observed in fuel consumption. Based on the meridional parameters obtained from the 1D optimization, 3D designs are created for both the turbine housing and the rotor. Finally, CFD evaluation and experimental testing are performed to verify the performance of optimized designs. 3D CFD predictions showed good agreement with experimental results, lying within the range of experimental uncertainty. The CFD analysis also showed a significant reduction in secondary flow features in the optimized design compared with the baseline turbine. While the developed framework can be used to improve existing turbine designs, it also facilitates the development and optimization of `tailor-made' turbines for new low carbon engine projects. Even though, for the particular case described, the optimization process indicates a moderate 0.2--0.4% reduction in the weighted cycle-averaged BSFC, this would translate to a reduction of at least 270,000 tonnes of CO2 considering the lifetime of all GDI engines manufactured each year in the EU. Thus, the developed turbine optimization framework has a massive potential, especially because it requires no new or additional technology.Open Acces

Modeling of Turbulence, Combustion and Knock for Performance Prediction, Calibration and Design of a Turbocharged Spark Ignition Engine

In this thesis work, a downsized VVA Spark Ignition engine is numerically and experimentally studied. In particular, the following topics are considered: •In-cylinder turbulence and combustion processes; •Knock and cycle by cycle variation (CCV) phenomena; •Techniques aiming to mitigate knock occurrence and improve fuel economy such as EGR and water injection methods; •Intake system redesign to reduce the emitted gas-dynamic noise; •Engine calibration. A deep experimental campaign is carried out to characterize the engine behaviour. Indeed, engine system is investigated both in terms of the overall performance (torque, power, fuel consumption, air flow rate, boost pressure etc.) and of the intake gas-dynamic noise at full load operation. In addition, proper experimental analyses are peformed on the engine to characterize the CCV phenomenon and the knock occurrence. Measured data are post-processed to derive experimental parameters which syntetize CCV and knock levels, according to the engine operating conditions. A 1D CFD model of the whole engine is realized in GT-PowerTM environment. Refined “in-house developed” sub-models capable to reproduce turbulence, combustion, CCVs and knock processes are introduced into 1D code through user routines. First of all, the whole engine model is validated against the experimental data both in terms of overall performance parameters and ensemble averaged pressure cycles and intake gas-dynamic noise at part and full load operation. Cycle by cycle variation is reproduced through a proper correlation and consequently a representative faster than average in-cylinder pressure cycle is obtained. Then, the knock model, with reference to the latter pressure cycle, allows to evaluate a proper knock index and to identify the knock limited spark advance (KLSA), basing on the same threshold level adopted in experimental knock analysis. In this way, the knock model taking into account the CCV is validated at full load operation. Once validated, the original engine architecture is modified by virtually installing a “Low pressure” EGR system. 1D simulations accounting for various EGR rates and mixture leaning are performed at full load points, showing improvements in the fuel economy with the same knock intensity of the base engine configuration. Water injection technique is also investigated by virtually mounting a water injector in the intake runners for each engine cylinder. In a similar way, 1D analyses are carried out for various water/fuel and air-to-fuel ratios, highlightinig BSFC improvements at full load operation. Since the engine under study is characterized by higher intake gas-dynamic noise levels, a partial redesign of the intake system is properly identified and subsequently tested with 1D and 3D CFD simulations to numerically quantify the gains in terms of reduction in the gas-dynamic noise emitted at the intake mouth. Finally, a numerical methodology aiming to calibrate the considered engine at high load knock-limited and at part load operations is developed. First, it shows the capability to identify with satisfactory accuracy the experimentally advised engine calibration. In addition, it allows the comparison of different intake valve strategies, underlining, in certain engine operating conditions, the fuel consumption benefits of an early intake valve closure (EIVC) strategy with respect to a Full Lift one, due to a better combustion phasing and a reduced mixture over-fuelling. The developed automatic procedure presents the capability to realize a “virtual” engine calibration on completely theoretical basis and proves to be very helpful in reducing time and costs related to experimental activities at the test bench

Experimental and Numerical Analysis of Ethanol Fueled HCCI Engine

Presently, the research on the homogeneous charge compression ignition (HCCI) engines has gained importance in the field of automotive power applications due to its superior efficiency and low emissions compared to the conventional internal combustion (IC) engines. In principle, the HCCI uses premixed lean homogeneous charge that auto-ignites volumetrically throughout the cylinder. The homogeneous mixture preparation is the main key to achieve high fuel economy and low exhaust emissions from the HCCI engines. In the recent past, different techniques to prepare homogeneous mixture have been explored. The major problem associated with the HCCI is to control the auto-ignition over wide range of engine operating conditions. The control strategies for the HCCI engines were also explored. This dissertation investigates the utilization of ethanol, a potential major contributor to the fuel economy of the future. Port fuel injection (PFI) strategy was used to prepare the homogeneous mixture external to the engine cylinder in a constant speed, single cylinder, four stroke air cooled engine which was operated on HCCI mode. Seven modules of work have been proposed and carried out in this research work to establish the results of using ethanol as a potential fuel in the HCCI engine. Ethanol has a low Cetane number and thus it cannot be auto-ignited easily. Therefore, intake air preheating was used to achieve auto-ignition temperatures. In the first module of work, the ethanol fueled HCCI engine was thermodynamically analysed to determine the operating domain. The minimum intake air temperature requirement to achieve auto-ignition and stable HCCI combustion was found to be 130 °C. Whereas, the knock limit of the engine limited the maximum intake air temperature of 170 °C. Therefore, the intake air temperature range was fixed between 130-170 °C for the ethanol fueled HCCI operation. In the second module of work, experiments were conducted with the variation of intake air temperature from 130-170 °C at a regular interval of 10 °C. It was found that, the increase in the intake air temperature advanced the combustion phase and decreased the exhaust gas temperature. At 170 °C, the maximum combustion efficiency and thermal efficiency were found to be 98.2% and 43% respectively. The NO emission and smoke emissionswere found to be below 11 ppm and 0.1% respectively throughout this study. From these results of high efficiency and low emissions from the HCCI engine, the following were determined using TOPSIS method. They are (i) choosing the best operating condition, and (ii) which input parameter has the greater influence on the HCCI output. In the third module of work, TOPSIS - a multi-criteria decision making technique was used to evaluate the optimum operating conditions. The optimal HCCI operating condition was found at 70% load and 170 °C charge temperature. The analysis of variance (ANOVA) test results revealed that, the charge temperature would be the most significant parameter followed by the engine load. The percentage contribution of charge temperature and load were63.04% and 27.89% respectively. In the fourth module of work, the GRNN algorithm was used to predict the output parameters of the HCCI engine. The network was trained, validated, and tested with the experimental data sets. Initially, the network was trained with the 60% of the experimental data sets. Further, the validation and testing of the network was done with each 20% data sets. The validation results predicted that, the output parameters those lie within 2% error. The results also showed that, the GRNN models would be advantageous for network simplicity and require less sparse data. The developed new tool efficiently predicted the relation between the input and output parameters. In the fifth module of work, the EGR was used to control the HCCI combustion. An optimum of 5% EGR was found to be optimum, further increase in the EGR caused increase in the hydrocarbon (HC) emissions. The maximum brake thermal efficiency of 45% was found for 170 °C charge temperature at 80% engine load. The NO emission and smoke emission were found to be below 10 ppm and 0.61% respectively. In the sixth module of work, a hybrid GRNN-PSO model was developed to optimize the ethanol-fueled HCCI engine based on the output performance and emission parameters. The GRNN network interpretive of the probability estimate such that it can predict the performance and emission parameters of HCCI engine within the range of input parameters. Since GRNN cannot optimize the solution, and hence swarm based adaptive mechanism was hybridized. A new fitness function was developed by considering the six engine output parameters. For the developed fitness function, constrained optimization criteria were implemented in four cases. The optimum HCCI engine operating conditions for the general criteria were found to be 170 °C charge temperature, 72% engine load, and 4% EGR. This model consumed about 60-75 ms for the HCCI engine optimization. In the last module of work, an external fuel vaporizer was used to prepare the ethanol fuel vapour and admitted into the HCCI engine. The maximum brake thermal efficiency of 46% was found for 170 °C charge temperature at 80% engine load. The NO emission and smoke emission were found to be below 5 ppm and 0.45% respectively. Overall, it is concluded that, the HCCI combustion of sole ethanol fuel is possible with the charge heating only. The high load limit of HCCI can be extended with ethanol fuel. High thermal efficiency and low emissions were possible with ethanol fueled HCCI to meet the current demand