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The use of Hybrid Predictive Control for model-based propulsion control applications is considered. A Variable Displacement Engine (VDE) control problem is considered, involving both continuous-time dynamics and discrete control actions in the form of activating/deactivating the engine cylinders. Hybrid Model Predictive Control is one of the most successful hybrid control schemes and builds upon predictive control methods developed for engine torque management. The ways in which preview information can be used to improve controller performance are considered, as well as simplifications to the hybrid control algorithms to reduce the computational burden. Several hybrid control design approaches are compared using a simulation of a VDE engine. The aim is to optimize the total system behaviour to provide good torque tracking, reduced fuel consumption and smooth cylinder switching. The main contribution is the demonstration that hybrid predictive control can provide a practical solution to an engine control application with the potential to enhance performance and with options to reduce complexity.

With advances in sensing, computing and communication technologies, Connected and Automated Vehicles (CAVs) are becoming feasible. The advent of CAVs presents new opportunities to improve the energy efficiency of individual vehicles. However, testing and verifying energy-efficient autonomous driving systems are difficult due to safety considerations and repeatability. In this paper, we present a co-simulation platform to develop and test novel vehicle eco-autonomous driving technologies named InfoRich, which incorporates the information from on-board sensors, V2X communications, and map database. The co-simulation platform includes eco-autonomous driving software, vehicle dynamics and powertrain (VD&PT) model, and a traffic environment simulator. Also, we utilize synthetic drive cycles derived from real-world driving data to test the strategies under realistic driving scenarios. To build road networks from the real-world driving data, we develop an Automated Parser and Calculator for Map/Scenario named AutoPASCAL. Overall, the simulation platform provides a realistic vehicle model, powertrain model, sensor model, traffic model, and road-network model to enable the evaluation of the energy efficiency of eco-autonomous driving.

This paper presents a control-oriented, reaction-based diesel combustion model that predicts the time-based rate of combustion, in-cylinder gas temperature, and pressure over one engine cycle. The model, based on the assumption of a homogeneous thermodynamic combustion process, uses a two-step chemical reaction mechanism that consists of six species: diesel fuel (C10.8H18.7), oxygen (O2), carbon dioxide (CO2), water (H2O), nitrogen (N2), and carbon monoxide (CO). The temperature variation rate is calculated based on the rate of change of species concentrations; the heat loss correlation is also used to study the model performance. The accuracy of the model is evaluated using test data from a GM 6.6 L, eight-cylinder Duramax engine. The main contribution is the model calibration under different key operational conditions over a large engine speed and load range as well as different injection timings and exhaust gas recirculation rates by solving the optimal calibration problem. The calibrated reaction-based model accurately predicts the indicated mean effective pressure, while keeping the errors of in-cylinder pressure and temperature small, and, at the same time, significantly reduces the calibration effort, especially when the engine is operated under multiple fuel injection operations compared with Wiebe-based combustion models. The calibrated model parameters have a strong correlation to engine speed, load, and injection timings, and, as a result, a universal parameter calibration structure is proposed for entire operational conditions.

With the rapid growth in the amount of computations that need to be performed by modern electronic control units and in the complexity of the algorithms, there is a pressing need to develop approaches to reduce chronometric loading in automotive vehicles. This paper presents a cyberphysical systems framework for the development of stochastic optimal decision policies that trigger the computations online. For a specific case study, we consider online triggering of the computations involved in obtaining a linearized model in a setting when such a linearized model is required by control or estimation algorithms. The objective is to define a policy for triggering the linearization that balances the average model accuracy (or expected closed loop performance) with the average computational cost. The problem is formulated as a stochastic optimal control problem and solved using stochastic dynamic programming (SDP). The approach is described, then illustrated with three examples, a pendulum, a turbocharged diesel engine, and a turbocharged spark ignition engine, that illustrate the trade-off between the computational cost and expected linearized model accuracy.

As a response to the ever more stringent emission standards, automotive engines have become more complex with more actuators. The traditional approach of using many single-input single output controllers has become more difficult to design, due to complex system interactions and constraints. Model predictive control offers an attractive solution to this problem because of its ability to handle multi-input multi-output systems with constraints on inputs and outputs. The application of model based predictive control to automotive engines is explored below and a multivariable engine torque and air-fuel ratio controller is described using a quasi-LPV model predictive control methodology. Compared with the traditional approach of using SISO controllers to control air fuel ratio and torque separately, an advantage is that the interactions between the air and fuel paths are handled explicitly. Furthermore, the quasi-LPV model-based approach is capable of capturing the model nonlinearities within a tractable linear structure, and it has the potential of handling hard actuator constraints. The control design approach was applied to a 2010 Chevy Equinox with a 2.4L gasoline engine and simulation results are presented. Since computational complexity has been the main limiting factor for fast real time applications of MPC, we present various simplifications to reduce computational requirements. A benchmark comparison of estimated computational speed is included.

To meet the more stringent emissions and fuel economy regulations, engine control system has become significantly more complex than before. As a result of this, engine calibration on the dynamometer now occupies one of the longest time sections in the vehicle development process. One strategy automakers have adopted is to use the same engine in multiple applications to reduce the calibration effort. Even then, vehicle design constraints often require changes to be made to the engine’s external components such as the intake and exhaust manifolds. These changes can create variations in the engine combustion behavior so that the engine must be recalibrated on the dyno, resulting in additional cost and effort. This paper explores the potential of reusing existing engine dyno data for a modified engine in these scenarios through the use of the so-called eigenvariable to describe engine operating conditions. Traditionally, engine dyno data is referenced by engine load and speed along with actuator positions (such as camphaser positions). The proposed approach describes dyno data using eigenvariables or variables that describe the engine in-cylinder condition prior to combustion. Eigenvariables are invariant with respect to external engine hardware. This invariance enables the same dyno data to be applied to a modified engine with the same combustion system design.

A control-oriented reaction-based combustion model is implemented and used to simulate the combustion process in a diesel engine. The model integrates a homogeneous thermodynamic system with a two-step chemical reaction mechanism that consists of six species. The accuracy of the model is evaluated by comparing with experimental data from a GM 6.6L, 8 cylinder Duramax engine. The model is calibrated for different key points over the entire engine map as well as various injection timings and exhaust gas recirculation (EGR) ratio using an automated calibration algorithm. The reaction-based model is shown to provide accurate predictions of incylinder pressure, temperature, mass-fraction-burned and heat release rate. As an alternative to Wiebe-based method, this approach could lead to a better model with less calibration effort. The improvement is due to the fact that the burn rate is online calculated based upon the dominated fuel chemical components and combustion chamber properties, such as temperature, oxygen and burned gas concentration, etc.

The role that supervisory control or upper-level multi-objective optimization can play in total engine control is considered. This involved the control of an SI engine for a combination of air-fuel ratio, torque, fuel consumption and residual gas fraction. The upper level optimization algorithm, based on Model Predictive Control principles, makes use of a ‘reference model.’ This is constructed as a closed-loop interconnection of the engine model and the lower-level classical torque/air charge and lambda controller. This model is derived in a linear parameter varying form, which facilitates the application of the predictive control law. The main contribution is the proposed hierarchical control structure, which has been assessed in simulation and on a test track, and provides a useful separation in tasks and functionality. The solution is also compared briefly with MPC at both upper and lower levels, using different sample rates.

Homogeneous charge compression ignition (HCCI) is one of the most promising piston-engine concepts for the future, providing significantly improved efficiency and emissions characteristics relative to current technologies. This paper presents a framework for controlling an HCCI engine with exhaust recompression and direct injection of fuel into the cylinder. A physical model is used to describe the HCCI process, with the model states being closely linked to the thermodynamic state of the cylinder constituents. Separability between the effects of the control inputs on the desired outputs provides an opportunity to develop a simple linear control scheme, where the fuel is used to control the work output and the valve timings are used to control the phasing of combustion. The controller is tested on both a single and multi-cylinder HCCI engine, demonstrating the value of a physical model-based control approach that allows an easy porting of the control structure from one engine to another. Experimental results show good tracking of both the work output and combustion phasing over a wide operating region on both engines. In addition, the controller is able to balance out differences between cylinders on the multi-cylinder engine testbed, and reduce the cycle-to-cycle variability of combustion.

White spot syndrome virus (WSSV), the causative agent of white spot syndrome in shrimp, has a wide host range which extends to crabs, copepods and other arthropods. In this study, benthic larvae of the mud crab Scylla serrata were captured from Taiwan's coastal waters and screened for the presence of WSSV by polymerase chain reaction (PCR) and in situ hybridization. WSSV was detected in around 60% of the larvae, and this prevalence rate remained fairly constant when the captured larvae were subsequently maintained in an aerated system in the laboratory. WSSV-free larvae obtained from a hatchery were challenged by immersion in a WSSV inoculum. Fifteen days after challenge, cumulative mortality in the experimental group reached 43% compared to 20% in the control group. PCR detection of WSSV in both moribund and surviving specimens clearly implicated the virus as the cause of death in most cases. Histological and in situ hybridization data confirmed that WSSV tissue tropism in Scylla serrata crab larvae is similar to that found in shrimp.

PCR and in situ hybridization analysis were used for detection of white spot syndrome virus (WSSV) in an infected, cultured shrimp population over a long period in the absence of disease outbreaks. The shrimp were derived from a single WSSV-carrier brooder and cultured first in a tank and then in outdoor ponds. Prior to harvest at 13 mo, no l-step PCR-positive specimens were found, even though most tested specimens were found to be 2-step PCR-positive. At 7 mo, 2-step PCR-positive tissues were found in 5 sampled shrimp. Heart, gill, integument, muscle and stomach tissues best supported viral replication At 13 mo several shrimp died, and l-step PCR-positive individuals were found for the first time Although superficially healthy, 10% of the surviving adults had tiny white spots on their carapace, and In sjtu hybridization analysis revealed WSSV-positive cells in 40% of the specimens examined. As before, most were found in the stomach, integument and gills, and only very few in the lymphoid organ and other organs. These observations contrasted to those for experimentally infected shrimp with gross signs of terminal WSSV infection, where strong positive signals were also observed in the lymphoid organ and in other organs of ectodermal or mesodermal origin. Our results showed clearly that whatever the source, WSSV was carried in the shrimp population at a low intensity (i.e. nested PCR was required for detection) for a very long time in the absence of massive mortality. We hypothesize that disease outbreaks do not occur if shrimp defense mechanisms manage to contain lowintensity viral infections under low-stress culture conditions. Conversely, outbreaks may occur under stressful conditions.

This paper discusses the application of linear observer theory to engine control with a specific focus on observers based on exhaust measurements. The motivation for this architecture is that a direct measurement of the relevant quantity (the exhaust chemistry entering the catalyst) will typically lead to the most accurate control. A secondary motivation is the need to reduce costs. If sufficient accuracy can be obtained with the exhaust sensor providing the primary information, the cost associated with intake manifold sensors can be reduced. The basic theory is discussed and experimental results are reported that illustrate the benefits and superior performance of linear observer based control.

The causative viral agent was purified from diseased shrimp Penaeus monodon with white spot syndrome. Negatively stained preparations show that the virus is pleiomorphic. It is fusiform or rod-shaped. In negatively stained preparations, the virion measures 70 to 150 nm at its broadest point and is 250 to 380 nm long. In some virions, a tail-like projection extends from one end. The capsid is apparently composed of rings of subunits in a stacked series. The rings are aligned perpendicular to the longitudinal axis of the capsid. The genome of the virus is a double-stranded DNA molecule which produces at least 22 Hind 111 fragments. The full length of the DNA is estimated to be longer than 150 kbp. Based on the morphological characteristics and genomic structures of the virus, we confirm that white spot syndrome associated virus (MJSSV) is a member of genus NOB (Non-Occluded Baculovirus) of the subfamily Nudibaculovirinae of Baculoviridae, name the present isolate PmNOBIII, and propose the use of WSBV (Baculovirus associated with White Spot syndrome) to indicate PmNOBIII related agents.

The oxygen concentration in the intake manifold of a lean-burn engine is a key variable that is directly related to the quality of combustion. In this paper, an adaptive observer is developed to estimate the intake oxygen concentration in the intake manifold of a lean-burn engine using measurements from existing sensors with minimum computational load. Performance of the observer is evaluated using transient experimental data obtained from a diesel engine.

Homogeneous charge compression ignition (HCCI) is currently being pursued as a cleaner and more efficient alternative to conventional engine strategies. Control of the load and phasing of combustion is critical in the effort to ensure reliable operation of an HCCI engine over a wide operating range. This paper presents an approach for modeling the effect of a small pilot injection during the recompression process of an HCCI engine, and a controller that uses the timing of this pilot injection to control the phasing of combustion. The model is a nonlinear physical model that captures the effect of fuel quantity and intake and exhaust valve timings on work output and combustion phasing. It is seen that around the operating points considered, the effect of a pilot injection can be modeled as a change in the Arrhenius threshold, an analytical construct used to model the phasing of combustion as a function of the thermodynamic state of the reactant mixture. The relationship between injection timing and combustion phasing can be separated into a linear, analytical component and a nonlinear, empirical component. Two different control strategies based on this model are presented, both of which enabled steady operation at low load conditions and effectively track desired load-phasing trajectories. These strategies demonstrate the potential of split injection as a practical cycle-by-cycle control knob requiring only minimal valve motion that would be easily achievable on current production engines equipped with cam phasers.

Homogeneous charge compression ignition (HCCI) is one of the most promising piston-engine concepts for the future, providing significantly improved efficiency and emissions characteristics relative to current technologies. This paper presents a framework for controlling a multi-cylinder HCCI engine with exhaust recompression and direct injection of fuel into the cylinder. A physical model is used to describe the HCCI process, with the model states being closely linked to the thermodynamic state of the cylinder constituents. Separability between the effects of the control inputs on the desired outputs provides an opportunity to develop a simple linear control scheme, where the fuel is used to control the work output and the valve timings are used to control the phasing of combustion. Experimental results show good tracking of both the work output and combustion phasing over a wide operating region. In addition, the controller is able to balance out differences between cylinders, and reduce the cycle-to-cycle variability of combustion.Copyright © 2009 by ASME