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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.

Connected and Automated Vehicles (CAVs) have real-time information from the surrounding environment by using local on-board sensors, V2X (Vehicle-to-Everything) communications, pre-loaded vehicle-specific lookup tables, and map database. CAVs are capable of improving energy efficiency by incorporating these information. In particular, Eco-Cruise and Eco-Lane Selection on highways and/or motorways have immense potential to save energy, because there are generally fewer traffic controllers and the vehicles keep moving in general. In this paper, we present a cooperative and energy-efficient lane-selection strategy named MultiCruise, where each CAV selects one among multiple candidate lanes that allows the most energy-efficient travel. MultiCruise incorporates an Eco-Cruise component to select the most energy-efficient lane. The Eco-Cruise component calculates the driving parameters and prospective energy consumption of the ego vehicle for each candidate lane, and the Eco-Lane Selection component uses these values. As a result, MultiCruise can account for multiple data sources, such as the road curvature and the surrounding vehicles' velocities and accelerations. The eco-autonomous driving strategy, MultiCruise, is tested, designed and verified by using a co-simulation test platform that includes autonomous driving software and realistic road networks to study the performance under realistic driving conditions. Our experimental evaluations show that our eco-autonomous Mul-tiCruise saves up to 8.5% fuel consumption.

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 aims at developing integrated onboard diagnosis and fault-tolerant control methods with experimental validation for a urea selective catalyst reduction (SCR) aftertreatment system to reduce vehicle tailpipe emissions. Diagnostics are performed for an SCR urea injection system by estimating and monitoring the injected urea mass flow with no need for a costly physical flow sensor. The estimation is derived from a first-principle-based urea injection system model, and the model parameters are identified by using system identification. During vehicle transient maneuvers, a Kalman filter (KF) is formulated to further reduce the estimation noise and improve diagnostic robustness. Once an injection fault is detected, an adaptation control algorithm is applied to compensate the urea injection command, thus correcting certain types of urea under/overdosing faults and maintaining the SCR $\mbox{NO}_{x}$ conversion performance. These methods have been validated through vehicle tests by utilizing an onboard rapid prototyping control system.

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.

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.

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.