Health conscious controller

#Controllers and configurations for such controllers.

#Modern automotive control generally makes use of a vehicle processing unit that controls a number of parameters to enhance engine performance. A variety of different control methodologies may be used, such as Model predictive control (MPC). MPC enables the user to formulate the control performance requirements (setpoint tracking quality, energy consumption and others) and technical/operational restrictions (physical constraints for valve positions and actuator values and others) into a form of objective function and accompanying constraints for the optimized variables. In automotive, these optimized variables usually correspond to manipulated variables, such as torques, normalized braking forces, valve openings etc. Using the internal model of the controlled system, the optimizer (optimization routine) finds the optimal values of the optimized variables over the specified prediction horizon such that the cost function is minimized, and the constraints are satisfied.

#Predictive maintenance criterion may be used to generate system warnings to prevent failure. FIG. 1 shows a prior art approach. For a given component of a system, such as a turbocharger, one or more parameters (and links between such parameters) may be monitored 10 to determine whether and when failure is likely. When the measured variable satisfies faultless case conditions 12, the process returns to monitoring 10 until a next iteration. Iterations may be performed at a desired/set frequency (such as every second, or longer or shorter time period). If the measured variable exceeds fault boundaries, a predictive maintenance approach will determine when the degradation of the underlying component has reached a point where failure is imminent, and proceeds from block 12 to block 14, where the component at issues is identified, and a flag is set at 16 for that component. The flag may result in issuance of an alert to the user, for example, and may be set and recorded for use in maintenance operations. For example, the pressure into, and out of, a turbocharger compressor may be monitored in relation to the speed of the compressor; the amount of pressure increase at a given speed can be modeled to determine whether the operation of the compressor is degrading to a point where a failure is likely to occur in a foreseeable timeline. While predictive maintenance is useful to avoid system performance degradation as well as catastrophic failure, it does not extend the life of the components of the system between maintenance processes. What is needed then is an approach that will extend component life, a consideration referred to as “health optimization” herein.

#In the drawings, which are not necessarily drawn to scale, like numerals may describe similar components in different views. Like numerals having different letter suffixes may represent different instances of similar components. The drawings illustrate generally, by way of example, but not by way of limitation, various embodiments discussed in the present document.

#FIG. 1 is a block method flow diagram of prior art predictive maintenance;

#FIG. 2 shows a model of an operational control system;

#FIG. 2 illustrates a general control architecture. A control apparatus is shown at 100 and includes a state observer 102, which feeds a set of current state variables x(k) to the optimizer 104. The optimizer calculates a solution for process parameters that can be applied to a set of actuators 112, which in turn control operation of the physical plant 114. The set of actuators may apply to control, for example and without limitation, fuel or other injectors, variable nozzle turbine position, throttle valve, engine brake, after-treatment (including exhaust), exhaust gas recirculation (EGR), turbocharger, an electric motor (in an electric turbocharger for example, which may be controlled via pulse width modulation (PWM)), the waste gate (WG) actuator position (stroke), position of the recirculation valve actuator, position of the variable compressor geometry actuator; and combinations thereof.

#The physical plant 114 may be, for example and without limitation, an internal combustion engine, whether diesel or gasoline, or a subsystem thereof, such as a turbocharger, the system airpath as a whole, catalysts, etc. The present innovations may also be used more broadly in other systems outside of the engine or vehicle context, including, for example and without limitation, the management of clean room and/or more general ventilation systems, boilers, power plants, furnaces, and/or factory/manufacturing equipment and lines of such equipment, including for example an extruder or a fixture in a manufacturing line.

#A plurality of sensors 116 are provided. Sensors 116 may include, for example, and without limitation, sensors detecting manifold absolute pressure (MAP), mass air flow (MAF), EGR flow, turbo speed, NOx, engine speed, fuel quantity, boost pressure, etc. Additional monitored parameters may include, for example, torque output of the electric motor of an electric turbocharger, waste gate (WG) normalized opening, recirculation valve (RCV) normalized opening, and/or a variable geometry compressor configuration. Such sensors may be configured to sample the underlying parameter being sensed and provide the result of such samples to the state observer 102. The state observer 102 may record the underlying sensed parameters, as well as actuator positions, over time to provide history of the system operation.