A system comprising engine driven pumps, control units, a rate control algorithm, and a trained algorithmic model. The control units store operation variables for the engine driven pumps. The rate control algorithm includes an instruction set to read the operation variables for the engine driven pumps. The instruction set comprises a trained algorithmic model with a parameter space based on historical operation variables. The trained algorithmic model determines an optimal distribution rate based on an objective. The instruction set generates rate control variables based on the determined optimal distribution rate. Each rate control variable comprises a selected control unit identifier and a rate value. The instruction set distributes each rate control variable based on the selected control unit identifier. The trained algorithmic model determine the optimal distribution rate using an objective that defines a desired mixture between a first fuel and a second fuel.

An optimum engine configuration is determined, based on a predicted required power, for a seafaring vessel having a plurality of thrust engines. The predicted required power is determined by inputting vessel operational data, environmental data, and voyage data to a required power model. At least some of the vessel operational data and environmental data is received from a plurality of sensors positioned onboard the vessel. The optimum engine configuration is selected from a plurality of candidate engine configurations. Each candidate engine configuration includes a specified number of thrust engines running and a specified power output level of each thrust engine. The optimum engine configuration is selected based on a candidate total predicted fuel consumption of each candidate engine configuration. The candidate total predicted fuel consumption amount is determined as a sum of the engine-specific predicted fuel consumptions determined for each running thrust engine of that candidate engine configuration.

A method for diagnosing a heating element of an exhaust gas sensor of an internal combustion engine. The exhaust gas sensor includes a temperature measurement device. A modeled temperature at the point of the exhaust gas sensor is continuously ascertained with the aid of a temperature model. The temperature of the exhaust gas sensor is increased by a heating process. The diagnosis of the heating element of the exhaust gas sensor is provided as the result an enable condition. Upon enablement of the diagnosis, a temperature difference between the modeled temperature of the exhaust gas sensor and the measured temperature of the exhaust gas sensor are ascertained. The heating element of the exhaust gas sensor is recognized as defective when the ascertained temperature difference exceeds a predefinable temperature threshold value.

An engine test method that causes a computer to execute a process including, acquiring, by a processer on the computer, a first test pattern in which an operation variable that is used for an engine test is changed in time series, inputting, based on the first test pattern, a first operation variable to a mathematical model that represents a time series response of an engine obtained by inputting a test pattern as a simulation of the engine test, monitoring, as a first monitoring parameter of engine abnormality, at least one of an air excess ratio, pressure and temperature of an intake manifold, pressure and temperature of an exhaust manifold, and a maximum cylinder pressure rise rate that are obtained by inputting the first operation variable to the mathematical model, holding, when the first monitoring parameter exceeds a first threshold value, the first operation variable until the first monitoring parameter is less than the first threshold value, creating, a history of the first operation variable in the simulation as a second test pattern, monitoring, as a second monitoring parameter, at least one of the air excess ratio, the pressure and the temperature of the intake manifold, the pressure and the temperature of the exhaust manifold, and the maximum cylinder pressure rise rate that are obtained by inputting a second operation variable to a real engine based on the second test pattern, holding, when the second monitoring parameter exceeds a second threshold value, the second operation variable until the second monitoring parameter is less than the second threshold value, and acquiring, time series data of the second operation variable and a controlled variable.

A skip fire control system for an engine of a vehicle includes a set of sensors configured to measure a set of operating parameters of the engine corresponding to a volumetric efficiency of the engine, a set of sub-systems having a set of operational states that affect transitions between different firing patterns/fractions of the engine, and a controller configured to, based on the set of operating parameters and the set of operational states of the set of sub-systems, determine a best firing pattern/fraction by taking into account losses or penalties to transition at least some of the set of operational states of the set of sub-systems to obtain a target firing pattern/fraction, and control the engine based on the target firing pattern/fraction to maximize an efficiency of the engine.

The present invention relates to a universal powertrain for controlling an effort request and/or a flow request to a powertrain based on a demanded effort or demanded flow for the powertrain. The universal controller includes a configurable powertrain model and a configurable optimiser module. The universal controller is configurable to control a class of generic powertrains comprising J generic power sources, K generic power sinks, and L generic couplings. The universal controller is arranged to receive an input file of a plurality of input parameters to configure the universal controller to control a specific powertrain having a powertrain architecture with N power sources, M power sinks, and X couplings, the configurable powertrain model comprising: (a) a generic powertrain component library configured to provide a model of each of the N power sources, M power sinks and X couplings of the specific powertrain, and (b) a connection parameter module configured to define a model architecture of the N power source models, M power sink models and X coupling models which is representative of the powertrain architecture based on flow weight parameters and effort weight parameters of the input file, the configurable optimiser module comprising: a generic performance objective function library comprising a plurality of configurable performance objective functions from which a cost function is configurable based on input parameters of the input file, wherein the configurable optimiser module is configurable to calculate at least one of an optimised effort request or an optimised flow request for each of the N power sources of the specific powertrain based on: the cost function, the powertrain model of the specific powertrain, the demanded effort request of demanded flow request.

To provide a controller and a control method for internal combustion engine which can calculate the shaft torque in unburning with good accuracy in all the operating condition in which calculation is required, using the shaft torque in unburning which was set in the specific operating condition, and can improve estimation accuracy of the parameter relevant to the combustion state. A controller for internal combustion engine calculates a specific shaft torque in unburning with reference to a specific unburning condition data; calculates specific and current generated torques of unburning assumption using the physical model equation; calculates a current shaft torque in unburning based on the specific shaft torque in unburning, and the specific and current generated torques of unburning assumption; and calculates an increment of gas pressure torque by burning based on the current shaft torque in unburning and the actual shaft torque in burning condition.

New and/or alternative approaches to engine performance control that can account for the need to robustly monitor performance and/or operation of the physical plant and actuators thereof, while avoiding or limiting performance degradation. Model predictive control (MPC) or other control configuration such as proportional-integral-derivative control may be used to control the system by identifying a performance optimized control solution. In some examples, a modification to the performance optimized solution analysis is made to weight control solutions in favor of robust monitoring conditions. In other examples, the performance optimized solution is post-processed and modified to favor robust monitoring conditions.

A method for determining an air mass in an air separator of a water direct injection system for injecting a water/fuel mixture into a combustion chamber of an engine of a motor vehicle. The air separator is disposed between a water pump for delivering water of the water/fuel mixture and a high-pressure pump for feeding the water/fuel mixture to a high-pressure injector for injecting the water/fuel mixture into the combustion chamber. The method includes increasing a pressure of the water from a first pressure value to a second pressure value by the water pump, determining a water volume delivered by the water pump during the increasing of the pressure of the water by the water pump, and determining the air mass in the air separator on a basis of the determined water volume delivered by the water pump.

Methods and systems for controlling a system such as an engine having an airflow system. A model predictive control calculation is configured in an off-line mode, having a linear part and a non-linear part. In an on-line mode, the linear part of the MPC and/or a Hessian matrix used with the MPC is modified responsive to special modes or other operating changes or conditions. The online mode is configured to respond to changing modes or conditions without requiring recalculation of the MPC. Certain changes of conditions and modes are used to modify feedforward, while others modify responsiveness.