Honeywell International Inc.

Honeywell G.m.b.h.

Ademco Inc.

Honeywell Technologies Sarl

Honeywell Spol, S.r.o.

Vladimir Havlena

Daniel Pachner

Jaroslav Pekar

Pavel Trnka

Michael L Rhodes

Jan Jelinek

Tariq Samad

Aravind Padmanabhan

Joseph Z Lu

Syed M Shahed

Lubomir Baramov

Petr Stluka

Jiri Findejs

Axel Hilborne-Clarke

Pavel Otta

Josef Horak

Josef Hor&aacute;k

Lubmir Baramov

Werner Hugger

Ondrej Basus

Jiri Dostál

Findejs Jiri

Samad Tariq

Devices, methods, systems, and computer-readable media for calculating a battery lifetime of an electric vehicle are described herein. One or more embodiments include receiving via a wide area network, at a network computing device, actual battery use data of a specific electric vehicle, expected battery use data, and battery use data from each of a one or more network connected electric vehicles and calculating an estimated lifetime of a battery of the specific electric vehicle based on the actual battery use data, the expected battery use data, and the battery use data from the one or more network connected electric vehicles.

Calculate lifetime of a battery based on battery use data from multiple network connected electric vehicles

During each of a plurality of iterations, a policy of a controller is updated and at least part of a process is controlled using the updated policy. The updated policy is associated with a performance level of the controller. For each iteration, the updated policy is determined using the associations generated during one or more previous iterations between the policies and the corresponding performance levels of the controller in controlling the at least part of the process, such that the updated policy is optimized to have a highest likelihood of producing a positive change in the performance level of the controller in controlling the at least part of the process rather than optimized to have a highest likelihood of producing a largest positive magnitude of change in the performance level of the controller in controlling the at least part of the process relative to the previous iteration.

Method and apparatus for tuning a regulatory controller

An approach for auto tuning a PID controller that may incorporate determining set-points for controlled variables, obtaining controlled variables from a controlled process module, taking a difference between the set-points and the controlled variable as inputs to a proportional, integral and derivative (PID) controller, calculating a loss or reward from the difference between the controlled variables and the set-points, and achieving set-point tracking. The loss or reward needs to be an increasing or decreasing function of a control error value. Also incorporated may be adding loss or reward components based on controlled variables time difference or control action time difference, which may effect a self-tuned performance of the PID controller.

PID controller autotuner using machine learning approaches

Systems and methods for pre-distortion pattern recognition are provided. In certain embodiments, the system includes an electronic device that provides an output signal, wherein the electronic device applies distortion to the output signal. The system also includes a pre-distorter that provides an input signal to the electronic device, wherein the pre-distorter applies a pre-distortion to the input signal before the electronic device receives the input signal. Further, the system includes a pre-distorter design block that identifies the distortion from an association between the output signal and a finite set of symbols and updates the pre-distortion based on the association.

Pre-distortion pattern recognition

A structure having convergence of a control system and a data analytics system. The control system may involve input-output models of items, for example, homes, buildings and process plants. The system may use sensor data and scientific principles to implement control models. The models may help to precisely achieve a desired value of a parameter. The data analytics system may involve analysis of diverse data and may create insights of the items. The control system plus the data analytics system may close the loop between insights and on-premise controls of the items represented by the input-output models. The control and data analytics systems may merge the best of both worlds to solve difficult issues pertaining to the items. The structure may be for comfort control, energy reduction, and so on, having improved performance based on the convergence.

Convergence structure for control and data analytics systems

A system and method for predicting future disturbance in MPC applications by segregating a transient part and a steady state value associated with the disturbance. A dynamic state space model that includes a variable disturbance prediction module can be utilized for analyzing a dynamic behavior of a physical process associated with a process model. The process model represents a dynamic behavior of the physical process being controlled and the dynamic state space model represents current deviations from the process model and future deviations over a predetermined prediction horizon. A predicted trajectory can be calculated as a response to the initial conditions estimated by a Kalman Filter for the process model extended by a disturbance model. The output of the dynamic state space model utilized for the disturbance prediction can be further provided as an estimated input to a MPC.

System and method for predicting future disturbances in model predictive control applications

Methods, systems, and devices for monitoring a battery are described herein. One method includes receiving a plurality of values, each value associated with a respective battery characteristic, and determining an internal state associated with the battery based, at least in part, on the plurality of values.

Model-based battery monitoring

A control method for boiler outlet temperatures includes predictive control of SH and RH desuperheater systems. The control method also includes control and optimization of steam generation conditions, for a boiler system, such as burner tilt and intensity, flue-gas recirculation, boiler fouling, and other conditions for the boiler. The control method assures a proportional-valve control action in the desuperheater system, that affects the boiler system.

Steam-generator temperature control and optimization

An improved process and corresponding controller provide a model predictive control approach that can be implemented with less computational resources and/or with greater speed than conventional MPC, while at the same time retaining all or a substantial portion of the robustness and advantages of conventional MPC. According to one aspect of the invention, the process provides an improved initial estimate of the MPC model trajectory to reduce the number of iterations to find the optimal one. The improved trajectory is obtained by applying a correction to the computed MPC manipulated value trajectory, and using the corrected manipulated value trajectory as the starting point for the next iteration of MPC manipulated value trajectory computation. As set forth in more detail below, the correction is determined from the LQR feedback control strategy. Since the sequence of control laws for the LQR feedback control strategy can be computed off-line and stored, the real time part of the LQR control strategy needed to determine the correction can be retrieved with relatively little computational resources.

Fast algorithm for model predictive control

The subject mater herein relates to coal mills and, more particularly, inferential pulverized fuel flow sensing and manipulation within a coal mill. Various embodiments provide systems, methods, and software to manipulate a primary air flow rate and a coal feed rate into a coal mill to produce a target pulverized fuel flow. Some embodiments include sensing a differential pressure between two or more locations within a coal mill to estimate a recirculated load of coal at one or more stages within the coal mill.

Inferential pulverized fuel flow sensing and manipulation within a coal mill

A method includes associating a plurality of valve balancing units with a plurality of valves in a hydronic network. The method also includes adjusting a setting of at least one of the valves using at least one of the valve balancing units to balance the hydronic network. Adjusting the setting could include identifying a differential pressure across a valve and a flow rate of material through that valve. Adjusting the setting could also include comparing the identified differential pressure to a target differential pressure and/or the identified flow rate to a target flow rate. Adjusting the setting could further include instructing an actuator to adjust the setting until the identified differential pressure is within a first threshold of the target differential pressure and/or the identified flow rate is within a second threshold of the target flow rate.

System and method for decentralized balancing of hydronic networks

A method and system for optimal model-based multivariable balancing for distributed hydronic networks based on global differential pressure/flow rate information. A simplified mathematical model of a hydronic system can be determined utilizing an analogy between hydronic systems and electrical circuits. Thereafter, unknown parameters can be identified utilizing the simplified mathematical model and a set of available measurements. Next, balancing valve settings can be calculated by reformulating the simplified mathematical model based on the parameterized model. The sum of pressure drops across selected balancing valves can be then minimized to achieve optimal economic performances of the system. The data can be collected and transferred to a central unit either by wireless communication or manually by reading the local measurement devices. Such a multivariable balancing approach provides a fast and accurate balancing of distributed hydronic heating systems based on a centralized and non-iterative approach.

Method and system for model-based multivariable balancing for distributed hydronic networks

The subject matter herein relates to coal mills and, more particularly, inferential pulverized fuel flow sensing and manipulation within a coal mill. Various embodiments provide systems, methods, and software to manipulate a primary air flow rate and a coal feed rate into a coal mill to produce a target pulverized fuel flow. Some embodiments include sensing a differential pressure between two or more locations within a coal mill to estimate a recirculated load of coal at one or more stages within the coal mill.

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Prague, Czechia

Vladimir Havlena