IP Library › Granted Patent US 12,572,135
Granted Patent B2
US 12,572,135 · App. 18/140,112 · Granted Mar 10, 2026

Building equipment control system with dynamic flow bounds

Inventors: Matthew J. Asmus (Watertown, WI); Ryan C. Beaty (Milwaukee, WI)
Assignee: TYCO FIRE & SECURITY GMBH
G05B19/416G05B2219/2614G05B2219/37371
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Quick Facts
Patent No.
US 12,572,135
App. No.
18/140,112
Granted
Mar 10, 2026
Kind
B2
Abstract

Systems and methods for dynamically updating flow bounds for one or more pumps are provided. An exemplary system includes one or more pumps, one or more processors, and one or more computer-readable media storing program instructions that, when executed by the one or more processors, cause the one or more processors to perform operations. The operations include updating bounds on flow provided by the one or more pumps based on one or more values of pump head, determining control decisions for the one or more pumps based on the bounds, and controlling the one or more pumps in accordance with the control decisions.

Claims (32)

1 . A system comprising:

one or more pumps; and

one or more processors and one or more non-transitory computer-readable media storing program instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:

updating bounds on flow provided by the one or more pumps based on one or more functions of pump head, the functions connecting points on different pump speed curves for the one or more pumps, the points having flow values defined as percentages of flow values of most efficient points on the pump speed curves;

determining control decisions for the one or more pumps based on the bounds; and

controlling the one or more pumps in accordance with the control decisions.

2 . The system of claim 1 , wherein determining the control decisions comprises performing an optimization of an objective function subject to constraints defined by the bounds.

3 . The system of claim 1 , wherein the control decisions comprise on/off decisions for the one or more pumps.

4 . The system of claim 1 , wherein updating the bounds on flow comprises determining an upper flow bound using a first function of the pump head and a lower flow bound using a second function of the pump head.

5 . The system of claim 4 , wherein the first function and the second function define straight lines.

6 . The system of claim 4 , wherein the first function defines a path between a first point of the points on a first curve of the different pump speed curves and a second point of the points on a second curve of the different pump speed curves, the first curve associated with a first pump speed and the second curve associated with a second pump speed, wherein the first function outputs flow based on the pump head.

7 . A method of controlling equipment comprising:

dynamically updating a constraint on a flow rate of a fluid created by operation of the equipment as a function of a pressure differential across the equipment, the function connecting points on different speed curves for the equipment, the points having flow rate values defined as percentages of flow rate values of most efficient points on the speed curves;

determining control decisions for the equipment subject to the constraint; and

operating the equipment in accordance with the control decisions.

8 . The method of claim 7 , wherein the equipment comprises a pump and the pressure differential across the equipment corresponds to pump head.

9 . The method of claim 7 , wherein determining the control decisions comprises optimizing an objective function subject to the constraint.

10 . The method of claim 7 , wherein the control decisions comprise on/off decisions for the equipment.

11 . The method of claim 7 , wherein dynamically updating the constraint comprises measuring a current value of the pressure differential and updating the constraint based on the current value of the pressure differential.

12 . The method of claim 7 , wherein dynamically updating the constraint comprises estimating a current value of the pressure differential and updating the constraint based on the current value of the pressure differential.

13 . The method of claim 7 , wherein dynamically updating the constraint comprises determining an upper flow bound using the function of the pressure differential and a lower flow bound using an additional function of the pressure differential.

14 . The method of claim 13 , wherein the first function of the pressure differential and the additional function define curved lines.

15 . The method of claim 7 , wherein the function defines a path between a first point of the points on a first curve of the different speed curves and a second point of the points on a second curve of the different speed curves, the first curve associated with a first equipment speed and the second curve associated with a second equipment speed, the first curve and the second curve representing relationships between the flow rate and the pressure differential.

16 . The method of claim 15 , wherein the second equipment speed is a maximum pump speed of the equipment.

17 . One or more non-transitory computer-readable media storing program instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:

dynamically updating a constraint on a flow rate of a fluid created by operation of the equipment as a function of a pressure differential across the equipment, the function connecting points on different speed curves for the equipment, the points having flow rate values defined as percentages of most efficient points on the speed curves;

determining control decisions for the equipment subject to the constraint; and

causing the equipment to operate in accordance with the control decisions.

18 . The one or more non-transitory computer-readable media of claim 17 , wherein dynamically updating the constraint on the flow rate of the fluid created by operation of the equipment as a function of the pressure differential across the equipment comprises:

measuring or estimating a current value of the pressure differential across the equipment; and

using a value of the function at the current value of the pressure differential as an upper or lower bound on the flow rate of the fluid;

wherein the function defines a path between points on curves representing relationships between the flow rate of the fluid and the pressure differential for different equipment operating speeds.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 9, 2024
From: JOHNSON CONTROLS TYCO IP HOLDINGS LLP
To: TYCO FIRE & SECURITY GMBH
Reel/Frame 067056/0552 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 28, 2023
From: ASMUS, MATTHEW J.; BEATY, RYAN C.
To: JOHNSON CONTROLS TYCO IP HOLDINGS LLP
Reel/Frame 063476/0516 →
Continuity (2)
Provisional Application 63336835 · Apr 29, 2022
Related Publication 20230350390A1 · Nov 2, 2023
References Cited (48)
US 20030055798A1 · Hittle et al. · 2003 [cited by applicant]
US 20150316907A1 · Elbsat et al. · 2015 [cited by applicant]
US 20160076549A1 · Kelly · 2016 [cited by examiner]
US 20170104345A1 · Wenzel et al. · 2017 [cited by applicant]
US 20170152310A1 · Barbour et al. · 2017 [cited by applicant]
US 20180129177A1 · Coin · 2018 [cited by examiner]
US 20180196456A1 · Elbsat · 2018 [cited by applicant]
US 20180197253A1 · Elbsat et al. · 2018 [cited by applicant]
US 20180285800A1 · Wenzel et al. · 2018 [cited by applicant]
US 20180357577A1 · Elbsat et al. · 2018 [cited by applicant]
US 20180372362A1 · Turney · 2018 [cited by examiner]
US 20190079473A1 · Kumar et al. · 2019 [cited by applicant]
EP 1156286A2 · 2001 [cited by applicant]
WO WO2012161804A1 · 2012 [cited by applicant]
WO WO2013130956A1 · 2013 [cited by applicant]
Afram et al., “Theory and Application of HVAC Control Systems—A review of Model Predictive Control (MPC),” Building and Environment, Feb. 2014, vol. 72 (pp. 343-355). [cited by applicant]
Ahn et al., “Optimal Control Development for Chilled Water Plants Using a Quadratic Representation,” Energy and Buildings, Apr. 2001, vol. 33, No. 4 (pp. 371-378). [cited by applicant]
Burer et al., “Non-convex Mixed-Integer Nonlinear Programming: A Survey,” Surveys in Operations Research and Management Science, Jul. 2012, vol. 17, No. 2 (pp. 97-106). [cited by applicant]
Cantoni, A., “Optimal Curve Fitting with Piecewise Linear Functions,” IEEE Transactions on Computers, Jan. 1971, vol. 20, No. (pp. 59-67). [cited by applicant]
Corbin et al., “A Model Predictive Control Optimization Environment for Real-Time Commercial Building Application,” Journal of Building Performance Simulation, 2013, (Published online: Jan. 11, 2012) vol. 6, No. 3 (pp. … [cited by applicant]
Drgona et al., “All you Need to Know about Model Predictive Control for Buildings,” Annual Reviews in Control, 2020, vol. 50 (pp. 190-232). [cited by applicant]
EPO Notice of Opposition to a European Patent issued in Appl. Ser. No. EP 16165681.4 dated May 2, 2023 (48 pages). [cited by applicant]
EPO Notice of Opposition to a European Patent issued in Appl. Ser. No. EP 16165681.4 dated May 2, 2023 (51 pages). [cited by applicant]
EPO Notice of Opposition to a European Patent with Consolidated List issued in EP Appl. Ser. No. 16165681.4 dated May 2, 2023 (4 pages). [cited by applicant]
EPO Office Action on EP Appl. Ser. No. 16165681.4 dated Apr. 6, 2021 (7 pages). [cited by applicant]
Extended European Search Report on EP Appl. Ser. No. 16165681.4 dated Oct. 20, 2016 (5 pages). [cited by applicant]
Extended European Search Report on EP Appl. Ser. No. 22177772.5 dated Sep. 26, 2022 (11 pages). [cited by applicant]
Hackner, J.R., “HVAC system dynamics and energy use in existing buildings,” Doctoral Dissertation, University of Madison, Wisconsin, 1984 (174 pages). [cited by applicant]
Haves et al., “Model Predictive Control of HVAC Systems: Implementation and Testing at the University of California, Merced,” Technical Report, U.S. Department of Energy Office of Scientific and Technical Information, J… [cited by applicant]
Huang et al., “A New Model Predictive Control Scheme for Energy and Cost Savings in Commercial Buildings: An Airport Terminal Building Case Study,” Building and Environment, Jul. 2015, vol. 89 (pp. 203-216). [cited by applicant]
Kelman et al., “Analysis of Local Optima in Predictive Control for Energy Efficient Buildings,” Journal of Building Performance Simulation, Apr. 16, 2012, vol. 6, No. 3 (pp. 236-255). [cited by applicant]
Koehler et al., “Building Temperature Distributed Control via Explicit MPC and ‘Trim and Respond’ Methods,” European Control Conference (ECC), Jul. 17-19, 2013, Zurich, Switzerland (pp. 4334-4339). [cited by applicant]
Kwadzogah et al., “Model Predictive Control for HVAC Systems—A Review,” 2013 IEEE International Conference on Automation Science and Engineering, Model Predictive Control for HVAC Systems—A Review, 2013 IEEE Internation… [cited by applicant]
McKenna et al., “A TRNSYS model of a building HVAC system with GSHP and PCM thermal energy storage—component modelling and validation,” Proceedings of BS2013: 13th Conference of International Building Performance Simula… [cited by applicant]
Mossolly et al., “Optimal Control Strategy for a Multizone Air Conditioning System Using a Genetic Algorithm,” Energy, Jan. 2009, vol. 34, No. 1 (pp. 58-66). [cited by applicant]
Nassif et al., “Optimization of HVAC Control System Strategy Using Two-Objective genetic Algorithm,” International Journal of HVA C&R Research, vol. 11, No. 3 (pp. 459-486). [cited by applicant]
Sourbon et al., “Dynamic Thermal Behaviour of Buildings with Concrete Core Activation,” Dissertation, Arenberg Doctoral School of Science, Engineering & Technology, Katholieke Universiteit Leuven—Faculty of Engineering … [cited by applicant]
Stluka et al., “Energy Management for Buildings and Microgrids,” 2011 50th IEEE Conference on Decision and Control and European Control Conference (CDCECC) Orlando, FL, USA, Dec. 12-15, 2011 (pp. 5150-5157). [cited by applicant]
Strurznegger, D., “Model Predictive Building Climate Control, Steps Towards Practice,” Doctoral Thesis, Automatic Control Laboratory, Zurich, Switzerland, 2014 (176 pages). [cited by applicant]
Sun et al., Optimal Control of Building HVAC&R Systems Using Complete Simulation-Based Sequential Quadratic Programming (CSB-SQP), Building and Environment, May 2005, vol. 40, No. 5 (pp. 657-669). [cited by applicant]
Third Party Observation Report on EP Appl. Ser. No. 16165681.4 dated Jan. 15, 2020 (8 pages). [cited by applicant]
Third Party Observation Report on EP Appl. Ser. No. 16165681.4 dated Oct. 5, 2018 (6 pages). [cited by applicant]
Verhelst et al., “Study of the Optimal Control Problem Formulation for Modulating Air-to-Water Heat Pumps Connected to a Residential Floor Heating System,” Energy and Buildings, Feb. 2012, vol. 45 (pp. 43-53). [cited by applicant]
Verhelst, C., “Model Predictive Control of Ground Coupled Heat Pump Systems in Office Buildinqs,” Dissertation, Arenberg Doctoral School of Science, Engineering & Technology, Katholieke Universiteit Leuven—Faculty of En… [cited by applicant]
Wang et al., “Model-Based Optimal Control of VAV Air-Conditioning System Using Genetic Algorithm,” Building and Environment, Aug. 2000, vol. 35, No. 6 (pp. 471-487). [cited by applicant]
Wang et al., “Supervisory and Optimal Control of Building HVAC Systems: A Review,” HVAC&R Research, Jan. 2008, vol. 14, No. 1 (pp. 3-32). [cited by applicant]
Xi et al., “Support Vector Regression Model Predictive Control on a HVAC Plant,” Control Engineering Practice, Aug. 2007, vol. 15, No. 8 (pp. 897-908). [cited by applicant]
Yao et al., “Global Optimization of a Central Air-Conditioning System Using Decomposition-Coordination Method,” Energy and Buildings, May 2010, vol. 42, No. 5 (pp. 570-583). [cited by applicant]