Thermal energy storage management system

Abstract

The present disclosure relates to systems and methods for monitoring, estimating, and managing thermal energy storage in temperature-controlled environments. A system receives sensor data associated with a thermal energy storage system and a surrounding environment, and processes the data using physics-based models, data-driven models, or combinations thereof to estimate a state of charge of the thermal energy storage system. The estimated state of charge is communicated to external control systems, including refrigeration or HVAC systems, to inform control actions that selectively charge or discharge the thermal energy storage system. The system dynamically updates state-of-charge estimates over time and adapts to changing operating conditions to improve energy efficiency while maintaining temperature compliance within the temperature-controlled environment.

Description

RELATED APPLICATION(S)

[0001] This application claims the benefit of priority to U.S. Provisional Pat. Application No. 63 / 756,057, filed Feb. 8, 2025, which is incorporated herein by reference in its entirety.FIELD OF THE DISCLOSURE

[0002] The disclosure relates to systems and methods for monitoring, modeling, and controlling energy storage and energy-consuming assets in temperature-controlled environments.BACKGROUND

[0003] As electricity demand has grown, and energy security has declined, so has the price of energy in many markets, motivating companies to prioritize energy reductions through optimization software and supplementing grid power with renewable energy sources and energy storage. The most popular energy storage methods currently employed are electrical energy storage via batteries (typically Lithium Ion battery packs) or mechanical energy stored via gravity (e.g., hydro-electric dams).

[0004] Thermal Energy Storage (TES) is an underutilized storage method that is particularly effective in temperature-controlled settings like cold storage, data centers, and beyond. Because it stores energy thermally directly, there is no additional loss converting from thermal to electrical or mechanical energy for storing, then back to thermal energy to cool or heat a space.

[0005] A common TES material is Phase Change Material (PCM). PCMs are composed of precise chemistries to solidify (“freeze”) and liquify (“melt”') at specific temperature, effectively absorbing thermal energy from the surrounding environment (“charging”) and dissipating that energy back to the surrounding environment (“discharging”) when it falls out of target temperature ranges.

[0006] The chemistry for PCMs has improved dramatically in recent the years, making TES cheaper per Kilowatt Hour than Lithium Ion storage. However, widespread adoption of TES has been limited by the absence of reliable systems for monitoring, managing, and integrating thermal energy storage with existing HVAC&R infrastructure.SUMMARY OF THE DISCLOSURE

[0007] The present disclosure describes a TES technology with monitoring hardware and software applied in ways to estimate the current state-of-charge (e.g., how much energy is stored in the system), and send that information to HVAC&R control systems to activate or deactivate and throttle up or down their assets to maintain a set temperature or temperatures in a target, temperature-controlled environment, consuming less overall energy. This system operates collectively as a Building Management System (BMS) for thermally stored energy.

[0008] These optimizations can be leveraged to further improve behind the meter renewable energy sources and energy storage systems.

[0009] In some aspects, the disclosure provides a method for managing a thermal energy storage system, the method comprising: reading sensor data from the thermal energy storage system, the sensor data comprising temperature data corresponding to a phase change material and temperature data corresponding to a temperature-controlled environment surrounding the phase change material; determining a direction of thermal energy transfer between the phase change material and the temperature-controlled environment surrounding the phase change material based on a comparison of the temperature of the phase change material and the temperature of the environment surrounding the phase change material; generating state-of-charge information by estimating a state-of-charge of the thermal energy storage system using a first model that maps temperature, time, or a combination thereof to a quantity of thermally stored energy in the phase change material, wherein the first model comprises a physics-based simulation that incorporates thermal properties of the phase change material, and estimates the quantity of thermally stored energy based on temperature change over time during charging or discharging of the phase change material; refining the state-of-charge information by inputting an output of the first model to a second model, wherein the second model comprises a regression model that calibrates the state-of-charge information using data obtained during one or more charging cycle; updating the state-of-charge information over time by reapplying the first model and the second model to newly received sensor data; transmitting the state-of-charge information to a heating, ventilation, air-conditioning, or refrigeration (HVAC&R) system associated with the temperature-controlled environment; and controlling, by the HVAC&R system, operation of a HVAC&R asset to selectively charge or discharge the thermal energy storage system while maintaining the temperature of the temperature-controlled environment surrounding the phase change material within a predefined temperature range.

[0010] In some embodiments, the phase change material comprises a material that undergoes a solid-liquid phase transition within an operating temperature range of the temperature-controlled environment.

[0011] In some embodiments, the sensor data comprises temperature data measured at a location proximate to the phase change material. In some embodiments, the sensor data comprises temperature data measured by a surface-mounted temperature sensor. In some embodiments, the sensor data comprises temperature data measured by a probe inserted into the phase change material. In some embodiments, the sensor data further comprises humidity data, energy consumption data, or environmental disturbance data indicative of heat ingress.

[0012] In some embodiments, the thermal properties of the phase change material comprise specific heat, latent heat, or heat transfer rate.

[0013] In some embodiments, the one or more charging cycle is selected from a controlled charge cycle and a controlled discharge cycle.

[0014] In some embodiments, the first model and / or the second model is executed on a cloud-based computing system independently from the thermal energy storage system.

[0015] In some embodiments, transmitting the state-of-charge information is performed with latency below a predefined threshold such that the transmission enables real-time control of the HVAC&R system. In some embodiments, transmitting the state-of-charge information comprises transmitting the state-of-charge information via an application programming interface to a building management system.

[0016] In some embodiments, updating the state-of-charge information over time comprises periodically recalculating the state-of-charge information at predetermined time intervals. In some embodiments, the state-of-charge information comprises a numerical representation of a percentage of thermal energy capacity remaining in the phase change material. In some embodiments, the state-of-charge information comprises a categorical indication selected from charged, charging, discharging, and discharged.

[0017] In some embodiments, controlling operation of the HVAC&R asset comprises activating, deactivating, or throttling a component of the HVAC&R system, wherein the component of the HVAC&R system is a compressor or refrigeration component. In some embodiments, controlling operation of the HVAC&R asset comprises delaying activation of the HVAC&R asset while the thermal energy storage system discharges thermal energy. In some embodiments, controlling operation of the HVAC&R asset is performed by applying one or more operational rules to the state-of-charge information, wherein the one or more operating thresholds comprise a state-of-charge threshold, a temperature threshold, or a safe operating limit of the temperature-controlled environment.

[0018] In some embodiments, maintaining the temperature of the temperature-controlled environment comprises maintaining the temperature within limits suitable for a cold storage or a freezer environment.

[0019] In some aspects, the disclosure provides a system comprising: a thermal energy storage system comprising a phase change material; one or more sensors configured to generate temperature data corresponding to the phase change material and a temperature-controlled environment surrounding the phase change material; a heating, ventilation, air-conditioning, or refrigeration (HVAC&R) system associated with the temperature-controlled environment; and a controller comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor of the computing node to cause the processor to perform a method comprising: reading sensor data from the thermal energy storage system, the sensor data comprising temperature data corresponding to a phase change material and temperature data corresponding to a temperature-controlled environment surrounding the phase change material; determining a direction of thermal energy transfer between the phase change material and the temperature-controlled environment surrounding the phase change material based on a comparison of the temperature of the phase change material and the temperature of the environment surrounding the phase change material; generating state-of-charge information by estimating a state-of-charge of the thermal energy storage system using a first model that maps temperature, time, or a combination thereof to a quantity of thermally stored energy in the phase change material, wherein the first model comprises a physics-based simulation that incorporates thermal properties of the phase change material, and estimates the quantity of thermally stored energy based on temperature change over time during charging or discharging of the phase change material; refining the state-of-charge information by inputting an output of the first model to a second model, wherein the second model comprises a regression model that calibrates the state-of-charge information using data obtained during one or more charging cycle; updating the state-of-charge information over time by reapplying the first model and the second model to newly received sensor data; transmitting the state-of-charge information to a heating, ventilation, air-conditioning, or refrigeration (HVAC&R) system associated with the temperature-controlled environment; and controlling, by the HVAC&R system, operation of a HVAC&R asset to selectively charge or discharge the thermal energy storage system while maintaining the temperature of the temperature-controlled environment surrounding the phase change material within a predefined temperature range.

[0020] In some aspects, the disclosure provides a computer program product, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions being executable by a processor to cause the processor to perform a method comprising: reading sensor data from the thermal energy storage system, the sensor data comprising temperature data corresponding to a phase change material and temperature data corresponding to a temperature-controlled environment surrounding the phase change material; determining a direction of thermal energy transfer between the phase change material and the temperature-controlled environment surrounding the phase change material based on a comparison of the temperature of the phase change material and the temperature of the environment surrounding the phase change material; generating state-of-charge information by estimating a state-of-charge of the thermal energy storage system using a first model that maps temperature, time, or a combination thereof to a quantity of thermally stored energy in the phase change material, wherein the first model comprises a physics-based simulation that incorporates thermal properties of the phase change material, and estimates the quantity of thermally stored energy based on temperature change over time during charging or discharging of the phase change material; refining the state-of-charge information by inputting an output of the first model to a second model, wherein the second model comprises a regression model that calibrates the state-of-charge information using data obtained during one or more charging cycle; updating the state-of-charge information over time by reapplying the first model and the second model to newly received sensor data; transmitting the state-of-charge information to a heating, ventilation, air-conditioning, or refrigeration (HVAC&R) system associated with the temperature-controlled environment; and controlling, by the HVAC&R system, operation of a HVAC&R asset to selectively charge or discharge the thermal energy storage system while maintaining the temperature of the temperature-controlled environment surrounding the phase change material within a predefined temperature range.BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings, which are incorporated into and constitute a part of this specification, illustrate various exemplary embodiments and together with the description, serve to explain the principles of the disclosed embodiments.

[0022] FIG. 1 is a flowchart illustrating an embodiment of a method for monitoring and controlling operation of a cooling asset within a temperature-controlled environment, in accordance with one or more embodiments of the present disclosure.

[0023] FIG. 2 is a schematic diagram of an exemplary computing node.DETAILED DESCRIPTION

[0024] In order for the present disclosure to be more readily understood, certain terms are first defined below. Additional definitions for the following terms and other terms are set forth throughout the specification. The publications and other reference materials referenced herein to describe the background of the disclosure and to provide additional detail regarding its practice are hereby incorporated by reference.

[0025] Unless otherwise defined, (i) the terms “a” and “an” are used herein to refer to one or to more than one (i.e., to at least one) of the grammatical object of the article; (ii) the term “or” may be understood to mean “and / or”; (iii) the terms “comprising” and “including” may be understood to encompass itemized components or steps whether presented by themselves or together with one or more additional components or steps; and (iv) where ranges are provided, endpoints are included.

[0026] For any methods described, regardless of whether the method is described in conjunction with a flow diagram, it should be understood that unless otherwise specified or required by context, any explicit or implicit ordering of steps performed in the execution of a method does not imply that those steps must be performed in the order presented but instead may be performed in a different order or in parallel.

[0027] As used in this application, the terms “system” and “component” are intended to refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution, examples of which are described herein. For example, a component can be, but is not limited to being, a process running on a processor, a processor, a hard disk drive, multiple storage drives (of optical and / or magnetic storage medium), an object, an executable, a thread of execution, a program, and / or a computer. By way of illustration, both an application running on a server and the server can be a component. One or more components can reside within a process and / or thread of execution, and a component can be localized on one computer and / or distributed between two or more computers.

[0028] Further, components may be communicatively coupled to each other by various types of communications media to coordinate operations. The coordination may involve the uni-directional or bi-directional exchange of information. For instance, the components may communicate information in the form of signals communicated over the communications media. The information can be implemented as signals allocated to various signal lines. In such allocations, each message is a signal. Further embodiments, however, may alternatively employ data messages. Such data messages may be sent across various connections. Exemplary connections include parallel interfaces, serial interfaces, and bus interfaces.

[0029] FIG. 1 illustrates an exemplary method 100 for managing a TES system, according to one or more embodiments of the present disclosure. The steps of method 100 presented below are intended to be illustrative, and the steps of exemplary method 100 (e.g., steps 102-114) may be performed automatically or in response to a request from a user. Additionally, the order in which the steps of method 100 are illustrated and described below is not intended to be limiting.

[0030] In some embodiments, method 100 may be implemented in one or more processing devices, such as one or more digital processors, analog processors, digital circuits designed to process information, analog circuits designed to process information, state machines, edge computing devices, cloud-based computing systems, and / or other mechanisms for electronically processing information. The one or more processing devices may execute some or all of the operations of method 100 in response to instructions stored electronically on one or more non-transitory electronic storage media. The one or more processing devices may be configured through hardware, firmware, software, or any combination thereof to perform one or more of the operations of method 100. In some embodiments, one or more steps of method 100 are performed in parallel, asynchronously, or in a distributed computing environment.

[0031] Step 102 may include reading sensor data from the thermal energy storage system, the sensor data comprising temperature data corresponding to a PCM and temperature data corresponding to a temperature-controlled environment surrounding the PCM. In some embodiments, the PCM comprises a material that undergoes a solid-liquid phase transition within an operating temperature range of the temperature-controlled environment. In some embodiments, the sensor data comprises temperature data measured at a location proximate to the PCM. In some embodiments, the sensor data comprises temperature data measured by a surface-mounted temperature sensor. In some embodiments, the sensor data comprises temperature data measured by a probe inserted into the PCM. In some embodiments, the sensor data further comprises humidity data, energy consumption data, or environmental disturbance data indicative of heat ingress.

[0032] In some embodiments, one or more sensors associated with a thermal energy storage (TES) system are mounted at various locations within a temperature-controlled environment. In some embodiments, the sensors are mounted on ceilings or walls of the environment, on an exterior surface of a PCM or heat sink casing, or via probes inserted directly into a PCM or heat sink casing. In some embodiments, sensor placement is selected based on installation constraints, desired measurement fidelity, or non-invasive deployment requirements.

[0033] In some embodiments, temperature sensors are selected to operate across a measurement range sufficient to support cold storage, freezer, and defrost operating conditions. In some embodiments, temperature sensors operate across a range from about −40° C. to about +40° C. to accommodate frozen environments and transient warming events. In some embodiments, temperature sensors provide an accuracy and resolution sufficient to detect gradual temperature changes associated with TES charging and discharging.

[0034] In some embodiments, temperature sensors provide accuracy in the order of about ±0.5° C. and resolution in the order of about 0.1° C. In some embodiments, temperature sensors sample temperature continuously at the device level and transmit data at periodic intervals. In some embodiments, transmission intervals are about 1 minute to about 10 minutes.

[0035] In some embodiments, the TES system comprises a set of sensors including at least one temperature sensor that measures the temperature of the PCM or a location proximate to the PCM, and at least one temperature sensor that measures the temperature of the surrounding environment. In some embodiments, the temperature of the PCM may be used to determine whether the PCM is above, below, or at a phase change temperature. In some embodiments, the temperature of the surrounding environment may be used to determine a temperature differential and a direction of charging or discharging of the TES system.

[0036] In some embodiments, additional sensors may be included to enhance monitoring accuracy or contextual awareness. For example, door sensors may be used to detect heat ingress events that influence a discharge rate of the TES system, whereas current or power sensors may be used to correlate compressor runtime or electrical energy consumption with TES charging behavior. In some embodiments, humidity sensors and energy metering devices may also be included to provide supplemental environmental or operational data.

[0037] In some embodiments, temperature measurements are obtained using surface-mounted sensors positioned on walls, ceilings, or external surfaces of a PCM enclosure. In some embodiments, temperature measurements are obtained using probe-based sensors inserted directly into the PCM to measure a core temperature. In some embodiments, surface-mounted temperature sensors provide sufficient accuracy for state-of-charge estimation due to the thermal mass of the PCM and a relatively slow rate of temperature change during phase transitions.

[0038] In some embodiments, a PCM undergoes volumetric expansion during charging and volumetric contraction during discharging, with a magnitude of such expansion or contraction dependent on the chemical composition of the PCM. In some embodiments, a change of a physical characteristic of the PCM (e.g., a change in volume or pressure exerted on a surrounding structure) is monitored to infer a state-of-charge of a thermal energy storage (TES) system.

[0039] In some embodiments, the PCM is contained within a flexible enclosure. In some embodiments, the flexible enclosure comprises a bag or pouch. In some embodiments, the flexible enclosure is further housed within a rigid or semi-rigid external casing formed from a material comprising a metal or plastic. In some embodiments, multiple flexible PCM enclosures may be housed within a single rigid external casing. In some embodiments, the rigid external casing includes an internal volume configured to accommodate expansion and contraction of a flexible enclosure. In some embodiments, one or more pressure sensors are positioned within the internal volume of the rigid casing to measure pressure variations associated with expansion and contraction of the PCM. In some embodiments, pressure data is processed by a local computing device associated with an individual PCM unit, a collection of PCM units, or transmitted via wired or wireless communication to a remote computing system to serve as a proxy for the state-of-charge of the TES system. In some embodiments, where the PCM is not contained within a rigid external casing, expansion and contraction of the PCM is detected using one or more image sensors. In some embodiments, the image sensors comprise RGB cameras, thermal cameras, or other optical imaging devices configured to capture visual information indicative of volumetric changes of the PCM. In some embodiments, image data is transmitted via wired or wireless communication to local or cloud-based computing systems for processing and analysis.

[0040] In some embodiments, sensor data is collected through a polling-based approach. In some embodiments, polling intervals are about 5 minutes and may be reduced to about 1 minute to increase temporal resolution. In some embodiments, polling intervals are adjustable based on system requirements, network conditions, or control objectives.

[0041] In some embodiments, the TES system comprises one or more PCMs configured for passive thermal energy storage and release. In some embodiments, the PCM is encapsulated in a rigid or semi-rigid enclosure that limits or accommodates volumetric expansion and contraction during phase transitions associated with thermal energy storage and release. In some embodiments, where the physical configuration of the PCM does not produce meaningful pressure variation during thermal energy storage and release, pressure monitoring is omitted. In some embodiments, thermal energy storage and release occur through passive heat transfer rather than active circulation of a working fluid, such that sensors associated with flow rate, valve position, or fluid transport are not required. For example, in a PCM having a phase change temperature of approximately −15 degrees Celsius, pressure monitoring is not required where the PCM is rigidly encapsulated such that volumetric expansion and contraction are mechanically constrained. In such embodiments, the TES system operates as a passive thermal storage system, and flow rate sensors or valve position sensors are not applicable due to the absence of active fluid circulation.

[0042] In some embodiments, sensors communicate data using one or more wired or wireless communication protocols. In some embodiments, wireless communication protocols comprise low-power wide-area network protocols or wireless local area network protocols. For example, sensors communicate using a Long Range Wide Area Network (LoRaWAN) protocol and transmit data to a LoRaWAN network server or cloud-based LoRaWAN service via a gateway device. In some embodiments, the gateway device transmits sensor data to a cloud-based event ingestion service for downstream processing and storage. In some embodiments, the cloud-based event ingestion service comprises event streaming service or message queue service. In some embodiments, wired communication protocols comprise industrial fieldbus protocols. For example, a controller may communicate with an edge computing device using a Modbus Remote Terminal Unit (RTU) protocol over a serial interface. In some embodiments, sensor data is transmitted through one or more intermediate devices (e.g., a gateway or an edge computing device) before being delivered to a local controller or cloud-based computing system.

[0043] In some embodiments, an edge computing device receives sensor data using a secure messaging protocol and forwards the data to a cloud-based system for storage, analysis, and state-of-charge estimation. For example, an edge computing device publishes sensor data using an Message Queuing Telemetry Transport (MQTT) protocol over a transport-layer security (TLS) connection to a message broker, and the data is subsequently ingested by one or more cloud-based processing services and databases. In some embodiments, communication between sensors, edge devices, controllers, and cloud systems is secured using encryption, authentication, or mutual authentication mechanisms. In some embodiments, mutual authentication may be performed using client-side digital certificates and cryptographic signing.

[0044] In some embodiments, a direct temperature sensor embedded within the PCM is not required. In some embodiments, a temperature sensor positioned within a predetermined distance from the PCM is used as a proxy for PCM temperature. For example, in one embodiment, a temperature sensor mounted less than approximately one meter from a PCM enclosure is used to infer the thermal state of the PCM. Such proxy temperature measurements are sufficient for estimating state-of-charge due to thermal coupling between the PCM and the surrounding environment.

[0045] In some embodiments, current deployments utilize surface-mounted temperature sensors positioned on walls or ceilings are a primary configuration, while probe-based temperature sensors inserted into the PCM remain supported as an alternative configuration. For example, surface-mounted temperature sensors installed on walls or ceilings of a temperature-controlled environment provide primary temperature inputs for state-of-charge estimation, while probe-based sensors configured for insertion into the PCM are optionally supported for higher-resolution core temperature measurements. In some embodiments, surface-mounted temperature sensing is preferred due to ease of installation, reduced invasiveness, and sufficient accuracy for TES state-of-charge determination. In some embodiment, no temperature sensors are physically embedded within the PCM, and proxy temperature measurements are used in place of direct PCM temperature sensing.

[0046] The temperature sensors can send data on the real-time temperature of individual PCM / heat sink units, or the overall temperature on a collection of those units, to destinations including but not limited to a BMS either hosted locally or in the cloud, an Internet of Things (IOT) platform, an automation software hosted either locally or in the cloud, or to an HVAC or Refrigeration Application Programming Interface (API). In some embodiments, data is transmitted using standard communication protocols including LoRaWAN, WiFi, MQTT over TLS, or Modbus RTU. It may also be made available via an API to developers of other technologies through REST-based or message-driven application programming interfaces.

[0047] A temperature sensor can be mounted in the same places described above, and connected via hardwiring to a local computer. That local computer can include, but is not limited to, an edge computer running advanced HVAC or Refrigeration control, a HVAC or Refrigeration analog controller, or an IoT device that collects data to be sent to other local or cloud destinations, APIs, or automation software as described above. In some embodiments, such local computers communicate with cloud-based systems using secure messaging protocols including MQTT over TLS with certificate-based authentication.

[0048] Another way to monitor temperature is via a detached thermal sensor. These sensors can register temperatures of specific points in space from large distances (several inches or feet). Instead of direct contact, these sensors can collect similar temperature information as a physical sensor and can integrate via hardwiring or over the air (WiFi, Bluetooth, etc.) to an endpoint comprising a local and cloud hosted IoT platform, a BMS, or a HVAC and / or Refrigeration control software. In some embodiments, detached thermal sensors include thermal imaging sensors configured to capture temperature data across a field of view.

[0049] In some embodiments, sensor data is transmitted to a cloud-based or local computing system using event streaming services, message brokers, or IoT platforms for storage, analysis, or control. In some embodiments, detached thermal sensors are used to supplement or replace contact-based temperature sensors, particularly in environments where physical installation of contact sensors is impractical or undesirable.

[0050] It should be understood that the sensor configurations described herein are illustrative and non-limiting, and that additional, fewer, or alternative sensors may be used in other embodiments depending on TES chemistry, packaging, environmental conditions, and control requirements.

[0051] Step 104 may include determining a direction of thermal energy transfer between the PCM and the temperature-controlled environment surrounding the PCM based on a comparison of the temperature of the PCM and the temperature of the environment surrounding the PCM.

[0052] In some embodiments, a software of the present disclosure processes temperature data from multiple sensors associated with the TES system. In some embodiments, the software aggregates temperature data from the multiple sensors to account for a spatial distribution of thermal energy within the TES system or a collection of TES units. In some embodiments, the aggregation comprises calculating an average temperature for general monitoring, identifying a minimum temperature to detect a coldest region, and identifying a maximum temperature to determine a limiting factor for the state-of-charge. In some embodiments, the aggregation further comprises applying weighted averages based on sensor location or performing spatial modeling to estimate charge distribution across a bank of TES units. For example, sensors positioned closer to a core region of a TES unit are assigned higher weighting when estimating the state-of-charge.

[0053] In some embodiments, the software determines a temperature associated with a surrounding environment of the TES system using one or more environmental temperature sensors. In some embodiments, the surrounding environment temperature corresponds to an air temperature within a temperature-controlled space in which the TES system is installed. In some embodiments the surrounding environment temperature corresponds to one or more product temperature measurements obtained from product temperature sensors. In some embodiments, the surrounding environment temperature is determined using an average of multiple room-level temperature sensors. In some embodiments, the surrounding environment temperature is determined for multiple zones within the temperature-controlled space. In some embodiments, the multiple zones may comprise locations proximate to an evaporator, a central region of the space, one or more access points, or the TES system. In some embodiments, the air temperature measurement may be used as a primary input. In some embodiments, the product temperature measurement may be used as a secondary input. In some embodiments, the air temperature measurement and the product temperature measurement are selected in combination to determine a surrounding environment temperature associated with the TES system.

[0054] In some embodiments, the software determines a direction of thermal energy transfer between the TES system and a surrounding environment based on a comparison between a temperature associated with the TES system and a temperature associated with the surrounding environment. In some embodiments, the TES system is determined to be discharging when the surrounding environment temperature is lower than the TES-associated temperature, charging when the surrounding environment temperature is higher than the TES-associated temperature, and in an equilibrium state when the temperatures are substantially equal, such that no net thermal energy transfer is occurring. In some embodiments, the TES-associated temperature is obtained from one or more temperature sensors associated with a PCM, and the surrounding environment temperature is obtained from one or more environmental temperature sensors. For example, when a room air temperature exceeds a PCM temperature, the software determines that the TES system is charging by absorbing thermal energy from refrigeration equipment, and when the PCM temperature exceeds the room air temperature, the software determines that the TES system is discharging stored thermal energy to the environment.

[0055] Step 106 may include generating state-of-charge information by estimating a state-of-charge of the thermal energy storage system using a first model that maps temperature, time, or a combination thereof to a quantity of thermally stored energy in the PCM, wherein the first model comprises a physics-based simulation that incorporates thermal properties of the PCM, and estimates the quantity of thermally stored energy based on temperature change over time during charging or discharging of the PCM. In some embodiments, the thermal properties of the PCM comprise specific heat, latent heat, or heat transfer rate. In some embodiments, the first model and / or the second model is executed on a cloud-based computing system independently from the thermal energy storage system. In some embodiments, execution of the first model and / or the second model is distributed across an edge computing device and a cloud-based computing system, such that at least a portion of the model execution is performed locally to support low-latency or safety-critical functions, and at least a portion of the model execution is performed in the cloud to support compute-intensive modeling, optimization, or learning.

[0056] In some embodiments, the disclosure provides a hardware-based monitoring system configured to determine a state-of-charge of a thermal energy storage (TES) system. The TES system may comprise a single thermal storage unit or a collection of thermal storage units, including but not limited to PCMs (PCMs) or other thermal energy sink units. In some embodiments, the monitoring system may comprise one or more sensing technologies, comprising internet-connected temperature sensors, hardwired temperature sensors, humidity sensors, electrical energy meters, current sensors, door or access sensors, image sensors, thermal imaging sensors, or pressure sensors.

[0057] In some embodiments, the state-of-charge of the TES system is determined based on temperature measurements. Temperature is a principal indicator of the state-of-charge of a PCM-based TES system, as the phase state of the PCM corresponds to the amount of thermal energy stored. In some embodiments, temperature data and a rate of temperature change may be used as inputs to a physics-based model or digital twin to evaluate the current state-of-charge given a prevailing cooling or heating condition.

[0058] In some embodiments, temperature measurements used for monitoring a thermal energy storage system comprise contact-based temperature measurements or non-contact temperature measurements. In some embodiments, contact-based temperature measurements are obtained from one or more temperature sensors that are physically coupled to, embedded in, or in direct thermal contact with a component of the thermal energy storage system or a surrounding environment. In some embodiments, non-contact temperature measurements are obtained from one or more sensors configured to measure temperature without direct physical contact with the thermal energy storage system, including sensors that infer temperature from emitted radiation, reflected signals, or other remote sensing techniques.

[0059] In some embodiments, the software of the present disclosure estimates a state-of-charge of the TES system by modeling a relationship between a temperature differential and stored thermal energy. In some embodiments, the software applies a regression model, a computational model, an artificial intelligence model, or a combination thereof, to represent how the TES system absorbs or releases thermal energy as a function of the temperature differential. In some embodiments, the relationship between temperature differential and stored thermal energy follows a linear, exponential, polynomial, or other non-linear behavior depending on properties of the PCM. In some embodiments, the software compares real-time sensor data to the modeled relationship to estimate a current state-of-charge corresponding to an amount of thermal energy stored in the TES system.

[0060] In some embodiments, a pressure measurement obtained from within a thermal energy storage (TES) container may be used to estimate a state-of-charge of the TES system. In some embodiments, the PCM expands during charging and contracts during discharging, with a magnitude of expansion or contraction that may follow a linear, exponential, polynomial, or other non-linear relationship as thermal energy is absorbed or released. In some embodiments, expansion and contraction of the PCM causes corresponding changes in pressure exerted on a rigid external encapsulation, and the pressure changes are indicative of a volumetric change of the PCM.

[0061] In some embodiments, the software models a relationship between volumetric change of the PCM and stored thermal energy using one or more regression models or other computational models. In some embodiments, real-time pressure data received from one or more pressure sensors is applied to the modeled relationship to estimate volumetric change and infer a state-of-charge of the PCM corresponding to an amount of stored thermal energy.

[0062] In some embodiments, where the PCM is not enclosed within a rigid external encapsulation, volumetric changes of the PCM are detected using one or more image sensors. In some embodiments, the image sensors comprise optical or imaging devices configured to capture visual data indicative of expansion or contraction of a flexible PCM enclosure. In some embodiments, the software may apply a computer vision model to track changes in volume over time and to estimate a state-of-charge based on the detected volumetric changes.

[0063] In some embodiments, data obtained from pressure sensors, image sensors, or combinations thereof is processed locally, remotely, or in a distributed manner to estimate the state-of-charge of the TES system based on inferred volumetric behavior of the PCM.

[0064] Step 108 may include refining the state-of-charge information by inputting an output of the first model to a second model, wherein the second model comprises a regression model that calibrates the state-of-charge information using data obtained during one or more charging cycle. In some embodiments, the one or more charging cycle is selected from a controlled charge cycle and a controlled discharge cycle.

[0065] In some embodiments, the non-contact temperature measurements are calibrated against the contact-based temperature measurements to apply correction factors that improve measurement accuracy. In some embodiments, calibration of temperature sensors used for monitoring the thermal energy storage system does not require a formal cross-calibration procedure between different sensor types. In some embodiments, a temperature probe associated with a controller is designated as a primary reference sensor for temperature measurement, and one or more additional sensors provide supplementary temperature coverage. For example, in one embodiment, a controller-connected temperature probe provides contact-based temperature measurements used as a primary reference input, while one or more IoT-based temperature sensors provide non-contact or indirect temperature measurements that provide additional spatial coverage without direct calibration against each other.

[0066] In some embodiments, temperature data from multiple sensors positioned at proximate locations is compared to validate measurement consistency. In some embodiments, discrepancies between temperature readings exceeding a predetermined threshold are identified and flagged for further analysis or investigation. In some embodiments, automated data quality validation is performed during data ingestion to detect anomalous or inconsistent sensor readings.

[0067] For example, temperature readings from sensors positioned at proximate locations are compared, and differences greater than about ±1° C. are automatically flagged for investigation. In some embodiments, an automated validation logic may be executed at ingestion time to detect out-of-range values or inconsistent sensor behavior before downstream processing.

[0068] In some embodiments, calibration data associated with one or more temperature sensors is stored and applied to adjust temperature measurements generated by the one or more temperature sensors. In some embodiments, the calibration data comprises one or more calibration offsets stored on a per-device basis, wherein the one or more calibration offsets are applied to sensor readings during processing. In some embodiments, the calibration data is maintained in controller registers or other configuration memory locations.

[0069] In some embodiments, a calibration procedure may comprise calibrating one or more temperature sensors against a reference thermometer. In some embodiments, the calibration procedure may generate one or more calibration offsets, wherein the one or more calibration offsets are stored and subsequently applied to improve measurement accuracy across different sensor types.

[0070] In some embodiments, thermal energy storage monitoring is performed using one or more contact-based temperature sensors. In some embodiments, the thermal energy storage monitoring further comprises deploying one or more thermal imaging sensors to provide spatial temperature information to improve the thermal energy storage monitoring or state-of-charge estimation. For example, the one or more contact-based temperature sensors provide primary inputs for state-of-charge estimation, while the one or more thermal imaging sensors are used to capture spatial temperature distributions across a thermal energy storage installation. In some embodiments, the one or more thermal imaging sensors are configured to capture temperature data across a field of view within a temperature-controlled environment to provide spatial temperature information associated with a thermal energy storage component and its surroundings, and to transmit the temperature data via wired or wireless communication to the same destinations as other temperature sensors described herein.

[0071] In some embodiments, temperature measurement methods are used to determine a state-of-charge of a thermal energy storage (TES) system. In some embodiments, the state-of-charge is determined by comparing a temperature associated with the TES system to a temperature of a surrounding environment. In some embodiments, the TES system is determined to be in a fully charged state when the temperature associated with the TES system is lower than the temperature of the surrounding environment. In some embodiments, the TES system is determined to be in a fully discharged state when the temperature associated with the TES system is higher than the temperature of the surrounding environment. In some embodiments, the TES system is determined to be in a charging or discharging state when the temperature associated with the TES system is substantially equal to the temperature of the surrounding environment. For example, a PCM that is colder than a cold room environment is identified as discharging stored thermal energy into the environment.

[0072] In some embodiments, the state-of-charge of the TES system is estimated using a time-and temperature-based approach. In some embodiments, the time-and temperature-based approach estimates the state-of-charge based on a duration of operation of a cooling asset during a charging period and a duration of thermal discharge while the cooling asset is inactive. In some embodiments, compressor runtime may be used as a proxy for energy input to charge the TES system, and a duration over which the TES system maintains an environmental temperature without active cooling is used as a proxy for discharge capacity. In some embodiments, a longer compressor runtime corresponds to a higher estimated state-of-charge, and a longer discharge duration corresponds to greater usable thermal storage capacity. In some embodiments, the time-and temperature-based approach is calibrated using controlled operating tests. In some embodiments, calibration comprises operating a cooling system under closed-door conditions for predefined charging durations and measuring a corresponding duration over which the TES system maintains an environmental temperature near a phase change temperature during discharge. In some embodiments, calibration data may establish a relationship between charging duration and discharge capacity. For example, a cooling system is operated for multiple predefined charging periods, and a resulting temperature hold time during discharge is measured to correlate charging time with thermal storage capacity.

[0073] In some embodiments, the state-of-charge of the TES system may be estimated using a physics-based thermal model. In some embodiments, the physics-based thermal model incorporates thermal properties of a PCM (e.g., specific heat and heat transfer characteristics). In some embodiments, the physics-based thermal model estimates the state-of-charge based on observed temperature decay curves over time. For example, a customized thermal simulation model is used to map time-temperature behavior of a thermal storage tank to an estimated state-of-charge.

[0074] In some embodiments, a relationship between the temperature differential and the state-of-charge of the TES system is calibrated using a controlled charge and discharge procedure. In some embodiments, the controlled charge and discharge procedure comprises fully discharging the TES system to a baseline condition in which the TES system reaches thermal equilibrium with a surrounding environment and recording an initial discharged temperature. In some embodiments, the controlled charge and discharge procedure comprises operating a cooling system at a predefined setpoint for a charging period. In some embodiments, the controlled charge and discharge procedure may occur while logging TES-associated temperature data, surrounding environment temperature data, operational state data of the cooling system, or energy consumption data. For example, the TES system is charged under closed-door conditions while temperature and compressor runtime data are recorded at predefined intervals.

[0075] In some embodiments, the controlled charge and discharge procedure further comprises disabling active cooling to allow the TES system to discharge while logging a temperature change of the TES system and the surrounding environment over time. In some embodiments, discharge data may comprise a duration over which the TES system maintains the surrounding environment temperature near a phase change temperature. In some embodiments, calibration data obtained from the charging and discharging procedure is used to fit a regression model that maps temperature trajectories to a state-of-charge of the TES system. In some embodiments, the regression model maps energy input during charging to temperature change and discharge duration to determine usable thermal storage capacity, charge rate, discharge rate, and phase change plateau characteristics. In some embodiments, the software derives a relationship between compressor runtime, temperature decay curves, and usable thermal capacity to calibrate subsequent state-of-charge estimates.

[0076] Step 110 may include updating the state-of-charge information over time by reapplying the first model and the second model to newly received sensor data. In some embodiments, updating the state-of-charge information over time comprises periodically recalculating the state-of-charge information at predetermined time intervals. In some embodiments, the state-of-charge information comprises a numerical representation of a percentage of thermal energy capacity remaining in the PCM.

[0077] In some embodiments, the thermal energy storage system further comprises a software for processing data collected from one or more hardware components described herein. In some embodiments, the software is executed on one or more computing systems comprising local computing devices, remote computing devices, cloud-based computing systems, or combinations thereof. In some embodiments, the software receives sensor data as input and applies the first model, the second model, or a combination thereof to estimate a state-of-charge of a thermal energy storage (TES) system within a temperature-controlled environment. In some embodiments, the first model and the second model comprise regression models, machine learning models, artificial intelligence models, or combinations thereof.

[0078] In some embodiments, the state-of-charge information further comprises a confidence metric, uncertainty range, or quality indicator associated with the estimated state-of-charge. In some embodiments, the confidence metric is derived from sensor availability, sensor agreement, model fit, or historical performance of the first model and the second model. In some embodiments, the confidence metric is used by the software to influence control decisions, modify operating thresholds, trigger fallback behavior, or generate notifications for an operator or external control system.

[0079] In some embodiments, the software adjusts one or more of the first model or the second model over time based on observed changes in thermal behavior of the thermal energy storage system to account for degradation, aging, or performance drift of a PCM or associated hardware. In some embodiments, when sensor data is unavailable, incomplete, or determined to be unreliable, the software estimates the state-of-charge using historical data, default assumptions, or reduced-order models. In some embodiments, estimation and updating of the state-of-charge are performed periodically and, in other embodiments, are triggered in response to detected events including temperature threshold crossings, compressor state changes, or environmental disturbances. In some embodiments, optimization or scheduling of charging or discharging further incorporates external signals including energy price data, demand response events, or grid-related constraints.

[0080] Step 112 may include transmitting the state-of-charge information to a heating, ventilation, air-conditioning, or refrigeration (HVAC&R) system associated with the temperature-controlled environment. In some embodiments, transmitting the state-of-charge information is performed with latency below a predefined threshold such that the transmission enables real-time control of the HVAC&R system. In some embodiments, transmitting the state-of-charge information comprises transmitting the state-of-charge information via an application programming interface to a BMS.

[0081] In some embodiments, once the state-of-charge of the TES system is estimated, the software communicates the estimated state-of-charge to one or more external control systems. In some embodiments, the external control systems comprise BMS, heating, ventilation, air-conditioning, or refrigeration (HVAC&R) control systems, or combinations thereof. In some embodiments, the software provides control signals or recommendations to the external control systems to activate, deactivate, or modulate one or more assets to charge or discharge the TES system. For example, the software may communicate a recommendation to delay compressor activation while the TES system is discharging stored thermal energy.

[0082] In some embodiments, communication between the software and an HVAC&R controller is performed using a direct industrial communication interface. In some embodiments, the industrial communication interface comprises a serial communication protocol. In some embodiments, a commands generated by the software is written directly to a register of a temperature controller or programmable logic controller. For example, the software may communicate with an HVAC&R controller via a Modbus RTU connection over an RS-485 interface and write control values to a controller register.

[0083] In some embodiments, the software is integrated with a BMS via an application programming interface. In some embodiments, the software transmits state-of-charge information and related operational data to the Building Management System for visualization, logging, or supervisory control. In some embodiments, the data transmitted to the BMS comprises a numerical or categorical representation of the TES state-of-charge, a current temperature associated with a PCM, an estimated time remaining in a current operating state, at least one recommended action, or at least one temperature limits.

[0084] In some embodiments, the software supports a standardized interface for third-party integration. In some embodiments, the standardized interface comprises an application programming interface or an industry-standard building automation protocol. In some embodiments, the software maps one or more thermal energy storage-related variables (e.g., state-of-charge information or temperature data) to corresponding points or objects defined by the standardized interface to enable integration with third-party systems or enterprise-scale building management platforms.

[0085] In some embodiments, estimation of the TES state-of-charge is performed by a cloud-based computing system. In some embodiments, a distributed computing architecture is used in which an edge computing device performs a local function and a cloud-based computing system performs a centralized function. In some embodiments, the edge computing device communicates with an HVAC&R controller using an industrial protocol, performs local data logging and buffering, monitors safety conditions in real time, and executes basic operational rules. In some embodiments, the edge computing device may override control commands if a measured temperature exceeds a predefined safety threshold. In some embodiments, the cloud-based computing system stores sensor data in one or more databases, executes artificial intelligence or machine learning models for state-of-charge estimation, performs optimization algorithms, and generates dashboards or reports. In some embodiments, the optimization algorithms comprise day-ahead planning algorithms configured to schedule charging or discharging of the TES system based on forecasted conditions. For example, the cloud-based computing system may execute an optimization algorithm that schedules TES charging during periods of lower energy cost.

[0086] In some embodiments, functions are divided between one or more local computing devices and one or more centralized computing systems based on latency, reliability, and computational requirements. In some embodiments, local functions executed by an edge computing device include safety-critical monitoring, real-time control, and fallback operation to support low-latency and network-independent behavior. In some embodiments, centralized functions executed by a cloud-based computing system include compute-intensive modeling, state-of-charge estimation, optimization, and analytics. In some embodiments, centralized functions associated with the centralized computing system enable cross-site analytics and model training across multiple thermal energy storage installations.

[0087] In some embodiments, a BMS receives an output generated by the centralized computing system as part of the centralized functions. In some embodiments, the BMS may use the output for monitoring, visualization, or supervisory control, without executing a computational model. In some embodiments, at least a portion of the state-of-charge estimation logic is executed by software components operating within an integration layer associated with the BMS to support local visualization, limited decision-making, or fallback operation when connectivity to the centralized computing system is unavailable.

[0088] Step 114 may include controlling, by the HVAC&R system, operation of a HVAC&R asset to selectively charge or discharge the thermal energy storage system while maintaining the temperature of the temperature-controlled environment surrounding the PCM within a predefined temperature range. In some embodiments, controlling operation of the HVAC&R asset comprises activating, deactivating, or throttling a component of the HVAC&R system, wherein the component of the HVAC&R system is a compressor or refrigeration component. In some embodiments, controlling operation of the HVAC&R asset comprises delaying activation of the HVAC&R asset while the thermal energy storage system discharges thermal energy. In some embodiments, controlling operation of the HVAC&R asset is performed by applying one or more operational rules to the state-of-charge information, wherein the one or more operating thresholds comprise a state-of-charge threshold, a temperature threshold, or a safe operating limit of the temperature-controlled environment. In some embodiments, maintaining the temperature of the temperature-controlled environment comprises maintaining the temperature within limits suitable for a cold storage or a freezer environment.

[0089] In some embodiments, multiple hardware sensing modalities are operated concurrently to provide complementary data indicative of the state-of-charge of the TES system. In some embodiments, data collected by the hardware components described herein is processed locally, remotely, or in a distributed manner. In some embodiments, the combination of the hardware components and associated software constitutes a thermal energy management system configured to monitor, estimate, and manage the state-of-charge of the TES system.

[0090] In an alternative embodiment, the system comprises at least one computing node within an authorized network of computing nodes forming a distributed thermal energy management and control infrastructure. Each computing node includes a computer-readable storage medium having program instructions embodied therewith, the instructions executable by a processor to cause the processor to perform operations comprising: receiving, from another authorized node, a thermal energy status record or control instruction request; verifying the authenticity of the request based on a stored cryptographic key associated with the submitting device or system; determining, by state comparison, that the current state of thermal energy storage data stored on the computing node matches the state maintained by at least one other node; validating the thermal energy status or control instruction based on the verified state; and authorizing propagation or execution of the thermal energy status update or control instruction within the distributed thermal energy management system. In some embodiments, the state validation and authorization steps are coordinated through a thermal energy management controller module described herein, ensuring that data integrity, consistency, and execution order are maintained across all nodes within the cloud-based and edge-based orchestration framework.

[0091] Referring now to FIG. 2, a schematic of an example of a computing node is shown. Computing node 210 is only one example of a suitable computing node and is not intended to suggest any limitation as to the scope of use or functionality of embodiments described herein. Regardless, computing node 210 is capable of implementing and / or performing any of the functionalities set forth hereinabove.

[0092] In computing node 210 there is a computer system / server 212, which is operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well-known computing systems, environments, and / or configurations that may be suitable for use with computer system / server 212 include, but are not limited to, personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing environments that include any of the above systems or devices, and the like.

[0093] Computer system / server 212 may be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, and so on that perform particular tasks or implement particular abstract data types. Computer system / server 212 may be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computer system storage media including memory storage devices.

[0094] As shown in FIG. 2, computer system / server 212 in computing node 210 is shown in the form of a general-purpose computing device. The components of computer system / server 212 may include, but are not limited to, one or more processors or processing units 216, a system memory 228, and a bus 218 that couples various system components including system memory 228 to processor 216.

[0095] Bus 218 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, Peripheral Component Interconnect (PCI) bus, Peripheral Component Interconnect Express (PCIe), and Advanced Microcontroller Bus Architecture (AMBA).

[0096] Computer system / server 212 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer system / server 212, and it includes both volatile and non-volatile media, removable and non-removable media.

[0097] System memory 228 can include computer system readable media in the form of volatile memory, such as random access memory (RAM) 230 and / or cache memory 222. Computer system / server 212 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 224 can be provided for reading from and writing to a non-removable, non-volatile magnetic media (not shown and typically called a “hard drive”). Although not shown, a magnetic disk drive for reading from and writing to a removable, non-volatile magnetic disk (e.g., a “floppy disk”), and an optical disk drive for reading from or writing to a removable, non-volatile optical disk such as a CD-ROM, DVD-ROM or other optical media can be provided. In such instances, each can be connected to bus 218 by one or more data media interfaces. As will be further depicted and described below, memory 228 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the disclosure.

[0098] Program / utility 240, having a set (at least one) of program modules 242, may be stored in memory 228 by way of example, and not limitation, as well as an operating system, one or more application programs, other program modules, and program data. Each of the operating system, one or more application programs, other program modules, and program data or some combination thereof, may include an implementation of a networking environment. Program modules 242 generally carry out the functions and / or methodologies of embodiments as described herein.

[0099] Computer system / server 212 may also communicate with one or more external devices 23 such as a keyboard, a pointing device, a display 224, etc.; one or more devices that enable a user to interact with computer system / server 212; and / or any devices (e.g., network card, modem, etc.) that enable computer system / server 212 to communicate with one or more other computing devices. Such communication can occur via Input / Output (I / O) interfaces 222. Still yet, computer system / server 212 can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and / or a public network (e.g., the Internet) via network adapter 220. As depicted, network adapter 220 communicates with the other components of computer system / server 212 via bus 218. It should be understood that although not shown, other hardware and / or software components could be used in conjunction with computer system / server 212. Examples, include, but are not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc.

[0100] The present disclosure may be embodied as a system, a method, and / or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present disclosure.

[0101] The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

[0102] Computer readable program instructions described herein can be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and / or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device.

[0103] Computer readable program instructions for carrying out operations of the present disclosure may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.

[0104] Aspects of the present disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer readable program instructions.

[0105] These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and / or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function / act specified in the flowchart and / or block diagram block or blocks.

[0106] The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0107] The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flowchart illustration, and combinations of blocks in the block diagrams and / or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.

[0108] The descriptions of the various embodiments of the present disclosure have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for managing a thermal energy storage system, the method comprising:reading sensor data from the thermal energy storage system, the sensor data comprising temperature data corresponding to a phase change material and temperature data corresponding to a temperature-controlled environment surrounding the phase change material;determining a direction of thermal energy transfer between the phase change material and the temperature-controlled environment surrounding the phase change material based on a comparison of the temperature of the phase change material and the temperature of the environment surrounding the phase change material;generating state-of-charge information by estimating a state-of-charge of the thermal energy storage system using a first model that maps temperature, time, or a combination thereof to a quantity of thermally stored energy in the phase change material, wherein the first model comprises a physics-based simulation that incorporates thermal properties of the phase change material, and estimates the quantity of thermally stored energy based on temperature change over time during charging or discharging of the phase change material;refining the state-of-charge information by inputting an output of the first model to a second model, wherein the second model comprises a regression model that calibrates the state-of-charge information using data obtained during one or more charging cycle;updating the state-of-charge information over time by reapplying the first model and the second model to newly received sensor data;transmitting the state-of-charge information to a heating, ventilation, air-conditioning, or refrigeration (HVAC&R) system associated with the temperature-controlled environment; andcontrolling, by the HVAC&R system, operation of a HVAC&R asset to selectively charge or discharge the thermal energy storage system while maintaining the temperature of the temperature-controlled environment surrounding the phase change material within a predefined temperature range.

2. The method of claim 1, wherein the phase change material comprises a material that undergoes a solid-liquid phase transition within an operating temperature range of the temperature-controlled environment.

3. The method of claim 1, wherein the sensor data comprises temperature data measured at a location proximate to the phase change material.

4. The method of claim 1, wherein the sensor data comprises temperature data measured by a surface-mounted temperature sensor.

5. The method of claim 1, wherein the sensor data comprises temperature data measured by a probe inserted into the phase change material.

6. The method of claim 1, wherein the sensor data further comprises humidity data, energy consumption data, or environmental disturbance data indicative of heat ingress.

7. The method of claim 1, wherein the thermal properties of the phase change material comprise specific heat, latent heat, or heat transfer rate.

8. The method of claim 1, wherein the one or more charging cycle is selected from a controlled charge cycle and a controlled discharge cycle.

9. The method of claim 1, wherein the first model and / or the second model is executed on a cloud-based computing system independently from the thermal energy storage system.

10. The method of claim 1, wherein transmitting the state-of-charge information is performed with latency below a predefined threshold such that the transmission enables real-time control of the HVAC&R system.

11. The method of claim 1, wherein updating the state-of-charge information over time comprises periodically recalculating the state-of-charge information at predetermined time intervals.

12. The method of claim 1, wherein the state-of-charge information comprises a numerical representation of a percentage of thermal energy capacity remaining in the phase change material.

13. The method of claim 1, wherein the state-of-charge information comprises a categorical indication selected from charged, charging, discharging, and discharged.

14. The method of claim 1, wherein transmitting the state-of-charge information comprises transmitting the state-of-charge information via an application programming interface to a building management system.

15. The method of claim 1, wherein controlling operation of the HVAC&R asset comprises activating, deactivating, or throttling a component of the HVAC&R system, wherein the component of the HVAC&R system is a compressor or refrigeration component.

16. The method of claim 1, wherein controlling operation of the HVAC&R asset comprises delaying activation of the HVAC&R asset while the thermal energy storage system discharges thermal energy.

17. The method of claim 1, wherein controlling operation of the HVAC&R asset is performed by applying one or more operational rules to the state-of-charge information, wherein the one or more operating thresholds comprise a state-of-charge threshold, a temperature threshold, or a safe operating limit of the temperature-controlled environment.

18. The method of claim 1, wherein maintaining the temperature of the temperature-controlled environment comprises maintaining the temperature within limits suitable for a cold storage or a freezer environment.

19. A system comprising:a thermal energy storage system comprising a phase change material;one or more sensors configured to generate temperature data corresponding to the phase change material and a temperature-controlled environment surrounding the phase change material;a heating, ventilation, air-conditioning, or refrigeration (HVAC&R) system associated with the temperature-controlled environment; anda controller comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor of the computing node to cause the processor to perform a method comprising:reading sensor data from the thermal energy storage system, the sensor data comprising temperature data corresponding to a phase change material and temperature data corresponding to a temperature-controlled environment surrounding the phase change material;determining a direction of thermal energy transfer between the phase change material and the temperature-controlled environment surrounding the phase change material based on a comparison of the temperature of the phase change material and the temperature of the environment surrounding the phase change material;generating state-of-charge information by estimating a state-of-charge of the thermal energy storage system using a first model that maps temperature, time, or a combination thereof to a quantity of thermally stored energy in the phase change material, wherein the first model comprises a physics-based simulation that incorporates thermal properties of the phase change material, and estimates the quantity of thermally stored energy based on temperature change over time during charging or discharging of the phase change material;refining the state-of-charge information by inputting an output of the first model to a second model, wherein the second model comprises a regression model that calibrates the state-of-charge information using data obtained during one or more charging cycles;updating the state-of-charge information over time by reapplying the first model and the second model to newly received sensor data;transmitting the state-of-charge information to a heating, ventilation, air-conditioning, or refrigeration (HVAC&R) system associated with the temperature-controlled environment; andcontrolling, by the HVAC&R system, operation of a HVAC&R asset to selectively charge or discharge the thermal energy storage system while maintaining the temperature of the temperature-controlled environment surrounding the phase change material within a predefined temperature range.

20. A computer program product, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions being executable by a processor to cause the processor to perform a method comprising:reading sensor data from the thermal energy storage system, the sensor data comprising temperature data corresponding to a phase change material and temperature data corresponding to a temperature-controlled environment surrounding the phase change material;determining a direction of thermal energy transfer between the phase change material and the temperature-controlled environment surrounding the phase change material based on a comparison of the temperature of the phase change material and the temperature of the environment surrounding the phase change material;generating state-of-charge information by estimating a state-of-charge of the thermal energy storage system using a first model that maps temperature, time, or a combination thereof to a quantity of thermally stored energy in the phase change material, wherein the first model comprises a physics-based simulation that incorporates thermal properties of the phase change material, and estimates the quantity of thermally stored energy based on temperature change over time during charging or discharging of the phase change material;refining the state-of-charge information by inputting an output of the first model to a second model, wherein the second model comprises a regression model that calibrates the state-of-charge information using data obtained during one or more charging cycles;updating the state-of-charge information over time by reapplying the first model and the second model to newly received sensor data;transmitting the state-of-charge information to a heating, ventilation, air-conditioning, or refrigeration (HVAC&R) system associated with the temperature-controlled environment; andcontrolling, by the HVAC&R system, operation of a HVAC&R asset to selectively charge or discharge the thermal energy storage system while maintaining the temperature of the temperature-controlled environment surrounding the phase change material within a predefined temperature range.

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