Virtual sensor for estimating charge state and health state in thermal energy storage

Abstract

A thermal management system is disclosed that provides a virtual sensor that estimates a charge state and / or a health state of an integrated thermal energy storage module. A physical sensor of the thermal management system measures values of a plurality of parameters of the thermal management system. A controller and / or cloud backend of the thermal management system implements one or more models or algorithms to provide a virtual sensor that estimates a charge state and / or health state of the integrated thermal energy storage module based on the measured parameters. The estimated charge state and health state of the thermal energy storage module provides additional useful information to a user or owner of the thermal management system and enables a controller to better control the thermal management system.

Description

Technical Field

[0001] The devices and methods disclosed in this document relate to thermal energy storage, and more specifically to estimating the state of charge and health of thermal energy storage. Background Technology

[0002] Unless otherwise stated herein, the material described in this section is not acknowledged as prior art simply because it is included in this section.

[0003] Increased deployment of renewable energy requires energy storage to accommodate the growing mismatch between times of high energy demand (e.g., cooling homes in the late afternoon / evening of summer) and times of high power generation rates (e.g., from wind farms that tend to peak at night or photovoltaics that peak at midday).

[0004] Thermal energy storage is a relatively inexpensive, safe, and convenient method of energy storage. It is anticipated that existing heat pump systems will begin to incorporate thermal energy storage to enhance energy efficiency and optimize performance by decoupling heating and cooling demands from fluctuations in energy supply. Such systems can utilize advanced thermal energy storage technologies, such as phase change materials or high-capacity water tanks, which will allow thermal energy to be stored during off-peak periods and released later when demand peaks.

[0005] Similar to battery energy storage, algorithms or sensors used to estimate the state of charge and state of health are crucial for the operation of thermal energy storage. However, measuring the state of charge and state of health of thermal energy storage presents several challenges. In particular, conventional sensors may struggle to capture the dynamic properties of energy storage, especially in systems employing phase change materials or other complex thermal storage media. Furthermore, such conventional sensors increase the cost and complexity of systems incorporating thermal energy storage. Summary of the Invention

[0006] A method for monitoring a thermal energy storage module in a thermal management system is disclosed. The method includes measuring the value of at least one parameter of the thermal management system using at least one sensor. The method further includes determining at least one of the following based on the measured value of the at least one parameter of the thermal management system using at least one processor: (i) the charging state of the thermal energy storage module or (ii) the health state of the thermal energy storage module. Attached Figure Description

[0007] The foregoing aspects and other features of the system and method are explained in the following description in conjunction with the accompanying drawings.

[0008] Figure 1 An overview of the workflow for providing virtual charge status and health status sensors in a thermal management system with an integrated thermal energy storage module is shown.

[0009] Figure 2 An exemplary component of a heat pump system for a building with integrated thermal energy storage is shown.

[0010] Figure 3 A flowchart is shown for a method of monitoring thermal energy storage modules in a thermal management system.

[0011] Figure 4 An exemplary graphical user interface is shown for using a mobile application of a mobile electronic device to monitor the charge status of a thermal energy storage module. Detailed Implementation

[0012] For the purpose of promoting an understanding of the principles of this disclosure, reference will now be made to embodiments illustrated in the accompanying drawings and described in the following written description. It should be understood that this is not intended to limit the scope of the disclosure. It should also be understood that this disclosure includes any changes and modifications to the illustrated embodiments, and includes further applications of the principles of this disclosure that would commonly occur to those skilled in the art to which this disclosure pertains.

[0013] Overview Figure 1 An overview of the workflow for providing virtual charge status and health status sensors in a thermal management system with an integrated thermal energy storage module is shown. In exemplary embodiments of this disclosure, the thermal management system is a heat pump system for heating and cooling buildings. However, although the features of this disclosure are described primarily with respect to heat pump systems, it should be understood that the thermal management system may similarly include an air conditioner for cooling buildings, a thermal management system for heating or cooling motor vehicles, or any other thermal management system operating on similar principles, such as a refrigerator or freezer.

[0014] Many existing thermal management systems, such as those with Internet of Things (IoT) capabilities, incorporate various sensors for monitoring the status of the thermal management system. However, for thermal management systems with integrated thermal energy storage modules, estimating the charge or health status of the thermal energy storage module can be challenging, and directly measuring the charge or health status using additional sensors can increase the cost of the thermal management system.

[0015] This document discloses a thermal management system and method for providing virtual sensors that estimate the charge status and / or health status of an integrated thermal energy storage module. Specifically, physical sensors (box 10) of the thermal management system measure values ​​of multiple parameters of the thermal management system, such as compressor frequency, indoor temperature, outdoor temperature, refrigerant flow rate, etc. The controller of the thermal management system and / or the cloud backend implements one or more models or algorithms to provide virtual sensors (box 20) that estimate the charge status and / or health status of the integrated thermal energy storage module.

[0016] In some embodiments, the controller of the thermal management system operates (box 30) at least one component of the thermal management system in a manner dependent on an estimated state of charge or an estimated state of health. The estimated state of charge and state of health of the thermal energy storage module provide additional useful information to the user or owner of the thermal management system and enable better control of the thermal management system. For example, the thermal management system can be operated based on the estimated state of charge to more effectively balance energy supply and demand, thereby optimizing comfort and reducing energy costs. Additionally, the thermal management system can be operated based on the estimated state of health to ensure reliable operation, extend the lifespan of the thermal energy storage module, and maintain optimal energy efficiency. Furthermore, in some embodiments, the user or owner of the thermal management system can remotely monitor and control (box 40) the thermal management system via a mobile application or web application.

[0017] An exemplary heat pump system with integrated thermal energy storage To provide a better understanding of the features of this disclosure, an exemplary thermal management system in the form of a heat pump system that incorporates thermal energy storage is described in detail.

[0018] Figure 2 Exemplary components of a heat pump system 100 for a building with integrated thermal energy storage are shown. In the example shown, the heat pump system 100 includes an external air heat exchanger 104, an internal air heat exchanger 108, a refrigerant circuit 112, a compressor 116, and an expander 120. Additionally, the heat pump system 100 includes a thermal energy storage module 130 or is integrated with a thermal energy storage module 130 via multiple on / off valves 140.

[0019] An external air heat exchanger 104 is configured to transfer heat between a first environment (i.e., including outside air 106) and refrigerant circulating through a refrigerant circuit 112. Structurally, in at least some embodiments, the external air heat exchanger 104 includes a series of wound metal tubes or fins (not shown) through which the refrigerant circulates, which increases the surface area for heat exchange and promotes efficient absorption or dissipation of heat energy. The external air heat exchanger 104 is disposed on the exterior of a building and, in at least some embodiments, is provided within an enclosure (not shown), such as a metal casing, to protect it from environmental factors. Additionally, in some embodiments, a fan is mounted inside the enclosure to blow air over the wound tubes or fins of the external air heat exchanger 104, thereby providing greater heat transfer.

[0020] An internal air heat exchanger 108 is configured to transfer heat between refrigerant circulating through a refrigerant circuit 112 and a second environment (i.e., including internal air 110). Structurally, in at least some embodiments, the internal air heat exchanger 108 includes a series of wound metal tubes or fins (not shown) through which the refrigerant circulates, which increases the surface area for heat exchange and promotes efficient absorption or dissipation of heat energy. The internal air heat exchanger 108 is arranged inside a building, and in at least some embodiments, within an indoor ventilation system, such that a fan installed in the ventilation system blows air over the wound tubes or fins of an external air heat exchanger 104 to distribute conditioned air throughout the building.

[0021] The refrigerant circuit 112 is a closed, continuous loop system that circulates refrigerant through various components of the heat pump system 100, thereby enabling the transfer of heat energy. In some embodiments, the refrigerant circuit 112 may more broadly take the form of a heat transport fluid circuit that circulates heat transport fluids, including refrigerant, water, ethylene glycol solution (antifreeze), etc. Therefore, references to refrigerant and refrigerant circuit 112 herein should be understood to alternatively incorporate any heat transport fluid. The refrigerant circuit 112 consists of piping connecting components of the heat pump system 100, including the external air heat exchanger 104, the internal air heat exchanger 108, the compressor 116, and the expander 120. The refrigerant flows through these components during circulation, undergoing a phase change between liquid and gas in some cases as it absorbs or releases heat. In addition, as will be discussed in more detail below, the refrigerant circuit 112 is also connected to the thermal energy storage module 130 via a plurality of switchable valves 140 to store thermal energy in the thermal energy storage module 130 and release thermal energy from the thermal energy storage module 130.

[0022] Compressor 116 is positioned in refrigerant circuit 112 along a first circulation path between internal air heat exchanger 108 and external air heat exchanger 104. Compressor 116 is configured to compress refrigerant and circulate refrigerant through refrigerant circuit 112. Compressor 116 includes a motor that typically uses electrical energy to compress the refrigerant to increase its pressure and temperature after the refrigerant has absorbed heat from elsewhere along refrigerant circuit 112. In some embodiments, heat pump system 100 includes multiple compressors 116.

[0023] An expander 120 is positioned in the refrigerant circuit 112 along a second circulation path between the internal air heat exchanger 108 and the external air heat exchanger 104, which is different from the first circulation path including the compressor 116. The expander 120 is configured to further regulate the pressure of the refrigerant as it moves through the refrigerant circuit 112. Specifically, the expander 120 includes an expansion valve or capillary tube configured to typically reduce the pressure and temperature of the refrigerant after it has released heat elsewhere along the refrigerant circuit 112. In some embodiments, the heat pump system 100 includes a plurality of expanders 120.

[0024] It should be understood that the illustrated embodiment of heat pump system 100 is in the form of an air-source heat pump. However, in alternative embodiments, heat pump system 100 may take the form of a ground-source (geothermal) heat pump or a water-source heat pump. Ground-source (geothermal) heat pumps transfer heat between a building and the ground or groundwater. These systems use an underground refrigerant loop that absorbs heat from the earth or releases heat back to the earth, which maintains a relatively constant temperature throughout the year. Similarly, water-source heat pumps exchange heat with a water tank in a building or with a body of water such as a lake, river, or well. These systems extract heat from the water for heating or release heat back into the water for cooling.

[0025] In any case, the heat pump system 100 advantageously includes a thermal energy storage module 130. The thermal energy storage module 130 is configured to store excess thermal energy for later release. Specifically, when the heat pump system 100 generates more thermal energy than immediately required, the thermal energy storage module 130 captures the excess thermal energy. For example, during periods of high heat pump efficiency or low demand, the heat pump system 100 can transfer excess thermal energy to the thermal energy storage module 130. When demand increases or the heat pump system 100 is not operating optimally, the stored thermal energy can be released back into the refrigerant circuit 112 to meet heating or cooling needs. This process helps balance the load, reduce peak energy consumption, and improve overall system efficiency.

[0026] The thermal energy storage module 130 typically comprises one or more insulated storage tanks (not shown) filled with a heat storage medium, such as water or a phase change material (TES). In the illustrated embodiment, the thermal energy storage module 130 includes a phase change material 134 and a TES heat exchanger 138. The TES heat exchanger 138 is connected to the refrigerant circuit 112 via a plurality of on / off valves 140, which are operated to direct refrigerant flow between the thermal energy storage module 130 and the remainder of the heat pump system 100. In some embodiments, the thermal energy storage module 130 has its own compressor or expander (not shown), making it easier to retrofit existing designs compared to compressors 116 and expanders 120 that rely entirely on the heat pump system 100. The compressor within the thermal energy storage module 130 can be specifically optimized for the thermal energy storage module 130 and can therefore be more efficient and less costly.

[0027] The phase change material 134 in the thermal energy storage module 130 is a substance that stores and releases thermal energy through a phase change (typically from solid to liquid or vice versa). It should be understood that the phase change material 134 may alternatively include any other thermal storage medium, such as water. When the heat pump system 100 generates excess thermal energy, the phase change material 134 can absorb the thermal energy and undergo a phase change, thereby effectively storing the thermal energy at a constant temperature. Conversely, when there is a demand for thermal energy, the phase change material 134 can release the stored thermal energy as it reverts to its original phase.

[0028] The TES heat exchanger 138 in the thermal energy storage module 130 is configured to transfer heat between the refrigerant circulating through the refrigerant loop 112 and the phase change material 134. Structurally, in at least some embodiments, the TES heat exchanger 138 includes a series of wound metal tubes or fins (not shown) through which the refrigerant circulates. The TES heat exchanger 138 is arranged within or adjacent to the phase change material 134 to maximize the contact surface area and ensure efficient heat exchange.

[0029] The TES heat exchanger 138 is connected between the inlet and outlet connections (not shown) of the thermal energy storage module 130, allowing refrigerant from the refrigerant circuit 112 to flow through the TES heat exchanger 138 to store or release thermal energy in the phase change material 134. It should be understood that the "inlet connection" and "outlet connection" of the thermal energy storage module 130 do not necessarily refer to specific refrigerant connections, as refrigerant can flow through the thermal energy storage module 130 in either direction. Therefore, the "inlet connection" and "outlet connection" of the thermal energy storage module 130 should be understood as interchangeable.

[0030] Multiple on / off valves 140 are suitably arranged and operated to manage the storage and release of thermal energy within the thermal energy storage module 130, and to direct refrigerant flow through the external air heat exchanger 104 and / or the internal air heat exchanger 108. In some embodiments, the multiple on / off valves 140 may comprise multi-way valves (e.g., three-way valves) or an equivalent arrangement of multiple valves. The multiple on / off valves 140 may include various possible configurations that allow the thermal energy storage module 130 to be selectively bypassed in a first on / off state, selectively connected in series with the external air heat exchanger 104 in a second on / off state, and selectively connected in series with the internal air heat exchanger 108 in a third on / off state.

[0031] Additionally, in some embodiments, the plurality of switchable valves 140 are configured such that the compressor 116 and / or expander 120 can be selectively connected in the refrigerant circuit 112 between the external air heat exchanger 104 and the TES heat exchanger 138, selectively connected in the refrigerant circuit 112 between the TES heat exchanger 138 and the internal air heat exchanger 108, and selectively connected in the refrigerant circuit 112 between the external air heat exchanger 104 and the internal air heat exchanger 108, respectively, under different switching states. Finally, in some embodiments, the plurality of switchable valves 140 are configured to reverse the compressor 116 and / or expander 120 in the refrigerant circuit 112 under different switching states.

[0032] In at least some embodiments, the heat pump system 100 also includes a controller 150 configured to manage the overall operation of the heat pump system 100. For this purpose, the controller 150 is configured to monitor various parameters, including, for example, internal and external temperatures, the temperature of the refrigerant entering and leaving the thermal energy storage module 130, the refrigerant flow rate at different points in the refrigerant circuit 112, the compressor frequency of the compressor 116, the density of the phase change material 134, and the electrical or magnetic properties of the phase change material 134. By continuously monitoring these parameters, the controller 150 makes real-time adjustments to the operation of the heat pump system 100, such as adjusting the speed of the compressor 116 or adjusting multiple on / off valves 140 to store or release thermal energy from the thermal energy storage module 130.

[0033] In some embodiments, the controller 150 is operatively connected to various temperature sensors configured to measure different temperatures within the heat pump system 100. These temperature sensors may include thermocouples or thermistors strategically positioned to measure the corresponding temperatures. In one embodiment, the indoor temperature sensor 152 measures one or both of the ambient indoor temperature (e.g., at a thermostat) and the temperature of the air entering the internal air heat exchanger 108. In one embodiment, the outdoor temperature sensor 154 measures one or both of the ambient outdoor temperature and the temperature of the air entering the external air heat exchanger 104. In one embodiment, the TES inlet temperature sensor 156 measures the temperature of the refrigerant entering the thermal energy storage module 130. In one embodiment, the TES outlet temperature sensor 158 measures the temperature of the refrigerant leaving the thermal energy storage module 130. It should be understood that the TES inlet temperature sensor 156 and the TES outlet temperature sensor 158 are interchangeable. In particular, whether each sensor measures inward or outward refrigerant flow depends on the direction of the refrigerant flow through the thermal energy storage module 130.

[0034] In some embodiments, the controller 150 is operatively connected to one or more refrigerant flow rate sensors configured to measure different refrigerant flow rates within the heat pump system 100. The refrigerant flow rate sensors (one or more) may include variable area flow meters or similar devices designed to detect changes in refrigerant flow rate. In some embodiments, the one or more refrigerant flow rate sensors include a corresponding refrigerant flow rate sensor that measures the flow rate through each heat exchanger in the heat pump system 100. Specifically, the TES refrigerant flow rate sensor 160 measures the flow rate of refrigerant circulating through the TES heat exchanger 138. The external refrigerant flow rate sensor 162 measures the flow rate of refrigerant circulating through the external air heat exchanger 104. The internal refrigerant flow rate sensor 164 measures the flow rate of refrigerant circulating through the internal air heat exchanger 108.

[0035] In some embodiments, the controller 150 is operatively connected to a compressor frequency sensor 166 (e.g., a tachometer), which is integrated into the compressor 116. The compressor frequency sensor 166 measures the rotational speed of the compressor 116 and / or the operating current frequency of the compressor 116.

[0036] Finally, in some embodiments, the controller 150 is operatively connected to the PCM sensor 168, which may be integrated with the thermal energy storage module 130. The PCM sensor 168 directly measures one or more properties of the phase change material 134. In one embodiment, the PCM sensor 168 includes an ultrasonic sensor that uses ultrasonic measurements to measure the density of the phase change material 134. In one embodiment, the PCM sensor 168 includes an electrical sensor that measures the electrical properties (e.g., resistance or impedance) of the phase change material 134. In one embodiment, the PCM sensor 168 includes a magnetic sensor that measures the magnetic properties of the phase change material 134.

[0037] The controller 150 is configured to selectively operate the heat pump system 100 in heating mode, cooling mode, or standby mode. In standby mode, the heat pump system 100 does not actively heat or cool, but is still ready to start when needed.

[0038] In heating mode, the heat pump system 100 operates by transferring heat from outside air to the interior of the building using a refrigerant circuit 112. A controller 150 operates a compressor 116 to compress the refrigerant, thereby increasing its temperature and pressure. The higher-temperature, higher-pressure refrigerant from the compressor 116 circulates through an internal air heat exchanger 108, where it releases heat to heat the internal air 110. Next, the refrigerant passes through an expander 120, where it undergoes a decrease in pressure and temperature. The lower-temperature, lower-pressure refrigerant from the expander 120 circulates through an external air heat exchanger 104, where it absorbs heat from the outside air, even under cold conditions. The refrigerant then returns to the compressor 116 to repeat the cycle.

[0039] In cooling mode, the heat pump system 100 operates in reverse to transfer heat from the building interior to the external environment. The controller 150 operates the compressor 116 to compress the refrigerant, thereby increasing its temperature and pressure. The higher-temperature, higher-pressure refrigerant from the compressor 116 circulates through the external air heat exchanger 104, where it releases heat into the outside air 106. Next, the refrigerant passes through the expander 120, where it undergoes a decrease in pressure and temperature. The lower-temperature, lower-pressure refrigerant from the expander 120 circulates through the internal air heat exchanger 108, where it absorbs heat from the internal air 110. The refrigerant then returns to the compressor 116 to repeat the cycle.

[0040] In addition to operating the heat pump system 100 in conventional heating or cooling modes, the controller 150 also operates a plurality of on / off valves 140 to control the heat pump system 100 to store or release thermal energy from the thermal energy storage module 130 as needed in either heating or cooling modes.

[0041] When the heat pump system 100 operates to store heat energy in the heat storage module 130 (i.e., in charging mode), the controller 150 operates a plurality of on / off valves 140 in a specific manner to direct refrigerant flow from the compressor 116 or from the expander 120 toward the heat storage module 130. In charging mode, the controller 150 operates the compressor 116 to circulate the refrigerant, such that excess heat energy generated during operation is transferred to the heat storage module 130 instead of being released into the internal air 110 or into the external air 106. Specifically, in cooling mode, the heat storage module 130 stores heat energy absorbed from the internal air 110 for the purpose of cooling the environment. Conversely, in heating mode, the heat storage module 130 stores heat energy absorbed from the external air 106.

[0042] When the heat pump system 100 operates to release heat from the thermal energy storage module 130 (i.e., in charging mode), the controller 150 operates a plurality of on / off valves 140 in a specific manner to direct a refrigerant flow from the thermal energy storage module 130 to the internal air heat exchanger 108 or the external air heat exchanger 104. In release mode, the controller 150 operates the compressor 116 to circulate the refrigerant, causing heat to be released from the thermal energy storage module 130, rather than absorbed from the external air 106 or the internal air 110. Specifically, in heating mode, the thermal energy storage module 130 releases heat into the internal air 110 for the purpose of heating the building. Conversely, in cooling mode, the thermal energy storage module 130 releases heat into the external air 106.

[0043] In some embodiments, the controller 150 includes a smart algorithm that determines the optimal time to charge and release energy into the thermal energy storage module 130 based on predictive analysis of energy demand, weather forecasts, and electricity rates. The controller 150 adjusts the operation of the compressor 116 and uses multiple on / off valves 140 to adjust the flow path of the refrigerant circuit 112 to store or release thermal energy in the thermal energy storage module 130, thereby minimizing energy costs, maximizing efficiency, and extending the lifespan of the heat pump system 100.

[0044] In at least some embodiments, the heat pump system 100 also includes an IoT gateway 170. The IoT gateway 170 acts as a communication bridge between the controller 150 and the cloud backend 180 and / or mobile electronic devices 190. The IoT gateway 170 includes, for example, a microprocessor and a network communication module including one or more transceivers (e.g., Wi-Fi, Ethernet, or cellular) for connecting to the cloud backend 180 and / or mobile electronic devices 190. The IoT gateway 170 enables data exchange and remote monitoring by transmitting system performance metrics (such as sensor data, energy usage, and operating status) to the cloud backend 180 and / or mobile electronic devices 190. Additionally, the IoT gateway 170 allows users to control the heat pump system 100 via a mobile application on the mobile electronic device 190 or via a web application on the cloud backend 180.

[0045] In at least some embodiments, the cloud backend 180 includes one or more servers that act as a central hub for data processing, storage, and system management. The cloud backend 180 receives data from the IoT gateway 170, including sensor data, energy usage, and operating status, and processes and stores this information for analysis and optimization. In some embodiments, the cloud backend 180 also facilitates user interaction by communicating with a mobile electronic device 190 or another computing device, allowing users to remotely monitor and control the heat pump system 100 via a mobile application or web application.

[0046] In at least some embodiments, the mobile electronic device 190 operates as a user interface for remotely monitoring and controlling the heat pump system 100. The mobile electronic device 190 communicates directly or via the cloud backend 180 with the IoT gateway 170, thereby allowing the user to monitor system performance, adjust temperature settings, schedule heating or cooling modes, etc.

[0047] Methods for estimating the state of charge and health in thermal energy storage Figure 3 A flowchart of a method 200 for monitoring a thermal energy storage module 130 in a thermal management system, such as a heat pump system 100, is shown. Method 200 advantageously uses measurements from various sensors of the heat pump system 100 to estimate the state of charge and health of the thermal energy storage module 130. Method 200 advantageously provides the estimates of the state of charge and health as virtual sensors of the heat pump system 100. In this way, the estimates of the state of charge and health can be monitored by a cloud backend 180 and by a user via a mobile electronic device 190. Furthermore, the estimates of the state of charge and health can be used to optimize the operation of the heat pump system 100 to improve energy efficiency and extend the lifespan of the thermal energy storage module 130.

[0048] Method 200 begins by measuring parameters of a thermal management system with thermal energy storage (block 210). Specifically, the controller 150 of the heat pump system 100 operates one or more sensors of the heat pump system 100 to measure values ​​of multiple parameters of the heat pump system 100. Specifically, the controller 150 operates the indoor temperature sensor 152 to measure one or both of the ambient indoor temperature and the air temperature entering the indoor air heat exchanger 108. The controller 150 operates the outdoor temperature sensor 154 to measure one or both of the ambient outdoor temperature and the air temperature entering the outdoor air heat exchanger 104. The controller 150 operates the TES inlet temperature sensor 156 to measure the temperature of the refrigerant entering the thermal energy storage module 130. The controller 150 operates the TES outlet temperature sensor 158 to measure the temperature of the refrigerant leaving the thermal energy storage module 130. The controller 150 operates the external refrigerant flow rate sensor 162 to measure the flow rate of the refrigerant circulating through the outdoor air heat exchanger 104. Controller 150 operates internal refrigerant flow rate sensor 164 to measure the flow rate of refrigerant circulating through internal air heat exchanger 108. Controller 150 operates compressor frequency sensor 166 to measure the rotational speed of compressor 116 and / or the operating current frequency of compressor 116. Finally, controller 150 operates PCM sensor 168 to measure the density, electrical properties, or magnetic properties of phase change material 134.

[0049] Method 200 continues to determine the charge status and / or health status of the thermal energy storage based on measured parameters (block 220). Specifically, the processor of controller 150 and / or cloud backend 180 determines one or both of the charge status and health status of thermal energy storage module 130. The charge status and / or health status are determined based on measured values ​​of multiple parameters of heat pump system 100.

[0050] In some embodiments, controller 150 and / or cloud backend 180 use a dynamic system model to determine one or both of power status and health status: in This indicates the charging status of the thermal energy storage module 130, while SOH indicates the health status of the thermal energy storage module 130. This indicates the compressor frequency of compressor 116. This represents the various temperatures measured within the heat pump system 100. This indicates the various refrigerant flow rates and / or refrigerant material parameters measured within the heat pump system 100, and This refers to any other parameter of the heat pump system 100 that is measured or otherwise monitored. In some embodiments, this refers to a function in a dynamic system model. This includes one or more of the following: physical models, empirical models, and machine learning models.

[0051] As used herein, a “fully charged” state or SOC = 100% means that the phase change material 134 (or other thermal storage medium) has completely converted to its highest energy state. Conversely, as used herein, a “fully released” state or SOC = 0% means that the phase change material 134 has completely converted to its lowest energy state. For example, in a solid-liquid phase change material, the highest energy state is liquid, and the lowest energy state is solid. However, it should be understood that the highest and lowest energy states may differ depending on the actual limitations of the possible degree of phase change of the phase change material 134. For example, some residual solid particles may be present in a “fully charged” phase change material 134, just as some residual liquid particles may be present in a “fully released” phase change material 134.

[0052] In at least some embodiments, the health status of the thermal energy storage module 130 includes at least two parameters. First, the health status includes the total thermal energy capacity of the phase change material 134, i.e., the total amount of energy that can circulate between SOC=1 and SOC=0. Second, the health status includes the heat transfer coefficient between the refrigerant flowing through the TES heat exchanger 138 and the phase change material 134.

[0053] It should be understood that the charge state and health state of the thermal energy storage module 130 typically have very different time scales. Therefore, in at least one embodiment, the controller 150 and / or the cloud backend 180 use a charge state model to determine the charge state and a health state model, separate from the charge state model, to determine the health state. Additionally, in some embodiments, the charge state model is deployed on an edge device, such as the controller 150, for real-time control of the heat pump system 100, while the health state model can be deployed on the cloud backend 180 because it changes very slowly. Specifically, the controller 150 of the heat pump system 100 determines the charge state, and the cloud backend 180 (i.e., the processor of a remote server) determines the health state.

[0054] In some embodiments, the controller 150 and / or the cloud backend 180 determine the rate of heat transfer between the refrigerant flowing through the TES heat exchanger 138 and the phase change material 134. As described below, this rate of heat transfer can be used to determine the charge status and / or health status of the thermal energy storage module 130.

[0055] In one embodiment, the controller 150 and / or the cloud backend 180 determine the rate of heat transfer by, for example, using a TES refrigerant flow rate sensor 160 to determine the flow rate of refrigerant flowing through the TES heat exchanger 138. Next, the controller 150 and / or the cloud backend 180 determine, for example, the temperature of the refrigerant flowing into the TES heat exchanger 138 using a TES inlet temperature sensor 156. Next, the controller 150 and / or the cloud backend 180 determine, for example, the temperature of the refrigerant flowing out of the TES heat exchanger 138 using a TES outlet temperature sensor 158. Finally, the controller 150 and / or the cloud backend 180 use an energy balance equation, based on the flow rate of refrigerant flowing through the TES heat exchanger 138, the temperature of the refrigerant flowing into the TES heat exchanger 138, and the temperature of the refrigerant flowing out of the TES heat exchanger 138, to determine the rate of heat transfer between the TES heat exchanger 138 and the phase change material 134.

[0056] The energy balance equation is an energy conservation equation that compares the heat energy flowing into the thermal energy storage module 130 with the heat energy flowing out of the thermal energy storage module 130 to estimate the rate at which heat energy is stored or released from the thermal energy storage module 130 (i.e., the rate of heat transfer between the TES heat exchanger 138 and the phase change material 134). The energy balance equation incorporates at least one material property of the refrigerant, such as the refrigerant's heat capacity and / or density.

[0057] In some embodiments, the energy balance equation includes one or more terms that take into account additional heat sources or radiators besides the refrigerant and phase change material 134. In one example, the energy balance equation includes terms that take into account frictional heating associated with the viscous flow of the refrigerant through the TES heat exchanger 138. In another example, the energy balance equation includes terms that take into account heating or cooling of the phase change material 134 or the TES heat exchanger 138 due to higher / lower ambient temperatures (i.e., based on measured internal and external temperatures) and the heat transfer coefficient estimated based on the insulation properties of the thermal energy storage module 130.

[0058] In one embodiment, the controller 150 and / or the cloud backend 180 determine the charging state by quantifying the amount of heat energy exchanged between the refrigerant flowing through the TES heat exchanger 138 and the phase change material 134. The controller 150 and / or the cloud backend 180 determine the amount of heat energy exchanged between the refrigerant flowing through the TES heat exchanger 138 and the phase change material 134 by integrating the heat transfer rate. Next, the controller 150 and / or the cloud backend 180 determine the amount of heat energy stored in the phase change material 134 based on the initial amount of heat energy stored in the phase change material 134 and the amount of heat energy exchanged between the refrigerant flowing through the TES heat exchanger 138 and the phase change material 134. Finally, the controller 150 and / or the cloud backend 180 determine the charging state of the thermal energy storage module 130 by dividing the amount of heat energy stored in the phase change material 134 by the total thermal capacity of the phase change material 134.

[0059] In another embodiment, the controller 150 and / or the cloud backend 180 determine the charging state directly based on the heat transfer rate rather than through integration. Specifically, the controller 150 and / or the cloud backend 180 estimates at least one heat transfer parameter based on the heat transfer rate. In one embodiment, the at least one heat transfer parameter includes the thermal conductivity of the phase change material 134. In another embodiment, the at least one heat transfer parameter includes the heat transfer coefficient between the refrigerant flowing through the TES heat exchanger 138 and the phase change material 134. Next, the controller 150 and / or the cloud backend 180 determine the charging state based on the estimated at least one heat transfer parameter using a known relationship between the charging state and the at least one heat transfer parameter, which tends to vary in a predictable manner depending on the charging state. The known relationship between the charging state and the at least one heat transfer parameter can be determined, for example, empirically and stored in a lookup table referenced by the controller 150 to determine the charging state.

[0060] In another embodiment, the controller 150 and / or the cloud backend 180 uses other measurement methods to determine the charging state. In one embodiment, the controller 150 and / or the cloud backend 180 uses an ultrasonic sensor to measure the density of the phase change material 134 and determines the charging state based on the density of the phase change material 134, which tends to change linearly with the charging state or follow some prior measurable trend. In one embodiment, the controller 150 and / or the cloud backend 180 uses an electrical sensor to measure the electrical properties (e.g., resistance or impedance) of the phase change material 134 and determines the charging state based on the electrical properties of the phase change material 134. In one embodiment, the controller 150 and / or the cloud backend 180 uses a magnetic sensor to measure the magnetic properties of the phase change material 134 and determines the charging state based on the magnetic properties of the phase change material 134.

[0061] In some embodiments, the controller 150 and / or the cloud backend 180 uses a machine learning model to determine the state of charge. This machine learning model is trained using a plurality of training samples that provide measured parameters of the heat pump system 100 associated with a baseline factual state of charge. The training samples are fed into the machine learning model iteratively, and the error between the state of charge predicted by the machine learning model and the baseline factual state of charge is used to optimize and refine the learnable parameters of the machine learning model during training. It should be understood that the machine learning model can employ a wide variety of architectures, including at least artificial neural networks. In some embodiments, the controller 150 and / or the cloud backend 180 uses a combination of the machine learning model and physical or empirical models (such as those discussed above) to determine the state of charge.

[0062] As used herein, the term "machine learning model" refers to a system or set of program instructions and / or dataset configured to implement an algorithm, process, or mathematical model (e.g., a neural network) that predicts or otherwise provides a desired output based on a given input. It should be understood that, typically, many or most parameters of a machine learning model are not explicitly programmed, and in the traditional sense, machine learning models are not explicitly designed to follow specific rules to provide a desired output for a given input. Instead, machine learning models are iteratively provided with a corpus of training data from which the model identifies or "learns" patterns and statistical relationships in the data, which are generalized to make predictions or otherwise provide outputs on new data inputs. The results of the training process are embodied in multiple learning parameters, kernel weights, and / or filter values ​​that are used in various parts of the machine learning model to perform various operations or functions.

[0063] A combination of techniques discussed above for determining the state of charge can be used, where weights are applied among multiple techniques to improve the accuracy of the state of charge determination. For example, a combination of techniques can help account for integral errors and unquantified heat sources and radiators that affect energy balance. Specifically, in one embodiment, controller 150 and / or cloud backend 180 use a first technique to determine a first estimated state of charge of thermal energy storage module 130, and use a second technique to determine a second estimated state of charge of thermal energy storage module 130. The first and second techniques can include integral-based techniques, heat transfer parameter-based techniques, density-based techniques, electrical property-based techniques, magnetic property-based techniques, machine learning-based techniques, or any other techniques used to determine the state of charge. Next, controller 150 and / or cloud backend 180 determine the state of charge as a weighted average or weighted sum of the first and second estimated states of charge. It should be understood that more than two techniques can be used.

[0064] As described above, in at least some embodiments, the health status of the thermal energy storage module 130 encompasses or includes the total thermal energy capacity of the phase change material 134, i.e., the total amount of energy that can be cycled between SOC=1 and SOC=0. In some embodiments, the controller 150 and / or the cloud backend 180 determine the total thermal energy capacity of the phase change material 134 and determine the health status based on the total thermal energy capacity of the phase change material 134.

[0065] In some embodiments, to determine the total thermal capacity, the controller 150 and / or the cloud backend 180 use direct measurement (e.g., using the density-based, electrical property-based, or magnetic property-based techniques described above) to determine the measured state of charge change (i.e., Next, the controller 150 and / or the cloud backend 180 use physical or empirical models based on measured values ​​of multiple parameters of the heat pump system 100, such as the aforementioned integration-based techniques or techniques based on heat transfer parameters, to determine the estimated state of charge change. Finally, the controller 150 and / or the cloud backend 180 determine the total thermal capacity based on the error between the estimated state of charge change and the measured state of charge change. In one example, the controller 150 and / or the cloud backend 180 determine the total thermal capacity based on the ratio of the measured state of charge change to the estimated state of charge change.

[0066] In another embodiment, to determine the total thermal capacity, the controller 150 and / or the cloud backend 180 use a first technique based on measurements of multiple parameters of the heat pump system 100, such as the integration-based technique discussed above, to determine a first estimated change in the state of charge. Next, the controller 150 and / or the cloud backend 180 use a second technique based on measurements of multiple parameters of the heat pump system 100, such as the heat transfer parameter-based technique discussed above, to determine a second estimated change in the state of charge. Finally, the controller 150 and / or the cloud backend 180 determine the total thermal capacity based on the error between a first estimated change in the state of charge and a second estimated change in the state of charge. In one example, the controller 150 and / or the cloud backend 180 determine the total thermal capacity based on the ratio of the estimated change in the state of charge determined using an integration-based technique to the estimated change in the state of charge determined using a technique based on heat transfer parameters.

[0067] As described above, in at least some embodiments, the health status of the thermal energy storage module 130 includes the heat transfer coefficient between the refrigerant flowing through the TES heat exchanger 138 and the phase change material 134. In some embodiments, the controller 150 and / or the cloud backend 180 determine the heat transfer coefficient between the refrigerant flowing through the TES heat exchanger 138 and the phase change material 134. Furthermore, the health status is determined based on the heat transfer coefficient. In some embodiments, in order to determine the heat transfer coefficient... The controller 150 and / or cloud backend 180 use energy balance equations to determine the rate of heat transfer between the refrigerant flowing through the TES heat exchanger 138 and the phase change material 134. Next, the controller 150 and / or cloud backend 180 determine the heat transfer coefficient based on the rate of heat transfer. .

[0068] Additionally, in one embodiment, the controller 150 and / or the cloud backend 180 determine the expected heat transfer coefficient based on the state of charge determined using one of the aforementioned techniques. Based on the temperature of the refrigerant flowing into the TES heat exchanger 138, the flow rate of the refrigerant flowing through the TES heat exchanger 138, and the expected heat transfer coefficient. The controller 150 and / or cloud backend 180 use an energy balance equation to determine the predicted temperature of the refrigerant flowing out of the TES heat exchanger 138. Next, the controller 150 and / or cloud backend 180 use, for example, the TES outlet temperature sensor 158 to determine the actual temperature of the refrigerant flowing out of the TES heat exchanger 138. If the measured actual temperature of the refrigerant flowing out of the TES heat exchanger 138 deviates from the predicted temperature of the refrigerant flowing out of the TES heat exchanger 138 (e.g., closer to the temperature of the refrigerant flowing into the TES heat exchanger 138 than expected), the controller 150 and / or cloud backend 180 determine the health status as the actual heat transfer coefficient determined using the energy balance equation. Compared with the expected heat transfer coefficient determined based on the charging state The ratio (i.e., ).

[0069] In some embodiments, the controller 150 and / or the cloud backend 180 uses a machine learning model to determine the health status. This machine learning model is trained using multiple training samples that provide measured parameters of the heat pump system 100 associated with a baseline factual health status. The training samples are fed into the machine learning model iteratively, and the error between the health status predicted by the machine learning model and the baseline factual health status is used to optimize and refine the learnable parameters of the machine learning model during training. It should be understood that the machine learning model can employ a wide variety of architectures, including at least artificial neural networks. In some embodiments, the controller 150 and / or the cloud backend 180 uses a combination of the machine learning model and physical or empirical models (such as those discussed above) to determine the health status.

[0070] In some embodiments, the controller 150 and / or the cloud backend 180 use state estimation techniques (such as extended Kalman filters, least squares regression, and moving time-domain estimators) to determine the charging state and / or health state over time. These state estimation techniques combine the state predicted by the model with the measured state to account for the error between the measured state and the state predicted by the model.

[0071] In at least some embodiments, controller 150 receives over-the-air updates to parameters of a model used to estimate the state of charge and / or health of thermal energy storage module 130. Specifically, when updates are available, controller 150 receives updated parameters of the model via a transceiver of IoT gateway 170, which may include adjustments based on new data or improved algorithms developed in cloud backend 180. In this way, as the formation model is improved and more field data is collected, heat pump system 100 is able to provide and utilize more accurate estimates of the state of charge and / or health.

[0072] Method 200 continues to provide the charging status and / or health status as virtual sensors for the thermal management system (block 230). Specifically, the heat pump system 100 is advantageously configured to provide estimated charging and health status of the thermal energy storage module 130 as virtual sensors for the heat pump system 100. In this way, the need for physical sensors is reduced. The estimated charging and health status of the thermal energy storage module 130 provides additional useful information to the user or owner of the heat pump system 100 and enables the controller 150 to better control the heat pump system 100.

[0073] In some embodiments, the controller 150 operates at least one component of the heat pump system 100 based on an estimated state of charge of the thermal energy storage module 130. In particular, it should be understood that the state of charge of the thermal energy storage module 130 is very useful information for optimizing the operation of the heat pump system 100. In some embodiments, in a heating mode, in response to the state of charge falling below a first predetermined lower threshold (indicating low remaining heating capacity), the controller 150 activates the heat pump system 100 to store additional heat energy in the thermal energy storage module 130. Similarly, in some embodiments, in a cooling mode, in response to the state of charge exceeding a first predetermined upper threshold (indicating low remaining cooling capacity), the controller 150 activates the heat pump system 100 to release additional heat energy from the thermal energy storage module 130 into the outside air. Conversely, in some embodiments, in a heating mode, in response to the state of charge exceeding a second predetermined upper threshold (indicating substantial remaining heating capacity), the controller 150 performs any necessary heating by releasing heat energy from the thermal energy storage module 130. Similarly, in some embodiments, in cooling mode, in response to a drop in charge state below a second predetermined lower threshold (indicating significant remaining cooling capacity), controller 150 performs any necessary cooling by storing thermal energy in thermal storage module 130. This dynamic management allows heat pump system 100 to more effectively balance energy supply and demand, thereby optimizing comfort and reducing energy costs.

[0074] In some embodiments, the controller 150 operates at least one component of the heat pump system 100 based on an estimated health status of the thermal energy storage module 130. Specifically, in one embodiment, in response to a health status decline below a first predetermined lower threshold, the controller 150 adjusts operating parameters, such as reducing charge and release rates, to prevent further degradation. Additionally, in one embodiment, in response to a health status decline below a second predetermined lower threshold (indicating more significant deterioration), the controller 150 transmits a maintenance alarm to a cloud backend 180 or a mobile electronic device 190, or initiates diagnostic routines to assess necessary repairs or replacements. By proactively monitoring and responding to health status, the controller 150 ensures reliable operation of the heat pump system 100, extends the lifespan of the thermal energy storage module 130, and maintains optimal energy efficiency.

[0075] Additionally, as described above, through the connectivity provided by the IoT gateway 170, users or owners of the heat pump system 100 can remotely monitor and control the heat pump system 100 via a mobile application or web application. To this end, in some embodiments, the controller 150 periodically or continuously transmits measured values ​​of multiple parameters of the heat pump system 100, as well as estimated charging and health status of the thermal energy storage module 130, to the cloud backend 180 via the IoT gateway 170. The cloud backend 180 receives the measured values ​​of the multiple parameters of the heat pump system 100 and, in at least some embodiments, stores them in a database.

[0076] Using a mobile electronic device 190 or any other computing device with a display screen, the user or owner of the heat pump system 100 can navigate to a mobile application or web application. The mobile electronic device 190 communicates directly with the cloud backend 180 or the heat pump system 100 to receive measurements of multiple parameters of the heat pump system 100, as well as estimated charge and health status of the thermal energy storage module 130. Within the mobile application or web application, the measurements of multiple parameters of the heat pump system 100 and the estimated charge and health status of the thermal energy storage module 130 are displayed on the display screen of the mobile electronic device 190 or other computing device, allowing the user to remotely monitor the heat pump system 100. Additionally, in some embodiments, the mobile application or web application enables the user to control or configure the heat pump system 100.

[0077] Figure 4 An exemplary graphical user interface 300 is shown for monitoring the charge status of a thermal energy storage module 130 using a mobile application of a mobile electronic device 190. The graphical user interface 300 includes a charge status summary 324 (e.g., “TES charge status, 73% (cold), 15 hours remaining”). Additionally, the graphical user interface 300 includes a graphical depiction of the charge status in the form of a charge meter 326, which is scaled to the estimated charge status of the thermal energy storage module 130. It should be understood that in cooling mode, a larger charge status of the thermal energy storage module 130 corresponds to a smaller remaining capacity for cooling. Therefore, in some embodiments, the graphical user interface 300 may present the charge status differently depending on whether the heat pump system 100 is operating in heating or cooling mode. In the example shown, “73% (cold)” could actually correspond to a 27% charge status of the thermal energy storage module 130. If the heat pump system 100 switches to heating mode, the same charge status would be indicated by “27% (hot)”.

[0078] Embodiments within the scope of this disclosure may also include non-transitory computer-readable storage media or machine-readable media for carrying or having computer-executable instructions (also known as program instructions) or data structures stored thereon. Such non-transitory computer-readable storage media or machine-readable media can be any available medium accessible by a general-purpose or special-purpose computer. By way of example and not limitation, such non-transitory computer-readable storage media or machine-readable media may include RAM, ROM, EEPROM, CD-ROM or other optical disc storage devices, magnetic disk storage devices or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code means in the form of computer-executable instructions or data structures. Combinations of the foregoing should also be included within the scope of non-transitory computer-readable storage media or machine-readable media.

[0079] Computer-executable instructions include, for example, instructions and data that cause a general-purpose computer, a special-purpose computer, or a special-purpose processing device to perform a specific function or group of functions. Computer-executable instructions also include program modules that are executed by a computer in a standalone or networked environment. Typically, program modules include routines, programs, objects, components, and data structures that perform a specific task or implement a specific abstract data type. Computer-executable instructions, associated data structures, and program modules represent examples of program code means for performing steps of the methods disclosed herein. A particular sequence of such executable instructions or associated data structures represents examples of corresponding actions for implementing the functions described in such steps.

[0080] Although this disclosure has been illustrated and described in detail in the accompanying drawings and the foregoing description, these should be considered illustrative rather than restrictive. It should be understood that only preferred embodiments have been presented, and protection is intended for all changes, modifications, and further applications falling within the spirit of this disclosure.

Claims

1. A method for monitoring a thermal energy storage module in a thermal management system, the method comprising: The value of at least one parameter of the thermal management system is measured using at least one sensor; as well as At least one of the following is determined using at least one processor based on measurements of at least one parameter of the thermal management system: (i) the charging state of the thermal energy storage module, or (ii) the health state of the thermal energy storage module.

2. The method of claim 1, further comprising at least one of the following: The at least one processor operates at least one component of the thermal management system based on at least one of (i) the charging state or (ii) the health state; and The at least one of (i) the charging state or (ii) the health state is transmitted to the electronic device using a transceiver, and the at least one of (i) the charging state or (ii) the health state is displayed on the display of the electronic device.

3. The method according to claim 1, further comprising determining the charging state: Determine the heat transfer rate between the heat transport fluid flowing through the heat exchanger of the heat storage module and the heat storage medium of the heat storage module.

4. The method according to claim 3, further comprising determining the heat transfer rate: Measure the flow rate of the heat transport fluid flowing through the heat exchanger; Measure the first temperature of the heat transport fluid flowing into the heat exchanger; Measure the second temperature of the heat transport fluid flowing out of the heat exchanger; as well as The rate of heat transfer between the heat exchanger and the heat storage medium is determined using an energy balance equation based on the flow rate of the heat transport fluid, the first temperature of the heat transport fluid, the second temperature of the heat transport fluid, and at least one material property of the heat transport fluid.

5. The method of claim 4, wherein the at least one material property of the thermal energy transport fluid includes at least one of heat capacity or density.

6. The method of claim 4, wherein the energy balance equation includes at least one heat source or radiator other than the thermal energy transport fluid and the thermal storage medium.

7. The method according to claim 3, further comprising determining the charging state: The amount of heat energy exchanged between the heat exchanger and the heat storage medium is determined by integrating the heat transfer rate. The amount of heat energy stored in the heat storage medium is determined based on the amount of heat energy exchanged between the heat exchanger and the heat storage medium. as well as The charging state is determined by dividing the amount of thermal energy stored in the thermal storage medium by the total thermal energy capacity of the thermal storage medium.

8. The method according to claim 3, further comprising determining the charging state: At least one heat transfer parameter of the heat storage medium is estimated based on the heat transfer rate. as well as The charging state is determined based on the at least one heat transfer parameter.

9. The method according to claim 8, wherein the at least one heat transfer parameter is the thermal conductivity of the heat storage medium.

10. The method according to claim 1, further comprising determining the charging state: Measure the density of the thermal storage medium in the thermal energy storage module; as well as The charging state is determined based on the density of the thermal storage medium.

11. The method according to claim 1, further comprising determining the charging state: The first estimated charge state of the thermal energy storage module is determined using a first technique. A second technique is used to determine a second estimated charge state of the thermal energy storage module; as well as The charging state is determined as a weighted sum of the first estimated charging state and the second estimated charging state.

12. The method according to claim 1, further comprising: Determine the total thermal energy capacity of the thermal storage medium of the thermal energy storage module.

13. The method according to claim 12, further comprising determining the total thermal energy capacity: The first change in the charging state is determined using a first technique; A second technique is used to determine a second change in the charging state; as well as The total thermal capacity is determined based on the error between the first change in the charging state and the second change in the charging state.

14. The method according to claim 1, further comprising determining the charging state: Determine the heat transfer coefficient between the heat transport fluid flowing through the heat exchanger of the heat storage module and the heat storage medium of the heat storage module.

15. The method of claim 14, further comprising determining the heat transfer coefficient: Measure the flow rate of the heat transport fluid flowing through the heat exchanger; Measure the first temperature of the heat transport fluid flowing into the heat exchanger; Measure the second temperature of the heat transport fluid flowing out of the heat exchanger; as well as The heat transfer coefficient is determined using an energy balance equation based on the flow rate of the heat transport fluid, the first temperature of the heat transport fluid, and the second temperature of the heat transport fluid.

16. The method of claim 1, wherein determining at least one of (i) the charging state or (ii) the health state further comprises: The charging state is determined using the controller of the thermal management system; as well as The health status is determined using the processor of a remote server.

17. The method of claim 1, wherein determining at least one of (i) the charging state or (ii) the health state further comprises: At least one of an extended Kalman filter, least squares regression, or a moving time-domain estimator is used to determine (i) the energy state or (ii) the health state.

18. The method of claim 1, wherein determining at least one of (i) the charging state or (ii) the health state further comprises: The at least one of (i) the energy state or (ii) the health state is determined using at least one of a machine learning model or a physical model.

19. The method of claim 18, wherein determining at least one of (i) the charging state or (ii) the health state further comprises: The combination of the machine learning model and the physical model is used to determine at least one of (i) the charging state or (ii) the health state.

20. The method of claim 18, further comprising: Receive updates to at least one parameter of either the machine learning model or the physical model from a remote server.

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