Systems, Apparatuses, and Methods for Appliances with Integrated Energy Storage - Patent application

Abstract

The intelligent energy system includes an energy consuming appliance, a battery module coupled to the appliance, and a bi-directional converter coupled to the appliance and the battery module by a power bus. The battery module is configured to provide power to the appliance. The bi-directional converter converts between alternating current (AC) and direct current (DC) and interfaces with a power infrastructure external to the appliance. The system further includes a control unit communicatively coupled to the battery module, the bi-directional converter, and the appliance.

Description

[Technical field]

[0001] (CROSS REFERENCE TO RELATED APPLICATIONS) This application claims the benefit of and priority to U.S. Provisional Patent Application No. 63 / 301,861, filed January 21, 2022, the entire disclosure of which is incorporated herein by reference in its entirety for all purposes.

[0002] The present disclosure relates generally to energy storage devices, and more specifically to systems, apparatus, and methods for optimized charging or discharging of energy storage devices for increased availability of peak power and sharing of stored energy across multiple appliances or electric power supply units. [Background technology]

[0003] The distribution, consumption, or conversion of electrical energy within buildings, such as homes, commercial buildings, industrial facilities, and non-building environments (e.g., agricultural, industrial, medical, and transportation systems) relies on available incoming power as a basis for distributing electricity. For example, if a user wants to purchase and install an appliance (e.g., a dryer) that is powered by electricity, the appliance may be required to have the necessary electrical infrastructure, such as specialized high-power 220V lines and / or plugs. Significant costs and efforts may then be required to install these specialized lines and / or plugs. In addition, the locations where such appliances may be located are also limited due to these requirements. Similarly, if a user wants to charge an electric vehicle, adding a specialized 220V line and a specialized plug may be necessary. A cooking range, cooking stove, or cooking table may face similar concerns due to its special power requirements.

[0004] During periods of electrical grid unavailability, users of electrical power have limited options. For example, users may purchase generators, fueled by gasoline or natural gas. Additionally or alternatively, users may also retrofit, re-install, or upgrade their electrical panels to include power storage devices, such as fixed-distribution battery systems. However, such approaches require significant expense and dedicated space, and are limited to providing power for the units based on the power available from the generator or fixed-distribution battery system. Furthermore, backup electrical options are limited by other factors, such as the use of other appliances in the home, appropriate in-home / in-building electrical infrastructure sizing methodologies, or by conditions such as when the generator runs out of fuel.

[0005] Additional limitations include, but are not limited to, that the heat and / or energy available during times of peak load is generally limited by the available power of the grid connection and may further be limited by the infrastructure. Peak available power to the appliances is therefore limited by existing shared and private infrastructure. For example, a single appliance drawing 20 kW may be impractical (or impossible, based on standards) due to branch circuit sizing limitations. Having some appliances in the home with lower peak power draws (e.g., 15 kW) may be impractical as well, since not only are there branch circuit limitations, but there may also be certain panel and feeder limitations. This may lead to performance limitations. For example, a user may experience long cooking / boiling times and / or an inability to cook certain types of food using a cooking range or cooking table, and the user may have to wait for water to heat up for washing clothes or dishes, bathing, or the like. All of these may draw additional costs in terms of energy and water, and undesirable performance of these appliances.

[0006] In addition, in the era of decarbonization, where there is significant attention on technologies and approaches that can limit the Earth from warming more than 1.5°C above pre-industrial levels, most countries, including the United States, have set a goal of reaching net zero by 2050. Net zero means that all greenhouse gas emissions produced are offset by an equal amount of emissions that are eliminated or captured. Achieving this will require a shift away from greenhouse gas emissions produced by fossil fuels, for example, by using renewable energy. Renewable energy is abundant in the right conditions, but it is intermittent. For example, solar panels in residential units may not generate energy at moments of high demand (e.g., early morning or evening). Thus, additional objectives of decarbonization include increased efficiency in the transmission, storage, distribution, and conversion of energy into use.

[0007] Thus, there exists an unmet need for effective approaches by which electrical power may be more efficiently and intelligently distributed within a particular residential, industrial, or commercial setting. Summary of the Invention [Means for solving the problem]

[0008] To address the aforementioned shortcomings, a system and method for intelligent energy management is provided. An exemplary method includes an appliance, a battery module coupled to the appliance, and a bi-directional converter coupled to the appliance and the battery module by a power bus. The battery module is configured to provide power to the appliance. The bi-directional converter converts between alternating current (AC) and direct current (DC) and interfaces with a power infrastructure external to the appliance. The system and method further includes a control unit communicatively coupled to the battery module, the bi-directional converter, and the appliance. The control unit is configured to determine an optimized charging and discharging schedule for the battery module.

[0009] The foregoing is a summary and thus contains, by necessity, simplifications, generalizations, and omissions of detail, such that this summary is illustrative only and is not intended to be in any way limiting. Other aspects, inventive features, and advantages of the systems and / or processes described herein will become apparent in the non-limiting detailed description set forth herein. [Brief description of the drawings]

[0010] The disclosed implementations have advantages and features that will become more readily apparent from the detailed description, the appended claims, and the accompanying figures (or drawings), a brief introduction of which is provided below.

[0011] [Figure 1] FIG. 1 illustrates a block diagram of an example system architecture for intelligent energy management, according to some implementations.

[0012] [Diagram 2] FIG. 2 illustrates a block diagram of example modules included within an intelligent energy management application, according to some implementations.

[0013] [Diagram 3] FIG. 3 is a plot illustrating an example power usage pattern of an appliance, a power production pattern of a power supply, and a charging and discharging schedule of a battery module, according to some implementations.

[0014] [Figure 4] FIG. 4 illustrates a block diagram of an example architecture of an instrument equipped with a battery module, according to some implementations.

[0015] [Diagram 5] FIG. 5 illustrates a block diagram of an example application scenario for energy sharing among different dwelling units in a building, according to some implementations.

[0016] [Figure 6] FIG. 6 is a flowchart of an example method for intelligent energy management, according to some implementations.

[0017] [Figure 7] FIG. 7 is a flowchart of another example method for intelligent energy management, according to some implementations. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0018] Detailed Description In the following detailed description of the implementations, reference is made to the accompanying drawings, which form a part of this specification and are shown by way of example. It should be understood that the features of the various described implementations may be combined, other implementations may be utilized, and structural changes may be made without departing from the spirit and scope of the present disclosure. It should also be understood that the features of the various implementations and examples herein may be combined, interchanged, or removed without departing from the spirit and scope of the present disclosure. In addition, reference numbers and descriptions of redundant elements between the figures may be omitted for clarity.

[0019] According to some implementations, the methods and functions described herein, such as controlling battery charging or discharging, may be implemented as one or more software programs running on a computer processor (e.g., a control unit or controller). According to some implementations, the methods and functions described herein may be implemented as one or more software or firmware programs running on a standalone computing device, or on an embedded device, such as a tablet computer, a smartphone, a personal computer, a server, or any other computing device, or on an appliance or device with a control program. Dedicated hardware implementations, including but not limited to application specific integrated circuits, programmable logic arrays, and other hardware devices, can also be constructed to implement the methods and functions described herein. Furthermore, the methods described herein may be implemented as a device, such as a non-transitory computer-readable storage medium or memory device, that includes instructions that, when executed, cause a processor to perform the methods and functions described herein.

[0020] According to some implementations, the methods, systems, and apparatus disclosed herein may relate to an energy storage device (e.g., a battery module) for a single or multiple appliances, appliances, or electric power supply units. The energy storage device may reside inside or outside the appliance, appliance, or electric power supply unit, and may be smartly charged and discharged to provide appropriate power to the directly coupled appliance, appliance, or electric power supply unit itself, or to other indirectly connected appliances, appliances, or electric power supply units. For example, the methods and functions described herein, when implemented as software or firmware programs, may provide a control mechanism that optimizes the charging or discharging of the energy storage device during periods of peak power consumption, high transient energy pricing, and / or during power outages. According to some implementations, the methods, systems, and apparatus disclosed herein may be applied in residential, industrial, or commercial settings for appliances or any other system that has power as an input and optionally energy in various forms as an output.

[0021] According to some implementations, the methods, systems, and devices disclosed herein may provide various benefits and advantages. For example, but not limited to, the implementations disclosed herein may provide the functional equivalent of a 220V, 50A (or even higher or lower value) outlet in an existing 110V electrical infrastructure environment without the need to deploy new 220V power delivery infrastructure (e.g., specialized lines and / or plugs). This, in turn, saves the cost and effectiveness of deploying or retrofitting a 220V power delivery infrastructure. In addition, the implementations disclosed herein may also provide an energy storage device within a battery module that allows for internal (or external) storage of energy within (or outside) an appliance, device, or electric power supply unit. As a result, the implementations disclosed herein may enable the use of electricity in a manner that bypasses the limitations of existing electrical infrastructure, such as branch circuits, circuit breakers, 100A panels, or the like. For example, the implementations disclosed herein may enable an appliance to be mobile or portable. Additionally, implementations disclosed herein may also enable power stored in a battery module within the appliance (or external to the appliance) to be redirected during periods of power loss to provide backup power for the appliance itself and for additional appliances (e.g., appliances in the same room, home, facility, building, etc.). For example, if a power outage occurs in the middle of the night, when kitchen appliances are unlikely to be used but the home is more likely to need to be heated, power stored in the battery module from the kitchen appliances may be provided as power to start a space heater or other furnace without interfering with the operation of the kitchen appliances.

[0022] It should be understood that the features, benefits, and advantages described herein are not all-inclusive, and that many additional features and advantages will become apparent to those of ordinary skill in the art in view of the figures and the following description. Overview of intelligent energy management systems.

[0023] As used herein, the terms "energy" and "power" shall be construed according to their broad and ordinary meanings and may refer to any form of electrical power resource and / or its transformed form (e.g., electricity generated from gas, charcoal, water or hydromotive, wind, solar, biomass, geothermal, etc.) for driving and operating as expected an appliance, equipment, or other electrically powered device. The term "battery module" is used herein according to its broad and ordinary meanings and may refer to any type of battery cells (the smallest unit of a battery) or modules organized in series and parallel into a single pack for charging from a resource and discharging to an appliance, equipment, or any other electrically powered unit.

[0024] 1 illustrates a block diagram of an example intelligent energy management architecture 100 according to some implementations of the present disclosure. As illustrated, the intelligent energy management system 100 may include distributed appliances 103a-103n (collectively or individually referred to as appliances 103) and a server environment comprising one or more servers, including, but not limited to, an intelligent energy management server 101a and / or one or more third party servers 101n. The one or more appliances 103 may be coupled to a battery module 107a or 107n (collectively or individually referred to as battery module 107) and may include an intelligent energy management application 105a-105n (collectively or individually referred to as intelligent energy management application 105) for managing the coupled battery module 107. Each battery module 107 may be disposed inside or outside the appliance and may include an associated bidirectional converter 111a or 111n (collectively or individually referred to as a bidirectional converter 111) for charging or discharging the battery. In some implementations, the storage battery may not be directly coupled to a specific appliance, but rather may provide electricity to a number of different appliances, and thus may be referred to as a standalone battery 117. Also included within the intelligent energy management system 100 is a communication network 115 for establishing communication between different components of the system, and a grid / electrical infrastructure 123 for electricity transmission between components within the system or with outside sources. In some implementations, the intelligent energy management system 100 may optionally include a user device 119 configured to communicate with one or more appliances 103 and / or the server 101 (e.g., for remotely controlling one or more appliances or battery modules coupled to the appliance).

[0025] It should be noted that the scale of the intelligent energy management system 100 may vary and may include additional or fewer components than those illustrated in FIG. 1. In one embodiment, in its basic form, the intelligent energy management system 100 may include only one appliance 103, without necessarily including a server 101 and / or a client device 119. In another embodiment, there may be additional sensors 109a-109n (collectively or individually referred to as sensors 109) included within the intelligent energy management system 100. Additionally, in some implementations, interfacing between components of the intelligent energy management system 100 may occur remotely, e.g., components of the intelligent energy management system 100 may be distributed across one or more devices of a distributed network. For example, one user device 119 or server 101 may be configured to manage multiple appliances in (or through) a remote location through a distributed intelligent energy management application 105.

[0026] The appliance 103 may specifically be configured to include a storage battery module 107 that is directly coupled to the appliance, for example, by being integrated into, embedded within, or attached to the appliance. Examples of such appliances may include, without limitation, certain heating, cooling, and climate control appliances (e.g., central air conditioners, window mounted air conditioners, space heaters, electric hot water heaters, dehumidifiers, fans, air purifiers, and the like), kitchen appliances (e.g., electric ranges, microwave ovens, cooktops, ovens, refrigerators, coffee makers, toasters, mixers, dishwashers, cooktop burners, electric kettles, teapots, and the like), lighting, a wide variety of entertainment electronic devices (e.g., televisions, audio receivers, game consoles, and the like), and other appliances such as clothes dryers, hair dryers, or any other device or electrical power supply unit that may have high peak power requirements for residential, commercial, or industrial applications.

[0027] The battery module 107 is a type of electric battery that can be charged, discharged into a load, and recharged many times, as opposed to a disposable or primary battery. The battery module 107 may be composed of one or more electrochemical cells. Rechargeable batteries can come in many different shapes and sizes, ranging from button cells to megawatt systems that are connected to stabilize the electrical distribution network. In some implementations, the battery module 107 may be designed to have a size and shape that fits into a concrete instrument 103. In some implementations, different combinations of electrode materials and electrolytes can be used for the battery module 107, including, but not limited to, lead acid, zinc air, nickel cadmium (NiCd), nickel metal hydride (NiMH), lithium ion (Li-ion), lithium iron phosphate (LiFePO4), and lithium ion polymer (Li-ion polymer). In some implementations, the battery module 107, which is located inside or outside the instrument 103, is also replaceable. That is, the battery module 107 can be easily replaced (eg, by pulling out / pushing in) without necessarily affecting the functionality of the device 103 to which it is coupled.

[0028] The bi-directional converter 111 may convert alternating current (AC) to direct current (DC) in the direction from the power source (e.g., grid / electrical infrastructure 123) to the battery module 107, and from DC to AC in the direction from the battery module 107 to a power source or directly connected load requiring AC power (e.g., a refrigerator connected directly to a cooking table containing a battery and a bi-directional converter). In some implementations, the power source is a 110V power supply. In some implementations, the power source can be a 220V power supply, depending on the grid structure used by the supplier (e.g., in some Eastern countries). In addition, the DC output provided by the converter can also be in different scales. For example, the output can be 6V, 12V, 24V, or any other suitable voltage, depending on the appliance to which the bi-directional converter is coupled. In some implementations, the bi-directional converter 111 includes more than one converter. For example, bi-directional converter 111 may include a first AC-DC converter for a 110V power supply and a second AC-DC converter for a 220V power supply. For another embodiment, bi-directional converter 111 may include a first converter for outputting 6V power and a second converter for outputting 12V power. In some implementations, bi-directional converter 111 may include a DC-DC converter with or without including an AC-DC converter. In some implementations, the bi-directional converter may automatically configure itself to receive a range of AC and DC voltages.

[0029] The grid / electrical infrastructure 123 may provide power to the battery module 107 through the coupled bi-directional converter 111. The grid / electrical infrastructure 123 may be provided from an existing electrical infrastructure (e.g., an electrical grid inside a building or set of buildings, also referred to as grid power, fixed power, or fixed AC current). In one example, an existing circuit containing a circuit breaker element with a limit of 20 A (whether on 110V or 220V) may provide the power input to the bi-directional converter. In some implementations, as in an industrial setting, the existing circuit may include any voltage level or any number of phases, such as a three-phase 480V configuration. In some implementations, the power source may alternatively be a DC source, such as current from solar panels, local DC generators (e.g., gas generators), and the like.

[0030] The sensors 109 may include any number and / or type of sensors configured to collect information from the surrounding environment or system. In an embodiment, the sensors 109 may include, but are not limited to, image sensors, video sensors, audio sensors, temperature sensors, humidity sensors, light detection sensors, different weather sensors (e.g., wind direction sensors, wind direction sensors, rain gauges, UV sensors, sunlight sensors, solar radiation sensors, air pressure sensors, noise sensors, rain / snow sensors, evaporation sensors, etc.), and the like. These different sensors may be locally (e.g., on-board temperature sensors) or remotely coupled to the appliance and / or power source and may provide certain context information to the appliance 103, for example, to the intelligent energy management application 105 of the appliance. In some implementations, each sensor 109 is further configured to process signals detected by the sensor. It may include a signal processing unit. For example, based on the configured complexity, an audio sensor may be configured to identify text from audio, the likely age of the person (e.g., infant or elderly), the temperament of the person in the audio file, and the like. If more than one person is included in the audio file, such information may be identified for each person. For another embodiment, based on the configured complexity, a video sensor coupled to the cooking table may be configured to monitor the cooking process, including capturing images of the food being cooked. In another embodiment, based on the configured complexity, an image sensor coupled to a storage portion of the refrigerator may be configured to detect the type and number of food items stored in the refrigerator (e.g., number of eggs, number of vegetables, type of vegetables, etc.). In some implementations, other different sensors may be present, as described below.

[0031] In some implementations, the intelligent energy management system 100 may optionally include a client device 119 configured to communicate with one or more appliances 103 and / or the server 101. For example, through an instance of an intelligent energy management application 105p disposed on the client device 119, a user may check the power level of a storage battery, configure certain parameters of the battery, such as preferred charging time, preferred appliance for discharging, etc. In some implementations, such configuration information may be transmitted directly to the appliance 103, the stand-alone battery 117, or the server 101 through the communication network 115.

[0032] In some implementations, different instances of the intelligent energy management application 105p may reside on a single server 101a or may be spread across multiple servers (not shown) as desired or practical. The intelligent energy management server 101a may be implemented according to executable code and / or associated server components used to support computations on the server 101a.

[0033] In some implementations, the intelligent energy management server 101a may be a cloud server that possesses greater computation / communication capabilities and computing resources than the appliances 103, and thus may perform more complex computations or communications than the appliances 103 may perform. For example, a complex decision process for automatically determining an ideal daily schedule for charging and / or dynamically adjusting the power levels provided from the battery module 107 to the coupled appliances 103 may be implemented within an instance of the intelligent energy management application 105p on the intelligent energy management server 101a, while receiving user inputs for manually setting charging or discharging tasks may be implemented within an instance of the intelligent energy management application 105a or 105n on the appliances 103. For another example, the intelligent energy management application 105p may automatically communicate with one or more sensors to gather real-time contextual information to determine whether to start, stop, or adjust the power provided to the appliances in a timely manner. In some implementations, the intelligent energy management server 101a and the intelligent energy management application 105p may include additional functionality and components, as described in more detail in FIG. 2. In some implementations, partial or complete functionality of the intelligent energy management application 105p may be released to an intelligent energy management application resident in a specific appliance. In some implementations, the intelligent energy management application 105p may be provided via a Software as a Service (SaaS) platform.

[0034] In some implementations, additional data storage units may be coupled to the server 101a locally (e.g., to the data store 121 illustrated in FIG. 1) or remotely (not shown). The data store 121 may include one or more non-transitory computer-readable media and may collectively comprise logic data, executable code, instructions, and / or associated components to support storage, data management, and retrieval of data related to energy management. Energy management related data may include appliance data (model, type, manufacture, maintenance date, etc.), storage battery data (e.g., size, power level, number of units, etc.), bi-directional converter data (e.g., input / output type, number of units, etc.), user profile (e.g., user age, household size, etc.), user preferences for an appliance (e.g., shower water temperature, shower duration, washing machine duty cycle, etc.), weather data, recipes for a cooking table, characteristics (e.g., capturing raw performance and command data and profiling usage over time), or any other data related to intelligent energy management. In some implementations, energy management related data may also include operational data of components included within the system, including settings, states, levels, and the like related to batteries, loads, etc. In some implementations, the collected and / or stored data may be transmitted to a server and used by the server for intelligent management. For example, intelligent management of the data may be provided to the supply side (e.g., optimizing charging or costs for battery life) or to the demand side (e.g., planning for common use cases not explicitly commanded by the user) for performance improvement and optimization.

[0035] Third party servers 101n may provide additional services to the intelligent energy management server 101a or services may be provided directly to the appliances 103. The third party servers 101n may be provided using other logical server instances or may be included inside the intelligent energy management server 101a according to some implementations. The server features and services provided may relate to intelligent decision-making processes and / or other context-based services. Examples of other server services may include, but are not limited to, weather forecast services, outage maps, alert services, instant recipes, and other information delivery, etc.

[0036] In some implementations, the intelligent energy management server 101a may be further configured to facilitate communication of information between the appliances 103, the stand-alone battery 117, the client device 119, and possibly other third party servers 101n. For example, the appliances 103 may exchange calendar information through direct device-to-device information exchange, such as through the intelligent energy management server 101a via the communications network 115, directly between the appliances 103 via the communications network 115, and / or via local pairing or network connection (e.g., Bluetooth, near field communication, infrared, power line networking, etc.).

[0037] The communication network 115 may be of a conventional type, i.e., wired and / or wireless, and may have a number of different configurations, including a star configuration, a token ring configuration, or other configurations. For example, the communication network 115 may include one or more local area networks (LANs), wide area networks (WANs) (e.g., the Internet), public networks, private networks, virtual networks, mesh networks, peer-to-peer networks, and / or other interconnected data paths across which multiple devices may communicate. The communication network 115 may also be coupled to or include a portion of a telecommunications network for transmitting data in a variety of different communication protocols. In some implementations, the communication network 115 includes a Bluetooth® communication network or a cellular communication network for transmitting and receiving data, including via Short Messaging Service (SMS), Multimedia Messaging Service (MMS), Hypertext Transfer Protocol (HTTP), direct data connection, Wireless Application Protocol (WAP), email, and the like. In some implementations, during data transmission, certain security mechanisms may be implemented within the communication network 115 to ensure that no user information is leaked during data transmission between different components of the intelligent management system 100.

[0038] In some implementations, the intelligent energy management system 100 may include additional or fewer components than those illustrated in Figure 1. For example, the intelligent energy management system 100 may additionally include a power bus for connecting the different components included within the system. Intelligent Energy Management Applications

[0039] 2 illustrates example modules included within an intelligent energy management application 105, which may be an application resident in any of the appliances 103a-103n, the stand-alone battery 117, the user device 119, or the intelligent energy management server 101a. According to one implementation, the intelligent energy management application 105 may include a load and power supply characterization module 201, an energy distribution decision module 203a, a context information collection module 205, and a dynamic adjustment module 207. In some implementations, when multiple battery modules are shared among different appliances and / or different residential units, a battery aggregation module 209 and an energy redistribution module 211 may further be included within the intelligent energy management application 105.

[0040] The load and power supply characterization module 201 may be configured to characterize the way in which input energy is delivered (e.g., to identify power supply patterns) and the way in which appliances or associated loads consume power (e.g., to identify power consumption patterns). In some implementations, the intelligent energy management system 100 may optionally include an infrastructure configured to collect data from the household loads. For example, there may be one or more sensors for monitoring the electricity usage of each appliance. Such sensors may include certain current transducer sensors configured to monitor electricity usage for large loads that are wired to dedicated circuits, such as air conditioners, washers, dryers, dishwashers, refrigerators, and the like. These sensors may be connected to in-panel meters and sample pre-circuit electricity usage every second. In some implementations, these sensors may be configured to be turned on only during the data collection phase for a period of time, e.g., for pattern identification, and turned off after the data collection phase is over to conserve energy.

[0041] In some implementations, the load and power supply characterization module 201 may be further configured to identify patterns of electricity usage per appliance based on the collected data. For example, the load and power supply characterization module 201 may generate daily usage lines (e.g., averaged electricity usage at any moment of a day during the pattern identification phase) for the appliances based on data collected over one month, three months, six months, twelve months, etc. FIG. 3 illustrates an example plot containing three example power usage patterns identified for three appliances. The X-axis of the plot shows a day starting from 12:00 AM to 12:00 PM (a complete 24-hour period), the top portion of the Y-axis (e.g., +Y) shows the power produced by the solar panels, and the bottom portion of the Y-axis (e.g., -Y) shows the power usage consumed by different appliances. Note that the power production and power usage patterns in FIG. 3 are not scaled on the Y-axis, but rather reflect overall patterns during a typical day. For example, the actual power production may be multiples (e.g., 5 times, 10 times, 15 times, etc.) of the appliance's power usage. In addition, the different patterns illustrated in FIG. 3 are for illustrative purposes, and actual daily patterns on a particular day may differ from those patterns illustrated in FIG.

[0042] As can be seen, line 301 indicates that the corresponding appliance uses less power during daytime hours and more power during nighttime hours. Line 302 indicates that the corresponding appliance uses more power during meal times (such as morning, midday, and evening). Line 303 indicates that the corresponding appliance uses more power during morning and evening hours. It can be seen that line 302 is considered to have more peaks than the other two lines 301 and 303, which indicates that short periods of one or more very high demands for power are possible for the corresponding appliance. When such high demands for power exceed the limits of the power supply during these periods, in the absence of a battery module as disclosed herein, the performance of the corresponding appliance may be limited (e.g., longer cooking times than desired, lower cooking temperatures than expected, etc.). However, in the present disclosure, due to the integration of a battery module, the integrated battery actually makes the appliance more capable than would otherwise be possible. That is, the integrated battery can act as a buffer for peak power and ensure that there is sufficient power supply for the appliance when there is a power requirement that exceeds the limits of the existing power supply provided by the grid / electrical infrastructure.

[0043] In some implementations, such patterns may be identified for all connected appliances in the residential unit (e.g., appliances directly or indirectly coupled to the battery module). Based on such information, the appliances at any moment of the day and the amount of power they will likely use can be predicted. In some implementations, power production patterns may be identified as well. FIG. 3 further illustrates a power production pattern corresponding to a solar panel, shown by line 304. From the peaks in line 304, it can be seen that the production of solar energy peaks at approximately solar noon and decreases during dark periods such as at night or with cloud cover. From the power production and power usage patterns shown in FIG. 3, it can be seen that the supply of solar energy is very high during daytime periods, while the demand for energy is high during evening and morning periods when a user is in the residence.

[0044] In some implementations, there may be more than one pattern identified for an appliance or power source, such as a solar panel. For example, for an appliance, the power usage pattern may include a weekday pattern, a weekend pattern, a spring pattern, a summer pattern, a fall pattern, a winter pattern, a winter sunny pattern, a winter snowy pattern, etc. Each pattern may exhibit some variation compared to the others. Similarly, for a power source, such as a solar panel, this may also include a sunny day pattern, a cloudy day pattern, a spring pattern, a summer pattern, a fall pattern, a winter pattern, a winter sunny day pattern, a winter snowy day pattern, etc. In some implementations, not all patterns are generated based on data collection. Instead, a machine learning model may infer a pattern based on other available patterns and by considering additional information. For example, a summer cloudy day pattern for a sun pattern may be generated based on a summer sunny day pattern and further based on the amount of clouds on a cloudy day (which may be obtained based on weather forecast information).

[0045] The energy allocation determination module 205 may be configured to determine a charging or discharging schedule for the battery module in the residential unit based on the identified power production and power usage patterns. For example, the energy allocation determination module 205 may forecast power usage for a day based on the identified patterns and then manage the charging and discharging of the battery module based on the forecast. For example, the charging and discharging schedule may ensure that energy is purchased for storage when supply exceeds demand or prices are relatively low, i.e., negative, which can be backfed and used at times when demand exceeds supply or prices are relatively high, i.e., positive. In particular, the energy allocation determination module 205 may determine to purchase energy to charge the battery module during periods of peak solar availability, which are also periods of low, i.e., negative pricing, and to discharge during periods of peak demand, which are also periods of peak, i.e., positive pricing. Additional charging from the AC grid source may also be performed during periods of low demand, such as during the middle of the night.

[0046] It should be noted that the battery charging or discharging described above may generally work well for most days and / or most families under certain circumstances, but for a specific family and / or a specific day, there may be some power usage changes and / or some power production changes. For example, if a family is away on vacation, discharging may be unnecessary. Thus, in some implementations, the intelligent energy management application 105 may additionally include a context information collection module 205 configured to collect context information related to appliance power usage and power production related information in a specific situation (e.g., for a specific family and / or a specific day).

[0047] For example, the context information collection module 205 may receive calendar and travel information for a family member from a third party service (e.g., Google Calendar®, check-in alerts from email, etc.) and identify that on a certain day, the family member will be out for a day of vacation. This information may then be automatically forwarded to the dynamic adjustment module 207 to adjust the charging and / or discharging plan for the battery module on that day. For another example, the context information collection module 205 may receive a weather forecast indicating that a certain day will be cloudy. This information may cause the dynamic adjustment module 207 to charge from the grid structure during daytime hours, rather than from the solar panel. In another example, if the context information collection module 205 obtains information indicating that the family will be out for dinner, the dynamic adjustment module 207 may determine not to charge the battery module embedded in the cooking table. In some implementations, the dynamic adjustment module 207 may also include dynamically adjusting the charge level of the modular battery. For example, if there is heavy cloud cover and / or rain forecasted on a certain day, the dynamic adjustment module 207 may adjust the charging of the battery module for the air conditioner much less than planned, since it may not be necessary to turn on the air conditioner during that day. In addition, there may also be cases where there is little power produced by the solar panels. By minimizing the charging of the battery module for the air conditioner, the battery modules for other appliances may get the necessary charge from the solar panels during daylight hours.

[0048] In some implementations, where there are multiple battery modules connected to form a battery network within a dwelling unit, or between dwelling units within a building, or facility, or small community, electricity redistribution can also be performed across or between appliances (and other electric power supply units) during periods of low demand to synchronize or redistribute stored power among storage batteries over the discharge period. For example, consider the case of battery modules used for cooking during the evening that become partially discharged after use earlier in the evening. Those battery modules may be embedded within appliances such as cooking tables or ovens. Those battery modules may also be connected to the battery network and thus can provide electricity later in the evening for use in other non-kitchen uses, such as water heating for baths or the like. After the evening demand cycle ends, the battery modules for the kitchen appliances may then be recharged either by charging from the grid / electrical infrastructure (e.g., also referred to as grid power, fixed power, fixed AC, solar, or locally generated power) or through redistribution from other appliances (or standalone battery modules) in an idle state, such that the battery modules of the kitchen appliances are recharged by the time there is demand for use in the morning, for example, to cook breakfast. This recharging approach can be applied within a dwelling unit, as well as across dwelling units, across buildings, and across districts or systems to optimize the charging and discharging of the battery modules.

[0049] Thus, in some implementations, the intelligent energy management application 105 may additionally include a battery aggregation module 209 and a stored energy redistribution module 211. The battery aggregation module 209 may be configured to aggregate information about the remaining power level for each battery module in the battery network and also aggregate the predicted usage for the upcoming hours of the day for the appliances coupled to each battery module. By collecting all this information, the energy redistribution module 211 may then determine whether to redistribute the energy stored in the battery module to another different battery module and / or different appliances within the same dwelling unit or across different dwelling units, different buildings, different areas, and the like. In this way, there is a large reservoir of battery modules that can be shared among different appliances, which may further improve the stability of the battery modules, improve the efficiency of the battery modules in controlling peak power consumption, reduce high transient energy pricing, and / or handle power outages.

[0050] It should be noted that the modules described above for the intelligent energy management application are for illustrative purposes. In some implementations, the intelligent energy management application may include additional components and / or functions not described above. For example, there may be additional machine learning engines that aid in the decision-making process, especially when making instant decisions based on collected contextual information and / or aggregated information. For example, based on the contextual information, the machine learning model may predict when a particular user in a family will take a shower, how long the shower will last, and the temperature of water that will be used in the shower. Based on this information, the machine learning model may predict the amount of power that should be applied to an electrically powered water heater to ensure that there is adequate hot water ready for the user to take a shower. The functions and components of the intelligent energy management application are further described in detail below with reference to a battery module coupled to an appliance and a plurality of battery modules in a battery network. Intelligent Energy Management for Single Battery Modules

[0051] FIG. 4 illustrates an example appliance equipped with a battery module according to some implementations. As shown, the appliance 403 is equipped with a battery module 407, which may be integrated into the appliance. The appliance can be a household appliance such as a kitchen appliance or any possible appliance. The battery 407 is coupled to both a power source (e.g., a grid / electrical infrastructure 423) and a sink energy (e.g., a load 413) via a bidirectional connection using a single-phase or multi-phase bidirectional converter 411. The power source may be provided from the existing electrical infrastructure of a building or set of buildings.

[0052] A high power bus 410 is provided between the power source / load and a bi-directional converter 411 that converts AC to DC in the direction from the power source 423 to the battery 407 and DC to AC in the direction from the battery 407 to the power source 423. The high power bus 410 may also be connected to the battery 407, the bi-directional converter 411, and the high peak loads 413. In addition, the control unit 405 (embodied by an intelligent energy management application and associated hardware therein) may include sensors 409 and switches at various locations, such as in the bi-directional converter 411 and the battery module 407, and at or near the peak loads 413. For example, the sensors 409 may be located inside or outside the appliance 403. If the sensors are located inside the appliance, they may include built-in sensors (e.g., for a temperature controller built into a stove or range, infrared and / or contact sensors may be built into the appliance).

[0053] In some implementations, the control unit 405 may provide a physical user interface 417, such as manual controls, to a user. Additionally or alternatively, the control unit 405 may automatically control battery charging and discharging by use of a deterministic process and / or machine learning or artificial intelligence based predictive models to distribute power to the peak load 413. In some implementations, the control unit 405 may provide both peak power or average power, where the peak power can provide abrupt and high peaks of power for the peak load 413. In some implementations, the control unit 405 may also be configured to enhance or boost battery performance by some existing means. As shown, the control unit 405 may communicate with different components (e.g., components 407, 409, 411, 413, and 417 in FIG. 4) through a communication and power bus 420, which may be a normal (i.e., at a voltage and current typical of those for powering buried electrical devices) power bus.

[0054] In some implementations, the appliance 403 may be coupled to a data storage unit 430. The data storage unit 430 may be a local or remote data storage unit and may be configured to store data generated by the intelligent energy management application and / or data collected from other sources, such as from a third party service. For example, the data stored in the data storage unit may include, but is not limited to, feature characterization 431, content 433, AI / ML 435, over-the-air (OTA) updates 437, and additional services 419.

[0055] The characterization 431 data may include data related to power usage patterns generated for the appliance 403 or load 413 and power supply patterns generated for the battery module. The content 433 may include contextual information that may affect the appliance's power usage and power supply for the battery module. Such information may include weather information, user schedule information, user preferences, the user's age or health conditions, or any other information that may affect the appliance's power usage and power supply for the battery module. In some implementations, the content 433 may also include different methods of use of the appliance. For example, with respect to a cooking utensil, the content 433 may include recipes, cooking instructions, and other types of content related to the type of cooking and / or the cooking utensil being used. For example, with respect to a recipe that calls for the use of a wok, the content may include instructions or commands to control the power supplied to the wok or limit the wok accordingly (e.g., limit peak power and keep food from burning). In some implementations, the content 433 may additionally include information related to the power supply limitations and energy requirements of the appliance. It should be noted that the content 433 described above is not comprehensive and that some other information may also be included within the content 433.

[0056] The AI / ML 435 may include machine learning models that are pre-trained prior to deployment to the control unit 405 or intelligent energy management application. The AI / ML 435 may also include data related to the training of machine learning models and partially trained or fully trained models that are not deployed to the control unit 405 or intelligent energy management application. In some implementations, the AI / ML 435 may additionally include algorithms or applications used to train the machine learning models. The OTA updates 437 may include any information received over the air, which may include instructions received from a user through a mobile device to manually set or adjust the charging or discharging schedule of the battery module at any time. The OTA updates 437 may also include other information received over the air. For example, the user may also provide information about the food or recipes stored in the refrigerator that the user is using to cook, the wash cycle selected by the user, etc. In some implementations, the OTA updates may actually include software or firmware updates that make changes to any of the aforementioned modules or modules described later. Additional services 439 may include certain notification services, alert services, display or delivery of information, and any other services related to energy management.

[0057] Note that in some implementations, there may be multiple appliances directly coupled to a single battery module. When multiple loads are present within or across a commercial, industrial, or residential unit, the control unit 405 may provide time shifting and management of resources so that loads or power may be shared within or across commercial, industrial, or residential units. In some implementations, multiple battery modules are shared within these units instead. Applications of energy sharing are described in further detail below. Sharing stored energy

[0058] FIG. 5 illustrates an exemplary application scenario for sharing energy stored in an apartment building 510 according to some implementations. As illustrated, the exemplary apartment building 510 includes four individual apartment units, namely, unit 101, unit 102, unit 201, and unit 202. Each apartment unit may have several appliances (only one appliance is shown in each apartment unit in FIG. 5 ) with a specific peak load. Among these apartment units, at least two apartment units include one or more battery modules coupled to the appliances in each apartment unit. Among at least two apartment units, at least one apartment unit may include two or more battery modules in the apartment unit. In some implementations, the battery modules in these apartment units and the appliances connected to these battery modules form a battery network. Thus, the apartment building 510 may enable sharing of energy stored in batteries between different appliances in the same apartment unit or between different apartment units. As described above, power input for the apartment building may be provided by an electric service provider (e.g., PG&E®) 523 and by solar panels 525. Electrical energy can be distributed to each of the apartment units using existing circuits and infrastructure, including to battery modules and / or appliances that are connected (e.g., through bi-directional converters). In each apartment unit, a unit-level circuit panel may be provided, with one or more connected appliances on one of the circuits. When there is an associated battery, the appliance may additionally include a bi-directional converter, a power bus, sensors, a control unit, and / or a user interface, as described above in FIG. 4.

[0059] In some circumstances, it may be beneficial to share the energy stored in the battery modules between different appliances or apartment units with the battery network to improve energy usage efficiency. For example, when one apartment unit (e.g., unit 101) is not in use, such as when the occupant of the apartment unit is out of town, the energy stored in the battery modules of the appliances in that apartment unit may be redistributed to other connected units during periods of peak power consumption, high transient energy pricing, and / or during power outages. Thus, short-term redundant emergency use power can be provided at the building level. Similarly, within the apartment unit itself, the battery modules may provide backup power for another appliance during periods of peak power consumption, high transient energy pricing, and / or during power outages. As a result, the apartment building may be able to manage electricity usage in the context of electricity pricing or emergency situations.

[0060] In some implementations, to enable sharing of stored energy, a control unit for an appliance (also referred to as an appliance-specific control unit) may be coordinated (e.g., through communication and / or a power bus) with other control units for other appliances in the same apartment unit or between different apartment units in the apartment building 401. At this point, the appliance-specific control units may be implemented in a primary / secondary mode, where one of the appliance-specific control units serves as a primary control unit, while all other appliance-specific control units serve as secondary control units and listen to this primary control unit. Alternatively or in addition, there may be a specifically configured central control unit (e.g., a control unit resident in the external first and / or third-party service 509 in FIG. 5) that controls all the appliance-specific control units.

[0061] In some implementations, the central or primary control unit may be configured to monitor the instantaneous power usage of the connected appliances and the remaining power levels in the connected battery modules. The central or primary control unit may redistribute the energy stored in the connected battery modules based on demand. For example, if the occupant in apartment unit 101 is out of town, the energy stored in the battery module in apartment unit 101 may be redistributed to other apartment units if those apartment units are running low on power stored in their battery modules due to high peak loads.

[0062] In some situations, such as when a power outage occurs, there is a shortage of stored energy among all the appliances and / or apartment units. At this moment, the central control unit or primary control unit may redistribute the stored energy based on the priority. Thus, in some implementations, the central control unit or primary control unit may additionally include a priority determination module configured to determine power usage priorities for the appliances within the apartment units and / or within the building.

[0063] In some implementations, a priority determination module in the central control unit or primary control unit may determine a priority for each appliance based on the general functionality of each appliance. For example, the priority determination module may determine that refrigerators generally have a higher priority because food stored in the refrigerator may become spoiled if the refrigerator loses power for an excessively long period of time. Similarly, a priority for a water heater may be lower than a refrigerator but higher than other appliances such as a clothes dryer or dishwasher. In this manner, the priority determination module may rank the appliances contained within an apartment unit and / or the entire building based on the determined priority of each appliance.

[0064] In some implementations, the priority determination module may also determine priorities at the apartment unit level. That is, the priority determination module may determine that some apartment units in an apartment building have a higher priority than other units. For example, if an apartment unit has elderly people who generally require more intensive treatment, the apartment unit may have a higher priority than other apartment units. In the event of a power outage, stored power may be provided to the apartment unit first to ensure that the apartment unit has the necessary power. In one embodiment, if an elderly person in the apartment unit uses an electrically powered medical support device, it can be ensured through priority-based energy sharing that the medical support device will not lose power immediately. In another embodiment, the priority determination module may determine that an apartment unit may have a newborn baby, and therefore decide to increase the priority of the apartment unit and ensure that the room temperature in the apartment unit is stable.

[0065] In some implementations, the disclosed intelligent power management system may include one or more sensors configured to monitor apartment units in an apartment building in determining appliance-level and / or apartment unit-level priorities. In one example, the disclosed intelligent power management system may include an audio sensor to detect audio from the apartment unit and determine whether a newborn is present (e.g., from an infant crying) or whether an elderly person is present (e.g., from frequent coughing). In some implementations, a machine learning model trained on voice recognition may be included within the audio sensor or within the intelligent power management system to provide the necessary information for determining priorities.

[0066] In some implementations, the information obtained from the sensor may also allow for further adjustment of the priority of certain appliances and / or apartment units. In one embodiment, the intelligent power management system disclosed herein may include a video sensor coupled to a cookstove to monitor the cooking process and determine whether an enhanced power supply should be provided to the cookstove instantaneously according to a recipe (or some other protocol). At this point, the priority for the cookstove can be instantaneously increased. In another embodiment, a video sensor coupled to the inside of a refrigerator may determine the type of food stored in the refrigerator. In general, some types of food, such as vegetables, may be more temperature sensitive than other types of food, such as eggs. Thus, if the video sensor detects that there are hardly any temperature sensitive foods stored inside the refrigerator, the priority of the refrigerator can be downgraded to a much lower level based on the information obtained from the video sensor.

[0067] In some implementations, based on the priorities determined at the appliance level and / or apartment unit level, the central control unit or the primary control unit may determine how to distribute the stored power among different appliances, with the same apartment unit or among different apartment units, when multiple appliances and / or apartment units need power supply at approximately the same time and when there is not enough power supply for all these needs. In general, higher priority appliances or apartment units get higher priority for power redistribution when many appliances or apartment units need power supply at the same time (e.g., when a power outage occurs). For example, for an appliance or equipment (e.g., a medical support device) with an extremely high priority, the central control unit and the primary control unit may reserve the energy stored in the battery network by the battery module and ensure that there is enough power to start the appliance or device for at least a certain period of time (e.g., 1 hour, 2 hours, 4 hours, 8 hours, 16 hours, 24 hours, etc.). This can ensure that a limited amount of stored energy is used appropriately to increase the efficiency of the energy stored by the battery module. implementation

[0068] The disclosed implementations additionally include several methods for intelligent energy management for appliances equipped with battery modules. Figure 6 illustrates an example method for management of a single battery module, and Figure 7 illustrates an example method for management of multiple battery modules, according to some implementations.

[0069] An exemplary method 600 is illustrated with reference to an application scenario in which a single appliance (or multiple appliances) is connected to a single battery module. In step 612, the power usage of the appliance coupled to the battery module is first monitored. This includes monitoring the power usage of the appliance over a period of time (e.g., one month, three months, six months, twelve months, etc.). In step 614, a power usage pattern of the appliance connected to the battery module is identified. The power usage pattern may include a single pattern or may include a set of patterns, such as a weekday pattern, a weekday pattern, a seasonal pattern, etc. The method 600 further monitors the power supply of one or more power sources in step 616. For example, the availability or cost of power from a solar panel or a grid may increase or decrease throughout the day. For another example, the power supply from a solar panel may reach a peak at approximately noon hours. In step 618, a power supply pattern of one or more power sources for the battery module is identified. Similar to the power usage pattern, the power supply pattern may also include a set of patterns, such as a seasonal pattern, a cloudy day pattern, a sunny day pattern, etc. At step 620, a power charging and discharging schedule is determined for the battery module based on the identified power usage patterns of the appliance and the power supply patterns of one or more power sources that power the battery module. In one example, if a day is a summer weekend, the weekend summer patterns for the appliance and / or power source can be used to determine the schedule for charging and discharging the battery module.

[0070] In some implementations, the determined charging and discharging schedule for the battery module can be dynamically adjusted for a specific day and time since not every day is the same. Thus, the method 600 may further include step 622 of collecting context information related to the appliance's power usage and the power supply of one or more power sources that may affect the charging and / or discharging of the battery module. For example, a normal dinner is canceled due to a busy schedule, which then affects the power usage of the cooking table. The power discharge to the cooking table by the battery module coupled to the cooking table can then be changed. In step 624, the power charging and / or discharging schedule can then be dynamically adjusted based on the context information related to the appliance's power usage and / or the power supply of one or more power sources.

[0071] Although not specifically illustrated in FIG. 6, in some implementations, the battery module illustrated in FIG. 6 can be applied to multiple appliances. That is, there are two or more appliances connected to the same battery module, and thus the battery module may discharge to two or more appliances within a period. For example, based on demand, the battery module may provide electricity to a cooking table from 5:45 to 6:30 PM, and then provide electricity to an electric powered water heater from 8:30 to 9:30 PM during that day. In some implementations, if there are two connected appliances that simultaneously request power, and the battery module cannot provide electricity to both appliances simultaneously, the battery module may provide electricity to the appliance with a higher priority. In some implementations, if the battery module is embedded in the appliance, the appliance may have a higher priority than other appliances connected to the battery module. In some implementations, the priority may be determined through other means than those described above. For example, if the appliance has a peak power draw that is so high that it is practically unacceptable to start up on any kind of existing licensed home infrastructure, the appliance may have a higher priority than other appliances. For example, a power usage pattern for the appliance may be determined, and a power charging and discharging schedule may then be determined and / or dynamically adjusted based on the power usage pattern for the appliance to ensure that the appliance operates properly. In this manner, the disclosed system can actually free up appliance requirements and allow certain unacceptable appliances (e.g., appliances that are unacceptable to run on the existing home infrastructure without the battery modules disclosed herein) to be deployed on the home infrastructure.

[0072] The exemplary method 700 further illustrates an exemplary scenario in which multiple battery modules are shared among different appliances and / or different dwelling units. The method 700 begins by identifying, at step 712, a power usage pattern of two or more appliances connected to a battery network, and, at step 714, a power supply pattern of two or more battery modules in the battery network. The battery network includes two or more battery modules that can be charged and / or discharged. In some implementations, the battery network may refer to all battery modules, all appliances connected to the battery modules, and other accessory components such as sensors, control units, bidirectional converters, peak loads, etc. The power usage pattern per appliance and the power supply pattern per battery module may be determined according to the approach described above (e.g., as described in FIG. 6). At step 716, a charging and discharging schedule for the two or more battery modules in the battery network is determined based on the power usage pattern and the power supply pattern. In some implementations, the determined charging and discharging schedules may also be dynamically adjusted according to context information for a particular time on a particular day associated with each particular appliance, as described above (e.g., as described in FIG. 6 ).

[0073] In step 718, the method 700 may further determine whether there is a redistribution of stored power required for one or more appliances at a time point in a day. The stored power may refer to the sum of the remaining power stored in all the battery modules in the battery network. In some implementations, the instantaneous charge level for each battery module in the battery network may be determined respectively, and thus the total available power in the battery network may be determined accordingly. To determine whether a redistribution of stored power is required, the method 700 may check whether there is still remaining power in the battery network when the battery module directly coupled to the appliance has no power left or does not have enough power to supply the expected power usage of the appliance. Here, the battery module directly coupled to the appliance is a battery module that is disposed inside or outside the appliance and is primarily used to provide power to the appliance. In step 720, if it is determined that there is no redistribution of stored power required, the battery module included in the battery network may be charged and / or discharged according to the schedule determined in step 716.

[0074] On the other hand, if it is determined that redistribution is required for one or more appliances, the method 700 may further determine in step 722 whether there is more than one appliance requesting redistribution of power. If there is only one appliance requesting redistribution of power, the power stored in the battery network is redistributed to the appliance requesting redistribution of power in step 724. Power may be redistributed from the battery module with the highest remaining power, regardless of whether it takes into account the necessary power supply required to power the appliances directly coupled to the battery module before the next scheduled battery charge. In some implementations, the battery modules are preferably selected from the same dwelling unit. If it is determined that redistribution is required for more than one appliance at a point in time of the day, the method 700 may further determine the priority of the two or more appliances requesting redistribution of power in step 726. One of the appliances may have a higher priority than another and therefore may be more likely to have power redistributed to it. In some implementations, the priority may also be determined at the dwelling unit level. If an appliance from a dwelling unit has a higher priority, even if the appliance itself may have a lower priority, the appliance may still have a higher priority but be more likely to get a redistribution of power than another appliance that is from a dwelling unit with a lower priority.

[0075] In some implementations, machine learning models or other algorithms may further be employed to predict which appliances should have higher priority at any given time by taking into account changing conditions to ensure that power is appropriately reallocated at a single point in time, which in turn improves the efficiency of power usage for the power stored in the battery network. Additional Considerations

[0076] Throughout this specification, multiple instances may implement components, operations, or structures described as a single instance. Although individual operations of one or more methods are illustrated and described as separate operations, one or more of the individual operations may be performed in parallel and there is no requirement that the operations be performed in the order illustrated. Structures and functionality presented as separate components in the example configurations may be implemented as a combined structure or component.

[0077] Similarly, structure and functionality presented as a single component may be implemented as separate components. These and other variations, modifications, additions, and improvements are within the scope of the subject matter herein.

[0078] An implementation is described herein as including logic or some components, modules, or mechanisms, for example as illustrated and described in the figures above. The modules may be either software modules (e.g., code encoded on a machine-readable medium) or hardware modules. A hardware module is a tangible unit capable of performing an operation, and may be configured or arranged in a certain manner. In an exemplary implementation, one or more computer systems (e.g., stand-alone, client, or server computer systems) or one or more hardware modules (e.g., a processor or processors) of a computer system may be configured by software (e.g., an application or application portion) as a hardware module that operates to perform an operation as described herein.

[0079] In various implementations, a hardware module may be implemented mechanically or electronically. For example, a hardware module may include dedicated circuitry or logic (e.g., a special purpose processor such as a field programmable gate array (FPGA) or an application specific integrated circuit (ASIC)) that is permanently configured to perform certain operations. A hardware module may also include programmable logic or circuitry (e.g., contained within a general purpose processor or other programmable processor) that is temporarily configured by software to perform certain operations. It should be understood that the decision to mechanically implement a hardware module in dedicated and permanently configured circuitry or temporarily configured circuitry (e.g., configured by software) may be determined by cost and time considerations.

[0080] Various operations of the example methods described herein may be performed, at least in part, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Such processors, whether temporarily or permanently configured, may constitute processor-implemented modules that operate to perform one or more operations or functions. Modules referred to herein may, in some example implementations, include processor-implemented modules.

[0081] The one or more processors may also operate to support performance of related operations within a "cloud computing" environment or as a "software as a service" (SaaS). For example, at least some of the operations may be performed by a group of computers (as an example of a machine, including a processor), which are accessible over a network (e.g., the Internet) and via one or more suitable interfaces (e.g., application program interfaces (APIs)).

[0082] Performance of an operation may be distributed among one or more processors and spread across several machines, rather than residing solely within a single machine. In some example implementations, one or more processors or processor-implemented modules may be located within a single geographic location (e.g., within a home environment, an office environment, or a server farm). In other example implementations, one or more processors or processor-implemented modules may be distributed across several geographic locations.

[0083] Some portions of this specification are presented in terms of algorithms or symbolic representations of operations on data stored as bits or binary digital signals in a machine memory (e.g., computer memory). These algorithms or symbolic representations are examples of techniques used by those skilled in the data processing arts to convey the significance of their work to others skilled in the art. As used herein, an "algorithm" is a self-consistent sequence of operations or similar processes leading to a desired result. In this context, algorithms and operations involve physical manipulations of physical quantities. Typically, though not necessarily, such quantities may take the form of electrical, magnetic, or optical signals capable of being stored, accessed, transferred, combined, compared, or otherwise manipulated by a machine. It is sometimes convenient, primarily for reasons of common usage, to refer to such signals using terms such as "data," "contents," "bits," "values," "elements," "symbols," "characters," "terms," ​​"numbers," "numeric values," or the like. However, these terms are merely convenient labels and are to be associated with the appropriate physical quantities.

[0084] Unless specifically stated otherwise, discussions herein using terms such as "processing," "computing," "calculating," "determining," "presenting," "displaying," or the like may refer to the action or process of a machine (e.g., a computer) manipulating or transforming data represented as physical (e.g., electronic, magnetic, or optical) quantities in one or more memories (e.g., volatile memory, non-volatile memory, or a combination thereof), registers, or other machine components that receive, store, transmit, or display information.

[0085] As used herein, any reference to "one implementation" or "an implementation" means that a particular element, feature, structure, or characteristic described in connection with an implementation is included in at least one implementation. Appearances of the phrase "in one implementation" in various places in this specification are not necessarily all referring to the same implementation.

[0086] Some implementations may be described using the phrases "coupled" and "connected," along with their derivatives. For example, some implementations may be described using the term "coupled" to indicate that two or more elements are in direct physical or electrical contact. However, the term "coupled" can also mean that two or more elements are not in direct contact with each other, yet still cooperate or interact with each other. This implementation is not limited in this context.

[0087] As used herein, the terms "comprises," "comprising," "includes," "including," "has," "having," or any other variation thereof, are intended to cover non-exclusive inclusions. For example, a process, method, article, or apparatus that includes a list of elements is not necessarily limited to only those elements and may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Furthermore, unless expressly stated to the contrary, "or" refers to an inclusive "or," not an exclusive "or." For example, a condition A or B is satisfied by any one of the following: A is true (or present) and B is false (or absent), A is false (or absent) and B is true (or present), and A and B are both true (or present). Additionally, "automatic" may involve a fully automatic or semi-automatic implementation involving user or operator control of certain aspects of the implementation, depending on the desired implementation of one of ordinary skill in the art practicing the present disclosure.

[0088] In addition, the use of "a" or "an" is employed to describe elements and components of implementations herein. This is done merely for convenience and to give a general sense of the invention being claimed. The description should be read as including one or at least one, and the singular also includes the plural, unless it is clear that it is meant otherwise.

[0089] Additionally, sequential terminology such as "first," "second," "third," etc. may be used in the description and claims merely for labeling purposes and should not be limited to referring to actions or articles described occurring in a described sequence. Actions or articles may be ordered in different sequences or performed in parallel or dynamically without departing from the scope of the disclosure.

[0090] Upon perusal of this disclosure, those skilled in the art will recognize additional and further alternative structural and functional designs for the system described above. Thus, while specific implementations and applications have been illustrated and described, it should be understood that the disclosed implementations are not limited to the precise structures and components disclosed herein. It will be apparent to those skilled in the art that various modifications, changes, and variations can be made in the arrangement, operation, and details of the methods and apparatus disclosed herein without departing from the spirit and scope as defined in the appended claims.

Claims

1. 1. A system for intelligent power management, comprising: Equipment and a battery module coupled to the instrument, the battery module configured to provide power to the instrument; and a bi-directional converter coupled to the fixture and the battery module by a power bus, the bi-directional converter converting between alternating current (AC) and direct current (DC) and interfacing with a power infrastructure external to the fixture; a control unit communicatively coupled to the battery module, the bi-directional converter, and the appliance, the control unit configured to determine a charging and discharging schedule for the battery module; A system comprising:

2. The system of claim 1 , wherein the battery module is coupled to the instrument by being integrated into the instrument.

3. The system of claim 1 , wherein the battery module provides power to the appliance according to the determined charging and discharging schedule.

4. The system of claim 1 , wherein the control unit is further configured to determine a power usage pattern of the appliance and a power supply pattern of the power infrastructure for providing power to the battery module.

5. The system of claim 4 , wherein the control unit is further configured to determine the charging and discharging schedule for the battery modules based on the power usage pattern and the power supply pattern.

6. 13. The system of claim 1, further comprising one or more sensors or third party services coupled to the control unit and configured to collect contextual information related to one or more of the battery module or the appliance.

7. The system of claim 6 , wherein the control unit further comprises a machine learning model that is trained to predict usage of the appliance at a point in time based on the collected contextual information.

8. The system of claim 6 , wherein the control unit is further configured to adjust the determined schedule based on the collected context information.

9. 10. The system of claim 1, further comprising a physical user interface configured to receive user input for manually editing the charge and discharge schedule for the battery module at a point in time.

10. 10. The system of claim 1, wherein the instrument comprises two or more instruments, the battery module comprises two or more battery modules, and at least two of the two or more instruments are coupled to the two or more battery modules.

11. 11. The system of claim 10, wherein the control unit is further configured to redistribute power stored in the two or more battery modules by providing power to the appliance from a battery module not directly coupled to the appliance.

12. 1. A method of intelligent power management, comprising: collecting electrical usage data for the appliance over a first period of time; determining a power usage pattern for the appliance based on the power usage data; collecting power supply data of a battery module over a second time period, the battery module being coupled to the appliance via a bi-directional converter and configured to supply power to the appliance; determining a power supply pattern of the battery module based on the power supply data; determining a charging and discharging schedule for the battery module based on a power usage pattern of the appliance and a power supply pattern of the battery module; A method comprising:

13. collecting contextual information relating to one or more of a power usage of the appliance or a power supply for the battery module for a point in time of day; adjusting the charging and discharging schedule for the time point based on the collected context information; The method of claim 12 further comprising:

14. The method of claim 13 , wherein the contextual information is collected from one or more sensors coupled to one or more of the instrument or battery modules.

15. The method of claim 13 , wherein the contextual information is collected from one or more third-party services.

16. 1. A method of intelligent power management, comprising: collecting electricity usage data for two or more appliances; determining a power usage pattern for each of the two or more appliances based on the power usage data; collecting power supply data of two or more battery modules, at least two of the two or more instruments being coupled to the two or more battery modules via a set of bi-directional converters; determining a power supply pattern for each of the two or more battery modules based on the power supply data; determining a charging and discharging schedule for each of the two or more battery modules based on a power usage pattern of each of the two or more appliances and a power supply pattern of each of the two or more battery modules; A method comprising:

17. 17. The method of claim 16, further comprising dynamically adjusting a charging and discharging schedule of at least one of the two or more battery modules based on contextual information related to power usage of the at least one appliance.

18. determining whether a reallocation of power is required for one of the two or more appliances; redistributing power from the decoupled battery module to one of the two or more appliances when it is determined that one of the two or more appliances requires redistribution of the power.

20. The method of claim 16, further comprising:

19. determining whether a reallocation of power is required for at least two of the two or more appliances; determining a priority for each of the at least two of the two or more appliances when it is determined that the at least two of the two or more appliances require a reallocation of the power; 20. The method of claim 16, further comprising:

20. 20. The method of claim 19, further comprising reallocating power to at least two of the two or more appliances according to the determined priority of each of the at least two appliances.

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