Battery pack

Abstract

The invention discloses a battery pack for supplying power to an electric tool, and the battery pack comprises a sensor which generates sensor data indicating the working parameters of the battery pack; and an electronic controller comprising a machine learning program, the electronic controller configured to: receive the sensor data, process the sensor data using the machine learning program, the machine learning program comprising a trained neural network model, generate an output based on the sensor data using the machine learning program, the output at least indicates at least one of the charge state, the temperature state, the health state and the power state of the battery cell. And the effective utilization rate of the battery is improved.

Description

Technical Field

[0001] The present application relates to a battery pack, belonging to the field of electric tools. Background Art

[0002] In the related technologies, battery packs and battery management systems are core technologies in the field of power tools. They are related to the life, working capacity and user experience of DC power tools. In the related technologies, battery packs or power management systems usually generate battery charging and discharging strategies based on laboratory data, and then solidify the charging and discharging strategies in the internal embedded software and hardware systems to manage and protect the batteries. Once the battery pack leaves the battery assembly plant, in the event of a failure, the battery supplier needs to go to the site to obtain battery data and perform a fault analysis. At the same time, the pre-established battery charging and discharging strategies cannot optimize the charging and discharging efficiency of the battery under different environments. Summary of the invention

[0003] One object of the present application is to solve or at least alleviate part or all of the above problems. The present application provides a more intelligent battery pack with higher charging and discharging efficiency.

[0004] To achieve the above objectives, this application adopts the following technical solutions: A battery pack for powering an electric tool comprises: a shell; a battery cell group accommodated in the shell, the battery cell group comprising a plurality of battery cells; a sensor electrically connected to the battery cell, the sensor being configured to generate sensor data indicating operating parameters of the battery pack; and an electronic controller electrically connected to the sensor, the electronic controller comprising an electronic processor and a memory, the memory comprising a machine learning program for execution by the electronic processor, the electronic controller being configured to: receive the sensor data, process the sensor data using the machine learning program, the machine learning program comprising a trained neural network model, and generate an output based on the sensor data using the machine learning program, the output indicating at least one of the charge state, temperature state, health state, and power state of the battery cell.

[0005] In some embodiments, the neural network model is a physical knowledge embedded neural network model.

[0006] In some embodiments, the neural network model with embedded physical knowledge uses the partial differential equation of heat conduction as a constraint condition of the neural network.

[0007] In some embodiments, the sensor data includes at least one of current data, voltage data, temperature data, and time data.

[0008] In some embodiments, the neural network model is a deep neural network.

[0009] In some embodiments, the electronic controller resamples from a preset original data set to create enhanced samples, and trains a series of extreme learning machines based on the samples.

[0010] In some embodiments, the neural network model is a recurrent neural network.

[0011] In some embodiments, a communication module is further included, and a machine learning control program is generated outside the battery pack through training based on preset sensor data and related outputs, and is received by the communication module.

[0012] In some embodiments, the battery pack is a detachable battery pack.

[0013] In some embodiments, the battery pack is a rechargeable battery pack.

[0014] A battery pack for powering an electric tool comprises: a shell; a battery cell group accommodated in the shell, the battery cell group comprising a plurality of battery cells; a power tool interface disposed in the shell and configured to couple with the power tool; a discharge circuit electrically connected to the battery cell and the power tool interface, respectively, the discharge circuit being configured to transmit electric energy to the power tool via the power tool interface; a sensor electrically connected to the battery cell, the sensor being configured to generate sensor data indicating operating parameters of the battery pack; and an electronic controller, the electronic controller being at least used to control the discharge circuit, the electronic controller comprising an electronic processor and a memory, the memory comprising a machine learning program for execution by the electronic processor, the electronic controller being configured to: receive sensor data, process the sensor data using the machine learning program, generate output based on the sensor data using the machine learning program, and adjust a protection threshold of the battery pack based on the generated output.

[0015] In some embodiments, the electronic controller further includes a second machine learning program, and the second machine learning program controls the discharge circuit based on a protection threshold of the battery pack.

[0016] In some embodiments, the sensor data includes current data, voltage data, temperature data, and time data.

[0017] In some embodiments, the protection threshold includes at least one of an over-temperature protection threshold, an under-temperature protection threshold, an over-voltage protection threshold, an under-voltage protection threshold, and an over-current protection threshold.

[0018] In some embodiments, the electronic controller controls the power supply of the battery pack to the power tool based on a protection threshold.

[0019] In some embodiments, the protection threshold is sent to the power tool via a power tool interface, and the power tool controls power usage of the power tool based on the protection threshold.

[0020] In some embodiments, the second electronic controller is at least used to control the discharge circuit, and the second electronic controller controls the discharge circuit based on a protection threshold of the battery pack.

[0021] In some embodiments, the second electronic controller includes a machine learning program.

[0022] In some embodiments, the second electronic controller is disposed on the same circuit board as the electronic controller.

[0023] In some embodiments, the second electronic controller and the electronic controller are disposed on different circuit boards.

[0024] A battery pack for powering an electric tool comprises: a shell; a battery cell group accommodated in the shell, the battery cell group comprising a plurality of battery cells; a power tool interface disposed in the shell and configured to couple with and communicate with the power tool; a discharge circuit electrically connected to the battery cell and the power tool interface, respectively, the discharge circuit being configured to transmit electric energy to the power tool via the power tool interface; and an electronic controller, the electronic controller comprising an electronic processor and a memory, the memory comprising a machine learning program for execution by the electronic processor, the electronic controller being configured to: collect working data from the power tool through the power tool interface, process the working data using the machine learning program, generate output based on the working data using the machine learning program, and select a discharge mode and control the discharge circuit based on the generated output.

[0025] In some embodiments, the discharge mode includes a performance mode, a power consumption mode, and a balance mode.

[0026] In some embodiments, the discharge mode includes a series mode and a parallel mode.

[0027] In some embodiments, the machine learning program identifies the type of power tool based on the operating data.

[0028] In some embodiments, the machine learning program identifies the usage habits of the user of the power tool based on the work data.

[0029] In some embodiments, the electronic controller controls the power supply of the battery pack to the power tool based on the discharge mode.

[0030] In some embodiments, the discharge mode is sent to the power tool via a power tool interface, and the power tool controls power usage of the power tool based on the discharge mode.

[0031] In some embodiments, a communication module is further included, and a machine learning control program is generated outside the battery pack through training based on preset sensor data and related outputs, and is received by the communication module.

[0032] In some embodiments, the machine learning control program is generated outside the battery pack by training based on preset sensor data and related outputs, and sent to the battery pack by the power tool via the power tool interface.

[0033] In some embodiments, each discharge mode predefines parameter information such as discharge voltage, discharge current, maximum discharge current, and discharge cut-off voltage, and combinations thereof.

[0034] A battery pack comprises: a shell; a battery cell group accommodated in the shell, the battery cell group comprising a plurality of battery cells; a display device disposed in the shell and configured to indicate a state of the battery pack; a sensor electrically connected to the battery cell group, the sensor being configured to generate sensor data indicating operating parameters of the battery pack; a circuit board electrically connected to the battery cell group and the sensor, the circuit board being provided with a plurality of electronic components; an electronic controller disposed in the circuit board, the electronic controller being electrically connected to the sensor, the electronic controller comprising an electronic processor and a memory, the memory comprising a machine learning program for execution by the electronic processor, the electronic controller being configured to: receive sensor data, process the sensor data using the machine learning program, generate output based on the sensor data using the machine learning program, the generated output indicating a fault of the plurality of battery cells or the plurality of electronic components, and control the display device to display an abnormal state according to the generated output.

[0035] In some embodiments, the battery pack further includes a communication module, the communication module is configured to establish a communication connection with a remote device, and the electronic controller is further configured to: based on the generated output, control the communication module to send the fault to the remote device.

[0036] In some embodiments, the remote device is at least one selected from the group consisting of: a server, a smart phone, a tablet computer, and a laptop computer.

[0037] In some embodiments, the communication module includes at least one device selected from the following group: Bluetooth, local area network, wireless, and cellular network.

[0038] In some embodiments, the output includes cell impedance, cell temperature data, life warning, aging warning, or installation fault warning.

[0039] In some embodiments, the display device receives the control signal and conveys the status information to the user through a device that generates a visual signal or an auditory signal.

[0040] In some embodiments, the display device includes an indicator light, a buzzer, or a display screen.

[0041] In some embodiments, the battery pack also includes: a power tool interface, disposed on the housing, configured to couple to and communicate with the power tool, and the electronic controller is further configured to: send a fault to the power tool through the power tool interface based on the generated output.

[0042] In some embodiments, the battery pack also includes: a charging power tool interface, which is disposed on the housing and is configured to couple with the charger and enable the charger to charge the battery pack. The electronic controller is also configured to: send a fault to the charger through the power tool interface based on the generated output.

[0043] In some embodiments, the electronic controller predicts battery lithium deposition, folding, and structural changes by implementing a neural network.

[0044] A battery pack comprises: a shell; a battery cell group accommodated in the shell, the battery cell group comprising a plurality of battery cells; a communication module, the communication module being configured to establish a communication connection with a remote device; a sensor, electrically connected to the battery cell group, the sensor being configured to generate sensor data indicating operating parameters of the battery pack; a circuit board, electrically connected to the battery cell group and the sensor, the circuit board being provided with a plurality of electronic components; an electronic controller, disposed on the circuit board, the electronic controller being electrically connected to the sensor, the electronic controller comprising an electronic processor and a memory, the memory comprising a machine learning program for execution by the electronic processor, the electronic controller being configured to: receive sensor data, process the sensor data using the machine learning program, generate output based on the sensor data using the machine learning program, the generated output indicating a fault of the plurality of battery cells or the plurality of electronic components, and control the communication module to send the fault to the remote device according to the generated output.

[0045] In some embodiments, the remote device is at least one selected from the group consisting of: a server, a smart phone, a tablet computer, and a laptop computer.

[0046] In some embodiments, the communication module includes at least one device selected from the following group: Bluetooth, local area network, wireless, and cellular network.

[0047] The benefit of the present application is that it provides a method for managing a battery pack based on a machine learning program. By using a machine learning program to indicate at least one of the state of charge, temperature, health, and power state of the battery cell group, the discharge strategy of the battery pack is optimized, and the fault alarm and protection threshold adjustment of the battery pack are more accurate and precise. The safety of battery use is improved, and the effective utilization rate of the battery is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is an exemplary diagram of a controlled device in the exemplary embodiment of the present application; Figure 2 is a schematic diagram of an exemplary battery management system in the present application; Figure 3 is a schematic diagram of a structure of a battery management system in this application; Figure 4a-4c is a schematic diagram of the second structure of the battery management system in this application; Figure 5a-5c is a schematic diagram of the third structure of the battery management system in this application; Figure 6 is a schematic diagram of the fourth structure of the battery management system in this application; Figure 7 is a schematic diagram of the fifth structure of the battery management system in this application; Figure 8 is a schematic diagram of the sixth structure of the battery management system in this application; Fig. 9 is a schematic diagram of the structure of the battery pack in this application; Fig.10 is a schematic diagram of the structure of a battery cell group of the battery pack in the present application; Fig.11 is a schematic diagram of the structure of the charger in this application; Fig.12 is a schematic diagram of another structure of the charger in this application; Fig.13 is a schematic diagram of another structure of a battery pack in the present application; Fig.14 is a schematic diagram of the third structure of the charger in this application; Fig.15 It is the control framework diagram of the battery module as a controlled device; Fig.16 It is a schematic diagram of the battery module structure; Fig.17a is a schematic diagram of the state of the half-bridge output terminals when the bypass circuit is inserted; Fig.17b is a schematic diagram of the state of the half-bridge output terminals when the bypass circuit is bypassed; Fig.18 is a method for building and implementing machine learning programs; Fig.19 is an exemplary block diagram of a power management system; Fig. 20 is an exemplary block diagram of a control component; Fig.21 is a flow chart of controlling the output of the battery pack usage status using a battery management system using a machine learning program; Fig. 22 It is a PINN framework diagram for estimating battery temperature distribution; Fig.23 It is a general framework for data cleaning, partial charge separation, feature extraction, SOH estimation, post-processing, and SOH and RUL prediction; Fig.24It is a flowchart of using a battery management system to use a machine learning program to control the charging and discharging management of a battery pack; Fig.25 is a block diagram of discharge management; Fig.26 It is a block diagram of charging management. DETAILED DESCRIPTION

[0049] Before any embodiments of the application are explained in detail, it is to be understood that the application is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the above drawings.

[0050] In this application, the terms "comprises", "includes", "has" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of more restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device that includes the element.

[0051] In this application, the term "and / or" is a description of the association relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this application generally indicates that the related objects before and after are in an "and / or" relationship.

[0052] In the present application, the terms "connect", "combine", "couple", and "install" may refer to direct connection, combination, coupling, or installation, or indirect connection, combination, coupling, or installation. For example, direct connection refers to two parts or components being connected together without the need for an intermediate piece, and indirect connection refers to two parts or components being connected to at least one intermediate piece respectively, and the two parts or components being connected via the intermediate piece. In addition, "connect" and "couple" are not limited to physical or mechanical connections or couplings, and may include electrical connections or couplings.

[0053] In this application, it will be understood by those of ordinary skill in the art that relative terms (e.g., "about," "approximately," "substantially," etc.) used in conjunction with quantities or conditions include the values ​​and have the meaning indicated by the context. For example, the relative terms include at least the degree of error associated with the measurement of a specific value, the tolerances caused by manufacturing, assembly, and use associated with a specific value, etc. Such terms should also be considered to disclose a range defined by the absolute values ​​of the two endpoints. Relative terms may refer to plus or minus a certain percentage (e.g., 1%, 5%, 10% or more) of the indicated value. Numerical values ​​that do not use relative terms should also be disclosed as specific values ​​with tolerances. In addition, "substantially" may refer to plus or minus a certain degree (e.g., 1 degree, 5 degrees, 10 degrees or more) on the basis of the indicated angle when expressing a relative angular position relationship (e.g., substantially parallel, substantially perpendicular).

[0054] In this application, it will be understood by those skilled in the art that the function performed by a component can be performed by one component, multiple components, one part, or multiple parts. Similarly, the function performed by a part can also be performed by one part, one component, or a combination of multiple parts.

[0055] In the present application, the terms "upper", "lower", "left", "right", "front", "back" and other directional words are described based on the orientation and positional relationship shown in the accompanying drawings, and should not be understood as limiting the embodiments of the present application. In addition, in the context, it is also necessary to understand that when it is mentioned that an element is connected to another element "upper" or "lower", it can not only be directly connected to another element "upper" or "lower", but also indirectly connected to another element "upper" or "lower" through an intermediate element. It should also be understood that directional words such as upper side, lower side, left side, right side, front side, back side, etc. not only represent the positive orientation, but can also be understood as the lateral orientation. For example, the bottom can include directly below, lower left, lower right, lower front, and lower back, etc.

[0056] In this application, the terms "controller", "processor", "central processing unit", "CPU", and "MCU" are interchangeable. When a unit "controller", "processor", "central processing unit", "CPU", or "MCU" is used to perform a specific function, unless otherwise specified, these functions can be performed by a single unit or multiple units.

[0057] In the present application, the terms “device”, “module” or “unit” may be implemented in the form of hardware or software to achieve specific functions.

[0058] In this application, the terms "calculate", "judge", "control", "determine", "identify", etc. refer to the operations and processes of a computer system or similar electronic computing device (e.g., controller, processor, etc.).

[0059] like Figure 1-2 As shown, a battery management system 100 is provided for use in power tools and non-road vehicles. The battery management system 100 is used for: discharging management when a battery pack 11 supplies power to a power tool or vehicle using the battery pack 11 and charging management when a charging device charges the battery pack 11.

[0060] In the present embodiment, the electric tool is a chain saw 12. In some embodiments, the electric tool can also be a garden tool, such as a pruning machine, a hair dryer, a lawn mower, a snow sweeper, a cleaning machine, etc. Alternatively, the electric tool can also be a decoration tool, such as a screwdriver, a wrench, an electric hammer, a nail gun, a sander, etc. Alternatively, the electric tool can also be a saw tool, such as a reciprocating saw, a jigsaw, etc. Alternatively, the electric tool can also be other bench-type tools, such as a bench saw, a metal cutter, a bakelite milling machine, etc. Alternatively, the electric tool can also be a grinding tool, such as an angle grinder, a sander, etc. Alternatively, the electric tool can also be other power tools, such as a lamp, a fan, etc. It can be understood that the electric tool can also be a power head, which includes a motor. The power head is used to adapt some output components to realize the function of the tool.

[0061] In some embodiments, the battery pack 11 supplies power to a walking device 13 using the battery pack. The walking device 13 is a riding lawn mower that can walk outdoors and mow grass. It is understandable that the walking device 13 can also be other vehicles that only walk outdoors, such as multi-purpose vehicles, and can also be beach cars, utility vehicles (UTV), golf carts, and all-terrain vehicles (ATV). The walking device 13 can also be a vehicle that can realize another function in addition to the walking function, such as a snowplow, which can walk outdoors and also realize the function of snow removal. It can also be an agricultural machinery vehicle, such as a harvester, a sprayer, etc. Of course, it is understandable that for the walking device, it can also be a cleaning machine.

[0062] like Fig.11As shown, the charging device is a charger 14 that can charge different types of rechargeable battery packs. The charger 14 includes: a housing 141, a battery pack interface 146, a power interface 142, a communication module 143, a communication protection module 144 and a power conversion circuit 145. Among them, the battery pack interface 146 includes: a charging terminal 1461 and a communication terminal 1462. Among them, the battery pack interface 146, the power interface 142, the communication module 143, the communication protection module and the power conversion circuit are arranged in the housing 141. The battery pack interface 142 is used to couple with the battery pack 11. The communication module is coupled with the communication terminal 1462 and is arranged in the housing for receiving, analyzing and processing the communication information of the battery pack sent by the battery pack. In this embodiment, the communication terminal of the charger and the communication terminal of the battery pack are single bus communication.

[0063] The power interface 142 is used to access the power supply. In this embodiment, the power supply can be selected as an AC power supply, and the power interface 142 can be connected to 120V or 220V AC mains. The power interface 142 is connected to a power conversion circuit to convert the connected AC power into charging power suitable for charging the battery pack 11 and auxiliary power for powering the internal components of the charger 14, such as power supply power for the communication module. It is understood that the power conversion circuit may include at least an AC / DC module. In an alternative implementation, the power interface can be connected to a photovoltaic panel, and the power conversion circuit can convert light energy into charging power suitable for charging the battery pack and auxiliary power for powering the internal components of the charger. It is understood that the power conversion circuit may include an MPPT (Maximum Power Point Tracking, solar controller) module, that is, a module that converts light energy into electrical energy. In this embodiment, the housing 141 is connected to a power cord plug as the power interface 142. In an alternative implementation, the power interface may also be an interface in other forms, which is not limited here.

[0064] The charging terminal 1462 is disposed on the housing and is detachably connected to a terminal of the battery pack 11. The communication terminal 1462 may be disposed adjacent to the charging port 1461 and is detachably connected to a communication terminal of the battery pack 11.

[0065] like Fig.12 As shown, the charger 14B includes a plurality of battery pack interfaces 146b, that is, the charger 14B can charge a plurality of battery packs 11 at the same time.

[0066] like Figure 9-10As shown, the battery pack 11 includes a housing 111, a battery cell group 112a, a sensor 18a, a display device 114, an interface and a battery pack controller 181. The housing 111 is assembled to form a housing space to fix the battery cell group 112a. The battery cell group 112a includes one or more battery cells 1121, and the battery cell 1121 is used to store energy. The interface is used to achieve electrical connection with an external circuit. In this embodiment, the interface includes a battery pack positive terminal 115 and a battery pack negative terminal 116 for achieving electrical connection. The battery pack 11 is coupled to the power tool through the interface to power the power tool. The battery pack 11 is coupled to the charger through the interface so that the charger charges the battery pack.

[0067] The battery pack 11 also includes a communication terminal 117, which communicates with the power tool. The communication terminal 117 is connected to a communication module 118. The battery pack 11 is detachably connected to the power tool to provide power to the power tool. When the battery pack needs to be charged, the battery pack is detachably connected to the charger. The positive terminal 115, the negative terminal 116 and the communication terminal 117 of the battery pack are respectively coupled to the battery pack interface 146 of the charger 14, and the battery pack is charged through the charging circuit.

[0068] like Figure 2 As shown, the battery management system 100 manages the battery pack 11 itself, the work between the battery pack 11 and the power tool 12 or the walking device 13, and the work between the battery pack 11 and the charger 14 based on the machine learning program. Among them, in the battery management system, the battery pack and the components coupled thereto constitute the controlled device 10. Optionally, the controlled device 10 includes the battery pack 11, the power tool such as the chain saw 12 or the walking device 13 powered by the battery pack 11, and the charger 14 for charging the battery pack 11. Optionally, the battery pack 11 and the electronic circuit or electrical component matched with the battery pack 11 constitute an electronic combination, for example, the battery pack and the battery pack charging and discharging circuit, the battery pack and the switch element for controlling the battery pack.

[0069] The sensor data includes sensor data collected by one or more sensors 18 disposed in the controlled device 10. The sensor 18 includes a voltage sensor, a current sensor, a temperature sensor, an inertial sensor, and a position sensor.

[0070] The sensor data includes: The operating parameters of the battery pack include, for example, the performance parameters of the battery pack: current data, voltage data, temperature data or internal resistance data. It also includes, for example, the current charging capacity of the battery pack (for example, the charging state of the battery pack), the number of charging cycles experienced by the battery pack, the estimated remaining service life of the battery pack, the re-retrieval time (for example, the time related to the speed at which the battery pack is removed from the power tool battery charger), and the operating time related to the battery pack.

[0071] The state of charge of a battery pack may be, for example, a one-dimensional state of charge (eg, percentage of charge, voltage, coulombs, ampere-hours (“Ah”), etc.).

[0072] It also includes, for example, the operating time of the battery pack, for example, how long the battery pack is used in each use cycle, the amount of time between two working uses of the battery pack, etc. It also includes, for example, the frequency of use of the battery pack, the frequency with which the battery pack is used with a specific power tool or type of power tool, the frequency with which the battery pack is charged on a specific charger or type of charger.

[0073] In some embodiments, the operating parameters may include data indicating the use of a particular battery. Other parameters of the battery pack may also be included, such as maintenance data of the battery pack.

[0074] In some embodiments, the sensor data also includes charging power input type data, such as AC power, DC power, battery power. Other examples include: 120V wall socket, 220V power socket, solar energy, gas inverter, wireless charger, another power tool battery pack, another power tool battery charger, built-in battery, supercapacitor, built-in energy storage device, vehicle. Sensor 18 data may also include: data on electrical characteristics or characteristics of the power grid or circuit associated with the charging power supply. For example, whether it trips frequently or has a connected power outage.

[0075] Sensor data also includes: environmental data. Environmental data includes data indicating characteristics or aspects of the environment in which the charger, battery pack, power tool, or walking device is located. For example, environmental data may include data associated with weather, temperature of the surrounding environment (e.g., outside temperature), humidity of the surrounding environment, etc.

[0076] The sensor data also includes: operator data. The operator data may include data indicating the operator and / or owner of the charger, battery pack, power tool, or walking device. For example, the operator data may include an operator identifier (ID), an owner ID, or both.

[0077] The sensor data also includes: location data. The location data may include data indicating the location of the charger, battery pack, power tool, or walking device. In some embodiments, the location data may indicate the physical location of the charger, battery pack, power tool, or walking device. For example, the physical location may be represented using geospatial coordinates, such as those determined via a Global Navigation Satellite System (GNSS) or the like. As another example, the physical location may be represented as a site location (e.g., an address, an identification of a site location) and may include a location within the site (e.g., a specific floor in a skyscraper or other building under construction). In some other embodiments, the location data may indicate the location of a power tool battery charger, battery pack, and / or power tool for inventory management and tracking.

[0078] In some embodiments, the battery management system includes a cell module 112. The cell module 112 includes a sensor 18b. The sensor 18b is used to collect operating parameters of the cell module 112 during use of the cell module 112, and the sensor 18b generates sensor data indicating the operating parameters of the cell module. This will be described in detail below.

[0079] like Figure 2 As shown, an exemplary battery management system 100 is shown. The battery management system 100 includes: a controlled device 10, a remote device 15, and a wireless communication device 16. Taking the controlled device as a battery pack 11 as an example, the battery pack 11 includes a sensor 18a. The sensor 18a is used to collect operating parameter data of the battery pack during use of the battery pack 11. The sensor 18a generates sensor data indicating the operating parameters of the battery pack.

[0080] In some embodiments, the remote device 15 includes, for example, a server 151 and a smart device 152. The smart device 152 includes a smart phone, a tablet computer, a cellular phone, a laptop computer, a smart watch, and the like.

[0081] The wireless communication device 16 may be a long-range wireless network, the wireless communication device 16 may communicate over different types of wireless networks using other protocols (e.g., Wi-Fi, cellular protocols, proprietary protocols, etc.). For example, the wireless communication device 16 may be configured to communicate via Wi-Fi over a network such as the Internet, a local area network (“LAN”), a wide area network (“WAN”), or a combination thereof, or to communicate via a piconet (e.g., using infrared or NFC communications). In other embodiments, the wireless communication device 16 may be a short-range communication protocol (such as Bluetooth), and in other embodiments, the wireless communication device 16 may be a wired network using, for example, a serial protocol (e.g., USB, USB-C, FireWire, etc.).

[0082] In some embodiments, the server 151 can send information to the smart device 152 for forwarding to the battery pack 11. In some embodiments, the communication module 118 of the battery pack 11 is equipped with a long-range transceiver instead of or in addition to a short-range transceiver. In such an embodiment, the battery pack 11 communicates directly with the server 151. In some embodiments, the battery pack 11 can communicate directly with the server 151 and the smart device 152. In such an embodiment, the smart device 152 can, for example, generate a graphical user interface to facilitate the control and programming of the battery pack 11, while the server 151 can store and analyze a larger amount of operating parameter data for future programming or operation of the battery pack 11. However, in other embodiments, the battery pack 11 can communicate directly with the server 151 without utilizing a short-range communication protocol with the smart device 152.

[0083] An electronic controller 17 implementing a machine learning program is provided in the battery management system 100 . The electronic controller 17 includes an electronic processor 171 and a memory 172 , wherein the memory 172 includes a machine learning program for execution by the electronic processor 171 .

[0084] The electronic controller 17 is configured to provide the required output from a given set of input data or data sets. Optionally, the machine learning program is trained by the data set and provides output for the test data. The learning information in the machine learning program is provided by the existing data. As the number of data samples increases, the accuracy of the machine learning program will increase. The machine learning program can be configured to use different types of methods to perform machine learning. For example, the electronic controller 17 can use decision tree learning, association rule learning, artificial neural networks, recurrent artificial neural networks, long short-term memory neural networks, inductive logic programming, support vector machines, clustering, Bayesian networks, reinforcement learning, representation learning, similarity and metric learning, sparse dictionary learning, genetic algorithms, k nearest neighbors (KNN), attention networks / transformers and others to implement machine learning programs.

[0085] The electronic controller 17 is programmed and trained to perform specific tasks. The electronic controller 17 can predict the characteristic data of the battery based on multiple operating parameters of different types through training. The electronic controller can generate an ideal charge and discharge strategy based on multiple operating parameters of different types and environmental data through training. The electronic controller 17 can generate an optimal battery protection control strategy based on multiple operating parameters of different types and other data through training. The electronic controller 17 can generate an optimal battery balancing control strategy based on multiple operating parameters of different types and other data through training.

[0086] A plurality of different training example data is provided to the electronic controller 17. The electronic controller 17 uses these training examples to generate a model (e.g., rules, set of equations, etc.) that helps to classify or estimate outputs based on new input data. The electronic controller 17 can weigh different training examples differently, for example, to prioritize different conditions or outputs from the electronic controller. In some embodiments, the training examples are weighted differently by associating different cost functions or values ​​with specific training examples or types of training examples.

[0087] In one embodiment, the electronic controller 17 implements an artificial neural network. The artificial neural network generally includes an input layer, a plurality of hidden layers or nodes, and an output layer. Generally, the input layer includes as many nodes as the inputs provided to the electronic controller 17. As described above, the number (and type) of inputs provided to the electronic controller 17 may vary based on the specific task of the electronic controller 17. Therefore, the input layer of the artificial neural network of the electronic controller 17 may have different numbers of nodes based on the specific task of the electronic controller 17. The input layer is connected to the hidden layer. The number of hidden layers is variable and may depend on the specific task of the electronic controller 17. In addition, each hidden layer may have different numbers of nodes and may be connected to the next layer differently. For example, each node of the input layer may be connected to each node of the first hidden layer. The connection between each node of the input layer and each node of the first hidden layer may be assigned a weight parameter. In addition, a bias value may also be assigned to each node of the artificial neural network. However, each node of the first hidden layer may not be connected to each node of the second hidden layer. That is, some nodes of the first hidden layer may not be connected to all nodes of the second hidden layer. The connections between the nodes of the first hidden layer and the second hidden layer are respectively assigned different weight parameters. Each node of the hidden layer is associated with an activation function. The activation function defines how the hidden layer processes the input received from the input layer or the previous input layer. These activation functions can vary and can vary not only based on the type of task associated with the electronic controller 17, but also based on the specific type of hidden layer implemented. Each hidden layer can perform different functions. For example, some hidden layers can be convolutional hidden layers, which can reduce the dimensionality of the input in some cases, while other hidden layers can perform more statistical functions, such as maximum pooling, which can reduce the group of inputs to a maximum value, averaging layers, etc. In some hidden layers (also called "dense layers"), each node is connected to each node of the next hidden layer. Some artificial neural networks that include more than, for example, three hidden layers can be considered deep neural networks. The last hidden layer is connected to the output layer. Similar to the input layer, the output layer typically has the same number of nodes as possible outputs.

[0088] The electronic controller 17 and the electronic processor 171 can implement different methods of combining the outputs from the electronic controller 17. During training, the artificial neural network receives inputs of training examples and generates outputs using the bias of each node and the connection between each node and the corresponding weight. The artificial neural network then compares the generated output with the actual output of the training example. Based on the generated output and the actual output of the training example, the neural network changes the weights and / or biases associated with each node connection. In some embodiments, the neural network also changes the weights associated with each node during training. Training will continue until the training condition is met. The training condition can correspond to, for example, a predetermined number of training examples being used, a minimum accuracy threshold being reached during training and validation, a predetermined number of validation iterations being completed, optimized training compared to testability testing, etc. Based on the training examples, different types of training algorithms can be used to adjust the bias values ​​and weights of the node connections. The training algorithms can include, for example, gradient descent, Newton's method, conjugate gradient, quasi-Newton, Levenberg Marquardt, etc.

[0089] In another embodiment, the electronic controller 17 implements a support vector machine to perform classification. The electronic controller 17 can, for example, classify whether a cell or an interface is aging. In such an embodiment, the electronic controller 17 can receive any two or more of the battery voltage V, battery current I, battery temperature T, battery internal resistance R, or usage time. The battery voltage V and the battery current I can be, for example, a discharge voltage and a discharge current or a charging voltage and a charging current, etc. Then, the electronic controller 17 uses a combination of some input variables as a support vector to define the margin to maximize the margin. In some embodiments, the electronic controller 17 uses the sum of more than one similar input variables to define the margin. The margin corresponds to the distance between the two closest vectors of different classifications. In some embodiments, the electronic controller 17 uses more than one support vector machine to perform a single classification. For example, when the electronic controller 17 classifies whether a cell or an interface is aging, the first support vector machine can determine whether the cell or the interface is aging based on the battery voltage V and the battery current I, and the second support vector machine can determine whether the cell or the interface is aging based on the battery internal resistance R and the usage time. Then, when both support vector machines classify a cell or interface as aged, the electronic controller 17 can determine aging. In other embodiments, a single support vector machine can use more than two input variables and define a hyperplane that separates cells or interfaces that are aging from cells or interfaces that are not aged.

[0090] A training example for a support vector machine includes: an input vector that includes values ​​for input variables; and an output classification that indicates whether a cell or interface indicates aging. During training, the support vector machine selects a support vector (e.g., a subset of the input vector) that maximizes the margin. In some embodiments, the support vector machine can define a line or hyperplane that accurately separates aging cells or interfaces from non-aging cells or interfaces. However, in other embodiments (e.g., in the case of inseparability), the support vector machine can define a line or hyperplane that maximizes the margin and minimizes the slack variable, which measures the error in the classification of the support vector machine. After the support vector machine is trained, new input data can be compared to the line or hyperplane to determine how to classify the new input data. In other embodiments, as described above, the electronic controller 17 can implement different machine learning algorithms to estimate or classify based on a set of input data.

[0091] like Figure 3 As shown, in the battery management system 200, the electronic controller 17a is set on the remote device 25. The transceiver of the remote device 25 communicates with the controlled device 20. The remote device 25 takes a server 251 as an example, and the server 251 also includes a processor 2511 of the server and a storage 2512 of the server. The electronic processor of the electronic controller 17a receives data or data sets from sensors (18a, 18b) of the battery pack or battery cell module, sensors of the power tool or walking equipment, or sensors of the charger. In some embodiments, the server 251 uses the received data or data sets as additional training examples. In other embodiments, the server 251 sends the received data or data sets to the trained electronic controller 17a. The electronic controller 17a then generates an estimate or classification based on the input data or data set. The processor 2511 of the server then generates recommendations for future operations of the battery pack or battery cell module.

[0092] The server 251 may send the suggested operating parameters to the smart device 252. The smart device 252 may display the suggested changes to the operating parameters and request confirmation from the user to implement the suggested changes before forwarding the changes to the controlled device 20 (battery pack or electronic combination). In other embodiments, the smart device 252 forwards the suggested changes to the controlled device 20 and displays the suggested changes to inform the user of the changes implemented by the controlled device 20.

[0093] In some embodiments, the electronic controller 17a provided on the server 251 communicates the generated required output data to the battery pack 21, that is, the battery pack 21 controls the charging and discharging of the battery pack 17a based on the received output data of the electronic controller 17a. In some embodiments, the electronic controller 17a provided on the server 251 communicates the generated required output data to the power tool 22 or walking device, and the controller on the power tool or walking device controls the discharge of the battery pack based on the output data. In some embodiments, the electronic controller 17a provided on the server 251 communicates the generated required output data to the charger 23, and the controller on the charger 23 controls the charging of the battery pack 21 based on the output data.

[0094] like Figure 4a-4c As shown, in the battery management system 300, the electronic controller 17b is disposed in the controlled device 30. The battery management system 300 includes the controlled device 30, the remote device 35 and the wireless communication device 16. In some embodiments, Figure 4a As shown, the electronic controller 17b is set in the battery pack 31 as an example. The battery pack 31 includes an electronic controller 17b, and the electronic controller 17b is provided with an offline trained machine learning program. In this embodiment, the communication module 118 of the battery pack 31 receives the offline trained machine learning program from the server 351 or the communication module 118 of the battery pack 31 receives the offline trained machine learning program from the server 351 through the wireless communication device 16. In some embodiments, the battery pack 31 receives the offline trained machine learning program during manufacturing, while in other embodiments, the user of the battery pack 31 can choose to receive the offline trained machine learning program after it has been manufactured and after the operation of the battery pack 31. The offline trained machine learning program is also a trained machine learning program similar to the electronic controller 17a set in the server 251. The offline trained machine learning program has been trained using different training examples and is configured to receive new input data and generate estimates or classifications for the new input data.

[0095] The controlled device 30 communicates with the server 351. The smart device 152 may also provide additional functionality to the controlled device 30 (e.g., generating a graphical user interface). The server 351 for offline training of a machine learning program may utilize operating parameter data from a battery pack similar to the battery pack 31, and use training examples of the operating parameter data of the battery pack 31 received from the battery pack 31 to train the offline trained machine learning program. The server 351 then sends the trained offline trained machine learning program to the electronic controller 17b of the controlled device, such as the battery pack 31, for execution during future operation of the battery pack 31.

[0096] Therefore, the offline trained machine learning program includes, for example, a trained machine learning program provided at the time of manufacture. During the future operation of the battery pack 31, the offline trained machine learning program analyzes the operating parameter data of the new battery pack 31 from the controlled device and generates recommendations or actions based on the new operating parameter data. The offline trained machine learning program has one or more specific tasks. In other embodiments, the tasks of the offline trained machine learning program may be different. In some embodiments, the user can use a graphical user interface generated by, for example, the smart device 152 to select a task for the offline trained machine learning program. The smart device 152 can then send the target task for the offline trained machine learning program to the server 351. The server 351 then sends the trained machine learning program to the offline trained machine learning program, which is trained for the target task. Based on the estimation or classification from the offline trained machine learning program, the controlled device 30 can change its operation, adjust one of the operating modes of the battery pack 31 and / or adjust different aspects of the battery pack 31. In some embodiments, the controlled device 30 may include more than one offline trained machine learning program, each offline trained machine learning program having a different target task. This will be described in detail below.

[0097] In this embodiment, the electronic controller 17b is disposed in the battery pack 31. The battery pack 31 includes a sensor 38a and a communication module 118, and receives the offline trained machine learning program through the communication module 118. The training process of the offline trained machine learning program is completed in the server 351. The battery pack 31 receives the offline trained machine learning program from the server 351. In some embodiments, the training process of the offline trained machine learning program can be completed in other smart devices 152. The battery pack 31 receives the offline trained machine learning program from the smart device 152. In some embodiments, the training process of the offline trained machine learning program is completed in the remote device 35 (server 351 and / or smart device 152), and the external device 35 sends the offline trained machine learning program to the power tool 32 or the charger 33 through the wireless communication device 16, and then when the battery pack 31 is connected to the power tool 32 or the charger 33 through the interface, the offline trained machine learning program is sent to the electronic controller 17b of the battery pack 31. The battery pack 31 controls the charging and discharging of the battery pack 31 based on the output data generated by the electronic controller 17b.

[0098] like Figure 4bAs shown, the electronic controller 17b is set in the charger 34. The charger 34 receives the offline trained machine learning program through the communication module 143. The training process of the offline trained machine learning program is completed in the remote device 143 (server 351 and / or smart device), and the remote device 35 sends the offline trained machine learning program to the electronic controller 17b in the charger 33 through the wireless communication device 16. The charger receives the working parameter data of the battery pack 31 input by the battery pack 31 or the battery cell module sensor through the interface, and controls the charging condition of the battery pack 31 through the data output by the machine learning program of the electronic controller 17b in the charger.

[0099] like Figure 4c As shown, the electronic controller 17b is set in the power tool 32 or the walking device. The power tool 32 or the walking device receives the offline trained machine learning program through an internal communication module (not shown). The training process of the offline trained machine learning program is completed in the remote device 35 (server 351 and / or smart device), and the remote device 35 sends the offline trained machine learning program to the electronic controller 17b in the power tool or the walking device through the wireless communication device 16. The power tool or the walking device receives the working parameter data of the battery pack 31 input by the battery pack 31 or the battery module sensor through the interface, and controls the discharge of the battery pack 31 through the data output by the machine learning program of the electronic controller 17b in the power tool 32 or the walking device.

[0100] Optionally, the data received by the electronic controller 17b include: current data, voltage data, temperature data or internal resistance data, and also include the charging state of the battery, the type of power source connected, the power supply cost of the connected power source, overheating of the battery and / or charger, and whether the circuit breaker on the connected circuit is tripped. The data received by the electronic controller 17b include: power tool equipment data, including other usage data, maintenance data, user feedback data, power data, environmental data, operator data, location data and other data. The data received by the electronic controller 17b include: the operating mode of the charger, for example, pre-charging mode, constant current regulation mode, constant voltage regulation mode, fast charging mode, the operating time of each mode, and the operating frequency of each mode.

[0101] like Figure 5a-5c As shown, in the battery management system 400, the electronic controller 17c is set in the controlled device 40, and the electronic controller 17c is provided with a dynamic machine learning program. The electronic controller 17c receives the machine learning program from the server 251 through the wireless communication device 16. Unlike the machine learning program trained offline, the server 251 can send an updated version of the machine learning program to the electronic controller 17c of the dynamic machine learning program to replace the previous version.

[0102] The controlled device 40 sends feedback about the operation of the dynamic machine learning program to the server 251. Taking the battery pack 41 as an example, the electronic controller 17c is set in the battery pack 41, and the battery pack 41 can send an indication of the number of operations incorrectly classified by the dynamic machine learning program to the server 251. The server 251 receives feedback from the battery pack 41, updates the machine learning program, and provides the updated program to the dynamic machine learning program to reduce the number of operations incorrectly classified. Therefore, the server 351 updates or retrains the dynamic machine learning program based on the feedback received from the battery pack 41. In some embodiments, the server 251 also uses feedback received from other similar battery packs to adjust the dynamic machine learning program. In some embodiments, the server 251 updates the dynamic machine learning program regularly (for example, every month). In other embodiments, when the server 251 receives a predetermined number of feedback indications (for example, after the server receives two feedback indications), the server 251 updates the dynamic machine learning program. The feedback indication may be positive (e.g., indicating that the dynamic machine learning program correctly classified the condition, event, action, or combination thereof), or the feedback may be negative (e.g., indicating that the dynamic machine learning program incorrectly classified the condition, event, action, or combination thereof). In some examples, the feedback indication may be numerical (e.g., a cell's State of Health (SoH)) or categorical.

[0103] In some embodiments, the server 251 also updates the dynamic machine learning program using usage data and / or sensor data (raw or processed) received from the battery pack 41 and other similar battery packs. For example, the server 251 can periodically retrain the dynamic machine learning program (or adjust the training) based on newly received usage data. The server 251 then sends the updated version of the dynamic machine learning program to the controlled device (e.g., battery pack).

[0104] When the controlled device 40 receives an updated version of the dynamic machine learning program (for example, when the updated machine learning program is provided to and stored on the electronic controller), the controlled device 40 replaces the current version of the dynamic machine learning program with the updated version. In some embodiments, the controlled device 40 is equipped with a first version of the dynamic machine learning program during manufacturing. In such an embodiment, a user can request a newer version of the dynamic machine learning program. In some embodiments, the implementation of updates and / or the use of machine learning functions can be provided by subscription, purchase and / or rental. In some embodiments, the user can select the frequency with which the dynamic machine learning program is sent to the electronic controller. In other examples, the operation or mode use of the controlled device 40 can be disabled until a newer version of the electronic controller 17c is established on the controlled device 40 and / or a calibration step of the electronic controller 17c and / or the controlled device is performed.

[0105] like Figure 5b As shown, the electronic controller 17c is disposed in the charger 44. The charger 44 receives the working parameter data of the battery pack 31 input by the battery pack 31 or the battery module sensor through the interface, and controls the charging condition of the battery pack 41 through the data output by the machine learning program of the electronic controller 17c in the charger.

[0106] like Figure 5c As shown, the electronic controller 17c is set in the power tool 42 or the walking device. The power tool 44 or the walking device receives the working parameter data of the battery pack 41 input by the battery pack 31 or the battery module sensor through the interface, and controls the discharge of the battery pack 31 through the data output by the machine learning program of the electronic controller 17b in the power tool 32 or the walking device.

[0107] like Figure 6 As described above, the dynamic machine learning program of the electronic controller 17d provided in the controlled device 40 can be self-updated. The self-updating dynamic machine learning program receives operating parameter data of a new battery pack from a sensor of the controlled device 40 (e.g., the battery pack 41), feedback information indicating a desired change in the operating parameter data (e.g., the user wants to increase the charging rate), feedback information indicating whether the classification made by the electronic controller 17d is incorrect, or a combination thereof. The self-updating dynamic machine learning program then uses the received information to retrain the self-updating dynamic machine learning program.

[0108] In some embodiments, the controlled device 40 retrains the self-updating dynamic machine learning program when the battery pack 41 is charged and discharged. The self-updating dynamic machine learning program is trained at the end of the battery pack charge and discharge to allow more processing power to be used during retraining without competing for computing resources normally used to manage the battery pack charge and discharge.

[0109] In some embodiments, the controlled device 40 also communicates with the smart device 152 and the server 251. Taking the battery pack 41 as an example, the smart device 152 communicates with the battery pack 41. The smart device 152, such as a smart phone, generates a graphical user interface to facilitate the adjustment of the operating parameter data of the battery pack 41. The smart device 152 can also bridge the communication between the battery pack 41 and the server 251. In some embodiments, the smart device 152 receives the selection of the target task for the electronic controller 17d. Then, the smart device 152 can request the corresponding machine learning program from the server 251 for transmission to the electronic controller 17d. The battery pack also communicates with the server 251 (e.g., via a smart device). In some embodiments, the server 251 can also retrain the self-updating machine learning program. The server 251 can use additional training examples from other similar battery packs. The use of these additional training examples can provide greater variability and ultimately make the electronic controller 17d more reliable. In some embodiments, when the battery pack is not charging or discharging, the battery pack retrains the self-updating machine learning program, and when the battery pack enters a charging or discharging state, the server 251 can retrain the machine learning program. Thus, in some embodiments, the self-updating machine learning program can be retrained on the battery pack, by a server, or by a combination thereof.

[0110] In some embodiments, the server 251 does not retrain the self-updating machine learning program, but still exchanges information with the battery pack. For example, the server 251 can provide other functions for the battery pack, such as sending information about various operating modes of the battery pack.

[0111] Figures 1 to 6 Each battery management system in which the controlled device communicates with a server and a smart device. The smart device can bridge the communication between the controlled device, such as a battery pack, and the server. That is, the battery pack can communicate directly with the smart device. The smart device can then forward the information received from the battery pack to the server. Similarly, the server can send information to the smart device, which forwards to the battery pack. In such an embodiment, the battery pack may include a communication module to communicate with the smart device via, for example, a short-range communication protocol (such as Bluetooth(R)). The smart device may include short-range communication to communicate with the battery pack, and may also include a long-range transceiver to communicate with the server. In some embodiments, a wired connection is provided between the battery pack and the power tool (e.g., via a USB data cable) to enable direct communication between the external device and the battery pack. Providing a wired connection can provide a faster and more reliable communication method between the external device and the battery pack.

[0112] like Figure 7As shown, in the battery management system 500, the controlled device 10 can directly communicate with the smart device 552. The electronic controller 17 is set in the smart device 552, wherein the electronic controller 17 can be any one of the electronic controller 17a, the electronic controller 17b, the electronic controller 17c and the electronic controller 17d.

[0113] The server includes at least a server electronic processor, a server memory, and a transceiver to communicate with the controlled device via a wireless communication device. The server electronic processor receives the operating parameter data of the battery pack, stores the operating parameter data of the battery pack in the server memory, and in some embodiments, uses the received operating parameter data of the battery pack to establish or adjust the electronic controller. Each of the remote devices is located outside the controlled device. In addition, in some embodiments, the remote device is a wireless hub, such as a beacon device placed at a construction site to monitor tools, used as a gateway network device (e.g., providing a Wi. network), or both. The remote device includes at least an input / output unit for communication (e.g., a wireless or wired transceiver), a memory for storing instructions, and an electronic processor that executes the instructions stored in the memory to perform functions attributed to the external system device.

[0114] like Figure 8 As shown, when the dynamic machine learning program can update itself, in the battery management system 400b, the controlled device 40b may not communicate with the smart device or server. In contrast, since the electronic controller 17d in the controlled device 40b includes a self-updating machine learning program, the electronic controller 17d can implement the machine learning program, receive user feedback, and update the electronic controller without communicating with the smart device or server.

[0115] As an embodiment of the present application, the battery management system includes a battery cell module 112. That is, the controlled device also includes a battery cell module 112. Optionally, the battery management system includes multiple groups of battery cell modules 112. Figure 15-16 As shown, each group of battery modules includes at least one battery cell 1121, a slave controller 1122, a sensor 18b, a wireless communication module 1123 and a bypass circuit 1124. The slave controller 1122 is electrically connected to the sensor 18b and the wireless communication module 1123, respectively. The slave controller 1122 is configured to receive sensor data indicating the working parameters of at least one battery cell 1121 generated by the sensor 18b, and send the sensor data to the electronic controller 17 through the wireless communication module 1123. The electronic controller 17 includes a machine learning program that can be implemented. The slave controller 1122 can also obtain the return information of the electronic controller 17 through the wireless communication module 1123, and control the bypass circuit 1124 based on the return information.

[0116] In this embodiment, the electronic controller 17 is configured to: receive sensor data of multiple groups of battery cell modules 112 from the multiple groups of battery cell modules 112 through the wireless communication module 1123, use a machine learning program to process the sensor data of the multiple groups of battery cell modules 112, use a machine learning program to generate return information based on the sensor data of the multiple groups of battery cell modules 112, and send the return information to the slave controller 1122 through the wireless communication module 1123.

[0117] In some embodiments, the electronic controller 17 is set on the remote device, and the electronic controller 17 communicates the generated required output data to the wireless communication device 16 to the wireless communication module 1123 of the battery module 112, and the battery module 112 controls the charging and discharging of the battery module 112 based on the received output data of the electronic controller 17. In some embodiments, the electronic controller 17 set on the remote device communicates the generated required output data to the power tool 12 or walking device, and the controller on the power tool or walking device controls the discharge of the battery module 112 based on the output data. In some embodiments, the electronic controller 17 set on the remote device communicates the generated required output data to the charger 13, and the controller on the charger 13 controls the charging of the battery module 112 based on the output data.

[0118] In some embodiments, the electronic controller 17 is disposed in the battery pack 11, the slave controller 1122 is disposed in the battery module 112, and the electronic controller 17 communicates with the slave controller 1122 via the wireless communication module 1123. Optionally, the electronic controller 17 is disposed in the power tool 12 or the walking device, and when the battery pack is connected to the power tool 12, the battery controller 17 communicates with the slave controller 1122 via the wireless communication module 1123, and the wireless communication module 1123 includes a transmitter disposed in the power tool and a receiver disposed in the battery module. Optionally, the electronic controller 17 is disposed in the charger 14, and the battery controller 17 communicates with the slave controller 1122 via the wireless communication module 1123, and the wireless communication module 1123 includes a transmitter disposed in the charger 14 and a receiver disposed in the battery module.

[0119] Optionally, the machine learning program can be set on a remote device, and can be an offline trained machine learning program, a dynamic machine learning program, or a self-updating dynamic machine learning program. It can be understood that the location of the electronic controller and the specific form of the machine learning program can be any one or a combination of the above disclosed embodiments.

[0120] For the battery cell module and the battery pack provided with the battery cell module. In this embodiment, the battery cell module 112 is electrically coupled to the slave controller 1122 through a half-bridge circuit. The half-bridge circuit includes a switch element, and the control signal of the electronic controller 17 is output to the slave controller 1122. The slave controller 1122 controls the on-off of the switch element to control the charging and discharging of the battery cell. The switch element controls the on-off of the bypass circuit. Fig.17a As shown in FIG. 1 , when the bypass circuit of the cell module is turned on, current flows through the cells inside it, that is, the cells are connected to the circuit. Fig.17b As shown, when the bypass circuit of the battery module is disconnected, the current bypasses the battery cell inside it, that is, bypasses the battery cell. By connecting one or more battery cells to the bypass circuit, the control of the battery pack is based on safety. During the charge and discharge control process of the battery pack, each battery cell 1121 has differences. In order to reduce the impact of the differences in the battery cell 1121 on the overall safety of the battery pack, it is necessary to balance the battery cells. In this embodiment, the balancing method using the bypass circuit is actively balanced by the bypass circuit without using a bidirectional DC-DC converter. Since no additional inductor / capacitor is used in the active balancing method, it has better efficiency. When performing battery pack control and protection, it can be performed from the battery cell. The slave controller determines which bypass circuit to control, and then which battery cell to control, through the control signal of the electronic controller. The control is more efficient and improves safety and reliability.

[0121] In this embodiment, the switch element includes a controllable semiconductor power device (such as a field effect transistor (FET), a bipolar junction transistor (BJT), an insulated gate bipolar transistor (IGBT), etc.). The sensor 18b monitors the cell voltage, current and temperature.

[0122] In some embodiments, different types of electronic controllers are used in conjunction with controlled devices (battery packs, power tools or walking equipment, chargers, battery modules). In some embodiments, the controlled device may include more than one electronic controller, and each electronic controller may be of a different type. In some embodiments, for example, two electronic controllers are included in the battery pack, or both the battery pack and the power tool are provided with electronic controllers. The battery pack may include an electronic controller 17b for setting an offline trained machine learning program, which may be set as a first electronic controller, and may also include an electronic controller 17d for setting a self-updating machine learning program, which may be set as a third electronic controller. Optionally, the battery pack may include a first electronic controller, but the offline trained machine learning program may be subsequently removed and replaced by, for example, a self-updating machine learning program.

[0123] In some embodiments, the electronic controller 17 includes a reinforcement learning program that allows the electronic controller 17 to continuously integrate feedback received from the user to optimize the performance of the electronic controller 17. In some embodiments, the reinforcement learning program controls the periodic evaluation of a reward function based on the performance of the machine learning control. In such embodiments, training the electronic controller includes increasing the operating time of the discharge or charge of the battery pack so that the reinforcement learning control receives sufficient feedback to optimize the execution of the machine learning program. In some embodiments, when reinforcement learning is implemented by the electronic controller, the first stage of operation (e.g., training) is performed during or before manufacturing so that when the user uses the battery pack, the machine learning program can achieve a predetermined minimum performance (e.g., accuracy). The machine learning program, once the user uses the battery pack, can continue to learn and evaluate the reward function to further improve its performance. Therefore, the battery management system can initially be equipped with a stable and predictable algorithm that can be adjusted over time. In some embodiments, reinforcement learning is limited to a portion of the machine learning program. For example, in some embodiments, instead of potentially updating weights / biases of all or most of a machine learning program, which may take up a lot of processing power and memory, the actual model is kept frozen or mostly frozen (e.g., except for the last layer or output), and one or a few output parameters or output features of the machine learning program (e.g., a final scaling or threshold) are updated based on the feedback.

[0124] like Fig.18 Method 1000 for establishing and implementing a machine learning program. The machine learning program may be established and operated by a server. Alternatively, the machine learning program may be established by a server but implemented by a controlled device. Alternatively, the controlled device establishes and implements the machine learning program.

[0125] 1010, access exemplary sensor data collected by the sensor. The server or the controlled device receives battery pack usage data from various controlled devices.

[0126] 1020, establish and train a machine learning program, which can be performed in a server or in a controlled device.

[0127] 1030, storing the machine learning program, i.e., storing it in the electronic controller.

[0128] 1040, controlling the power management system according to the machine learning program, the discharge management when the battery pack supplies power to the electric tool or walking equipment using the battery pack, and the charging management when the charging device charges the battery pack.

[0129] 1050, receiving user feedback.

[0130] 1060, adjusting a machine learning program based on user feedback.

[0131] When the electronic processor adjusts the machine learning program based on user feedback, the electronic processor can adjust the machine learning program based on feedback from different users. In embodiments where the electronic controller is fully implemented on the battery management system, the electronic processor can use feedback indications from only the battery pack to adjust the electronic controller of the battery management system. Using feedback information from a particular user can help customize the operation of the battery management system for the user of that particular battery management system.

[0132] 1070, controlling a power management system based on the regulated machine learning program.

[0133] In some embodiments, the user may also select a learning rate for the electronic controller 17. Adjusting the learning rate of the electronic controller 17 affects the speed at which the electronic controller adjusts based on received user feedback. For example, when the learning rate is high, even a small number of feedback indications from a user (or multiple users) may affect the performance of the electronic controller. On the other hand, when the learning rate is lower, more feedback indications from the user are used to produce the same performance change of the electronic controller. Using a learning rate that is too high may cause the electronic controller to change unnecessarily due to abnormal operation of the battery pack. On the other hand, using a learning rate that is too low may cause the electronic controller to remain unchanged until a large number of feedback indications requesting similar changes are received. In some embodiments, the battery management system includes a dedicated actuator to adjust the learning rate of the electronic controller.

[0134] like Fig.19As shown, in another embodiment, the activation switch 185 is used to enable or disable the electronic controller and can also be used to adjust the learning rate of the electronic controller. In other embodiments, an external device (e.g., a smart phone, a tablet computer, a laptop computer, an ASIC, etc.) can be coupled to, for example, a battery pack and provide a user interface, for example, to select a learning rate. In some embodiments, the selection of the learning rate can include the selection of a low, medium, or high learning rate. In other embodiments, more or fewer options can be used to set the learning rate and can include the ability to turn off learning (i.e., setting the learning rate to zero).

[0135] When an electronic controller implements a recurrent neural network, the learning rate (or sometimes referred to as the "switching rate") affects how previous inputs or training examples affect the output of the current input or training example. For example, when the switching rate is high, the previous input has the least effect on the output associated with the current input. That is, when the switching rate is high, each input is treated more as an independent input. On the other hand, when the switching rate is low, the previous input has a high correlation with the output of the current input. That is, the output of the current input is highly dependent on the output determined for the previous input. In some embodiments, the user can select a switching rate that is related to the learning rate (e.g., using the same actuator). However, in other embodiments, a separate actuator (or graphical user interface element) is generated to change the switching rate independently of the learning rate. The methods or components for setting the switching rate are similar to those described above for setting the learning rate.

[0136] The electronic controller 17 may be provided with any one or more of the machine learning programs described above. The electronic controller 17 is electrically and / or communicatively connected to various modules or components of the battery management system 100. The electronic controller 17 controls the battery pack based on outputs and determinations from the machine learning programs.

[0137] like Figure 19-20As shown, the battery management system also includes a general controller 181. Taking the battery pack 11 as an example, the general controller 181 and the electronic controller 17 form a control component. Among them, the general controller 181 includes a general processor 1811, wherein the general processor 1811 includes a plurality of electrical and electronic components that provide power, operation control and protection for the general processor and / or the components and modules within the battery management system. Both the general controller 181 and the electronic controller 17 are configured with corresponding activation switches 185, which can selectively enable and disable the machine learning controller. The general processor 1811 includes a processing unit 1831 (e.g., a microprocessor, a microcontroller or other suitable programmable device), a general memory 1832, an input unit 1833 and an output unit 1834. The general processing unit 1831 includes a control unit 1831a, an arithmetic logic unit 1831b ("ALU") and a plurality of registers 1831c. In some embodiments, the electronic processor is partially or entirely included in a single-chip microcomputer, a microcontroller module MCU (Microcontroller Unit).

[0138] In this embodiment, the electronic controller 17 is disposed in the battery pack, and the universal controller 181 is also disposed in the battery pack. The electronic controller 17 includes a first processor 171 and a first memory 172, and the electronic controller 17 is supported by the housing of the battery pack and coupled to the sensor. The electronic controller is configured to: receive sensor data, use a machine learning program to process the sensor data, wherein the machine learning program is a trained machine learning model, and use the machine learning program to generate output based on the sensor data. The switching element is used to control the input current or output current of the battery pack. The universal controller 181 includes a second processor and a second memory, and the universal controller is also supported by the housing and is electrically connected to the switching element and the electronic controller, and the universal controller is configured to: receive output from the electronic controller, and control the switching element based on the output.

[0139] Optionally, the electronic controller 17 is arranged in the battery pack, and the universal controller 182 is arranged in the power tool or walking device powered by the battery pack. Optionally, the electronic controller 17 is arranged in the battery pack, and the universal controller 183 is arranged in the charger for powering the battery pack. Optionally, the universal controller is arranged in the battery pack, and the electronic controller is arranged in the power tool or walking device powered by the battery pack. Optionally, the universal controller is arranged in the battery pack, and the electronic controller is arranged in the charger for powering the battery pack.

[0140] The electronic processor is configured to retrieve and execute instructions related to the control processes and methods described herein from the electronic memory, etc. The electronic processor is also configured to store battery pack information on the memory, including battery pack usage information, information identifying the type of battery pack, a unique identifier for a specific battery pack, user characteristics (e.g., identity, industry type, skill level), and other information related to operating or maintaining the battery management system. Battery pack usage information, such as current level, voltage level, usage time, and endurance time, can be captured or inferred from the data output by the sensor.

[0141] The electronic controller 17 is positioned on a separate printed circuit board (PCB). The PCB of the general processor 181 and the PCB of the electronic controller 17 are coupled using, for example, wires or cables to enable the general processor of the battery management system to control the charging and discharging of the battery pack based on the output and determination from the electronic controller. However, in other embodiments, the machine learning program can be stored in the general memory of the general processor and implemented. In such embodiments, the electronic controller includes a single electronic processor. In some other embodiments, the machine learning program is implemented in a separate electronic processor but is located on the same PCB as the general processor of the battery management system. The embodiment in which the machine learning control is implemented as a processing unit separate from the general processor, whether on the same or different PCB, allows the processing unit to be selected to implement each of the machine learning control and the general electronic processor. Its capabilities (e.g., processing power and memory capacity) are suitable for the specific needs of each unit. This customization can reduce costs and improve the efficiency of the power tool. In some embodiments, the electronic controller is implemented in a plug-in chip or controller that is easy to add to the battery management system. For example, the machine learning controller may include a plug-in chip. In this embodiment, the machine learning controller is provided on a Renesas chip.

[0142] like Fig.21 As shown, a flowchart of controlling the output of the battery pack usage status using a battery management system using a machine learning program.

[0143] 1110, receiving a start switch signal and starting the power management system.

[0144] 1120, receiving sensor data from a sensor, including, for example, current data, voltage data, temperature data or internal resistance data, power tool equipment data, maintenance data, user feedback data, power data, environment data, operator data, position data, and the like.

[0145] At 1130 , the sensor data is input to the electronic controller.

[0146] 1140, calculate and estimate battery pack usage status information based on sensor data, where the battery pack usage status information includes at least one of state of charge (SoC), state of temperature (SoT), state of health (SoH), and state of power (SoP).

[0147] 1150, receiving a charging and / or discharging mode, a connected power source type, a power supply cost of the connected power source, a circuit breaker on a connected circuit, a pre-charging mode, a constant current regulation mode, a constant voltage regulation mode, and a fast charging mode.

[0148] 1160, predicting component failure in a battery management system based on an electronic controller, and comparing estimated battery pack usage status information with a threshold.

[0149] 1170, component failure, if yes, go to 1180, otherwise return to 1120.

[0150] 1180, alarm or change charging and / or discharging mode.

[0151] like Figure 9-10 The battery pack 21 may be a lithium battery pack, a solid-state battery pack, or a soft pack battery pack. In some embodiments, the nominal voltage of the battery pack may be greater than or equal to 10V and less than or equal to 20V. For example, the nominal voltage of the battery pack may be 12V, 18V, 20V, etc. Fig.13 As shown, the nominal voltage of the battery pack 11B may be greater than or equal to 20V and less than or equal to 100V. For example, the nominal voltage of the battery pack may be 24V, 40V, 56V, 80V, etc. Fig.14 As shown in B', the charger 14B' charges 11B.

[0152] In one embodiment, the positive terminal 115 can be divided into a discharge positive terminal and a charge positive terminal, wherein the discharge positive terminal is two terminals connected in parallel, and the charge positive terminal is one terminal. The negative terminal 116 is two terminals connected in parallel. The communication terminal is one terminal. In one embodiment, the battery pack output terminal will display 6 interfaces corresponding to the output terminals on the battery pack housing. In an optional embodiment, the housing can be provided with more or fewer interfaces according to the electrical characteristics of the battery pack. It can be understood that the interface corresponding to the output terminal on the battery pack housing is the same as the interface corresponding to the input terminal of the power tool.

[0153] In some embodiments, the number of battery cells 1121 included in a battery cell group 112a may be greater than or equal to 3 and less than or equal to 5, for example, the number of battery cells 1121 included in a battery cell group 112a may be 3, 4 or 5. In some embodiments, the number of battery cells 1121 included in a battery cell group 112a may be greater than or equal to 6 and less than or equal to 25, for example, the number of battery cells 1121 included in a battery cell group 112a may be 6, 10, 14, 20, etc. In some embodiments, the battery pack 11 may include only one battery cell group 112a, so that the volume of the battery pack will be relatively small. In some embodiments, the battery pack 11B may also include three battery cell groups 112a or four battery cell groups 112a, so that the capacity of the battery pack can be increased and the battery life of the battery pack can be extended. The battery cell group 112a is composed of a plurality of battery cells connected in series, in parallel, or in series and in parallel. In some embodiments, the battery cell 1121 is a flat bag structure, multiple battery cells are stacked and arranged in the vertical direction, and the battery cell 1121 can also be bent into an arc. In other embodiments, the battery cell 1121 is a cylindrical structure. It is understood that the present application is not limited to the disclosed embodiments, and there is no limitation on the structure of the battery cell.

[0154] In this example, the capacity of the battery pack is greater than or equal to 1.5Ah and less than or equal to 20Ah. The battery cell 1121 is a cylindrical battery cell, and further, the battery cell 1121 is a cylindrical lithium battery cell. The weight of each battery cell unit is greater than or equal to 35g and less than or equal to 55g, and the capacity of each battery cell 1121 is greater than or equal to 1200mAh and less than or equal to 3600mAh. In some other embodiments, the weight of each battery cell 1121 is greater than or equal to 60g and less than or equal to 75g, and the capacity of each battery cell 1121 is greater than or equal to 3000mAh and less than or equal to 5000mAh.

[0155] In some embodiments, the communication module 118 of the battery pack can be integrated into the battery pack controller 181 or can be set separately. The communication module 118 is used for remote device connection. In some embodiments, the communication terminal and the communication module of the battery pack can also be designed in an integrated manner. The communication module can also include an Internet of Things (IoT) communication module to share various parameters and control instructions of the battery pack with other devices in the IoT network.

[0156] In some embodiments, the battery pack also includes a human-computer interaction component such as a display device 114, which is used to display or feedback information of the battery pack. For example, working status information display, fault alarm / indication and other information to be indicated to the user. Of course, in some embodiments, an external device is used to implement the human-computer interaction function. For example, a smart device is used to communicate with the battery pack communication module 118 to input and feedback relevant information through the smart device.

[0157] As an embodiment of the present application, a lithium battery pack is taken as an example. Lithium-ion batteries have been widely used in electric vehicles, portable electronic products and smart grid systems due to their high energy / power density, long cycle life and high efficiency. However, there are technical bottlenecks related to heat. This bottleneck stems from the complex impact of temperature on the safety, performance and life of lithium-ion batteries. Therefore, temperature monitoring is the basis of battery management systems. In related technologies, battery temperature can be monitored by temperature sensors placed on the surface of the battery cell. However, in real-life battery packs, it is impractical to place temperature sensors on the surface of each battery cell 1121 due to cost and complexity considerations. Therefore, in this embodiment, a machine learning program is used to estimate the temperature state (SoT).

[0158] The machine learning program includes a trained neural network model, which is a physical embedded neural network model (PINN). PINN can quickly solve the underlying nonlinear heat transfer partial differential equation (PDE) with a small amount of data and provide an output indicating the temperature distribution inside the battery. PINN mimics the data pattern controlled by the heat transfer partial differential equation, so that the temperature distribution inside the battery cell can be estimated. The PINN framework for estimating battery temperature distribution is shown in Figure 1. Fig. 22 As shown, a cylindrical battery cell is used as an example. When collecting training data, the current, voltage, surface and core temperature can be measured (by inserting sensors into the battery cell). The measured data is considered as training data for the deep neural network, and the loss is calculated based on the predicted temperature and the measured temperature. In addition, the temperature of the core, surface and any point within the domain should obey the heat transfer partial differential equation and its initial and boundary conditions. The difference in temperature with respect to time and position can be calculated by automatic differentiation so that the physical loss is obtained accordingly according to the heat transfer partial differential equation. In PINN, the loss function consists of the loss of training data and the physical loss. By minimizing the total loss, the weights and bias of the neural network can be adjusted, and the unknown coefficients in the partial differential equation can be identified. For a trained PINN, the prediction can be of high accuracy while also obeying the heat transfer law so that it can be used to estimate the internal temperature distribution under other operating conditions.

[0159] A framework for temperature distribution estimation using PINN includes: measuring temperature data at the surface and core of the battery for training a recurrent neural network (e.g., based on a deep neural network). A physical part is then added as a regularization term to further train the deep neural network so that the estimated temperature not only follows the data pattern in the training set, but also obeys the laws of space-time physics in the physical part. The network is trained by minimizing the loss of the measured data and the loss of the physical part over a certain number (e.g., 10,000) or more, where an appropriate λ should be selected to adjust the relative importance between the data loss and the physical loss. The training process can adjust the weights and biases in the neural network, as well as simultaneously identify unknown parameters in the partial differential equation (where T represents temperature, t represents time, I represents current, V represents voltage, r represents spatial distribution, Q represents heat generation, C represents the relative importance of the data loss and the physical loss). p represents specific heat capacity, V b represents the volume of the battery, MSE represents the mean square error, MSE data represents the mean square error of the temperature estimation based on the measured data, MSE physics represents the mean square error of the temperature estimation of the physical part, k t represents the thermal conductivity of the pixel, and λ represents the coefficient between data loss and physical loss). Accurate SoT estimation using PINN is the first step to achieve the goal of long-term (e.g., 10 minutes in advance) temperature prediction, which will lead to optimal operation and reduce safety issues of smart batteries.

[0160] As an embodiment of the present application, a lithium battery pack is taken as an example. As the battery is used over time, the battery will age, resulting in different battery characteristics at different times, which is specifically reflected in the size of the battery's state of health (SOH) value. Therefore, the state of health (SOH) value intuitively reflects the overall performance of the battery and its ability to release electrical energy under certain conditions. Although empirical methods such as direct measurement of charge throughput or indirect analysis of incremental capacity have been used to quantify the SOH mechanism, their stability severely limits their use in practical applications. Therefore, in this embodiment, a machine learning program is used to estimate the state of health (SOH). Among them, the machine learning program includes methods such as support vector machines, artificial neural networks, deep learning (DL), and random forests.

[0161] Deep neural networks are able to extract global features from raw multidimensional data. However, due to the latent nature of SOH, it is often impossible to obtain the amount of SOH information required to train such neural networks in practical applications. In order to improve the estimation accuracy for small amounts of data, an ensemble method based on bagging (bootstrap aggregating) is proposed. Bagging creates augmented samples by resampling from an exemplary raw dataset and trains a series of extreme learning machines based on these samples. The bagging extreme learning machine (ELM) method has many advantages of deep learning (DL), such as automatic feature extraction, while requiring less data to train and performing well when estimating SOH. In addition to using fuzzy entropy-based features and an ensemble extreme learning machine method, data augmentation is used to increase the amount of useful information extracted from a single partial charge of the battery. Augmentation allows the extraction of not only the charge voltage, but also each sequence of partial charge voltages found in any charge, no matter how large. As Fig.23 Described above is a general framework for data cleaning, partial charge separation, feature extraction, and SOH estimation.

[0162] The purpose of any Remaining Useful Life (RUL) algorithm is to predict the end of useful life (EOL) time of the battery. However, before predicting the end of useful life (EOL), it is necessary to predict the SOH. Given a mission profile and a short-term SOH prediction method, the long-term behavior of the SOH can be predicted until the end of useful life (EOL). In physics-based lifetime models, while the nonlinear and time-varying characteristics of electrochemical systems can be accounted for, the parameters of these models are difficult to identify because they rely on destructive testing methods. Therefore, the development of physics-based models requires time and resources and is not necessarily a viable option for real-time predictions.

[0163] In this embodiment, a deep learning life model can be built based on the collected data and continuously updated using gradient optimization. That is, the relationship between features (i.e., health indicators such as voltage, current, and temperature) and the cycle and calendar life of the battery cell can be established. Optionally, deep neural networks, convolutional neural networks, and recurrent neural networks (RNNs) are used as algorithms for remaining useful life (RUL) prediction because they inherently model parameters that change over time. The biggest challenge of the above-mentioned SOH and RUL prediction methods is that they require SOH measurements to work. Obtaining SOH measurements means stopping the operation of the battery and running a full cycle, that is, fully charging and discharging the battery. In addition, in order for these prediction methods to be effective, they need to be performed regularly and as often as possible. SOH and RUL predictions are run on a SOH estimation model that can provide an estimate of the SOH for each partial charge of the battery. Given the estimated SOH, post-processing needs to be applied to eliminate the effects of dependencies (such as temperature and C rate) (if this effect cannot be eliminated by constructing invariant features). When predicting future SOH, using the estimated SOH should stabilize the uncertainty predictions of SOH and RUL. Furthermore, as new measurements of SOH are made, the difference between the predicted and measured SOH will be used to update the SOH estimation model, ultimately leading to better SOH predictions. Fig.23 As described, the general framework of post-processing and the general framework of SOH and RUL prediction are presented.

[0164] As an embodiment of the present application, accurate estimation of the state of charge (SOC) of the battery pack is an important basis for battery charge and discharge control in the power tool, which directly affects the service life of the battery pack and the performance of the power tool, and can predict the battery life of the battery pack. In the related art, the estimation of the SOC state of the battery pack mostly adopts the ampere-hour integration method and the model-based closed-loop estimation method, such as the Kalman filter method, the sliding mode state observer, the particle filter method, etc. Such methods have high requirements on the accuracy of the current measurement value. If a large current measurement error occurs, it will directly lead to inaccurate estimation results, which will then cause serious safety problems. Therefore, in this embodiment, a machine learning program is used to estimate the state of charge (SoC). The machine learning program includes a trained neural network model. The neural network model includes a feedforward neural network (FNN), a radial basis function (RBF) neural network, an extreme learning machine (ELM), a support vector machine (SVM) and a recurrent neural network (RNN). These methods and techniques use measurable signals, such as battery terminal voltage, current and temperature. Among them, a recurrent neural network (RNN) is taken as an example. A recurrent neural network is a neural network that uses past information in a closed-loop manner.

[0165] Taking lithium battery pack as an example, the steps of using recurrent neural network (RNN) to estimate the state of charge (SoC) include: performing multiple cycle charge and discharge tests on lithium-ion batteries, measuring the battery current I, voltage V, internal resistance R, temperature T, power P and battery capacity Q at each moment in each cycle. The state of charge value SOC of the lithium-ion battery at each moment is obtained by dividing the actual capacity of the battery by the rated capacity. The external characteristic parameters of the battery are used as the input of the improved recurrent neural network, that is, the long short-term memory model LSTM (Long Short-Term Memory) network structure, and the SOC of the lithium-ion battery is used as the output of the network structure. The collected input data is preprocessed, redundant data is deleted, and data normalization is performed for better training of the next model. The sliding window algorithm is used to transform the multivariate time series problem into a supervised learning problem, and the battery charge and discharge state is used as the input label quantity to establish an input data sequence that is easier for model learning. According to the input and output characteristics, a fully connected LSTM network structure is established to determine the number of hidden layer nodes; the preprocessed battery external characteristic data is input into the initialized LSTM network structure, the training network is forward-transferred, and the network parameters and deviation values ​​are adjusted according to the actual SOC value through back-propagation to determine the final LSTM network structure suitable for predicting accurate SOC values. The test sample is input into the trained neural network for verification, and the prediction error is calculated.

[0166] The process of the LSTM network structure estimation SOC method is as follows: The input of this network structure is as follows: (1) Current and past input values: V(t), V(t-1), V(t-2), I(t), I(t-1), I(t-2) and T, P, R. Among them, V(t), I(t) and T(t) are all external inputs from external sensors of the network, and the delayed values ​​of the previous moment are stored in the external memory. (2) The delayed values ​​corresponding to the output of each network: SOC(t), SOC(t-1) and SOC(t-2) of the SOC estimator and the estimated value of SOC. Therefore, the network's output SOC(t+1) is always one step ahead of all inputs. The dynamic behavior of each network structure can be expressed by the formula:

[0167] The battery dynamic condition test data was analyzed and 5 SOC influencing factors were determined. The number of LSTM input layer nodes is 5, the number of hidden layer nodes is 50, and the number of output layer nodes is 1. The mean absolute error (MAE) is used as the loss function and the tangent function (than) is used as the activation function. The number of simulation training epochs is set to 200 times, and different learning efficiencies α=0.05 and α=0.005 are set to verify the effectiveness of the model. In order to ensure that this number of training times is achieved, the target error is set to 0 to prevent early exit from training due to reaching the training target error.

[0168] The electronic controller 17 detects the occurrence of harmful battery conditions of the battery pack, other faults indicating multiple battery cells, or faults indicating multiple electronic components by implementing a neural network, and controls the indicator 184 device to display an abnormal state according to the generated output. Among them, the indicator 184 device includes a display device 114 set on the battery pack, or a human-computer interaction device set on an electric tool or walking equipment, and a human-computer interaction device set on a charger. In some embodiments, the communication module is controlled according to the generated output to send the fault to a remote device, and the indicator device is controlled by the remote device to display an abnormal state. The indicator device receives the control signal through a device that generates a visual signal or an auditory signal and conveys the status information to the user. Optionally, the display device includes an indicator light, a buzzer, an LED or a display screen. The generated output includes battery cell impedance data, battery cell temperature data, etc. Optionally, the fault of the electronic component includes a life warning, an aging warning, or an installation fault warning of the battery pack, the battery cell, and the battery pack accessories. Harmful battery conditions include overtemperature, undertemperature, overvoltage, undervoltage, overcurrent, battery cell imbalance, overcharge, overdischarge, abnormal internal resistance, and internal and external circuit problems. Other faults of the battery cell include, for example, component failure (connection failure, insulation failure, thermal runaway and other thermal management system failures) and sensor failure (voltage, current, temperature). Optionally, when the power management system is equipped with energy recovery, whether the battery pack implements energy recovery and the energy recovery efficiency are also indicated items. When the sensor data includes battery cell impedance data, the electronic controller predicts battery lithium deposition, folding, and structural changes by implementing a neural network.

[0169] In some embodiments, the electronic controller combines the battery pack mode, the battery pack type, and the type of components connected to the battery pack when detecting anomalies. The electronic controller can perform the above classification and identification work by using one of the aforementioned machine learning algorithms.

[0170] As an embodiment, the electronic combination includes a battery pack 11 and a switch element 191 for controlling an input current or an output current of the battery pack. The electronic controller includes: a first machine learning program and a second machine learning program, wherein the sensor data is processed using the first machine learning program and a first output is generated based on the sensor data, the sensor data is processed using the second machine learning program and a second output is generated based on a real-time parameter of the sensor data, a third output is generated based on the first output and the second output, and the switch element is controlled based on the generated third output.

[0171] like Fig.24 Flowchart 1200 of the control scheme for the above electronic combination.

[0172] 1210, the electronic controller receives sensor data from the sensor, the sensor data including, for example, current data, voltage data, temperature data or internal resistance data, power tool equipment data, maintenance data, user feedback data, power data, environmental data, operator data, position data and the like.

[0173] At 1221, a first machine learning program processes the sensor data.

[0174] 1222, a second machine learning program processes the sensor data. 1221 and 1222 are performed substantially simultaneously.

[0175] At 1231 , a first machine learning program generates a first output based on the sensor data.

[0176] 1232, the second machine learning program generates a second output based on the sensor data. 1231 and 1232 are performed substantially simultaneously. The second machine learning program receives real-time data.

[0177] 1240, the first output and the second output generate a third output.

[0178] 1250, based on the generated third output, the switch element is controlled, that is, the charging and discharging process can be controlled.

[0179] like Fig.25 As shown, it is a block diagram of discharge management. The battery pack is electrically coupled to a switch element 191, and the switch element 191 is used to control the input current or output current of the battery pack. Among them, the switch element 191 can be set in the electric pack. Optionally, the switch element is set in an electric tool or walking equipment to control the discharge of the battery pack. Optionally, the switch element is set in a charger to control the charging of the battery pack.

[0180] The battery pack includes a discharge circuit 192. The discharge circuit 192 includes a switch element 191, and the discharge circuit 192 is electrically connected to the battery cell or battery cell group 112a and the terminals of the battery pack interface (battery pack positive terminal 115, battery pack negative terminal 116, and communication terminal 117). When the battery pack is connected to the power tool or walking equipment, the discharge circuit 192 is configured to transmit electric energy to the power tool via the terminals of the battery pack. The battery pack can be detachably connected to the above-mentioned power tool, or fixedly set in the above-mentioned power tool.

[0181] The electronic controller 17 is used to control the discharge circuit 192. The electronic controller 17 is configured to control the on and off of the switch element 191. The electronic controller 17 includes an electronic processor and a memory, and the memory includes a machine learning program for execution by the electronic processor. The electronic controller 17 is configured to: receive sensor data of the battery pack and / or the battery module. Use the machine learning program to process the sensor data, use the machine learning program to generate an output based on the sensor data, and adjust the protection threshold of the battery pack based on the generated output. Among them, the protection threshold includes at least one of an over-temperature protection threshold, an under-temperature protection threshold, an overvoltage protection threshold, an under-voltage protection threshold, and an over-current protection threshold. The sensor data includes current data, voltage data, or temperature data. The electronic controller 17 controls the current transmission between the battery pack and the power tool based on the adjusted protection threshold. Optionally, the electronic controller 17 is set in the battery pack, and the electronic controller controls the power supply of the battery pack to the power tool based on the protection threshold. Optionally, the electronic controller 17 is set in the power tool or walking device, and the protection threshold is sent to the power tool or walking device via the battery pack terminal, and the power tool or walking device controls the power consumption of the power tool or walking device based on the protection threshold.

[0182] The electronic controller may implement a neural network (e.g., a feed-forward neural network, a recurrent neural network, etc.) to detect battery conditions that are detrimental to the battery pack. The electronic controller generates and / or utilizes various thresholds to detect battery conditions that are detrimental to the battery pack. For example, when the temperature of the battery pack or the battery cell continues to increase beyond a threshold, the electronic controller is more likely to detect that there is a problem with the temperature state (SoT) of the battery pack and the battery pack is in a poor discharge state. During the discharge of the battery pack, when the electronic controller detects that the battery pack is in a poor discharge state, the electronic controller limits (disconnects) the discharge circuit.

[0183] The third party may provide feedback to the electronic controller regarding the accuracy of the electronic controller detection feedback. For example, the electronic controller receives a first type of negative feedback indicating that the electronic controller did not detect that the battery pack has overheated. The electronic controller also receives a second type of negative feedback indicating that the machine learning controller is too sensitive in detecting battery pack overheating. For example, a detection sensor may be set in the battery pack, ambient temperature detection may be added, etc.

[0184] The electronic controller may adjust a threshold value generated or used by the electronic controller in response to the received feedback, or may adjust the machine learning control itself. For example, an increase in a threshold value may result in a delay in the detection of a battery condition that is harmful to a battery pack in a power tool. In other embodiments, the feedback does not directly change the threshold value generated or used by the electronic controller, but allows the machine learning control to be retrained based on the received feedback. Because the occurrence of a battery condition that is harmful to a battery pack is not obvious to the user, or requires the assistance of an instrument, feedback and threshold updates for the battery management system are more likely to occur during manufacturing or laboratory conditions.

[0185] As mentioned above, the learning rate can affect how new information with feedback is used to retrain the machine learning control, as well as how feedback is used to more directly adjust the thresholds generated or used by the electronic controller.

[0186] The first machine learning program is used to process the sensor data, and a first output is generated based on the sensor data, and a protection threshold is adjusted based on the first output. The second machine learning program is used to process the sensor data, and a second output is generated based on the real-time parameters of the sensor data. Wherein, whether to discharge the battery cell module is determined based on the comparison result between the second output and the protection threshold. The battery pack discharge mode is adjusted based on the difference between the second output and the protection threshold or the calculated value of the first-order equation or the multi-order equation.

[0187] The machine learning program may be any one or more of the machine learning programs described above, based on the classification of one of the aforementioned machine learning algorithms.

[0188] Since the discharge mode of the battery pack 11 has a strong correspondence with the user's usage scenario, this has a great impact on the usage status of the battery pack. Manually adjusting the discharge mode requires the user to have sufficient experience. Use an electronic controller to collect working data from the power tool through the battery pack output terminal, use a machine learning program to generate output based on the working data, and select a discharge mode and control the discharge circuit based on the generated output. Among them, the discharge mode includes a performance mode, a power consumption mode and a balance mode. Optionally, the discharge mode includes a series mode and a parallel mode. The machine learning program identifies the type of power tool based on the working data. The machine learning program identifies the user's usage habits of the power tool based on the working data.

[0189] The discharge mode setting can specify the cell discharge parameters and protection thresholds of the battery pack or cell module during discharge in a specific mode. For example, each discharge mode can limit parameter information such as discharge voltage, discharge current, maximum discharge current and discharge cut-off voltage and their combinations. The combination of two or more discharge parameters and protection thresholds defines the usage profile or mode of the battery pack.

[0190] The electronic controller distinguishes between several discharge modes. Training examples of the electronic controller include input vectors indicating discharge voltage, discharge current, cell temperature, maximum discharge current, and discharge cutoff voltage, and output labels indicating the type of discharge mode. The electronic controller implements an artificial neural network to perform the classification. The artificial neural network includes, for example, five input nodes, and, for example, one hundred output nodes. Each output node corresponds to, for example, a different type of discharge mode recognizable by the electronic controller, and an additional output indicating the battery pack when the discharge mode does not correspond to any recognizable discharge mode type. The artificial neural network may include more or fewer output nodes based on the number of fasteners that can be distinguished. In some embodiments, the neural network includes an additional layer that includes a single node. The additional layer may determine which output node has the highest value (which may correspond to the probability that the discharge mode type is identified as the discharge mode type corresponding to the output node), and output (e.g., one, two, three, or four) values ​​associated with the output node. The value of the output node may correspond to the type of discharge mode recognized by the electronic controller. When the electronic controller selects a discharge mode, the electronic processor controls the battery pack or the power tool according to discharge parameters and protection thresholds specified by the selected mode, and the discharge parameters and protection thresholds may be stored in a memory.

[0191] The electronic controller also receives information about the operating mode of the power tool (e.g., speed associated with the mode, mode torque, mode slope, etc.). The electronic controller also receives sensor information indicating operating parameters of the power tool (e.g., motor current, motor voltage, trigger activation or feedback from the trigger, movement of the power tool, motor speed, output shaft speed, etc.). The electronic controller distinguishes between several types of power tools. In some embodiments, the electronic controller distinguishes between types of usage habits of users of several power tools. The electronic controller implements an artificial neural network to perform the classification.

[0192] In this embodiment, the charger further includes a charging circuit 194. The charging circuit 194 is used to control the charger to output a voltage matching the battery pack to charge the battery pack, so that the output voltage of the battery pack interface is the same as the nominal voltage of the battery pack. In some embodiments, the charging circuit 194 is disposed in the charger housing, and the charging circuit is arranged on the circuit board assembly. In other embodiments, the charging circuit 194 may also be disposed in the battery pack.

[0193] When the battery pack is connected to the charger, the charging circuit 194 is configured to electrically connect to the battery pack interface (battery pack positive terminal 115, battery pack negative terminal 116, and communication terminal 117) and charge the battery pack. The charger also includes a clock module, which is used to detect the charging time. The charging time data includes a charging timestamp or a charging duration. The clock module can enable a safety function, where the battery pack is disabled (e.g., locked and non-chargeable) when the time of the clock module exceeds a user-determined lock time.

[0194] The electronic controller 17 is used to control the charging circuit. The charging circuit 194 includes a switching element 191, and the electronic controller is configured to control the on and off of the switching element. The electronic controller includes an electronic processor and a memory, and the memory includes a machine learning program for execution by the electronic processor. The electronic controller is configured to: obtain the charging time data of the charging circuit charging the battery pack through the clock module 147, process the charging time data using the machine learning program, generate an output based on the charging time data using the machine learning program, output an indication of a parameter value associated with the charging operation of the charger, and control the charging circuit according to the generated output. The parameter value includes a charging rate or a pulse charging interval. In some embodiments, the parameter value also includes a full charge rate. When the charge state of the battery pack is greater than or equal to the full charge rate, the charging circuit stops charging the battery pack.

[0195] The machine learning program is generated on the external system device through training based on sample data and related outputs, and is received by the charger from the external system device. Optionally, the charger obtains the working data of the battery pack via the battery pack interface, and the machine learning program adjusts the output based on the working data.

[0196] Optionally, the electronic controller is set in the battery pack, and the electronic controller receives the charging time data through the port, and generates an output parameter value based on the charging time data to control the amount of electricity charged into the battery cell of the battery pack. Optionally, the electronic controller is set in the charger, and the electronic controller controls the charger to supply power to the battery pack based on the output parameter value based on the charging time data.

[0197] As an embodiment, the electronic controller includes: a first machine learning program and a second machine learning program, wherein the first machine learning program is used to process sensor data, and a first output is generated based on the sensor data, and a protection threshold is adjusted based on the first output. The second machine learning program is used to process sensor data, and a second output is generated based on real-time parameters of the sensor data, wherein whether to charge the battery cell module is determined based on a comparison result between the second output and the protection threshold. The battery pack charging mode is adjusted based on a difference between the second output and the protection threshold or a calculated value of a first-order equation or a multi-order equation.

[0198] The machine learning program is classified according to one of the aforementioned machine learning algorithms and can be any one or several of the aforementioned machine learning programs.

[0199] Since the charging mode of the battery pack has a strong correspondence with the user's usage scenarios and usage habits, this has a great impact on the charging state of the battery pack. An electronic controller is used to collect charging data from the power tool through the battery pack output terminal, and a machine learning program is used to generate output based on the working data, and a charging mode is selected based on the generated output and the charging circuit is controlled. Among them, the charging modes include night mode and fast charging mode. The machine learning program identifies different scenarios according to the charging data, such as different charging depths, different charging frequencies, and different charging time points.

[0200] The charging mode setting can specify the parameter values ​​and protection thresholds associated with the cell charging operation of the battery pack or cell module during charging in a specific mode. For example, each operation mode can define parameter information such as charging rate, charging timestamp, and full charge rate, and their combinations. The combination of two or more charging parameters and protection thresholds defines the usage profile or mode of the battery pack.

[0201] The electronic controller distinguishes between several charging modes. The training examples of the electronic controller include input vectors indicating charging current, charging time, charging at night or during the day, timely power off and remaining power when charging, and output labels indicating the type of charging mode. The electronic controller implements an artificial neural network to perform the classification. The artificial neural network includes, for example, five input nodes, and, for example, one hundred output nodes. Each output node corresponds to, for example, a different type of charging mode recognizable by the electronic controller, and an additional output indicating the battery pack when the charging mode does not correspond to any recognizable charging mode type. The artificial neural network may include more or fewer output nodes based on the number of fasteners that can be distinguished. In some embodiments, the neural network includes an additional layer that includes a single node. The additional layer may determine which output node has the highest value (which may correspond to the probability that the charging mode type is identified as the charging mode type corresponding to the output node), and outputs (for example, one, two, three or four) values ​​associated with the output node. The value of the output node may correspond to the charging mode type recognized by the electronic controller. When the electronic controller selects a charging mode, the electronic processor controls the battery pack or the power tool according to charging parameters and protection thresholds specified by the selected mode, which may be stored in a memory.

[0202] The electronic controller also receives information about the operating mode of the power tool. The electronic controller also receives sensor information indicating operating parameters of the power tool. The electronic controller distinguishes between types of several power tools. In some embodiments, the electronic controller distinguishes between types of usage habits of users of several power tools. The electronic controller implements an artificial neural network to perform the classification.

[0203] The above shows and describes the basic principles, main features and advantages of the present application. Those skilled in the art should understand that the above embodiments do not limit the present application in any form, and any technical solution obtained by equivalent replacement or equivalent transformation falls within the protection scope of the present application.

Claims

1. A battery pack for powering an electric tool, comprising: case; A battery cell group, contained in the housing, wherein the battery cell group includes at least one battery cell; It is characterized by further comprising: a sensor electrically connected to the battery cell group, the sensor being configured to generate sensor data indicative of an operating parameter of the battery pack; and an electronic controller electrically connected to the sensor, the electronic controller comprising an electronic processor and a memory, the memory comprising a machine learning program for execution by the electronic processor, the electronic controller being configured to: receiving the sensor data; processing the sensor data using the machine learning program, the machine learning program comprising a trained neural network model; The machine learning program is used to generate an output based on the sensor data, the output indicating at least one of a state of charge, a temperature state, a health state, and a power state of the battery cell group.

2. The battery pack according to claim 1, characterized in that: The neural network model is a neural network model with embedded physical knowledge.

3. The battery pack according to claim 2, characterized in that: The embedded physical knowledge neural network model uses the heat conduction partial differential equation as a constraint condition of the neural network.

4. The battery pack according to claim 1, characterized in that: The sensor data includes at least one of current data, voltage data, temperature data, and time data.

5. The battery pack according to claim 1, characterized in that: The neural network model is a deep neural network.

6. The battery pack according to claim 5, characterized in that: The electronic controller resamples a preset original data set to create enhanced samples and trains a series of extreme learning machines based on the samples.

7. The battery pack according to claim 1, characterized in that: The neural network model is a recurrent neural network.

8. The battery pack according to claim 1, characterized in that: Also included is a communication module, in which a machine learning control program is generated outside the battery pack through training based on preset sensor data and related outputs and is received by the communication module.

9. The battery pack according to claim 1, characterized in that: The battery pack is a detachable battery pack.

10. The battery pack according to claim 1, characterized in that: The battery pack is a rechargeable battery pack.

11. A battery pack for powering an electric tool, comprising: case; A battery cell group, contained in the housing, wherein the battery cell group includes at least one battery cell; A power tool interface, disposed on the housing and configured to be coupled to the power tool; a discharge circuit, electrically connected to the battery pack and the power tool interface respectively, the discharge circuit being configured to transmit electric energy to the power tool via the power tool interface; It is characterized by further comprising: a sensor electrically connected to the battery cell group, the sensor being configured to generate sensor data indicative of an operating parameter of the battery pack; and An electronic controller, the electronic controller being used to control at least the discharge circuit, the electronic controller comprising an electronic processor and a memory, the memory comprising a machine learning program for execution by the electronic processor, the electronic controller being configured to: receiving the sensor data, Processing the sensor data using the machine learning program, An output is generated based on the sensor data using the machine learning program, and a protection threshold of the battery pack is adjusted based on the generated output.

12. The battery pack according to claim 11, characterized in that: The electronic controller also includes a second machine learning program that controls the discharge circuit based on a protection threshold of the battery pack.

13. The battery pack according to claim 11, characterized in that: The sensor data includes current data, voltage data, temperature data and time data.

14. The battery pack according to claim 11, characterized in that: The protection threshold includes at least one of an over-temperature protection threshold, an under-temperature protection threshold, an over-voltage protection threshold, an under-voltage protection threshold, and an over-current protection threshold.

15. The battery pack according to claim 11, characterized in that: The electronic controller controls the power supply from the battery pack to the power tool based on the protection threshold.

16. The battery pack according to claim 11, characterized in that: The protection threshold is sent to the power tool via the power tool interface, and the power tool controls power consumption of the power tool based on the protection threshold.

17. The battery pack according to claim 11, characterized in that: The battery pack also includes a second electronic controller, which is at least used to control the discharge circuit. The second electronic controller controls the discharge circuit based on a protection threshold of the battery pack.

18. The battery pack according to claim 17, characterized in that: The second electronic controller includes a machine learning program.

19. The battery pack according to claim 17, characterized in that: The second electronic controller is arranged on the same circuit board as the electronic controller.

20. The battery pack according to claim 17, characterized in that: The second electronic controller and the electronic controller are arranged on different circuit boards.

21. A battery pack for powering an electric tool, comprising: case; A battery cell group, contained in the housing, wherein the battery cell group includes at least one battery cell; a power tool interface, disposed on the housing and configured to couple with and communicate with the power tool; a discharge circuit, electrically connected to the battery pack and the power tool interface respectively, the discharge circuit being configured to transmit electric energy to the power tool via the power tool interface; It is characterized by further comprising: An electronic controller comprising an electronic processor and a memory, the memory comprising a machine learning program for execution by the electronic processor, the electronic controller being configured to: collecting working data from the power tool through the power tool interface, Processing the working data using the machine learning program, An output is generated based on the operating data using the machine learning program, and a discharge mode is selected and the discharge circuit is controlled based on the generated output.

22. The battery pack according to claim 21, characterized in that: The discharge modes include a performance mode, a power consumption mode and a balance mode.

23. The battery pack according to claim 21, characterized in that: The discharge mode includes a series mode and a parallel mode.

24. The battery pack according to claim 21, characterized in that: The machine learning program identifies the type of the power tool based on the operating data.

25. The battery pack according to claim 21, characterized in that: The machine learning program identifies usage habits of a user of the power tool based on the work data.

26. The battery pack according to claim 21, characterized in that: The electronic controller controls the power supply from the battery pack to the power tool based on the discharge mode.

27. The battery pack according to claim 21, characterized in that: The discharge mode is sent to the power tool via the power tool interface, and the power tool controls power usage of the power tool based on the discharge mode.

28. The battery pack according to claim 21, characterized in that: Also included is a communication module, in which a machine learning control program is generated outside the battery pack through training based on preset sensor data and related outputs and is received by the communication module.

29. The battery pack according to claim 21, characterized in that: The machine learning control program is generated outside the battery pack by training based on preset sensor data and related outputs, and is sent to the battery pack by the power tool via the power tool interface.

30. The battery pack according to claim 21, characterized in that: Each discharge mode predefines parameter information such as discharge voltage, discharge current, maximum discharge current and discharge cut-off voltage and their combinations.

31. A battery pack comprising: case; A battery cell group, contained in the housing, the battery cell group comprising a plurality of battery cell monomers; a display device, disposed on the housing and configured to indicate a status of the battery pack; It is characterized by further comprising: a sensor electrically connected to the battery cell group, the sensor being configured to generate sensor data indicative of an operating parameter of the battery pack; A circuit board, electrically connected to the battery cell group and the sensor, and having a plurality of electronic components arranged on the circuit board; an electronic controller, disposed on the circuit board, the electronic controller being electrically connected to the sensor, the electronic controller comprising an electronic processor and a memory, the memory comprising a machine learning program for execution by the electronic processor, the electronic controller being configured as follows: receiving the sensor data, Processing the sensor data using the machine learning program, generating an output based on the sensor data using the machine learning program, the generated output indicating a failure of the plurality of battery cells or the plurality of electronic components, and The display device is controlled to display an abnormal state according to the generated output.

32. The battery pack according to claim 31, characterized in that: The battery pack further includes a communication module, which is configured to establish a communication connection with a remote device. The electronic controller is further configured to: control the communication module to send the fault to the remote device based on the generated output.

33. The battery pack according to claim 32, characterized in that: The remote device is at least one selected from the group consisting of: a server, a smart phone, a tablet computer, and a laptop computer.

34. The battery pack according to claim 32, characterized in that: The communication module includes at least one device selected from the following group: Bluetooth, local area network, wireless, and cellular network.

35. The battery pack according to claim 31, characterized in that: The output includes cell impedance, cell temperature data, life warning, aging warning or installation fault warning.

36. The battery pack according to claim 31, characterized in that: The display device receives the control signal and conveys the status information to the user through a device that generates a visual signal or an auditory signal.

37. The battery pack according to claim 34, characterized in that: The display device includes an indicator light, a buzzer or a display screen.

38. The battery pack according to claim 31, characterized in that: The battery pack also includes: a power tool interface, which is arranged on the shell and is configured to couple with the power tool and communicate with the power tool. The electronic controller is also configured to: send the fault to the power tool through the power tool interface according to the generated output.

39. The battery pack according to claim 31, characterized in that: The battery pack also includes: a charging power tool interface, which is arranged on the shell and is configured to couple with a charger and enable the charger to charge the battery pack. The electronic controller is also configured to: send the fault to the charger through the power tool interface based on the generated output.

40. The battery pack according to claim 31, characterized in that: The electronic controller predicts battery lithium deposition, folding, and structural changes by implementing a neural network.

41. A battery pack comprising: case; A battery cell group, contained in the housing, the battery cell group comprising a plurality of battery cell monomers; A communication module, the communication module is configured to establish a communication connection with a remote device; It is characterized by further comprising: a sensor electrically connected to the battery cell group, the sensor being configured to generate sensor data indicative of an operating parameter of the battery pack; A circuit board, electrically connected to the battery cell group and the sensor, and having a plurality of electronic components arranged on the circuit board; an electronic controller, disposed on the circuit board, the electronic controller being electrically connected to the sensor, the electronic controller comprising an electronic processor and a memory, the memory comprising a machine learning program for execution by the electronic processor, the electronic controller being configured as follows: receiving the sensor data, Processing the sensor data using the machine learning program, generating an output based on the sensor data using the machine learning program, the generated output indicating a failure of the plurality of battery cells or the plurality of electronic components, and The communication module is controlled according to the generated output to send the fault to the remote device.

42. The battery pack according to claim 41, characterized in that: The remote device is at least one selected from the group consisting of: a server, a smart phone, a tablet computer, and a laptop computer.

43. The battery pack according to claim 41, characterized in that: The communication module includes at least one device selected from the following group: Bluetooth, local area network, wireless, and cellular network.

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