WO2023185601A1

WO2023185601A1 - Method and device for determining state of health information of battery, and battery system

Info

Publication number: WO2023185601A1
Application number: PCT/CN2023/083218
Authority: WO
Inventors: 贺宏胜; 顾辉; 高超; 于静美
Original assignee: 北京芯虹科技有限责任公司
Priority date: 2022-03-29
Filing date: 2023-03-22
Publication date: 2023-10-05
Also published as: CN114675188A

Abstract

A method and device for determining the state of health information of a battery, and a battery system. The battery system (150) comprises: at least one battery module (151), which comprises at least one battery cell; at least one storage medium, which comprises a set of instructions; and one or more processors (310) in communication with the at least one storage medium. When the instructions are executed, the one or more processors (310) are used for: acquiring charging data related to the battery system (150) (610), wherein the charging data comprises at least one of charging voltage data, charging current data, charging time data, charging temperature data and historical usage data; and determining state of health information related to the battery system (150) on the basis of the charging data (620), wherein the state of health information comprises one or more of capacity offset information, SOH value, self-discharging consistency, differential pressure and internal resistance consistency, insulation state and temperature state related to the battery system (150).

Description

A method, device and battery system for determining battery health status information

priority statement

This application claims priority from Chinese application No. 202210316191.7 filed on March 29, 2022, the entire content of which is incorporated herein by reference.

Technical field

The present application relates to the field of charging technology, and in particular to a method, device and battery system for determining battery health status information.

Background technique

In recent years, with the rapid development of battery cells and combined battery technology, the market retention rate of new energy electric vehicles has continued to increase. Under normal circumstances, the operating data of electric vehicles can be transmitted and stored in the data monitoring system server for secondary data development, such as remaining battery capacity estimation, fault warning, operation statistics, etc. As the number of new energy electric vehicles increases, the computing pressure on the system server will inevitably increase.

Contents of the invention

One embodiment of this specification provides a battery system. The battery system includes at least one battery module including at least one battery cell; at least one storage medium including a set of instructions; and one or more processors in communication with the at least one storage medium, wherein, When executing the instructions, the one or more processors are configured to: obtain charging data related to the battery system, the charging data including charging voltage, charging current, charging time, charging temperature data and historical usage data. At least one; and determining health status information related to the battery system based on the charging data; wherein the health status information includes capacity offset information, SOH value, self-discharge consistency, voltage and pressure related to the battery system. The difference is related to one or more of internal resistance consistency, insulation status, and temperature status.

In some embodiments, the system further includes a communication module configured to communicate with a reading device so that the reading device reads the health status information through the communication module.

In some embodiments, determining health status information related to the battery system based on the charging data includes: processing the charging data through a preconfigured machine learning model to determine the health status information, The machine learning model is stored in the storage medium.

In some embodiments, determining the health status information related to the battery system based on the charging data includes: obtaining a trained machine learning model from a server; and charging the battery system through the trained machine learning model. The data is processed to determine the health status information.

In some embodiments, the machine learning model includes a trained state information determination model; processing the charging data through the machine learning model to determine the health state information includes: determining through the state information The model processes the charging data to determine current health information of the battery system.

In some embodiments, the machine learning model further includes a trained state information prediction model; the one or more processors are further configured to: use the state information prediction model to predict the charging data and the current health state. The information is processed to determine the future health status information of the battery system in the next time period.

In some embodiments, the machine learning model also includes a trained health status judgment model; the one or more processors are also configured to: obtain two or more future health status information of the battery system, the two The two or more future health status information respectively correspond to different future time periods; through the health status judgment model, the two or more future health status information are processed to determine the health status of the battery system.

In some embodiments, the machine learning model is obtained by: obtaining training samples, the training samples include a plurality of sample groups, each of the sample groups includes historical charging data of the corresponding sample battery, through the status information The historical future health status information obtained by the prediction model and the historical health status obtained by the health status judgment model; training the initial model based on the training samples to obtain a trained machine learning model; wherein, the label of the training sample It includes historical actual health status information corresponding to the historical future health status information, and historical actual health status corresponding to the historical health status.

In some embodiments, when the number of negative samples in the training samples is less than a preset value, data simulation or parameter adjustment is used to The number of negative samples is filled.

In some embodiments, the one or more processors are further configured to update the machine learning model.

In some embodiments, the one or more processors are further configured to: obtain usage information of the battery system, the usage information including at least one of usage time, charging times, and maintenance times; determine based on the usage information The health category of the battery system includes at least a high health category, a medium health category and a low health category.

In some embodiments, the one or more processors are further configured to: in response to the battery system belonging to the low health category, perform at least one of the following operations on the battery system: determine whether the battery system is charged before each charge. The health status of the battery system is described, the target data of the battery system is reported to the server, and a backup battery is preset for the battery system.

In some embodiments, the one or more processors are further configured to: obtain usage feedback information related to the battery system; analyze the usage feedback information to determine characteristic information of the battery system; and pass health status A judgment model processes the characteristic information of the battery system to determine the health status of the battery system.

In some embodiments, the charging voltage includes at least one of a charging starting voltage, a charging process characteristic voltage, and a charging cutoff voltage; the charging temperature data includes at least one of a charging starting temperature and a charging process temperature weight; The historical usage data includes at least one of cumulative charge and discharge capacity, cumulative charge and discharge times, and historical battery health status.

In some embodiments, the system further includes: one or more sensors for detecting at least one of the charging voltage, the charging current, and the charging temperature; a battery management system for managing the battery The charging and discharging behavior of the system; a power supply module for using the electric energy stored in the battery system to provide power for at least one component of the battery system; and/or a positioning module for obtaining location information of the battery system.

One embodiment of this specification provides a battery health status information determining device, configured to estimate health status information related to the battery system in a battery system. The device includes: a first acquisition module for acquiring charging data related to the battery system, the charging data including at least one of charging voltage, charging current, charging time, charging temperature data and historical usage data; An evaluation module for determining health status information related to the battery system based on the charging data; wherein the health status information includes capacity offset information, SOH value, and self-discharge consistency related to the battery system. , one or more of voltage difference and internal resistance consistency, insulation status and temperature status.

In some embodiments, the device includes a storage module and a communication module. The storage module is used to store the health status information. The communication module is used to communicate with a reading device so that the reading device passes The communication module reads the health status information stored in the storage module.

One embodiment of this specification provides a device for determining battery health status information, which is configured in a charging device to determine health status information related to a battery system. The device includes: a second acquisition module for acquiring charging data related to the battery system, the charging data including at least one of charging voltage, charging current, charging time, charging temperature data and historical usage data; Two evaluation modules, configured to determine health status information related to the battery system based on the charging data; wherein the health status information includes capacity offset information, SOH value, and self-discharge consistency related to the battery system. , one or more of voltage difference and internal resistance consistency, insulation status and temperature status.

One embodiment of this specification provides a method for determining battery health status information, which is executed by a processor in a battery system. The battery system includes at least one battery module, and the battery module includes at least one battery cell. The method includes: obtaining charging data related to the battery system, the charging data including at least one of charging voltage, charging current, charging time, charging temperature data and historical usage data; and determining based on the charging data and Health status information related to the battery system; wherein the health status information includes capacity offset information, SOH value, self-discharge consistency, pressure difference and internal resistance consistency, insulation status and temperature related to the battery system one or more of the states.

One embodiment of this specification provides a method for determining battery health status information, which is executed by a processor in a charging device. The method includes: obtaining charging data related to the battery system, the charging data including at least one of charging voltage, charging current, charging time, charging temperature data and historical usage data; and determining the relationship between the battery and the battery based on the charging data. System-related health status information; wherein the health status information includes capacity offset information, SOH value, self-discharge consistency, pressure difference and internal resistance consistency, insulation status, and temperature status related to the battery system. one or more.

Description of drawings

This specification is further explained by way of example embodiments, which are described in detail by means of the accompanying drawings. These embodiments are not limiting. In these embodiments, the same numbers represent the same structures, where:

Figure 1 is a schematic diagram of an application scenario of a charging system according to some embodiments of the present application;

Figure 2 is a schematic diagram of a battery system according to some embodiments of the present application;

Figure 3 is a software and/or hardware schematic diagram of an exemplary computer device according to some embodiments of this specification;

Figure 4 is a schematic module diagram of a device for determining battery health status information according to some embodiments of the present application;

Figure 5 is a schematic connection diagram of a battery system according to some embodiments of the present application;

Figure 6 is an exemplary flow chart of a method for determining battery health status information according to some embodiments of the present application;

Figure 7 is a diagram showing the corresponding relationship between module capacity and cell number according to some embodiments of the present application;

Figure 8 is a diagram showing the corresponding relationship between offset and cell number according to some embodiments of the present application;

Figure 9 is a diagram showing the corresponding relationship between DC impedance and cell number according to some embodiments of the present application;

Figure 10 is an exemplary flow chart of a battery status determination method according to some embodiments of the present application;

Figure 11 is an exemplary flow chart of a method for determining battery status information according to some embodiments of the present application;

Figure 12 is a schematic module diagram of a device for determining battery health status information according to other embodiments of the present application; and

Figure 13 is an exemplary flow chart of a method for determining battery health status information according to other embodiments of the present application.

Detailed ways

In order to explain the technical solutions of the embodiments of this specification more clearly, the accompanying drawings needed to be used in the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some examples or embodiments of this specification. For those of ordinary skill in the art, without exerting any creative efforts, this specification can also be applied to other applications based on these drawings. Other similar scenarios. Unless obvious from the locale or otherwise stated, the same reference numbers in the figures represent the same structure or operation.

It should be understood that the terms "system," "apparatus," and/or "module" as used herein are a means of distinguishing between different components, elements, parts, portions, or assemblies at different levels. However, said words may be replaced by other expressions if they serve the same purpose.

As shown in this specification and claims, words such as "a", "an", "an" and/or "the" do not specifically refer to the singular and may include the plural unless the context clearly indicates an exception. "Plural" can mean "two or more". Generally speaking, the terms "comprising" and "comprising" only imply the inclusion of clearly identified steps and elements, and these steps and elements do not constitute an exclusive list. The method or apparatus may also include other steps or elements.

Flowcharts are used in this specification to illustrate operations performed by systems according to embodiments of this specification. It should be understood that preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. At the same time, you can add other operations to these processes, or remove a step or steps from these processes.

In the battery system, the battery management system (Battery Management System, BMS) can obtain the voltage, current, temperature, time and other parameters of the battery system during charging or discharging, and then transmit the above parameters to the remote server (for example, remote data platform or monitoring system). The server uses the received data to perform secondary data development, such as battery remaining capacity calculation, fault warning, operation statistics, etc. At present, with the increase in the number of new energy electric vehicles, the computing pressure on the data monitoring system server will inevitably increase.

In order to solve the above problems, embodiments of the present application provide a method, device and battery system for determining battery health status information, which can obtain charging data related to the battery system, store and calculate the charging data, and determine the charging data related to the battery system. Relevant health status information is then sent to the server. In this application, the operation of evaluating the battery system status can be performed locally by the battery system determination device instead of being uniformly performed by the remote server, thereby reducing the computing load of the server to a certain extent.

The method, device, and battery system for determining battery health status information provided by embodiments of the present application will be described in detail below with reference to the accompanying drawings.

Figure 1 is a schematic diagram of an application scenario of a charging system according to some embodiments of the present application.

The charging system 100 may be used in the field of charging management. In some embodiments, charging data can be stored and/or calculated in the battery system, charging equipment, or electrical equipment (for example, electric vehicles, etc.) loaded with the battery system, thereby reducing the calculation of the remote server. Stress and monitoring costs.

Referring to FIG. 1 , the charging system 100 may include a charging device 110 , a server 120 , a memory 130 , a network 140 , a battery system 150 and a terminal 160 . The charging device 110 or the battery system 150 may include a computing device (eg, the computing device 300 ) for processing charging data related to the battery system 150 .

In some embodiments, charging device 110 may refer to a device for charging battery system 150 (eg, a battery system used to power an electric vehicle). In some embodiments, the charging device 110 may be a mobile device or a fixed device, for example, it may be fixed on the ground or a wall. In some embodiments, the charging device 110 may refer to a charging pile installed in a parking lot, a residential area or a charging station. In some embodiments, the charging device 110 may also include a battery replacement station for replacing the battery system 150 .

In some embodiments, as shown in FIG. 2 , the battery system 150 may include one or more battery modules 151 , where each battery module 151 may be obtained by a plurality of single cells 153 connected in series and/or in parallel. A processing device (eg, processor 310) in the battery system 150 may obtain charging data related to the battery system and process it. For example, the processing device in the battery system 150 may be used to obtain charging data of the battery system, battery modules or cells, and determine health status information of the battery system, battery modules or cells based on the charging data. In some embodiments, the charging data may include at least one of charging voltage, charging current, charging time, charging temperature data, and historical usage data of the battery system 150 , and the health status information may include capacity offset information, SOH value, One or more of self-discharge consistency, pressure difference and internal resistance consistency, insulation status, and temperature status.

In some embodiments, the server 120 can perform data transmission with other devices in the charging system 100 (eg, charging device 110, memory 130, battery system 150, terminal 160). For example, the server 120 may receive charging data and/or health status information sent by the charging device 110 or the battery system 150 . For another example, the server 120 may send a trained machine learning model or update data for updating the machine learning model to the charging device 110 or the battery system 150 . In some embodiments, server 120 may be an independent server or a group of servers. The server group may be centralized or distributed (eg, server 120 may be a distributed system). In some embodiments, the server 120 may be local or remote. For example, the server 120 can access information and/or data stored in the charging device 110, the memory 130, and the battery system 150 through the network 140. In some embodiments, server 120 may be directly connected to charging device 110, memory 130, and battery system 150 to access information and/or data stored therein. In some embodiments, server 120 may be implemented on a cloud platform. For example, the cloud platform may include private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, inter-cloud cloud, multi-cloud, etc. or any combination thereof. In some embodiments, the server 110 may be a server of a battery monitoring platform, such as an owner of a replacement battery pack, a maintenance platform of a battery system, or a manufacturer of a battery system.

Memory 130 may provide data storage functionality for charging system 100 . In some embodiments, memory 130 may be part of charging device 110 , battery system 150 , and/or server 120 . For example, in some embodiments, memory 130 may be used to store trained machine learning models. In some embodiments, memory 130 may be used to store charging data and/or status information related to the battery system (eg, current health status information, future health status information, health status, health categories, etc.). In some embodiments, the memory 130 may include multiple data pools for storing charging data related to the battery system. In some embodiments, memory 130 may store information and/or instructions that are executed or used by processing devices in charging device 110 or battery system 150 to perform the example methods described herein. In some embodiments, memory 130 may include bulk memory, removable memory, volatile read-write memory, read-only memory (ROM), etc., or any combination thereof. Exemplary mass storage may include magnetic disks, optical disks, solid state disks, and the like. Exemplary removable storage may include flash drives, floppy disks, optical disks, memory cards, compact disks, tapes, and the like. Exemplary volatile read-write memory may include random access memory (RAM). In some embodiments, the memory 130 may be implemented on a cloud platform.

Network 140 may facilitate the exchange of data and/or information. In some embodiments, one or more components in charging system 100 may send data and/or information to other components in charging system 100 over network 140 . For example, the server 120 may receive status information related to the battery system sent by the charging device 110 or the battery system 150 through the network 140 . For another example, the server 120 may send the trained machine learning model or update data for updating the machine learning model to the battery system 150 and/or the memory 130 through the network 140 . In some embodiments, network 140 may be any type of wired or wireless network. For example, network 140 may be or include a public network (eg, the Internet), a private network (eg, a local area network (LAN)), a wired network, a wireless network (eg, an 802.11 network, a Wi-Fi network), a frame relay network , virtual private network (VPN), satellite network, telephone network, router, hub, switch, server computer and/or any combination thereof. For example, network 140 may include a fiber optic network, a telecommunications network, an intranet, a wireless local area network (WLAN), a metropolitan area network (MAN), a public switched telephone network (PSTN), a Bluetooth ^™ network, a ZigBee ^™ network, near field communications ( NFC) network, etc. or any combination thereof. In some embodiments, network 140 may include one or more network access points. For example, network 140 may include wired or wireless network access points, such as base stations and/or Internet switching points, through which one or more components of charging system 100 may be connected to network 140 to exchange data and /or information.

The battery system 150 may be electrically connected to the charging device 110 for charging. In some embodiments, battery system 150 It can be a battery pack used to power electric vehicles, electric bicycles, electric motorcycles or other electrical equipment. In some embodiments, the battery system 150 can perform data transmission with the charging device 110 . For example, during the charging process of the battery system 150, the battery system 150 may obtain charging data from the charging device 110, and the charging data may include at least one of charging voltage, charging current, and charging time. In some embodiments, the battery system 150 may include one or more battery modules, each battery module may include one or more single cells, and the one or more battery modules may cooperate to provide power. In some embodiments, the battery system 150 may further include one or more sensors, and/or a battery management system (BMS). The single cell can be used to store electrical energy. Each single cell may include a positive terminal and a negative terminal. In this application, the single battery can be any type of battery, such as lead-acid single battery, nickel metal hydride single battery, lithium ion (Li-ion) single battery, etc., which is not limited in this application. The BMS can be used to manage the charging and discharging behaviors of the battery system 150, collect data related to charging and discharging of the battery system 150, and transmit the collected data. In some embodiments, the battery system 150 may transmit data through the BMS. In some embodiments, the BMS can transmit data to one or more devices of the charging system 100, such as the charging device 110, the server 120, the memory 130, and the terminal 160. One or more sensors within battery system 150 may detect one or more characteristics of battery system 150 . For example, the one or more sensors may include a temperature sensor configured to detect the overall internal temperature of the battery system 150 during charging and discharging, and/or the temperature of one or more internal locations. For another example, the one or more sensors may detect charging and discharging current, voltage, etc. of the battery system 150 . The one or more sensors may send detected data to the BMS. In some embodiments, the battery system 150 may further include a power supply module, which may be used to utilize the electrical energy stored in the battery system and power external devices and/or at least one component in the battery system 150 under the control of the BMS. In some embodiments, the battery system 150 may also include a positioning module for obtaining location information of the battery system. The location information of the battery system can be obtained in real time through the positioning module, so that when the battery fails or is abnormal, corresponding processing can be carried out in a timely manner based on the location information. For example, the server 120 can obtain the location information obtained by the battery system 150 through the positioning module, so as to arrange for maintenance personnel to repair when the battery fails, or to trace the battery assets, monitor the battery status, or terminate the use of the battery when it is damaged, etc. In some embodiments, the battery system 150 may also include other customized modules, such as a display module, which is not limited in this specification.

The terminal 160 may be various types of devices with information receiving and/or sending functions. Users can interact with server 120 through terminal 160. For example, after the battery system 150 locally evaluates the health status information related to the battery system, the evaluation results can be sent to the server 120 through the network 140 . The user can receive the battery health status information estimation result from the server 120 through the terminal 160 . For another example, the user may send a request to evaluate battery health status information to the server 120 through the terminal 160 . The server 120 may send the request to the battery system 150 so that the battery system 150 locally evaluates the relevant health status information. In some embodiments, the terminal 160 may include a mobile phone 160-1, a tablet computer 160-2, a personal computer 160-3, and other electronic devices, such as vehicle-mounted equipment, maintenance equipment, etc. In some embodiments, the users may include battery users (eg, electric vehicle users), battery manufacturers, electric vehicle manufacturers, etc.

It should be noted that the above description of the charging system 100 is for illustrative purposes only and is not intended to limit the scope of this specification. For those of ordinary skill in the art, various variations and modifications can be made based on this description. For example, the charging system 100 may also include electrical equipment, such as electric vehicles. However, these changes and modifications do not depart from the scope of this specification.

Figure 3 is a software and/or hardware schematic diagram of an exemplary computer device according to some embodiments of this specification.

In some embodiments, the computing device 300 may be part of the charging device 110 or the battery system 150 for determining status information (eg, current health status information, future health status information) of the battery system based on charging data related to the battery system. , health status, health category, etc.). For example, when the computing device 300 is integrated into the charging device 110, the charging device 110 can directly determine the current health status information and future health status information of the battery system based on charging data such as charging voltage, charging current, and charging time of the battery system currently being charged. Health status information, or health status, etc. For another example, when the computing device 300 is integrated into the battery system 150, the battery system 150 can obtain charging data such as charging voltage, charging current, charging time, etc. from the charging device 110, and determine the current health status information and future health status information of the battery system based on the charging data. Health status information, or health status, etc.

As shown in Figure 3, in some embodiments, computing device 300 may include processor 310, memory 320, input/output 330, and communication port 340.

Processor 310 may execute computer instructions (eg, program code) and determine health-related information of the battery system according to the methods described herein. Computer instructions may include, for example, routines, programs, objects, components, data structures, procedures, modules, and functions that perform the specific functions described herein. For example, processor 310 may obtain charging data related to battery system 150 and determine current health information, future health information, or health status of the battery system based on the charging data. In some embodiments, processor 310 may include at least one processing device (eg, a single-core processing device or a multi-core multi-core processing device). By way of example only, a processing device may include a central processing unit (CPU), an application specific integrated circuit (ASIC), an application specific instruction processor (ASIP), a graphics processor (GPU), physical processor (PPU), digital signal processor (DSP), field programmable gate array (FPGA), programmable logic circuit (PLD), controller, microcontroller unit, reduced instruction set computer (RISC) ), microprocessor, etc. or any combination of the above.

For illustration only, only one processor is depicted in computing device 300 . However, it should be noted that in some embodiments, the computing device 300 may also include multiple processors, and the operations and/or method steps performed by one processor described in the specification may also be performed jointly or individually by multiple processors. . For example, if the processor of computing device 300 performs operations A and B simultaneously, it should be understood that operations A and B may be performed jointly or individually by two or more different processors in computing device 300 (e.g., One processor performs operation A and a second processor performs operation B, or the first processor and the second processor jointly perform operations A and B).

Memory 320 may store data/information obtained from charging device 110 , battery system 150 , terminal 160 , and/or any other component in charging system 100 . The term data as used herein may be any information including, for example, numbers, text, speech, images, videos, parameters, codes, formulas, files, algorithms, programs, etc., or any combination thereof. For example, memory 320 may be used to store trained machine learning models. As another example, memory 320 may be used to store charging data and/or status information related to battery system 150 . For another example, the processor 310 may receive the evaluation model update information sent by the server 120 through the network 140 and store it in the memory 320 . The processor 310 may update the evaluation model according to the model update information. In some embodiments, memory 320 may store programs and/or instructions, such as computer programs, for execution by processor 310 . In some embodiments, memory 320 may include mass storage, removable storage, volatile read-write memory, read-only memory (ROM), or any combination thereof.

Input/output 330 may be used to input and/or output signals, data, information, etc. In some embodiments, input/output 330 may enable a user to interact with components in charging system 100 (eg, charging device 110, server 120). In some embodiments, input/output 330 may include input devices and output devices. Exemplary input devices may include a keyboard, mouse, touch screen, microphone, or any combination thereof. Exemplary output devices may include display devices, speakers, printers, projectors, or any combination thereof. Exemplary display devices may include liquid crystal displays (LCDs), light emitting diode (LED) based displays, flat panel displays, curved displays, television devices, cathode ray tubes, or any combination thereof.

Communication port 340 may connect with a network (eg, network 140) to facilitate data communications. The communication port 340 may establish a connection between the server 120 and the charging device 110, the memory 130, the battery system 150, and/or the terminal 160. Connections can include wired and wireless connections. In some embodiments, communication port 340 may be and/or include a standardized communication port, such as RS232, RS485, etc. In some embodiments, communication port 340 may be a specially designed communication port.

Figure 4 is a schematic module diagram of a device for determining battery health status information according to some embodiments of the present application.

In some embodiments, the battery health status information determining device 400 may be part of the aforementioned battery system 150 . For example, when the computing device 300 is integrated into the battery system 150, the battery health status information determining device 400 may be part of the processor 310. In some embodiments, the battery health status information determination device 400 can be added to a traditional battery system as an independent additional device, thereby providing a localized battery health status information determination function for the traditional battery system. It should be noted that in some embodiments, by adding the battery health status information determination device 400 to the traditional battery system to realize the localized battery health status information determination function, replacement of the battery system can be avoided.

As shown in FIG. 4 , in some embodiments, the battery health status information determining device 400 may include a first acquisition module 410 and a first evaluation module 420 .

The first acquisition module 410 may be used to acquire charging data related to the battery system 150 . In some embodiments, the charging data may include at least one of charging voltage, charging current, charging time, charging temperature data, and historical usage data. For example, the first acquisition module 410 may acquire the charging data related to the battery system 150 from the BMS in the battery system 150 and store it in the storage module (eg, the memory 320). In some embodiments, the first acquisition module 410 may be used to acquire a trained machine learning model.

The first evaluation module 420 may be configured to determine health status information related to the battery system 150 based on the charging data related to the battery system 150 . For example, the first evaluation module 420 can read the health status judgment model from the memory 320 to evaluate the battery health status. In some embodiments, the health status information may include one or more of capacity offset information, SOH value, self-discharge consistency, pressure difference and internal resistance consistency, insulation status, and temperature status related to the battery system 150 . In some embodiments, the first evaluation module 420 may use any evaluation algorithm or model to evaluate the battery health status information related to the battery system 150 , which is not limited by this application. The method for the first evaluation module 420 to evaluate the battery health status information is detailed in the description of FIG. 6 .

In some embodiments, the battery health status information determining device 400 may further include a communication module (not shown in the figure). In some embodiments, the communication module may be used to communicate with the reading device, so that the reading device reads the charging data and/or related to the battery system 150 stored in the storage module (eg, the memory 320) through the communication module. or health status information. This facilitates inspection, maintenance or escalation Charging data and/or health status information related to the battery system 150 is quickly obtained when battery cells are screened. In some embodiments, the reading device may refer to a terminal device (eg, terminal 160) with a data reading function, such as maintenance equipment, etc. In some embodiments, charging data and/or health status information related to the battery system 150 can be read by a reading device, so as to determine the current status of the battery system 150 based on the charging data and/or health status information. In some embodiments, the communication connection between the communication module and the reading device may include a short-range communication connection, such as NFC, radio frequency identification (RFID), Bluetooth, ZigBee, infrared, etc. In some embodiments, the communication connection between the communication module and the reading device may also include a long-distance communication connection. In some embodiments, the communication module can implement communication between the battery health status information determining device 400 and the BMS. For example, the first acquisition module 410 can communicate through a Controller Area Network (CAN) to receive charging data of the battery system 150 during the charging process from the BMS 152, such as battery cell or module voltage, battery cell or Module temperature, battery cell or module charging current, charging duration, etc. The communication module may send the charging data to the second evaluation module 420 and/or the storage module.

Figure 5 is a schematic connection diagram of a device for determining battery health status information according to some embodiments of the present application.

As shown in Figure 5, in some embodiments, the battery system 150 can include a battery module 151 and a BMS 152, wherein the battery module 151 and the BMS 152 can include a communication interface (such as a plug interface), and the BMS 152 can use the The communication interface is connected to the battery module 151 and obtains voltage, current, temperature, time and other parameters of the battery module 151 during charging or discharging.

The battery health status information determining device 400 may include one or more communication interfaces and be connected to the battery module 151 and/or the BMS 152 through the communication interface to obtain information related to the battery module 151 from the battery module 151 or the BMS 152. charging data. In some embodiments, the battery health status information determining device 400 may be connected between the battery module 151 and the BMS 152. In some embodiments, the battery health status information determining device 400 may be connected only to the battery module 151 or the BMS 152. In some embodiments, the aforementioned one or more sensors may be connected to the battery health status information determining device 400 through the communication interface.

It should be noted that the above description is provided for illustrative purposes only and is not intended to limit the scope of the application. For those of ordinary skill in the art, various changes and modifications can be made based on the description of this application. However, such changes and modifications would not depart from the scope of the present application.

Figure 6 is an exemplary flowchart of a method for determining battery health status information according to some embodiments of the present application.

In some embodiments, the method 600 may be performed by the battery system 150 (eg, the computing device 300 integrated in the battery system 150, the battery health status information determining device 400 shown in FIG. 4).

Referring to Figure 6, in some embodiments, a method for determining battery health status information may include the following steps:

Step 610: Obtain charging data related to the battery system.

In some embodiments, the battery health status information determining device 400 may obtain charging data related to the battery system 150 . The battery system 150 may be a power supply device used to power electric vehicles, electric bicycles, electric motorcycles or other electrical equipment.

In some embodiments, when the charging device 110 provides charging services for the battery system 150, the battery health status information determining device 400 may obtain charging data related to the battery system 150. In some embodiments, the charging data may include at least one of charging voltage, charging current, charging time, charging temperature data, and historical usage data of a single battery, a module, or the entire battery system 150 . In some embodiments, the battery health status information determining device 400 (eg, communication module) may obtain the charging data from the battery system 150 through wired communication or wireless communication. Exemplary wired communication methods may include CAN communication, and exemplary wireless communication methods may include Bluetooth, NFC, ZigBee, etc. In some embodiments, when the charging device 110 provides charging services for the battery system 150, the communication module can obtain charging data from the battery system 150 in real time and send the charging data to the first evaluation module 420 in real time. During the charging process, the first evaluation module 420 may evaluate the battery health status of the battery system 150 in real time according to the charging data. In some embodiments, when the charging device 110 provides charging services for the battery system 150, the communication module can obtain charging data from the battery system 150 in real time and store the charging data in the storage module. The first evaluation module 420 may evaluate the battery health status of the battery system 150 according to the charging data after charging is completed.

In some embodiments, if the current battery system belongs to the low health category, before the charging device 110 provides charging services for the battery system 150, the first acquisition module 410 can obtain the charging data from the storage module (for example, the memory 320) of the battery system 150. , and sends the charging data to the first evaluation module 420. The first evaluation module 420 can evaluate the battery health status of the battery system 150 in real time based on the charging data before charging. More details can be found in Figure 11 and its associated description.

In some embodiments, the aforementioned charging voltage may include a charging start voltage, a charging process characteristic voltage, and a charging end voltage. Among them, the charging starting voltage may refer to the input voltage when charging starts; the charging process characteristic voltage may refer to the change characteristics of the input voltage during the charging process; and the charging end voltage may refer to the input voltage when charging ends. In some embodiments, the charging voltage may be constant voltage or variable voltage. pressure.

In some embodiments, the charging current may be an input current during charging, and the input current may be variable or constant. In some embodiments, the charging current may be a constant current.

In some embodiments, the charging time may refer to the time used for this charging. In some embodiments, the charging time can be combined with the charging current to reflect the total power of this charge.

In some embodiments, the charging temperature data may include charging starting temperature and charging process temperature weight. In some embodiments, the charging process temperature weight can be understood as the proportion of the charging starting temperature during the charging process in the health status evaluation. It is understandable that there is a certain relationship between battery temperature and battery capacity. The specific performance is as follows: when the temperature drops, the battery capacity also decreases accordingly. In other words, the health of the battery system 150 is related to the charging starting temperature and the charging process temperature weight. Therefore, in some embodiments, the health status assessment result of the battery system 150 can be made more accurate by taking the charging start temperature and the charging process temperature weight into consideration. In some embodiments, the charging temperature data may include temperature change data during the charging process, and abnormality during the charging process may be reflected through the temperature change data during the charging process.

In some embodiments, the charging process temperature weight may change accordingly as the charging starting temperature changes. For example, when the charging starting temperature is 8°C, the corresponding charging process temperature weight can be 0.2; when the charging starting temperature is 10°C, the corresponding charging process temperature weight can be 0.21; when the charging starting temperature is 11°C , the corresponding charging process temperature weight can be 0.215. It should be noted that the above corresponding relationship between the charging start temperature and the charging process temperature weight is only an exemplary description. In the embodiment of the present application, the corresponding relationship between the charging starting temperature and the charging process temperature weight may be, but not Limited to the above examples.

In some embodiments, historical usage data may include historical battery health status of battery system 150 . In some embodiments, the historical usage data may also include the cumulative charge and discharge capacity of the battery system 150, charge and discharge cycles, number of charge and discharge cycles, charge and discharge depth (ratio of a single charge or discharge amount to battery capacity), discharge time The voltage and current, single discharge time, discharge temperature, driving mileage, battery factory time, etc. In some embodiments, the historical usage data may include data related to the battery system 150 when the electrical device (eg, an electric vehicle, an electric bicycle, an electric motorcycle, or other electrical device) is in use or not in use. Taking an electric vehicle as an example, the historical usage data may include the voltage and current, temperature, accumulated charge and discharge capacity, and total voltage of the battery system of the single cells of the battery system 150 during the driving and/or parking of the electric vehicle. In some embodiments, the battery system 150 may include multiple cells (also referred to as single cells) or battery modules, and the historical usage data may include historical battery health corresponding to each cell or battery module. Status, accumulated charge and discharge capacity, charge and discharge cycle, number of charge and discharge cycles, charge and discharge depth, driving mileage, battery factory time, etc. In some embodiments, the historical usage data of the battery system 150 can be uploaded to the server 120, and the charging device can obtain the data from the server based on the ID corresponding to the battery system 150 (such as the battery number, the chassis number corresponding to the battery system, etc.) or identification information. 120 obtains historical usage data corresponding to the battery system 150 . In some embodiments, the battery health status information determining device 400 may obtain the historical usage data from the BMS of the battery system 150 .

It should be noted that the historical usage data of the battery system 150 can reflect the current health status of the battery system 150 to a certain extent. Therefore, in some embodiments, taking the historical usage data of the battery system 150 into consideration can make the battery system 150 more accurate. The health status assessment results are more accurate.

Step 620: Determine health status information related to the battery system based on the charging data.

In some embodiments, the battery health status information determining device 400 may determine the health status information of the battery system 150 based on the charging data of the battery system 150 . In some embodiments, the health status information may include one or more of capacity offset information, SOH value, self-discharge consistency, pressure difference and internal resistance consistency, insulation status, and temperature status related to the battery system 150 .

In some embodiments, the health status information may include capacity offset information, SOH value, insulation status, temperature status, etc. corresponding to each battery cell or battery module. In some embodiments, the health status information may also include system capacity offset information, self-discharge consistency, pressure difference and internal resistance consistency, insulation status, temperature status, and SOH value of the entire battery system 150 .

In some embodiments, the capacity offset information may refer to the difference between the current capacity of the cell or module and the standard capacity, and is used to characterize the consistency of the electric vehicle battery system. In some embodiments, the standard capacity may be determined based on the average capacity of multiple cells or modules. In some embodiments, the standard capacity may be determined based on the capacity of the first fully charged cell or module. In some embodiments, the capacity offset information can be obtained based on the aforementioned charging data. In some embodiments, the capacity offset information may be capacity offset information corresponding to a single cell or battery module, or may be capacity offset information corresponding to a battery system composed of multiple cells or battery modules. . For example, referring to Figures 7 and 8, in some embodiments, the current capacity of each cell or battery module (ie, the module capacity shown in Figure 7) can be obtained based on the aforementioned charging data, and then each The current capacity of the battery cell or module is compared with the aforementioned standard capacity to obtain the capacity offset information of each battery cell or module as shown in Figure 8.

The SOH (state-of-health) value can be used to characterize the health status of cells, modules or battery systems. In some embodiments, the SOH value can be the ratio of actual capacity to rated capacity. Among them, the actual capacity can refer to the current maximum battery capacity. The actual capacity may change with the use time, the number of uses (ie, the number of charge and discharge cycles), the use environment temperature, usage habits, etc. In other words, the SOH value may change with the use time. , usage frequency, usage environment temperature, usage habits and other reasons. In some embodiments, the SOH value can be obtained based on the aforementioned charging data. In some embodiments, the SOH value may be the SOH value corresponding to a single cell or battery module, or the SOH value corresponding to a battery system composed of multiple cells or battery modules.

Self-discharge consistency can characterize the charge retention capability of cells or modules in a battery system. Through self-discharge consistency, we can know whether the amount of electricity that automatically reduces or disappears in the battery cell or module is consistent when not in use. In some embodiments, the self-discharge consistency can be obtained from the aforementioned charging data.

In some embodiments, the aforementioned health status information may include consistency of pressure difference and internal resistance, and the consistency of pressure difference and internal resistance may include consistency of pressure difference and internal resistance consistency. In some embodiments, the voltage difference consistency and internal resistance consistency between each cell or module in the battery system can be obtained through the aforementioned charging data. For example, the voltage corresponding to each battery cell or module can be determined based on the aforementioned charging voltage, and then the voltage difference between each battery cell or module can be determined based on the voltage. For another example, the internal resistance of each battery cell or module can be determined based on the aforementioned charging voltage and charging current, and then the internal resistance consistency between each battery cell or module can be determined based on the internal resistance.

In some embodiments, the aforementioned insulation state may refer to the insulation relationship between the charging object and the ground, or may also refer to the insulation relationship between the charging object and an adjacent object (such as a car body). In some embodiments, the aforementioned charging data may include insulation voltage and/or insulation resistance between the charging object and other objects, and the insulation status of the charging object can be obtained through the insulation voltage and/or insulation resistance.

In some embodiments, the battery health status information determining device 400 can process the obtained charging data to obtain the aforementioned health status information of the battery system 150, so as to determine whether there is an abnormality in the battery system 150 and predict the potential of the battery system 150. risk of failure. In some embodiments, the battery health status information determining device 400 can preprocess the obtained charging data, and obtain the aforementioned health status information based on the preprocessed charging data. For example, preprocessing may include but is not limited to filtering (such as filtering abnormal data), cleaning, increasing data dimensions, etc.

In some embodiments, the aforementioned health status information may also include impedance information related to the battery system 150 , and the impedance information may be used to characterize the charging and discharging performance of the cell or battery module and the consistency of the battery system. In some embodiments, the impedance information may be the impedance information corresponding to a single cell or battery module, or the impedance information corresponding to a battery system composed of multiple cells or battery modules. In some embodiments, the impedance information may be obtained based on the charging voltage and charging current associated with the battery system 150 . For example, referring to Figure 9, in some embodiments, the minimum DCR (DC impedance) is 0.154mΩ, the maximum DCR is 0.173mΩ, the average DCR of the module is 0.163mΩ, and the maximum DCR difference between modules is about 12%, which can be expressed The consistency of current battery systems is relatively good.

In some embodiments, the battery health status information determining device 400 may use a trained machine learning model to process the obtained charging data to determine health status information related to the battery system 150 . In some embodiments, the battery health status information determining device 400 may use a trained machine learning model to process the preprocessed charging data to determine health status information related to the battery system 150 . In some embodiments, the trained machine learning model may include at least one of a state information determination model, a state information prediction model, and a health state judgment model. For example, the battery health status information determination device 400 can use a trained status information determination model to process the charging data (such as data mining, feature extraction, etc.) to determine the current health status information of the battery system 150 . For another example, the battery health status information determining device 400 can use a trained status information prediction model to process the charging data and the current health status information to determine future health status information of the battery system in the next time period. As another example, the battery health status information determination device 400 can use a trained health status judgment model to process two or more future health status information of the battery system 150 to determine the health status of the battery system 150, such as health, abnormality, etc. In some embodiments, the machine learning model may be pre-configured locally in the battery health information determining device 400 (eg, stored in the memory 320), or obtained from the server 120 when the charging service is started. For more information on using machine learning models to determine status information of the battery system, see Figure 10 and its related description.

In some embodiments, the machine learning model may include one of neural networks, transfer learning, gradient boosting decision trees, cluster analysis, outlier analysis, etc., or any combination thereof.

In some embodiments, the battery health status information determining device 400 may determine a matching machine learning model based on the type of the battery system 150 . For example, in some embodiments, the battery system 150 may be divided into battery types (such as lithium-ion batteries, nickel-metal hydride batteries, fuel cells, lead-acid batteries, sodium-sulfur batteries, etc.) or battery capacity levels/voltage levels/current levels. Different types, and then configure the corresponding machine learning models respectively. When the battery health status information determining device 400 processes the charging data of the battery system 150, the corresponding machine learning model can be obtained according to the type of the battery system 150 to process the charging data, thereby better adapting to different conditions. The differences between the battery systems 150 ensure the accuracy of the health status information processed by them.

It should be noted that the above machine learning model can be trained through several samples. In some embodiments, the state information determination model, the state information prediction model, and the health state judgment model can be trained based on corresponding sample data respectively.

In some embodiments, when the above machine learning model includes at least two of a state information determination model, a state information prediction model, and a health state judgment model, the machine learning model can be obtained through joint training. Taking the above machine learning model as an example, including a state information determination model, a state information prediction model and a health state judgment model, in some embodiments, the processor (for example, the server 120, the processor 310) can obtain training samples, and the training samples Including multiple sample groups, each sample group includes the historical charging data of the corresponding sample battery, the current health status information determined by the above-mentioned status information determination model, the historical future health status information obtained by the above-mentioned status information prediction model, and the above-mentioned health status. Judge the historical health status obtained by the model. Further, the processor can train the initial model based on the training samples to obtain the trained machine learning model, wherein the labels of the training samples include the historical actual health status information of the sample battery corresponding to the historical current health status information, and Historical actual health status information corresponding to historical future health status information, and historical actual health status corresponding to the historical health status.

It should be noted that when the above machine learning model includes any two of the state information determination model, the state information prediction model and the health status judgment model, the training samples corresponding to the joint training include sample data corresponding to any two of the models, The tags contain historical actual data corresponding to any two of the models described. For example, the above-mentioned machine learning model includes a state information determination model and a state information prediction model. Each sample group in the training sample includes historical charging data of the sample battery, current health status information determined by the state information determination model, and the above state information. The labels of the historical and future health status information obtained by the prediction model include the historical actual health status information of the sample battery corresponding to the historical and current health status information, and the historical actual health status information corresponding to the historical and future health status information.

In some embodiments, various dimensions of historical original data (such as charging/discharging voltage, charging/discharging current, battery capacity, charging/discharging time, charging/discharging temperature, etc.) can be preprocessed to realize the characteristics of the sample data. Select to obtain training samples. In some embodiments, the obtained training samples can be divided into training data and test data according to a preset ratio (such as 8:2, 6:4, or 7:3, etc.). Among them, the training data is used for model training, and the test data is used to test the prediction accuracy of the model. For example, 70% of the above training samples can be used as training data, 30% of the samples can be used as test data, and the training data can be used to train the initial model to obtain a trained machine learning model. In some embodiments, the prediction results of the test data (such as current health status information, future health status information, health status) and the labels of the test data (such as the historical actual health status corresponding to the current health status information) can be based on the machine learning model. information, historical actual health status information corresponding to future health status information, historical actual health status corresponding to health status) to construct a loss function. The loss function can reflect the difference between the prediction result and the label. The battery health status information determining device 400 or the server 120 may adjust the parameters of the machine learning model based on the loss function to reduce the difference between the prediction results and the labels. For example, by continuously adjusting the parameters of the machine learning model, the value of the loss function is reduced or minimized. In some embodiments, the trained machine learning model can also be obtained according to other training methods, for example, setting a corresponding initial learning rate (for example, 0.1) and a learning rate decay strategy for the training process. This application is not limited here.

In some embodiments, when the number of negative samples in the training samples is less than a preset value, the number of negative samples can be filled through data simulation or parameter adjustment. For example, when the number of abnormal or faulty batteries is small, the number of corresponding negative samples may be smaller than the preset value. For example, when the number of negative samples is less than a preset value, the parameter threshold of the battery can be adaptively reduced based on the corresponding parameter value when the faulty battery fails. For example, if the fault temperature corresponding to a battery that has failed in history is 50°C, the initial threshold of the battery safety temperature can be 55°C and adaptively adjusted to 50°C. When the temperature of a battery is greater than the safe temperature, the probability of failure is higher. After lowering the safe temperature threshold, batteries greater than the temperature threshold will be defined as faulty batteries when obtaining samples, so the number of negative samples will increase. For another example, the battery charging and discharging process can be simulated based on the parameter information of the sample battery, and the sample data can be annotated based on the simulated data.

When the number of negative samples is less than the preset value, the negative samples are filled to make the training samples richer, thereby improving the accuracy of the prediction results of the trained machine learning model.

In some embodiments, the training of the machine learning model may be performed by the battery health status information determining device 400 (eg, the first evaluation module 420). In some embodiments, training of the machine learning model may also be performed by the server 120 or other devices.

In some embodiments, the above machine learning model can be updated regularly (such as daily, weekly, or monthly, etc.). For example, the battery health status information determination device 400 may update the status information determination model based on the actual health status information corresponding to the current health status information predicted by the battery system 150 every Monday, and update the status information based on the actual health status information corresponding to the future health status information. The prediction model updates the health status judgment model based on the health status. In some embodiments, the trained machine learning model can be updated in real time. In some embodiments, the above machine learning model may be updated in response to update instructions (for example, instructions issued by an external monitoring platform). new. The model update may be performed by the battery health status information determining device 400 (for example, the first evaluation module 420), or may be performed by the server 120 or other devices. The updated model may be sent to the battery health status information determining device 400 . In some embodiments, the battery health status information determining device 400 may obtain a program for updating the model from a server or other device (eg, a mobile storage device), and update the locally stored machine learning model according to the program for updating the model.

Step 630: Send the health status information to the server.

In some embodiments, after the battery health status information determining device 400 determines the corresponding health status information based on the charging data related to the battery system 150, the health status information (such as capacity offset information, SOH value, self-discharge properties, pressure difference and internal resistance consistency, insulation status, and temperature status) and/or health status (eg, healthy, abnormal) are sent to the server 120 . In some embodiments, the battery health status information determining device 400 can send the charging data and health status information of the battery system 150 to the server 120 to facilitate remote monitoring of the current status of the battery system 150 . In some embodiments, the battery health status information determining device 400 may send the health status of the battery system 150 together with related data (such as a plurality of predicted future health status information) to the server 120 . In some embodiments, the charging device 110 may send the aforementioned health status information, the health status, and the ID or identification information corresponding to the battery system 150 to the server together.

In some embodiments, the server 120 can further process the health status information of the battery cells or battery modules. For example, one or more preset thresholds can be set. When a certain battery cell or battery in the battery system 150 is detected, When the status of the module does not meet the preset threshold (for example, less than or greater than the preset threshold), it is determined that the cell or battery module is abnormal, and it is prompted that it needs to be repaired or replaced.

In some embodiments, when the server 120 determines that there is an abnormality based on the health status information sent by the battery health status information determination device 400, it may further determine the ID or identification information corresponding to the abnormal battery cell or battery module, and then based on The ID or identification information determines the location of the abnormal cell or battery module to facilitate subsequent repair or replacement. In some embodiments, the server 120 determines the health status sent by the device 400 based on the battery health status information. For an abnormal battery system, the server 120 may further determine the ID or identification information corresponding to the abnormal battery cell or battery module, and then based on The ID or identification information determines the location of the abnormal cell or battery module to facilitate subsequent repair or replacement.

In some embodiments, the server 120 may perform further processing based on the received status data to monitor the operating status of the battery system 150 . For example, in some embodiments, server 120 may convert received health status information into monitoring charts. For another example, the server 120 can perform capacity fading assessment, cell internal resistance assessment, consistency assessment, cell voltage distribution analysis, temperature rise rate assessment, static voltage difference assessment, and undervoltage data assessment on the battery system based on the received status data. , cumulative charging behavior evaluation, cumulative usage status evaluation, cumulative throughput capacity evaluation, etc. In some embodiments, the server 120 can predict the failure risk of the battery system 150 based on the received health status information, and make corresponding warnings when it is predicted that the battery system 150 may have a risk of failure, for example, perform abnormal pressure difference warning, temperature difference warning, etc. Abnormal warning, insulation abnormal warning, abnormal self-discharge rate, etc.

In some embodiments, the terminal 160 may obtain charging data and/or status data of the battery system 150 from the server 120 . In some embodiments, the battery health status information determining device 400 can also directly send the charging data and/or status data of the battery system 150 to the terminal 160 . In some embodiments, the terminal 160 can display the charging data and/or status data of the battery system 150 through an output module (such as a display screen), and/or issue an early warning prompt based on the status data. In some embodiments, terminal 160 may include the reading device described above.

It should be noted that the above description of the battery health status information determination method and its process is provided for illustrative purposes only and is not intended to limit the scope of the present application. For those of ordinary skill in the art, various modifications and changes can be made based on the description of this application. For example, the battery health status information determining device 400 may determine the health status information and/or the health status of the battery system based on the discharge data of the battery system, or the discharge data and charging data. As another example, the battery health status information determining device 400 can determine the health status information and/or the health status of the battery system when the battery system is in a discharge state or a resting state (that is, not working). However, such modifications and changes would not depart from the scope of the present application.

Figure 10 is an exemplary flow chart of a battery status information determination method according to some embodiments of the present application.

In some embodiments, the method 1000 may be performed by the battery system 150 (eg, the computing device 300 integrated in the battery system 150, the battery health status information determining device 400 shown in FIG. 4). As shown in Figure 10, in some embodiments, the battery health status information determining device 400 can use a trained machine learning model to process the charging data of the battery system, etc., to determine the status information of the battery system, for example, the current health status information. , future health status information, health status, etc.

Referring to Figure 10, in some embodiments, the battery status information determining method may include the following steps:

Step 1010: Obtain charging data related to the battery system.

In some embodiments, the battery health status information determining device 400 may obtain charging data related to the battery system 150 . In some embodiments, the charging data may include at least one of charging voltage, charging current, charging time, charging temperature data, and historical usage data of a single battery, a module, or the entire battery system 150 . More details can be seen in Figure 6 (eg step 610), which will not be described again here.

Step 1020: Process the charging data through a state information determination model to determine current health state information of the battery system.

In some embodiments, the battery health status information determination device 400 may use a trained status information determination model to process the charging data to determine the current health status information of the battery system 150 . The current health status information may refer to one or more of the capacity offset information, SOH value, self-discharge consistency, voltage difference and internal resistance consistency, insulation status, and temperature status of the battery system at the current moment. For example, the current moment may include the current time point corresponding to when the battery system is charging, the corresponding time point after charging is completed, the corresponding time point when discharging, etc.

In some embodiments, the state information determination model may include an acquisition layer and a fusion layer. The acquisition layer can be used to obtain the current charging data of the battery system, and the fusion layer can fuse the current charging data to obtain fused charging data. In some embodiments, the fusion layer may fuse the current charging data of the battery system 150 based on a preset weight value. For example, the fusion layer can fuse the charging temperature data of the battery system 150 based on the charging process temperature weight and the charging starting temperature of the battery system 150 to obtain the fused charging temperature. For another example, according to the impact of charging voltage, charging current, charging time, charging temperature, etc. on the health status of the battery system, different weight values can be set for each type of data, and the fusion layer can be based on the preset weight value corresponding to the battery system 150 , fuse the charging voltage, charging current, charging time, and charging temperature of the battery system 150 to obtain a set of fused charging data. In some embodiments, the preset weight value can be statistically obtained by the server 120 based on historical data of the battery system, or manually set by the user. It can be any reasonable value, and this specification does not limit this. In some embodiments, different battery systems, or different single cells, or different battery modules may correspond to different preset weight values. In some embodiments, the usage time of the battery system is different, and the corresponding preset weight values are different. For example, new batteries and batteries that have been used for a period of time can correspond to different preset weight values.

Step 1030: Process the charging data and the current health status information through a status information prediction model to determine future health status information of the battery system in the next time period.

The next time period can include any time period after the current moment, such as the next 30 minutes, 1 hour, 3 hours, 5 hours, one day, one week, etc. Future health status information may refer to one or more of the capacity offset information, SOH value, self-discharge consistency, voltage difference and internal resistance consistency, insulation status, and temperature status of the battery system in the next period of time.

In some embodiments, the battery health status information determination device 400 may utilize a trained status information prediction model to process the charging data of the battery system 150 and the current health status information obtained through the status information determination model to determine the battery system 150 future health status information. The input data of the state information prediction model is the charging data and current health status information of the battery system, and the output is the future health status information of the battery system in the next preset time period (such as one day, one week, etc.).

In some embodiments, the battery health status information determining device 400 may use a trained status information prediction model to process the charging data of the battery system 150 and the first future health status information in the first time period to determine the battery system 150 Second future health status information in a second time period. Among them, the first time period is earlier than the second time period. For example, if the current time is February 1st, the first time period can be the period from February 1st to February 2nd, and the second time period can be the period from February 2nd to February 3rd. . In some embodiments, the battery health status information determining device 400 may utilize a trained status information prediction model to determine future health status information of the battery system 150 in one or more different next time periods.

Step 1040: Process two or more future health status information through the health status judgment model to determine the health status of the battery system.

The health status can reflect whether the battery system is healthy or not, for example, healthy or abnormal. In some embodiments, the battery health status information determining device 400 can use a trained health status determination model to process two or more future health status information of the battery system 150 to determine the health status of the battery system 150 . Among them, the input of the health status judgment model is two or more future health status information of the battery system, and the output can be the health or abnormality of the current battery system, such as 1 indicating health and 0 indicating abnormality, or it can be the health or abnormality of the current battery system. probability value. When the output is a probability value, the battery health status information determining device 400 may further determine the health or abnormality of the battery system based on a preset probability threshold. For example, the battery health status information determining device 400 can input the first future health status information of the battery system 150 in the first time period and the second future health status information in the second time period into the trained health status judgment model to obtain the health status judgment. The model outputs the health status of the battery system 150 .

In some embodiments, the battery health status information determining device 400 may obtain usage feedback information related to the battery system 150 interest. In some embodiments, usage feedback information may include pictures, text, sounds, videos, etc. For example, the user can upload through the terminal 160 pictures containing the fault location of the battery system 150 , audio or video containing abnormal sounds of the battery system 150 , or input text or voice related to the use of the battery system 150 .

In some embodiments, the battery health status information determining device 400 can analyze the usage feedback information to determine the characteristic information of the battery system 150 . In some embodiments, the battery health status information determining device 400 can determine the characteristic information of the battery system 150 through image recognition, sound recognition, or keyword extraction. For example, the battery health status information determining device 400 can classify the text feedback information reported by multiple users on the same battery system by extracting keywords from the text to determine the characteristic information of the battery system 150 . For another example, the battery health status information determining device 400 can compare the abnormal sounds of the battery system in the audio or video uploaded by the user with the original prompt sounds or abnormal sounds of the battery system 150 to obtain the characteristic information of the battery system 150 .

In some embodiments, the battery health status information determining device 400 may process the characteristic information through a health status determination model to determine the health status of the battery system 150 .

By evaluating the health status of the battery system based on the usage feedback information uploaded by the user, the health status evaluation can also be achieved for battery systems that do not have charging data (such as new batteries) or have less charging data, improving the applicability of the battery system. sex.

In some embodiments, when the battery health status information determining device 400 determines that the battery system 150 is abnormal, the battery system's identification, health status, charging data, and health status information may be sent to the server 120 . After receiving the information, the server 120 can issue a warning, such as sending prompt information and/or battery system related data to the terminal 160 . In some embodiments, the battery health status information determining device 400 may issue a warning in response to determining that the battery system 150 is abnormal.

Figure 11 is an exemplary flowchart of a battery status information determination method according to some embodiments of the present application.

In some embodiments, the method 1100 may be performed by the battery system 150 (eg, the computing device 300 integrated in the battery system 150, the battery health status information determining device 400 shown in FIG. 4). As shown in Figure 11, in some embodiments, the battery health status information determining device 400 can determine the health category of the battery system based on the usage information, and perform corresponding operations on the battery system based on the health category.

Referring to Figure 11, in some embodiments, the battery status information determining method may include the following steps:

Step 1110: Obtain usage information of the battery system.

The usage information may include at least one of the usage time, charging times, maintenance times, etc. of the single battery, battery module or battery system. The usage time may refer to the time from the start of use of the battery (for example, the time it was installed on the electrical equipment) to the current moment. The number of charging times can refer to the number of historical charging times of the battery from the factory to the present.

In some embodiments, the battery health status information determining device 400 may obtain the usage information of the battery system 150 from the server 120 . In some embodiments, the battery health status information determining device 400 may obtain the usage information of the battery system 150 from the storage module. In some embodiments, the battery health status information determining device 400 may determine the usage information of the battery system 150 based on its identification information.

Step 1120: Determine the health category of the battery system based on the usage information.

The health category can reflect the health level of the battery system, such as high health, low health, and medium health. The higher the health level, the lower the probability of failure of the battery system and the corresponding longer service life. For example, if the battery is new or the usage time is less than the first preset value, the probability of failure is low, and it can be defined as high health; the battery has been repaired many times or the usage time is greater than or equal to the second preset value, and the battery has failure If the probability is high, it is defined as low health; if the battery life is greater than or equal to the first preset value and less than the second preset value, it is defined as medium health.

In some embodiments, the battery health status information determining device 400 may use a trained classification model to determine the health category of the battery system 150 based on the usage information. Classification models can be trained based on a large amount of labeled sample data. The input of the classification model can be the usage information of the battery system, and the output is the health level, such as first level, second level, and third level, where the lower the level, the healthier it is, or low health, medium health, or high health.

In some embodiments, the battery health status information determining device 400 may perform different operations for battery systems of different health categories. For example, for a battery system with a high health category, the frequency of evaluating the status information of the battery system can be reduced, such as evaluating the health status information and/or health status every 3 times of charging or once a week; for a battery with a medium health category, system, evaluate health status information and/or health status every 2 times of charging or once a day; for battery systems with low health categories, adaptively increase the frequency of evaluation of status information of the battery system, such as real-time or every time it is charged. their health status information and/or health status. In some embodiments, the battery health status information determining device 400 can determine the health category of the battery system 150 in real time or regularly (such as every day), and perform corresponding early warning operations for abnormal conditions of the battery system in a timely manner, for example, for low health categories. The battery system provides timely warnings or data reporting. In a In some embodiments, the frequency of identifying the health category of the battery system 150 may be higher than the frequency of determining its status information. For example, the battery health status information determining device 400 can determine the health category of the battery system 150 in real time. When the health category reaches a preset condition (such as greater than the first level), or every week, the current health status information and future health status information are determined. and/or estimates of health status.

Step 1130: In response to the battery system belonging to the low health category, perform corresponding operations on the battery system.

In some embodiments, in response to the battery system belonging to the low health category, at least one of the following operations may be performed on the battery system: determining the health status of the battery system before each charge, reporting target data of the battery system to the server, and The battery system is preset with backup batteries.

The target data can include charging temperature, charging voltage, charging current, charging time and other data that have a greater impact on the health of the battery. In some embodiments, the battery health status information determining device 400 may determine target data based on historical data.

In some embodiments, when the battery system fails, the battery health status information determining device 400 can autonomously switch to the connection line with the backup battery to ensure the normal use of the battery system.

It should be noted that the above descriptions of the battery status information determining method 1000, the method 1100 and their processes are provided for illustrative purposes only and are not intended to limit the scope of the present application. For those of ordinary skill in the art, various modifications and changes can be made based on the description of this application. For example, in step 1130, the battery health status information determining device 400 can perform corresponding operations, such as alarming, status assessment, etc., based on the health category of the battery system. However, such modifications and changes would not depart from the scope of the present application.

Figure 12 is a schematic module diagram of a device for determining battery health status information according to other embodiments of the present application.

In some embodiments, the battery health status information determining device 1200 may be part of the aforementioned charging device 110 . For example, when the computing device 300 is integrated into the battery system 110, the battery health status information determining device 1200 may be a part of the processor 310. In some embodiments, the battery health status information determining device 1200 can be added to a traditional charging device as an independent additional device. By installing the battery health status information determining device 1200 on the charging equipment, the battery health status information determining device 1200 obtains the charging data related to the battery system, stores and calculates the charging data, and determines the health status information related to the battery system. , and then sending the health status information to the server 120, which can reduce the computing load of the server.

As shown in FIG. 12 , in some embodiments, the battery health status information determining device 1200 may include a second acquisition module 1210 and a second evaluation module 1220 .

The second acquisition module 1210 may be used to acquire charging data related to the battery system 150 . For example, the second acquisition module 1210 may acquire charging data related to the battery system 150 from the BMS in the battery system 150 and store it in the storage module (eg, the accessor 130 or the memory 320). For another example, the second acquisition module 1210 can acquire the charging voltage, charging current, charging time and other data of the currently charging battery system from its own storage module based on the identification information of the battery system.

The second evaluation module 1220 may be configured to determine health status information related to the battery system 150 based on the charging data related to the battery system 150 . In some embodiments, the second evaluation module 1220 may use any evaluation algorithm or model to evaluate battery health status information related to the battery system 150 , which is not limited by this application. The method for the second evaluation module 1220 to evaluate the battery health status information is detailed in the description of FIG. 13 .

In some embodiments, the battery health status information determining device 1200 may further include a communication module (not shown in the figure). In some embodiments, the communication module may be used to communicate with the reading device, so that the reading device reads the charging data and/or related to the battery system 150 stored in the storage module (eg, the memory 320) through the communication module. or health status information. In some embodiments, the communication module may be used to communicate with the battery system 150 to obtain charging data of the battery system.

In some embodiments, the method 1300 may be performed by the charging device 110 (eg, the computing device 300 integrated in the charging device 110, the battery health status information determining device 1200 shown in FIG. 12).

Referring to Figure 13, in some embodiments, a method for determining battery health status information may include the following steps:

Step 1310: Obtain charging data related to the battery system.

In some embodiments, the battery health status information determining device 1200 may obtain charging data related to the battery system 150 . In some embodiments, when the battery system 150 is being charged using the charging device 110, the battery health status information determining device 1200 may obtain the charging data of the battery system. In some embodiments, the charging data may include at least one of charging voltage, charging current, charging time, charging temperature data, and historical usage data of a single battery, a module, or the entire battery system 150 . In some embodiments, the battery health status information determining device 1200 may obtain its charging data from the battery system 150 through the communication module.

Step 1320: Determine health status information related to the battery system based on the charging data.

In some embodiments, the battery health status information determining device 1200 may determine the health status information of the battery system 150 based on the charging data of the battery system 150 . In some embodiments, when the battery system belongs to the low health category, the battery health status information determining device 1200 can determine the status information of the battery system based on charging data before each charge. In some embodiments, the battery health status information determining device 1200 may use a trained machine learning model to process the obtained charging data to determine status information related to the battery system 150 . In some embodiments, the battery health status information determining device 1200 may obtain the trained machine learning model from a storage module or server. The method for the charging device to determine the battery system status information is similar to the battery system. For more details, see Figure 6-11 and its related descriptions, which will not be described again here.

Step 1330: Send the health status information to the server.

In some embodiments, the battery health status information determining device 1200 may send the health status information and/or the health status to the server 120 after determining the corresponding health status information based on the charging data related to the battery system 150 . In some embodiments, the battery health status information determining device 1200 can send the charging data and health status information of the battery system 150 to the server 120 to facilitate remote monitoring of the current status of the battery system 150 . In some embodiments, the battery health status information determining device 1200 may send the health status of the battery system 150 together with related data (such as a plurality of predicted future health status information) to the server 120 . In some embodiments, the battery health status information determining device 1200 may send the aforementioned health status information, the health status, and the ID or identification information corresponding to the battery system 150 to the server.

It should be noted that the above description of the battery health status information determination method and its process is provided for illustrative purposes only and is not intended to limit the scope of the present application. For those of ordinary skill in the art, various modifications and changes can be made based on the description of this application. However, such modifications and changes would not depart from the scope of the present application.

Possible beneficial effects brought by the embodiments of this specification include but are not limited to: (1) Integrating a battery health status information determination device or adding a battery health status information determination device in the battery system to realize localized assessment of battery health status information; (2) ) The battery health status information determination device transmits the battery health status information results to a remote data platform or monitoring system through wired or wireless means. The remote data platform or monitoring system does not need to evaluate the battery status, which reduces the need for data from the remote data platform or monitoring system. The processing and computing load reduces the construction and operation costs of a remote data platform or monitoring system; (3) battery health status information can be evaluated through the battery system, charging equipment or monitoring device (for example, server 120), improving battery health status information. Flexibility of status information assessment; (4) Battery status assessment based on charging data, usage information, usage feedback and other information of the battery system can not only quickly assess the health status, but also detect battery faults or hazards in time, so as to conduct advance Repair or maintenance can accurately locate the fault point and significantly reduce after-sales costs.

It should be noted that different embodiments may produce different beneficial effects. In different embodiments, the possible beneficial effects may be any one or a combination of the above, or any other possible beneficial effects.

The basic concepts have been described above. It is obvious to those skilled in the art that the above detailed disclosure is only an example and does not constitute a limitation of this specification. Although not explicitly stated herein, various modifications, improvements, and corrections may be made to this specification by those skilled in the art. Such modifications, improvements, and corrections are suggested in this specification, and therefore such modifications, improvements, and corrections remain within the spirit and scope of the exemplary embodiments of this specification.

At the same time, this specification uses specific words to describe the embodiments of this specification. For example, "one embodiment," "an embodiment," and/or "some embodiments" means a certain feature, structure, or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that “one embodiment” or “an embodiment” or “an alternative embodiment” mentioned twice or more at different places in this specification does not necessarily refer to the same embodiment. . In addition, certain features, structures or characteristics in one or more embodiments of this specification may be appropriately combined.

In addition, unless explicitly stated in the claims, the order of the processing elements and sequences, the use of numbers and letters, or the use of other names in this specification are not intended to limit the order of the processes and methods in this specification. Although the foregoing disclosure discusses by various examples some embodiments of the invention that are presently considered useful, it is to be understood that such details are for purposes of illustration only and that the appended claims are not limited to the disclosed embodiments. To the contrary, rights The claims are intended to cover all modifications and equivalent combinations consistent with the spirit and scope of the embodiments of this specification. For example, while the system components described above may be implemented as hardware devices, they may also be implemented as software-only solutions. Such as installing the described system on an existing server or mobile device.

Similarly, it should be noted that, in order to simplify the expression disclosed in this specification and thereby help understand one or more embodiments of the invention, in the previous description of the embodiments of this specification, multiple features are sometimes combined into one embodiment. accompanying drawings or descriptions thereof. However, this method of disclosure does not imply that the subject matter of the description requires more features than are mentioned in the claims. In fact, embodiments may have less than all features of a single disclosed embodiment.

In some embodiments, numbers are used to describe the quantities of components and properties. It should be understood that such numbers used to describe the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Grooming. Unless otherwise stated, "about," "approximately," or "substantially" means that the stated number is allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending on the desired features of the individual embodiment. In some embodiments, numerical parameters should account for the specified number of significant digits and use general digit preservation methods. Although the numerical ranges and parameters used to identify the breadth of ranges in some embodiments of this specification are approximations, in specific embodiments, such numerical values are set as accurately as is feasible.

Each patent, patent application, patent application publication and other material, such as articles, books, instructions, publications, documents, etc. cited in this specification is hereby incorporated by reference into this specification in its entirety. Application history documents that are inconsistent with or conflict with the content of this specification are excluded, as are documents (currently or later appended to this specification) that limit the broadest scope of the claims in this specification. It should be noted that if there is any inconsistency or conflict between the descriptions, definitions, and/or the use of terms in the accompanying materials of this manual and the content described in this manual, the descriptions, definitions, and/or the use of terms in this manual shall prevail. .

Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Accordingly, by way of example and not limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to those expressly introduced and described in this specification.

Claims

A battery system, characterized by including:

At least one battery module, the battery module including at least one battery cell;

at least one storage medium including a set of instructions; and

One or more processors in communication with the at least one storage medium, wherein when executing the instructions, the one or more processors are configured to:

Obtain charging data related to the battery system, the charging data including at least one of charging voltage, charging current, charging time, charging temperature data and historical usage data; and

Determine health status information related to the battery system based on the charging data; wherein the health status information includes capacity offset information, SOH value, self-discharge consistency, pressure difference and internal resistance related to the battery system One or more of consistency, insulation status, and temperature status.
The battery system according to claim 1, characterized in that the system further includes a communication module, the communication module is used to communicate with a reading device, so that the reading device reads the data through the communication module. Describe health status information.
The battery system according to claim 1, wherein the determining health status information related to the battery system based on the charging data includes:

The charging data is processed through a preconfigured machine learning model, which is stored in the storage medium, to determine the health status information.
The battery system according to claim 1, wherein the determining health status information related to the battery system based on the charging data includes:

Obtain the trained machine learning model from the server;

The charging data is processed through the trained machine learning model to determine the health status information.
The battery system according to claim 3 or 4, characterized in that,

The machine learning model includes a trained state information determination model;

The charging data is processed through the machine learning model to determine the health status information, including:

The charging data is processed through the state information determination model to determine current health state information of the battery system.
The battery system according to claim 5, characterized in that:

The machine learning model also includes a trained state information prediction model;

The one or more processors are also used to:

The charging data and the current health status information are processed through the status information prediction model to determine future health status information of the battery system in the next time period.
The battery system of claim 6, wherein

The machine learning model also includes a trained health status judgment model;

The one or more processors are also used to:

Obtain two or more future health status information of the battery system, and the two or more future health status information respectively correspond to different future time periods;

Through the health status judgment model, the two or more future health status information are processed to determine the health status of the battery system.
The battery system according to claim 7, wherein the machine learning model is obtained by:

Obtain training samples, the training samples include a plurality of sample groups, each of the sample groups includes historical charging data of the corresponding sample battery, historical future health state information obtained through the state information prediction model, and health state judgment based on the state information prediction model. Historical health status obtained by the model;

The initial model is trained based on the training samples to obtain a trained machine learning model; wherein the labels of the training samples include historical actual health status information corresponding to the historical future health status information, and historical health status information corresponding to the historical health status information. The historical actual health status corresponding to the status.
The battery system of claim 8, wherein when the number of negative samples in the training samples is less than a preset value, the number of negative samples is filled through data simulation or parameter adjustment.
The battery system according to claim 3 or 4, wherein the one or more processors are also used for:

The machine learning model is updated.
The battery system of claim 1, wherein the one or more processors are also used to:

Obtain usage information of the battery system, where the usage information includes at least one of usage time, charging times, and maintenance times;

A health category of the battery system is determined based on the usage information, and the health category at least includes a high health category, a medium health category and a low health category.
The battery system of claim 11, wherein the one or more processors are further used to:

In response to the battery system belonging to the low health category, perform at least one of the following operations on the battery system: determine the health status of the battery system before each charge, and report target data of the battery system to a server . Preset a backup battery for the battery system.
The battery system of claim 1, wherein the one or more processors are also used to:

Obtain usage feedback information related to the battery system;

Analyze the usage feedback information to determine the characteristic information of the battery system;

Through the health status judgment model, the characteristic information of the battery system is processed to determine the health status of the battery system.
The battery system according to any one of claims 1 to 13, wherein the charging voltage includes at least one of a charging starting voltage, a charging process characteristic voltage, and a charging cutoff voltage; and the charging temperature data includes charging At least one of starting temperature and charging process temperature weight; the historical usage data includes at least one of cumulative charge and discharge capacity, cumulative charge and discharge times, and historical battery health status.
The battery system of claim 1, further comprising:

One or more sensors for detecting at least one of the charging voltage, the charging current, and the charging temperature;

A battery management system, used to manage the charging and discharging behavior of the battery system;

A power supply module, configured to use the electrical energy stored in the battery system to power at least one component of the battery system; and/or

A positioning module is used to obtain the position information of the battery system.
A battery health status information determining device, configured to estimate health status information related to the battery system in a battery system, which is characterized in that it includes:

A first acquisition module, configured to acquire charging data related to the battery system, where the charging data includes at least one of charging voltage, charging current, charging time, charging temperature data and historical usage data;

A first evaluation module configured to determine health status information related to the battery system based on the charging data; wherein the health status information includes capacity offset information, SOH value, and self-discharge consistency related to the battery system. one or more of the following: consistency of voltage difference and internal resistance, insulation status, and temperature status.
The device according to claim 16, characterized in that the device includes a storage module and a communication module, the storage module is used to store the health status information, and the communication module is used to communicate with a reading device to connect. The reading device is caused to read the health status information stored in the storage module through the communication module.
A device for determining battery health status information, which is used to determine health status information related to the battery system in a charging device, which is characterized by including:

a second acquisition module, configured to acquire charging data related to the battery system, where the charging data includes at least one of charging voltage, charging current, charging time, charging temperature data and historical usage data;

A second evaluation module, configured to determine health status information related to the battery system based on the charging data; wherein the health status information includes capacity offset information, SOH value, and self-discharge consistency related to the battery system. one or more of the following: consistency of voltage difference and internal resistance, insulation status, and temperature status.
A method for determining battery health status information, executed by a processor in a battery system, the battery system including at least one battery module, the battery module including at least one battery cell, characterized in that it includes:

Obtain charging data related to the battery system, the charging data including at least one of charging voltage, charging current, charging time, charging temperature data and historical usage data; and

Determine health status information related to the battery system based on the charging data; wherein the health status information includes capacity offset information, SOH value, self-discharge consistency, pressure difference and internal resistance related to the battery system One or more of consistency, insulation status, and temperature status.
A method for determining battery health status information, executed by a processor in a charging device, is characterized by including:

Obtain charging data related to the battery system, the charging data including at least one of charging voltage, charging current, charging time, charging temperature data, and historical usage data; and

Determine health status information related to the battery system based on the charging data; wherein the health status information includes capacity offset information, SOH value, self-discharge consistency, pressure difference and internal resistance related to the battery system One or more of consistency, insulation status, and temperature status.