Battery state dynamic monitoring and balancing method and system and electronic equipment

Abstract

The invention discloses a battery state dynamic monitoring and balancing method and system and electronic equipment, and belongs to the technical field of battery balancing management. The method comprises the following steps: calculating an initial health score of each single battery in a battery pack; taking the single battery corresponding to the initial health score lower than a preset initial health score threshold as a first target equalization battery, and calculating an initial equalization current of the first target equalization battery; calculating the initial equalization time of the first target equalization battery; controlling an equalization circuit to carry out current equalization on the first target equalization battery according to the initial equalization current until the initial equalization time is reached; and generating a first balanced data set, and transmitting the first balanced data set to the cloud platform. According to the scheme, the state of each single battery of the battery pack is monitored in real time, the equalization current and equalization time are intelligently calculated, the battery state change can be dynamically adapted, the battery health management is optimized, the charging and discharging efficiency is improved, and the battery life is prolonged.

Description

Technical Field

[0001] The present application belongs to the technical field of battery balancing management, and specifically relates to a method, system and electronic device for dynamic monitoring and balancing of battery status. Background Art

[0002] As a key component for energy storage and conversion, batteries play a vital role in various electronic devices, electric vehicles and energy storage systems. However, due to the influence of various factors such as production process, material properties, use environment and aging degree, there are often inconsistencies in parameters such as voltage, capacity and internal resistance between battery cells. This inconsistency will gradually accumulate during the battery's charge and discharge cycle, causing some battery cells to be overcharged or over-discharged, seriously affecting the overall performance, service life and safety of the battery pack.

[0003] Traditional battery monitoring technology mainly relies on simple electronic components to monitor basic parameters such as battery voltage and current, such as SOC and SOH. Traditional battery balancing technology mainly uses passive balancing to dissipate excess energy in the form of heat using a resistor network to achieve voltage balancing between battery cells.

[0004] However, traditional battery monitoring technology has the problem of insufficient monitoring accuracy and cannot provide detailed battery status information, making it difficult to meet the current demand for accurate monitoring of battery status. In addition, traditional battery balancing technology has the disadvantages of low energy utilization, slow balancing speed, and possible heat generation that affects battery life. Passive balancing achieves balancing through resistor discharge, but this method is not only inefficient, but also increases the thermal load of the battery pack and accelerates the aging process of the battery. Summary of the invention

[0005] The embodiments of the present application provide a method, system and electronic device for dynamic monitoring and equalization of battery status, which solves the problem of insufficient monitoring accuracy in traditional battery monitoring technology, and is unable to provide detailed battery status information, making it difficult to meet the current demand for accurate monitoring of battery status. In addition, traditional battery equalization technology has the disadvantages of low energy utilization, slow equalization speed and possible heat generation that affects battery life. Passive equalization achieves equalization through resistor discharge, but this method is not only inefficient, but also increases the thermal load of the battery pack and accelerates the aging process of the battery.

[0006] In a first aspect, an embodiment of the present application provides a method for dynamic monitoring and balancing of battery status, the method comprising:

[0007] Acquire real-time status data of each single battery in the battery pack, and if the real-time status data is initial status data, calculate the initial health score of each single battery according to the real-time status data and a preset initial health score calculation formula; wherein the real-time status data includes voltage data, current data, temperature data and internal resistance data;

[0008] If there is an initial health score lower than the preset initial health score threshold, the single cell corresponding to the initial health score lower than the preset initial health score threshold is used as the first target balancing cell, and the initial balancing current of the first target balancing cell is calculated according to the initial health score, voltage data, temperature data and the preset initial current balancing formula; wherein there is at least one first target balancing cell;

[0009] Acquire maximum voltage data and minimum voltage data of each single cell in the battery pack, determine a voltage difference according to the maximum voltage data and the minimum voltage data, and calculate an initial balancing time of a first target balancing cell according to the voltage difference, the initial balancing current and a preset initial balancing time calculation formula;

[0010] Controlling the balancing circuit to perform current balancing on the first target balancing battery according to the initial balancing current until an initial balancing time is reached;

[0011] Send a network request to the cloud platform. If a success status code sent by the cloud platform is received, generate a first balancing data set according to the real-time status data, the initial health score, the first target balancing battery, the initial balancing current, and the initial balancing time, and transmit the first balancing data set to the cloud platform.

[0012] Furthermore, the preset initial health score calculation formula is:

[0013] H cell =f(V cell , I cell , T cell , R internal );

[0014] Among them, H cell is the initial health score; V cell is the voltage data; I cell is the current data; T cell is the temperature data; R internal is the internal resistance data; f() is a function used to combine multiple parameters such as voltage data, current data, temperature data and internal resistance data to calculate the initial health score of the battery;

[0015] The preset initial current balancing formula is:

[0016] I balance=f(V cell , T cell , H cell );

[0017] Among them, I balance is the initial balancing current; f() is a function that combines the battery voltage data, temperature data and initial health score to output a suitable initial balancing current value; the preset initial balancing time calculation formula is:

[0018]

[0019] Among them, t balance is the initial equilibrium time; V diff is the voltage difference; k is the preset balancing efficiency factor.

[0020] Furthermore, after transmitting the first balanced data set to the cloud platform, the method further includes:

[0021] If the preset data collection time point is reached, a network request is sent to the cloud platform. If a success status code is received from the cloud platform, a historical health score acquisition request is sent to the cloud platform.

[0022] If the historical health score sent by the cloud platform is received, the charge and discharge times of the battery pack are obtained, the real-time status data of each single battery in the battery pack is updated, and the dynamic health score of each single battery is calculated according to the real-time status data, the charge and discharge times, the historical health score and the preset dynamic health score calculation formula;

[0023] If there is a dynamic health score lower than the preset dynamic health score threshold, the single cell corresponding to the dynamic health score lower than the preset dynamic health score threshold is used as the second target balancing cell, and the dynamic balancing current of the second target balancing cell is calculated according to the dynamic health score, voltage data, temperature data and a preset dynamic current balancing formula; wherein there is at least one second target balancing cell;

[0024] Updating the maximum voltage data and the minimum voltage data of each single cell in the battery pack, updating the voltage difference according to the maximum voltage data and the minimum voltage data, and calculating the dynamic balancing time of the second target balancing cell according to the voltage difference, the dynamic balancing current and a preset dynamic balancing time calculation formula;

[0025] Performing current balancing on the second target balancing battery according to the dynamic balancing current control balancing circuit until the dynamic balancing time is reached;

[0026] A second balancing data set is generated according to the real-time status data, the dynamic health score, the second target balancing battery, the dynamic balancing current, and the dynamic balancing time, and the second balancing data set is transmitted to the cloud platform.

[0027] Furthermore, the preset dynamic health score calculation formula is:

[0028]

[0029] in, V is the dynamic health score; cell is the voltage data; I cell is the current data; T cell is the temperature data; R internal is the internal resistance data; f() is a function used to combine multiple parameters such as voltage data, current data, temperature data, internal resistance data, historical health score, charge and discharge times, etc. to calculate the dynamic health score of the battery; is the historical health score; Cycle Count is the number of charge and discharge times;

[0030] The preset dynamic current balancing formula is:

[0031]

[0032] in, is the dynamic balancing current; f() is a function that combines the battery voltage data, temperature data and dynamic health score to output a suitable initial balancing current value;

[0033] The preset dynamic balancing time calculation formula is:

[0034]

[0035] in, is the dynamic equilibrium time; V diff is the voltage difference; k is the preset balancing efficiency factor.

[0036] Furthermore, after acquiring the real-time status data of each single battery in the battery pack, the method further includes:

[0037] A target thermal management strategy is determined according to the temperature data and a preset multi-level thermal management method, and thermal management is performed on each single battery in the battery pack according to the target thermal management strategy.

[0038] Furthermore, after acquiring the real-time status data of each single battery in the battery pack, the method further includes:

[0039] Inputting the real-time status data into a preset fault detection model to determine a fault score of each single cell;

[0040] If there is a fault score exceeding a preset fault score threshold, the single cell corresponding to the fault score exceeding the preset fault score threshold is used as a target faulty cell, and the fault type of the target faulty cell is output through the preset fault detection model; wherein the number of the target faulty cells is at least one;

[0041] If the fault type is a minor risk type, a self-repair scheme is output through the preset fault detection model, and the target faulty battery is self-repaired according to the self-repair scheme;

[0042] Accordingly, after outputting the fault type through the preset fault detection model, the method further includes:

[0043] If the fault type is a major risk type, the target faulty battery is dynamically isolated and major risk information is sent to the control center.

[0044] Furthermore, the training process of the preset fault detection model includes:

[0045] Acquire historical scoring records, and determine first historical state data and historical scores of each historical battery according to the historical scoring records;

[0046] Obtain historical fault records, determine the second historical state data and historical fault type of each historical fault battery according to the historical fault records, and determine the third historical state data of the minor risk type and the corresponding historical self-repair solution; wherein the historical fault type includes a minor risk type and a major risk type;

[0047] marking a score tag of the first historical status data according to the historical score;

[0048] marking a fault type label of the second historical state data according to the historical fault type;

[0049] Marking the third historical state data with a self-repair solution label according to the historical self-repair solution;

[0050] Constructing a fault detection model, and training a scoring branch of the fault detection model according to the first historical state data and the scoring label;

[0051] Training a fault classification branch of the fault detection model according to the second historical state data and the fault type label;

[0052] The self-repair solution output branch of the fault detection model is trained according to the third historical state data and the self-repair solution label.

[0053] In a second aspect, an embodiment of the present application provides a battery status dynamic monitoring and balancing system, the system comprising:

[0054] A scoring module, used to obtain real-time status data of each single battery in the battery pack, and if the real-time status data is initial status data, calculate the initial health score of each single battery according to the real-time status data and a preset initial health score calculation formula; wherein the real-time status data includes voltage data, current data, temperature data and internal resistance data;

[0055] a balancing current determination module, configured to, if there is an initial health score lower than a preset initial health score threshold, use a single cell corresponding to the initial health score lower than the preset initial health score threshold as a first target balancing cell, and calculate an initial balancing current of the first target balancing cell according to the initial health score, voltage data, temperature data and a preset initial current balancing formula; wherein there is at least one first target balancing cell;

[0056] A health score calculation module, used to obtain maximum voltage data and minimum voltage data of each single cell in the battery pack, determine a voltage difference according to the maximum voltage data and the minimum voltage data, and calculate an initial balancing time of a first target balancing cell according to the voltage difference, the initial balancing current and a preset initial health score calculation formula;

[0057] A balancing module, configured to control the balancing circuit to perform current balancing on the first target balancing battery according to the initial balancing current until an initial balancing time is reached;

[0058] The data transmission module is used to send a network request to the cloud platform. If a success status code sent by the cloud platform is received, a first balancing data set is generated according to the real-time status data, the initial health score, the first target balancing battery, the initial balancing current and the initial balancing time, and the first balancing data set is transmitted to the cloud platform.

[0059] In a third aspect, an embodiment of the present application provides an electronic device, which includes a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements the steps of the method described in the first aspect.

[0060] In a fourth aspect, an embodiment of the present application provides a readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented.

[0061] In an embodiment of the present application, real-time status data of each single battery in the battery pack is obtained. If the real-time status data is initial status data, the initial health score of each single battery is calculated according to the real-time status data and a preset initial health score calculation formula; wherein the real-time status data includes voltage data, current data, temperature data and internal resistance data; if there is an initial health score lower than a preset initial health score threshold, the single battery corresponding to the initial health score lower than the preset initial health score threshold is used as the first target balancing battery, and the initial balancing current of the first target balancing battery is calculated according to the initial health score, voltage data, temperature data and a preset initial current balancing formula; wherein the first target balancing battery The battery pack is at least one; obtain the maximum voltage data and the minimum voltage data of each single cell in the battery pack, determine the voltage difference according to the maximum voltage data and the minimum voltage data, calculate the initial balancing time of the first target balancing battery according to the voltage difference, the initial balancing current and the preset initial balancing time calculation formula; control the balancing circuit according to the initial balancing current to balance the current of the first target balancing battery until the initial balancing time is reached; send a network request to the cloud platform, if a success status code sent by the cloud platform is received, generate a first balancing data set according to the real-time status data, the initial health score, the first target balancing battery, the initial balancing current and the initial balancing time, and transmit the first balancing data set to the cloud platform. Through the above-mentioned battery status dynamic monitoring and balancing method, by real-time monitoring of the status of each single cell of the battery pack, intelligent calculation of the balancing current and balancing time, it is possible to dynamically adapt to changes in battery status, optimize battery health management, improve charging and discharging efficiency, and extend battery life. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a flowchart of a method for dynamic monitoring and equalization of battery status provided in Example 1 of the present application;

[0063] Figure 2 It is a flowchart of a method for dynamic monitoring and equalization of battery status provided in Embodiment 2 of the present application;

[0064] Figure 3 It is a structural diagram of a battery status dynamic monitoring and balancing system provided in Embodiment 3 of the present application;

[0065] Figure 4 It is a schematic diagram of the structure of the electronic device provided in Example 4 of the present application. DETAILED DESCRIPTION

[0066] In order to make the purpose, technical scheme and advantages of the present application clearer, the specific embodiments of the present application are further described in detail below in conjunction with the accompanying drawings. It is understood that the specific embodiments described herein are only used to explain the present application, rather than to limit the present application. It should also be noted that, for the convenience of description, only the part related to the present application but not all the contents are shown in the accompanying drawings. Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow chart describes each operation (or step) as a sequential process, many of the operations therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of each operation can be rearranged. The process can be terminated when its operation is completed, but it can also have additional steps not included in the accompanying drawings. The process can correspond to a method, a function, a procedure, a subroutine, a subprogram, etc.

[0067] The following will be combined with the drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the present application belong to the scope of protection of this application.

[0068] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0069] In combination with the accompanying drawings, an RSMC chip, a chip multi-stage startup method and a Beidou communication navigation device provided in the embodiments of the present application are described in detail through specific embodiments and their application scenarios.

[0070] Embodiment 1

[0071] Figure 1 1 is a flow chart of a method for dynamic monitoring and equalization of battery status provided in the first embodiment of the present application. Figure 1 As shown, the specific steps include:

[0072] S101, obtaining real-time status data of each single cell in the battery pack. If the real-time status data is initial status data, calculating the initial health score of each single cell according to the real-time status data and a preset initial health score calculation formula; wherein the real-time status data includes voltage data, current data, temperature data and internal resistance data.

[0073] First, the usage scenario of this solution can be to collect real-time status data of each single cell in the battery pack, perform battery scoring based on this, and calculate the balancing current and balancing time when the battery score is lower than a certain threshold, and perform current balancing on the batteries below a certain threshold.

[0074] Based on the above usage scenarios, it can be understood that the executor of the present application can be a battery status dynamic monitoring and balancing system, and no excessive limitations are made here.

[0075] In this solution, the equalizer can be a device specially designed for voltage equalization of single cells in a battery pack. Its main function is to achieve a balanced state of the entire battery pack by accurately controlling the voltage difference of each single cell in the battery pack, thereby preventing some cells from being damaged or affecting the performance of the battery pack due to excessive voltage differences.

[0076] A battery pack can be a system composed of multiple single cells (usually lithium batteries or lead-acid batteries) connected in series and parallel to provide high capacity and high voltage. Each battery in the battery pack has its own independent voltage, current, temperature and other conditions, but they work together to provide power output.

[0077] A single cell can be the basic component unit in a battery pack. Each single cell has independent voltage, temperature, internal resistance and other characteristics. They are connected in series or in parallel to form a battery pack.

[0078] The real-time status data may be the status information of each single battery at the current moment, including the voltage, current, temperature and internal resistance of the battery.

[0079] The voltage data may be a voltage value of a battery cell, indicating the power state of the battery.

[0080] Current data can be the current flowing into or out of the battery, which is used to measure the discharge or charge of the battery.

[0081] The temperature data may be the temperature of the battery, which directly affects the performance and life of the battery.

[0082] The internal resistance data may be the internal resistance of the battery, which generally increases as the battery ages and is an important indicator of battery health.

[0083] Initial state data refers to the battery state data collected for the first time at a certain point in time, usually referring to the state of the system when it starts running or is monitored for the first time. It can be regarded as a situation where there is no previous data comparison, that is, a state without historical data.

[0084] The preset initial health score calculation formula can be used to calculate the initial health score based on the real-time status data of the single battery. Usually this formula combines the battery's voltage, current, temperature, internal resistance and other data to calculate a health score that reflects the overall health status of the battery.

[0085] The initial health score may be calculated using a preset health score calculation formula, and represents the health status of the battery when data is first collected.

[0086] Sensors or monitoring systems can be used to collect the voltage, current, temperature and internal resistance data of each single cell in the battery pack in real time. The collected real-time status data (voltage, current, temperature, internal resistance) is then input into the preset initial health score calculation formula to calculate the initial health score of each single cell.

[0087] Based on the above technical solution, optionally, after obtaining the real-time status data of each single battery in the battery pack, the method further includes:

[0088] Inputting the real-time status data into a preset fault detection model to determine a fault score of each single cell;

[0089] If there is a fault score exceeding a preset fault score threshold, the single cell corresponding to the fault score exceeding the preset fault score threshold is used as a target faulty cell, and the fault type of the target faulty cell is output through the preset fault detection model; wherein the number of the target faulty cells is at least one;

[0090] If the fault type is a minor risk type, a self-repair scheme is output through the preset fault detection model, and the target faulty battery is self-repaired according to the self-repair scheme;

[0091] Accordingly, after outputting the fault type through the preset fault detection model, the method further includes:

[0092] If the fault type is a major risk type, the target faulty battery is dynamically isolated and major risk information is sent to the control center.

[0093] In this solution, the preset fault detection model can be designed based on historical data, sensor input and algorithmic models (such as machine learning models, rule engines, etc.) to detect and evaluate potential battery failures. The model evaluates each single battery through real-time status data (such as battery voltage, current, temperature, internal resistance, etc.) and outputs a fault score that reflects the current health status of the battery.

[0094] The fault score can be a quantitative value of the degree of battery fault, which generally indicates the abnormality of the current state of the battery. The higher the score, the greater the risk of battery failure.

[0095] The preset fault score threshold can be a numerical limit used to distinguish between a normal state of a battery and a potential fault state. Batteries above this threshold are considered to have a greater risk of failure and further measures need to be taken.

[0096] The target faulty battery may refer to a battery whose fault score exceeds a preset threshold according to the fault score, and the battery needs further processing (such as self-repair or isolation).

[0097] The fault type is output by a preset fault detection model, describing the specific fault risk of the battery. For example, the fault type can be a minor risk (such as no significant impact in the short term) or a major risk (such as a serious fault that may cause battery damage or endanger safety).

[0098] Minor risk types could be those faults that are minor and unlikely to immediately affect battery performance or safety. For example, a small deviation in battery voltage from normal values ​​that does not affect overall functionality.

[0099] A self-repair solution can be a series of repair measures for a slightly faulty battery, designed to restore the battery to a normal state. Self-repair can be a software adjustment (such as changing the charging strategy), a hardware operation (such as slightly adjusting the battery balance), or other automated operations.

[0100] Major risk types can be those that may cause complete battery failure, a safety incident (such as fire, explosion), or have a significant impact on the overall performance of the battery pack, which requires immediate attention.

[0101] Major risk information can be an alarm message or report when a serious risk of battery failure occurs. It can contain key information such as the type, location, and severity of the failure, and is intended to notify relevant personnel to take emergency measures.

[0102] The control center can be a monitoring and command center that centrally manages battery packs or battery management systems (BMS). It is responsible for receiving fault reports, assessing risks and making corresponding decisions, and coordinating subsequent repairs, isolation and other operations.

[0103] The real-time status data of the battery pack (such as voltage, current, temperature, internal resistance, etc.) can be input into the fault detection model. According to the real-time status data, the fault detection model will output the fault score of each single cell. According to the preset fault score threshold, the fault score of each battery is judged: if the fault score of the battery exceeds the preset threshold, it is considered that the battery has a fault, and its fault score corresponds to the target fault battery. The number of target fault batteries is at least 1, and there may be multiple fault batteries. For each target fault battery, the fault classification branch is used to determine its fault type: if the fault type is a minor risk type, a self-repair solution is output. If the fault type is a major risk type, other measures are taken (such as isolating the battery and sending major risk information). When the fault type is a minor risk, the self-repair solution output branch of the fault detection model will output the corresponding self-repair solution. This solution may include: adjusting the battery charging / discharging strategy, battery balancing operation, and temperature control adjustment, etc. According to the self-repair solution recommended by the model, the repair operation is automatically performed to restore the normal working state of the battery. When the fault type is a major risk, the system will initiate a dynamic isolation operation: isolate the target faulty battery from the battery pack to prevent it from affecting other batteries or the entire system. And send major risk information to the control center to notify the control center to take further measures. This information may include the location of the battery, the type of fault, possible consequences, etc. When the control center receives the major risk information, it can take corresponding emergency processing operations according to the alarm content, such as: starting the backup battery system, shutting down or restricting certain operations of the battery pack, and dispatching maintenance personnel for inspection and repair.

[0104] In this solution, faulty batteries can be quickly identified through fault scoring and fault type classification, especially the dynamic isolation of batteries with major risks, which can effectively avoid the impact of faulty batteries on other batteries, thereby ensuring the overall safety of the battery pack. The fault detection model automatically calculates the fault score of each battery based on the input of real-time status data, and determines whether repair or isolation measures are needed based on the threshold, reducing manual intervention and improving management efficiency.

[0105] Based on the above technical solution, the optional, preset fault detection model training process includes:

[0106] Acquire historical scoring records, and determine first historical state data and historical scores of each historical battery according to the historical scoring records;

[0107] Obtain historical fault records, determine the second historical state data and historical fault type of each historical fault battery according to the historical fault records, and determine the third historical state data of the minor risk type and the corresponding historical self-repair solution; wherein the historical fault type includes a minor risk type and a major risk type;

[0108] marking a score tag of the first historical status data according to the historical score;

[0109] marking a fault type label of the second historical state data according to the historical fault type;

[0110] Marking the third historical state data with a self-repair solution label according to the historical self-repair solution;

[0111] Constructing a fault detection model, and training a scoring branch of the fault detection model according to the first historical state data and the scoring label;

[0112] Training a fault classification branch of the fault detection model according to the second historical state data and the fault type label;

[0113] The self-repair solution output branch of the fault detection model is trained according to the third historical state data and the self-repair solution label.

[0114] In this solution, the historical scoring record may be a record generated when the battery health status was scored in the past. Each record corresponds to the battery status score at a time point, and is used to reflect the health status of the battery.

[0115] A historical battery can be a battery cell that is used in historical data to assess health. The historical data for each battery cell is used to determine its health score and possible failure type.

[0116] The first historical status data may refer to the status data of each battery (such as voltage, current, temperature, internal resistance, etc.) and other possible influencing factors (such as environmental conditions) in the historical scoring records.

[0117] The historical score may be a score calculated by evaluating these status data, typically a numerical value, to quantify the health of the battery.

[0118] The historical fault record may be historical data recording battery faults, and may include the time, type, and battery status of the fault.

[0119] The historical fault battery may be a battery cell that has failed in the historical fault record. Each historical fault battery has corresponding status data and fault type information.

[0120] The second historical status data may be status data of the battery when a fault occurs, such as the voltage, current, internal resistance, temperature, etc. of the battery.

[0121] The historical failure type can refer to the type of failure that occurred in the battery, which is usually divided into minor risk and major risk types. The minor risk type may not immediately affect the use of the battery, while the major risk type may cause the battery to fail or have an impact on other batteries.

[0122] The third historical status data may be status data when a minor fault occurs to the battery, including health status, parameters, and environmental information of the battery when the minor fault occurs.

[0123] The historical self-repair scheme may be a repair scheme designed for a battery with a minor fault, which may include adjusting the charging strategy, optimizing the discharge process, or other operations to delay further deterioration of the fault.

[0124] The history score can be a numerical value or a label used to quantify the battery's health status.

[0125] The scoring label is a label annotated on the first historical state data, which is used to indicate the battery health score in this state. It can be a numerical label used to train the scoring branch.

[0126] The fault type label can be used to annotate the second historical state data to determine the fault type of the battery (such as slight risk or major risk) for training the fault classification branch.

[0127] The self-repair solution tag can be used to mark the third historical state data, indicating the self-repair solution applied when a minor battery failure occurs. The self-repair solution may include measures such as adjusting the charging current and temperature control management.

[0128] The scoring branch can be a part of the fault detection model that is responsible for calculating the health score based on the battery status data. This branch outputs a health score based on the input historical status data.

[0129] The fault classification branch can be a part of the fault detection model, which is responsible for predicting the fault type of the battery (such as minor risk, major risk, etc.) based on the status data of the battery.

[0130] The self-repair scheme output branch may be a part of the fault detection model, responsible for outputting a self-repair scheme for the battery.

[0131] The historical scoring records, historical fault records, and historical self-repair solution data of the battery can be collected and organized. These historical data provide the necessary input for the subsequent training model. Based on the historical data, the system uses the score, fault type, and self-repair solution as labels and annotates them to the corresponding status data. These labels will be used as the target output when training the model. Then, based on the historical scoring records and the corresponding scoring labels, the scoring branch in the fault detection model is trained using the historical status data so that it can predict the health score based on the battery status. According to the fault type label in the historical fault record, the fault classification branch of the model is trained so that it can judge the fault type according to the battery status. According to the historical self-repair solution label of the minor fault type, the self-repair solution output branch is trained so that it can generate appropriate self-repair solutions. Through the above training steps, the various branches are integrated to build a comprehensive fault detection model that can output the health score, fault type, and self-repair solution based on the input real-time status data. When the battery status data is input into the model in real time, the model will output the corresponding fault score, fault type, and self-repair solution according to the learned rules and patterns, so as to accurately evaluate the health status of the battery and take corresponding measures.

[0132] S102, if there is an initial health score lower than a preset initial health score threshold, a single cell corresponding to the initial health score lower than the preset initial health score threshold is used as a first target balancing cell, and an initial balancing current of the first target balancing cell is calculated according to the initial health score, voltage data, temperature data and a preset initial current balancing formula; wherein there is at least one first target balancing cell.

[0133] The preset initial health score threshold may refer to a threshold value used to determine whether the battery needs to be balanced after the initial health score is calculated. If the health score of the battery is lower than the threshold, it is considered that the health of the battery is poor and needs to be balanced.

[0134] The first target balanced battery may refer to batteries whose initial health scores are lower than a preset threshold. Due to the poor health of these batteries, they need to be charged and discharged through a balanced current to restore their health state and ensure the overall performance of the battery pack.

[0135] The preset initial current balancing formula may be used to calculate the current value required for balancing the low health score battery.

[0136] The initial balancing current can be a current value calculated according to a preset initial current balancing formula, and is used to balance the target balancing battery. The balancing current is generally used to adjust the power difference between batteries to ensure that the power of each single battery in the battery pack tends to be consistent.

[0137] The initial health score, voltage data, and temperature data may be substituted into a preset initial current balancing formula to calculate an initial balancing current of the first target balancing battery.

[0138] S103, obtaining maximum voltage data and minimum voltage data of each single cell in the battery pack, determining a voltage difference according to the maximum voltage data and the minimum voltage data, and calculating an initial balancing time of a first target balancing cell according to the voltage difference, the initial balancing current and a preset initial balancing time calculation formula.

[0139] The maximum voltage data may be the highest voltage value among all the single cells in the battery pack.

[0140] The minimum voltage data may be the lowest voltage value among all the single cells in the battery pack.

[0141] The voltage difference can be the difference between the maximum and minimum voltages in a battery pack, which is used to reflect the voltage distribution of each single cell in the battery pack. In a battery pack, the voltages of each single cell are usually different. These differences may be caused by factors such as the health status, aging, temperature, and charge and discharge history of different batteries. If the voltage difference between the batteries is large, some batteries may need more time to be balanced in order to adjust their voltage to a level that matches that of other batteries. The voltage difference of a battery pack can reflect the charge distribution and battery health of each single cell in the entire battery pack. In order to balance each single cell in the battery pack, it is usually necessary to estimate the required balancing time for each single cell based on the voltage difference. The purpose of this is to ensure that the voltage difference of the battery is reduced and the charge level of each single cell is consistent, thereby improving the overall performance and life of the battery pack. A larger voltage difference means that more current needs to be applied to balance the battery voltage, which may cause the balancing current to last longer. Batteries with large voltage differences receive more balancing current to achieve voltage balance of the battery pack faster. A smaller voltage difference may mean that the batteries are close to each other, so a shorter balancing time is required.

[0142] The preset initial balancing time calculation formula may be a formula for calculating the initial balancing time of the battery according to data such as the voltage difference and balancing current of each single battery in the battery pack.

[0143] The voltage data can be obtained from each single cell in the battery pack in real time, the battery with the highest voltage and the battery with the lowest voltage in the battery pack can be found, and the voltage difference can be calculated. Then the voltage difference and the initial balancing current are substituted into the preset initial balancing time calculation formula to calculate the initial balancing time of the first target balancing battery.

[0144] S104, controlling the balancing circuit to perform current balancing on the first target balancing battery according to the initial balancing current until an initial balancing time is reached.

[0145] The balancing circuit can refer to the circuit part integrated in the balancing instrument (or battery balancing management system). The main function of this circuit is to control and adjust the voltage difference of each single cell in the battery pack to ensure that each single cell in the battery pack can maintain a balanced voltage and charge state during the charging and discharging process. Its role is to adjust the current and voltage of the single cell through different methods (such as active balancing or passive balancing) to avoid excessive damage to the battery due to imbalance. The balancing instrument can include multiple balancing circuits and balancing channels, and the number of balancing channels can be expanded as needed to adapt to battery packs of different sizes.

[0146] In the equalizer, current balancing can be achieved by controlling the current path or by adjusting the charging current of each single cell. The equalizer will continue to apply current to the target battery according to the calculated initial balancing current. According to the preset initial balancing time, the equalizer will control the circuit to provide this current to the first target balancing battery. The balancing current can be applied to the first target balancing battery by passive balancing (consuming the excess power of the battery as heat) or active balancing (transferring power to the battery with lower power). Once the balancing current is set, the current output will continue until the predetermined initial balancing time is reached.

[0147] S105, sending a network request to the cloud platform. If a success status code sent by the cloud platform is received, generating a first balancing data set according to the real-time status data, the initial health score, the first target balancing battery, the initial balancing current and the initial balancing time, and transmitting the first balancing data set to the cloud platform.

[0148] A cloud platform can be a platform that provides services based on cloud computing architecture, aiming to provide functions such as data storage, computing, analysis and access.

[0149] A network request may refer to a network communication request initiated from a local device (such as a balancer) to send data or request data to a cloud platform via the Internet or a local area network. Typically, these requests may be HTTP, HTTPS, WebSocket, and other protocol requests.

[0150] The success status code can be part of the HTTP request response, indicating whether the request is successful. Common status codes include: 200OK: Indicates that the request is successful, the server processes the request normally, and returns the response content. 201Created: Indicates that the resource is successfully created. 202Accepted: Indicates that the request has been accepted, but the processing has not yet been completed.

[0151] The first balancing data set may refer to a data set generated by the balancing instrument based on real-time data (such as battery voltage, current, temperature, health score, etc.) after completing the preliminary balancing operation.

[0152] The balancer can send a request to the cloud platform, and the request can be sent through an HTTP POST request. After receiving the network request, the cloud platform will return a status code. If the request is successful, the cloud platform will return a success status code such as 200OK or 201Created. After receiving the success status code, the balancer generates a first balancing data set based on the real-time status data, the initial health score, the first target balancing battery, the initial balancing current, and the initial balancing time. The first balancing data set is uploaded to the cloud platform through a network request, specifically, it can be achieved through an HTTP request. The purpose of networking can be to ensure that the battery pack and the balancing system can obtain historical data for more accurate analysis and decision-making: If the battery pack or balancer needs to evaluate the long-term health status of the battery or needs to compare the current balancing status with past performance, it needs to obtain previously collected historical data from the cloud platform (for example, past health scores, status data, etc.).

[0153] In the embodiment of the present application, real-time status data of each single battery in the battery pack is obtained. If the real-time status data is initial status data, the initial health score of each single battery is calculated according to the real-time status data and a preset initial health score calculation formula; wherein the real-time status data includes voltage data, current data, temperature data and internal resistance data; if there is an initial health score lower than a preset initial health score threshold, the single battery corresponding to the initial health score lower than the preset initial health score threshold is used as the first target balancing battery, and the initial balancing current of the first target balancing battery is calculated according to the initial health score, voltage data, temperature data and a preset initial current balancing formula; wherein the first target balancing battery The battery pack is at least one; obtain the maximum voltage data and the minimum voltage data of each single cell in the battery pack, determine the voltage difference according to the maximum voltage data and the minimum voltage data, calculate the initial balancing time of the first target balancing battery according to the voltage difference, the initial balancing current and the preset initial balancing time calculation formula; control the balancing circuit according to the initial balancing current to balance the current of the first target balancing battery until the initial balancing time is reached; send a network request to the cloud platform, if a success status code sent by the cloud platform is received, generate a first balancing data set according to the real-time status data, the initial health score, the first target balancing battery, the initial balancing current and the initial balancing time, and transmit the first balancing data set to the cloud platform. Through the above-mentioned battery status dynamic monitoring and balancing method, by real-time monitoring of the status of each single cell of the battery pack, intelligent calculation of the balancing current and balancing time, it is possible to dynamically adapt to changes in battery status, optimize battery health management, improve charging and discharging efficiency, and extend battery life.

[0154] Based on the above technical solution, an optional, preset initial health score calculation formula is:

[0155] H cell =f(V cell , I cell , T cell , R internal );

[0156] Among them, H cell is the initial health score; V cell is the voltage data; I cell is the current data; T cell is the temperature data; R internal is the internal resistance data; f() is a function used to combine multiple parameters such as voltage data, current data, temperature data and internal resistance data to calculate the initial health score of the battery;

[0157] The preset initial current balancing formula is:

[0158] Ibalance =f(V cell , T cell , H cell );

[0159] Among them, I balance is the initial balancing current; f() is a function that combines the battery voltage data, temperature data and initial health score to output a suitable initial balancing current value;

[0160] The preset initial equilibrium time calculation formula is:

[0161]

[0162] Among them, t balance is the initial equilibrium time; V diff is the voltage difference; k is the preset balancing efficiency factor.

[0163] In this scheme, H cell =f(V cell , I cell , T cell , R internal ) is used to combine parameters such as the battery's voltage data, current data, temperature data, and internal resistance data to calculate the battery's initial health score. The battery's health status is usually affected by these parameters, especially the changes in the battery's temperature and internal resistance, which are often related to the aging of the battery. For example, traditional linear or simple nonlinear models may not be able to fully capture the complexity of battery behavior. Using machine learning (such as neural networks, support vector machines, random forests, etc.) to fit the relationship between these variables allows the model to adaptively learn complex nonlinear patterns and make more accurate predictions about battery health. Neural networks use deep neural networks (DNNs) to model input variables (V cell , I cell , T cell , R internal ) and the initial health score. The formula can be:

[0164] H cell =f(V cell , I cell , T cell , R internal )=NN(V cell , I cell , T cell ,

[0165] R internal )

[0166] Among them, NN represents the neural network model, the input data passes through the nonlinear activation function of multiple hidden layers, and the output is the initial health score.

[0167] Fuzzy logic can handle the uncertainty and ambiguity of input variables and is suitable for the calculation of battery health scores, especially when the input data has certain uncertainty or fluctuations. The formula can be:

[0168] H cell =f(V cell , I cell , T cell , R internal )=FuzzyLogic(V cell , I cell , T cell ,

[0169] R internal )

[0170] The input variables are converted into health scores through fuzzy rules (such as "the initial health score is good when the battery voltage is high and the internal resistance is low"). This method can handle the fuzziness and uncertainty of battery status parameters and improve the flexibility of prediction.

[0171] The health status of the battery is affected by multiple objectives (such as battery capacity, internal resistance, temperature, etc.), and these objectives usually have a certain trade-off relationship. Through multi-objective optimization algorithms (such as genetic algorithms, particle swarm optimization, etc.), multiple factors can be considered simultaneously to calculate the health score. The formula can be:

[0172] H cell =f(V cell , I cell , T cell , R internal )=Optimization(V cell , I cell , T cell , R internal )

[0173] In this model, the battery health score H is calculated by optimizing the algorithm. cell It is calculated based on the comprehensive weights of multiple objectives.

[0174] I balance =f(V cell , T cell , H cell The □() function in ) is used to combine the battery voltage data, temperature data and initial health score to output a suitable initial balancing current value. Neural networks can be used to model the complex nonlinear relationship between battery status and balancing current. The formula is:

[0175] I balance =f(V cell , T cell , H cell )=DNN(V cell , T cell , H cell )

[0176] The fuzzy logic model can handle the fuzziness and uncertainty of the input variables and is suitable when there is no precise linear relationship between the battery health score and other parameters (such as temperature, voltage, etc.). Through the fuzzy rule system, the battery state can be inferred and the appropriate balancing current can be output. The formula is:

[0177] I balance =f(V cell , T cell , H cell )=FuzzyLogic(V cell , T cell , H cell )

[0178] For example, the fuzzy rules can be set as follows:

[0179] If the battery voltage V cell Lower and temperature T cell If it is higher, the balancing current will be reduced;

[0180] If the battery health score is H cell Lower and voltage V cell If the voltage is high, the balancing current will be increased.

[0181] This method performs fuzzy reasoning on the battery status through a rule base to dynamically adjust the balancing current.

[0182] Genetic algorithm (GA) is a global optimization method that simulates natural selection and optimizes the objective function through operations such as selection, crossover, and mutation. Through genetic algorithms, different combinations of battery state parameters can be explored to find the optimal balanced current output. The formula is:

[0183] I balance =f(V cell , T cell , H cell )=GA(V cell , T cell , H cell )

[0184] The genetic algorithm can continuously adjust the balancing current through an evolutionary process to maximize the health of the battery and extend its life cycle.

[0185] Based on the above technical solution, optionally, after transmitting the first balanced data set to the cloud platform, the method further includes:

[0186] If the preset data collection time point is reached, a network request is sent to the cloud platform. If a success status code is received from the cloud platform, a historical health score acquisition request is sent to the cloud platform.

[0187] If the historical health score sent by the cloud platform is received, the charge and discharge times of the battery pack are obtained, the real-time status data of each single battery in the battery pack is updated, and the dynamic health score of each single battery is calculated according to the real-time status data, the charge and discharge times, the historical health score and the preset dynamic health score calculation formula;

[0188] If there is a dynamic health score lower than the preset dynamic health score threshold, the single cell corresponding to the dynamic health score lower than the preset dynamic health score threshold is used as the second target balancing cell, and the dynamic balancing current of the second target balancing cell is calculated according to the dynamic health score, voltage data, temperature data and a preset dynamic current balancing formula; wherein there is at least one second target balancing cell;

[0189] Updating the maximum voltage data and the minimum voltage data of each single cell in the battery pack, updating the voltage difference according to the maximum voltage data and the minimum voltage data, and calculating the dynamic balancing time of the second target balancing cell according to the voltage difference, the dynamic balancing current and a preset dynamic balancing time calculation formula;

[0190] Performing current balancing on the second target balancing battery according to the dynamic balancing current control balancing circuit until the dynamic balancing time is reached;

[0191] A second balancing data set is generated according to the real-time status data, the dynamic health score, the second target balancing battery, the dynamic balancing current, and the dynamic balancing time, and the second balancing data set is transmitted to the cloud platform.

[0192] In this solution, the preset data collection time point can be a time point set by the system to collect real-time data of the battery pack at a regular interval, which is usually set based on periodic collection, system requirements or external conditions.

[0193] The historical health score acquisition request refers to sending a request to the cloud platform to obtain the battery health score data corresponding to a historical time point (such as the last collection cycle).

[0194] The historical health score can refer to the battery health score calculated based on the battery status data (such as voltage, current, temperature, internal resistance, etc.) and the number of charge and discharge times at a certain point in time or a certain period of time in the past. For example, after obtaining the initial status data, the historical health score corresponding to the next preset data collection time point is the initial health score, and the historical health score corresponding to the next preset data collection time point is the first dynamic health score, and so on.

[0195] The charge and discharge times may be the cumulative number of complete charge and discharge processes completed by the battery.

[0196] The preset dynamic health score calculation formula may be a formula used to calculate the battery health score.

[0197] The dynamic health score can be a battery health value calculated based on the operating data of the battery in its current state (such as voltage, current, temperature, internal resistance, etc.) and historical data, and is used to evaluate the current operating health status of the battery.

[0198] The preset dynamic health score threshold may be a preset standard value used to determine whether the battery health state meets the requirements. If the dynamic health score is lower than the threshold, the battery needs to be balanced.

[0199] The second target balanced cells may be those whose health scores are below the dynamic health score threshold. The system needs to balance these cells to restore the overall balance of the battery pack.

[0200] The preset dynamic current balancing formula may be a formula for calculating the balancing current.

[0201] The dynamic balancing current may be a current value determined according to a calculation formula and is used to balance the charge state of the target battery.

[0202] The dynamic balancing time calculation formula may be a formula used to calculate the duration of the balancing process.

[0203] The dynamic equalization time may be the time required for the equalization process.

[0204] The second balancing data set may be a collection of all relevant data for describing the second target balancing battery, including real-time status data of the battery, dynamic health score, dynamic balancing current, balancing time and other information.

[0205] When the preset time point is reached, a network request can be automatically triggered to send a request to the cloud platform, indicating that it is ready for the next step. The cloud platform will return a status code based on the legitimacy of the request and the response time. Common success status codes are 200 (OK). If a success status code is received, it means that the network is successfully connected, and the cloud platform can be queried for the historical health score of the battery pack. The cloud platform may store historical data to support subsequent dynamic health score calculations. Then, the number of charge and discharge times of the battery pack can be obtained by calling the BMS (battery management system) interface, and the real-time status data can be updated by regularly reading the battery information in the battery management system (possibly through the CAN bus, serial port or other communication protocols). The dynamic health score of each battery is calculated by combining the real-time status data, the number of charge and discharge times and the historical health score through the preset dynamic health score calculation formula. If the dynamic health score of some batteries is lower than the preset dynamic health score threshold, the battery is considered to need to be balanced and is used as the second target balanced battery. The balancing current is calculated based on the dynamic health score, voltage data, temperature data and the preset dynamic current balancing formula of the battery, and the maximum voltage and minimum voltage data of each single battery are updated, and then the voltage difference is calculated. Then, the time required for equalization is calculated based on the voltage difference, dynamic balancing current, and the preset dynamic balancing time calculation formula. In the equalizer, current balancing can be achieved by controlling the current path or by adjusting the charging current of each single cell. The equalizer will continuously apply current to the target battery according to the calculated dynamic balancing current. According to the preset dynamic balancing time, the equalizer will control the circuit to provide this current to the second target balancing battery. The balancing current can be applied to the second target balancing battery by passive balancing (consuming the excess power of the battery as heat) or active balancing (transferring power to the battery with lower battery power). Once the balancing current is set, the current output will continue until the predetermined dynamic balancing time is reached. Then, a second balancing data set is generated based on the real-time status data, dynamic health score, second target balancing battery, dynamic balancing current, and dynamic balancing time, and the second balancing data set is uploaded to the cloud platform through an Internet request, and the cloud platform will receive and store this data for subsequent analysis.

[0206] In this solution, by regularly monitoring the dynamic health score of the battery and performing equalization on the cells below the threshold, it is possible to effectively avoid excessive loss of some cells due to imbalance, thereby extending the overall service life of the battery pack. Based on real-time data, charge and discharge times, health score, temperature and other factors, the equalization current and equalization time are dynamically calculated, and the appropriate equalization strategy can be accurately selected for each battery. This dynamic adjustment can avoid over-balancing or under-balancing and improve the balancing efficiency.

[0207] Based on the above technical solution, an optional, preset dynamic health score calculation formula is:

[0208]

[0209] in, V is the dynamic health score; cell is the voltage data; I cell is the current data; T cell is the temperature data; R internal is the internal resistance data; f() is a function used to combine multiple parameters such as voltage data, current data, temperature data, internal resistance data, historical health score, charge and discharge times, etc. to calculate the dynamic health score of the battery; is the historical health score; Cycle Count is the number of charge and discharge times;

[0210] The preset dynamic current balancing formula is:

[0211]

[0212] in, is the dynamic balancing current; f() is a function that combines the battery voltage data, temperature data and dynamic health score to output a suitable initial balancing current value;

[0213] The preset dynamic balancing time calculation formula is:

[0214]

[0215] in, is the dynamic equilibrium time; V diff is the voltage difference; k is the preset balancing efficiency factor.

[0216] In this scheme, In Used to combine the battery's voltage data, current data, temperature data, internal resistance data, historical health score, charge and discharge times and other parameters to calculate the battery's dynamic health score. The battery's health status is often affected by these parameters, especially the changes in the battery's temperature and internal resistance, which are often related to the aging of the battery. For example, traditional linear or simple nonlinear models may not fully capture the complexity of battery behavior. Using machine learning (such as neural networks, support vector machines, random forests, etc.) to fit the relationship between these variables allows the model to adaptively learn complex nonlinear patterns and make more accurate predictions about battery health. Neural networks use deep neural networks (DNNs) to model input variables The complex relationship between and dynamic health score. The formula can be:

[0217]

[0218] Among them, NN represents the neural network model, the input data passes through the nonlinear activation function of multiple hidden layers, and the output is a dynamic health score.

[0219] Fuzzy logic can handle the uncertainty and ambiguity of input variables and is suitable for the calculation of battery health scores, especially when the input data has certain uncertainty or fluctuations. The formula can be:

[0220]

[0221] Input variables such as voltage, internal resistance, charge and discharge times, and historical health scores are converted into health scores through fuzzy rules (for example, "When the battery voltage is high, the internal resistance is low, and the number of charge and discharge times is low", this method can effectively handle the fuzziness and uncertainty of battery status parameters, thereby improving the flexibility and adaptability of prediction.

[0222] The health status of the battery is affected by multiple objectives (such as battery capacity, internal resistance, temperature, historical health score, and number of charge and discharge times, etc.), and these objectives usually have a certain trade-off relationship. Through multi-objective optimization algorithms (such as genetic algorithms, particle swarm optimization, etc.), multiple factors can be considered simultaneously to calculate the health score. The formula can be:

[0223]

[0224] In this model, the battery health score is optimized by the algorithm. It is calculated based on the comprehensive weights of multiple objectives.

[0225] The f() function in is used to combine the battery voltage data, temperature data and dynamic health score to output the appropriate dynamic balancing current value. Neural networks can be used to model the complex nonlinear relationship between battery status and balancing current. The formula is:

[0226]

[0227] The fuzzy logic model can handle the fuzziness and uncertainty of the input variables and is suitable when there is no precise linear relationship between the battery health score and other parameters (such as temperature, voltage, etc.). Through the fuzzy rule system, the battery state can be inferred and the appropriate balancing current can be output. The formula is:

[0228]

[0229] For example, the fuzzy rules can be set as follows:

[0230] If the battery voltage V cell Lower and temperature Tcell If it is higher, the balancing current will be reduced;

[0231] If the battery health score Lower and voltage V cell If the voltage is high, the balancing current will be increased.

[0232] This method performs fuzzy reasoning on the battery status through a rule base to dynamically adjust the balancing current.

[0233] Genetic algorithm (GA) is a global optimization method that simulates natural selection and optimizes the objective function through operations such as selection, crossover, and mutation. Through genetic algorithms, different combinations of battery state parameters can be explored to find the optimal balanced current output. The formula is:

[0234]

[0235] The genetic algorithm can continuously adjust the balancing current through an evolutionary process to maximize the health of the battery and extend its life cycle.

[0236] Embodiment 2

[0237] Figure 2 is a flow chart of the method for dynamic monitoring and equalization of battery status provided in the second embodiment of the present application. Figure 2 As shown, the specific steps include:

[0238] S201, acquiring real-time status data of each single cell in a battery pack, determining a target thermal management strategy according to the temperature data and a preset multi-level thermal management method, and performing thermal management on each single cell in the battery pack according to the target thermal management strategy.

[0239] The preset multi-level thermal management method may refer to adopting different thermal management strategies and means according to different temperature ranges or different working states of the battery pack during the thermal management process of the battery pack. Its main purpose is to control the thermal state of the battery according to the temperature change of the battery to avoid the battery temperature being too high or too low, thereby improving the service life and safety of the battery. Specifically, it may include: Low temperature protection: When the battery temperature is lower than the set minimum temperature threshold, the heating device, such as a heating film or electric belt, is enabled to ensure that the battery can work normally. Medium temperature management: When the battery operating temperature is within a safe range, natural heat dissipation or air cooling, liquid cooling, etc. are used for thermal management. High temperature protection: When the battery temperature exceeds the set maximum temperature threshold, the cooling system, such as air cooling or liquid cooling system, is started, or thermal unloading measures (such as transferring excess current to other battery cells) are taken to reduce the battery temperature. Dynamic thermal regulation: Dynamically adjust the thermal management strategy according to the working state of the battery (such as charging and discharging state, battery usage frequency, temperature change, etc.) to ensure that the battery temperature can be kept within a safe range under different working conditions.

[0240] The target thermal management strategy may refer to the specific thermal management operation determined and implemented based on the real-time temperature data of each single cell in the battery pack and the preset multi-level thermal management mode. It includes adopting different temperature control strategies for different single cells in the battery pack, and specifically, it may include: starting the heating or cooling system. Starting or stopping the air cooling or liquid cooling system. Indirectly affecting the battery temperature by adjusting the charge and discharge rate.

[0241] Real-time temperature data can be obtained from the sensors of each single cell in the battery pack. Based on the real-time temperature data, a preset multi-level thermal management method is used to determine whether the battery is in a low temperature, medium temperature or high temperature state, and the corresponding thermal management strategy is selected according to different states. If the temperature of the battery is lower than the preset minimum temperature threshold, the heating device is enabled. If the temperature of the battery is in the medium range, natural heat dissipation or air cooling can be used to control the temperature. If the battery temperature is too high, the cooling system is started, such as air cooling, liquid cooling or thermal unloading. According to the target thermal management strategy, the heating, cooling or heat dissipation system is started or adjusted to ensure that the battery temperature remains within a safe range. During the battery operation process, the changes in battery temperature are monitored in real time and adjusted according to the preset thermal management strategy. If the load of the battery pack changes (for example, during the charging and discharging process), the thermal management strategy is adjusted accordingly.

[0242] S202, if the real-time status data is initial status data, calculating the initial health score of each single cell according to the real-time status data and a preset initial health score calculation formula; wherein the real-time status data includes voltage data, current data, temperature data and internal resistance data.

[0243] S203, if there is an initial health score lower than a preset initial health score threshold, a single cell corresponding to the initial health score lower than the preset initial health score threshold is used as a first target balancing cell, and an initial balancing current of the first target balancing cell is calculated according to the initial health score, voltage data, temperature data and a preset initial current balancing formula; wherein there is at least one first target balancing cell.

[0244] S204, obtaining maximum voltage data and minimum voltage data of each single cell in the battery pack, determining a voltage difference according to the maximum voltage data and the minimum voltage data, and calculating an initial balancing time of a first target balancing cell according to the voltage difference, the initial balancing current and a preset initial balancing time calculation formula.

[0245] S205 , controlling the balancing circuit to perform current balancing on the first target balancing battery according to the initial balancing current until an initial balancing time is reached.

[0246] S206, sending a network request to the cloud platform. If a success status code sent by the cloud platform is received, generating a first balancing data set according to the real-time status data, the initial health score, the first target balancing battery, the initial balancing current and the initial balancing time, and transmitting the first balancing data set to the cloud platform.

[0247] In this embodiment, by dynamically adjusting the temperature management strategy, safety risks caused by over-high or under-low battery temperatures, such as thermal runaway, damage or fire caused by overheating or overcooling, are avoided. Dynamic thermal management can enable different cooling or heating schemes according to actual needs, avoiding energy waste and achieving energy-saving effects.

[0248] Embodiment 3

[0249] Figure 3 Schematic diagram of the structure of the battery status dynamic monitoring and balancing system provided in the third embodiment of the present application. Figure 3 As shown, specifically including:

[0250] The scoring module 301 is used to obtain the real-time status data of each single battery in the battery pack. If the real-time status data is initial status data, the initial health score of each single battery is calculated according to the real-time status data and a preset initial health score calculation formula; wherein the real-time status data includes voltage data, current data, temperature data and internal resistance data;

[0251] The balancing current determination module 302 is used to, if there is an initial health score lower than a preset initial health score threshold, use the single cell corresponding to the initial health score lower than the preset initial health score threshold as a first target balancing cell, and calculate the initial balancing current of the first target balancing cell according to the initial health score, voltage data, temperature data and a preset initial current balancing formula; wherein there is at least one first target balancing cell;

[0252] The health score calculation module 303 is used to obtain the maximum voltage data and the minimum voltage data of each single cell in the battery pack, determine the voltage difference according to the maximum voltage data and the minimum voltage data, and calculate the initial balancing time of the first target balancing battery according to the voltage difference, the initial balancing current and a preset initial health score calculation formula;

[0253] A balancing module 304, configured to control the balancing circuit to perform current balancing on the first target balancing battery according to the initial balancing current until an initial balancing time is reached;

[0254] The data transmission module 305 is used to send a network request to the cloud platform. If a success status code sent by the cloud platform is received, a first balancing data set is generated according to the real-time status data, the initial health score, the first target balancing battery, the initial balancing current and the initial balancing time, and the first balancing data set is transmitted to the cloud platform.

[0255] In an embodiment of the present application, a scoring module is used to obtain real-time status data of each single cell in a battery pack. If the real-time status data is initial status data, the initial health score of each single cell is calculated according to the real-time status data and a preset initial health score calculation formula; wherein the real-time status data includes voltage data, current data, temperature data and internal resistance data; a balancing current determination module is used to, if there is an initial health score lower than a preset initial health score threshold, take the single cell corresponding to the initial health score lower than the preset initial health score threshold as the first target balancing battery, and calculate the initial balancing current of the first target balancing battery according to the initial health score, voltage data, temperature data and a preset initial current balancing formula; wherein the first target balancing battery is at least A health score calculation module, used to obtain the maximum voltage data and minimum voltage data of each single cell in the battery pack, determine the voltage difference according to the maximum voltage data and the minimum voltage data, and calculate the initial balancing time of the first target balancing battery according to the voltage difference, the initial balancing current and the preset initial health score calculation formula; a balancing module, used to control the balancing circuit to balance the current of the first target balancing battery according to the initial balancing current until the initial balancing time is reached; a data transmission module, used to send a network request to the cloud platform, and if a success status code sent by the cloud platform is received, a first balancing data set is generated according to the real-time status data, the initial health score, the first target balancing battery, the initial balancing current and the initial balancing time, and the first balancing data set is transmitted to the cloud platform. Through the above-mentioned battery status dynamic monitoring and balancing system, by real-time monitoring of the status of each single cell of the battery pack, intelligent calculation of the balancing current and balancing time, it is possible to dynamically adapt to changes in battery status, optimize battery health management, improve charging and discharging efficiency, and extend battery life.

[0256] The battery status dynamic monitoring and balancing system provided in the embodiment of the present application can achieve Figure 1 To avoid repetition, the various processes implemented by the method embodiment are not described here.

[0257] Embodiment 4

[0258] like Figure 4As shown, an embodiment of the present application also provides an electronic device 400, including a processor 401, a memory 402, and a program or instruction stored in the memory 402 and executable on the processor 401. When the program or instruction is executed by the processor 401, each process of the above-mentioned battery status dynamic monitoring and balancing method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0259] It should be noted that the electronic devices in the embodiments of the present application include the mobile electronic devices and non-mobile electronic devices mentioned above.

[0260] Embodiment 5

[0261] An embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, each process of the above-mentioned cable installation process based on the tension adaptive control system embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0262] The processor is a processor in the electronic device described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0263] It should be noted that, in this article, the term "comprises", "includes" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or system including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the process, method, article or system including the element. In addition, it should be pointed out that the scope of the method and system in the embodiment of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0264] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM / RAM, a disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0265] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

[0266] The above are only preferred embodiments of the present application and the technical principles used. The present application is not limited to the specific embodiments described herein, and various obvious changes, readjustments and substitutions that can be made by those skilled in the art will not deviate from the scope of protection of the present application. Therefore, although the present application is described in more detail through the above embodiments, the present application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present application, and the scope of the present application is determined by the scope of the claims.

Claims

1. A method for dynamic monitoring and equalization of battery status, characterized in that: The method is performed by an equalizer, and the method comprises: Acquire real-time status data of each single battery in the battery pack, and if the real-time status data is initial status data, calculate the initial health score of each single battery according to the real-time status data and a preset initial health score calculation formula; wherein the real-time status data includes voltage data, current data, temperature data and internal resistance data; If there is an initial health score lower than the preset initial health score threshold, the single cell corresponding to the initial health score lower than the preset initial health score threshold is used as the first target balancing cell, and the initial balancing current of the first target balancing cell is calculated according to the initial health score, voltage data, temperature data and the preset initial current balancing formula; wherein there is at least one first target balancing cell; Acquire maximum voltage data and minimum voltage data of each single cell in the battery pack, determine a voltage difference according to the maximum voltage data and the minimum voltage data, and calculate an initial balancing time of a first target balancing cell according to the voltage difference, the initial balancing current and a preset initial balancing time calculation formula; Controlling the balancing circuit to perform current balancing on the first target balancing battery according to the initial balancing current until an initial balancing time is reached; Send a network request to the cloud platform. If a success status code sent by the cloud platform is received, generate a first balancing data set according to the real-time status data, the initial health score, the first target balancing battery, the initial balancing current, and the initial balancing time, and transmit the first balancing data set to the cloud platform.

2. The method for dynamic monitoring and equalization of battery status according to claim 1, characterized in that: The preset initial health score calculation formula is: H cell =f(V cell ,I cell ,T cell ,R internal ); Among them, H cell is the initial health score; V cell is the voltage data; I cell is the current data; T cell is the temperature data; R internal is the internal resistance data; f() is a function used to combine multiple parameters such as voltage data, current data, temperature data and internal resistance data to calculate the initial health score of the battery; The preset initial current balancing formula is: I balance =f(V cell ,T cell ,H cell ); Among them, I balance is the initial balancing current; f() is a function that combines the battery voltage data, temperature data and initial health score to output a suitable initial balancing current value; the preset initial balancing time calculation formula is: Among them, t balance is the initial equilibrium time; V diff is the voltage difference; k is the preset balancing efficiency factor.

3. The method for dynamic monitoring and equalization of battery status according to claim 1, characterized in that: After transmitting the first balanced data set to the cloud platform, the method further includes: If the preset data collection time point is reached, a network request is sent to the cloud platform. If a success status code is received from the cloud platform, a historical health score acquisition request is sent to the cloud platform. If the historical health score sent by the cloud platform is received, the charge and discharge times of the battery pack are obtained, the real-time status data of each single battery in the battery pack is updated, and the dynamic health score of each single battery is calculated according to the real-time status data, the charge and discharge times, the historical health score and the preset dynamic health score calculation formula; If there is a dynamic health score lower than the preset dynamic health score threshold, the single cell corresponding to the dynamic health score lower than the preset dynamic health score threshold is used as the second target balancing cell, and the dynamic balancing current of the second target balancing cell is calculated according to the dynamic health score, voltage data, temperature data and a preset dynamic current balancing formula; wherein there is at least one second target balancing cell; Updating the maximum voltage data and the minimum voltage data of each single cell in the battery pack, updating the voltage difference according to the maximum voltage data and the minimum voltage data, and calculating the dynamic balancing time of the second target balancing cell according to the voltage difference, the dynamic balancing current and a preset dynamic balancing time calculation formula; Performing current balancing on the second target balancing battery according to the dynamic balancing current control balancing circuit until the dynamic balancing time is reached; A second balancing data set is generated according to the real-time status data, the dynamic health score, the second target balancing battery, the dynamic balancing current, and the dynamic balancing time, and the second balancing data set is transmitted to the cloud platform.

4. The method for dynamic monitoring and equalization of battery status according to claim 3, characterized in that: The preset dynamic health score calculation formula is: in, V is the dynamic health score; cell is the voltage data; I cell is the current data; T cell is the temperature data; R internal is the internal resistance data; f() is a function used to combine multiple parameters such as voltage data, current data, temperature data, internal resistance data, historical health score, charge and discharge times, etc. to calculate the dynamic health score of the battery; is the historical health score; Cycle Count is the number of charge and discharge times; the preset dynamic current balancing formula is: in, is the dynamic balancing current; f() is a function that combines the battery voltage data, temperature data and dynamic health score to output a suitable initial balancing current value; The preset dynamic balancing time calculation formula is: in, is the dynamic equilibrium time; V diff is the voltage difference; k is the preset balancing efficiency factor.

5. The method for dynamic monitoring and equalization of battery status according to claim 1, characterized in that: After acquiring the real-time status data of each single battery in the battery pack, the method further includes: A target thermal management strategy is determined according to the temperature data and a preset multi-level thermal management method, and thermal management is performed on each single battery in the battery pack according to the target thermal management strategy.

6. The method for dynamic monitoring and equalization of battery status according to claim 1, characterized in that: After acquiring the real-time status data of each single battery in the battery pack, the method further includes: Inputting the real-time status data into a preset fault detection model to determine a fault score of each single cell; If there is a fault score exceeding a preset fault score threshold, the single cell corresponding to the fault score exceeding the preset fault score threshold is used as a target faulty cell, and the fault type of the target faulty cell is output through the preset fault detection model; wherein the number of the target faulty cells is at least one; If the fault type is a minor risk type, a self-repair scheme is output through the preset fault detection model, and the target faulty battery is self-repaired according to the self-repair scheme; Accordingly, after outputting the fault type through the preset fault detection model, the method further includes: If the fault type is a major risk type, the target faulty battery is dynamically isolated and major risk information is sent to the control center.

7. The method for dynamic monitoring and equalization of battery status according to claim 6, characterized in that: The training process of the preset fault detection model includes: Acquire historical scoring records, and determine first historical state data and historical scores of each historical battery according to the historical scoring records; Obtain historical fault records, determine the second historical state data and historical fault type of each historical fault battery according to the historical fault records, and determine the third historical state data of the minor risk type and the corresponding historical self-repair solution; wherein the historical fault type includes a minor risk type and a major risk type; marking a score tag of the first historical status data according to the historical score; marking a fault type label of the second historical state data according to the historical fault type; Marking the third historical state data with a self-repair solution label according to the historical self-repair solution; Constructing a fault detection model, and training a scoring branch of the fault detection model according to the first historical state data and the scoring label; Training a fault classification branch of the fault detection model according to the second historical state data and the fault type label; The self-repair solution output branch of the fault detection model is trained according to the third historical state data and the self-repair solution label.

8. A battery status dynamic monitoring and balancing system, characterized in that: The system is configured in an equalizer, and the system comprises: A scoring module, used to obtain real-time status data of each single battery in the battery pack, and if the real-time status data is initial status data, calculate the initial health score of each single battery according to the real-time status data and a preset initial health score calculation formula; wherein the real-time status data includes voltage data, current data, temperature data and internal resistance data; a balancing current determination module, configured to, if there is an initial health score lower than a preset initial health score threshold, use a single cell corresponding to the initial health score lower than the preset initial health score threshold as a first target balancing cell, and calculate an initial balancing current of the first target balancing cell according to the initial health score, voltage data, temperature data and a preset initial current balancing formula; wherein there is at least one first target balancing cell; A health score calculation module, used to obtain maximum voltage data and minimum voltage data of each single cell in the battery pack, determine a voltage difference according to the maximum voltage data and the minimum voltage data, and calculate an initial balancing time of a first target balancing cell according to the voltage difference, the initial balancing current and a preset initial health score calculation formula; A balancing module, configured to control the balancing circuit to perform current balancing on the first target balancing battery according to the initial balancing current until an initial balancing time is reached; The data transmission module is used to send a network request to the cloud platform. If a success status code sent by the cloud platform is received, a first balancing data set is generated according to the real-time status data, the initial health score, the first target balancing battery, the initial balancing current and the initial balancing time, and the first balancing data set is transmitted to the cloud platform.

9. An electronic device, characterized in that: It includes a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements the steps of the method for dynamic monitoring and balancing of battery status as described in any one of claims 1 to 7.

10. A readable storage medium, characterized in that: The readable storage medium stores a program or instruction, and when the program or instruction is executed by the processor, the steps of the battery status dynamic monitoring and balancing method as described in any one of claims 1 to 7 are implemented.

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