A Method, System and Electronic Device for Dynamically Monitoring and Equalizing Battery State

Abstract

The present application discloses a method, system and electronic device for dynamically monitoring and equalizing battery states, and the present application belongs to the technical field of battery equalization management. The method includes: calculating the initial health scores of individual batteries in the battery pack; taking the individual batteries corresponding to the initial health scores lower than a preset initial health score threshold as the first target equalization batteries, and calculating the initial equalization current of the first target equalization batteries; calculating the initial equalization time of the first target equalization batteries; controlling an equalization circuit to perform current equalization on the first target equalization batteries according to the initial equalization current until the initial equalization time is reached; generating a first equalization data set and transmitting the first equalization data set to a cloud platform. This solution can dynamically adapt to changes in battery states, optimize battery health management, improve charge and discharge efficiency, and extend battery life by real-time monitoring the states of individual batteries in the battery pack and intelligently calculating the equalization current and equalization time.

Description

Technical Field

[0001] This application belongs to the technical field of battery equalization management, and particularly relates to a method, system and electronic device for dynamically monitoring and equalizing battery states. Background Art

[0002] As a key component for energy storage and conversion, batteries play a crucial role in various electronic devices, electric vehicles and energy storage systems. However, due to the influence of various factors such as production processes, material properties, usage environments and aging degrees, there are often inconsistencies in parameters such as voltage, capacity and internal resistance among battery cells. This inconsistency will gradually accumulate during the charge and discharge cycles of the battery, resulting in overcharging or over-discharging of some battery cells, seriously affecting the overall performance, service life and safety of the battery pack.

[0003] Traditional battery monitoring technologies mainly rely on simple electronic components to monitor basic parameters of the battery, such as SOC and SOH. Traditional battery equalization technologies mainly adopt passive equalization methods, using a resistor network to dissipate the excess energy in the form of heat to achieve voltage equalization among battery cells.

[0004] However, traditional battery monitoring technologies have the problem of insufficient monitoring accuracy, unable to provide detailed battery state information, and difficult to meet the current precise monitoring requirements for battery states. And traditional battery equalization technologies have the disadvantages of low energy utilization rate, slow equalization speed and possible heat generation affecting the battery life. The passive equalization method realizes equalization by discharging through a resistor, 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] Embodiments of this application provide a method, system and electronic device for dynamically monitoring and equalizing battery states, which solve the problems that traditional battery monitoring technologies have insufficient monitoring accuracy, are unable to provide detailed battery state information, and are difficult to meet the current precise monitoring requirements for battery states. And traditional battery equalization technologies have the disadvantages of low energy utilization rate, slow equalization speed and possible heat generation affecting the battery life. The passive equalization method realizes equalization by discharging through a resistor, 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, embodiments of this application provide a method for dynamically monitoring and equalizing battery states, the method including:

[0007] Obtain the real-time status data of each single battery in the battery pack. If the real-time status data is the initial status data, calculate the initial health score of each single battery according to the real-time status data and the 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, take the single battery corresponding to the initial health score lower than the preset initial health score threshold as the first target equalization battery, and calculate the initial equalization current of the first target equalization battery according to the initial health score, voltage data, temperature data, and the preset initial current equalization formula; wherein, the first target equalization battery is at least one;

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

[0010] Control the equalization circuit to perform current equalization on the first target equalization battery according to the initial equalization current until the initial equalization time is reached;

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

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

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

[0014] Wherein, 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 equalization formula is:

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

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

[0018]

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

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

[0021] If a preset data collection time point is reached, send a networking request to the cloud platform. If a successful status code sent by the cloud platform is received, send a historical health score acquisition request to the cloud platform;

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

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

[0024] Update the maximum voltage data and minimum voltage data of each single battery in the battery pack, update the voltage difference according to the maximum voltage data and the minimum voltage data, and calculate the dynamic equilibrium time of the second target equilibrium battery according to the voltage difference, the dynamic equilibrium current, and a preset dynamic equilibrium time calculation formula;

[0025] Control the equilibrium circuit to perform current equilibrium on the second target equilibrium battery according to the dynamic equilibrium current until the dynamic equilibrium time is reached;

[0026] Generate a second equalization dataset based on the real-time status data, dynamic health score, second target balanced battery, dynamic equalization current, and dynamic equalization time, and transmit the second equalization dataset to the cloud platform.

[0027] Further, the preset formula for calculating the dynamic health score is:

[0028]

[0029] Wherein, is the dynamic 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, 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 charge and discharge times;

[0030] The preset dynamic current equalization formula is:

[0031]

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

[0033] The preset formula for calculating the dynamic equalization time is:

[0034]

[0035] Wherein, is the dynamic equalization time; V diff is the voltage difference; k is the preset equalization efficiency factor.

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

[0037] Determine the target thermal management strategy according to the temperature data and the preset multi-level thermal management method, and perform thermal management on each single battery in the battery pack according to the target thermal management strategy.

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

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

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

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

[0042] Correspondingly, after the fault type is output through the preset fault detection model, the method further includes:

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

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

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

[0046] Obtain historical fault records, and determine the second historical state data and historical fault types of each historical fault battery, and determine the third historical state data of the minor risk type and the corresponding historical self-repair solution; where the historical fault types include minor risk types and major risk types;

[0047] Label the score labels of the first historical state data according to the historical scores;

[0048] Label the fault type labels of the second historical state data according to the historical fault types;

[0049] Label the self-repair solution labels of the third historical state data according to the historical self-repair solutions;

[0050] Construct a fault detection model, and train the score branch of the fault detection model according to the first historical state data and the score labels;

[0051] Train the fault classification branch of the fault detection model according to the second historical state data and the fault type labels;

[0052] Train the self-repair solution output branch of the fault detection model according to the third historical state data and the self-repair solution labels.

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

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

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

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

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

[0058] A data transmission module, configured to send a networking request to a cloud platform. If a success status code sent by the cloud platform is received, generate a first equalizing data set according to the real-time state data, initial health score, first target equalizing battery, initial equalizing current, and initial equalizing time, and transmit the first equalizing data set to the cloud platform.

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

[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 the embodiment of the present application, the real-time status data of each single battery in the battery pack is obtained. If the real-time status data is the 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. Among them, 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 equalization battery, and the initial equalization current of the first target equalization battery is calculated according to the initial health score, voltage data, temperature data, and a preset initial current equalization formula. Among them, the first target equalization battery is at least one. The maximum voltage data and the minimum voltage data of each single battery in the battery pack are obtained, the voltage difference is determined according to the maximum voltage data and the minimum voltage data, and the initial equalization time of the first target equalization battery is calculated according to the voltage difference, the initial equalization current, and a preset initial equalization time calculation formula. The equalization circuit is controlled according to the initial equalization current to perform current equalization on the first target equalization battery until the initial equalization time is reached. A networking request is sent to the cloud platform. If a successful status code sent by the cloud platform is received, a first equalization data set is generated according to the real-time status data, initial health score, first target equalization battery, initial equalization current, and initial equalization time, and the first equalization data set is transmitted to the cloud platform. Through the above battery status dynamic monitoring and equalization method, by monitoring the status of each single battery in the battery pack in real time and intelligently calculating the equalization current and equalization time, it can dynamically adapt to the change of battery status, optimize battery health management, improve charge and discharge efficiency, and extend battery life. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 FIG. is a flowchart of the battery status dynamic monitoring and equalization method provided in Embodiment 1 of the present application;

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

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

[0065] Figure 4 FIG. is a structural diagram of the electronic device provided in Embodiment 4 of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0066] In order to make the objectives, technical solutions, and advantages of this application clearer, the following further describes the specific embodiments of this application in detail with reference to the accompanying drawings. It can be understood that the specific embodiments described herein are only used to explain this application, rather than limiting this application. Additionally, it should be noted that for ease of description, only parts related to this application are shown in the drawings, rather than all the content. Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the operations (or steps) as sequential processes, many of the operations can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. When the operations are completed, the process can be terminated, but there can also be additional steps not included in the drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, and so on.

[0067] Next, the technical solutions in the embodiments of this application will be clearly described with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art belong to the scope of protection of this application.

[0068] The terms "first", "second", etc. in the specification and claims of this application are used to distinguish similar objects, rather than to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of this application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. generally belong to the same category, and the number of objects is not limited. For example, the first object can be one or multiple. In addition, "and / or" in the specification and claims means at least one of the connected objects, and the character " / " generally indicates an "or" relationship between the associated objects before and after.

[0069] Next, with reference to the accompanying drawings, a RSMC chip, a multi-stage chip startup method, and a Beidou communication and navigation device provided by the embodiments of this application will be described in detail through specific embodiments and their application scenarios.

[0070] Embodiment 1

[0071] Figure 1 is a schematic flowchart of the battery state dynamic monitoring and balancing method provided by Embodiment 1 of this application. As Figure 1 shown, it specifically includes the following steps:

[0072] S101. Obtain the real-time status data of each single battery in the battery pack. 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. Among them, the real-time status data includes voltage data, current data, temperature data, and internal resistance data.

[0073] First of all, the usage scenario of this solution can be a scenario where the real-time status data of each single battery in the battery pack is collected, battery scoring is performed based on this, and when the battery score is lower than a certain threshold, the equalization current and equalization time are calculated, and current equalization is performed on the batteries lower than a certain threshold.

[0074] Based on the above usage scenario, it can be understood that the execution subject of this application can be a battery state dynamic monitoring and equalization system, and no excessive limitation is made here.

[0075] In this solution, the equalizer can be a device specifically designed for voltage equalization of single batteries in a battery pack. Its main function is to precisely control the voltage difference between each single battery in the battery pack, so that the entire battery pack reaches an equalized state, avoiding damage to some batteries due to excessive voltage difference or affecting the performance of the battery pack.

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

[0077] The single battery can be the basic component unit in the battery pack. Each single battery has independent characteristics such as voltage, temperature, and internal resistance. They are combined into a battery pack in series or parallel.

[0078] The real-time status data can 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 can be the voltage value of the battery single body, indicating the electrical energy state of the battery.

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

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

[0082] The internal resistance data can be the internal resistance of the battery, which usually increases with battery aging 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 undergoes initial monitoring. It can be regarded as a situation without comparison with previous data, 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 state data of individual batteries. Usually, this formula combines data such as the voltage, current, temperature, and internal resistance of the battery to calculate a health score, reflecting the overall health status of the battery.

[0085] The initial health score can be calculated through the preset health score calculation formula and represents the health state of the battery when the data is first collected.

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

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

[0088] Input the real-time state data into a preset fault detection model to determine the fault score of each individual battery;

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

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

[0091] Correspondingly, after the fault type is output through the preset fault detection model, the method further includes:

[0092] If the fault type is a major risk type, dynamically isolate the target fault battery and send a major risk message to the control center.

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

[0094] The fault score can be a quantified value of the degree of battery fault, usually indicating the degree of abnormality of the current battery state. The higher the score, the greater the risk of battery failure.

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

[0096] The target faulty battery can refer to a battery whose fault score exceeds the preset threshold based on the fault score judgment. This battery needs to be further processed (such as self-repair or isolation).

[0097] The fault type is output by the preset fault detection model and describes the specific fault risks existing in 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 can be those faults that are less severe and less likely to immediately affect battery performance or safety. For example, the battery voltage deviates slightly from the normal value, but it does not affect the overall function.

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

[0100] Major risk types can be those faults that may cause the battery to fail completely, result in safety accidents (such as fires, explosions), or have a significant impact on the overall performance of the battery pack. Such faults need to be dealt with immediately.

[0101] Major risk information can be alarm information or reports when serious risks occur in battery faults, which can include key information such as the type, location, and severity of the fault, aiming to notify relevant personnel to take emergency measures.

[0102] The control center can be a monitoring and command center that centrally manages the battery pack or the battery management system (BMS). It is responsible for receiving fault reports, evaluating risks, making corresponding decisions, and coordinating subsequent repair, isolation, etc. 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. Based on the real-time status data, the fault detection model will output the fault score of each single battery. 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, a 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 a major risk message). 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 can include: adjusting the battery charging / discharging strategy, battery equalization operation, and temperature control adjustment, etc. According to the self-repair solution recommended by the model, the repair operation is automatically executed to restore the normal working state of the battery. When the fault type is a major risk, the system will start a dynamic isolation operation: isolate the target fault battery from the battery pack to prevent it from affecting other batteries or the entire system. And send a major risk message to the control center to notify the control center to take further measures. This information can include the location of the battery, the fault type, possible consequences, etc. When the control center receives the major risk message, it can take corresponding emergency handling 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, through fault scoring and fault type classification, faulty batteries can be quickly identified. Especially for the dynamic isolation of batteries with major risk types, it can effectively avoid the impact of faulty batteries on other batteries, thus ensuring the overall safety of the battery pack. Based on the input of real-time status data, the fault detection model automatically calculates the fault score of each battery and judges whether repair or isolation measures are needed according to the threshold, reducing manual intervention and improving management efficiency.

[0105] On the basis of the above technical solution, optionally, the training process of the preset fault detection model includes:

[0106] Obtain historical score records, and determine the first historical status data and historical scores of each historical battery according to the historical score records;

[0107] Obtain historical fault records, and determine the second historical status data and historical fault types of each historical faulty battery, and determine the third historical status data of the minor risk type and the corresponding historical self-repair solution; wherein, the historical fault types include minor risk types and major risk types;

[0108] Annotate the scoring label of the first historical status data according to the historical score;

[0109] Annotate the fault type label of the second historical status data according to the historical fault type;

[0110] Annotate the self - repair solution label of the third historical status data according to the historical self - repair solution;

[0111] Construct a fault detection model, and train the scoring branch of the fault detection model according to the first historical status data and the scoring label;

[0112] Train the fault classification branch of the fault detection model according to the second historical status data and the fault type label;

[0113] Train the self - repair solution output branch of the fault detection model according to the third historical status data and the self - repair solution label.

[0114] In this solution, the historical score record can be a record generated when scoring the battery health status 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] The historical battery can be a battery cell used to evaluate the health status in the historical data. The historical data of each battery cell is used to determine its health score and possible fault types.

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

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

[0118] The historical fault record can be the historical data recording the battery faults, which can include the time, type and battery status when the fault occurs.

[0119] The historical fault battery can 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 can be the status data of the battery when a fault occurs, such as the voltage, current, internal resistance, temperature, etc. of the battery.

[0121] The historical fault type can refer to the fault types that occur in the battery, which are generally divided into minor risk and major risk types. The minor risk type may not immediately affect the battery usage, while the major risk type may lead to battery failure or have an impact on other batteries.

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

[0123] The historical self - repair solution can be a repair solution designed for batteries with minor faults, which can include adjusting the charging strategy, optimizing the discharging process, or other operations to delay the further deterioration of the fault.

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

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

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

[0127] The self - repair solution label can be used to mark the third historical status data, indicating the self - repair solution applied when the battery has a minor fault. 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, responsible for calculating the health score based on the status data of the battery. This branch will output a health score according to the input historical status data.

[0129] The fault classification branch can be a part of the fault detection model, 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 solution output branch can be a part of the fault detection model, responsible for outputting the self - repair solution of the battery.

[0131] 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 inputs for subsequent model training. Based on the historical data, the system labels the scoring, fault type, and self - repair solution as tags to the corresponding status data respectively. These tags will serve as the target outputs during model training. Then, according to the historical scoring records and the corresponding scoring tags, the historical status data is used to train the scoring branch in the fault detection model so that it can predict the health score based on the battery status. According to the fault type tags in the historical fault records, the fault classification branch of the model is trained so that it can determine the fault type according to the battery status. According to the historical self - repair solution tags for minor fault types, the self - repair solution output branch is trained so that it can generate appropriate self - repair solutions. Through the above training steps, each branch is integrated to build a comprehensive fault detection model that can output the health score, fault type, and self - repair solution according to 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 the preset initial health score threshold, the single battery corresponding to the initial health score lower than the preset initial health score threshold is taken as the first target equalization battery, and according to the initial health score, voltage data, temperature data, and the preset initial current equalization formula, the initial equalization current of the first target equalization battery is calculated; where the first target equalization battery is at least one.

[0133] The preset initial health score threshold can refer to the boundary value used to determine whether the battery needs equalization after the initial health score is calculated. If the health score of the battery is lower than this threshold, it is considered that the health status of the battery is poor and equalization processing is required.

[0134] The first target equalization battery can refer to those batteries whose initial health scores are lower than the preset threshold. Due to their poor health status, these batteries need to be adjusted for charge and discharge through the equalization current to restore their health status and ensure the overall performance of the battery pack.

[0135] The preset initial current equalization formula can be used to calculate the current value required for equalizing the battery with a low health score.

[0136] The initial equalization current can be the current value calculated according to the preset initial current equalization formula and is used for equalizing the target equalization battery. The equalization 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 can be substituted into a preset initial current balancing formula to calculate the initial balancing current of the first target balancing battery.

[0138] S103, obtain the maximum voltage data and the minimum voltage data of each single battery 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 balancing time calculation formula.

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

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

[0141] The voltage difference can be the difference between the maximum voltage and the minimum voltage in the battery pack, which is used to reflect the voltage distribution of each single battery in the battery pack. In a battery pack, the voltages of individual single batteries usually vary. These differences may be caused by factors such as the health status, aging degree, temperature, charging and discharging history of different batteries. If the voltage differences between batteries are large, some batteries may require more time to balance in order to adjust their voltages to a level matching that of other batteries. The voltage difference of the battery pack can reflect the charge distribution and battery health status of each single battery in the entire battery pack. To balance each single battery in the battery pack, it is usually necessary to estimate the balancing time required for each single battery based on the voltage difference. The purpose of this is to ensure that the voltage differences of the batteries are reduced and the charge levels of individual single batteries tend to be consistent, thereby improving the overall performance and lifespan of the battery pack. A larger voltage difference means that more current needs to be applied to balance the battery voltages, which may result in a longer duration of the balancing current. Batteries with a large voltage difference will receive more balancing current to reach the voltage balance of the battery pack faster. A smaller voltage difference may mean that the batteries are already relatively close, so less balancing time is required.

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

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

[0144] S104. According to the initial equalization current, control the equalization circuit to perform current equalization on the first target equalization battery until the initial equalization time is reached.

[0145] The equalization circuit can refer to the circuit part integrated in the equalizer (or called battery equalization management system). The main function of this circuit is to control and regulate the voltage differences among individual cells in the battery pack, ensuring that each individual cell in the battery pack can maintain an equal voltage and charge state during the charge and discharge processes. Its role is to adjust the current and voltage of individual cells through different methods (such as active equalization or passive equalization) to avoid excessive damage to the battery due to imbalance. The equalizer can include multiple equalization circuits and equalization channels, and the number of equalization channels can be expanded as needed to adapt to battery packs of different scales.

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

[0147] S105. Send a networking request to the cloud platform. If a successful status code sent by the cloud platform is received, generate a first equalization dataset based on the real-time status data, initial health score, first target equalization battery, initial equalization current, and initial equalization time, and transmit the first equalization dataset to the cloud platform.

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

[0149] The networking request can refer to a network communication request initiated from a local device (such as an equalizer) to send data to the cloud platform or request to obtain data through the Internet or local area network. Usually, these requests can be requests based on protocols such as HTTP, HTTPS, and WebSocket.

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

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

[0152] The equalizer can send a request to the cloud platform, and the request can be sent via an HTTP POST request. After receiving the networking request, the cloud platform will return a status code. If the request is successful, the cloud platform will return a successful status code such as 200 OK or 201 Created. After receiving the successful status code, the equalizer generates the first balanced data set based on the real-time status data, the initial health score, the first target balanced battery, the initial balancing current, and the initial balancing time. The first balanced data set is uploaded to the cloud platform through the networking 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 the equalizer 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 the previously collected historical data (such as past health scores, status data, etc.) from the cloud platform.

[0153] In the embodiments of the present application, the real-time status data of each single battery in the battery pack is obtained. If the real-time status data is the 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. Among them, 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 equalization battery, and the initial equalization current of the first target equalization battery is calculated according to the initial health score, voltage data, temperature data, and a preset initial current equalization formula. Among them, the first target equalization battery is at least one. The maximum voltage data and the minimum voltage data of each single battery in the battery pack are obtained, the voltage difference is determined according to the maximum voltage data and the minimum voltage data, and the initial equalization time of the first target equalization battery is calculated according to the voltage difference, the initial equalization current, and a preset initial equalization time calculation formula. The equalization circuit is controlled according to the initial equalization current to perform current equalization on the first target equalization battery until the initial equalization time is reached. A networking request is sent to the cloud platform. If a successful status code sent by the cloud platform is received, a first equalization data set is generated according to the real-time status data, initial health score, first target equalization battery, initial equalization current, and initial equalization time, and the first equalization data set is transmitted to the cloud platform. Through the above battery status dynamic monitoring and equalization method, by real-time monitoring the status of each single battery in the battery pack, intelligently calculating the equalization current and equalization time, it can dynamically adapt to the change of battery status, optimize battery health management, improve charge and discharge efficiency, and extend battery life.

[0154] Based on the above technical solution, optionally, the 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 equalization formula is:

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

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

[0160] The preset formula for the initial equilibrium time is:

[0161]

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

[0163] In this solution, H cell = f(V cell , I cell , T cell , R internal ) in f() is used to combine parameters such as the voltage data, current data, temperature data, and internal resistance data of the battery to calculate the initial health score of the battery. The health state of the battery is usually affected by these parameters, especially the changes in the temperature and internal resistance of the battery, which are often related to the degree of battery aging. For example, traditional linear or simple non-linear 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 non-linear patterns and make more accurate predictions of battery health. The neural network uses a deep neural network (DNN) to model the complex relationship between the 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 non-linear activation functions 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 calculating the battery health score, especially when the input data has a certain degree of uncertainty or fluctuation. 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 a health score through fuzzy rules (such as "when the battery voltage is high and the internal resistance is low, the initial health score is better"). This method can handle the ambiguity and uncertainty of battery state parameters and improve the flexibility of prediction.

[0171] The health state of the battery is affected by multiple objectives (such as battery capacity, internal resistance, temperature, etc.), and there is usually a certain trade-off relationship between these objectives. The health score can be calculated by considering multiple factors simultaneously through multi-objective optimization algorithms (such as genetic algorithms, particle swarm optimization, etc.). 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, through the optimization algorithm, the battery health score H cell is calculated based on the comprehensive weights of multiple objectives.

[0174] I balance = f(V cell , T cell , H cell ) The f() function is used to combine the battery voltage data, temperature data, and the initial health score to output an appropriate initial equalization current value. A neural network can be used to model the complex non-linear relationship between the battery state and the equalization 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 input variables and is applicable when there is no precise linear relationship between the health score of the battery and other parameters (such as temperature, voltage, etc.). Through the fuzzy rule system, the battery state can be inferred and an appropriate equalization 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 is low and the temperature T cell is high, then reduce the equalization current;

[0180] If the battery health score H cell is low and the voltage V cell is high, then increase the equalization current.

[0181] This method performs fuzzy inference on the battery state through the rule base, thereby dynamically adjusting the equalization current.

[0182] The 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 the genetic algorithm, different combinations of battery state parameters can be explored to find the optimal equalization 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 equalization current through the evolutionary process to optimize the health state of the battery and extend its life cycle to the greatest extent.

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

[0186] If a preset data collection time point is reached, send a networking request to the cloud platform. If a successful status code sent by the cloud platform is received, send a historical health score acquisition request to the cloud platform;

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

[0188] If there is a dynamic health score lower than the preset dynamic health score threshold, use the single battery corresponding to the dynamic health score lower than the preset dynamic health score threshold as the second target balanced battery, and calculate the dynamic balance current of the second target balanced battery according to the dynamic health score, voltage data, temperature data, and a preset dynamic current balance formula; where the second target balanced battery is at least one;

[0189] Update the maximum voltage data and minimum voltage data of each single battery in the battery pack, update the voltage difference according to the maximum voltage data and the minimum voltage data, and calculate the dynamic balance time of the second target balanced battery according to the voltage difference, the dynamic balance current, and a preset dynamic balance time calculation formula;

[0190] Control the balance circuit to perform current balance on the second target balanced battery according to the dynamic balance current until the dynamic balance time is reached;

[0191] Generate a second balanced data set according to the real-time status data, dynamic health score, second target balanced battery, dynamic balance current, and dynamic balance time, and transmit the second balanced data set to the cloud platform.

[0192] In this solution, the preset data collection time point can be the time point when the system is set to regularly collect the real-time data of the battery pack. These time points are 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 previous collection period).

[0194] The historical health score can refer to the battery health score calculated based on factors such as the battery's state data (such as voltage, current, temperature, internal resistance, etc.) and the number of charge and discharge cycles at a certain time point or during a certain period in the past. For example, if after obtaining the initial state 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 number of charge and discharge cycles can be the cumulative number of complete charge and discharge processes completed by the battery.

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

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

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

[0199] The second target balanced battery can be those batteries with a health score lower than the dynamic health score threshold. The system needs to balance these batteries to restore the overall balance of the battery pack.

[0200] The preset dynamic current balance formula can be a formula for calculating the balance current.

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

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

[0203] The dynamic balance time can be the time required for the balance process.

[0204] The second balance data set can be a set of all relevant data used to describe the second target balanced battery, including the battery's real-time state data, dynamic health score, dynamic balance current, balance time and other information.

[0205] When a preset time point is reached, a network connection request can be automatically triggered to send a request to the cloud platform, indicating readiness for the next operation. The cloud platform will return a status code based on the legitimacy of the request and the response time. Common successful status codes include 200 (OK). If a successful status code is received, it indicates a successful network connection, and the historical health score of the battery pack can be queried from the cloud platform. The cloud platform may store historical data to support subsequent dynamic health score calculations. Then, the charge and discharge times of the battery pack can be obtained by calling the interface of the BMS (Battery Management System), 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). Through a preset dynamic health score calculation formula, the dynamic health score of each battery is calculated by combining the real-time status data, charge and discharge times, and historical health score. If the dynamic health score of some batteries is lower than the preset dynamic health score threshold, the battery is considered to require balancing and is used as the second target balancing battery. The balancing current is calculated based on the dynamic health score, voltage data, temperature data of the battery, and a preset dynamic current balancing formula, and the maximum voltage and minimum voltage data of each single battery are updated, and then the voltage difference is calculated. Then, based on the voltage difference, dynamic balancing current, and a preset dynamic balancing time calculation formula, the time required for balancing is calculated. In the balancer, current balancing can be achieved by controlling the current path or by adjusting the charging current of each single battery. The balancer will continuously apply current to the target battery according to the calculated dynamic balancing current. According to the preset dynamic balancing time, the balancer will control the circuit to supply 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 the power to the battery with a lower battery charge). Once the balancing current is set, the current output will continue until the preset 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 a network connection request. The cloud platform will receive and store these data for subsequent analysis.

[0206] In this solution, by regularly monitoring the dynamic health score of the battery and balancing the single batteries below the threshold, it is possible to effectively avoid excessive loss of some batteries due to imbalance, thereby extending the overall service life of the battery pack. Dynamically calculating the balancing current and balancing time based on factors such as real-time data, charge and discharge times, health score, and temperature can accurately select an appropriate balancing strategy for each battery. This dynamic adjustment can avoid overbalancing or underbalancing and improve the balancing efficiency.

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

[0208]

[0209] Among them, is the dynamic 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, 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 charge and discharge times;

[0210] The preset dynamic current equalization formula is:

[0211]

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

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

[0214]

[0215] Among them, is the dynamic equalization time; V diff is the voltage difference; k is the preset equalization efficiency factor.

[0216] In this solution, The f() in is used to combine parameters such as the voltage data, current data, temperature data, internal resistance data, historical health score, charge and discharge times of the battery to calculate the dynamic health score of the battery. The health state of the battery is usually affected by these parameters, especially the temperature and internal resistance changes of the battery, which are often related to the degree of battery aging. For example, traditional linear or simple non-linear 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 non-linear patterns and make more accurate predictions of battery health. The neural network uses a deep neural network (DNN) to model the complex relationship between the input variables

[0217]

[0218] Among them, NN represents the neural network model. The input data passes through the non-linear activation functions of multiple hidden layers, and the output is the dynamic health score.

[0219] Fuzzy logic can handle the uncertainty and ambiguity of input variables and is suitable for calculating the battery health score, especially when the input data has a certain degree of uncertainty or fluctuation. The formula can be:

[0220]

[0221] Through fuzzy rules (e.g., "when the battery voltage is high, the internal resistance is low, and the number of charge and discharge cycles is small, the dynamic health score is good"), input variables such as voltage, internal resistance, number of charge and discharge cycles, and historical health score are transformed into the health score. This method can effectively handle the ambiguity and uncertainty of battery state parameters, thereby improving the flexibility and adaptability of prediction.

[0222] The health state of the battery is affected by multiple objectives (such as battery capacity, internal resistance, temperature, historical health score, and number of charge and discharge cycles, etc.), and there is usually a certain trade-off relationship between these objectives. 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, through the optimization algorithm, the health score of the battery is calculated according to 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 an appropriate dynamic balancing current value. A neural network can be used to model the complex non-linear relationship between the battery state and the balancing current. The formula is:

[0226]

[0227] The fuzzy logic model can handle the ambiguity and uncertainty of 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 an 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 is low and the temperature Tcell If it is relatively high, reduce the equalization current;

[0231] If the battery health score is relatively low and the voltage V cell is high, increase the equalization current.

[0232] This method performs fuzzy inference on the battery state through a rule base, thereby dynamically adjusting the equalization current.

[0233] The 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. Different combinations of battery state parameters can be explored through the genetic algorithm to find the optimal equalization current output. The formula is:

[0234]

[0235] The genetic algorithm can continuously adjust the equalization current through the evolution process to optimize the battery health state to the greatest extent and extend its life cycle.

[0236] Embodiment 2

[0237] Figure 2 is a schematic flow chart of the battery state dynamic monitoring and equalization method provided by the second embodiment of the present application. As Figure 2 shown, it specifically includes the following steps:

[0238] S201, Obtain the real-time state data of each single battery in the battery pack, determine the target thermal management strategy according to the temperature data and the preset multi-level thermal management method, and perform thermal management on each single battery in the battery pack according to the target thermal management strategy.

[0239] The preset multi-level thermal management method may refer to taking different thermal management strategies and means according to different temperature ranges or different working states of the battery pack during the battery pack thermal management process. Its main purpose is to control the thermal state of the battery according to the temperature change of the battery, 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, enable heating devices such as heating films or heating tapes to ensure that the battery can work normally. Medium-temperature management: When the battery working temperature is within the safe range, use natural heat dissipation or air cooling, liquid cooling, etc. for thermal management. High-temperature protection: When the battery temperature exceeds the set maximum temperature threshold, start the cooling system such as air cooling or liquid cooling system, or take thermal unloading measures (such as transferring excessive current to other battery units) to reduce the battery temperature. Dynamic thermal regulation: Dynamically adjust the thermal management strategy according to the working state of the battery (such as charge and discharge state, battery usage frequency, temperature change, etc.) to ensure that the battery temperature can be maintained within the safe range under different working conditions.

[0240] The target thermal management strategy can refer to the specific thermal management operations determined and implemented according to the real-time temperature data of each single battery in the battery pack and the preset multi-level thermal management method. It includes adopting different temperature control strategies for different single batteries in the battery pack. Specifically, it can 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] The real-time temperature data can be obtained from the sensors of each single battery in the battery pack. According to the real-time temperature data, use the preset multi-level thermal management method to judge whether the battery is in a low-temperature, medium-temperature or high-temperature state, and select the corresponding thermal management strategy 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 and other methods can be adopted 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, start or adjust the heating, cooling or heat dissipation system to ensure that the battery temperature is kept within a safe range. During the operation of the battery, the change of the battery temperature is monitored in real time and adjusted according to the preset thermal management strategy. If the load of the battery pack changes (such as the charge and discharge process), the thermal management strategy is adjusted accordingly.

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

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

[0244] S204, obtain the maximum voltage data and the minimum voltage data of each single battery in the battery pack, determine the voltage difference according to the maximum voltage data and the minimum voltage data, and calculate the initial equalization time of the first target equalization battery according to the voltage difference, the initial equalization current and the preset initial equalization time calculation formula.

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

[0246] S206. Send a networking request to the cloud platform. If a successful status code sent by the cloud platform is received, generate a first equalization data set based on the real-time status data, the initial health score, the first target equalization battery, the initial equalization current, and the initial equalization time, and transmit the first equalization data set to the cloud platform.

[0247] In this embodiment, by dynamically adjusting the temperature management strategy, the safety risks caused by the battery being too hot or too cold are avoided, such as damage or fire caused by thermal runaway, overheating or overcooling. Dynamic thermal management can enable different cooling or heating schemes according to actual needs, avoid energy waste, and achieve an energy-saving effect.

[0248] Embodiment Three

[0249] Figure 3 is a schematic structural diagram of the battery state dynamic monitoring and equalization system provided in Embodiment Three of this application. As Figure 3 shown, it specifically includes:

[0250] A scoring module 301, configured to obtain the real-time status data of each single battery in the battery pack. If the real-time status data is the 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;

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

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

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

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

[0255] In the embodiment of the present application, the scoring module is used to obtain the real-time status data of each single battery in the battery pack. If the real-time status data is the 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; the equalization current determination module is used to, if there is an initial health score lower than a preset initial health score threshold, use the single battery corresponding to the initial health score lower than the preset initial health score threshold as the first target equalization battery, and calculate the initial equalization current of the first target equalization battery according to the initial health score, voltage data, temperature data, and a preset initial current equalization formula; wherein, the first target equalization battery is at least one; the health score calculation module is used to obtain the maximum voltage data and the minimum voltage data of each single battery in the battery pack, determine the voltage difference according to the maximum voltage data and the minimum voltage data, and calculate the initial equalization time of the first target equalization battery according to the voltage difference, the initial equalization current, and a preset initial health score calculation formula; the equalization module is used to control the equalization circuit to perform current equalization on the first target equalization battery according to the initial equalization current until the initial equalization time is reached; the data transmission module is used to send a networking request to the cloud platform. If a successful status code sent by the cloud platform is received, a first equalization data set is generated according to the real-time status data, the initial health score, the first target equalization battery, the initial equalization current, and the initial equalization time, and the first equalization data set is transmitted to the cloud platform. Through the above battery state dynamic monitoring and equalization system, by monitoring the state of each single battery in the battery pack in real time and intelligently calculating the equalization current and equalization time, it can dynamically adapt to the change of the battery state, optimize the battery health management, improve the charge and discharge efficiency, and extend the battery life.

[0256] The battery state dynamic monitoring and equalization system provided by the embodiment of the present application can achieve Figure 1 each process implemented by the method embodiment. To avoid repetition, it will not be elaborated here.

[0257] Embodiment 4

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

[0259] It should be noted that the electronic device in the embodiment of the present application includes the above-mentioned mobile electronic device and non-mobile electronic device.

[0260] Embodiment Five

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

[0262] Wherein, the processor is the 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 disc, etc.

[0263] It should be noted that in this article, the term "including", "comprising" or any other variant thereof is intended to cover a non-exclusive inclusion, so that a process, method, article or system including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or system. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or system including that element. In addition, it should be pointed out that the scope of the methods and systems in the embodiments 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 a reverse order according to the functions involved. For example, the described methods may be performed in an order different from that described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0264] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented 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, in essence, or the part that contributes to the prior art can be embodied in the form of a computer software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions for causing a terminal (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present application.

[0265] The embodiments of the present application have been described above in conjunction with the accompanying drawings. However, the present application is not limited to the above specific implementation manners. The above specific implementation manners are merely illustrative and not restrictive. Under the inspiration of the present application, those of ordinary skill in the art can also make many forms without departing from the purpose of the present application and the scope protected by the claims, and all of them belong to the protection scope of the present application.

[0266] The above is only the preferred embodiment of the present application and the technical principles applied. The present application is not limited to the specific embodiments described herein. Various obvious changes, re-adjustments, and substitutions that can be made by those skilled in the art will not depart from the protection scope of the present application. Therefore, although the present application has been described in more detail through the above embodiments, the present application is not limited to the above embodiments. Without departing from the concept of the present application, it can also include more other equivalent embodiments, and the scope of the present application is determined by the scope of the claims.

Claims

1. A method for dynamically monitoring and equalizing the state of a battery, characterized in that, The method is executed by an equalizer, and the method includes: Obtaining real-time state data of each single battery in the battery pack. If the real-time state data is initial state data, calculating an initial health score of each single battery according to the real-time state data and a preset initial health score calculation formula; wherein, the real-time state 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, regarding the single battery corresponding to the initial health score lower than the preset initial health score threshold as a first target equalization battery, and calculating an initial equalization current of the first target equalization battery according to the initial health score, voltage data, temperature data, and a preset initial current equalization formula; wherein, the first target equalization battery is at least one; Obtaining the maximum voltage data and the minimum voltage data of each single battery in the battery pack, determining a voltage difference according to the maximum voltage data and the minimum voltage data, and calculating an initial equalization time of the first target equalization battery according to the voltage difference, the initial equalization current, and a preset initial equalization time calculation formula; Controlling an equalization circuit to perform current equalization on the first target equalization battery according to the initial equalization current until the initial equalization time is reached; Sending a networking request to the cloud platform. If a successful status code sent by the cloud platform is received, generating a first equalization data set according to the real-time state data, initial health score, first target equalization battery, initial equalization current, and initial equalization time, and transmitting the first equalization data set to the cloud platform; If a preset data collection time point is reached, sending a networking request to the cloud platform. If a successful status code sent by the cloud platform is received, sending a historical health score acquisition request to the cloud platform; If the historical health score sent by the cloud platform is received, obtaining the charge and discharge times of the battery pack, updating the real-time state data of each single battery in the battery pack, and calculating a dynamic health score of each single battery according to the real-time state data, charge and discharge times, historical health score, and a preset dynamic health score calculation formula; If there is a dynamic health score lower than a preset dynamic health score threshold, regarding the single battery corresponding to the dynamic health score lower than the preset dynamic health score threshold as a second target equalization battery, and calculating a dynamic equalization current of the second target equalization battery according to the dynamic health score, voltage data, temperature data, and a preset dynamic current equalization formula; wherein, the second target equalization battery is at least one; Updating the maximum voltage data and the minimum voltage data of each single battery in the battery pack, updating the voltage difference according to the maximum voltage data and the minimum voltage data, and calculating a dynamic equalization time of the second target equalization battery according to the voltage difference, the dynamic equalization current, and a preset dynamic equalization time calculation formula; Controlling an equalization circuit to perform current equalization on the second target equalization battery according to the dynamic equalization current until the dynamic equalization time is reached; Generate a second equalization dataset based on the real-time status data, dynamic health score, second target balanced battery, dynamic equalization current, and dynamic equalization time, and transmit the second equalization dataset to the cloud platform.

2. The battery state dynamic monitoring and equalization method 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 equalization formula is: I balance = f(V cell , T cell , H cell ); where I balance is the initial equilibrium current; f() is a function that combines the voltage data, temperature data, and initial health score of the battery to output a suitable initial equilibrium current value; the preset calculation formula for the initial equilibrium time is: Among them, t balance is the initial equilibrium time; V diff is the voltage difference; k is a preset equilibrium efficiency factor.

3. The battery state dynamic monitoring and equalization method according to claim 1, characterized in that The preset dynamic health score calculation formula is: wherein, is the dynamic 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, 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 charge and discharge times; The preset dynamic current equalization formula is: Among them, is the dynamic balance current; f() is a function that combines the voltage data, temperature data, and dynamic health score of the battery to output a suitable initial balance current value; The preset dynamic equalization time calculation formula is: Among them, is the dynamic equilibrium time; V diff is the voltage difference; k is a preset equilibrium efficiency factor.

4. The battery state dynamic monitoring and equalization method according to claim 1, characterized in that, After obtaining the real-time status data of each single battery in the battery pack, the method further includes: Determine a target thermal management strategy according to the temperature data and the preset multi-level thermal management method, and perform thermal management on each single battery in the battery pack according to the target thermal management strategy.

5. The battery state dynamic monitoring and equalization method according to claim 1, characterized in that, After obtaining the real-time status data of each single battery in the battery pack, the method further includes: Input the real-time status data into a preset fault detection model to determine the fault score of each single battery; If there is a fault score exceeding the preset fault score threshold, regard the single battery corresponding to the fault score exceeding the preset fault score threshold as the target fault battery, and output the fault type of the target fault battery through the preset fault detection model; where the number of target fault batteries is at least one; If the fault type is a minor risk type, output a self-repair solution through the preset fault detection model, and perform self-repair on the target fault battery according to the self-repair solution; Correspondingly, after outputting the fault type through the preset fault detection model, the method further includes: If the fault type is a major risk type, perform dynamic isolation on the target fault battery and send a major risk message to the control center.

6. The battery state dynamic monitoring and equalization method according to claim 5, characterized in that The training process of the preset fault detection model includes: Obtain historical score records, and determine the first historical status data and historical scores of each historical battery according to the historical score records; Obtain historical fault records, and determine the second historical status data and historical fault types of each historical fault battery according to the historical fault records, and determine the third historical status data of the minor risk type and the corresponding historical self-repair solution; where the historical fault types include minor risk types and major risk types; Label the score tags of the first historical status data according to the historical scores; Label the fault type tags of the second historical status data according to the historical fault types; Label the self-repair solution tags of the third historical status data according to the historical self-repair solutions; Construct a fault detection model, and train the score branch of the fault detection model according to the first historical status data and the score tags; Train the fault classification branch of the fault detection model according to the second historical status data and the fault type tags; Train the self-repair solution output branch of the fault detection model according to the third historical status data and the self-repair solution tags.

7. A dynamic battery state monitoring and equalization system, characterized in that, The system is configured in an equalizer, and the system includes: The scoring module is used to obtain the real-time status data of each single battery in the battery pack. If the real-time status data is the initial status data, the initial health scores of each single battery are calculated according to the real-time status data and a preset initial health score calculation formula. Among them, the real-time status data includes voltage data, current data, temperature data, and internal resistance data; The 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 battery 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. Among them, the first target balancing battery is at least one; The health score calculation module is used to obtain the maximum voltage data and the minimum voltage data of each single battery 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; The balancing module is used to control the balancing circuit to perform current balancing on the first target balancing battery according to the initial balancing current until the initial balancing time is reached; The data transmission module is used to send a networking request to the cloud platform. If a successful status code sent by the cloud platform is received, a first balancing data set is generated according to the real-time status data, initial health score, first target balancing battery, initial balancing current, and initial balancing time, and the first balancing data set is transmitted to the cloud platform; The system is also used for: If a preset data collection time point is reached, send a networking request to the cloud platform. If a successful status code sent by the cloud platform is received, send a historical health score acquisition request to the cloud platform; If the historical health score sent by the cloud platform is received, obtain the charge and discharge times of the battery pack, update the real-time status data of each single battery in the battery pack, and calculate the dynamic health scores of each single battery according to the real-time status data, charge and discharge times, historical health score, and a preset dynamic health score calculation formula; If there is a dynamic health score lower than a preset dynamic health score threshold, take the single battery corresponding to the dynamic health score lower than the preset dynamic health score threshold as the second target balancing battery, and calculate the dynamic balancing current of the second target balancing battery according to the dynamic health score, voltage data, temperature data, and a preset dynamic current balancing formula. Among them, the second target balancing battery is at least one; Update the maximum voltage data and the minimum voltage data of each single battery in the battery pack, update the voltage difference according to the maximum voltage data and the minimum voltage data, and calculate the dynamic balancing time of the second target balancing battery according to the voltage difference, the dynamic balancing current, and a preset dynamic balancing time calculation formula; Control the balancing circuit to perform current balancing on the second target balancing battery according to the dynamic balancing current until the dynamic balancing time is reached; Generate a second equalization data set based on the real-time status data, dynamic health score, second target balanced battery, dynamic equalization current, and dynamic equalization time, and transmit the second equalization data set to the cloud platform.

8. An electronic device, characterized in that, It includes a processor, a memory, and a program or instruction stored on the memory and executable on the processor. When the program or instruction is executed by the processor, it implements the steps of the battery state dynamic monitoring and equalization method according to any one of claims 1-6.

9. A readable storage medium, characterized in that, A program or instruction is stored on the readable storage medium. When the program or instruction is executed by the processor, it implements the steps of the battery state dynamic monitoring and equalization method according to any one of claims 1-6.

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