A battery management system for a lithium battery pack and a fault warning method thereof

Abstract

The present application provides a battery management system for a lithium battery pack and a fault warning method thereof, which relate to the technical field of battery fault warning, monitor the load state of each lithium battery cell in the lithium battery pack; extract the corresponding load-temperature curve from the load state of the lithium battery cell, and determine the thermal load entropy of the lithium battery cell according to the load-temperature curve; obtain the limit load current of each lithium battery cell, and determine the load failure coefficient of each lithium battery cell based on the thermal load entropy and the limit load current, and determine the fault warning factor of the lithium battery pack under the limit load state through all the load failure coefficients; send a fault warning to a monitoring center based on the load failure coefficient of each lithium battery cell and the fault warning factor of the lithium battery pack under the limit load state. The present application can accurately identify the faulty lithium battery cell in the lithium battery pack and quantify the risk level of the faulty lithium battery cell, so as to improve the fault warning performance of the battery management system.

Description

Technical Field

[0001] The present application relates to the technical field of battery failure warning, and more specifically, to a battery management system for a lithium battery pack and a failure warning method thereof. Background Art

[0002] The battery management system (BMS) of lithium battery packs is a key system for monitoring, managing and protecting battery packs. Its core function is to monitor the working status of the battery pack in real time, including parameters such as voltage, current, temperature, and charge and discharge status, and to ensure that the battery works within a safe range through a series of algorithms and control strategies to avoid overcharging, overdischarging, overcurrent, overtemperature and other situations that may cause failures or safety hazards. The main goal of the battery management system is to reduce operating costs while ensuring battery pack safety, extending battery life, improving energy efficiency and providing accurate status estimation.

[0003] In order to prevent lithium battery packs from unexpected failures during use, the battery management system will integrate a fault warning function, which is based on continuous monitoring and analysis of battery operating parameters, and determines whether the battery pack is in an abnormal state through real-time data. For example, if the voltage of a lithium battery cell suddenly drops, the battery management system can immediately identify this situation and issue an alarm, indicating that there may be a short circuit or other abnormal situation. However, in the prior art, lithium battery packs are usually composed of hundreds or even thousands of lithium battery cells. Even if each lithium battery cell has the same specifications when it leaves the factory, the performance of the lithium battery will decline inconsistently over time, which leads to the battery management system needing to have strong balancing management capabilities and fault warning capabilities to ensure the balanced charging and discharging of each lithium battery cell, and to accurately locate the faulty lithium battery cell in the lithium battery pack. Therefore, how to accurately identify the faulty lithium battery cell in the lithium battery pack and quantify the risk level of the faulty lithium battery cell to improve the fault warning performance of the battery management system is a difficult problem faced by the industry. Summary of the invention

[0004] The present application provides a battery management system for a lithium battery pack and a fault warning method thereof, which can accurately identify faulty lithium battery cells in a lithium battery pack and quantify the risk level of the faulty lithium battery cells to improve the fault warning performance of the battery management system.

[0005] In a first aspect, the present application provides a fault warning method for a lithium battery pack, the warning method comprising the following steps:

[0006] Monitor the load status of each lithium battery unit in the lithium battery pack;

[0007] For each lithium battery cell, extracting a load-temperature curve corresponding to the lithium battery cell in the load state of the lithium battery cell, determining a thermal load entropy of the lithium battery cell according to the load-temperature curve, and then obtaining a thermal load entropy of each lithium battery cell;

[0008] Obtaining the limit load current of each lithium battery unit in the lithium battery pack, determining the load failure coefficient of each lithium battery unit based on the corresponding thermal load entropy and the corresponding limit load current, and determining the fault warning factor of the lithium battery pack under the limit load state through all the load failure coefficients;

[0009] A fault warning of the lithium battery pack is sent to a monitoring center according to the load failure coefficient of each lithium battery unit and the fault warning factor of the lithium battery pack under the extreme load state.

[0010] In this embodiment, the load state includes current data, voltage data and lithium battery temperature data of the lithium battery unit.

[0011] In this embodiment, the load state of each lithium battery unit in the lithium battery pack is monitored by a monitoring sensor in the battery management system.

[0012] In this embodiment, extracting the load-temperature curve corresponding to the lithium battery cell in the load state of the lithium battery cell specifically includes:

[0013] Acquiring current data, voltage data and lithium battery temperature data of the lithium battery unit in a load state of the lithium battery unit;

[0014] Determine the load data of the lithium battery unit through the current data and the voltage data;

[0015] Performing data alignment on the load data and the lithium battery temperature data to obtain load-temperature data of the lithium battery unit;

[0016] Generate a load-temperature scatter plot of the lithium battery unit based on the load-temperature data;

[0017] The load-temperature data scatter plot is fitted to obtain a load-temperature curve corresponding to the lithium battery unit.

[0018] In this embodiment, determining the thermal load entropy of the lithium battery unit according to the load-temperature curve specifically includes:

[0019] Segmenting the load-temperature curve to obtain a plurality of load-temperature segments;

[0020] Determine the heat increase rate of the lithium battery corresponding to each load-temperature segment;

[0021] The thermal load entropy of the lithium battery unit is determined according to the heat increase rate of the lithium battery corresponding to all load-temperature sections.

[0022] In this embodiment, the limit carrying current of each lithium battery unit in the lithium battery pack is obtained from the database of the battery management system.

[0023] In this embodiment, determining the load failure coefficient of each lithium battery unit based on the corresponding heat load entropy and the corresponding limit load current specifically includes:

[0024] For each lithium battery cell, obtain the thermal load entropy and the corresponding limit carrying current corresponding to the lithium battery cell;

[0025] Obtaining the current carrying current of the lithium battery unit;

[0026] The load failure coefficient of the lithium battery unit is determined according to the current load current, the heat load entropy and the limit load current, and then the load failure coefficient of each lithium battery unit is obtained.

[0027] In this embodiment, sending the fault warning of the lithium battery pack to the monitoring center according to the load failure coefficient of each lithium battery unit and the fault warning factor of the lithium battery pack under the extreme load state specifically includes:

[0028] For each lithium battery cell, when the load failure coefficient of the lithium battery cell exceeds a set load failure threshold, the lithium battery cell is determined to be a faulty lithium battery cell;

[0029] When the fault warning factor of the lithium battery pack under the extreme load state exceeds the set warning value, the lithium battery pack is determined to be a faulty lithium battery pack;

[0030] When there is a faulty lithium battery cell in the lithium battery pack or the lithium battery pack is a faulty lithium battery pack, a fault warning of the lithium battery pack is sent to a monitoring center.

[0031] In this embodiment, sending the fault warning of the lithium battery pack to the monitoring center refers to sending the fault monitoring report of each faulty lithium battery unit and the faulty lithium battery pack in the lithium battery pack to the monitoring center.

[0032] In a second aspect, the present application provides a battery management system for a lithium battery pack, which is used to execute a fault warning method for a lithium battery pack, and the battery management system includes:

[0033] Lithium battery monitoring module, used to monitor the load status of each lithium battery unit in the lithium battery pack;

[0034] A thermal load extraction module is used to extract a load-temperature curve corresponding to each lithium battery cell in the load state of the lithium battery cell, determine the thermal load entropy of the lithium battery cell according to the load-temperature curve, and then obtain the thermal load entropy of each lithium battery cell;

[0035] A fault quantification module is used to obtain the limit load current of each lithium battery unit in the lithium battery pack, determine the load fault coefficient of each lithium battery unit based on the corresponding thermal load entropy and the corresponding limit load current, and determine the fault warning factor of the lithium battery pack under the limit load state through all the load fault coefficients;

[0036] The fault warning module is used to send a fault warning of the lithium battery pack to a monitoring center according to the load failure coefficient of each lithium battery unit and the fault warning factor of the lithium battery pack under the extreme load state.

[0037] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects:

[0038] By monitoring the load status of each lithium battery cell in the lithium battery pack; for each lithium battery cell, extracting the load-temperature curve corresponding to the lithium battery cell in the load status of the lithium battery cell, determining the thermal load entropy of the lithium battery cell according to the load-temperature curve, and then obtaining the thermal load entropy of each lithium battery cell; obtaining the limit load current of each lithium battery cell in the lithium battery pack, determining the load failure coefficient of each lithium battery cell based on the corresponding thermal load entropy and the corresponding limit load current, and determining the fault warning factor of the lithium battery pack under the limit load status through all the load failure coefficients; sending the fault warning of the lithium battery pack to the monitoring center according to the load failure coefficient of each lithium battery cell and the fault warning factor of the lithium battery pack under the limit load status.

[0039] It can be seen that in the present application, the faulty lithium battery cells in the lithium battery pack can be accurately identified, and the risk level of the faulty lithium battery cells can be quantified; wherein, by extracting the load-temperature curve of each lithium battery cell, the load-temperature curve is used to capture the temperature response of the lithium battery cell under different working loads, thereby identifying cells that exhibit different thermal response characteristics from other battery cells, and the process of determining the thermal load entropy of the lithium battery cell according to the load-temperature curve quantifies the degree of aging of the lithium battery cell; then, the battery failure risk is judged by combining the thermal load entropy with the limit load current, that is, the failure risk of the lithium battery cell is quantified by calculating the load failure coefficient of each lithium battery cell, thereby helping the battery management system to make a clear judgment on the risk level of each lithium battery cell, and determining the fault warning factor based on the load failure coefficient of each lithium battery cell, thereby realizing the prediction and warning of whether there is a potential risk for the overall battery pack system based on the failure risk of a single lithium battery cell, thereby providing the battery management system with potential fault warning information under the limit load state, thereby improving the fault warning performance of the battery management system.

[0040] In summary, the technical solution adopted in the present application can accurately identify faulty lithium battery cells in a lithium battery pack and quantify the risk level of the faulty lithium battery cells to improve the fault warning performance of the battery management system. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0042] Figure 1 is a flow chart of a fault warning method for a lithium battery pack provided in the present application;

[0043] Figure 2 is an exemplary flow chart for determining heat load entropy according to the present application;

[0044] Figure 3 is an exemplary flow chart for determining a load failure factor according to the present application;

[0045] Figure 4 It is a module structure diagram of the battery management system of the lithium battery pack provided in this application. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0047] The embodiment of the present application provides a battery management system for a lithium battery pack and a fault warning method thereof, the core of which is to monitor the load state of each lithium battery cell in the lithium battery pack; for each lithium battery cell, extract the load-temperature curve corresponding to the lithium battery cell in the load state of the lithium battery cell, determine the thermal load entropy of the lithium battery cell according to the load-temperature curve, and then obtain the thermal load entropy of each lithium battery cell; obtain the limit load current of each lithium battery cell in the lithium battery pack, determine the load failure coefficient of each lithium battery cell based on the corresponding thermal load entropy and the corresponding limit load current, and determine the fault warning factor of the lithium battery pack under the limit load state through all the load failure coefficients; send the fault warning of the lithium battery pack to the monitoring center according to the load failure coefficient of each lithium battery cell and the fault warning factor of the lithium battery pack under the limit load state. The above scheme can accurately identify the faulty lithium battery cells in the lithium battery pack and quantify the risk level of the faulty lithium battery cells to improve the fault warning performance of the battery management system.

[0048] Embodiment 1: In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. Figure 1 As shown in FIG. 1 , this figure is an exemplary flow chart of a fault warning method of a lithium battery pack according to this embodiment of the present application, and the warning method includes the following steps:

[0049] In step S1 , the load state of each lithium battery unit in the lithium battery pack is monitored.

[0050] It should be noted that, in the present application, the load state includes current data, voltage data and lithium battery temperature data of the lithium battery unit, and the data contained in the load state is a series of data arranged in chronological order.

[0051] In specific implementation, the load status of each lithium battery cell in the lithium battery pack can be monitored by the monitoring sensor in the battery management system; the charging and discharging current of the lithium battery pack and each lithium battery cell can be monitored in real time by the current sensor (usually a Hall effect sensor or) in the battery management system; the voltage of each lithium battery cell can be monitored in real time by the voltage sensor in the battery management system; the temperature of the lithium battery pack and each lithium battery cell can be monitored in real time by the temperature sensor (such as a thermocouple, thermistor or infrared temperature sensor) in the battery management system.

[0052] In step S2, for each lithium battery cell, a load-temperature curve corresponding to the lithium battery cell is extracted from the load state of the lithium battery cell, and the thermal load entropy of the lithium battery cell is determined according to the load-temperature curve, thereby obtaining the thermal load entropy of each lithium battery cell.

[0053] In this embodiment, the load-temperature curve corresponding to the lithium battery cell can be extracted from the load state of the lithium battery cell in the following manner, namely:

[0054] Acquiring current data, voltage data and lithium battery temperature data of the lithium battery unit in a load state of the lithium battery unit;

[0055] Determine the load data of the lithium battery unit through the current data and the voltage data;

[0056] Performing data alignment on the load data and the lithium battery temperature data to obtain load-temperature data of the lithium battery unit;

[0057] Generate a load-temperature scatter plot of the lithium battery unit based on the load-temperature data;

[0058] The load-temperature data scatter plot is fitted to obtain a load-temperature curve corresponding to the lithium battery unit.

[0059] In the specific implementation, first, the current data, voltage data and lithium battery temperature data of the lithium battery cell are obtained in the load state of the lithium battery cell, and the load data of the lithium battery cell is determined by the current data and voltage data, that is, the current data and the voltage data are multiplied in time sequence to obtain the load data of the lithium battery cell. For example, if there is a data sample of the load state:

[0060] Time series (unit: seconds): t = [1, 2, 3, 4, 5]

[0061] Current data (in amperes): I = [10, 12, 15, 18, 20]

[0062] Voltage data (unit: volt): V = [3.7, 3.6, 3.55, 3.5, 3.4]

[0063] Lithium battery temperature data (unit: Celsius): T = [25, 26, 28, 31, 35]

[0064] The load data of the lithium battery cell can be calculated according to the formula P=I×VP, so as to obtain the load data of the lithium battery cell: P = [37.0, 43.2, 53.25, 63.0, 68.0] (unit: watt); then, in order to ensure the consistency of the load data and the temperature data, it is necessary to synchronize the load data and the temperature data in time, that is, align the data, so that the load and temperature data at the same time point can be matched to generate a load-temperature pair, and the load-temperature data of the lithium battery cell can be obtained. For example, after time series alignment, the load and temperature can be matched to generate load-temperature data: [(37.0, 25), (43.2, 26), (53.25, 28), (63.0, 31), (68.0, 35)].

[0065] In addition, in a specific implementation, a load-temperature scatter plot of the lithium battery unit is generated based on the load-temperature data. The load-temperature scatter plot can help intuitively display the relationship between load and temperature. For example, the load-temperature scatter plot can be expressed as:

[0066] X-axis of the load-temperature scatter plot (load, in watts): [37.0, 43.2, 53.25, 63.0, 68.0]

[0067] Y axis of the load-temperature scatter plot (temperature, unit: Celsius): [25, 26, 28, 31, 35];

[0068] Finally, the load-temperature data scatter plot is fitted to obtain the load-temperature curve corresponding to the lithium battery cell. The fitting process can capture the trend relationship between load and temperature. The fitting method used in this application is linear fitting. For example, the slope and intercept can be calculated by the least squares method to obtain the load-temperature curve corresponding to the lithium battery cell. The above data can be used in this application to calculate the load-temperature curve corresponding to the lithium battery cell as T=0.14P+20.8T. The fitted load-temperature curve can more accurately describe the thermal response characteristics of the lithium battery cell under different load conditions. If the slope of the load-temperature curve is large, it indicates that the temperature of the lithium battery rises rapidly when the load increases, and there may be a risk of overheating. If the load-temperature curve is smoother, it indicates that the lithium battery maintains a relatively stable thermal state during load fluctuations, indicating that the thermal management of the lithium battery cell is good.

[0069] Preferably, in this embodiment, reference Figure 2 As shown, this figure is an exemplary flow chart of determining the thermal load entropy in an embodiment of the present application. In this embodiment, determining the thermal load entropy of the lithium battery unit according to the load-temperature curve can be implemented by the following steps:

[0070] First, in step S21, the load-temperature curve is segmented to obtain a plurality of load-temperature segments;

[0071] Then, in step S22, the heat increase rate of the lithium battery corresponding to each load-temperature segment is determined respectively;

[0072] Finally, in step S23, the thermal load entropy of the lithium battery unit is determined according to the heat increase rate of the lithium battery corresponding to all load-temperature sections.

[0073] In specific implementation, first, the entire load-temperature curve can be divided into multiple load-temperature segments. This division can be performed based on the load range or the temperature range. For example, the load-temperature curve can be divided into several equally spaced load segments or temperature segments, each segment representing the thermal response of the battery within a certain load or temperature range. For example, if there is a load-temperature curve of [(37.0, 25), (43.2, 26), (53.25, 28), (63.0, 31), (68.0, 35)], then multiple load-temperature segments can be divided into [37.0, 43.2], (43.2, 53.25] and (53.25, 68.0]; then, for each load-temperature segment, the heat increase rate of the lithium battery corresponding to the load-temperature segment can be calculated. The heat increase rate of the lithium battery represents the temperature change rate caused by the load change. In actual implementation, the heat increase rate of the lithium battery can be determined according to the following formula:

[0074]

[0075] in, represents the thermal increase rate of the lithium battery corresponding to the i-th load-temperature segment, and represent the final temperature and initial temperature in the i-th load-temperature segment, respectively. and Respectively represent the final load and initial load in the i-th load-temperature segment. The thermal increase rate of the lithium battery corresponding to each load-temperature segment can be obtained by the above method. For example, the thermal increase rate of the lithium battery corresponding to the first load-temperature segment = (26-25) / (43.2-37.0) = 0.1613; Finally, the thermal load entropy of the lithium battery unit can be determined according to the thermal increase rate of the lithium battery corresponding to all load-temperature segments. The thermal load entropy is used to measure the thermal change regularity of the lithium battery unit in the entire load range. The thermal load entropy can be determined according to the following formula:

[0076]

[0077] in, represents the thermal load entropy of the lithium battery cell, represents the thermal increase rate of the lithium battery corresponding to the i-th load-temperature segment, represents the sum of the heat increase rates of the lithium battery corresponding to all load-temperature sections, and N represents the number of load-temperature sections. For example, according to the data in this embodiment, it can be calculated that The value of is 0.1613+0.1990+0.4746=0.8349, so the thermal load entropy of the lithium battery cell can be calculated according to the above formula The value of is 0.9469; the higher the thermal load entropy, the more irregular the impact of load changes on battery temperature and the greater the volatility, which may reflect the aging of materials inside the lithium battery unit or poor thermal management. On the contrary, a lower thermal load entropy indicates that the thermal response of the lithium battery unit under different loads is relatively stable.

[0078] It should be noted that in the present application, for each lithium battery cell, the thermal load entropy of each lithium battery cell can be obtained by the above method, which will not be repeated here.

[0079] In addition, it should be noted that by extracting the load-temperature curve of each lithium battery cell, the temperature response of the lithium battery cell under different workloads can be captured. By calculating the thermal load entropy of each lithium battery cell, the aging process of the lithium battery cell can be quantified, and it helps to identify those cells that exhibit different thermal response characteristics from other battery cells, thereby avoiding chain failures or thermal runaway of the entire lithium battery pack due to the failure of a single lithium battery cell.

[0080] In step S3, the limit load current of each lithium battery cell in the lithium battery pack is obtained, and the load failure coefficient of each lithium battery cell is determined based on the corresponding thermal load entropy and the corresponding limit load current. The fault warning factor of the lithium battery pack under the limit load state is determined through all the load failure coefficients.

[0081] In specific implementation, the limit carrying current of each lithium battery cell in the lithium battery pack is obtained in the database of the battery management system; the limit carrying current of each lithium battery cell in the lithium battery pack can be extracted through SQL query, wherein the limit carrying current represents the maximum load current of the lithium battery cell. The database in the battery management system includes the following fields:

[0082] Lithium battery unit ID: ID that uniquely identifies each lithium battery unit (Primary Key).

[0083] Lithium battery model / specification: Indicates the specification information of the battery unit, such as capacity, voltage level, etc.

[0084] Limit carrying current: stores the limit carrying current of each lithium battery unit (unit: A).

[0085] Lithium battery cell status: current working status of the battery cell (for example: charging, discharging, standby, etc.).

[0086] Update timestamp: records the last update time of the data to ensure real-time performance.

[0087] Preferably, in this embodiment, reference Figure 3 As shown, this figure is an exemplary flow chart for determining the load failure coefficient in an embodiment of the present application. In this embodiment, the load failure coefficient of each lithium battery cell is determined based on the corresponding thermal load entropy and the corresponding limit load current, which can be implemented by the following steps:

[0088] First, in step S31, for each lithium battery cell, the thermal load entropy and the corresponding limit carrying current corresponding to the lithium battery cell are obtained;

[0089] Then, in step S32, the current carrying current of the lithium battery unit is obtained;

[0090] Finally, in step S33, the load failure coefficient of the lithium battery unit is determined according to the current load current, the heat load entropy and the limit load current, and then the load failure coefficient of each lithium battery unit is obtained.

[0091] In specific implementation, first, for each lithium battery cell, the thermal load entropy and the corresponding limit carrying current corresponding to the lithium battery cell are obtained, and the thermal load entropy corresponding to the lithium battery cell can be obtained by the method in step S2; then, the current sensor in the battery management system can be used for real-time acquisition to obtain the current carrying current of the lithium battery cell; finally, the carrying failure coefficient of the lithium battery cell can be determined according to the current carrying current, the thermal load entropy and the limit carrying current, that is, the product of the ratio of the current carrying current to the limit carrying current and the thermal load entropy is used as the carrying failure coefficient of the lithium battery cell. The carrying failure coefficient is a measure of the failure risk of the lithium battery cell under the current load state, reflecting the balance between its thermal management capability and carrying capacity. The carrying failure coefficient of each lithium battery cell can be obtained by the above method. For example, assuming that the thermal load entropy corresponding to the lithium battery cell is 0.9469, the limit carrying current (unit: A) corresponding to the lithium battery cell is 50, and the current carrying current (unit: A) of the lithium battery cell is 30, then according to the above formula, the carrying failure coefficient of the lithium battery cell can be calculated to be 0.5681.

[0092] In addition, in the specific implementation, in the present application, the thermal load entropy and the limit load current in the historical experiments and the manually assessed load failure coefficient can be used as neural network training samples, and a single hidden layer neural network can be used for training and learning. The thermal load entropy and the limit load current can be input into the trained neural network model for prediction, thereby obtaining the load failure coefficient of each lithium battery unit.

[0093] In this embodiment, the fault warning factor of the lithium battery pack under the extreme load state is determined by all the load failure coefficients, and the standard deviation of all the load failure coefficients is used as the fault warning factor of the lithium battery pack under the extreme load state; for example, if the load failure coefficients of each lithium battery unit in the lithium battery pack are [0.5681, 0.8018, 0.6316] respectively, then it can be calculated that the value of the fault warning factor of the lithium battery pack under the extreme load state is 0.0985, and the fault warning factor is the overall risk assessment indicator of the entire lithium battery pack under the extreme load state, and the fault warning factor can be used to trigger the fault warning mechanism.

[0094] It should be noted that by combining the thermal load entropy with the limiting load current, it is possible to more accurately identify whether the lithium battery cell is in an overheated or overloaded state, and by calculating the load failure coefficient of each lithium battery cell, the failure risk of the lithium battery cell can be quantified, which can help the battery management system make a clear judgment on the risk level of each lithium battery cell. By determining the fault warning factor based on the load failure coefficient of each lithium battery cell, a comprehensive health status assessment indicator can be provided for the battery management system, and potential fault warning information can be provided for the battery management system under the limit load state.

[0095] In step S4, a fault warning of the lithium battery pack is sent to a monitoring center according to the load failure coefficient of each lithium battery unit and the fault warning factor of the lithium battery pack under the extreme load state.

[0096] In this embodiment, the following method can be used to send the fault warning of the lithium battery pack to the monitoring center according to the load failure coefficient of each lithium battery unit and the fault warning factor of the lithium battery pack under the extreme load state, namely:

[0097] For each lithium battery cell, when the load failure coefficient of the lithium battery cell exceeds a set load failure threshold, the lithium battery cell is determined to be a faulty lithium battery cell;

[0098] When the fault warning factor of the lithium battery pack under the extreme load state exceeds the set warning value, the lithium battery pack is determined to be a faulty lithium battery pack;

[0099] When there is a faulty lithium battery cell in the lithium battery pack or the lithium battery pack is a faulty lithium battery pack, a fault warning of the lithium battery pack is sent to a monitoring center.

[0100] In the specific implementation, first, each lithium battery cell is monitored in real time to obtain the load failure coefficient of each lithium battery cell. The battery management system pre-sets a threshold value of the load failure coefficient, namely, the load failure threshold. When the load failure coefficient of the lithium battery cell exceeds the load failure threshold, it indicates that the lithium battery cell has an abnormal working state, such as overload or thermal runaway risk, and the lithium battery cell is judged to be a faulty lithium battery cell; then, a warning value can be set according to historical experiments and data analysis. When the fault warning factor of the lithium battery pack under the extreme load state exceeds the set warning value, it indicates that the lithium battery pack as a whole may be in a high risk state of overload or thermal runaway, and the lithium battery pack is judged to be a faulty lithium battery pack; finally, when the load failure coefficient of any lithium battery cell exceeds the set threshold, it is immediately judged. The unit is determined to be a faulty lithium battery unit and a fault warning mechanism is triggered. When the fault warning factor of the lithium battery pack exceeds the set warning value, the entire lithium battery pack is determined to be a faulty lithium battery pack. Even if individual units do not exceed the limit individually, the system will issue a warning. Once a single lithium battery unit or the entire battery pack is detected to have a fault, the battery management system will send a fault warning for the lithium battery pack to the monitoring center through the communication module. Sending a fault warning for the lithium battery pack to the monitoring center refers to sending a fault monitoring report of each faulty lithium battery unit and faulty lithium battery pack in the lithium battery pack to the monitoring center. The fault monitoring report includes the identification of the faulty lithium battery unit and its specific fault information (such as load failure coefficient, current load current, thermal load entropy, etc.), the fault warning factor of the overall lithium battery pack and detailed information on its exceeding the warning value.

[0101] It should be noted that by real-time monitoring of the load failure coefficient and the fault warning factor, the battery management system can issue warnings in time to avoid serious lithium battery damage or safety accidents.

[0102] It can be seen that in the present application, the faulty lithium battery cells in the lithium battery pack can be accurately identified, and the risk level of the faulty lithium battery cells can be quantified; wherein, by extracting the load-temperature curve of each lithium battery cell, the load-temperature curve is used to capture the temperature response of the lithium battery cell under different working loads, thereby identifying cells that exhibit different thermal response characteristics from other battery cells, and the process of determining the thermal load entropy of the lithium battery cell according to the load-temperature curve quantifies the degree of aging of the lithium battery cell; then, the battery failure risk is judged by combining the thermal load entropy with the limit load current, that is, the failure risk of the lithium battery cell is quantified by calculating the load failure coefficient of each lithium battery cell, thereby helping the battery management system to make a clear judgment on the risk level of each lithium battery cell, and determining the fault warning factor based on the load failure coefficient of each lithium battery cell, thereby realizing the prediction and warning of whether there is a potential risk for the overall battery pack system based on the failure risk of a single lithium battery cell, thereby providing the battery management system with potential fault warning information under the limit load state, thereby improving the fault warning performance of the battery management system.

[0103] In summary, the technical solution adopted in the present application can accurately identify faulty lithium battery cells in a lithium battery pack and quantify the risk level of the faulty lithium battery cells to improve the fault warning performance of the battery management system.

[0104] Embodiment 2: This application provides a battery management system for a lithium battery pack, referring to Figure 4 As shown, this figure is a schematic diagram of a battery management system according to this embodiment of the present application, and the battery management system includes:

[0105] The lithium battery monitoring module 100 is used to monitor the load status of each lithium battery unit in the lithium battery pack;

[0106] The thermal load extraction module 200 is used to extract the load-temperature curve corresponding to each lithium battery cell in the load state of the lithium battery cell, determine the thermal load entropy of the lithium battery cell according to the load-temperature curve, and then obtain the thermal load entropy of each lithium battery cell;

[0107] A fault quantification module 300 is used to obtain the limit load current of each lithium battery unit in the lithium battery pack, determine the load fault coefficient of each lithium battery unit based on the corresponding thermal load entropy and the corresponding limit load current, and determine the fault warning factor of the lithium battery pack under the limit load state through all the load fault coefficients;

[0108] The fault warning module 400 is used to send a fault warning of the lithium battery pack to a monitoring center according to the load failure coefficient of each lithium battery unit and the fault warning factor of the lithium battery pack under the extreme load state.

[0109] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0110] A person skilled in the art may understand that all or part of the steps in the various methods of the above embodiments may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, the storage medium including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically-erasable programmable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0111] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

Claims

1. A fault warning method for a lithium battery pack, characterized in that: The early warning method comprises the following steps: Monitor the load status of each lithium battery unit in the lithium battery pack; For each lithium battery cell, extracting a load-temperature curve corresponding to the lithium battery cell in the load state of the lithium battery cell, determining a thermal load entropy of the lithium battery cell according to the load-temperature curve, and then obtaining a thermal load entropy of each lithium battery cell; Obtaining the limit load current of each lithium battery unit in the lithium battery pack, determining the load failure coefficient of each lithium battery unit based on the corresponding thermal load entropy and the corresponding limit load current, and determining the fault warning factor of the lithium battery pack under the limit load state through all the load failure coefficients; Sending a fault warning of the lithium battery pack to a monitoring center according to the load failure coefficient of each lithium battery unit and the fault warning factor of the lithium battery pack under the extreme load state; Wherein, determining the thermal load entropy of the lithium battery unit according to the load-temperature curve specifically includes: Segmenting the load-temperature curve to obtain a plurality of load-temperature segments; Determine the heat increase rate of the lithium battery corresponding to each load-temperature segment; The thermal load entropy of the lithium battery unit is determined according to the heat increase rate of the lithium battery corresponding to all load-temperature sections; wherein the heat increase rate of the lithium battery is determined according to the following formula: in, represents the thermal increase rate of the lithium battery corresponding to the i-th load-temperature segment, and represent the final temperature and initial temperature in the i-th load-temperature segment, respectively. and represent the final load and initial load in the i-th load-temperature section, respectively, wherein the heat load entropy is determined according to the following formula: in, represents the thermal load entropy of the lithium battery cell, represents the thermal increase rate of the lithium battery corresponding to the i-th load-temperature segment, It represents the sum of the thermal increase rates of lithium batteries corresponding to all load-temperature sections, and N represents the number of load-temperature sections; The method of determining the load failure coefficient of each lithium battery unit based on the corresponding heat load entropy and the corresponding limit load current specifically includes: For each lithium battery cell, obtain the thermal load entropy and the corresponding limit carrying current corresponding to the lithium battery cell; Obtaining the current carrying current of the lithium battery unit; Determine the load failure coefficient of the lithium battery unit according to the current load current, the thermal load entropy and the limit load current, and then obtain the load failure coefficient of each lithium battery unit, wherein the product of the ratio of the current load current to the limit load current and the thermal load entropy is used as the load failure coefficient of the lithium battery unit; The method of determining the fault warning factor of the lithium battery pack under the extreme load state through all the load failure coefficients is to use the standard deviation of all the load failure coefficients as the fault warning factor of the lithium battery pack under the extreme load state.

2. A lithium battery pack fault warning method as claimed in claim 1, characterized in that: The load state includes current data, voltage data and lithium battery temperature data of the lithium battery unit.

3. A lithium battery pack fault warning method as claimed in claim 1, characterized in that: The load status of each lithium battery unit in the lithium battery pack is monitored by the monitoring sensor in the battery management system.

4. A lithium battery pack fault warning method as claimed in claim 1, characterized in that: Extracting the load-temperature curve corresponding to the lithium battery cell in the load state of the lithium battery cell specifically includes: Acquiring current data, voltage data and lithium battery temperature data of the lithium battery unit in a load state of the lithium battery unit; Determine the load data of the lithium battery unit through the current data and the voltage data; Performing data alignment on the load data and the lithium battery temperature data to obtain load-temperature data of the lithium battery unit; Generate a load-temperature scatter plot of the lithium battery unit based on the load-temperature data; The load-temperature data scatter plot is fitted to obtain a load-temperature curve corresponding to the lithium battery unit.

5. A lithium battery pack fault warning method as claimed in claim 1, characterized in that: The limit carrying current of each lithium battery cell in the lithium battery pack is obtained from the database of the battery management system.

6. A lithium battery pack fault warning method as claimed in claim 1, characterized in that: Sending a fault warning of the lithium battery pack to a monitoring center according to the load failure coefficient of each lithium battery unit and the fault warning factor of the lithium battery pack under the extreme load state specifically includes: For each lithium battery cell, when the load failure coefficient of the lithium battery cell exceeds a set load failure threshold, the lithium battery cell is determined to be a faulty lithium battery cell; When the fault warning factor of the lithium battery pack under the extreme load state exceeds the set warning value, the lithium battery pack is determined to be a faulty lithium battery pack; When there is a faulty lithium battery cell in the lithium battery pack or the lithium battery pack is a faulty lithium battery pack, a fault warning of the lithium battery pack is sent to a monitoring center.

7. A lithium battery pack fault warning method as claimed in claim 6, characterized in that: Sending the fault warning of the lithium battery pack to the monitoring center refers to sending the fault monitoring report of each faulty lithium battery unit and the faulty lithium battery pack in the lithium battery pack to the monitoring center.

8. A battery management system for a lithium battery pack, used to execute a fault warning method for a lithium battery pack as claimed in any one of claims 1 to 7, characterized in that: The management system comprises: Lithium battery monitoring module, used to monitor the load status of each lithium battery unit in the lithium battery pack; A thermal load extraction module is used to extract a load-temperature curve corresponding to each lithium battery cell in the load state of the lithium battery cell, determine the thermal load entropy of the lithium battery cell according to the load-temperature curve, and then obtain the thermal load entropy of each lithium battery cell; A fault quantification module is used to obtain the limit load current of each lithium battery unit in the lithium battery pack, determine the load fault coefficient of each lithium battery unit based on the corresponding thermal load entropy and the corresponding limit load current, and determine the fault warning factor of the lithium battery pack under the limit load state through all the load fault coefficients; The fault warning module is used to send a fault warning of the lithium battery pack to a monitoring center according to the load failure coefficient of each lithium battery unit and the fault warning factor of the lithium battery pack under the extreme load state.

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