Battery system health detection method, electronic equipment, storage medium and program product

Abstract

The embodiment of the invention provides a battery system health detection method, electronic equipment, a storage medium and a program product. The method comprises the following steps: acquiring voltage lag data of each battery module in the battery system and state parameters of the battery system; establishing a standard lag model corresponding to each battery module based on the state parameters and the voltage lag data; according to the state parameters and a standard lag model, standard lag time corresponding to each battery module is obtained through calculation; analyzing health state data corresponding to each battery module according to the standard lag time and the voltage lag data of each battery module; the method effectively reduces the calculation and analysis complexity, improves the analysis precision, can achieve the real-time monitoring and calculation, and directly reflects the dynamic performance and health condition of the battery system.

Description

Technical Field

[0001] The present application relates to the field of battery detection, and in particular to a battery system health detection method, electronic equipment, storage medium and program product. Background Art

[0002] Battery health testing can accurately analyze the maximum available capacity of the battery and is a key indicator for determining whether battery performance has declined. As the battery ages, its internal resistance increases, which causes the battery to generate more heat during the charge and discharge process, increasing the possibility of thermal runaway. There are a large number of battery modules in the battery system, and the health status of each battery module has an impact on the battery system. The health of the battery system directly affects the endurance of the device. Through testing, the remaining service life of the battery can be predicted, helping users maintain the battery system.

[0003] The existing technology is not capable of analyzing the dynamic performance of battery system health detection. The existing technology usually uses static parameter monitoring, such as open circuit voltage method, internal resistance detection method, etc. The open circuit voltage method determines the capacity of the battery based on the voltage measurement of the battery in a no-load state. It is necessary to place the battery in a static state for a period of time, and then place the battery in a rated current load for continuous discharge, and record the data for calculation; the internal resistance detection method analyzes the capacity by measuring the internal resistance of the battery. It is necessary to determine the initial internal resistance of the battery through the relationship between the no-load open circuit voltage and the point battery internal resistance, and then place the battery under the corresponding current load for discharge, and test the internal resistance of the battery during the discharge process. That is, it is necessary to detect the health status of the battery when the battery is not working, and it is impossible to reflect the dynamic performance of the battery under actual working conditions in real time. In addition, the test operation of static parameter monitoring is relatively complicated and the accuracy is difficult to guarantee. Summary of the invention

[0004] The embodiments of the present application provide a battery system health detection method, an electronic device, a storage medium, and a program product to achieve the effect of quickly and accurately detecting the health of a battery system.

[0005] In a first aspect, an embodiment of the present application provides a battery system health detection method, comprising: obtaining voltage hysteresis data of each battery module in a battery system and a state parameter of the battery system, wherein the battery system comprises a plurality of battery modules, and the state parameter of each battery module is the same;

[0006] Establishing a standard hysteresis model corresponding to each battery module based on the state parameter and the voltage hysteresis data;

[0007] Calculating a standard hysteresis time corresponding to each battery module according to the state parameter and the standard hysteresis model;

[0008] Analyze the health status data corresponding to each battery module according to the standard hysteresis time and voltage hysteresis data of each battery module;

[0009] The health status data is averaged to obtain the health status of the battery system.

[0010] In a possible implementation manner, the obtaining voltage hysteresis data of each battery module in the battery system includes:

[0011] Acquire voltage data of the battery module in real time, and obtain the voltage change rate according to the voltage data analysis;

[0012] When the voltage data is greater than a preset voltage threshold, the start time of the voltage change is obtained;

[0013] When the voltage change rate is less than a preset change threshold, a termination time of the voltage change is obtained;

[0014] The voltage hysteresis data corresponding to the battery module is calculated according to the start time and the end time.

[0015] In a possible implementation manner, the calculating, according to the start time and the end time, voltage hysteresis data corresponding to the battery module includes:

[0016] Calculate a first time difference according to the start time and the end time;

[0017] Determining whether the first time difference is within a preset hysteresis time range;

[0018] When the first time difference is within the preset hysteresis time range, using the first time difference as voltage hysteresis data corresponding to the battery module;

[0019] When the first time difference is not within the preset hysteresis time range, performing a first-order differential analysis on the voltage data to obtain a second time difference;

[0020] Determining whether the second time difference is within a preset lag time range;

[0021] When the second time difference is within the preset hysteresis time range, using the second time difference as the voltage hysteresis data corresponding to the battery module;

[0022] When the second time difference is not within the preset hysteresis time range, a second-order differential analysis is performed on the voltage data to obtain voltage hysteresis data of the battery module.

[0023] In a possible implementation manner, performing a first-order differential analysis on the voltage data to obtain a second time difference value includes:

[0024] Performing a first-order differentiation on the voltage data to obtain a first-order derivative corresponding to the voltage;

[0025] Acquire a first moment corresponding to the peak data and a second moment when the derivative is zero according to the first-order derivative;

[0026] A second time difference is calculated based on the first moment and the second moment.

[0027] In a possible implementation manner, performing a second-order differential analysis on the voltage data to obtain the voltage hysteresis data of the battery module includes:

[0028] Performing a second-order differentiation on the voltage data to obtain a second-order derivative corresponding to the voltage;

[0029] Obtaining, according to the second-order derivative, a third moment when the derivative changes from negative to positive and a fourth moment when the derivative is zero after the third moment;

[0030] The voltage hysteresis data of the battery module is calculated based on the third moment and the fourth moment.

[0031] In a possible implementation manner, establishing a standard hysteresis model corresponding to each battery module based on the state parameter and the voltage hysteresis data includes:

[0032] Acquiring voltage hysteresis data of a standard battery module as control data, wherein the state parameter of the standard battery module is the same as the state parameter of each battery module;

[0033] Establishing a target model corresponding to each battery module according to the state parameter and the voltage hysteresis data;

[0034] The target model is optimized based on the control data and a preset accuracy threshold to obtain a standard hysteresis model corresponding to each battery module.

[0035] In a possible implementation manner, the step of calculating the standard hysteresis time corresponding to each battery module according to the state parameter and the standard hysteresis model includes:

[0036] Inputting the state parameters into the standard hysteresis model of all battery modules to obtain hysteresis time data corresponding to each battery module;

[0037] The corresponding standard delay time of each battery module is calculated according to the delay time data.

[0038] In a possible implementation manner, the calculating the corresponding standard hysteresis time of each battery module according to the hysteresis time data includes:

[0039] Extracting lag time data corresponding to a target battery module from the lag time data, and using the remaining lag time data as target calculation data, wherein the target battery module is a battery module to be calculated;

[0040] The target calculation data is averaged to obtain the standard lag time corresponding to the target battery module.

[0041] In a possible implementation manner, analyzing the health status data corresponding to each battery module according to the standard hysteresis time and voltage hysteresis data of each battery module includes:

[0042] Comparing the voltage hysteresis data of each battery module with the standard hysteresis time to obtain ratio data;

[0043] The ratio data is matched with a plurality of preset state intervals to obtain health state data; each preset state interval corresponds to different health state data.

[0044] In a second aspect, an embodiment of the present application provides a battery system health detection device, comprising:

[0045] an acquisition module, used for acquiring voltage hysteresis data of each battery module in a battery system and a state parameter of the battery system, wherein the battery system comprises a plurality of battery modules, and the state parameter of each battery module is the same;

[0046] A model building module, used for building a standard hysteresis model corresponding to each battery module based on the state parameter and the voltage hysteresis data;

[0047] A time calculation module, used to calculate the standard hysteresis time corresponding to each battery module according to the state parameters and the standard hysteresis model

[0048] An analysis module, configured to analyze health status data corresponding to each battery module according to standard hysteresis time and voltage hysteresis data of each battery module;

[0049] The health calculation module is used to perform mean calculation on the health status data to obtain the health status of the battery system.

[0050] In a possible implementation, the acquisition module is specifically configured to:

[0051] Acquire voltage data of the battery module in real time, and obtain the voltage change rate according to the voltage data analysis;

[0052] When the voltage data is greater than a preset voltage threshold, the start time of the voltage change is obtained;

[0053] When the voltage change rate is less than a preset change threshold, a termination time of the voltage change is obtained;

[0054] The voltage hysteresis data corresponding to the battery module is calculated according to the start time and the end time.

[0055] In a possible implementation, the acquisition module is further configured to:

[0056] Calculate a first time difference according to the start time and the end time;

[0057] Determining whether the first time difference is within a preset hysteresis time range;

[0058] When the first time difference is within the preset hysteresis time range, using the first time difference as voltage hysteresis data corresponding to the battery module;

[0059] When the first time difference is not within the preset hysteresis time range, performing a first-order differential analysis on the voltage data to obtain a second time difference;

[0060] Determining whether the second time difference is within a preset lag time range;

[0061] When the second time difference is within the preset hysteresis time range, using the second time difference as the voltage hysteresis data corresponding to the battery module;

[0062] When the second time difference is not within the preset hysteresis time range, a second-order differential analysis is performed on the voltage data to obtain voltage hysteresis data of the battery module.

[0063] In a possible implementation, the acquisition module is further configured to:

[0064] Performing a first-order differentiation on the voltage data to obtain a first-order derivative corresponding to the voltage;

[0065] Acquire a first moment corresponding to the peak data and a second moment when the derivative is zero according to the first-order derivative;

[0066] A second time difference is calculated based on the first moment and the second moment.

[0067] In a possible implementation, the acquisition module is further configured to:

[0068] Performing a second-order differentiation on the voltage data to obtain a second-order derivative corresponding to the voltage;

[0069] Obtaining, according to the second-order derivative, a third moment when the derivative changes from negative to positive and a fourth moment when the derivative is zero after the third moment;

[0070] The voltage hysteresis data of the battery module is calculated based on the third moment and the fourth moment.

[0071] In a possible implementation, the model building module is specifically used to:

[0072] Acquiring voltage hysteresis data of a standard battery module as control data, wherein the state parameter of the standard battery module is the same as the state parameter of each battery module;

[0073] Establishing a target model corresponding to each battery module according to the state parameter and the voltage hysteresis data;

[0074] The target model is optimized based on the control data and a preset accuracy threshold to obtain a standard hysteresis model corresponding to each battery module.

[0075] In a possible implementation manner, the time calculation module is specifically used to:

[0076] Inputting the state parameters into the standard hysteresis model of all battery modules to obtain hysteresis time data corresponding to each battery module;

[0077] The corresponding standard delay time of each battery module is calculated according to the delay time data.

[0078] In a possible implementation manner, the time calculation module is further configured to:

[0079] Extracting lag time data corresponding to a target battery module from the lag time data, and using the remaining lag time data as target calculation data, wherein the target battery module is a battery module to be calculated;

[0080] The target calculation data is averaged to obtain the standard lag time corresponding to the target battery module.

[0081] In a possible implementation, the analysis module is specifically used to:

[0082] Comparing the voltage hysteresis data of each battery module with the standard hysteresis time to obtain ratio data;

[0083] The ratio data is matched with a plurality of preset state intervals to obtain health state data; each preset state interval corresponds to different health state data.

[0084] In a third aspect, an embodiment of the present application provides a battery system health detection device, including: a memory, a processor;

[0085] The memory stores computer-executable instructions;

[0086] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the above first aspect and / or various possible implementations of the first aspect.

[0087] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the first aspect above and / or various possible implementations of the first aspect.

[0088] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above first aspect and / or various possible implementation methods of the first aspect.

[0089] The battery system health detection method, electronic device, storage medium and program product provided in the embodiments of the present application obtain the voltage hysteresis data of each battery module in the battery system and the state parameters of the battery system; establish a standard hysteresis model corresponding to each battery module based on the state parameters and the voltage hysteresis data; calculate the standard hysteresis time corresponding to each battery module according to the state parameters and the standard hysteresis model; analyze the health status data corresponding to each battery module according to the standard hysteresis time and voltage hysteresis data of each battery module; and calculate the health status data to obtain the health of the battery system by averaging, which reduces the complexity of calculation and analysis, effectively saves a lot of computing resources, and can realize real-time monitoring calculation; the analysis has high accuracy and fast calculation speed, and can directly reflect the dynamic performance and health status of the battery system. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0091] Figure 1 Schematic diagram of the battery system health detection method provided in this application Figure 1 ;

[0092] Figure 2 Schematic diagram of the battery system health detection method provided in this application Figure 2 ;

[0093] Figure 3 It is a schematic diagram of the voltage response to the current change;

[0094] Figure 4 Schematic diagram of the battery system health detection method provided in this application Figure 3 ;

[0095] Figure 5 Schematic diagram of the battery system health detection method provided in this application Figure 4 ;

[0096] Figure 6Schematic diagram of the battery system health detection method provided in this application Figure 5 ;

[0097] Figure 7 Schematic diagram of the battery system health detection method provided in this application Figure 6 ;

[0098] Figure 8 A schematic diagram of the structure of the battery system health detection device provided in this application;

[0099] Fig. 9 A schematic diagram of the structure of the battery system health detection equipment provided in this application.

[0100] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0101] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0102] First, the terms involved in this application are explained:

[0103] Battery State of Health (SOH): The ratio of the actual performance of a battery to its initial performance, used to analyze the degree of battery degradation and remaining life. SOH is usually expressed as a percentage, with 100% indicating that the battery is in a new state and 0% indicating that the battery is completely damaged.

[0104] State of Charge (SOC): The ratio of the amount of electricity currently stored in a battery to its maximum capacity. SOC is usually expressed as a percentage, with 100% indicating that the battery is fully charged and 0% indicating that the battery is completely discharged.

[0105] Voltage hysteresis time: The delay time of the battery voltage response when the current changes, that is, the time required for the voltage to change from the initial value to the new steady-state value. The hysteresis time reflects the battery response speed and its internal impedance and capacitance characteristics.

[0106] Open Circuit Voltage (OCV): The voltage of a battery when no external current passes through it, reflecting the internal potential difference of the battery.

[0107] Internal Resistance (Rint): The resistance to current flow inside the battery, including ohmic resistance, polarization resistance, etc. The size of the internal resistance directly affects the voltage hysteresis time and thermal effect of the battery.

[0108] Capacitance (C): The electrochemical capacitance in the battery equivalent circuit reflects the battery's ability to store and release charge when the current changes. The presence of capacitance causes a time delay in voltage response.

[0109] Current step change: The phenomenon that the current suddenly changes from one value to another at a certain moment, which is usually used to test the dynamic response characteristics of the battery.

[0110] Steady-state value: After the current changes, the battery voltage gradually tends to a stable voltage value after a certain period of time, that is, the voltage value in the new equilibrium state.

[0111] Lag time calculation: The voltage lag time is calculated by recording the time it takes for the cell voltage response to reach a steady-state value and determining the time point when the voltage change reaches 99% of the new steady-state value.

[0112] Existing battery health analysis technologies mainly include the following methods:

[0113] Internal resistance measurement method: Analyze the health status of the battery by measuring the internal resistance of the battery. The internal resistance usually increases as the battery ages, causing the battery performance to deteriorate. The internal resistance measurement method is relatively simple, but in actual use it is greatly affected by temperature and current changes, and there are errors in the measurement.

[0114] Capacity decay method: Analyze the health status of the battery by measuring the charge and discharge capacity of the battery. As the battery ages, its actual capacity will gradually decrease. The capacity decay method requires a long charge and discharge test, and it is not easy to achieve real-time monitoring in actual use.

[0115] Open circuit voltage method: The SOC and health status of the battery are inferred by measuring the open circuit voltage of the battery in a static state. The open circuit voltage method is simple and intuitive, but it is greatly affected by temperature and battery chemical characteristics, has limited accuracy, and is more suitable for use with batteries / battery packs with fewer series and parallel connections.

[0116] Impedance spectroscopy (EIS): The battery's health status is analyzed by measuring the impedance spectrum characteristics of the battery through AC impedance spectroscopy. The EIS method can provide detailed information inside the battery, but it requires expensive equipment and complex data analysis and is not suitable for real-time monitoring.

[0117] Combining the above-mentioned battery health analysis technology, it can be seen that in the prior art, there are technical problems of long testing time and poor accuracy.

[0118] The battery system health detection method provided in the present application analyzes the health of the battery system by modeling and analyzing the lag time of the battery module, thereby solving the technical problem that the existing health analysis process is complex and has poor accuracy.

[0119] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0120] Figure 1 Schematic diagram of the battery system health detection method provided in this application Figure 1 ,like Figure 1 As shown, the method includes:

[0121] S101. Obtain voltage hysteresis data of each battery module in a battery system and a state parameter of the battery system. The battery system includes a plurality of battery modules, and the state parameters of each battery module are the same.

[0122] Specifically, the voltage response time of the battery is more delayed than the current time. That is, when the current changes, the voltage takes a certain amount of time to reach a stable value. The reason is that the equivalent circuit model of the battery usually includes internal resistance and capacitance. The presence of electronic components will cause a certain delay in the voltage response to the current change. The lag time reflects the battery response speed and its internal impedance and capacitance characteristics. The battery lag time will increase as it ages and its health deteriorates.

[0123] Status parameters include service life, temperature, current change, etc. Multiple battery modules in a battery system are from the same batch and have the same status parameters. However, due to the unevenness of the chemical reactions inside the batteries, there may be certain differences between individuals during the aging process over time.

[0124] In the process of acquiring voltage hysteresis data, the voltage hysteresis time is identified by analyzing the voltage change trend over time, and the voltage hysteresis data of each battery module is obtained.

[0125] S102: Establish a standard hysteresis model corresponding to each battery module based on the state parameters and voltage hysteresis data.

[0126] Specifically, by establishing a standard voltage hysteresis model for each battery module, it is convenient to analyze the hysteresis time of each battery module under a given state, and to judge the health status of the battery module through comparative analysis. The standard hysteresis model can also obtain the hysteresis time of the battery module at the target time by inputting the time state parameters of different years, so as to compare the hysteresis time of the previous time with the hysteresis time of the target time, and check the changes in the health status of the battery module between different years, which is convenient for staff to conduct comparative analysis.

[0127] S103: Calculate the standard hysteresis time corresponding to each battery module according to the state parameters and the standard hysteresis model.

[0128] Specifically, the standard hysteresis time is the voltage hysteresis time corresponding to the state parameter of the battery module under normal conditions. In actual conditions, the battery module may be different from the standard hysteresis time due to reasons such as the acceleration of individual aging speed. The standard hysteresis time is used to compare with the actual situation to check whether the current battery module is undergoing a normal aging process in a healthy state.

[0129] S104: Analyze the health status data corresponding to each battery module according to the standard hysteresis time and voltage hysteresis data of each battery module.

[0130] Specifically, by comparing and analyzing the standard hysteresis time and voltage hysteresis data, the health status of the battery can be intuitively reflected.

[0131] S105. Calculate the mean of the health status data to obtain the health status of the battery system.

[0132] Specifically, the health status data is averaged to obtain the overall health of the battery system. The health status of the entire battery is analyzed from the level of the entire system. Based on the results of the average health status data, it is decided whether the battery system needs to be maintained, some battery modules need to be replaced, or other measures need to be taken. If the health status is high, it means that the overall performance of the battery system is good; if the health status is low, it may be necessary to optimize the charging strategy or improve the usage conditions.

[0133] It is also possible to analyze the overall performance and reliability of the battery system by comparing the health of the battery system with the preset standard value, so that operation and maintenance personnel can adjust the maintenance strategy in a timely manner.

[0134] The battery system health detection method provided in the embodiment of the present application effectively saves a large amount of computing resources by acquiring the lag time of each battery module in the battery system for modeling and analysis, and can realize real-time monitoring and calculation; the analysis has high accuracy and fast calculation speed, and can directly reflect the dynamic performance and health status of the battery system.

[0135] Figure 2Schematic diagram of the battery system health detection method provided in this application Figure 2 ,like Figure 2 As shown, in this embodiment Figure 1 Based on the embodiment, a battery system health detection method is described in detail, and the method includes:

[0136] S201. Acquire voltage data of a battery module in real time, and obtain a voltage change rate based on voltage data analysis.

[0137] Specifically, the measurement of the lag time during data acquisition can be achieved through relative current sensors and voltage sensors. It can also be directly obtained through the battery management system (BMS), and the data acquisition method is simple and easy to implement. The voltage change rate can be obtained by calculating the difference between two consecutive voltage readings.

[0138] S202: When the voltage data is greater than a preset voltage threshold, obtain a start time of a voltage change.

[0139] S203: When the voltage change rate is less than a preset change threshold, the end time of the voltage change is obtained.

[0140] Specifically, the start time and the end time of the voltage change are determined by presetting thresholds.

[0141] S204. Calculate voltage hysteresis data corresponding to the battery module according to the start time and the end time.

[0142] The following is explained by way of example: Figure 3 As shown:

[0143] Before time t0, the operating power of the battery module is P0, the corresponding current is I0, and the voltage is U0.

[0144] At time t0, the power of the battery module changes from P0 to P1. At this time:

[0145]

[0146] The voltage response value changes with time, from U0 to the steady-state voltage U1.

[0147] The start time t1 and the end time t2 of the hysteresis time are determined by presetting the absolute value or relative value of the voltage change.

[0148] When identifying t1, a threshold V is pre-set th (For example, an absolute voltage difference or a relative difference with respect to a steady-state voltage may be used.) When the voltage change exceeds a threshold, it is determined that a voltage response has begun.

[0149] t1=min{t||V(t)-V steady |≥V th}

[0150] V(t) is the transient voltage that changes with time, V steady is the steady-state voltage before voltage change

[0151] When identifying t2, a voltage change rate threshold dV is pre-set th When the voltage change rate is lower than the threshold, it is determined that the voltage has stabilized and the voltage hysteresis time ends.

[0152]

[0153] The hysteresis time can be obtained by calculating the time difference, which reflects the time required for the battery voltage to change from the beginning to the change rate to decrease below the specified threshold.

[0154] The process of calculating the lag time does not require complex algorithms and a large amount of computing resources, and has low hardware requirements. It can realize real-time monitoring, real-time acquisition of lag time data and calculation of health status, which helps to timely discover changes in battery performance and perform corresponding maintenance and management.

[0155] The battery system health detection method provided in the embodiment of the present application monitors the hysteresis time in real time through simple current and voltage measurements. Compared with the traditional SOH calculation method, it not only greatly saves computing power, but the hysteresis time can also intuitively reflect the dynamic performance of the battery.

[0156] Figure 4 Schematic diagram of the battery system health detection method provided in this application Figure 3 ,like Figure 4 As shown, in this embodiment Figure 2 Based on the embodiment, a battery system health detection method is described in detail, and the method includes:

[0157] S301. Calculate a first time difference according to the start time and the end time.

[0158] S302: Determine whether the first time difference is within a preset lag time range.

[0159] Specifically, whether the state of the battery module is normal is determined by determining whether the first time difference is within a preset hysteresis time range.

[0160] S303: When the first time difference is within a preset hysteresis time range, the first time difference is used as voltage hysteresis data corresponding to the battery module.

[0161] Specifically, when the first time difference is within a preset hysteresis time range, it indicates that the battery module is in a normal state.

[0162] S304: When the first time difference is not within the preset hysteresis time range, perform a first-order differential analysis on the voltage data to obtain a second time difference.

[0163] When the first time difference is not within the preset hysteresis time range, it indicates that the battery module has an abnormality in identifying the hysteresis time through the threshold method. When the threshold method cannot accurately identify the hysteresis time, the hysteresis time is re-analyzed through the first-order differential method to improve the analysis accuracy. The analysis process is as follows:

[0164] Performing a first-order differentiation on the voltage data, obtaining a first-order derivative corresponding to the voltage; obtaining a first moment corresponding to the peak data and a second moment when the derivative is zero according to the first-order derivative; and calculating a second time difference based on the first moment and the second moment.

[0165] S305: Determine whether the second time difference is within a preset lag time range.

[0166] S306: When the second time difference is within a preset hysteresis time range, the second time difference is used as voltage hysteresis data corresponding to the battery module.

[0167] Specifically, the hysteresis time obtained after the first-order differentiation is judged, and when the second time difference is within a preset hysteresis time range, the second time difference is the voltage hysteresis data corresponding to the battery module.

[0168] S307: When the second time difference is not within the preset hysteresis time range, perform a second-order differential analysis on the voltage data to obtain voltage hysteresis data of the battery module.

[0169] Specifically, when the first-order differential method still cannot accurately identify the lag time, the second-order differential method is used for further analysis:

[0170] Perform second-order differentiation on the voltage data to obtain the second-order derivative corresponding to the voltage; obtain the third moment when the derivative changes from negative to positive and the fourth moment when the derivative is zero after the third moment based on the second-order derivative; and calculate the voltage hysteresis data of the battery module based on the third moment and the fourth moment.

[0171] By more accurately analyzing abnormal data, consistency differences can be eliminated to the greatest extent possible, improving data accuracy and reliability. At the same time, the preset lag time range can be adjusted according to actual conditions to ensure that it can reflect the actual performance of the battery module.

[0172] For battery modules that still have abnormalities after multiple analyses, records can be made and reported, making it easier for staff to maintain the battery system.

[0173] The battery system health detection method provided in the embodiment of the present application eliminates consistency differences to the greatest extent and improves data accuracy and reliability by performing more accurate analysis on abnormal data.

[0174] Figure 5 Schematic diagram of the battery system health detection method provided in this application Figure 4 ,like Figure 5 As shown, in this embodiment Figure 1 Based on the embodiment, a battery system health detection method is described in detail, and the method includes:

[0175] S401. Obtain voltage hysteresis data of a standard battery module as control data, wherein a state parameter of the standard battery module is the same as a state parameter of each battery module.

[0176] Specifically, the standard battery module is a battery module in good condition and with stable performance, and voltage hysteresis data of the standard battery module is obtained.

[0177] S402: Establish a target model corresponding to each battery module according to the state parameters and voltage hysteresis data.

[0178] Specifically, the voltage hysteresis data of each battery module is used in combination with the state parameters of the battery module (such as temperature, service life, number of charge and discharge times, etc.) to establish a preliminary target model. The target model is used to describe the relationship between the battery state parameters and the voltage hysteresis time.

[0179] S403 , optimizing the target model based on the control data and the preset accuracy threshold to obtain a standard hysteresis model corresponding to each battery module.

[0180] Specifically, the actual data of each battery module is compared with the data of the standard battery module, and analytical methods such as the least squares method and machine learning algorithms are used to optimize the target model so that the target model can better adapt to the specific conditions of each battery module.

[0181] By presetting an accuracy threshold, when the difference between the lag time predicted by the model and the actual observed value is less than the preset accuracy threshold, the target model is valid and serves as the standard lag model of the battery module.

[0182] The battery system health detection method provided in the embodiment of the present application can accurately predict the voltage hysteresis time of the battery module under a given state by establishing a standard hysteresis model.

[0183] Figure 6 Schematic diagram of the battery system health detection method provided in this application Figure 5 ,like Figure 6 As shown, in this embodiment Figure 1Based on the embodiment, a battery system health detection method is described in detail, and the method includes:

[0184] S501 , inputting state parameters into standard hysteresis models of all battery modules to obtain hysteresis time data corresponding to each battery module.

[0185] Specifically, the hysteresis time data is the standard hysteresis time that should correspond to the given state parameters in the health state corresponding to the battery module.

[0186] S502: Calculate the corresponding standard hysteresis time of each battery module according to the hysteresis time data.

[0187] Specifically, the process of calculating the corresponding standard hysteresis time of each battery module according to the hysteresis time data is as follows:

[0188] The lag time data corresponding to the target battery module is extracted from the lag time data, and the remaining lag time data is used as the target calculation data, and the target battery module is the battery module to be calculated; the target calculation data is averaged to obtain the standard lag time corresponding to the target battery module.

[0189] The battery system health detection method provided in the embodiment of the present application takes into account the possible performance differences among different battery modules and reduces the impact of individual abnormal values ​​on the overall system by averaging.

[0190] Figure 7 Schematic diagram of the battery system health detection method provided in this application Figure 6 ,like Figure 7 As shown, in this embodiment Figure 1 Based on the embodiment, a battery system health detection method is described in detail, and the method includes:

[0191] S601. Compare the voltage hysteresis data of each battery module with the standard hysteresis time to obtain ratio data.

[0192] Specifically, for each battery module, the ratio of its actual voltage hysteresis time to the standard hysteresis time is calculated, and the ratio reflects the performance deviation of the battery module relative to the standard health state.

[0193] S602, matching the ratio data with a plurality of preset state intervals to obtain health state data; each preset state interval corresponds to different health state data.

[0194] Specifically, multiple preset status intervals are set, and each interval corresponds to a different health status. For example:

[0195] Excellent: The ratio is between 0.9 and 1.1 (indicating that the battery performance is very close to the standard and the health status data is 90%).

[0196] Good: Ratio between 0.8 and 0.9 or 1.1 and 1.2 (indicating a slight performance degradation, with a healthy status of 80%).

[0197] Normal: The ratio is between 0.7 and 0.8 or 1.2 and 1.3 (indicating a significant performance degradation, with a healthy status of 70%).

[0198] Poor: Ratio less than 0.7 or greater than 1.3 (indicating serious performance issues, health status data is less than 70%).

[0199] The health status of each battery module is determined by matching the ratio data of each battery module with its corresponding status interval.

[0200] The relationship between hysteresis time and health can also be analyzed through multi-segment SOH mapping formulas. For example, the difference between voltage hysteresis data and standard hysteresis time is described by Effect. The trend of Effect change is usually related to the battery's state of health (SOH). As the battery module ages, the internal impedance increases, and the chemical reaction rate slows down, resulting in an increase in hysteresis time. Therefore, by monitoring the change in hysteresis time, the health status of each battery module can be indirectly inferred.

[0201]

[0202] Among them, E1 and E2 are the thresholds of Effect, which are used to divide different intervals.

[0203] The first section is a constant section. When Effect is less than or equal to E1, SOH is 100%;

[0204] The second linear decrease coefficient is k1, which indicates the decrease rate of SOH with the change of Effect in the medium Effect range.

[0205] The third stage is the exponential decline stage, which means that when the Effect is large, the SOH decreases rapidly.

[0206] The battery system health detection method provided in the embodiment of the present application can effectively monitor and manage the health status of battery modules and promptly detect battery modules with poor health status.

[0207] In some embodiments, the accuracy of the standard hysteresis model can also be improved by correcting the SOC. The hysteresis time indicates the speed at which the battery module responds to external current changes or load changes. Effect refers to the difference between the voltage hysteresis data of the battery module and the standard hysteresis time. When the Effect increases, the internal resistance of the battery increases, the polarization phenomenon increases, or the electrochemical reaction rate decreases, which affects the actual available capacity of the battery and causes errors in the SOC.

[0208] The SOC can be corrected by correction. For example, assuming that the effect of Effect on the SOC estimation error is linear, the correction formula is:

[0209] SOC corrected =SOC initial ·(1-α·Effect)

[0210] SOC corrected : Corrected SOC value.

[0211] SOC initial : Initial estimated SOC value.

[0212] α: Empirical coefficient, a constant obtained by fitting experimental data, which indicates the degree of influence of Effect on SOC error. The α value can be obtained by fitting a large amount of experimental data. It indicates the proportion of SOC that needs to be corrected for each unit increase of Effect.

[0213] Figure 8 The structural diagram of the battery system health detection device provided in this application is as follows: Figure 8 As shown, the battery system health detection device 80 provided in this embodiment includes:

[0214] An acquisition module 801 is used to acquire voltage hysteresis data of each battery module in a battery system and a state parameter of the battery system, wherein the battery system includes a plurality of battery modules and the state parameter of each battery module is the same;

[0215] A model building module 802, used to build a standard hysteresis model corresponding to each battery module based on state parameters and voltage hysteresis data;

[0216] The time calculation module 803 is used to calculate the standard hysteresis time corresponding to each battery module according to the state parameters and the standard hysteresis model.

[0217] An analysis module 804 is used to analyze the health status data corresponding to each battery module according to the standard hysteresis time and voltage hysteresis data of each battery module;

[0218] The health calculation module 805 is used to perform mean calculation on the health status data to obtain the health of the battery system.

[0219] In a possible implementation, the acquisition module 801 is specifically configured to:

[0220] Obtain the voltage data of the battery module in real time, and obtain the voltage change rate based on the voltage data analysis;

[0221] When the voltage data is greater than a preset voltage threshold, the start time of the voltage change is obtained;

[0222] When the voltage change rate is less than a preset change threshold, the end time of the voltage change is obtained;

[0223] The voltage hysteresis data corresponding to the battery module is calculated based on the start time and the end time.

[0224] In a possible implementation manner, the acquisition module 801 is further configured to:

[0225] Calculate the first time difference according to the start time and the end time;

[0226] Determining whether the first time difference is within a preset lag time range;

[0227] When the first time difference is within a preset hysteresis time range, the first time difference is used as voltage hysteresis data corresponding to the battery module;

[0228] When the first time difference is not within the preset hysteresis time range, performing a first-order differential analysis on the voltage data to obtain a second time difference;

[0229] Determining whether the second time difference is within a preset lag time range;

[0230] When the second time difference is within the preset hysteresis time range, the second time difference is used as the voltage hysteresis data corresponding to the battery module;

[0231] When the second time difference is not within the preset hysteresis time range, a second-order differential analysis is performed on the voltage data to obtain voltage hysteresis data of the battery module.

[0232] In a possible implementation manner, the acquisition module 801 is further configured to:

[0233] Perform first-order differentiation on the voltage data to obtain the first-order derivative corresponding to the voltage;

[0234] Obtaining a first moment corresponding to the peak data and a second moment when the derivative is zero according to the first-order derivative;

[0235] A second time difference is calculated based on the first moment and the second moment.

[0236] In a possible implementation manner, the acquisition module 801 is further configured to:

[0237] Perform second-order differentiation on the voltage data to obtain the second-order derivative corresponding to the voltage;

[0238] Obtain, according to the second-order derivative, a third moment when the derivative changes from negative to positive and a fourth moment when the derivative is zero after the third moment;

[0239] Voltage hysteresis data of the battery module is calculated based on the third moment and the fourth moment.

[0240] In a possible implementation, the model building module 802 is specifically configured to:

[0241] Obtaining voltage hysteresis data of a standard battery module as control data, the state parameters of the standard battery module being the same as the state parameters of each battery module;

[0242] Establish a target model corresponding to each battery module according to state parameters and voltage hysteresis data;

[0243] The target model is optimized based on the control data and the preset accuracy threshold to obtain the standard hysteresis model corresponding to each battery module.

[0244] In a possible implementation, the time calculation module 803 is specifically configured to:

[0245] Input the state parameters into the standard hysteresis model of all battery modules to obtain the hysteresis time data corresponding to each battery module;

[0246] The corresponding standard delay time of each battery module is calculated according to the delay time data.

[0247] In a possible implementation manner, the time calculation module 803 is further configured to:

[0248] Extracting the lag time data corresponding to the target battery module from the lag time data, and using the remaining lag time data as the target calculation data, the target battery module being the battery module to be calculated;

[0249] The target calculation data is averaged to obtain the standard lag time corresponding to the target battery module.

[0250] In a possible implementation, the analysis module 804 is specifically configured to:

[0251] Compare the voltage hysteresis data of each battery module with the standard hysteresis time to obtain ratio data;

[0252] The ratio data is matched with a plurality of preset state intervals to obtain health state data; each preset state interval corresponds to different health state data.

[0253] The battery system health detection device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and this embodiment will not be described in detail here.

[0254] Fig. 9 This is a schematic diagram of the structure of the battery system health detection device provided in this application. Fig. 9As shown, the battery system health detection device 90 provided in this embodiment includes: at least one processor 901 and a memory 902. Optionally, the device 90 also includes a communication component 903. The processor 901, the memory 902 and the communication component 903 are connected via a bus 904.

[0255] In a specific implementation process, at least one processor 901 executes the computer execution instructions stored in the memory 902, so that at least one processor 901 executes the above method.

[0256] The specific implementation process of the processor 901 can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.

[0257] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0258] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (Non-volatile Memory, NVM), such as at least one disk memory.

[0259] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application is not limited to only one bus or one type of bus.

[0260] The present application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0261] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above method is implemented.

[0262] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0263] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. The readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as ASIC). The processor and the readable storage medium can also exist in a device as discrete components.

[0264] The division of units is only a logical function division, and there may be other divisions in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0265] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0266] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0267] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0268] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0269] Finally, it should be noted that those skilled in the art will readily conceive of other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention, which follow the general principles of the present invention and include common knowledge or customary technical means in the art not disclosed by the present invention, are not limited to the precise structure described above and shown in the drawings, and may be modified and changed in various ways without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A battery system health detection method, characterized in that: include: Acquire voltage hysteresis data of each battery module in a battery system and a state parameter of the battery system, wherein the battery system comprises a plurality of battery modules, and the state parameter of each battery module is the same; Establishing a standard hysteresis model corresponding to each battery module based on the state parameter and the voltage hysteresis data; Calculating a standard hysteresis time corresponding to each battery module according to the state parameter and the standard hysteresis model; Analyze the health status data corresponding to each battery module according to the standard hysteresis time and voltage hysteresis data of each battery module; The health status data is averaged to obtain the health status of the battery system.

2. The method according to claim 1, characterized in that The obtaining of voltage hysteresis data of each battery module in the battery system includes: Acquire voltage data of the battery module in real time, and obtain the voltage change rate according to the voltage data analysis; When the voltage data is greater than a preset voltage threshold, the start time of the voltage change is obtained; When the voltage change rate is less than a preset change threshold, a termination time of the voltage change is obtained; The voltage hysteresis data corresponding to the battery module is calculated according to the start time and the end time.

3. The method according to claim 2, characterized in that The voltage hysteresis data corresponding to the battery module is calculated according to the start time and the end time and includes: Calculate a first time difference according to the start time and the end time; Determining whether the first time difference is within a preset hysteresis time range; When the first time difference is within the preset hysteresis time range, using the first time difference as voltage hysteresis data corresponding to the battery module; When the first time difference is not within the preset hysteresis time range, performing a first-order differential analysis on the voltage data to obtain a second time difference; Determining whether the second time difference is within a preset lag time range; When the second time difference is within the preset hysteresis time range, using the second time difference as the voltage hysteresis data corresponding to the battery module; When the second time difference is not within the preset hysteresis time range, a second-order differential analysis is performed on the voltage data to obtain voltage hysteresis data of the battery module.

4. The method according to claim 3, characterized in that The performing first-order differential analysis on the voltage data to obtain the second time difference value comprises: Performing a first-order differentiation on the voltage data to obtain a first-order derivative corresponding to the voltage; Acquire a first moment corresponding to the peak data and a second moment when the derivative is zero according to the first-order derivative; A second time difference is calculated based on the first moment and the second moment.

5. The method according to claim 3, characterized in that: The step of performing a second-order differential analysis on the voltage data to obtain the voltage hysteresis data of the battery module includes: Performing a second-order differentiation on the voltage data to obtain a second-order derivative corresponding to the voltage; Obtaining, according to the second-order derivative, a third moment when the derivative changes from negative to positive and a fourth moment when the derivative is zero after the third moment; The voltage hysteresis data of the battery module is calculated based on the third moment and the fourth moment.

6. The method according to claim 1, characterized in that The establishing of a standard hysteresis model corresponding to each battery module based on the state parameter and the voltage hysteresis data comprises: Acquiring voltage hysteresis data of a standard battery module as control data, wherein the state parameter of the standard battery module is the same as the state parameter of each battery module; Establishing a target model corresponding to each battery module according to the state parameter and the voltage hysteresis data; The target model is optimized based on the control data and a preset accuracy threshold to obtain a standard hysteresis model corresponding to each battery module.

7. The method according to claim 1, characterized in that The step of calculating the standard hysteresis time corresponding to each battery module according to the state parameter and the standard hysteresis model includes: Inputting the state parameters into the standard hysteresis model of all battery modules to obtain hysteresis time data corresponding to each battery module; The corresponding standard delay time of each battery module is calculated according to the delay time data.

8. The method according to claim 7, characterized in that The step of calculating the corresponding standard hysteresis time of each battery module according to the hysteresis time data comprises: Extracting lag time data corresponding to a target battery module from the lag time data, and using the remaining lag time data as target calculation data, wherein the target battery module is a battery module to be calculated; The target calculation data is averaged to obtain the standard lag time corresponding to the target battery module.

9. The method according to claim 1, characterized in that: Analyzing the health status data corresponding to each battery module according to the standard hysteresis time and voltage hysteresis data of each battery module includes: Comparing the voltage hysteresis data of each battery module with the standard hysteresis time to obtain ratio data; The ratio data is matched with a plurality of preset state intervals to obtain health state data; each preset state interval corresponds to different health state data.

10. A battery system health detection device, characterized in that: include: an acquisition module, used for acquiring voltage hysteresis data of each battery module in a battery system and a state parameter of the battery system, wherein the battery system comprises a plurality of battery modules, and the state parameter of each battery module is the same; A model building module, used for building a standard hysteresis model corresponding to each battery module based on the state parameter and the voltage hysteresis data; A time calculation module, used to calculate the standard hysteresis time corresponding to each battery module according to the state parameters and the standard hysteresis model An analysis module, configured to analyze health status data corresponding to each battery module according to standard hysteresis time and voltage hysteresis data of each battery module; The health calculation module is used to perform mean calculation on the health status data to obtain the health status of the battery system.

11. A battery system health detection device, characterized in that: include: Memory, processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the method according to any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 9 when executed by a processor.

13. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 9 when being executed by a processor.

Images