A power battery multi-fault risk assessment and early warning method

Abstract

The present invention discloses a method for assessing and warning multiple fault risks in power batteries, including: requesting recent lifecycle data of in-service electric vehicle power batteries from a cloud server, performing data preprocessing, and obtaining the temperature value of any battery cell in the power battery through interpolation; performing differentiation on the voltage and temperature values ​​of any battery cell to obtain a fault discrimination signal; establishing a multiple fault risk discrimination model based on the fault discrimination signal to calculate the absolute fault severity scores corresponding to different battery faults; statistically analyzing the data distribution of various battery faults and fault severity scores, determining the fault classification range based on the distribution results, and drawing a multiple fault risk warning map; and issuing warnings based on the severity of results within a specific fault classification range. The present invention can assess and separate multiple faults in power batteries, and can provide timely battery maintenance and repair references to vehicle owners based on the multiple fault risk warning map. The calculation is simple, the deployment is convenient, and it has practical application value.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-fault assessment and early warning of power battery systems, and in particular to a multi-fault risk assessment and early warning method for power batteries of electric vehicles. Background Art

[0002] As new energy vehicle sales climb, safety concerns are becoming increasingly prominent, particularly issues like smoke, spontaneous combustion, and explosions from onboard power batteries. These issues not only increase travel anxiety for owners and passengers, but also pose a serious threat to public safety and property, hindering the sustainable development of the new energy vehicle industry. Therefore, power battery fault assessment and early warning are crucial for both vehicle owners and automakers.

[0003] Currently, power battery fault diagnosis methods primarily fall into two categories: model-driven and data-driven. Model-driven methods can reliably track fault signals under idealized approximate conditions, but struggle to adapt to the complexities of real-world usage. Meanwhile, data-driven methods, while currently the mainstream, still struggle to handle the diverse and concurrent faults that exist in real-world situations. Therefore, a method for multi-fault risk assessment and early warning in power batteries is urgently needed. Summary of the Invention

[0004] The purpose of the present invention is to address the deficiencies in the above-mentioned technologies and to provide a method for assessing and early warning the risks of multiple faults in power batteries. Based on the recent life cycle data of power batteries in a cloud data center, the median difference of the voltage and temperature values ​​of each battery cell in the power battery is calculated to reveal different fault characteristics. In the median difference data space, the statistical characteristics of battery cell data with different fault levels vary significantly. Therefore, a multi-fault risk discrimination model for power batteries can be constructed based on the statistical characteristics to achieve multi-fault risk assessment and early warning for any battery cell in the power battery.

[0005] The purpose of the present invention can be achieved by taking the following technical solutions:

[0006] A method for risk assessment and early warning of multiple faults in electric vehicle power batteries, comprising:

[0007] S1. Request the recent life cycle data of the power batteries of in-service electric vehicles from the cloud server, perform data preprocessing, and obtain the temperature value of any battery cell in the power battery system through interpolation;

[0008] S2. Differentiating the voltage and temperature of any battery cell in the power battery system to obtain a fault identification signal;

[0009] S3. Build a multi-fault risk discrimination model based on the fault discrimination signal to comprehensively evaluate battery faults. Battery faults include abnormal inter-battery connection in the power battery system, voltage sampling distortion, abnormal battery self-discharge, sudden internal short circuit faults, voltage inconsistency, and temperature inconsistency. Calculate the absolute fault severity scores corresponding to these battery faults.

[0010] S4. Calculate the data distribution of different battery faults and their absolute fault severity scores, determine the warning level and fault level based on the distribution results, and draw a multi-fault risk warning map;

[0011] S5. According to the preset method, the vehicle owner is prompted with the situations and treatment methods at different warning levels and fault levels in the multi-fault risk warning map, so as to achieve the multi-fault warning goal of the power battery.

[0012] Furthermore, the process of step S1 is as follows:

[0013] S101. Request power battery lifecycle data from the cloud big data center. The data fields include: collection time, vehicle status, charging status, operating mode, vehicle speed, total voltage, total current, accumulated mileage, state of charge, battery cell voltage, maximum battery cell voltage, maximum voltage battery cell code, minimum battery cell voltage, minimum voltage battery cell code, maximum temperature, maximum temperature subsystem number, maximum temperature probe number, maximum alarm level, probe temperature, and general alarm flag.

[0014] S102, sorting the data in order of acquisition time, separating data of different data fields, filtering the data using mean filtering and median filtering to filter out impulse noise in the data, and then using the least squares method to interpolate missing data;

[0015] S103 . Based on the layout of the limited temperature probes in the power battery system, the known temperature probe data are weightedly fused using an interpolation method to obtain the temperature value of any battery cell in the power battery system, thereby compensating for the sparsity of temperature data in the power battery system.

[0016] Furthermore, the process of step S2 is as follows:

[0017] S201. Load the voltage values ​​of all battery cells in the power battery system and calculate the median differential voltage. Calculate the median differential temperature based on the battery cell temperature values ​​to generate a fault discrimination signal. The fault discrimination signal highlights the differences in data between battery cells, forming a high-resolution data modality that helps extract data features of different battery faults. Calculate using the following method:

[0018] exist At this moment, the power battery system Voltage value of each battery cell and temperature values Forming a battery cell voltage sequence and battery cell temperature series , and the The median differential voltage is obtained by subtracting the voltage value of each battery cell from the median of the battery cell voltage sequence. , let the The temperature value of each battery cell is subtracted from the median of the battery cell temperature sequence to obtain the median differential temperature. When the data of the tested battery cell is normal, and The closer the value is to the 0 value line; otherwise, there is data anomaly. and The value is far away from the 0 value line;

[0019] S202 , analyzing the data performance of the fault discrimination signal in the charging and discharging intervals, and extracting data features of different battery faults in the median difference data space.

[0020] Furthermore, the process of step S3 is as follows:

[0021] S301, for battery voltage sampling distortion and battery connection abnormality, perform logical mutual exclusion judgment: divide the charging interval and discharging interval in a charge and discharge segment, according to Slice out the first The median differential voltage of each battery cell and the first The median differential voltage of each battery cell , and calculate the arithmetic mean of the extreme values and and the variance of the time series difference statistic with extreme values and , the arithmetic mean without extreme values and The calculation process is as follows:

[0022]

[0023]

[0024] In the above formula and Respectively represent The maximum median differential voltage and the minimum median differential voltage within the charging interval of each battery cell, and Respectively represent The maximum median differential voltage and the minimum median differential voltage within the discharge interval of each battery cell, and They are the start and end times of charging in a single continuous charge and discharge process, and are the start and end times of discharge respectively. The calculation process of the statistical variance of the time series difference of extreme values ​​is as follows:

[0025]

[0026] In the above formula and Respectively represent The time dimension of each battery cell and The sequence after difference, operator Indicates that and After sorting, remove the first and last two extreme value samples to obtain the extreme value time series difference sequence and , and finally calculate the statistical variance of the two sequences and The above statistical descriptive analysis results are used to characterize the central tendency and dispersion of different battery failures within the charge and discharge range, extract and quantify the data modes within the range, and enhance the transparency of fault analysis.

[0027] If voltage sampling distortion occurs, most battery cells in the same module will have data distortion, while abnormal inter-battery connection will cause data distortion of a few battery cells in the module and there will be differences in the charging process. To address this feature, a mutually exclusive logic judgment process is designed. Greater than the charging distortion judgment threshold ,or Exceeding the discharge distortion judgment threshold The number of battery cells is greater than 1, and Below the charging overlimit threshold , voltage sampling distortion occurs. Exceed If the number of is equal to 1, the battery connection is abnormal, otherwise no fault occurs. If voltage sampling distortion occurs, the mutually exclusive symbol If the battery connection is abnormal, the mutual exclusion symbol is set to -1. is +1;

[0028] In order to expand the distance between voltage sampling anomalies and battery connection anomalies in the data space, and thus more effectively achieve fault separation, the first Amplification factor of each battery cell , calculated by the following formula:

[0029]

[0030] Definition The data distortion fraction of each battery cell is , calculated by the following formula:

[0031]

[0032] In the above formula, Representation function , since the value range is [-1, +1], the data distortion score is normalized to the interval [-1, +1] by using this function;

[0033] Data distortion score Indicates the degree of fault caused by voltage sampling distortion and abnormal inter-cell connection. If the battery cell is healthy, the data distortion score approaches 0. If voltage sampling distortion occurs, it will diverge to the edge of -1. If an abnormal inter-cell connection occurs, it will diverge to the edge of +1.

[0034] S302, for battery self-discharge anomalies and sudden internal short circuit faults, feature confrontation discrimination is performed: the battery self-discharge anomaly is manifested in the data as a larger voltage drop, which exists in the entire charge and discharge process, and the median differential voltage expands to the negative domain, gradually outliers, and the consistency difference is obvious. Therefore, using and Calculate the Difference score of each battery cell Characterize and design attenuation factors To reduce the interference of healthy battery data, the calculation formula is as follows:

[0035]

[0036]

[0037] Use median filter to The median differential temperature of each battery cell Filter to get , and in the time dimension Perform the difference to obtain the temperature time difference sequence ;

[0038] Definition The sudden internal short circuit fraction of each battery cell is , the specific calculation process is as follows:

[0039]

[0040] In the above formula For the The voltage variation factor of each battery cell, For the Temperature variation factor of each battery cell, correction factor Used to adjust the impact of data distortion on sudden internal short circuits; It means to find the maximum value of the sequence. Express request The battery with a sudden internal short circuit will experience a voltage drop during the charging phase, and the coupling of different battery failures will cause data distortion. Introducing data distortion , adjust the correction factor To control data distortion The analytical sensitivity of the battery system is high. In the temperature control system of the battery system, the temperature of the normal battery is controllable. When the battery has an internal short circuit, the temperature rises suddenly, which can induce spontaneous combustion and explosion. Therefore, the maximum value of the temperature time series difference is used to represent the most significant abnormal temperature change of the battery. The function is to strengthen the impact of short circuits within the burst and weaken the interference caused by other data features.

[0041] right and Difference The mutation score of each battery cell is calculated using The calculation process is as follows:

[0042]

[0043] Mutation score Indicates the degree of battery self-discharge abnormality and sudden internal short circuit fault. Used to normalize the mutation score to the range of [-1, +1]. If the battery cell is healthy, the mutation score approaches 0. If a self-discharge anomaly occurs, it will diverge to the edge of -1. If a sudden internal short circuit occurs, it will diverge to the edge of +1.

[0044] S303: For the voltage inconsistency and temperature inconsistency of the battery, define The voltage inconsistency score of each battery cell is , No. The temperature inconsistency fraction of each battery cell is , the calculation process is as follows:

[0045]

[0046]

[0047] In the above formula The sensitivity adjustment factor representing the voltage inconsistency fraction, The sensitivity adjustment factor of the temperature inconsistency fraction. The voltage inconsistency of the battery is The battery temperature consistency is proportional to Inversely proportional to the value of the battery cell inconsistency.

[0048] S304. Take the absolute value of the calculated fault severity score as the basis for fault severity assessment. This fully utilizes the voltage and temperature values ​​of the battery cells to construct a universal fault discrimination signal that reflects multiple types of battery faults. This signal is used to discover the data characteristics of multiple types of faults, avoiding complex data transformations and improving diagnostic efficiency.

[0049] Furthermore, the process of step S4 is as follows:

[0050] S401. Count the frequencies of all battery cells under different fault types and absolute fault severity scores, and calculate the relative density to obtain the data distribution of each fault and its fault severity;

[0051] S402. Based on the data distribution result and the general principle that the number of normal battery cells is far greater than the number of faulty battery cells, the fault severity intervals are divided to obtain the warning level and the fault level.

[0052] S403: Determine the fault classification range based on the warning level and fault level, and draw a multi-fault risk warning map based on the fault type. Quantifying and visualizing battery faults helps vehicle owners obtain clear and intuitive fault information.

[0053] Furthermore, the process of step S5 is as follows:

[0054] S501. Based on the multi-fault risk warning map, if the absolute fault severity score of a battery cell is within the corresponding fault classification range, a warning is issued or the battery cell is determined to be faulty; otherwise, the battery cell is determined to be safe.

[0055] S502. Preset corresponding prompt methods for different warning levels and fault levels, and remind and warn the vehicle owner according to the preset prompt methods to achieve multiple fault warnings for the power battery, avoiding potential safety accidents and expensive repair costs.

[0056] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0057] 1. This invention uses an interpolation weighted fusion method to expand from low-density temperature sampling to full-cell temperature monitoring, thereby improving the accuracy and reliability of identifying multiple types of faults in battery cells under abnormal temperature conditions;

[0058] 2. This invention uses the voltage and temperature values ​​of battery cells to construct a universal fault discrimination signal that can reflect multiple types of battery faults. Based on this signal, the data characteristics of multiple types of faults are discovered, avoiding complex data conversion and improving diagnostic efficiency.

[0059] 3. In the assessment of multiple types of faults, a multi-fault risk discrimination model is constructed based on mathematical statistics. This model can quantify the fault severity of multiple types of faults in any battery cell in the power battery system without analyzing the complex internal mechanisms of the physical battery. The calculation process is fast and efficient, making it suitable for online applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0061] Figure 1 1 is a flow chart of a method for assessing and early warning multiple fault risks of a power battery according to an embodiment of the present invention;

[0062] Figure 2 is an initial data graph in an embodiment of the present invention;

[0063] Figure 3 is a data graph after preprocessing in an embodiment of the present invention;

[0064] Figure 4 is a layout diagram of a power battery system in an embodiment of the present invention;

[0065] Figure 5 This is a diagram showing the temperature calculation results of some battery cells in an embodiment of the present invention.

[0066] Figure 6 1 is a schematic diagram of a median differential voltage when the battery connection is abnormal in an embodiment of the present invention;

[0067] Figure 7 1 is a schematic diagram of a median differential voltage of voltage sampling distortion in an embodiment of the present invention;

[0068] Figure 8 1 is a schematic diagram of median differential voltage of abnormal battery self-discharge and inconsistent voltage in an embodiment of the present invention;

[0069] Figure 9 1 is a schematic diagram of the median differential voltage of a sudden internal short circuit fault in an embodiment of the present invention;

[0070] Figure 10 Schematic diagram of median differential voltage with inconsistent temperature in an embodiment of the present invention;

[0071] Figure 11 This is a multi-fault risk assessment and early warning diagram in an embodiment of the present invention. DETAILED DESCRIPTION

[0072] In order to enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0073] Example

[0074] This embodiment discloses a power battery multi-fault risk assessment and early warning method, the implementation process is as follows Figure 1 As shown, the method specifically includes the following steps:

[0075] S1. Request the recent life cycle data of the power batteries of in-service electric vehicles from the cloud server, perform data preprocessing, and obtain the temperature value of any battery cell in the power battery system through interpolation;

[0076] S2. Differentiating the voltage and temperature of any battery cell in the power battery system to obtain a fault identification signal;

[0077] S3. Build a multi-fault risk discrimination model based on the fault discrimination signal to comprehensively evaluate battery faults. Battery faults include abnormal inter-battery connection in the power battery system, voltage sampling distortion, abnormal battery self-discharge, sudden internal short circuit faults, voltage inconsistency, and temperature inconsistency. Calculate the absolute fault severity scores corresponding to these battery faults.

[0078] S4. Calculate the data distribution of different battery faults and their absolute fault severity scores, determine the warning level and fault level based on the distribution results, and draw a multi-fault risk warning map;

[0079] S5. According to the preset method, the vehicle owner is prompted with the situations and treatment methods at different warning levels and fault levels in the multi-fault risk warning map, so as to achieve the multi-fault warning goal of the power battery.

[0080] In this embodiment, the implementation process of the power battery multi-fault risk assessment and early warning method is described by taking the in-service data of 20 passenger electric vehicles as an example. The specific implementation steps are as follows:

[0081] S1. Request the recent life cycle data of the in-service electric vehicle power battery from the cloud server and perform data preprocessing.

[0082] In this step, the original format of the collected data can optionally comply with GB / T 32960.3-2016 "Technical Specifications for Electric Vehicle Remote Service and Management Systems Part 3: Communication Protocol and Data Format," including: collection time, vehicle status, charging status, operating mode, vehicle speed, total voltage, total current, accumulated mileage, state of charge, battery cell voltage, maximum battery cell voltage, maximum voltage battery cell code, minimum battery cell voltage, minimum voltage battery cell code, maximum temperature, maximum temperature subsystem number, maximum temperature probe serial number, maximum alarm level, probe temperature, and general alarm flag. As a preferred embodiment of the present invention, the electric vehicle power battery system includes a total of 92 battery cells and 34 temperature probes, and the collection time, charging status, total voltage, total current, battery cell voltage, and probe temperature values ​​are further preferred.

[0083] It should be noted that the data collected in step S1 is not limited to the above-mentioned data types, and other types of data may also be collected, such as battery cell impedance and battery cell current of the power battery, etc., and the specific data may be optimized according to actual needs.

[0084] Data disturbances may occur due to vehicle power failure, abnormal operation of the communication module, or interference with information collection, which may cause data coding loss or disorder. Figure 2 As shown, the data needs to be pre-processed. As a preferred embodiment of the present invention, the data pre-processing further selectively includes: sorting the above data by sampling time; separating the data according to the battery cell number and the probe temperature number; and filtering and interpolating the screened data.

[0085] The screened data is filtered by using mean filtering and median filtering to filter the impulse noise in the data, and the least squares method is used to interpolate the missing values, which can remove the noise and burrs in the data, reconstruct the missing data, and improve the data quality. Figure 3 shown.

[0086] Since the number of temperature probes in the power battery does not match the number of battery cells in the module, it is difficult to distinguish the temperature abnormality of a single battery using only the probe temperature value. Therefore, a full battery temperature generation equation is designed to construct the full battery temperature data. As a preferred embodiment of the present invention, the power battery system layout diagram is as follows Figure 4 As shown, "1P5S" represents a "1 parallel 5 series" battery module mixed form, "1P6S" and so on, all battery modules are connected in parallel to form a power battery.

[0087] At the time of collection Under this condition, the calculation method of the battery cell temperature value in the battery system is as follows:

[0088] For parallel type 1 edge modules, the calculation process includes:

[0089]

[0090] For the middle module of parallel type 1, the calculation process includes:

[0091]

[0092] Finally, the temperature data of the battery cells far away from the temperature probe in the module are jointly corrected (such as 、 wait):

[0093]

[0094]

[0095] In the above calculation process, The temperature of the temperature probe is , in this case ;No. The temperature of each battery cell is Indicates the generated battery cell temperature value, ; A temporary variable representing the battery cell temperature value during the calculation process. and Respectively represent the probe point temperature weight and the inter-group temperature transfer weight. For the temperature of a certain battery, the adjacent temperature probe data is given a larger weight, and the other temperature probe data are integrated; since the temperature transfer efficiency within the module is generally higher than that between modules, the temperature probe data group between modules is given a lower weight. Preferably, here let and A value of 0.8 can obtain calculation results that are unbiased with the temperature probe data, making up for the sparsity of battery temperature values ​​in the power battery system and fixing abnormal values ​​after preprocessing. Some results are shown in Figure 2. Figure 5 The temperature calculations for the remaining battery cells can be deduced in the same way and will not be elaborated here.

[0096] It should be noted that the power battery of the aforementioned passenger electric vehicle can be selectively constructed from multiple battery cells connected in series, parallel, or in a hybrid configuration. The hybrid configuration refers to a connection configuration that combines both series and parallel connections. Furthermore, methods for generating battery temperature values ​​are not limited to the weighted fusion method described above; weighted least squares methods, Kalman filtering, and other methods may also be employed.

[0097] S2. Perform differentiation on the preprocessed data to obtain a fault discrimination signal, and extract data features of different faults in the median difference data space.

[0098] By calculating the median differential voltage and median differential temperature of the 92 battery cells in the power battery system, a fault identification signal is obtained. Taking the fault type of abnormal connection between batteries as a reference case, an analysis of the median differential voltage is given. Figure 6 As shown, the legend label is The curve indicates that there are battery cells with abnormal inter-battery connections. The corresponding median differential voltage, with the darkest grayscale curve corresponding to a fault-free battery cell, serves as a reference. The battery cells with connection abnormalities in the figure are clearly scattered across different modules. These battery cells not only exhibit data anomalies during discharge, but also experience an increase in median differential voltage during charging. The former is due to loose connections, resulting from vehicle vibrations that cause the tightly connected electrodes to shake, leading to unstable electrode voltage and data distortion. The latter is due to increased contact resistance as the connection becomes looser. At high charging current rates, Ohm's law indicates that high-resistance components will have higher voltages at high currents.

[0099] It should be noted that the analysis process for other fault types is similar to the above, so we will not elaborate on it here. We only give the corresponding data features as shown in Table 1. The specific manifestations are as follows: Figure 7 、 Figure 8 、 Figure 9 、 Figure 10 shown.

[0100] Table 1. Data feature description of various fault types

[0101]

[0102] In addition, the power battery failures targeted are not limited to the above-mentioned failure types, but may also selectively include battery cell internal resistance inconsistency, battery cell life inconsistency, etc.

[0103] S3. Build a multi-fault risk discrimination model based on the fault discrimination signal, conduct a comprehensive assessment of the battery fault, and calculate the absolute fault severity score corresponding to the battery fault.

[0104] The preprocessed data is divided into multiple charge and discharge cycles based on adjacent charge and discharge behaviors, and then into charge and discharge intervals. The median differential voltage and median differential temperature of all batteries within the charge and discharge intervals are calculated and input into the multi-fault risk discrimination model to obtain the data distortion score, mutation score, voltage inconsistency score, and temperature inconsistency score for all batteries. The absolute value of the calculated scores is used to characterize the fault severity of abnormal inter-battery connection, voltage sampling distortion, abnormal battery self-discharge, sudden internal short circuit faults, voltage inconsistency, and temperature inconsistency. The relevant parameters of the multi-fault risk discrimination model are determined using expert experience combined with the grid method, as shown in Table 2.

[0105] Table 2. Related parameters and value selection methods involved in the multi-fault risk judgment model

[0106]

[0107] S4. Statistically calculate the data distribution of different battery faults and their absolute fault severity scores, determine the warning level and fault level based on the distribution results, and draw a multi-fault risk warning diagram. As a preferred embodiment of the present invention, the frequency of different faults and different fault severity of all batteries in the 20 passenger electric vehicles is counted, and the frequency density is calculated to obtain the data distribution of each fault and its fault severity. According to the general principle that the number of normal batteries is far greater than the number of faulty batteries, the range of normal data and fault data in terms of fault severity is divided. The fault classification range of power batteries is shown in Table 3:

[0108] Table 3. Power battery fault classification range

[0109]

[0110] As a preferred embodiment of the present invention, the multi-fault risk warning map is represented by a multi-dimensional radar map. The corresponding labels in the multi-fault risk warning map are the degree of abnormal connection between batteries, the degree of voltage sampling distortion, the abnormal level of battery self-discharge, the risk of sudden internal short circuit failure, and the degree of voltage inconsistency and temperature inconsistency. Figure 11 As shown in the figure, the fault severity of different faults is displayed to the car owner through a multi-fault risk warning diagram.

[0111] It should be noted that the multi-fault risk warning map is not limited to the presentation form of a multi-dimensional radar map, and can also be presented using any other two-dimensional or three-dimensional map.

[0112] S5. According to the preset method, the vehicle owner is prompted with the situations and treatment methods at different warning levels and fault levels in the multi-fault risk warning map, so as to achieve the multi-fault warning goal of the power battery.

[0113] Preferably, according to the classification range of step S4, the preset prompt is:

[0114] (1) If the absolute failure degree scores of all cells in the power battery are in the safe zone, it indicates that the vehicle's main power battery system is safe;

[0115] (2) If the absolute fault degree score of a battery in the power battery is in the warning level I zone, the owner will be prompted that the battery has the corresponding fault risk, and the owner can choose to conduct a routine inspection of the power battery;

[0116] (3) If the absolute fault degree score of a battery in the power battery is in the warning level II zone, the owner will be notified that the battery has a high risk of corresponding failure and the owner needs to conduct a routine inspection of the power battery;

[0117] (4) If the absolute fault degree score of a battery in the power battery is in the fault level I zone, the owner will be prompted that the battery has a corresponding fault and the owner needs to inspect the power battery and optionally replace the power battery system components;

[0118] (5) If the absolute fault degree score of a battery in the power battery is in the fault level II zone, the owner will be prompted that the battery has a serious fault and the owner needs to replace the power battery system components according to the power battery inspection results.

[0119] In summary, the above embodiments present an implementation process of a method for multi-fault risk assessment and early warning of power batteries. The data preprocessing method reduces data analysis and modeling errors and improves data reliability. The interpolation method makes full use of low-density temperature sampling data to generate full battery cell temperature values, thereby improving the accuracy of identifying multiple types of faults in battery cells under abnormal temperature conditions. Based on the battery cell voltage value and the battery cell temperature value, a fault discrimination signal is constructed by a differential method, and data features of different faults are extracted from it without the need for complex data transformation, thereby improving calculation efficiency. The use of mathematical statistics methods to construct a multi-fault risk discrimination model realizes the detection and separation of multiple faults of power batteries. Based on the multi-fault risk warning map, a reference for battery maintenance and repair can be provided to car owners in a timely manner to prevent potential dangers caused by battery system failures.

[0120] References to "embodiments" in this application mean that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described in this application may be combined with other embodiments.

[0121] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be considered as equivalent replacement methods and are included in the scope of protection of the present invention.

Claims

1. A power battery multi-fault risk assessment and early warning method, characterized in that: The risk assessment and early warning methods include: S1. Request the recent life cycle data of the power batteries of in-service electric vehicles from the cloud server, perform data preprocessing, and obtain the temperature value of any battery cell in the power battery system through interpolation; S2. Differentiating the voltage and temperature of any battery cell in the power battery system to obtain a fault identification signal; S3. Build a multi-fault risk discrimination model based on the fault discrimination signal to comprehensively evaluate battery faults. Battery faults include abnormal inter-battery connection in the power battery system, voltage sampling distortion, abnormal battery self-discharge, sudden internal short circuit faults, voltage inconsistency, and temperature inconsistency. Calculate the absolute fault severity scores corresponding to these battery faults. S4. Calculate the data distribution of different battery faults and their absolute fault severity scores, determine the warning level and fault level based on the distribution results, and draw a multi-fault risk warning map; S5. According to the preset method, the vehicle owner is prompted with the situations and treatment methods at different warning levels and fault levels in the multi-fault risk warning map, so as to achieve the multi-fault warning goal of the power battery.

2. A power battery multi-fault risk assessment and early warning method according to claim 1, characterized in that: The process of step S1 is as follows: S101. Request power battery lifecycle data from the cloud big data center. The data fields include: collection time, vehicle status, charging status, operating mode, vehicle speed, total voltage, total current, accumulated mileage, state of charge, battery cell voltage, maximum battery cell voltage, maximum voltage battery cell code, minimum battery cell voltage, minimum voltage battery cell code, maximum temperature, maximum temperature subsystem number, maximum temperature probe number, maximum alarm level, probe temperature, and general alarm flag. S102, sorting the data in order of acquisition time, separating data of different data fields, filtering the data using mean filtering and median filtering to filter out impulse noise in the data, and then using the least squares method to interpolate missing data; S103 , according to the layout of the limited temperature probes in the power battery system, use the interpolation method to weightedly fuse the known temperature probe data to calculate the temperature value of any battery cell in the power battery system.

3. A power battery multi-fault risk assessment and early warning method according to claim 2, characterized in that: The process of step S2 is as follows: S201, load the voltage values ​​of all battery cells in the power battery system and calculate the median differential voltage, calculate the median differential temperature based on the battery cell temperature values, and generate a fault judgment signal, which is calculated by the following method: exist At this moment, the power battery system Voltage value of each battery cell and temperature values Forming a battery cell voltage sequence and battery cell temperature series , and the The median differential voltage is obtained by subtracting the voltage value of each battery cell from the median of the battery cell voltage sequence. , let the The temperature value of each battery cell is subtracted from the median of the battery cell temperature sequence to obtain the median differential temperature. , and That is the fault discrimination signal; S202: Analyze the fault discrimination signal and extract data features of different faults in the median difference data space.

4. A power battery multi-fault risk assessment and early warning method according to claim 3, characterized in that: The process of step S3 is as follows: S301, for battery voltage sampling distortion and battery connection abnormality, perform logical mutual exclusion judgment: divide the charging interval and discharging interval in a charge and discharge segment, according to Slice out the first The median differential voltage of each battery cell and the first The median differential voltage of each battery cell , and calculate the arithmetic mean of the extreme values and and the variance of the time series difference statistic with extreme values and , the arithmetic mean without extreme values and The calculation process is as follows: In the above formula and Respectively represent The maximum median differential voltage and the minimum median differential voltage within the charging interval of each battery cell, and Respectively represent The maximum median differential voltage and the minimum median differential voltage within the discharge interval of each battery cell, and They are the start and end times of charging in a single continuous charge and discharge process, and are the moments when discharge starts and ends, respectively; The calculation process of the statistical variance of the extreme value time series difference is as follows: In the above formula and Respectively represent The time dimension of each battery cell and The sequence after difference, operator Indicates that and After sorting, remove the first and last two extreme value samples to obtain the extreme value time series difference sequence and , and finally calculate the statistical variance of the two sequences and ; In the same battery module, the battery cells Greater than the charging distortion judgment threshold ,or Exceeding the discharge distortion judgment threshold The number of battery cells is greater than 1, and Below the charging overlimit threshold , voltage sampling distortion occurs; Battery cell Exceed If the number of is equal to 1, the battery connection is abnormal, otherwise no fault occurs; If voltage sampling distortion occurs, the mutually exclusive symbol If the battery connection is abnormal, the mutual exclusion symbol is set to -1. is +1; Definition Amplification factor of each battery cell It is used to enhance the diagnosis of abnormal voltage sampling and abnormal inter-battery connection, and is calculated using the following formula: Definition The data distortion fraction of each battery cell is , calculated by the following formula: In the above formula, Representation function , since the value range is [-1, +1], the data distortion score is normalized to the interval [-1, +1] by using this function; Data distortion score Indicates the fault severity of voltage sampling distortion and abnormal inter-cell connection. If the battery cell is healthy, the data distortion score approaches 0. If voltage sampling distortion occurs, it will diverge to the -1 edge. If an abnormal inter-cell connection occurs, it will diverge to the +1 edge. S302, for battery self-discharge anomalies and sudden internal short circuit faults, perform feature confrontation identification: use and Calculate the Voltage difference fraction of each battery cell , and design the attenuation factor To reduce the interference of healthy battery data, the specific calculation formula is as follows: Use median filter to The median differential temperature of each battery cell Filter to get , and in the time dimension Perform the difference to obtain the temperature time difference sequence ; Definition The sudden internal short circuit fraction of each battery cell is , the specific calculation process is as follows: In the above formula For the The voltage variation factor of each battery cell, For the Temperature variation factor of each battery cell, correction factor Used to adjust the impact of data distortion on sudden internal short circuits; It means to find the maximum value of the sequence. Express request the power of right and Difference The mutation score of each battery cell is calculated using To express; The specific calculation process is as follows: Mutation score Indicates the degree of battery self-discharge abnormality and sudden internal short circuit fault. Used to normalize the mutation score to the interval [-1, +1]; If the battery cell is healthy, the mutation score approaches 0. If a self-discharge anomaly occurs, it will diverge to the -1 edge. If a sudden internal short circuit occurs, it will diverge to the +1 edge. S303: For the voltage inconsistency and temperature inconsistency of the battery, define The voltage inconsistency score of each battery cell is , No. The temperature inconsistency fraction of each battery cell is , the calculation process is as follows: In the above formula The sensitivity adjustment factor representing the voltage inconsistency fraction, The sensitivity adjustment factor representing the temperature inconsistency fraction; S304: Take the absolute value of the fault severity score obtained by the above calculation as the basis for fault severity assessment.

5. A power battery multi-fault risk assessment and early warning method according to claim 4, characterized in that: The process of step S4 is as follows: S401. Count the frequencies of all battery cells with different faults and absolute fault severity scores, and calculate the relative density to obtain the data distribution of each fault and its fault severity. S402: Divide the fault degree intervals according to the data distribution result to obtain the warning level and fault level; S403: Determine the fault classification range based on the warning level and the fault level, and draw a multi-fault risk warning map according to the fault type.

6. A power battery multi-fault risk assessment and early warning method according to claim 5, characterized in that: The process of step S5 is as follows: S501. Based on the multi-fault risk warning map, if the absolute fault severity score of a battery cell is within the corresponding fault classification range, a warning is issued or the battery cell is determined to be faulty; otherwise, the battery cell is determined to be safe. S502: Preset corresponding prompting methods for different warning levels and fault levels, and provide the vehicle owner with a multi-fault warning for the power battery according to the preset prompting methods.

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