Battery defect detection method based on charging process

Abstract

The invention relates to a battery defect detection method and battery defect detection equipment based on a charging process, a computer readable storage medium, a vehicle and a charging facility. The battery defect detection method based on the charging process comprises the following steps: S1, determining one or more battery parameters of a battery in the charging process of the battery; s2, when the deviation of a battery parameter value in the determined one or more battery parameters from a preset value corresponding to the battery parameter reaches a first deviation threshold value, performing defect diagnosis counting; and S3, determining that the battery has defects related to the battery parameters when the counting reaches a preset time threshold value.

Description

Technical Field

[0001] The present disclosure relates to the field of batteries, and in particular, to a battery defect detection method based on a charging process, a battery defect detection device, a computer-readable storage medium, a vehicle, and a charging facility. Background Art

[0002] With the rapid development of the new energy vehicle industry, the quality and safety of power batteries have become key issues. Power batteries not only need to ensure their performance and safety through design and manufacturing, but also require an effective, online method to identify potential defects inside the battery, and promptly notify users to perform maintenance or background alarms to avoid further deterioration of defects and effectively curb the occurrence of safety or major quality problems. At present, the monitoring of power batteries mainly relies on the upper and lower threshold alarms of characteristic signals such as voltage, temperature, and current. This method only issues an alarm when the battery is close to or reaches the critical state of failure, and the response is relatively slow. Summary of the invention

[0003] In view of the above problems, the present disclosure aims to provide a battery defect detection method based on the charging process, a battery defect detection device, a computer-readable storage medium, a vehicle and a charging facility.

[0004] According to a first aspect of the present disclosure, a battery defect detection method based on a charging process is provided, comprising the following steps: S1: during the battery charging process, determining one or more battery parameters of the battery; S2: when a battery parameter value in the determined one or more battery parameters deviates from a predetermined value corresponding to the battery parameter by reaching a first deviation threshold, performing a defect diagnosis count; and S3: when the count reaches a predetermined number of times, determining that the battery has a defect related to the battery parameter.

[0005] According to the battery defect detection method based on the charging process of an embodiment of the present disclosure, the battery parameters include one or more of the following: voltage, temperature, current, charge, internal resistance, voltage change rate, and internal resistance change rate.

[0006] According to the battery defect detection method based on the charging process of an embodiment of the present disclosure, when the battery parameter is the voltage change rate or the internal resistance change rate, the method further includes: when the battery parameter value in the determined battery parameter is less than 0, determining that the battery has a defect related to the battery parameter.

[0007] According to the battery defect detection method based on the charging process of an embodiment of the present disclosure, wherein one or more battery parameters each have a weight, the method further includes: determining a battery defect detection score based on the difference between each battery parameter and a predetermined threshold value corresponding to the battery parameter and the weight of each battery parameter, and determining that the battery is defective when the score exceeds the predetermined score.

[0008] According to the battery defect detection method based on the charging process of an embodiment of the present disclosure, wherein the charging process includes multiple charging sub-segments, the method further includes: when a battery parameter value among the determined one or more battery parameters deviates twice from a predetermined value corresponding to the battery parameter in one charging sub-segment among the multiple charging sub-segments by a first deviation threshold, determining that the battery has a defect related to the battery parameter.

[0009] According to the battery defect detection method based on the charging process of an embodiment of the present disclosure, the method further includes: after determining that the battery has a defect, performing early warning processing on the vehicle, the early warning processing includes one or more of the following: setting a fault code for the vehicle, shutting down the vehicle, and alarming the vehicle or the charging station.

[0010] According to a second aspect of the present disclosure, a battery defect detection device is provided, which includes a memory and a processor, wherein the memory stores instructions, and when the instructions are executed by the processor, the method described in any one of the aforementioned embodiments is implemented.

[0011] According to a third aspect of the present disclosure, a computer-readable storage medium is provided. The computer-readable storage medium stores instructions, and when the instructions are executed by a processor, the method described in any of the above embodiments is implemented.

[0012] According to a fourth aspect of the present disclosure, a vehicle is provided, which may include a battery defect detection device according to any one of the aforementioned embodiments.

[0013] According to a fifth aspect of the present disclosure, a charging facility is provided, which may include a battery defect detection device according to any one of the aforementioned embodiments.

[0014] By analyzing the changes in characteristic signals of the battery during the charging process, the present disclosure can identify defects inside the battery earlier, thereby preventing battery failure or malfunction from occurring in a timely manner, improving user experience and enhancing vehicle safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The above and other objects and advantages of the present disclosure will become more fully apparent from the following detailed description taken in conjunction with the accompanying drawings, wherein the same or similar elements are denoted by the same reference numerals.

[0016] Figure 1 A schematic flow chart of a battery defect detection method 100 based on a charging process according to some embodiments is shown. DETAILED DESCRIPTION

[0017] The description of the following specific embodiments is merely exemplary in nature and is not intended to limit the disclosed technology or the application and use of the disclosed technology. In addition, it is not intended to be bound by any express or implied theory presented in the aforementioned technical field, background technology or the following specific embodiments.

[0018] In the following detailed description of the embodiments, many specific details are set forth in order to provide a more thorough understanding of the disclosed technology. However, it is apparent to one of ordinary skill in the art that the disclosed technology can be practiced without these specific details. In other instances, well-known features are not described in detail to avoid unnecessarily complicating the description.

[0019] Terms such as “comprise” and “include” indicate that in addition to the units and steps directly and explicitly stated in the specification, the technical solution of the present disclosure does not exclude the situation where it has other units and steps that are not directly or explicitly stated.

[0020] Hereinafter, various exemplary embodiments according to the present disclosure will be described in detail with reference to the accompanying drawings.

[0021] Figure 1 A schematic flow chart of a battery defect detection method 100 based on a charging process according to some embodiments is shown. The battery defect detection method 100 based on a charging process disclosed in the present invention may include the following steps:

[0022] In step S1, during the battery charging process, one or more battery parameters of the battery are determined. In some examples, one or more key parameters of the battery are collected and determined in real time during the battery charging process by an online monitoring system. These parameters include, but are not limited to, voltage (V), temperature (T), current (I), charge (Q) and internal resistance (R). Specifically, the collection of voltage and current can be achieved through sensors in the battery management system (BMS), and the temperature is monitored by a built-in temperature sensor. In addition, the charge (Q) can be obtained by integrally calculating the voltage and current data during the charging process, and the internal resistance (R) can be calculated by the ratio of the voltage change to the current change. Through multi-dimensional parameter collection, the present disclosure can more comprehensively reflect the state of the battery during the charging process and provide rich data support for subsequent defect diagnosis.

[0023] In step S2, when the battery parameter value in the determined one or more battery parameters deviates from the predetermined value corresponding to the battery parameter to reach the first deviation threshold, a defect diagnosis count is performed. A series of predetermined thresholds are set to determine whether the battery parameters deviate from the normal range. When any battery parameter value (such as voltage, temperature, current, power or internal resistance) deviates from its corresponding predetermined value to reach the first deviation threshold, the system will trigger a defect diagnosis count. For example, if the voltage drops by more than a preset voltage threshold (such as 0.1V) in a short period of time, or the internal resistance increases by more than a preset internal resistance threshold (such as 0.5Ω) in a short period of time, it is considered that the parameter is abnormal. The system will record this abnormality and perform a count. In this way, the present disclosure can capture subtle changes in battery parameters in a timely manner, thereby identifying potential internal defects at an early stage and avoiding further deterioration of the problem.

[0024] In step S3, when the count reaches a predetermined number threshold, it is determined that the battery has defects related to the battery parameters. A predetermined number threshold is set to finally determine whether the battery has internal defects. When the abnormal count of a battery parameter reaches a predetermined number threshold, the system will confirm that the battery has internal defects related to the parameter. For example, if the voltage abnormal count reaches 3 times during continuous charging, the system will determine that the battery has internal voltage abnormalities, which may be caused by poor welding, pole piece tearing or other internal structural problems. For another example, if the battery's internal resistance change rate abnormal count reaches 5 times during multiple charge and discharge processes, the system will determine that the battery may have internal polarization or material aging problems, affecting the battery's charge and discharge efficiency and stability. For another example, during the charging process, if the battery temperature change abnormal count reaches 4 times, the system may confirm that there is an overheating problem inside the battery, which may be caused by battery design defects, cooling system failures or battery cell problems, etc.

[0025] By counting multiple times, the present disclosure can effectively avoid misjudgment caused by accidental noise or interference, ensuring the accuracy and reliability of defect diagnosis. Ultimately, it is convenient to generate corresponding warning information to notify users or the background to conduct further inspection and maintenance, thereby ensuring the safety and performance of the battery.

[0026] Through the above method, the present disclosure can achieve early, accurate and reliable detection of internal defects of power batteries. First, by real-time acquisition and analysis of multi-dimensional parameters of the battery (such as voltage, temperature, current, charge and internal resistance), the system can comprehensively monitor the state of the battery during the charging process and provide rich data support for defect diagnosis. Secondly, by setting a first deviation threshold and performing defect diagnosis counting when the parameter value deviates from the normal range, the system can capture slight changes in battery parameters in a timely manner, thereby identifying potential internal defects at an early stage and avoiding further deterioration of the problem. Finally, by setting a predetermined number of thresholds to finally determine whether the battery has internal defects, the system can effectively avoid misjudgments caused by accidental noise or interference, and ensure the accuracy and reliability of defect diagnosis. Overall, these steps jointly improve the monitoring capability and fault warning level of the battery management system, significantly improve the user experience and improve the safety of the vehicle.

[0027] In some examples, the method disclosed herein can be applied to batteries with various types of electrolytes, including but not limited to liquid electrolytes, solid-liquid mixed electrolytes, and all-solid electrolytes, to meet the detection requirements of different types of power batteries. In addition, the applicable battery cell states cover 0% SOC to 100% SOC, that is, the entire charging process can be detected, so the method disclosed herein has wide applicability.

[0028] In some embodiments, the battery parameters include one or more of the following: voltage, temperature, current, charge, internal resistance, voltage change rate, and internal resistance change rate. For example, the voltage change rate can capture abnormal voltage fluctuations and quickly identify defects in pole ear welding or internal short circuits, while the internal resistance change rate can reveal problems with lithium ion migration or interface stability. These parameters are monitored in real time during the charging process and compared with historical data or predetermined thresholds. By calculating deviations and change trends, early potential defects are identified and accurate warnings are provided. Furthermore, by monitoring the charging behavior, for example, by changes in the voltage differential, the detection sensitivity can be improved, while adapting to a variety of battery forms and complex working conditions, significantly improving the efficiency of identifying battery defects.

[0029] In some embodiments, when the battery parameter is a voltage change rate or an internal resistance change rate, the method further includes: when the battery parameter value in the determined battery parameter is less than 0, determining that the battery has a defect related to the battery parameter. A voltage change rate less than 0 indicates that the battery voltage drops per unit time, which may be related to a defect in the internal tab welding of the battery cell, abnormal interface stability, or a short circuit; an internal resistance change rate less than 0 may indicate uneven material distribution or abnormal polarization inside the battery cell.

[0030] In some embodiments, one or more battery parameters each have a weight, and method 100 further includes: determining a battery defect detection score based on the difference between each battery parameter and a predetermined threshold value corresponding to the battery parameter and the weight of each battery parameter, and determining that the battery is defective when the score exceeds the predetermined score. For example, weights (such as 0.4, 0.35, and 0.25) can be assigned to the voltage difference, the voltage change rate, and the internal resistance change rate, respectively, and a comprehensive score can be calculated based on the accumulated deviation values, for example, weighted calculations are performed based on their deviation values. For example, if the voltage difference deviation is 0.2, the change rate is 0.15, and the internal resistance change rate is 0.05, then based on the deviation values ​​and weights of these parameters, a score can be calculated: score = 0.4 × 0.2 + 0.35 × 0.15 + 0.25 × 0.05 = 0.145. If the score does not exceed the set threshold (such as 0.2), it can be determined that the score of 0.145 does not belong to an abnormality or defect.

[0031] The thresholds are usually set based on historical data, experimental results, and the working characteristics of the battery. For example, based on a large amount of battery usage data, the parameter changes when different defects occur are analyzed to select a suitable threshold. By simulating various defect scenarios and testing the parameter changes of the battery under these conditions, a reasonable threshold is determined. The range of thresholds set can also be combined with industry standards and expert experience to ensure accuracy and reliability in identifying defects.

[0032] The method 100 disclosed in the present invention takes into account the comprehensiveness of multiple parameters, flexibly adapts to different battery defect types, improves the accuracy and robustness of diagnosis, and can be used for real-time monitoring and defect identification of various operating conditions and battery forms.

[0033] In some embodiments, the charging process includes multiple charging sub-segments, and the method 100 further includes: when the battery parameter value in the determined one or more battery parameters deviates from the predetermined value corresponding to the battery parameter twice in one of the multiple charging sub-segments and reaches the first deviation threshold, determining that the battery has a defect related to the battery parameter. The charging process includes multiple charging sub-segments, and the method 100 may, for example, further include: when one or more battery parameters (such as voltage change rate, internal resistance change rate, etc.) deviate from their corresponding predetermined values ​​twice in a certain charging sub-segment and reach the first deviation threshold, determining that the battery has a defect related to the parameter.

[0034] The division of multiple charging sub-segments is usually based on the characteristics of the charging process, such as the constant current stage, the constant voltage stage, or the dynamic load test stage. In different charging sub-segments, the parameters of the battery may show different changing trends due to factors such as load, electrochemical reaction and temperature. For example, in the constant current charging segment, if the voltage change rate deviates from the set threshold twice in a row, it may indicate that the internal pole ear of the battery cell is broken or short-circuited; in the constant voltage stage, the abnormality of the internal resistance change rate may indicate the deterioration of interface stability or the precipitation of lithium metal. By dynamically monitoring the deviation behavior of parameters in different charging stages, more sensitive defect identification can be achieved. Multi-segment analysis can capture subtle but indicative defect features, ensure the accuracy of the diagnostic results, and avoid misjudgment due to individual abnormal points. Therefore, the method 100 disclosed in the present invention is applicable to a variety of battery types and complex working conditions, effectively improving the detection efficiency, safety and accuracy.

[0035] In some embodiments, method 100 further includes: after determining that the battery is defective, performing early warning processing on the vehicle, and the early warning processing includes one or more of the following: setting a fault code for the vehicle, shutting down the vehicle, and causing the vehicle or charging pile to alarm. Early warning processing may, for example, include one or more of the following measures, such as setting a fault code for the vehicle and uploading abnormal information to the vehicle management system; forcing the vehicle to shut down to prevent further damage or safety accidents caused by defects; or causing the vehicle or charging pile to emit an audible and visual alarm to remind users or maintenance personnel to check in time. In addition, the alarm information can be sent to the background management system via wireless communication to trigger a remote monitoring and maintenance mechanism. For example, if an abnormal internal resistance change rate is detected to cause severe interface degradation, the system can send a fault alarm to the user and the service center at the same time, providing accurate positioning and diagnostic information. This multi-level early warning mechanism can achieve rapid response, improve the practicality of the detection results, and significantly reduce the risk of failures caused by battery defects.

[0036] In some examples, the power batteries involved in the present disclosure are in various forms, including but not limited to soft-pack batteries, square-shell batteries and cylindrical batteries. Soft-pack batteries are widely used in electric vehicles and portable electronic devices due to their lightweight design and flexible shape adaptability; square-shell batteries have high energy density and good mechanical stability, and are suitable for large energy storage systems and electric vehicles; cylindrical batteries are common in power tools and small electric vehicles due to their standardized size and mature production process. The selection of battery positive electrode materials is also very rich, including but not limited to nickel cobalt manganese oxide (ternary) positive electrode materials, which have high energy density and good cycle performance; nickel cobalt aluminum oxide (ternary) positive electrode materials, which are suitable for high power output occasions; lithium manganese oxide positive electrode materials, which have low cost and good safety; lithium cobalt oxide positive electrode materials, which are mainly used in consumer electronics products and have high energy density; lithium iron phosphate positive electrode materials, which have excellent safety and long life characteristics; in addition, modified materials doped with transition metal elements can also be used to further optimize electrochemical performance. Battery negative electrode materials are also diverse, including but not limited to graphite negative electrode materials, which have good stability and low cost; silicon oxide negative electrode materials, which provide higher specific capacity; silicon-carbon composite negative electrode materials, which combine the high capacity of silicon and the stable performance of carbon; lithium titanate negative electrode materials, which have excellent cycle stability and fast charge and discharge capabilities; lithium metal negative electrode materials, which have extremely high energy density; nano-negative electrode materials, which use nanotechnology to increase the surface area and reaction activity of materials, thereby improving the overall performance of the battery. The disclosed solution can effectively identify and detect various defects inside the battery cell, such as poor welding, pole piece tearing, diaphragm folding and lithium precipitation, for different battery forms and material combinations, thereby ensuring the stability and safety of the battery and improving the reliability and service life of the overall system.

[0037] According to another aspect of the present disclosure, a battery defect detection device is provided, which includes a memory and a processor, wherein the memory stores instructions, and when the instructions are executed by the processor, any one of the battery defect detection methods 100 based on the charging process described above is implemented.

[0038] According to another aspect of the present disclosure, a computer-readable storage medium is provided, in which instructions are stored. When the instructions are executed by a processor, the processor executes any one of the battery defect detection methods 100 based on the charging process as described above. The computer-readable medium referred to in the present disclosure includes various types of computer storage media, which can be any available medium that can be accessed by a general or special computer. For example, the computer-readable medium can include RAM, ROM, EPROM, EEPROM, etc. 2PROM, register, hard disk, removable disk, CD-ROM or other optical disk storage, disk storage or other magnetic storage device, or can be used to carry or store the desired program code unit with instruction or data structure form and can be accessed by general or special computer, or any other temporary or non-temporary medium of general or special processor. As used herein, the disk usually copies data magnetically, while the dish uses laser to optically copy data. The above combination should also be included in the protection scope of computer-readable medium. Exemplary storage medium is coupled to the processor so that the processor can read and write information from / to the storage medium. In an alternative solution, the storage medium can be integrated into the processor. The processor and the storage medium can reside in the ASIC. The ASIC can reside in the user terminal. In an alternative solution, the processor and the storage medium can reside in the user terminal (e.g., test, diagnosis, detection equipment) as discrete components.

[0039] According to another aspect of the present disclosure, a vehicle is provided, which includes a battery defect detection device according to any of the aforementioned embodiments. The detection device in the present disclosure is applicable to various types of vehicles, including but not limited to pure electric vehicles, hybrid electric vehicles, plug-in hybrid vehicles, and fuel cell vehicles. These vehicles usually include a power battery system, and the detection device monitors the characteristic parameters of the battery in real time to identify potential defects and ensure the safety and reliability of the vehicle. In addition, the detection device can also be applied to other vehicles that use battery systems, such as electric buses, electric trucks, electric motorcycles, and even industrial vehicles and unmanned equipment.

[0040] According to another aspect of the present disclosure, a charging facility is provided, which includes a battery defect detection device according to any of the aforementioned embodiments. The battery defect detection device in the present disclosure is applicable to various types of charging facilities, including but not limited to public charging stations, private charging piles, fast charging and slow charging stations, etc. The detection device can be integrated into existing charging equipment to monitor the health status of the battery being charged in real time, identify potential battery defects, and ensure the safety of the charging process. In addition, the detection device can also be applied to charging facilities in specific scenarios, such as electric vehicle fast battery replacement stations, electric bus charging stations, etc.

[0041] The above are only specific embodiments of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Those skilled in the art can think of other feasible changes or substitutions based on the technical scope disclosed in the present disclosure, and such changes or substitutions are all included in the protection scope of the present disclosure. In the absence of conflict, the embodiments of the present disclosure and the features in the embodiments can also be combined with each other. The protection scope of the present disclosure shall be based on the records of the claims.

Claims

1. A battery defect detection method based on the charging process, characterized in that: The method comprises the following steps: S1: during a battery charging process, determining one or more battery parameters of the battery; S2: when a battery parameter value among the determined one or more battery parameters deviates from a predetermined value corresponding to the battery parameter by reaching a first deviation threshold, performing defect diagnosis counting; and S3: When the count reaches a predetermined number threshold, determine that the battery has a defect related to the battery parameter.

2. The method according to claim 1, characterized in that The battery parameters include one or more of the following: voltage, temperature, current, charge, internal resistance, voltage change rate, and internal resistance change rate.

3. The method according to claim 2, characterized in that In the case where the battery parameter is a voltage change rate or an internal resistance change rate, the method further comprises: when a battery parameter value in the determined battery parameter is less than 0, determining that the battery has a defect related to the battery parameter.

4. The method according to claim 1, characterized in that The one or more battery parameters each have a weight, and the method further includes: determining a battery defect detection score based on the difference between each battery parameter and a predetermined threshold corresponding to the battery parameter and the weight of each battery parameter, and determining that the battery is defective when the score exceeds a predetermined score.

5. The method according to claim 1, characterized in that The charging process includes multiple charging sub-segments, and the method further includes: when a battery parameter value among the determined one or more battery parameters deviates twice from a predetermined value corresponding to the battery parameter in one of the multiple charging sub-segments by a first deviation threshold, determining that the battery has a defect related to the battery parameter.

6. The method according to claim 1, characterized in that The method further includes: after determining that the battery is defective, performing early warning processing on the vehicle, the early warning processing includes one or more of the following: setting a fault code for the vehicle, shutting down the vehicle, and causing the vehicle or charging station to sound an alarm.

7. The method according to claim 1, characterized in that The form of the battery includes one or more of the following: soft-pack battery, square shell battery, cylindrical battery, the positive electrode material of the battery includes one or more of the following: nickel cobalt manganese oxide positive electrode material, nickel cobalt aluminum oxide lithium positive electrode material, lithium manganese oxide positive electrode material, lithium cobalt oxide positive electrode material, lithium iron phosphate positive electrode material, the negative electrode material of the battery includes one or more of the following: graphite negative electrode material, silicon oxide negative electrode material, silicon-carbon composite negative electrode material, lithium titanate negative electrode material, lithium metal negative electrode material, nano negative electrode material.

8. A battery defect detection device, characterized in that: The battery defect detection device comprises a memory and a processor, wherein the memory stores instructions, and when the instructions are executed by the processor, the method according to any one of claims 1 to 7 is implemented.

9. A vehicle, characterized in that: The vehicle includes the battery defect detection device according to claim 8.

10. A charging facility, characterized in that: The charging facility includes the battery defect detection device according to claim 8.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, which, when executed by a processor, implement the method according to any one of claims 1 to 7.

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