A method and system for rapid detection of lithium battery health status

Abstract

The present application relates to the technical field of electrical variable detection, and specifically to a method and system for rapid detection of the health status of lithium batteries, the method comprising: determining the battery charging impedance of each battery pack during the charging stage of each cycle; determining the aging degree of each battery pack based on the change trend and distribution of all temperature data during the charging stage of each battery pack in each cycle, combined with the battery charging impedance; determining the health of the battery system based on the difference between the capacity data of all battery packs in the battery system during the charging stage of the same cycle, combined with all the aging degrees, so as to detect the battery. The present application determines the real capacity data of the battery system by analyzing the health and aging of the battery system, thereby improving the accuracy of battery health status detection.

Description

Technical Field

[0001] The present application relates to the technical field of electrical variable detection, and in particular to a method and system for rapid detection of the health status of a lithium battery. Background Art

[0002] Lithium batteries are currently widely used in many fields such as electric vehicles, energy storage systems, and aerospace. In actual operation, the health status estimation of lithium-ion batteries is crucial to maintaining the safe and stable operation of the battery. In addition, accurate health status estimation can also provide important guidance for the subsequent operation of the battery, which is conducive to extending the battery life.

[0003] The internal resistance of a battery is an important indicator for evaluating the battery's health status. Lithium batteries have very complex electrochemical properties. However, directly measuring the internal resistance of a battery is difficult to meet the current demand for rapid detection of the battery's health status. In order to achieve rapid online detection, the changing characteristics of the voltage and current during the charging and discharging of lithium batteries are usually analyzed to extract the health status of lithium batteries. In the actual detection process, the accuracy of battery health status detection is affected by the internal chemical reaction rate and strength instability of the battery under different working conditions, as well as the change in ambient temperature. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a method and system for rapid detection of the health status of a lithium battery. The technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present application provides a method for quickly detecting the health status of a lithium battery, the method comprising the following steps:

[0006] Obtain the current, voltage and temperature data of each battery pack in the battery system at all sampling moments during the charging stage of each cycle;

[0007] The integral of the current data at each sampling moment and the next sampling moment in each cycle charging stage of each battery pack is used as the capacity data of each battery pack in each cycle charging stage;

[0008] Determine the battery charging impedance of each battery pack in each cycle based on the change and distribution of all capacity data with voltage data in the charging stage of each battery pack in each cycle;

[0009] Determine the aging degree of each battery pack based on the change trend and distribution of all temperature data in the charging stage of each cycle of each battery pack and the battery charging impedance;

[0010] Based on the difference between the capacity data of all battery packs in the battery system at the end of the charging stage in the same cycle, the health status of the battery system is determined in combination with all the aging degrees to detect the batteries.

[0011] Preferably, the method for determining the battery charging impedance of each battery pack in each cycle is:

[0012] All capacity data and all voltage data of the charging stage of each battery pack in each cycle are used as inputs of the capacity increase analysis method to obtain the IC curve of the charging stage of each battery pack in each cycle;

[0013] Determine the area enclosed by the IC curve and the coordinate axis where the voltage data is located between the two inflection points before and after the maximum value point on the IC curve of each battery pack in the charging stage of each cycle;

[0014] Analyze the degree of dispersion of the horizontal coordinates of the maximum value points on all IC curves in each cycle of each battery pack and the previous preset number of cycles;

[0015] Analyze the product of the maximum value on the IC curve of each battery pack in the charging stage in each cycle and the area, and record it as the first product of each battery pack in each cycle;

[0016] The battery charging impedance of each battery group in each cycle is determined based on the discrete degree and the first product.

[0017] Preferably, the battery charging impedance of each battery pack in each cycle is the ratio of the discreteness of the horizontal coordinates of the maximum value points on all IC curves of each battery pack in each cycle to the first product.

[0018] Preferably, the process of determining the aging degree of each battery pack is as follows:

[0019] The ratio of the difference between the temperature data at each sampling moment in the charging stage of each cycle of each battery pack and the temperature data at the previous sampling moment to the difference between the corresponding sampling moments is used as the slope value of each temperature data at the charging stage of each cycle of each battery pack;

[0020] Analyze the product of the sum of the slope values ​​of all temperature data in the charging stage of each cycle of each battery pack and the range of all temperature data, and record it as the first temperature product of each cycle of each battery pack;

[0021] The aging degree of each battery group is determined based on the product of all the battery charging impedances and all the first temperatures.

[0022] Preferably, the determining the aging degree of each battery pack based on the product of all the battery charging impedances and all the first temperatures further includes:

[0023] Analyze the difference between the product of the battery charging impedance and the first temperature in each cycle of each battery group, and record it as the first difference in each cycle of each battery group;

[0024] The aging degree of each battery group is the result of the inverse fusion of the first differences of all cycles of each battery group.

[0025] Preferably, the method for determining the health of the battery system is: the result of normalizing the capacity data at the end of the charging stage in all cycles of each battery pack to form a battery capacity sequence of each battery pack;

[0026] Analyze the mean value of the distance between the battery capacity sequences of all two-by-two battery groups in the battery system, and record it as the first mean value of the battery system;

[0027] Analyze the mean aging degree of all battery packs in the battery system and record it as the second mean of the battery system;

[0028] A health status of a battery system is determined based on the first average value and the second average value.

[0029] Preferably, the health status of the battery system is expressed as: ; In the formula, Indicates the health of the battery system; represents the first mean value of the battery system; Represents the second mean value of the battery system.

[0030] Preferably, the testing of the battery includes:

[0031] The normalized result of the mean value of the capacity data at the end of the charging stage in the last cycle of all battery packs in the battery system is recorded as the capacity value of the battery system;

[0032] Determine a capacity correction value of the battery system based on the capacity value and health status of the battery system;

[0033] If the ratio of the capacity correction value to the capacity value of the battery system is greater than a preset value, the battery is normal, otherwise, the battery is abnormal.

[0034] Preferably, the expression of the capacity correction value of the battery system is: ; In the formula, Q represents the capacity correction value of the battery system; P is the capacity value of the battery system; F represents the health of the battery system; Represents the base 2 logarithmic function.

[0035] In a second aspect, an embodiment of the present application also provides a lithium battery health status rapid detection system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above methods when executing the computer program.

[0036] This application has at least the following beneficial effects:

[0037] This application determines the battery charging impedance of each battery pack in each cycle based on the change and distribution of the first-order derivative of the capacity data of the charging stage of each battery pack with respect to all voltage data as the voltage data changes, accurately reflecting the internal chemical reaction rate and intensity characteristics of the battery in each cycle; based on the change trend and distribution of all temperature data of the charging stage of each battery pack in each cycle, the aging degree of each battery pack is determined in combination with the battery charging impedance; based on the difference between the capacity data of all battery packs in the battery system in the charging stage of the same cycle, the health of the battery system is determined in combination with all the aging degrees, so as to correct the capacity data of the battery system. This application determines the true capacity data of the battery system by analyzing the health and aging of the battery system, thereby improving the accuracy of battery health status detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. 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 creative work.

[0039] Figure 1 A flowchart of a method for quickly detecting the health status of a lithium battery provided in one embodiment of the present application;

[0040] Figure 2 A schematic diagram of the battery charging impedance acquisition process provided in this application;

[0041] Figure 3 A schematic diagram of the process of extracting the health status of a battery system provided for this application;

[0042] Figure 4 A flow chart of battery health status assessment provided for this application;

[0043] Figure 5 Schematic diagram of the battery health detection process provided for this application. DETAILED DESCRIPTION

[0044] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, characteristics and effects of a lithium battery health status rapid detection method and system proposed in the present application in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0045] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0046] The specific scheme of a lithium battery health status rapid detection method and system provided by the present application is described in detail below with reference to the accompanying drawings.

[0047] See also Figure 1 , which shows a flowchart of a method for rapid detection of the health status of a lithium battery provided by an embodiment of the present application, the method comprising the following steps:

[0048] Step S1: collecting voltage, current and temperature data of each battery pack during each charge and discharge cycle.

[0049] The single cell voltage of lithium batteries is usually small, while the battery system in practical applications usually requires a higher operating voltage. Therefore, the battery system is often composed of battery packs connected in series and in parallel, so that the required total voltage can be achieved and sufficient total capacity can be provided. The health status of different battery packs may vary. In order to obtain a more accurate battery health status, this embodiment first analyzes a single battery pack, and then comprehensively judges the health status of the entire battery system based on the status of all battery packs.

[0050] A complete charging and discharging process of the lithium batteries in each battery pack is regarded as one cycle of each battery pack. Since there are many unstable factors in the discharge process of the battery pack, this embodiment collects relevant data of the charging stage in each cycle of each battery pack.

[0051] For each battery pack, a voltage sensor and a current sensor are installed respectively to collect the voltage data and current data of each battery pack in the charging stage of each cycle. The integration of the current data at each sampling moment and the next sampling moment in the charging stage of each cycle of each battery pack is used as the capacity data of each battery pack in the charging stage of each cycle.

[0052] A temperature sensor is installed near each battery pack to collect temperature data of each battery pack during the charging stage of each cycle. The time interval for collecting data by each sensor is set to T seconds, and the duration of data collection is the charging duration.

[0053] It should be noted that the value of the time interval T is manually set. In this embodiment, the value of the time interval T is 1s. The implementer can also set it according to the specific situation. This embodiment does not impose any special restrictions.

[0054] Step S2: Determine the battery charging impedance of each battery pack in each cycle based on the change and distribution of all capacity data during the charging phase of each battery pack in each cycle along with the voltage data.

[0055] As lithium batteries are used, each charge and discharge cycle may affect the battery status, such as battery capacity attenuation, changes in internal structure or degradation of electrolyte performance leading to changes in charge and discharge rates, increased internal resistance and increased side reactions leading to changes in thermal effects, etc. Therefore, by analyzing the possible slight differences in battery status during the charging stage of each cycle, the battery health status characteristics can be extracted.

[0056] The incremental capacity analysis (ICA) method is a common mathematical model method, which is usually used to analyze the small change characteristics of voltage and capacity during the charge and discharge process of lithium batteries, so as to identify the changes in active substances inside the battery, thereby reflecting the aging state and electrochemical performance of the battery.

[0057] The core step of the incremental capacity analysis (ICA) method is to obtain the electromagnetic incremental capacity curve. The initial IC curve is obtained by calculating the ratio of the capacity change to the voltage change per unit time. The process of obtaining the initial IC curve is a well-known technology, and the specific process of the acquisition process will not be repeated.

[0058] Therefore, the capacity data and all voltage data of the charging stage of each battery pack in each cycle are used as inputs of the capacity increase analysis algorithm to obtain the IC curve of the charging stage of each battery pack in each cycle.

[0059] Since the obtained initial IC curve contains a lot of noise, a filtering algorithm is used to reduce the noise of the initial IC curve.

[0060] It should be noted that there are many filtering methods. In this embodiment, the Savitzky-Golay filter is used for noise reduction. The characteristics of this filtering algorithm are that it can ensure that the shape and width of the signal remain unchanged, retain the low-frequency data signal while removing the relevant high-frequency part, so as to obtain the IC curve of the charging stage of each battery pack in each cycle after preprocessing, which is helpful for extracting the health characteristics of the battery; in actual application, as other implementation methods, the implementer can also use other filtering methods such as low-pass filters, and this embodiment does not make special restrictions.

[0061] The working principle of the Savitzky-Golay filter is a well-known technology, and its specific noise reduction process will not be described in detail.

[0062] The IC curve of the charging stage of each battery pack in each cycle usually shows a trend of first increasing and then decreasing. Its peak position, size and peak area reflect the rate and intensity of the chemical reaction inside the lithium battery to a certain extent. At the same time, as the number of cycles increases, the battery gradually ages, and the structure of the electrode material may change, resulting in a shift in the voltage threshold of the electrochemical reaction, which causes fluctuations in the peak position. Therefore, based on the change and distribution of the first-order derivative of the capacity data of the charging stage of each battery pack in each cycle with respect to all voltage data, the battery charging impedance of each battery pack in each cycle is determined, specifically:

[0063] Determine the area enclosed by the IC curve and the coordinate axis where the voltage data is located between the two inflection points before and after the maximum value point on the IC curve of each battery pack in the charging stage of each cycle;

[0064] Analyze the degree of dispersion of the horizontal coordinates of the maximum value points on all IC curves in each cycle of each battery pack and the previous preset number of cycles;

[0065] It should be understood that the value of the preset number is artificially set. In this embodiment, the value of the preset number is 5. The implementer can also set it according to the specific situation. For the corresponding cycles of each battery pack that are less than the preset number before each cycle, they will not be analyzed.

[0066] It should be noted that there are many methods for measuring the degree of discreteness. In this embodiment, the standard deviation of all peak positions in each cycle of each battery pack and the previous preset number of cycles is used as the measure of its discreteness. In actual application, the implementer can also use other methods of measuring the degree of discreteness such as variance according to the specific situation, and this embodiment does not impose any special restrictions.

[0067] Further, the product of the maximum value on the IC curve of each battery pack in the charging stage in each cycle and the area is analyzed and recorded as the first product of each battery pack in each cycle;

[0068] The battery charging impedance in the charging stage of each battery pack in each cycle is the ratio of the dispersion degree of the horizontal coordinates of the maximum value points on all IC curves of each battery pack in each cycle to the first product.

[0069] The greater the degree of dispersion of the maximum values ​​on the IC curve of adjacent charging stages, the faster the structural change rate of the electrode material in the lithium battery during the charging and discharging process, and the smaller the first product, indicating that the intensity of the chemical reaction generated by the internal discharge of the battery during the charging stage of each cycle is lower. Therefore, the greater the battery charging impedance, the greater the impedance encountered by the chemical reaction inside the battery during the charging stage of each cycle, and the lower the rate and intensity of the chemical reaction; conversely, the smaller the degree of dispersion of the maximum values ​​on the IC curve of adjacent charging stages, the larger the first product, indicating that the impedance encountered by the chemical reaction inside the battery during the charging stage of each cycle is smaller, and the rate and intensity of the chemical reaction are higher.

[0070] Preferably, the battery charging impedance acquisition process schematic diagram provided in the present application is as follows Figure 2 shown.

[0071] Step S3: determining the aging degree of each battery pack based on the change trend and distribution of all temperature data in the charging stage of each cycle of each battery pack in combination with the battery charging impedance.

[0072] If the battery is in a high temperature environment for a long time during the charging and discharging process, it may accelerate the aging of the battery and cause capacity decay. As the battery is used and the number of cycles increases, the internal resistance is usually larger, and the temperature change rate during use is usually larger. Therefore, by analyzing the temperature change rate, the thermal state and aging degree of the battery can be reflected. For example, if the temperature rises too quickly during the charging process, it means that a violent chemical reaction may have occurred inside the battery, that is, the battery health state may deteriorate.

[0073] Therefore, based on the change trend and distribution of all temperature data in the charging stage of each battery pack in each cycle, the aging degree of each battery pack is determined in combination with the battery charging impedance, specifically:

[0074] The ratio of the difference between the temperature data at each sampling moment in the charging stage of each cycle of each battery pack and the temperature data at the previous sampling moment to the difference between the corresponding sampling moments is used as the slope value of each temperature data at the charging stage of each cycle of each battery pack;

[0075] Analyze the product of the sum of the slope values ​​of all temperature data in the charging stage of each cycle of each battery pack and the range of all temperature data, and record it as the first temperature product of each cycle of each battery pack;

[0076] Analyze the difference between the product of the battery charging impedance and the first temperature in each cycle of each battery group, and record it as the first difference in each cycle of each battery group;

[0077] It should be noted that there are many ways to measure the difference. In this embodiment, the difference between the battery charging impedance and the first temperature product is measured by calculating the absolute value of the difference between the battery charging impedance and the first temperature product of each battery pack in each cycle. In actual application, as other embodiments, the implementer may also adopt methods such as ratios to measure the difference according to specific circumstances, and this embodiment does not impose any special restrictions.

[0078] Furthermore, the aging degree of each battery group is the result of the inverse fusion of the first differences of all cycles of each battery group.

[0079] It should be understood that fusion refers to combining two or more indicators by adding or multiplying them together in order to obtain a comprehensive indicator, thereby more comprehensively and accurately evaluating a phenomenon or problem. This fusion method is not limited to simple arithmetic operations, but can also include more complex statistical models and analysis methods. The implementer can choose according to the specific situation, and this embodiment does not impose any special restrictions.

[0080] Preferably, the aging degree of each battery pack in the present embodiment is the cumulative sum of the reciprocals of the first differences of all cycles of each battery pack. In actual application, as other embodiments, the implementer may also use an exponential function value with a natural constant as the base and the sum of the reciprocals of the first differences of all cycles of each battery pack as the independent variable as the aging degree of each battery pack.

[0081] The greater the battery temperature change and the greater the battery charging impedance, the smaller the difference between the battery temperature change and the battery charging impedance, and the greater the aging of the battery pack; conversely, the greater the difference between the battery temperature change and the battery charging impedance, the smaller the aging of the battery pack.

[0082] Step S4: Based on the difference between the capacity data of all battery packs in the battery system at the end of the charging stage in the same cycle, the health status of the battery system is determined in combination with all the aging degrees.

[0083] The capacity data at the end of the charging stage in all cycles of each battery pack are normalized to form the battery capacity sequence of each battery pack. The size of the battery capacity data indicates the health status of the battery to a certain extent. As the number of cycles increases, the battery capacity tends to decrease.

[0084] Due to the interference of the battery working state and environmental factors during the detection process, the capacity data fluctuates abnormally. Therefore, the health status of different battery packs may be different. Therefore, based on the difference between the capacity data of all battery packs in the battery system at the end of the charging stage in the same cycle, the health of the battery system is determined in combination with all the aging degrees to judge the health status of the battery, specifically:

[0085] Analyze the mean value of the distance between the battery capacity sequences of all two-by-two battery groups in the battery system, and record it as the first mean value of the battery system;

[0086] Analyze the mean aging degree of all battery packs in the battery system and record it as the second mean of the battery system;

[0087] The expression of the health of the battery system is: ; In the formula, Indicates the health of the battery system; represents the first mean value of the battery system; Represents the second mean value of the battery system.

[0088] The larger the mean distance of all pairwise combinations of battery packs in the battery system, the greater the degree of battery pack aging, indicating that the capacity state difference between different battery packs is greater and the battery system is more seriously aged, and the healthier the battery system is; conversely, the smaller the mean distance of all pairwise combinations of battery packs in the battery system, the smaller the degree of battery pack aging is, and the healthier the battery system is.

[0089] Preferably, the schematic diagram of the battery system health extraction process provided in the present application is as follows: Figure 3 shown.

[0090] Step S5: Based on the health of the battery system, the health status of the battery is detected.

[0091] The normalized result of the mean value of all capacity data at the end of the charging stage in the last cycle of all battery packs in the battery system is recorded as the capacity value of the battery system.

[0092] The health status reflects the deviation between the battery and its true health status. The capacity data is corrected according to the health status of the battery. The expression of the capacity correction value of the battery system is: ; In the formula, Q represents the capacity correction value of the battery system; P is the capacity value of the battery system; F represents the health of the battery system; Represents the base 2 logarithmic function.

[0093] Among them, the values ​​of Q and P are both between, The 0.1 in the value is to prevent The adjustment factor is set too large, and in order to only affect The carry position changes while the digits that make up the value remain unchanged, and avoid It is always less than 0.1 and affects the judgment effect, so it cannot be set to 1 and 0.01, but is set to 0.1; The 1 in the formula is to prevent the is a negative value and is guaranteed not to affect Where F is the accuracy of the independent variable on the calculation result of the capacity correction value of the final battery system, which is set to 1 instead of other values.

[0094] Furthermore, if the ratio of the capacity correction value to the capacity value of the battery system is greater than a preset value, the battery is normal; conversely, if the ratio of the capacity correction value to the capacity value of the battery system is less than or equal to the preset value, the battery is abnormal.

[0095] The value of the preset value needs to be set according to different battery specifications. In this embodiment, the preset value is 0.7. The implementer needs to set it according to the actual situation. This embodiment does not impose any special restrictions.

[0096] At this point, the battery status is detected based on the health of the battery system.

[0097] Preferably, the battery health status assessment flow chart provided in this application is as follows: Figure 4 shown.

[0098] Preferably, the battery health detection process diagram provided in this application is as follows Figure 5 shown.

[0099] Based on the same inventive concept as the above method, an embodiment of the present application also provides a lithium battery health status rapid detection system, including a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, the steps of any one of the above-mentioned lithium battery health status rapid detection methods are implemented.

[0100] It should be noted that the above sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0101] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0102] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for rapid detection of lithium battery health status, characterized in that: The method comprises the following steps: Obtain the current, voltage and temperature data of each battery pack in the battery system at all sampling moments during the charging stage of each cycle; The integral of the current data at each sampling moment and the next sampling moment in each cycle charging stage of each battery pack is used as the capacity data of each battery pack in each cycle charging stage; Determine the battery charging impedance of each battery pack in each cycle based on the change and distribution of all capacity data with voltage data in the charging stage of each battery pack in each cycle; Determine the aging degree of each battery pack based on the change trend and distribution of all temperature data in the charging stage of each cycle of each battery pack and the battery charging impedance; Based on the difference between the capacity data of all battery packs in the battery system at the end of the charging stage in the same cycle, the health of the battery system is determined in combination with all the aging degrees to detect the batteries; The method for determining the battery charging impedance of each battery pack in each cycle is: All capacity data and all voltage data of the charging stage of each battery pack in each cycle are used as inputs of the capacity increase analysis method to obtain the IC curve of the charging stage of each battery pack in each cycle; Determine the area enclosed by the IC curve and the coordinate axis where the voltage data is located between the two inflection points before and after the maximum value point on the IC curve of each battery pack in the charging stage of each cycle; Analyze the degree of dispersion of the horizontal coordinates of the maximum value points on all IC curves in each cycle of each battery pack and the previous preset number of cycles; Analyze the product of the maximum value on the IC curve of each battery pack in the charging stage in each cycle and the area, and record it as the first product of each battery pack in each cycle; The battery charging impedance of each battery group in each cycle is determined based on the discrete degree and the first product.

2. A lithium battery health status rapid detection method as claimed in claim 1, characterized in that: The battery charging impedance of each battery pack in each cycle is the ratio of the discrete degree of the horizontal coordinates of the maximum value points on all IC curves of each battery pack in each cycle to the first product.

3. A lithium battery health status rapid detection method as claimed in claim 1, characterized in that: The process of determining the aging degree of each battery pack is as follows: The ratio of the difference between the temperature data at each sampling moment in the charging stage of each cycle of each battery pack and the temperature data at the previous sampling moment to the difference between the corresponding sampling moments is used as the slope value of each temperature data at the charging stage of each cycle of each battery pack; Analyze the product of the sum of the slope values ​​of all temperature data in the charging stage of each cycle of each battery pack and the range of all temperature data, and record it as the first temperature product of each cycle of each battery pack; The aging degree of each battery group is determined based on the product of all the battery charging impedances and all the first temperatures.

4. A lithium battery health status rapid detection method as claimed in claim 3, characterized in that: The determining the aging degree of each battery pack based on the product of all the battery charging impedances and all the first temperatures further includes: Analyze the difference between the product of the battery charging impedance and the first temperature in each cycle of each battery group, and record it as the first difference in each cycle of each battery group; The aging degree of each battery group is the result of the inverse fusion of the first differences of all cycles of each battery group.

5. A lithium battery health status rapid detection method as claimed in claim 1, characterized in that: The method for determining the health of the battery system is: The result of normalizing the capacity data at the end of the charging stage in all cycles of each battery pack constitutes the battery capacity sequence of each battery pack; Analyze the mean value of the distance between the battery capacity sequences of all two-by-two battery groups in the battery system, and record it as the first mean value of the battery system; Analyze the mean aging degree of all battery packs in the battery system and record it as the second mean of the battery system; A health status of a battery system is determined based on the first average value and the second average value.

6. A lithium battery health status rapid detection method as claimed in claim 5, characterized in that: The expression of the health of the battery system is: ; In the formula, Indicates the health of the battery system; represents the first mean value of the battery system; Represents the second mean value of the battery system.

7. A lithium battery health status rapid detection method as claimed in claim 1, characterized in that: The battery detection includes: The normalized result of the mean value of the capacity data at the end of the charging stage in the last cycle of all battery packs in the battery system is recorded as the capacity value of the battery system; Determine a capacity correction value of the battery system based on the capacity value and health status of the battery system; If the ratio of the capacity correction value to the capacity value of the battery system is greater than a preset value, the battery is normal, otherwise, the battery is abnormal.

8. A lithium battery health status rapid detection method as claimed in claim 7, characterized in that: The expression of the capacity correction value of the battery system is: ; In the formula, Q represents the capacity correction value of the battery system; P is the capacity value of the battery system; F represents the health of the battery system; Represents the base 2 logarithmic function.

9. A lithium battery health status rapid detection system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of a method for quickly detecting the health status of a lithium battery as described in any one of claims 1-8 are implemented.

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