Methods, systems, equipment and media for detecting battery health status

Abstract

This invention discloses a method, system, device, and medium for detecting battery health status. It extracts a target curve segment from the first charging characteristic curve of the battery under test; based on the correspondence between the characteristic parameters of the target curve segment and the battery health status, it determines the battery health status that matches the characteristic parameters of the target curve segment, and identifies this battery health status as the battery health status of the battery under test. This invention uses the correspondence between the characteristic parameters of the target curve segment of the battery under test and the battery health status as the calculation basis, improving the reliability and accuracy of battery health status detection; furthermore, it statistically analyzes the number of abuse conditions that accelerate battery aging, determines whether abuse conditions will accelerate battery degradation, and provides early warnings.

Description

Technical Field

[0001] This invention relates to the field of battery technology, and in particular to a method, system, device and medium for detecting the health status of a battery. Background Technology

[0002] With advancements in power battery technology and fast charging technology, users are no longer concerned about the impact of driving range on their driving experience, leading to a surge in the selection of new energy vehicles. However, after prolonged use or repeated charging, the performance of power batteries will significantly decline. In recent years, there have been numerous incidents of vehicles losing control, spontaneously combusting, or even exploding due to power battery malfunctions.

[0003] When the rate at which the battery releases heat exceeds its rate of heat dissipation, the battery temperature rises as heat accumulates. High temperatures increase the chemical activity of the battery's internal materials, accelerating chemical reactions or introducing new side reactions, which in turn release even more heat, creating a vicious cycle. Continued heating can lead to the melting or decomposition of the battery's internal materials, and in severe cases, can cause serious accidents such as internal short circuits and thermal runaway.

[0004] However, there is currently no reliable method to assess the health status of batteries, making it impossible to prevent the continued use of power batteries with malfunctions in a timely manner, which could lead to safety accidents. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to overcome the defects of poor reliability and low accuracy in the existing battery health status detection, and to provide a method, system, device and medium for detecting battery health status.

[0006] The present invention solves the above-mentioned technical problems through the following technical solution:

[0007] Firstly, a method for detecting the health status of a battery is provided, the method comprising:

[0008] Extract a target curve segment from the first charging characteristic curve of the battery under test; wherein the first charging characteristic curve is determined based on the first charging data obtained by charging the battery under test with target charging parameters, and the target curve segment is at least one curve segment with the highest correlation to the battery health status.

[0009] Based on the correspondence between the characteristic parameters of the target curve segment and the battery health status, the battery health status that matches the characteristic parameters of the target curve segment is determined, and the battery health status is determined as the battery health status of the battery under test; wherein, the correspondence is determined based on the second charging data obtained by charging a test battery of the same type as the battery under test with the target charging parameters.

[0010] Optionally, the characteristic parameters include charging time, and the extraction of the target curve segment from the first charging characteristic curve of the battery under test includes:

[0011] The first charging characteristic curve is divided into multiple first curve segments;

[0012] Calculate the correlation coefficient between the isobaric rise time characteristic and the battery health status for each of the first curve segments;

[0013] The first curve segment with the largest correlation coefficient is taken as the target curve segment of the first charging characteristic curve.

[0014] Optionally, the correspondence can be established through the following steps:

[0015] The test battery is charged M times to obtain the second charging data corresponding to each charge; the second charging data includes the second charging characteristic curve and the battery health status; M≥1 and M is an integer;

[0016] For each of the second charging characteristic curves, the second charging characteristic curve is divided into multiple second curve segments according to the same division rules. The correlation coefficient between the charging time of each second curve segment and the battery health status is calculated, and at least one second curve segment with the largest correlation coefficient is determined as the target curve segment of the second charging characteristic curve.

[0017] Fit the target curve segments corresponding to M second charging characteristic curves and M battery health states, and determine the fitting results as the correspondence.

[0018] Secondly, the present invention provides a battery health status detection system, the detection system comprising:

[0019] An extraction module is used to extract a target curve segment from the first charging characteristic curve of the battery under test; wherein the first charging characteristic curve is determined based on the first charging data obtained by charging the battery under test with target charging parameters, and the target curve segment is at least one curve segment with the highest correlation to the battery health status.

[0020] The first determining module is used to determine the battery health state that matches the characteristic parameters of the target curve segment based on the correspondence between the characteristic parameters of the target curve segment and the battery health state, and to determine the battery health state as the battery health state of the battery under test; wherein, the correspondence is determined based on the second charging data obtained by charging a test battery of the same type as the battery under test with the target charging parameters.

[0021] Optionally, the feature parameters include charging time, and the extraction module includes:

[0022] The first division unit is used to divide the first charging characteristic curve into multiple first curve segments;

[0023] A calculation unit is used to calculate the correlation coefficient between the charging time and the battery health status of each of the first curve segments;

[0024] The selection unit is used to select at least one of the first curve segments with the largest correlation coefficient as the target curve segment of the first charging characteristic curve.

[0025] Optionally, the correspondence can be established using the following units:

[0026] The acquisition unit is used to charge the test battery M times to obtain the second charging data corresponding to each charge; the second charging data includes a second charging characteristic curve and battery health status; M≥1 and M is an integer;

[0027] The second division unit is used to divide each second charging characteristic curve into multiple second curve segments according to the same division rules, calculate the correlation coefficient between the charging time of each second curve segment and the battery health status, and determine at least one second curve segment with the largest correlation coefficient as the target curve segment of the second charging characteristic curve.

[0028] The fitting unit is used to fit the target curve segments corresponding to the M second charging characteristic curves and the M battery health states, and to determine the fitting result as the correspondence.

[0029] Optionally, the detection system further includes:

[0030] The judgment module is used to determine whether the number of times the battery under test has been charged under abuse conditions meets the threshold. If the judgment result is yes, the extraction module is called; if the judgment result is no, the second determination module is called.

[0031] The detection system also includes:

[0032] The second determining module is used to determine the capacity and DC internal resistance of the battery under test during the current charging and at least one previous charging.

[0033] The calculation module is used to calculate the rate of change of the current internal resistance of the battery under test relative to its capacity based on the capacity and the DC internal resistance.

[0034] The detection module is used to perform abuse condition detection on the battery under test based on the current relative change rate of internal resistance and capacity and the correspondence between the relative change rate of internal resistance and capacity and the abnormality type.

[0035] Thirdly, the present invention provides an electronic device including a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the battery health status detection method described in the first aspect.

[0036] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the battery health status detection method described in the first aspect.

[0037] The positive and progressive effects of this invention are as follows: by using the correspondence between the characteristic parameters of the target curve segment of the battery under test and the battery health status as the calculation basis, the battery health status that matches the characteristic parameters of the target curve segment is taken as the battery health status of the battery under test, thereby improving the reliability and accuracy of battery health status detection; and by statistically analyzing the number of abuse conditions that cause accelerated battery aging, it can determine whether abuse conditions will accelerate battery degradation and issue a warning. Attached Figure Description

[0038] Figure 1 This is a flowchart of the battery health status detection method according to Embodiment 1 of the present invention.

[0039] Figure 2 This is a flowchart of step S1 of the battery health status detection method according to Embodiment 1 of the present invention.

[0040] Figure 3 This is a flowchart illustrating the establishment of the correspondence in step S2 of the battery health status detection method according to Embodiment 1 of the present invention.

[0041] Figure 4 This is a flowchart of the battery health status detection method according to Embodiment 2 of the present invention.

[0042] Figure 5 This is a schematic diagram of the battery health status detection system according to Embodiment 3 of the present invention.

[0043] Figure 6 This is a schematic diagram of the battery health status detection system according to Embodiment 4 of the present invention.

[0044] Figure 7 This is a schematic diagram of the hardware structure of the electronic device according to Embodiment 5 of the present invention. Detailed Implementation

[0045] The present invention will be further illustrated by way of embodiments below, but the present invention is not limited to the scope of the embodiments described herein.

[0046] Example 1

[0047] This embodiment provides a method for detecting battery health status, such as... Figure 1 As shown, the detection method includes the following steps:

[0048] S1. Extract the target curve segment from the first charging characteristic curve of the battery under test.

[0049] The first charging characteristic curve is determined based on the first charging data obtained by charging the battery under test with the target charging parameters. The target curve segment is at least one curve segment with the highest correlation to the battery health status, that is, the curve segment that best characterizes the battery aging status.

[0050] The target charging parameters include at least one of the following: temperature, charging current / charging voltage, maximum single-cell voltage, maximum allowable charging current, and standard charging current.

[0051] A charging characteristic curve represents the relationship between battery voltage and charging time when charging at a constant current value; or the relationship between charging current and charging time when charging at a constant voltage value. The following explanation uses constant current charging as an example to illustrate the process of detecting battery health status.

[0052] S2. Based on the correspondence between the characteristic parameters of the target curve segment and the battery health status, determine the battery health status that matches the characteristic parameters of the target curve segment, and determine the battery health status as the battery health status of the battery under test.

[0053] The correspondence is determined based on the second charging data obtained by charging a test battery of the same type as the battery under test with the target charging parameters.

[0054] For example, suppose we obtain the following correspondence: feature parameter a - battery health state a', feature parameter b - battery health state b', feature parameter c - battery health state c', feature parameter d - battery health state d'. If the feature parameter of the target curve segment of the battery under test is b, then the battery health state of the battery under test is determined to be b' based on the correspondence.

[0055] In one embodiment, a first charging characteristic curve is obtained by acquiring a first number of charging batteries through an off-board conductive charger of an electric vehicle charging the battery under test, and steps S1 and S2 are executed to determine the battery health status of the battery under test.

[0056] In one embodiment, the BMS (Building Management System) obtains the first charging characteristic curve by acquiring the first number of charging batteries based on the communication protocol between the BMS and the off-board conductive charger of the electric vehicle, and executes steps S1 and S2 to determine the battery health status of the battery under test.

[0057] In one embodiment, the target curve segment of the battery under test is determined based on historical experience. For batteries of the same type, they have the same target curve segment, that is, the upper limit voltage of the target curve segment of each battery of the same type is the same, and the lower limit voltage of the target curve segment of each battery of the same type is also the same.

[0058] In one embodiment, a target curve segment is determined based on the first charging data of the battery under test, that is, based on the characteristics and real-time charging status of the battery under test. When extracting the target curve segment, the first charging characteristic curve obtained from the first charging data is divided into multiple first curve segments. The correlation coefficient P between the characteristic parameters corresponding to each first curve segment and the SOH value (State of Health, battery health state) is calculated, and the target curve segment is selected from the multiple first curve segments based on the magnitude of the correlation coefficient P. The correlation coefficient P can be characterized by, but is not limited to, the Pearson correlation coefficient.

[0059] In one embodiment, the characteristic parameters include at least one of charging time, slope, gradient, etc. If the characteristic parameter is characterized by charging time, then when calculating the correlation coefficient P, the magnitude of the correlation coefficient P between the charging time corresponding to each first curve segment and the SOH value is calculated.

[0060] In the case where the first charging characteristic curve represents the relationship between battery voltage and charging time, the first curve segment also represents the relationship between battery voltage and charging time. The charging time of the first curve segment is the charging time required for the lower voltage limit of the curve segment to rise to the upper voltage limit.

[0061] The following section uses the characteristic parameter including charging time as an example to further illustrate the process of determining the target curve segment. Figure 2 As shown, the characteristic parameters include charging time, and step S1 includes:

[0062] S11. Divide the first charging characteristic curve into multiple first curve segments.

[0063] The rules for dividing the first charging characteristic curve can be set according to the actual situation, but they must be the same as the rules for dividing the second charging characteristic curve described below.

[0064] S12. Calculate the correlation coefficient between the charging time and battery health status for each first curve segment.

[0065] S13. Take at least one first curve segment with the largest correlation coefficient as the target curve segment of the first charging characteristic curve.

[0066] The list of correlation coefficients corresponding to N curve segments can be: [P1, P2, P3, P4.......P N The target curve segment can be selected from N curve segments with the highest correlation coefficient; or multiple first curve segments with the closest correlation coefficient to 1 can be selected as candidate curve segments, and one or more of the largest correlation coefficients can be selected from all candidate curve segments as the target curve segment of the first charging characteristic curve.

[0067] In one embodiment, in step S2 above, when the feature parameter includes charging time, the maximum charging voltage of the single cell in the battery under test is obtained from the second charging data, and the charging time corresponding to the target curve segment is obtained. Combining the correspondence between the feature parameter of the target curve segment and the battery health status, the battery health status matching the charging time is calculated, and the calculated battery health status is used as the battery health status of the battery under test under the current charging condition.

[0068] The following describes the specific implementation method for determining the correspondence, such as... Figure 3 As shown, the correspondence in step S2 is established through the following steps:

[0069] S21. Charge the test battery M times to obtain the second charging data corresponding to each charge; the second charging data includes the second charging characteristic curve and the battery health status; M≥1 and M is an integer.

[0070] It should be noted that the test batteries can be multiple batteries of the same model.

[0071] S22. For each second charging characteristic curve, the second charging characteristic curve is divided into multiple second curve segments according to the same division rules. The correlation coefficient between the charging time of each second curve segment and the battery health status is calculated, and at least one second curve segment with the largest correlation coefficient is determined as the target curve segment of the second charging characteristic curve.

[0072] S23. Fit the target curve segments corresponding to M second charging characteristic curves and M battery health states, and determine the fitting results as the correspondence.

[0073] During M charging operations of the test battery, M second charging characteristic curves were obtained. Each charge corresponds to a SOH value, resulting in M ​​SOH values. Taking the test battery's SOH value decreasing from 100% to 75% as an example, an experiment was conducted. The SOH value can be calculated using the following formula:

[0074] SOH = C_now / C_rated

[0075] Wherein, C_now represents the actual charge capacity of the test battery at the current charge, obtained from the BMS; C_rated represents the factory rated capacity of the test battery.

[0076] The second charging characteristic curve is divided into multiple second curve segments, and the target curve segment is selected from them. Specifically, the correlation coefficient P corresponding to each second curve segment is calculated. The specific formula for calculating P is as follows:

[0077]

[0078] Where 1≤i≤M, ΔT i This represents the charging duration of the i-th charge corresponding to each second curve segment. S represents the average charging time corresponding to each second curve segment M charging operations. i This represents the SOH value of the i-th charge corresponding to each second curve segment. This represents the average SOH value corresponding to each of the M charges for each second curve segment. The second curve segment corresponding to the largest correlation coefficient P is taken as the target curve segment of the second charging characteristic curve.

[0079] Each second curve segment corresponds to M SOH values. The correlation coefficient P corresponding to each second curve segment is calculated using the M charging times and the M SOH values.

[0080] The M target curve segments and M battery health states are fitted using higher-order or exponential equations. The specific calculation formula is as follows:

[0081] SOH i =αΔT i 2 +βΔT i +γ

[0082] Where, ΔT i SOH represents the charging duration of the i-th charge corresponding to the target curve segment. i The value of SOH during the i-th charge corresponds to the target curve segment, and α, β, and γ represent the fitting parameters.

[0083] In this embodiment, by using the correspondence between the feature parameters of the target curve segment of the battery under test and the battery health status as the calculation basis, the battery health status that matches the feature parameters of the target curve segment is taken as the battery health status of the battery under test, thereby improving the reliability and accuracy of battery health status detection.

[0084] Example 2

[0085] Based on Example 1, this example provides a method for detecting battery health status, such as... Figure 4As shown, improvements have been made compared to Example 1, specifically:

[0086] Prior to step S1, the detection method further includes the following steps:

[0087] S0. Determine whether the number of times the battery under test is charged under abuse conditions meets the threshold.

[0088] If the result of step S0 is yes, proceed to step S1; if the result of step S0 is no, proceed to step S3.

[0089] Charging data may include, but is not limited to, ambient temperature, charging rate, charging voltage, and charging current. The BMS detects the ambient temperature (e.g., high or low temperature) of the battery under test during charging; parameters characterizing the battery's lifespan (e.g., charging rate); and parameters characterizing the battery's state of charge (e.g., charging voltage and charging current). Based on the charging data acquired by the BMS, it determines whether the battery is under any of the following abuse conditions during each charge: high temperature, low temperature, high rate, overcharge, or over-discharge. If the number of charges under these abuse conditions meets a threshold, step S1 is automatically executed. This threshold is determined based on actual needs, for example, set to 4 times. If the number of charges under abuse conditions exceeds the threshold, step S3 is automatically executed.

[0090] Following step S2, the detection method further includes the following steps:

[0091] S3. Determine the capacity and DC internal resistance of the battery under test during the current charging and at least one historical charging.

[0092] The relative rate of change of internal resistance capacity can be calculated by using the capacity and DC internal resistance of the battery under test during multiple historical charging cycles, or by using the average capacity and average DC internal resistance of the battery under test during each historical charging cycle, or by using the weighted result of the capacity and the weighted result of the DC internal resistance of the battery under test during each historical charging cycle.

[0093] S4. Calculate the relative change rate of the current internal resistance capacity of the battery under test based on the capacity and DC internal resistance.

[0094] Experiments have shown that the relative change rate of internal resistance capacity can effectively diagnose abnormal battery aging and provide early warning of battery abuse.

[0095] S5. Based on the current relative change rate of internal resistance and capacity and the correspondence between the relative change rate of internal resistance and capacity and the abnormality type, perform abuse condition testing on the battery under test.

[0096] If the current charging capacity of the battery under test is Cn_charge, the SOC value of the battery before charging is SOC1, and the SOC value of the battery after charging is SOC2, the capacity Cn is calculated based on SOC1 and SOC2. The specific calculation formula for Cn is as follows:

[0097] Cn=(Cn_charge) / (SOC2-SOC1)

[0098] When the battery under test reaches 90% SOC, let it rest for 5 minutes. Then, use 75% Imax as the pulse current to charge the battery under test for 10 seconds, and let it rest for another 5 minutes. Obtain the corresponding pulse voltage rise ΔU on the battery under test. Calculate the DC internal resistance Rn based on the pulse voltage rise ΔU. The specific formula for calculating Rn is as follows:

[0099]

[0100] In step S3, the first capacity Cn1 of the battery under test during the current charging and the second capacity Cn2 of the battery under test during at least one previous charging are obtained. Alternatively, the second capacity Cn2 can be determined based on the weighted or average result of the capacities of the batteries under test during multiple previous chargings. The first difference between the first and second capacities, |Cn1-Cn2|, is calculated. The first DC internal resistance Rn1 during the current charging and the second DC internal resistance Rn2 during at least one previous charging are obtained. Alternatively, the second DC internal resistance Rn2 can be determined based on the weighted or average result of the DC internal resistances of the batteries under test during multiple previous chargings. The second difference between the first and second DC internal resistances, |Rn1-Rn2|, is calculated.

[0101] In step S4, the relative change rate K of the current internal resistance capacitance is calculated based on the first difference and the second difference. mn K mn The specific calculation formula is as follows:

[0102]

[0103] In step S5, multiple sets of comparative tests can be conducted in advance on batteries of the same type and rated capacity, including single-variable tests such as normal aging test, overcharge aging test, high temperature aging test, and low temperature aging test. During the test, charging is performed under constant current conditions, and discharging can be performed under any accelerated aging conditions. The normal range and abnormal range of the relative change rate of internal resistance and capacity under different aging test conditions are determined to provide a basis for subsequent abuse test of the batteries under test.

[0104] After determining the abnormality type of the battery under test, it is determined whether the relative change rate of the current internal resistance capacity is within the abnormal range under the abnormality type. If it is, it indicates that the current charging of the battery under test is an abuse condition and has caused permanent damage to the battery, and an alarm prompt is automatically triggered.

[0105] In this embodiment, the number of times the battery under test is charged under abuse conditions is compared with a threshold number. If the threshold is met, the battery health state that matches the characteristic parameters of the target curve segment is taken as the battery health state of the battery under test. If the threshold is not met, the relative change rate of the current internal resistance capacity of the battery under test is calculated using capacity and DC internal resistance to achieve abuse condition detection of the battery under test. This invention improves the reliability and accuracy of battery health state detection through normal decay SOH value calculation and abnormal decay alarm.

[0106] Example 3

[0107] This embodiment provides a battery health status detection system, such as... Figure 5 As shown, the detection system includes an extraction module 110 and a first determination module 120.

[0108] Extraction module 110 is used to extract target curve segments from the first charging characteristic curve of the battery under test.

[0109] The first charging characteristic curve is determined based on the first charging data obtained by charging the battery under test with the target charging parameters. The target curve segment is at least one curve segment with the highest correlation to the battery health status, that is, the curve segment that best characterizes the battery aging status.

[0110] The target charging parameters include at least one of the following: temperature, charging current / charging voltage, maximum single-cell voltage, maximum allowable charging current, and standard charging current.

[0111] A charging characteristic curve represents the relationship between battery voltage and charging time when charging at a constant current value; or the relationship between charging current and charging time when charging at a constant voltage value. The following explanation uses constant current charging as an example to illustrate the process of detecting battery health status.

[0112] The first determining module 120 is used to determine the battery health state that matches the characteristic parameters of the target curve segment based on the correspondence between the characteristic parameters of the target curve segment and the battery health state, and to determine the battery health state as the battery health state of the battery under test.

[0113] The correspondence is determined based on the second charging data obtained by charging a test battery of the same type as the battery under test with the target charging parameters.

[0114] For example, suppose we obtain the following correspondence: feature parameter a - battery health state a', feature parameter b - battery health state b', feature parameter c - battery health state c', feature parameter d - battery health state d'. If the feature parameter of the target curve segment of the battery under test is b, then the battery health state of the battery under test is determined to be b' based on the correspondence.

[0115] In one embodiment, a first charging characteristic curve is obtained by acquiring the first number of charging batteries through an off-board conductive charger of an electric vehicle charging the battery under test, and the extraction module 110 and the first determination module 120 are invoked to determine the battery health status of the battery under test.

[0116] In one embodiment, the BMS (Building Management System) obtains the first charging characteristic curve by acquiring the first number of charging batteries based on the communication protocol between the BMS and the off-board conductive charger of the electric vehicle, and calls the extraction module 110 and the first determination module 120 to determine the battery health status of the battery under test.

[0117] In one embodiment, the target curve segment of the battery under test is determined based on historical experience. For batteries of the same type, they have the same target curve segment, that is, the upper limit voltage of the target curve segment of each battery of the same type is the same, and the lower limit voltage of the target curve segment of each battery of the same type is also the same.

[0118] In one embodiment, a target curve segment is determined based on the first charging data of the battery under test, that is, based on the characteristics and real-time charging status of the battery under test. When extracting the target curve segment, the first charging characteristic curve obtained from the first charging data is divided into multiple first curve segments. The correlation coefficient P between the characteristic parameters corresponding to each first curve segment and the SOH value (State of Health, battery health state) is calculated, and the target curve segment is selected from the multiple first curve segments based on the magnitude of the correlation coefficient P. The correlation coefficient P can be characterized by, but is not limited to, the Pearson correlation coefficient.

[0119] In one embodiment, the characteristic parameters include at least one of charging time, slope, gradient, etc. If the characteristic parameter is characterized by charging time, then when calculating the correlation coefficient P, the magnitude of the correlation coefficient P between the charging time corresponding to each first curve segment and the SOH value is calculated.

[0120] In the case where the first charging characteristic curve represents the relationship between battery voltage and charging time, the first curve segment also represents the relationship between battery voltage and charging time. The charging time of the first curve segment is the charging time required for the lower voltage limit of the curve segment to rise to the upper voltage limit.

[0121] The following section uses the characteristic parameter including charging time as an example to further illustrate the process of determining the target curve segment. Figure 5 As shown, the feature parameters include charging time, and the extraction module 110 includes:

[0122] The first division unit 111 is used to divide the first charging characteristic curve into multiple first curve segments.

[0123] The rules for dividing the first charging characteristic curve can be set according to the actual situation, but they must be the same as the rules for dividing the second charging characteristic curve described below.

[0124] The calculation unit 112 is used to calculate the correlation coefficient between the charging time of each first curve segment and the battery health status.

[0125] Selection unit 113 is used to select at least one first curve segment with the largest correlation coefficient as the target curve segment of the first charging characteristic curve.

[0126] The list of correlation coefficients corresponding to N curve segments can be: [P1, P2, P3, P4.......P N The target curve segment can be selected from N curve segments with the highest correlation coefficient; or multiple first curve segments with the closest correlation coefficient to 1 can be selected as candidate curve segments, and one or more of the largest correlation coefficients can be selected from all candidate curve segments as the target curve segment of the first charging characteristic curve.

[0127] In one embodiment, in the first determining module 120, when the feature parameter includes charging time, the maximum charging voltage of the single cell in the battery under test is obtained from the second charging data, and the charging time corresponding to the target curve segment is obtained. Combining the correspondence between the feature parameter of the target curve segment and the battery health status, the battery health status matching the charging time is calculated, and the calculated battery health status is used as the battery health status of the battery under test under the current charging condition.

[0128] The following describes the specific implementation method for determining the correspondence, such as... Figure 5 As shown, the correspondence in the first determining module 120 is established through the following units:

[0129] The acquisition unit 121 is used to charge the test battery M times to obtain second charging data corresponding to each charge; the second charging data includes the second charging characteristic curve and the battery health status; M≥1 and M is an integer.

[0130] It should be noted that the test batteries can be multiple batteries of the same model.

[0131] The second division unit 122 is used to divide each second charging characteristic curve into multiple second curve segments according to the same division rules, calculate the correlation coefficient between the charging time of each second curve segment and the battery health status, and determine at least one second curve segment with the largest correlation coefficient as the target curve segment of the second charging characteristic curve.

[0132] Fitting unit 123 is used to fit the target curve segments corresponding to M second charging characteristic curves and M battery health states, and to determine the fitting results as a correspondence.

[0133] During M charging operations of the test battery, M second charging characteristic curves were obtained. Each charge corresponds to a SOH value, resulting in M ​​SOH values. Taking the test battery's SOH value decreasing from 100% to 75% as an example, an experiment was conducted. The SOH value can be calculated using the following formula:

[0134] SOH = C_now / C_rated

[0135] Wherein, C_now represents the actual charge capacity of the test battery at the current charge, obtained from the BMS; C_rated represents the factory rated capacity of the test battery.

[0136] The second charging characteristic curve is divided into multiple second curve segments, and the target curve segment is selected from them. Specifically, the correlation coefficient P corresponding to each second curve segment is calculated. The specific formula for calculating P is as follows:

[0137]

[0138] Where 1≤i≤M, ΔT i This represents the charging duration of the i-th charge corresponding to each second curve segment. S represents the average charging time corresponding to each second curve segment M charging operations. i This represents the SOH value of the i-th charge corresponding to each second curve segment. This represents the average SOH value corresponding to each of the M charges for each second curve segment. The second curve segment corresponding to the largest correlation coefficient P is taken as the target curve segment of the second charging characteristic curve.

[0139] Each second curve segment corresponds to M SOH values. The correlation coefficient P corresponding to each second curve segment is calculated using the M charging times and the M SOH values.

[0140] The M target curve segments and M battery health states are fitted using higher-order or exponential equations. The specific calculation formula is as follows:

[0141] SOH i=αΔT i 2 +βΔT i +γ

[0142] Where, ΔT i SOH represents the charging duration of the i-th charge corresponding to the target curve segment. i The value of SOH during the i-th charge corresponds to the target curve segment, and α, β, and γ represent the fitting parameters.

[0143] In this embodiment, by using the correspondence between the feature parameters of the target curve segment of the battery under test and the battery health status as the calculation basis, the battery health status that matches the feature parameters of the target curve segment is taken as the battery health status of the battery under test, thereby improving the reliability and accuracy of battery health status detection.

[0144] Example 4

[0145] Based on Example 3, this example provides a battery health status detection system, such as... Figure 6 As shown, compared to Embodiment 3, this detection system includes:

[0146] The judgment module 100 is used to determine whether the number of times the battery under test is charged under abuse conditions meets the threshold.

[0147] If the judgment result of the judgment module 100 is yes, the extraction module 110 is called; if the judgment result of the judgment module 100 is no, the second determination module 130 is called.

[0148] Charging data may include, but is not limited to, ambient temperature, charging rate, charging voltage, and charging current. The BMS detects the ambient temperature (e.g., high or low temperature) of the battery under test during charging; parameters characterizing the battery's lifespan (e.g., charging rate); and parameters characterizing the battery's charging state (e.g., charging voltage and charging current). Based on the charging data acquired by the BMS, it determines whether the battery is under any of the following abuse conditions during each charge: high temperature, low temperature, high rate, overcharge, or over-discharge. If the number of charges under abuse conditions meets a threshold, the extraction module 110 is automatically invoked. This threshold is determined based on actual needs, for example, set to 4 times. If the number of charges under abuse conditions exceeds the threshold, the second determination module 130 is automatically invoked.

[0149] The second determining module 130 is used to determine the capacity and DC internal resistance of the battery under test during the current charging and at least one historical charging.

[0150] The relative rate of change of internal resistance capacity can be calculated by using the capacity and DC internal resistance of the battery under test during multiple historical charging cycles, or by using the average capacity and average DC internal resistance of the battery under test during each historical charging cycle, or by using the weighted result of the capacity and the weighted result of the DC internal resistance of the battery under test during each historical charging cycle.

[0151] The calculation module 140 is used to calculate the relative rate of change of the current internal resistance capacity of the battery under test based on the capacity and DC internal resistance.

[0152] Experiments have shown that the relative change rate of internal resistance capacity can effectively diagnose abnormal battery aging and provide early warning of battery abuse.

[0153] The detection module 150 is used to perform abuse condition detection on the battery under test based on the current relative change rate of internal resistance and capacity and the correspondence between the relative change rate of internal resistance and capacity and the abnormality type.

[0154] If the current charging capacity of the battery under test is Cn_charge, the SOC value of the battery before charging is SOC1, and the SOC value of the battery after charging is SOC2, the capacity Cn is calculated based on SOC1 and SOC2. The specific calculation formula for Cn is as follows:

[0155] Cn=(Cn_charge) / (SOC2-SOC1)

[0156] When the battery under test reaches 90% SOC, let it rest for 5 minutes. Then, use 75% Imax as the pulse current to charge the battery under test for 10 seconds, and let it rest for another 5 minutes. Obtain the corresponding pulse voltage rise ΔU on the battery under test. Calculate the DC internal resistance Rn based on the pulse voltage rise ΔU. The specific formula for calculating Rn is as follows:

[0157]

[0158] In the second determining module 130, the first capacity Cn1 of the battery under test during the current charging and the second capacity Cn2 of the battery under test during at least one historical charging are obtained. Alternatively, the second capacity Cn2 can be determined based on the weighted or average result of the capacities of the batteries under test during multiple historical chargings. A first difference |Cn1-Cn2| between the first and second capacities is calculated. Similarly, the first DC internal resistance Rn1 during the current charging and the second DC internal resistance Rn2 during at least one historical charging are obtained. Alternatively, the second DC internal resistance Rn2 can be determined based on the weighted or average result of the DC internal resistances of the batteries under test during multiple historical chargings. A second difference |Rn1-Rn2| between the first and second DC internal resistances is calculated.

[0159] In the calculation module 140, the relative change rate K of the current internal resistance capacitance is calculated based on the first difference and the second difference.mn K mn The specific calculation formula is as follows:

[0160]

[0161] In the testing module 150, multiple sets of comparative tests can be performed in advance on batteries of the same type and rated capacity, including single-variable tests such as normal aging test, overcharge aging test, high temperature aging test, and low temperature aging test. During the test, charging is performed under constant current conditions, and discharging can be performed under any accelerated aging conditions. The normal range and abnormal range of the relative change rate of internal resistance and capacity under different aging test conditions are determined to provide a basis for subsequent abuse test of the batteries under test.

[0162] After determining the abnormality type of the battery under test, it is determined whether the relative change rate of the current internal resistance capacity is within the abnormal range under the abnormality type. If it is, it indicates that the current charging of the battery under test is an abuse condition and has caused permanent damage to the battery, and an alarm prompt is automatically triggered.

[0163] In this embodiment, the number of times the battery under test is charged under abuse conditions is compared with a threshold number. If the threshold is met, the battery health state that matches the characteristic parameters of the target curve segment is taken as the battery health state of the battery under test. If the threshold is not met, the relative change rate of the current internal resistance capacity of the battery under test is calculated using capacity and DC internal resistance to achieve abuse condition detection of the battery under test. This invention improves the reliability and accuracy of battery health state detection through normal decay SOH value calculation and abnormal decay alarm.

[0164] Example 3

[0165] Figure 7 This is a schematic diagram of the structure of an electronic device provided in this embodiment. The smart helmet includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the battery health status detection method of Embodiment 1 or Embodiment 2. Figure 7 The electronic device 60 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0166] Electronic device 60 may be in the form of a general-purpose computing device, such as a server device. Components of electronic device 60 may include, but are not limited to: at least one processor 61, at least one memory 62, and a bus 63 connecting different system components (including memory 62 and processor 61).

[0167] Bus 63 includes a data bus, an address bus, and a control bus.

[0168] The memory 62 may include volatile memory, such as random access memory (RAM) 621 and / or cache memory 622, and may further include read-only memory (ROM) 623.

[0169] The memory 62 may also include a program / utility 625 having a set (at least one) of program modules 624, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0170] The processor 61 executes various functional applications and data processing by running computer programs stored in the memory 62, such as the battery health status detection method of Embodiment 1 or Embodiment 2 of the present invention.

[0171] Electronic device 60 can also communicate with one or more external devices 64 (e.g., keyboard, pointing device, etc.). This communication can be performed via input / output (I / O) interface 65. Furthermore, the model-generated electronic device 60 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public network, such as the Internet) via network adapter 66. As shown, network adapter 66 communicates with other modules of the model-generated electronic device 60 via bus 63. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the model-generated electronic device 60, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems.

[0172] It should be noted that although several units / modules or sub-units / modules of the electronic device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided and embodied by multiple units / modules.

[0173] Example 4

[0174] This embodiment provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the battery health status detection method of Embodiment 1 or Embodiment 2.

[0175] The readable storage medium may be more specifically adopted, including but not limited to: portable disk, hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0176] In a possible implementation, the present invention can also be implemented as a program product comprising program code, which, when the program product is run on a terminal device, is used to cause the terminal device to execute the battery health status detection method of Embodiment 1 or Embodiment 2.

[0177] The program code for executing the present invention can be written in any combination of one or more programming languages. The program code can be executed entirely on the user device, partially on the user device, as a standalone software package, partially on the user device and partially on a remote device, or entirely on a remote device.

[0178] While specific embodiments of the present invention have been described above, those skilled in the art should understand that these are merely illustrative examples, and the scope of protection of the present invention is defined by the appended claims. Those skilled in the art can make various changes or modifications to these embodiments without departing from the principles and essence of the present invention, but all such changes and modifications fall within the scope of protection of the present invention.

Claims

1. A method for detecting the health status of a battery, characterized in that, The detection method includes: Extract a target curve segment from the first charging characteristic curve of the battery under test; wherein the first charging characteristic curve is determined based on the first charging data obtained by charging the battery under test with target charging parameters, and the target curve segment is at least one curve segment with the highest correlation to the battery health status. Based on the correspondence between the characteristic parameters of the target curve segment and the battery health status, the battery health status that matches the characteristic parameters of the target curve segment is determined, and the battery health status is determined as the battery health status of the battery under test; wherein, the correspondence is determined based on the second charging data obtained by charging a test battery of the same type as the battery under test with the target charging parameters; The characteristic parameters include charging time, and the extraction of the target curve segment from the first charging characteristic curve of the battery under test includes: The first charging characteristic curve is divided into multiple first curve segments; Calculate the correlation coefficient between the charging time and battery health status for each of the first curve segments; The first curve segment with the largest correlation coefficient is taken as the target curve segment of the first charging characteristic curve. The corresponding relationship is established through the following steps: The test battery is charged M times to obtain the second charging data corresponding to each charge; the second charging data includes the second charging characteristic curve and the battery health status; M≥1 and M is an integer; For each of the second charging characteristic curves, the second charging characteristic curve is divided into multiple second curve segments according to the same division rules. The correlation coefficient between the charging time of each second curve segment and the battery health status is calculated, and at least one second curve segment with the largest correlation coefficient is determined as the target curve segment of the second charging characteristic curve. Fit the target curve segments corresponding to M second charging characteristic curves and M battery health states, and determine the fitting results as the correspondence.

2. The method for detecting battery health status as described in claim 1, characterized in that, The step of extracting the target curve segment from the first charging characteristic curve of the battery under test also includes: When determining whether the number of times the battery under test is charged under abuse conditions meets the threshold; If the judgment result is yes, then the step of extracting the target curve segment from the first charging characteristic curve of the battery under test is performed.

3. The method for detecting battery health status as described in claim 2, characterized in that, The detection method further includes: If the determination result is negative, determine the capacity and DC internal resistance of the battery under test during the current charging and at least one previous charging. Calculate the rate of change of the current internal resistance of the battery under test relative to its capacity based on the capacity and the DC internal resistance. Based on the current relative change rate of internal resistance and capacity and the correspondence between the relative change rate of internal resistance and capacity and the abnormality type, the battery under test is subjected to abuse condition detection.

4. A battery health status detection system, characterized in that, The detection system includes: An extraction module is used to extract a target curve segment from the first charging characteristic curve of the battery under test; wherein the first charging characteristic curve is determined based on the first charging data obtained by charging the battery under test with target charging parameters, and the target curve segment is at least one curve segment with the highest correlation to the battery health status. The first determining module is used to determine the battery health state that matches the characteristic parameters of the target curve segment based on the correspondence between the characteristic parameters of the target curve segment and the battery health state, and to determine the battery health state as the battery health state of the battery under test; wherein, the correspondence is determined based on the second charging data obtained by charging a test battery of the same type as the battery under test with the target charging parameters. The feature parameters include charging time, and the extraction module includes: The first division unit is used to divide the first charging characteristic curve into multiple first curve segments; A calculation unit is used to calculate the correlation coefficient between the charging time and the battery health status of each of the first curve segments; The selection unit is used to select at least one of the first curve segments with the largest correlation coefficient as the target curve segment of the first charging characteristic curve. The correspondence is established using the following units: The acquisition unit is used to charge the test battery M times to obtain the second charging data corresponding to each charge; the second charging data includes a second charging characteristic curve and battery health status; M≥1 and M is an integer; The second division unit is used to divide each second charging characteristic curve into multiple second curve segments according to the same division rules, calculate the correlation coefficient between the charging time of each second curve segment and the battery health status, and determine at least one second curve segment with the largest correlation coefficient as the target curve segment of the second charging characteristic curve. The fitting unit is used to fit the target curve segments corresponding to the M second charging characteristic curves and the M battery health states, and to determine the fitting result as the correspondence.

5. The battery health status detection system as described in claim 4, characterized in that, The detection system also includes: The judgment module is used to determine whether the number of times the battery under test has been charged under abuse conditions meets the threshold. If the judgment result is yes, the extraction module is called; if the judgment result is no, the second determination module is called.

6. The battery health status detection system as described in claim 5, characterized in that, The detection system also includes: The second determining module is used to determine the capacity and DC internal resistance of the battery under test during the current charging and at least one previous charging. The calculation module is used to calculate the rate of change of the current internal resistance of the battery under test relative to its capacity based on the capacity and the DC internal resistance. The detection module is used to perform abuse condition detection on the battery under test based on the current relative change rate of internal resistance and capacity and the correspondence between the relative change rate of internal resistance and capacity and the abnormality type.

7. An electronic device comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the battery health status detection method according to any one of claims 1-3.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the method for detecting the battery health status according to any one of claims 1-3.

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