Method and device for determining state of health (SOH) of battery

Abstract

The present disclosure provides a method and apparatus for determining a state of health (SOH) of a battery, the method comprising: updating a battery full charge capacity (FCC) database including battery FCC data of the battery based on charging data acquired during a battery charging process, the battery FCC database is used to record battery capacity estimation data for a combination of reference values for a set of detected quantities during charging of the battery, and the SOH of the battery is determined at least based on the battery FCC database when the filling of the battery FCC database is completed by updating. According to the method, more accurate and stable SOH estimation of the battery can be provided, and the user experience is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of new energy technology, in particular to battery management technology, and more particularly to a method, device, computer program product and computer readable storage medium for determining the state of health (SOH) of a battery. Background Art

[0002] New batteries such as lithium batteries are widely used in electric vehicles (EV), robots, power storage and other fields due to their long life, high capacity and wide operating temperature range, and are gradually expanding to aviation, aerospace and other fields. In order to ensure operational safety, reliability and durability, timely and accurate monitoring of battery status is essential. However, long-term and frequent use of batteries will inevitably shorten their service life. In addition, improper charging and use will accelerate battery aging and even cause safety problems. Therefore, accurate estimation of battery state of health (SOH) has become a key factor to ensure safe operation.

[0003] Existing battery SOH methods are divided into two categories: model prediction and data-driven prediction. Model prediction methods mainly include electrochemical models, equivalent circuit models, and empirical degradation models. These methods need to explore the aging mechanism inside the battery, and it is difficult to accurately describe the degradation process of the battery capacity, resulting in poor prediction robustness and accuracy. Data-driven prediction methods directly mine implicit battery health status information and its evolution law from battery performance test data to achieve battery health estimation. Through a large amount of charging and discharging data, its internal dynamic characteristics are directly learned to establish a nonlinear relationship between SOH and the original data. Among them, convolutional neural networks (CNNs) are widely used in online battery SOH estimation due to their advantages in time series prediction. However, their single serial network structure cannot extract rich serialization information, resulting in poor model generalization and inability to accurately estimate battery SOH. Summary of the invention

[0004] A brief overview of the disclosure is given below in order to provide a basic understanding of certain aspects of the disclosure. It should be understood that this overview is not an exhaustive overview of the disclosure. It is not intended to identify key or important parts of the disclosure, nor is it intended to limit the scope of the disclosure. Its purpose is simply to give certain concepts in a simplified form as a prelude to a more detailed description discussed later.

[0005] According to one aspect of the present disclosure, a method for determining a state of health (SOH) of a battery is provided, comprising: updating a battery FCC database including full charge capacity (FCC) data of the battery based on charging data acquired during a battery charging process, wherein the battery FCC database is used to record battery capacity estimation data for a combination of benchmark values ​​of a set of detection quantities during the battery charging process, and when the battery FCC database is filled through the update, the SOH of the battery is determined at least based on the battery FCC database.

[0006] According to another aspect of the present disclosure, a device for determining the SOH of a battery is provided, comprising: a processing circuit, which is configured to: update a battery FCC database including the battery full charge capacity FCC data of the battery based on charging data acquired during the battery charging process, wherein the battery FCC database is used to record battery capacity estimation data for a benchmark value combination of a set of detection quantities during the battery charging process, and when the battery FCC database is filled through the update, the SOH of the battery is determined at least based on the battery FCC database.

[0007] According to other aspects of the present disclosure, a computer program code and a computer program product for implementing the above method and a computer-readable storage medium having the computer program code for implementing the above method recorded thereon are also provided.

[0008] The method and device according to the embodiments of the present disclosure can achieve a more accurate estimation of the battery SOH, thereby improving the user experience.

[0009] These and other advantages of the present disclosure will become more apparent through the following detailed description of the preferred embodiments of the present disclosure in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to further illustrate the above and other advantages and features of the present disclosure, the specific embodiments of the present disclosure are further described in detail below in conjunction with the accompanying drawings. The accompanying drawings together with the following detailed description are included in this specification and form a part of this specification. Elements with the same function and structure are represented by the same reference numerals. It should be understood that these drawings only describe typical examples of the present disclosure and should not be regarded as limiting the scope of the present disclosure. In the drawings:

[0011] Figure 1 A flow chart of a method for determining the state of health SOH of a battery according to a first embodiment is shown.

[0012] Figure 2 A flow chart of a method for determining the state of health SOH of a battery according to a second embodiment is shown.

[0013] Figure 3 A flow chart of a method for determining the state of health SOH of a battery according to a third embodiment is shown.

[0014] Figure 4 A flowchart showing steps of updating a battery FCC database using battery charging data according to a third embodiment of the present disclosure.

[0015] Figure 5 The capacity decay curve of the battery recorded by the cycler after 400 charge and discharge cycles at a discharge depth of 0%-100% is shown.

[0016] Figure 6 The estimated SOH value of a lithium battery based on the FCC database according to an embodiment of the present disclosure is shown. f (without Kalman filter) versus the number of cycles.

[0017] Figure 7 The figure shows the comparison between the estimated value soh-est (after Kalman filtering) of the lithium battery SOH based on the FCC database according to an embodiment of the present disclosure and the actual value of the lithium battery SOH recorded by the cycler.

[0018] Figure 8 A functional module block diagram of a device for determining battery SOH according to a sixth embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0019] Exemplary embodiments of the present disclosure will be described below in conjunction with the accompanying drawings. For the sake of clarity and conciseness, not all features of the actual implementation are described in the specification. However, it should be understood that many implementation-specific decisions must be made in the process of developing any such actual implementation in order to achieve the developer's specific goals, such as meeting those constraints related to the system and business, and these constraints may vary from implementation to implementation. In addition, it should be understood that although the development work may be very complex and time-consuming, it is only a routine task for those skilled in the art who benefit from the content of this disclosure.

[0020] It is also necessary to explain here that, in order to avoid obscuring the present disclosure due to unnecessary details, only the device structure and / or processing steps closely related to the scheme according to the present disclosure are shown in the accompanying drawings, while other details that are not very relevant to the present disclosure are omitted.

[0021] <First Embodiment>

[0022] This embodiment provides an exemplary method for determining the state of health (SOH) of a battery. The method adopts an adaptive fusion strategy to achieve accurate estimation of the SOH.

[0023] Figure 1 FIG. 4 is a flow chart showing a method for determining a battery state of health SOH according to a first embodiment. Figure 1 As shown, the method includes: updating a battery FCC database including FCC data of the battery based on charging data acquired during the battery charging process (S11), wherein the battery FCC database is used to record battery capacity estimation data for a combination of benchmark values ​​of a set of detection quantities during the battery charging process; and determining the SOH of the battery at least based on the battery FCC database (S12) when the battery FCC database is filled through the update.

[0024] In this embodiment, by using the battery charging data to update the FCC database recording the battery capacity estimation data, various factors affecting the battery SOH, such as temperature, current, discharge depth, operating conditions, etc., are integrated multiple times, effectively improving the accuracy of the battery SOH estimation. Here, the various factors can be embodied as a set of detection quantities. As a non-limiting example, the set of detection quantities can include one or more of temperature, current, and impedance.

[0025] During the battery charging process, corresponding charging data can be collected, and various processing can be performed on the collected charging data. For example, the charging data that can be obtained may include one or more of the detectable parameters during the battery charging process, such as one or more of the following: the maximum temperature value, the minimum temperature value, the maximum current value, the minimum current value, the impedance, the battery voltage value, the coulomb accumulation, the SOC at the start of charging, the SOC at the end of charging, the battery energy density, and the estimated value of the charge amount of the current charging cycle.

[0026] In step S11, based on the acquired charging data, the battery FCC database is updated, and the battery FCC database is used to record the battery capacity estimation data under different reference value combinations of a set of detection quantities. Exemplarily, the battery capacity estimation data in the battery FCC database may include the battery capacity initial value, the battery capacity estimation value and the number of updates for the corresponding value combination.

[0027] For example, each data point in the database is used to record battery capacity estimation data for a set of reference value combinations. Alternatively, each data point in the database can record battery capacity estimation data in association with a set of reference value combinations. In other words, each data point in the database represents information related to the charging capacity of the battery when the corresponding charging cycle is completed when a set of detection quantities is a reference value combination. When there are K detection quantities in a set of detection quantities, the reference value combination is composed of a combination of detection quantities for battery physical quantities in K dimensions, wherein each detection quantity can be distributed at predetermined intervals in each dimension, thereby constituting a plurality of discrete reference value combinations of the database. As a simple example that is illustrative and not restrictive, one of the reference value combinations can be a combination of the first detection quantity and the second detection quantity of the battery, so there will be a plurality of data points in the database that are composed of battery capacity estimation data corresponding to discrete specific first detection quantities and specific second detection quantities; and the value of the detection quantity corresponding to the reference value combination, for example, includes the first detection quantity and the second detection quantity values ​​obtained by real-time detection of the battery. When the battery FCC database is initially constructed, the above data points can be assigned initial values ​​using data provided by the battery manufacturer and updated in subsequent steps, or all of them can be assigned zero values. Figure 1 Not shown, but the above method may also include the steps of building and initializing a battery FCC database.

[0028] In one example, step S11 includes obtaining the value of each detection quantity in a set of detection quantities in a battery charging process based on charging data, and performing multi-dimensional linear interpolation on the battery capacity estimation data based on a comparison of the value of each detection quantity with a corresponding benchmark value to obtain a cumulative capacity value; and updating the battery capacity estimation data for a combination of corresponding benchmark values ​​based on the cumulative capacity value and the charge capacity estimation value from the charging data.

[0029] In one example, the process of performing multidimensional linear interpolation on the battery capacity estimation data to obtain a cumulative capacity value in step S11 may include: determining, for each detected quantity, at least one benchmark value that is the nearest neighbor to the value of the detected quantity; determining the weight of the battery capacity estimation data corresponding to the corresponding benchmark value based on the difference between the value of each detected quantity and at least one benchmark value of its nearest neighbor; and calculating the weighted sum of the battery capacity estimation values ​​in the battery capacity estimation data corresponding to the corresponding benchmark values ​​based on the weight as the cumulative capacity value.

[0030] For example, at least one benchmark value of the nearest neighbor can be the nearest benchmark value that is less than the value of the detection amount in the dimension of a specific detection amount and / or the nearest benchmark value that is greater than or equal to the value of the detection amount. For ease of understanding, taking the detection amount as current as an example, for example, two benchmark points of 5 amperes and 6 amperes are set and there are no other benchmark points in between. When the detected current value is 5.3 amperes, at least one benchmark value of its nearest neighbor can be determined as the above two benchmark points of 5 amperes and 6 amperes. In addition, in the weight calculation process, a variety of known weight calculation methods can be used according to the difference without limitation. For example, a benchmark value that is closer to the value of the detection amount can be assigned a larger weight.

[0031] In one example, the cumulative capacity value obtained above can be used to update the battery capacity estimation data as follows: calculate the difference between the charge capacity estimation value from the charging data and the cumulative capacity value as the capacity error value; and for each of the reference value combinations consisting of at least one reference value of the nearest neighbor of each detected quantity, update the battery capacity estimation value in the battery capacity estimation data based on the capacity error value and the weight. That is, for the battery capacity estimation value currently corresponding to the nearest neighbor reference value combination, use the capacity error value to correct it, wherein the degree of correction of the battery capacity estimation value is further determined according to the weight, for example, the result of weighting the capacity error value using the weight can be added to the original battery capacity estimation value as the correction value of the original battery capacity estimation value to perform the update. In addition, the update number is also updated when performing the update, for example, the update number is increased by 1 each time the update is performed.

[0032] The charge capacity estimation value from the charging data may be determined based on the accumulated charge capacity in coulombs during the battery charging process and / or the remaining capacity before charging, or may be determined based on the accumulated charge capacity in coulombs during the battery charging process and / or the remaining capacity before charging, or may be determined based on the accumulated charge capacity in coulombs during the battery charging process, the remaining capacity before charging, and the capacity correction value of the cut-off current. It should be noted that this is not restrictive, and it may be the charge capacity estimation value obtained from the charging data in any appropriate manner.

[0033] In step S12, when the filling of the battery FCC database is completed, the SOH of the battery is calculated according to the ratio of the battery capacity estimation value of the battery capacity estimation data with the most updates to the battery capacity initial value and the calculated value is used as the final output battery SOH.

[0034] For example, a battery FCC database may be traversed, or a comparison may be performed to find a group of reference value combinations with the most updates and their corresponding battery capacity estimation data.

[0035] In addition, step S12 may also include: continuing to fill the battery FCC database when the filling of the battery FCC database is not completed, and in this case, the SOH of the battery can be determined according to the aging factor of the preset battery health value and the number of charging cycles and used as the final output of the battery SOH.

[0036] In addition, in the process of updating the battery capacity estimation data, the initial value of the battery capacity can also be selectively updated. For example, the initial value of the battery capacity can be updated once or several times only when the number of updates is equal to a preset threshold (for example, the preset threshold is updated 5 times, 10 times, 20 times, etc.). Note that the above update of the initial value of the battery capacity is only to improve the accuracy of the initial value of the battery capacity, and will no longer be updated in subsequent calculations to ensure the stability of the battery SOH calculation benchmark.

[0037] In summary, the method according to this embodiment can estimate the SOH of the battery more accurately in an adaptive manner by updating the battery FCC database in real time based on charging data. In addition, the method can be applied to a wide temperature range, has a small amount of calculation and good stability.

[0038] <Second Embodiment>

[0039] The second embodiment according to the present disclosure is described below. As a variation of the first embodiment, in addition to the steps described in the first embodiment, an additional step S13 may be included after step S12: performing Kalman filtering on the calculated SOH of the battery to use the filtered SOH as the SOH of the battery. Figure 2 A flow chart of a method for determining the state of health SOH of a battery according to a second embodiment is shown.

[0040] Exemplarily, in the Kalman filter, the system noise and measurement noise of the Kalman filter equation are dynamically adjusted according to the temperature, and the state equation and the observation equation are constructed, wherein

[0041] The state equation is: k =soh k-1 -dropratio / 10+w

[0042] The observation equation is: k =soh f +v

[0043] Among them, soh k is the SOH of the battery at the kth iteration, soh k-1 is the SOH of the battery at the k-1th iteration, soh fis the SOH of the battery calculated based on the battery FCC database, dropratio is the aging factor of the battery health value, w is the system noise, and v is the measurement noise.

[0044] By including Kalman filtering, the increase or rapid decrease of SOH with the increase of cycle number can be suppressed, so that the output noise of SOH in abnormal conditions such as overtemperature and too low temperature can be effectively reduced, and better stability can be obtained while maintaining estimation accuracy.

[0045] A person skilled in the art may adjust the Kalman filtering method and related parameters according to the method described in the embodiment, and these adjustments will not deviate from the inventive concept of the present disclosure, and thus should be deemed to fall within the protection scope of the present disclosure.

[0046] <Third Embodiment>

[0047] Combine the following Figure 3 and Figure 4 The third embodiment of the present disclosure is described. The third embodiment takes current and temperature as a group of detection quantities to more specifically introduce the method for determining the SOH of a battery of the present application.

[0048] Figure 3 FIG. 4 is a flow chart showing a method for determining a battery state of health SOH according to a third embodiment. Figure 3 As shown, the method includes: updating a battery FCC database including FCC data of the battery based on charging data acquired during battery charging (S31); and determining the SOH of the battery based at least on the battery FCC database when it is determined that the filling of the battery FCC database has been completed through updating; or determining the SOH of the battery based on an aging factor and the number of charging cycles when the filling of the battery FCC database has not been completed (S32).

[0049] In one example, step S31 includes the steps of obtaining charging data ( S311 ) and updating a battery FCC database using the battery charging data ( S312 ).

[0050] In one example, step S311 includes collecting battery charging data, processing the charging data, and obtaining the maximum temperature value, minimum temperature value, maximum current value, minimum current value, battery impedance, coulomb accumulation, SOC at the start of charging, SOC at the end of charging, and estimated value of charging amount of the current cycle, etc.

[0051] In this embodiment, current and temperature are used as a set of detection quantities. Therefore, the reference values ​​of the battery in the two-dimensional battery FCC database are current reference values ​​and temperature reference values, respectively. For example, the temperature reference value can be set to the average temperature of the battery in the charging cycle, and the current reference value can be set to the maximum current of the battery. Correspondingly, the obtained charging data at least includes the average temperature of the battery (for example, obtained by the maximum and / or minimum temperature) and the maximum current.

[0052] In one example, step S311 also includes constructing a two-dimensional array of different temperature points (i.e., temperature reference values) and different current points (i.e., current reference values) as a lithium battery FCC database. For the purpose of illustration and not limitation, the battery FCC database in this embodiment is an N×M-dimensional two-dimensional array, where N represents the number of temperature reference values ​​and M represents the number of current reference values. For example, there are 4 temperature points, namely 5°C, 20°C, 40°C, and 65°C, and 3 current points, namely 2A, 5A, and 10A. The battery capacity estimation data packet corresponding to each combination of temperature point and current point includes the initial value of the battery capacity, the estimated value of the battery capacity, and the number of updates. The initial values ​​of the initial value of the battery capacity, the estimated value of the battery capacity, and the number of updates can all be set to 0 to complete the initialization.

[0053] In one example, combining Figure 4 The step (S312) of updating the battery FCC database using the battery charging data in step S31 is specifically introduced. Figure 4 A flowchart showing steps of updating a battery FCC database using battery charging data according to a third embodiment of the present disclosure.

[0054] In one example, step S312 includes: using the maximum temperature value and the minimum temperature value of the charging data to calculate the temperature difference and the average temperature temp aver (S3121).

[0055] In one example, step S312 includes: when the absolute value of the temperature difference is greater than the temperature threshold temp_th (20°C) or the capacity estimation value fcc_est of the charging data, c When ≤0, exit the update of the battery FCC database (S3122); otherwise proceed to the next step.

[0056] In one example, step S312 includes: searching the two-dimensional battery FCC database for the average temperature temp aver The nearest temperature point temp 0 and temp 1 , find the maximum current curr in the two-dimensional battery FCC database max The nearest current point curr 0and curr 1 , thereby obtaining the four nearest FCC data in the two-dimensional battery FCC database, and calculating the corresponding temperature weight and current weight (S3123). aver When the temperature is greater than the maximum value of 65℃ or less than the minimum value of 5℃, the temp 0 and temp 1 Set to the same value of 65℃ or 5℃ respectively; when the maximum current curr max When the current is greater than the maximum value of 10A or less than the minimum value of 2A, curr 0 and curr 1 Set them to the same value of 10A or 2A respectively. 0 =temp 1 When the temperature weight is set to temp_weight 0 =temp_weight 1 =500, in curr 0 =curr 1 When the current weight is set to curr_weight 0 =curr_weight 1 = 500. In other cases, the calculation formulas of the exemplary temperature weight and current weight of the four nearest neighbor FCC data can be as follows:

[0057]

[0058] temp_weight 1 =1000-temp_weight 0

[0059]

[0060] curr_weight 1 =1000-curr_weight 0

[0061] In one example, step S312 includes: calculating a capacity error value (S3124). The cumulative capacity value is obtained by multiplying and adding the temperature weight, current weight, and battery capacity estimation value of four adjacent FCC data, and the capacity error value fcc_error is obtained by subtracting the charge estimation value of the battery charging data from the cumulative capacity value;

[0062]

[0063] Among them, fcc_est cThe estimated value of the charge capacity of the battery charging data. In one example, the estimated value of the charge capacity may be equal to the sum of the accumulated coulomb charge capacity during the battery charging process, the remaining capacity before charging, and the capacity correction value of the cut-off current, as shown in the following formula:

[0064]

[0065] Where CC is the coulomb cumulative charge, It is the remaining capacity before charging, start_dodsoc is the SOC before charging, (save_min_current-eocma) / 10 is the tail capacity value fitted by current, save_min_current is the minimum current in the charging process, eocma is the charging cut-off current. Since the charging is divided into the constant current CC stage and the constant voltage CV stage, in the CV stage, the charging cut-off current is relied on to catch up, that is, the current reaches the charging cut-off current corresponding to the SOC must reach 100%, and the charging is completed.

[0066] temp_weight i , curr_weight j , are the temperature weight, current weight and current battery capacity estimation value of the four adjacent FCC data, where 0≤i≤1,0≤j≤1.

[0067] In one example, step S312 includes determining the update times (S3125), and updating four adjacent FCC data of the battery capacity estimation data in sequence according to the determination result of S3125.

[0068] When it is determined in step S3125 that the update count of the FCC data is greater than or less than the battery update count threshold update_th (eg, 10), the battery capacity estimation value of the FCC data is updated as follows without updating the initial capacity value of the FCC data (S3126), and the update count is increased by 1.

[0069]

[0070] When it is determined in step S3125 that the number of updates of the FCC data is equal to the battery update number threshold update_th (such as 10), in addition to updating the battery capacity estimation value and the number of updates in the same manner as above, the initial value of the battery capacity is also updated to the current value. (S3127) In addition, it can also be set to update the initial value of the battery capacity when the number of updates of the FCC data is less than or equal to the battery update number threshold.

[0071] Return below Figure 3The description continues with step S32 of the method for determining the state of health SOH of the battery according to the third embodiment.

[0072] In one example, step S32 calculates the SOH of the battery according to the ratio of the battery capacity estimation value of the battery capacity estimation data with the most update times to the battery capacity initial value.

[0073] In one example, step S32 includes: searching the two-dimensional FCC database to find the battery capacity data with the most updates, and determining the update times (S321). When the update times of the battery capacity data are greater than a predetermined threshold (for example, the predetermined threshold is 10 times) and the initial value of the battery capacity and the estimated value of the battery capacity are both greater than 0, it is considered that the two-dimensional FCC database is filled, and sub-step S322 is performed; otherwise, it is determined that the data in the database is not filled, and sub-step S323 is performed.

[0074] In one example, step S32 includes: calculating soh using the ratio of the battery capacity estimation value with the most updates to the battery capacity initial value. f , the calculated soh f The value of is used as the final output battery health value SOH (S322).

[0075]

[0076] Among them, fcc_est is the estimated value of battery capacity, and fcc_init is the initial value of battery capacity.

[0077] As another case, step S32 includes: calculating the number of times the battery is charged, and calculating the battery health value soh according to the battery aging factor as follows: d And output as the final battery health value SOH (S323).

[0078] soh d =10000-chg_cycle_cnt*dropratio / 10

[0079] Among them, chg_cycle_cnt is the number of times the battery is charged (the number of charging cycles), and dropratio is the aging factor of the battery health value. An exemplary empirical value is that the battery health value drops by 3% after 100 cycles of charge and discharge, that is, dropratio=30.

[0080] In addition, the method of this embodiment may further include, after step S32, calculating the calculated SOH estimated value (ie, the SOH calculated in sub-step S322). f )Step S33 of performing Kalman filtering.

[0081] Similar to the above method, step S33 includes: dynamically adjusting the system noise and observation noise of the Kalman filter according to the temperature, and constructing the Kalman filter equation as follows:

[0082] The state equation is: k =soh k-1 -dropratio / 10+w

[0083] The observation equation is: k =soh f +v

[0084] Among them, soh k is the SOH of the battery at the kth iteration, soh k-1 is the SOH of the battery at the k-1th iteration, soh f is the SOH of the battery calculated based on the battery FCC database, dropratio is the aging factor of the battery health value, w is the system noise, and v is the measurement noise. At this time, the filtered SOH value is used as the final output of the battery SOH.

[0085] It should be noted that the method described in this embodiment is only exemplary and not restrictive, and each step thereof can be used in combination with the method steps described in the first and second embodiments or can be used to complement or replace each other, and the technical solution combinations thus formed are all within the scope of this application. In addition, the numerical values ​​appearing in some steps are merely examples given for the purpose of convenience of explanation, and therefore should not be regarded as limitations on the described method of this disclosure.

[0086] <Fourth Embodiment>

[0087] In the fourth embodiment, the performance of the method for estimating SOH using the FCC database according to the present disclosure is verified. In this embodiment, the entity to be detected and calculated is a lithium battery built into a mobile phone, and some parameters thereof are shown in Table 1 below.

[0088] Table 1 Some parameters of lithium battery

[0089] Parameter name Numeric Rated capacity 3130mAh Rated voltage 3.85V Constant current charging cut-off voltage 4.4V Charging cut-off current 0.1A Discharge cut-off voltage 2.45V

[0090] The lithium battery was used to perform a set of 400 charge and discharge cycles, with a discharge depth of 0%-100% and a room temperature. The charge and discharge data of the lithium battery were recorded by a cycler and a fuel gauge, respectively, which can reflect the true SOH value of the battery. The following is a comparison of the true SOH value, the SOH value estimated based on the FCC database (without Kalman filtering), and the SOH value estimated based on the FCC database (after Kalman filtering).

[0091] Figure 5The capacity decay curve of the battery recorded by the cycler after 400 charge and discharge cycles at a discharge depth of 0%-100% is shown. Figure 5 It can be seen from the capacity decay curve that the capacity of the battery shows an overall downward trend with the increase in the number of charge and discharge cycles. Due to changes in external ambient temperature and periodic capacity regeneration of the battery, the capacity curve has jitters, which is the burr-like part in the figure.

[0092] Figure 6 The estimated SOH value of a lithium battery based on the FCC database according to an embodiment of the present disclosure is shown. f (without Kalman filter) versus the number of cycles.

[0093] like Figure 6 Although the SOH shown still has some curve jitter, with a maximum difference of 0.85%, it is much better than Figure 5 The actual value of the battery SOH corresponding to the capacity decay curve recorded by the cycler shown in the figure, and the estimated value of the battery SOH according to the embodiment of the present disclosure is soh f The amplitude of the curve jitter is significantly attenuated, resulting in a more stable SOH estimation output, which improves the user experience.

[0094] Further, Figure 7 The figure shows the comparison between the estimated value soh-est (after Kalman filtering) of the lithium battery SOH based on the FCC database according to an embodiment of the present disclosure and the actual value of the lithium battery SOH recorded by the cycler. Figure 7 The SOH of the lithium battery after Kalman filtering is represented by the blue line soh-est; the true value of the lithium battery SOH recorded by the cycler is the black curve soh-true; in addition, Figure 7 It also shows Figure 6 The estimated SOH value of the lithium battery shown in f (without Kalman filter), soh f The red curve.

[0095] Among them, the actual value of the battery SOH after each charge and discharge cycle is

[0096]

[0097] Among them, fcc 1 The capacity of the battery at the first charge recorded by the cycler, that is, the health value of the battery at the first charge is 100%, fcc i is the capacity of the battery after the i-th cycle of charge and discharge recorded by the cycler. Figure 7It can be seen that there is no jump in the battery SOH during the entire cycle. The difference between the SOH estimated based on the FCC database and the actual SOH value recorded by the cycler is within 2%, and the maximum error value is 1.05%. After 400 cycles, the estimated value of the battery SOH (after Kalman filtering) is 85%, and the true value (SOH_true) is 85.72%, with a difference of 0.72%. When the external ambient temperature changes and the battery periodic capacity regeneration occurs, the relationship between the battery capacity and the number of cycles will change, which may cause the difference between the estimated value and the true value of the battery SOH to be greater than 0.5%.

[0098] Since the true value of the SOH of the above-mentioned lithium battery is calculated using the capacity ratio, changes in capacity will cause certain fluctuations in the true value of SOH. The state equation of the Kalman filter can suppress this fluctuating capacity change and present a trend of a continuous decrease in the health value SOH, which is reflected in the figure as the difference between the SOH filtered value and the true value.

[0099] from Figure 7 It can be seen that the estimated SOH value of the lithium battery without the Kalman filter has a more stable battery SOH estimation result and higher accuracy compared to the true value. Furthermore, the SOH estimation method with the addition of the Kalman filter can obviously provide more stable and effective estimation results. Since the Kalman filter is added to the adaptive fusion SOH estimation algorithm, the increase or rapid decrease of the lithium battery SOH with the increase in the number of cycles is suppressed, so that the output noise of the lithium battery SOH in abnormal conditions such as over-temperature and too low is effectively reduced during the entire cycle charge and discharge process, the stability is better, and the accuracy of the lithium battery SOH estimation can also meet the needs.

[0100] <Sixth Embodiment>

[0101] This embodiment provides an apparatus for determining the SOH of a battery.

[0102] Figure 8 FIG. 4 is a functional module block diagram of an apparatus 60 for determining a battery SOH according to a sixth embodiment of the present disclosure.

[0103] The device 60 includes a processing circuit 601, which is configured to: update a battery FCC database including the battery full charge capacity FCC data of the battery based on the charging data obtained during the battery charging process, wherein the battery FCC database is used to record the battery capacity estimation data for a set of benchmark value combinations of detection quantities during the battery charging process, and when the battery FCC database is filled by the update, the SOH of the battery is determined at least based on the battery FCC database. Optionally, the device 60 may also include an acquisition circuit 602, which is configured to acquire the charging data during the battery charging process. The acquisition circuit 602 may be implemented, for example, as a sensor or detection circuit for measuring various detection quantities of the battery.

[0104] In the above-mentioned process of the first to fifth embodiments for describing the method for determining the battery SOH, it has actually been disclosed as follows: Figure 8 The processing and functions implemented by each functional module of the device for determining the battery SOH shown in FIG. Figure 8 Therefore, in the following, an overview of the functions of these processing circuits is given without repeating some of the details discussed above.

[0105] Here, the processing circuit 601 may be implemented as a chip, a processor, various microcontrollers, etc., and may be implemented by one or more dedicated integrated circuits and / or corresponding software programs.

[0106] In one example, the processing circuit 601 is further configured to perform the update as follows: obtaining the value of each detection quantity in the set of detection quantities in the battery charging process based on the charging data, performing multi-dimensional linear interpolation on the battery capacity estimation data based on a comparison of the value of each detection quantity with a corresponding benchmark value to obtain a cumulative capacity value; and updating the battery capacity estimation data for the combination of the corresponding benchmark values ​​based on the cumulative capacity value and the charge capacity estimation value from the charging data.

[0107] In one example, the processing circuit 601 is further configured to: determine, for each detected quantity, at least one benchmark value that is the nearest neighbor to the value of the detected quantity; determine the weight of the battery capacity estimation data corresponding to the corresponding benchmark value based on the difference between the value of each detected quantity and at least one benchmark value of its nearest neighbor; and calculate the weighted sum of the battery capacity estimation values ​​in the battery capacity estimation data corresponding to the corresponding benchmark values ​​based on the weight as the cumulative capacity value, thereby performing multi-dimensional linear interpolation.

[0108] In one example, the processing circuit 601 is further configured to: calculate the difference between the charge capacity estimation value from the charging data and the cumulative capacity value as a capacity error value; and for each of the reference value combinations consisting of at least one reference value of the nearest neighbor of each detected quantity, update the battery capacity estimation value in the battery capacity estimation data based on the capacity error value and the weight, and update the update number, thereby updating the battery capacity estimation data.

[0109] In an example, the processing circuit 601 is further configured to: update the initial value of the battery capacity when the update number is equal to a preset threshold.

[0110] In one example, the processing circuit 601 is further configured to: obtain the estimated charge capacity value by the sum of the coulomb accumulated charge capacity during the battery charging process, the remaining capacity before charging, and the capacity correction value of the cut-off current.

[0111] In one example, the processing circuit 601 is further configured to: determine the SOH of the battery according to an aging factor of a preset battery health value and the number of charging cycles when the filling of the battery FCC database is not completed.

[0112] In one example, the processing circuit 601 is further configured to: after completing filling of the battery FCC database, calculate the SOH of the battery according to the ratio of the battery capacity estimation value of the battery capacity estimation data with the most updates to the battery capacity initial value.

[0113] In one example, the processing circuit 601 is further configured to: perform Kalman filtering on the calculated SOH of the battery, so as to use the filtered SOH as the SOH of the battery.

[0114] The basic principles of the present disclosure are described above in conjunction with specific embodiments. However, it should be pointed out that for those skilled in the art, it is understandable that all or any steps or components of the methods and devices of the present disclosure can be implemented in any computing device (including processors, storage media, etc.) or a network of computing devices in the form of hardware, firmware, software, or a combination thereof. This can be achieved by those skilled in the art using their basic circuit design knowledge or basic programming skills after reading the description of the present disclosure.

[0115] Furthermore, the present disclosure also proposes a program product storing machine-readable instruction codes. When the instruction codes are read and executed by a machine, the method according to the embodiment of the present disclosure can be executed.

[0116] Accordingly, the storage medium for carrying the program product storing the machine-readable instruction code is also included in the disclosure of the present disclosure. As a common application form, the storage medium may be the storage medium of the device itself where the battery to be estimated is located. Other storage media include but are not limited to floppy disks, optical disks, magneto-optical disks, memory cards, memory sticks, etc.

[0117] When the present disclosure is implemented by software or firmware, programs constituting the software are installed from a storage medium or a network to various types of computers having a dedicated hardware structure, and the computer can execute various functions and the like when various programs are installed.

[0118] It should also be noted that in the apparatus, method and system of the present disclosure, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present disclosure. In addition, the steps of performing the above series of processes can naturally be performed in chronological order according to the order of description, but do not necessarily need to be performed in chronological order. Some steps can be performed in parallel or independently of each other.

[0119] Finally, it should be noted that the terms "comprises", "includes" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also includes other elements that are not explicitly listed, or also includes elements that are inherent to such process, method, article or device. In addition, in the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, article or device that includes the elements.

[0120] Although the embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings, it should be understood that the embodiments described above are only used to illustrate the present disclosure and do not constitute a limitation of the present disclosure. For those skilled in the art, various modifications and changes can be made to the above embodiments without departing from the essence and scope of the present disclosure. Therefore, the scope of the present disclosure is limited only by the attached claims and their equivalent meanings.

[0121] The present technology may also be configured as follows.

[0122] (1) A method for determining a battery state of health (SOH), comprising:

[0123] Based on the charging data acquired during the battery charging process, a battery FCC database including the battery full charge capacity FCC data of the battery is updated, wherein the battery FCC database is used to record battery capacity estimation data for a set of reference value combinations of detection quantities during the battery charging process,

[0124] In case that the battery FCC database is populated by the updating, the SOH of the battery is determined based at least on the battery FCC database.

[0125] (2) The method according to (1), wherein the battery capacity estimation data includes a battery capacity initial value, a battery capacity estimation value, and an update number for a corresponding reference value combination.

[0126] (3) The method according to (2), wherein the updating comprises:

[0127] Obtaining the value of each detection quantity in the set of detection quantities during the battery charging process based on the charging data, and performing multi-dimensional linear interpolation on the battery capacity estimation data based on comparison between the value of each detection quantity and a corresponding reference value to obtain a cumulative capacity value; and

[0128] Based on the cumulative capacity value and the charge amount estimate value from the charging data, the battery capacity estimation data for the combination of the corresponding reference values ​​is updated.

[0129] (4) The method according to (3), wherein performing the multidimensional linear interpolation comprises:

[0130] For each detected quantity, determining at least one reference value that is closest to the value of the detected quantity;

[0131] Determining the weight of the battery capacity estimation data corresponding to the corresponding reference value based on the difference between the value of each detected quantity and at least one reference value of its nearest neighbor; and

[0132] A weighted sum of the battery capacity estimation values ​​in the battery capacity estimation data corresponding to the corresponding reference values ​​is calculated based on the weights as the cumulative capacity value.

[0133] (5) The method according to (4), wherein updating the battery capacity estimation data comprises:

[0134] calculating a difference between a charge amount estimation value from the charging data and the cumulative capacity value as a capacity error value; and

[0135] For each of the reference value combinations consisting of at least one reference value of the nearest neighbor of each detection amount, the battery capacity estimation value in the battery capacity estimation data is updated based on the capacity error value and the weight, and the update number is updated.

[0136] (6) The method according to (5), wherein updating the battery capacity estimation data further comprises: when the number of updates is equal to a preset threshold, updating the initial value of the battery capacity.

[0137] (7) A method according to (1), wherein the charging data includes one or more of the following: maximum temperature value, minimum temperature value, maximum current value, minimum current value, coulomb accumulation, state of charge (SOC) at the start of charging, SOC at the end of charging, and estimated charge capacity of the current charging cycle.

[0138] (8) The method according to (7), wherein the charge capacity estimation value is the sum of the coulomb cumulative charge capacity during battery charging, the remaining capacity before charging, and the capacity correction value of the cutoff current.

[0139] (9) The method according to (1), further comprising:

[0140] In the case that the filling of the battery FCC database is not completed, the SOH of the battery is determined according to the aging factor of the preset battery health value and the number of charging cycles.

[0141] (10) The method according to (2), wherein, when the filling of the battery FCC database is completed, the SOH of the battery is calculated based on the ratio of the battery capacity estimation value of the battery capacity estimation data with the largest number of updates to the battery capacity initial value.

[0142] (11) The method according to (10) further includes performing Kalman filtering on the calculated SOH of the battery to use the filtered SOH as the SOH of the battery.

[0143] (12) The method according to (11), wherein, in the Kalman filter, the system noise and measurement noise of the Kalman filter equation are dynamically adjusted according to the temperature, and the state equation and the observation equation are constructed, wherein

[0144] The state equation is: k =soh k-1 -dropratio / 10+w

[0145] The observation equation is: k =soh f +v

[0146] Among them, soh k is the SOH of the battery at the kth iteration, soh k-1 is the SOH of the battery at the k-1th iteration, soh f is the SOH of the battery calculated based on the battery FCC database, dropratio is the aging factor of the battery health value, w is the system noise, and v is the measurement noise.

[0147] (13) The method according to (1), wherein the set of detected quantities includes current and temperature.

[0148] (14) The method according to (13), wherein the current is a maximum current during the battery charging process, and / or the temperature is an average temperature during the battery charging process.

[0149] (15) The method according to (13), wherein the battery FCC database is a two-dimensional array of N×M, where N and M are positive integers greater than 1.

[0150] (16) The method according to (1) also includes constructing and initializing the battery FCC database.

[0151] (17) A device for determining a battery state of health (SOH), comprising:

[0152] The processing circuit is configured to:

[0153] Based on the charging data acquired during the battery charging process, a battery FCC database including the battery full charge capacity FCC data of the battery is updated, wherein the battery FCC database is used to record battery capacity estimation data for a set of reference value combinations of detection quantities during the battery charging process,

[0154] In case that the battery FCC database is populated by the updating, the SOH of the battery is determined based at least on the battery FCC database.

[0155] (18) The apparatus according to (17), wherein:

[0156] The battery capacity estimation data includes an initial value of the battery capacity, an estimated value of the battery capacity, and an update number for a corresponding reference value combination.

[0157] (19) The apparatus of (18), wherein the processing circuit is configured to perform the updating as follows:

[0158] Obtaining the value of each detection quantity in the set of detection quantities during the battery charging process based on the charging data, and performing multi-dimensional linear interpolation on the battery capacity estimation data based on comparison between the value of each detection quantity and a corresponding reference value to obtain a cumulative capacity value; and

[0159] Based on the cumulative capacity value and the charge amount estimate value from the charging data, the battery capacity estimation data for the combination of the corresponding reference values ​​is updated.

[0160] (20) The apparatus according to (19), wherein the processing circuit is configured to perform the multidimensional linear difference as follows:

[0161] For each detected quantity, determining at least one reference value that is closest to the value of the detected quantity;

[0162] Determining the weight of the battery capacity estimation data corresponding to the corresponding reference value based on the difference between the value of each detected quantity and at least one reference value of its nearest neighbor; and

[0163] A weighted sum of the battery capacity estimation values ​​in the battery capacity estimation data corresponding to the corresponding reference values ​​is calculated based on the weights as the cumulative capacity value.

[0164] (21) The apparatus of (20), wherein the processing circuit is configured to update the battery capacity estimation data as follows:

[0165] calculating a difference between a charge amount estimation value from the charging data and the cumulative capacity value as a capacity error value; and

[0166] For each of the reference value combinations consisting of at least one reference value of the nearest neighbor of each detection amount, the battery capacity estimation value in the battery capacity estimation data is updated based on the capacity error value and the weight, and the update number is updated.

[0167] (22) The device according to (21), wherein the processing circuit is further configured to update the initial value of the battery capacity when the update number is equal to a preset threshold.

[0168] (23) A device according to (17), wherein the charging data includes one or more of the following: maximum temperature value, minimum temperature value, maximum current value, minimum current value, coulomb accumulation, state of charge (SOC) at the beginning of charging, SOC at the end of charging, and estimated charge amount of the current charging cycle.

[0169] (24) The device according to (23), wherein the charge capacity estimation value is a sum of a coulomb cumulative charge capacity during battery charging, a remaining capacity before charging, and a capacity correction value of a cutoff current.

[0170] (25) The device according to (17), wherein the processing circuit is further configured to determine the SOH of the battery based on an aging factor of a preset battery health value and a number of charging cycles without completing the filling of the battery FCC database.

[0171] (26) A device according to (18), wherein the processing circuit is configured to calculate the SOH of the battery based on the ratio of the battery capacity estimation value of the battery capacity estimation data with the most updates to the battery capacity initial value after the filling of the battery FCC database is completed.

[0172] (27) The device according to (26), wherein the processing circuit is further configured to perform Kalman filtering on the calculated SOH of the battery to use the filtered SOH as the SOH of the battery.

[0173] (28) The device according to (27), wherein, in the Kalman filter, the system noise and measurement noise of the Kalman filter equation are dynamically adjusted according to the temperature, and the state equation and the observation equation are constructed, wherein

[0174] The state equation is: k =soh k-1 -dropratio / 10+w

[0175] The observation equation is: k =soh f +v

[0176] Among them, soh k is the SOH of the battery at the kth iteration, soh k-1 is the SOH of the battery at the k-1th iteration, soh f is the SOH of the battery calculated based on the battery FCC database, dropratio is the aging factor of the battery health value, w is the system noise, and v is the measurement noise.

[0177] (29) The device according to (17), wherein the set of detected quantities includes current and temperature.

[0178] (30) The device according to (29), wherein the current is a maximum current during the battery charging process, and / or the temperature is an average temperature during the battery charging process.

[0179] (31) The device according to (29), wherein the battery FCC database is a two-dimensional array of N×M, wherein N and M are positive integers greater than 1.

[0180] (32) The apparatus of (17), wherein the processing circuit is configured to construct and initialize the battery FCC database.

[0181] (33) A computer program product comprising computer executable instructions, which, when executed by a processor, cause the processor to perform the method according to any one of (1) to (16).

[0182] (34) A computer-readable storage medium having computer-executable instructions stored thereon, which, when executed by a processor, causes the processor to perform a method according to any one of (1) to (16).

Claims

1. A method for determining the state of health (SOH) of a battery, comprising: Based on the charging data acquired during the battery charging process, a battery FCC database including the battery full charge capacity FCC data of the battery is updated, wherein the battery FCC database is used to record battery capacity estimation data for a set of reference value combinations of detection quantities during the battery charging process, In case that the battery FCC database is populated by the updating, the SOH of the battery is determined based at least on the battery FCC database.

2. The method according to claim 1, wherein: The battery capacity estimation data includes an initial value of the battery capacity, an estimated value of the battery capacity, and an update number for a corresponding reference value combination.

3. The method according to claim 2, wherein: The updates include: Obtaining the value of each detection quantity in the set of detection quantities during the battery charging process based on the charging data, and performing multi-dimensional linear interpolation on the battery capacity estimation data based on comparison between the value of each detection quantity and a corresponding reference value to obtain a cumulative capacity value; and Based on the cumulative capacity value and the charge amount estimate value from the charging data, the battery capacity estimation data for the combination of the corresponding reference values ​​is updated.

4. The method according to claim 3, wherein: Performing the multidimensional linear difference comprises: For each detected quantity, determining at least one reference value that is closest to the value of the detected quantity; Determining the weight of the battery capacity estimation data corresponding to the corresponding reference value based on the difference between the value of each detected quantity and at least one reference value of its nearest neighbor; and A weighted sum of the battery capacity estimation values ​​in the battery capacity estimation data corresponding to the corresponding reference values ​​is calculated based on the weights as the cumulative capacity value.

5. The method according to claim 4, wherein: Updating the battery capacity estimation data includes: calculating a difference between a charge amount estimation value from the charging data and the cumulative capacity value as a capacity error value; and For each of the reference value combinations consisting of at least one reference value of the nearest neighbor of each detection amount, the battery capacity estimation value in the battery capacity estimation data is updated based on the capacity error value and the weight, and the update number is updated.

6. The method according to claim 5, wherein: Updating the battery capacity estimation data further includes: when the number of updates is equal to a preset threshold, updating the initial value of the battery capacity.

7. The method according to claim 1, wherein: The charging data includes one or more of the following: maximum temperature value, minimum temperature value, maximum current value, minimum current value, coulomb accumulation, state of charge SOC when starting charging, SOC when ending charging, and estimated charge capacity of the current charging cycle.

8. The method according to claim 7, wherein: The estimated value of the charge capacity is the sum of the coulomb accumulated charge capacity during the battery charging process, the remaining capacity before charging, and the capacity correction value of the cut-off current.

9. The method according to claim 1, further comprising: In the case that the filling of the battery FCC database is not completed, the SOH of the battery is determined according to the aging factor of the preset battery health value and the number of charging cycles.

10. The method according to claim 2, wherein: When the filling of the battery FCC database is completed, the SOH of the battery is calculated according to the ratio of the battery capacity estimation value of the battery capacity estimation data with the largest number of updates to the battery capacity initial value. 11 . The method according to claim 10 , further comprising performing Kalman filtering on the calculated SOH of the battery to use the filtered SOH as the SOH of the battery.

12. The method according to claim 11, wherein: In the Kalman filter, the system noise and measurement noise of the Kalman filter equation are dynamically adjusted according to the temperature, and the state equation and observation equation are constructed, wherein The state equation is: k =soh k-1 -dropratio / 10+w The observation equation is: k =soh f +v Among them, soh k is the SOH of the battery at the kth iteration, soh k-1 is the SOH of the battery at the k-1th iteration, soh f is the SOH of the battery calculated based on the battery FCC database, dropratio is the aging factor of the battery health value, w is the system noise, and v is the measurement noise.

13. The method according to claim 1, wherein: The set of detected quantities includes current and temperature.

14. The method according to claim 13, wherein: The current is a maximum current during the battery charging process, and / or the temperature is an average temperature during the battery charging process.

15. The method according to claim 13, wherein: The battery FCC database is a two-dimensional array of N×M, where N and M are positive integers greater than 1.

16. The method of claim 1, further comprising building and initializing the battery FCC database.

17. A device for determining a battery state of health (SOH), comprising: The processing circuit is configured to: Based on the charging data acquired during the battery charging process, a battery FCC database including the battery full charge capacity FCC data of the battery is updated, wherein the battery FCC database is used to record battery capacity estimation data for a set of reference value combinations of detection quantities during the battery charging process, In case that the battery FCC database is populated by the updating, the SOH of the battery is determined based at least on the battery FCC database.

18. The apparatus according to claim 17, wherein: The battery capacity estimation data includes an initial battery capacity value, an estimated battery capacity value, and an update number for a corresponding value combination.

19. The apparatus according to claim 17, wherein: The processing circuit is further configured to perform the updating as follows: Obtaining the value of each detection quantity in the set of detection quantities during the battery charging process based on the charging data, and performing multi-dimensional linear interpolation on the battery capacity estimation data based on comparison between the value of each detection quantity and a corresponding reference value to obtain a cumulative capacity value; as well as Based on the cumulative capacity value and the charge amount estimate value from the charging data, the battery capacity estimation data for the combination of the corresponding reference values ​​is updated.

20. The apparatus of claim 18, wherein: The processing circuit is further configured to: When the filling of the battery FCC database is completed, the SOH of the battery is calculated according to the ratio of the battery capacity estimation value of the battery capacity estimation data with the largest number of updates to the battery capacity initial value.

21. The apparatus according to claim 20, wherein: The processing circuit is further configured to: Kalman filtering is performed on the calculated SOH of the battery to use the filtered SOH as the SOH of the battery.

22. A computer program product comprising computer executable instructions which, when executed by a processor, cause the processor to perform the method according to any one of claims 1 to 16.

23. A computer-readable storage medium having computer-executable instructions stored thereon, which, when executed by a processor, cause the processor to perform the method according to any one of claims 1 to 16.

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