A battery health status assessment method based on equivalent cycle number and stress

Abstract

The present invention relates to a method for evaluating the state of health of a battery based on the equivalent cycle number and stress, which comprises the following steps: S1: Obtain in real time the stress P corresponding to n batteries included in the battery pack of the battery system n , the running time Day, the current I, and the corresponding sampling time interval t, and calculate the equivalent cycle number N in real time; S2: Collect the stress values of each battery in the battery pack aging test, and then obtain the fitting curve of the average stress of each battery and the battery SOH; S3: Calculate the corresponding average stress; S4: Design a stress calculation and judgment window according to the calendar life, cycle life, and equivalent cycle number. If the stress calculation and judgment window is satisfied, go to step S5; S5: Perform SOH n correction. This method combines the real-time monitoring of the stress changes of each battery, can obtain accurate battery state of health evaluation results, can provide an important basis for battery maintenance and replacement, and effectively improves the accuracy and reliability of battery state of health evaluation.

Description

Technical Field

[0001] The present invention belongs to the technical field of batteries, and in particular relates to a battery health status assessment method based on equivalent cycle number and stress. Background Art

[0002] With the rapid development of energy technology, lithium batteries, as an important part of power and energy storage, have become the first choice in many fields. Whether it is electric vehicles, storage of renewable energy, or power supply systems for electronic devices, lithium batteries play a vital role. However, as their use time increases, the health status and life of batteries have gradually become the focus of people's attention.

[0003] Estimation of battery health status is an important part of battery management and maintenance. It is not only related to the service life of the battery, but also affects the performance of the device. Currently, most evaluations of battery health status only consider the impact of calendar decay and cycle decay, such as:

[0004] Chinese patent CN108983106A discloses a novel power battery health status assessment method, which includes: recording the start time t1, end time t2 and charging capacity △SOC of each charging of new energy vehicles, as well as the total charging times n, total charging time h, battery installation and operation days d, total mileage l and power battery charge and discharge cycle theoretical value g of different types of new energy vehicles; extracting the user charging behavior characteristics of the average charging interval T, total mileage l, slow charging frequency ratio θ, average charge and discharge depth e and deep charge and discharge cycle ratio k of new energy vehicles; weighted average calculation of the comprehensive mean w, and then judging the power battery health status assessment result.

[0005] Chinese patent CN115407211A discloses an online prediction method and system for the health status of lithium batteries in electric vehicles, wherein the method includes: obtaining electric vehicle battery data; performing SOH prediction through a vehicle-side prediction model to obtain a second SOH prediction result at the next moment; judging whether the difference between the number of charge and discharge cycles at the current moment and the number of charge and discharge cycles at the last round of correction reaches a preset correction period, otherwise the second SOH prediction result is used as the lithium battery SOH prediction result, and if so, the cloud-based battery monitoring platform is used to implement: performing SOH prediction through a cloud-based prediction model to obtain a first SOH prediction result at the next moment; taking the first SOH prediction result as a measurement value and the second SOH prediction result as an observation value, fusing them through a Kalman filter to obtain a lithium battery SOH prediction result at the next moment.

[0006] The above-mentioned traditional calendar life and cycle life attenuation methods use coefficient attenuation, which does not take into account the changes in the characteristics of the battery itself, and there are large errors. After a lot of research, the applicant found that the force signal can show abnormal characteristics earlier in the battery aging process, and the signal is more sensitive. Therefore, the battery health status can be evaluated based on stress to improve the safety and reliability of the energy storage system. Chinese patent CN116540135A discloses a stress-based battery remaining life definition, evaluation and prediction method. The invention combines multiple definition methods such as battery stress, capacity, internal resistance, etc., performs weighted summation, and comprehensively evaluates the remaining life of the battery. Finally, the model is used to take current, voltage, capacity, stress and other characteristics as input to predict the remaining life of the battery online. The defect of this invention is that the implementation of the method relies on a large data model, the software and hardware requirements are high, and the predicted data has a large uncertainty. Summary of the invention

[0007] The technical problem to be solved by the present invention is to provide a battery health status assessment method based on equivalent cycle number and stress, which implements battery health status assessment based on equivalent cycle number and stress change, and has high prediction result accuracy.

[0008] The present invention provides a battery health status assessment method based on equivalent cycle number and stress, which comprises the following steps:

[0009] S1: Real-time acquisition of the stress P corresponding to the n batteries contained in the battery pack of the battery system n , running time Day, current I, corresponding sampling time interval t, and real-time calculation of equivalent cycle number N;

[0010] The equivalent cycle number N is the historical cumulative capacity change divided by twice the rated capacity, and its calculation formula is shown in formula (1):

[0011] (1)

[0012] Among them, C N Indicates the rated capacity of the battery;

[0013] S2: Collect the stress value of each battery in the battery pack aging test, obtain the maximum stress-SOH curve during charging and the minimum stress-SOH curve during discharging, calculate the average stress data of each battery under different SOH, and then obtain the fitting curve of the average stress of each battery and the battery SOH;

[0014] S3: According to the stress P of step S1 n Update the current maximum stress and minimum stress of each battery in real time and calculate the corresponding average stress;

[0015] S4: Design a stress calculation judgment window according to the calendar life, cycle life and equivalent cycle number. If the stress calculation judgment window is satisfied, proceed to step S5;

[0016] S5: Set initial SOH n = 100%, and the estimated SOH is obtained by fitting the curve of step S2 according to the average stress of step S3 n-est , if the current SOH is met n >SOH n-est , then perform SOH n Correction, corrected SOH n Indicates the current battery health status.

[0017] Preferably, in step S2, the average stress data of each battery at different SOH is calculated using formula (2):

[0018] (2)

[0019] in, is the mean stress, represents the maximum stress of the nth battery under the current SOH, Indicates the minimum stress of the nth battery at the current SOH.

[0020] Preferably, in step S2, formula (3) is used to perform least squares fitting curve on the average stress data of each battery:

[0021] (3)

[0022] in, y Mean stress , x Indicates SOH n , k represents gain, and b represents offset.

[0023] Preferably, in formula (3), k is -10 and b is 10.5.

[0024] Preferably, the calculation formula of the stress calculation judgment window in step S4 is as shown in formula (4),

[0025] (4)

[0026] Among them, B represents the initial calendar life provided by the battery manufacturer, C represents the initial cycle life provided by the battery manufacturer, D is the SOH value of the retired battery, Day is the current number of operating days, N is the current equivalent number of cycles, and e is the SOH change threshold.

[0027] Preferably, in formula (4), B is 3650 days, C is 5000 times, D is 80%, and e is 0.2%.

[0028] Preferably, in step S5, the estimated SOH is obtained by fitting the curve n-est The calculation formula is shown in formula (5);

[0029] (5)

[0030] Where y represents the mean stress , k represents gain, and b represents offset.

[0031] Preferably, in step S5, SOH n The correction formula of is shown in formula (6):

[0032] (6)

[0033] in, w Indicates the compensation coefficient, 0.2 <w<0.8。

[0034] Preferably, in step S5, whenever SOH n If a correction occurs, the maximum stress and minimum stress in step S3 are updated according to the maximum stress-SOH curve and the minimum stress-SOH curve in step S2. Considering that during the data acquisition (collection) process, special abnormal environments and external interferences are likely to interfere with the collected data, thereby affecting the maximum stress and minimum stress values, the stress value is updated in time after the SOH correction to improve the robustness of the algorithm.

[0035] The battery health status assessment method of the present invention can provide an important basis for battery operation. For example, when the SOH value of the retired battery in step S4 is set to 80%, when the SOHn in step S5 is corrected to below 80%, the user is promptly reminded to maintain and replace the battery.

[0036] Preferably, the average stress in step S3 is the arithmetic mean of the maximum stress and the minimum stress.

[0037] The present invention has the following technical effects:

[0038] 1. The present invention obtains the average stress through the stress-SOH curve and generates a fitting curve, and further realizes the battery health status assessment based on the stress change. This method combines the real-time monitoring of the stress change of each battery to obtain accurate battery health status assessment results, and overcomes the defect that the traditional calendar life and cycle life use coefficient attenuation will cause greater deviations in SOH. It is easy to implement and convenient for engineering application, and can provide an important basis for battery maintenance and replacement, effectively improving the accuracy and reliability of battery health status assessment.

[0039] 2. The present invention monitors the number of days each battery has been running, current and other data in real time, and designs the stress calculation judgment window according to the calendar life, cycle life and equivalent number of cycles, which can effectively monitor the changes in the health status of the battery and avoid unnecessary waste of computing power. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a flow chart of a battery health status assessment method based on equivalent cycle number and stress of the present invention;

[0041] Figure 2 This is a schematic diagram of the stress-SOH curve of the battery of the present invention;

[0042] Figure 3 This is a comparison chart of the application of the battery health status assessment method of the present invention and the existing method. DETAILED DESCRIPTION

[0043] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention is described in detail below with reference to the accompanying drawings.

[0044] like Figure 1 As shown, a battery health status assessment method based on equivalent cycle number and stress includes the following steps:

[0045] S1: Real-time acquisition of the stress P corresponding to the n batteries contained in the battery pack of the battery system n (in MPa), running time Day (in days), current I (in A), corresponding sampling time interval t (in h), and real-time calculation of equivalent cycle number N;

[0046] The equivalent cycle number N is the historical cumulative capacity change divided by twice the rated capacity, and its calculation formula is shown in formula (1):

[0047] (1)

[0048] Among them, C N Indicates the rated capacity of the battery (in Ah);

[0049] S2: Collect the stress value of each battery in the battery pack aging test, obtain the maximum stress-SOH curve during charging and the minimum stress-SOH curve during discharging, calculate the average stress data of each battery under different SOH, and then obtain the fitting curve of the average stress of each battery and the battery SOH;

[0050] like Figure 2As shown, the upper curve is the maximum stress-SOH curve, the lower curve is the minimum stress-SOH curve, the middle curve is the average stress-SOH curve, and the fitting curve is generated according to the middle curve.

[0051] S3: According to the stress P of step S1 n Update the current maximum stress and minimum stress of each battery in real time, and calculate the corresponding average stress, which is the arithmetic mean of the maximum stress and the minimum stress;

[0052] S4: Design a stress calculation judgment window according to the calendar life, cycle life and equivalent cycle number. If the stress calculation judgment window is satisfied, proceed to step S5;

[0053] S5: Set initial SOH n = 100%, and the estimated SOH is obtained by fitting the curve of step S2 according to the average stress of step S3 n-est , if the current SOH is met n >SOH n-est , then perform SOH n Correction, corrected SOH n Indicates the current battery health status.

[0054] In step S2, the average stress data of each battery at different SOH is calculated using formula (2):

[0055] (2)

[0056] in, is the mean stress, represents the maximum stress of the nth battery under the current SOH, Indicates the minimum stress of the nth battery at the current SOH.

[0057] In step S2, the least squares method is used to fit the average stress data of each battery to a curve:

[0058] (3)

[0059] in, y Mean stress , x Indicates SOH n , k represents gain, and b represents offset. In formula (3), k is -10, and b is 10.5, that is, the fitting formula is y=-10x+10.5.

[0060] The calculation formula of the stress calculation judgment window in step S4 is shown in formula (4):

[0061] (4)

[0062] Among them, B represents the initial calendar life provided by the battery manufacturer, C represents the initial cycle life provided by the battery manufacturer, D is the SOH value of the retired battery, Day is the current number of operating days, N is the current equivalent number of cycles, and e is the SOH change threshold.

[0063] In formula (4), B is 3650 days, C is 5000 times, D is 80%, and e is 0.2%. When the number of running days in a certain period is 18 days and the current equivalent cycle number is 25 times, the calculation according to formula (4) is as follows, the judgment condition is met, and the next step is carried out.

[0064]

[0065] In step S5, the estimated SOH is obtained by fitting the curve n-est The calculation formula is shown in formula (5);

[0066] (5)

[0067] Where y represents the mean stress , k represents gain, b represents offset. k is -10, b is 10.5, the current average stress When y is 1Mpa, then SOH n-est =95%.

[0068] In step S5, SOH n The correction formula of is shown in formula (6):

[0069] (6)

[0070] in, w Indicates the compensation coefficient, 0.2 <w<0.8。

[0071] If the current health status of the first battery is 100%, the SOH calculated by formula (5) is n-est =99.9%, e is 0.2%, w is 0.5, then SOH1 is corrected to 99.9%; if the SOH calculated by formula (5) n-est =98%, SOH1 corrected to 98.9%.

[0072] If the current health status SOH1 of battery 1 is 96%, SOH n-est is 95%, e is 0.2%, w is 0.5, then SOH1 is corrected to 95.4%.

[0073] In step S5, whenever SOH nIf a correction occurs, the maximum stress and minimum stress in step S3 are updated according to the maximum stress-SOH curve and the minimum stress-SOH curve in step S2. This is to avoid disturbances caused by special abnormal environments and external interference to the collected data and improve the robustness of the algorithm.

[0074] When the current maximum stress of the first battery is 1.5Mpa and the minimum stress is 0.5Mpa, and SOH1 is 95.4% after updating, the current maximum stress is updated to 1.45Mpa and the minimum stress is 0.47Mpa according to the maximum stress-SOH curve and the minimum stress-SOH curve in step S2 (refer to Figure 2 ).

[0075] The battery health status assessment method based on equivalent cycle number and stress of the present invention is compared with the battery health status assessment method in the prior art through application analysis to evaluate the beneficial effects of the present invention.

[0076] Existing method 1: Battery capacity verification method, which measures the actual capacity of the battery by filling and discharging the battery. The actual capacity / rated capacity is the actual SOH value.

[0077] Existing method 2: The power battery health status assessment method disclosed in Chinese patent CN108983106A records the start time, end time and charging power of each charge of new energy vehicles, as well as the total number of charges, total charging time, battery installation and operation days, total mileage and theoretical value of power battery charge and discharge cycle times of different types of new energy vehicles; extracts the user charging behavior characteristics of the average charging interval, total mileage, slow charging ratio, average charge and discharge depth and deep charge and discharge cycle ratio of new energy vehicles; calculates the comprehensive mean by weighted average, and then determines the power battery health status assessment result. That is, the current battery health status is assessed by mainly considering the impact of charging times and charging time on battery health status.

[0078] Figure 3 The relationship curves between the battery health status and the operating time calculated by three methods are demonstrated. Among them, the existing method 1 calculates the actual SOH of the battery, which is the most accurate, but it is the most harmful to the battery and is not suitable as a daily battery health status monitoring method. The battery health status calculated by the existing method 2 only considers the impact of the number of charging times and charging time on the battery health status, and does not consider the changes in the characteristics of the battery itself. Therefore, it has a large deviation from the actual value measured by the existing method 1.

[0079] The method of the present invention fully considers the strong correlation between battery stress change and battery health status, estimates the current battery health status based on the fitting curve of average stress and battery SOH, and makes further corrections. Figure 3It can be seen that the corrected SOH fits the actual SOH curve of the battery with a small deviation, which effectively improves the accuracy and reliability of the battery health status assessment.

[0080] The embodiments described above are only descriptions of the preferred modes of the present invention, and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A battery health status assessment method based on equivalent cycle number and stress, characterized in that: It includes the following steps: S1: Real-time acquisition of the stress P corresponding to the n batteries contained in the battery pack of the battery system n , running time Day, current I, corresponding sampling time interval t, and real-time calculation of equivalent cycle number N; The equivalent cycle number N is the historical cumulative capacity change divided by twice the rated capacity, and its calculation formula is shown in formula (1): (1)， Among them, C N Indicates the rated capacity of the battery; S2: Collect the stress value of each battery in the battery pack aging test, obtain the maximum stress-SOH curve during charging and the minimum stress-SOH curve during discharging, calculate the average stress data of each battery under different SOH, and then generate a fitting curve of the average stress of each battery and the battery SOH; S3: According to the stress P of step S1 n Update the current maximum stress and minimum stress of each battery in real time and calculate the corresponding average stress; S4: Design a stress calculation judgment window according to the calendar life, cycle life and equivalent cycle number. If the stress calculation judgment window is satisfied, proceed to step S5; S5: Set initial SOH n = 100%, and the estimated SOH is obtained by fitting the curve of step S2 according to the average stress of step S3 n-est , if the current SOH is met n >SOH n-est , then perform SOH n Correction, corrected SOH n Indicates the current battery health status.

2. The battery health status assessment method based on equivalent cycle number and stress according to claim 1, characterized in that: In step S2, the average stress data of each battery at different SOH is calculated using formula (2): (2)， in, is the mean stress, represents the maximum stress of the nth battery under the current SOH, Indicates the minimum stress of the nth battery at the current SOH.

3. The battery health status assessment method based on equivalent cycle number and stress according to claim 2, characterized in that: In step S2, the least squares method is used to fit the average stress data of each battery to a curve: (3)， in, y Mean stress , x Indicates SOH n , k represents gain, and b represents offset.

4. The battery health status assessment method based on equivalent cycle number and stress according to claim 3, characterized in that: In formula (3), k is -10 and b is 10.

5.

5. The battery health status assessment method based on equivalent cycle number and stress according to claim 1, characterized in that: The calculation formula of the stress calculation judgment window in step S4 is shown in formula (4): (4)， Among them, B represents the initial calendar life provided by the battery manufacturer, C represents the initial cycle life provided by the battery manufacturer, D is the SOH value of the retired battery, Day is the current number of operating days, N is the current equivalent number of cycles, and e is the SOH change threshold.

6. The battery health status assessment method based on equivalent cycle number and stress according to claim 5, characterized in that: In formula (4), B is 3650 days, C is 5000 times, D is 80%, and e is 0.2%.

7. The battery health status assessment method based on equivalent cycle number and stress according to claim 3, characterized in that: In step S5, the estimated SOH is obtained by fitting the curve n-est The calculation formula is shown in formula (5); (5)， Where y represents the mean stress , k represents gain, and b represents offset.

8. A battery health status assessment method based on equivalent cycle number and stress according to claim 5 or 6, characterized in that: In step S5, SOH n The correction formula of is shown in formula (6): (6)， in, w Indicates the compensation coefficient, 0.2 <w<0.8。 9. The battery health status assessment method based on equivalent cycle number and stress according to claim 1, characterized in that: In step S5, whenever SOH n If a correction occurs, the maximum stress and the minimum stress in step S3 are updated according to the maximum stress-SOH curve and the minimum stress-SOH curve in step S2.

10. A battery health status assessment method based on equivalent cycle number and stress according to claim 1 or 9, characterized in that: The average stress in step S3 is the arithmetic mean of the maximum stress and the minimum stress.

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