Online calibration method for OCV-SOC curve of energy storage lithium ion battery

Abstract

The invention relates to the technical field of lithium ion batteries, and particularly discloses an energy storage lithium ion battery OCV-SOC curve online calibration method, which comprises the following steps: fitting an existing OCV-SOC curve at the initial stage of the service life of an energy storage lithium ion battery; determining the upper limit voltage and the lower limit voltage of the energy storage lithium ion battery in the operation of the energy storage power station; fitting the obtained OCV-SOC curve by adopting a high-order polynomial; carrying out a small amount of OCV online calibration test on the aged energy storage lithium ion battery; and calculating an OCV-SOC curve after aging according to a calibration test result. According to the online calibration method for the OCV-SOC curve of the energy storage lithium ion battery, high-precision calculation of the OCV-SOC curve of the aged battery can be achieved through a small number of online calibration tests, complex equipment and models do not need to be used, and operation is simple and easy to implement. The mathematical model before and after aging is derived from the mechanism theory of the OCV-SOC curve, the theoretical property is high, the accuracy is high, and the application range is wide.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium-ion batteries, and particularly to an online calibration method for the OCV-SOC curve of an energy storage lithium-ion battery. Background Art

[0002] There is a definite one-to-one correspondence between the open circuit voltage (OCV) and the state of charge (SOC) of an energy storage lithium-ion battery, and this relationship can be described by the OCV-SOC curve. The OCV-SOC curve directly affects the accurate assessment of the battery state and is the basis for the BMS to monitor and manage the battery state. Only with an accurate OCV-SOC curve can the actual state of charge, remaining power, internal resistance change, capacity attenuation, etc. of the battery be accurately estimated, thereby improving the battery usage efficiency, preventing safety hazards such as overcharging and over-discharging, and extending the battery life.

[0003] However, in actual applications, as the battery ages, the OCV-SOC curve will change, resulting in an increase in the error of battery state estimation based on the factory-given OCV-SOC curve.

[0004] Among the existing OCV-SOC calibration methods, the most accurate one is the off-line calibration of the battery under laboratory conditions, but this method cannot be applied during the operation of the energy storage lithium-ion battery. Another method is the open circuit voltage method, which obtains the OCV by leaving the battery static for a long time. On the one hand, it is difficult to obtain the static conditions at all SOCs during the operation of the energy storage lithium-ion battery, and on the other hand, the SOC calculated by the ampere-hour integration method also has obvious errors, so it is also difficult to be applied in engineering. Some studies use complex algorithms such as neural network algorithms and Kalman filter algorithms to estimate the OCV-SOC curve, but these algorithms often require a large number of data samples for training, and have a large amount of calculation and storage, increasing the cost of online application and reducing the operation speed of the system.

[0005] Therefore, an online calibration method for the OCV-SOC curve of an energy storage lithium-ion battery has become an urgent problem to be solved. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide an online calibration method for the OCV-SOC curve of an energy storage lithium-ion battery. Starting from the theoretical model of the OCV-SOC curve of the energy storage lithium-ion battery, the present invention derives and models the aged OCV-SOC curve, and establishes an online calibration scheme for the OCV-SOC curve based on a small amount of online test data, solving the problems of difficult calibration and low accuracy of the current OCV-SOC curve of the energy storage lithium-ion battery.

[0007] To solve the above technical problems, the technical solution provided by the present invention is: an online calibration method for the OCV-SOC curve of a energy storage lithium-ion battery, comprising the following steps:

[0008] S1. Fit the existing OCV-SOC curve at the initial stage of the life of the energy storage lithium-ion battery:

[0009] (1) Determine the upper voltage and the lower voltage of the energy storage lithium-ion battery during operation in the energy storage power station;

[0010] (2) Use a high-order polynomial to fit the obtained OCV-SOC curve;

[0011] S2. Conduct a small amount of OCV online calibration tests on the aged energy storage lithium-ion battery:

[0012] (1) Online adjust the SOC of the aged battery to 100%;

[0013] (2) Conduct online calibration of the aged battery: Starting from 100% SOC, perform multi-stage discharge at constant current or constant power, and stop discharging when the BMS shows 90%, 50%, 10%, 0% SOC respectively. After each stage of discharge is completed, let it stand for 2 h, calculate the total discharge capacity by integrating the discharge time according to the discharge current respectively, and extract the voltage at the end of standing;

[0014] S3. Calculate the OCV-SOC curve after aging according to the calibration test results:

[0015] (1) Calculate the calibrated OCV-SOC points;

[0016] (2) Establish an OCV-SOC curve model for the aged battery;

[0017] (3) Fit and calculate the OCV-SOC curve of the aged battery.

[0018] Further, the method for determining the upper voltage and the lower voltage of the energy storage lithium-ion battery during operation in the energy storage power station is as follows:

[0019] According to the upper voltage U max and the lower voltage U min set during the operation of the energy storage lithium-ion battery in the energy storage power station, perform linear scaling on the SOC range of the OCV-SOC test data provided by the battery supplier when the energy storage lithium-ion battery leaves the factory, to ensure that when the OCV is equivalent to U max , the SOC is 1; when the OCV is equivalent to U min , the SOC is 0;

[0020] In the OCV-SOC test data provided by the battery supplier, let the OCV be U and the SOC be z ’ , corresponding to Umax The SOC of 1 corresponding to U is z min The SOC of 0 is z. Then the scaled SOC is denoted as z and calculated as:

[0021]

[0022] Furthermore, the method for fitting the obtained OCV-SOC curve with a high-order polynomial is as follows:

[0023] The fitting formula is:

[0024]

[0025] where U i and z i respectively represent the OCV and SOC of the i-th point participating in the fitting. N is the fitting order, and a suitable number is selected according to the actual data volume and fitting effect. a k is the fitting coefficient, and all the fitting coefficients form the fitting coefficient vector A = {a k , k = 0, 1, 2,..., N}.

[0026] Furthermore, the method for online adjusting the SOC of the aged battery to 100% is as follows:

[0027] Charge the aged battery in the energy storage power station online in a constant current or constant power manner until U max , then continuously reduce the charging current or power and continue charging until the battery reaches 100% SOC shown by the BMS, and let it stand for 1 h.

[0028] Furthermore, the detailed online calibration process of the aged battery is as follows:

[0029] ① Start discharging from 100% SOC in a constant current or constant power manner. Stop discharging when the BMS shows 90% SOC, let it stand for 2 h, and calculate the total discharge capacity Q by integrating the discharge current over the discharge time 1 , and extract the voltage U aged,1 at the end of the standing;

[0030] ② Continue to discharge in a constant current or constant power manner. Stop discharging when the BMS shows 50% SOC, let it stand for 2 h, and calculate the total discharge capacity Q by integrating the discharge current over the discharge time 2 , and extract the voltage U aged,2 at the end of the standing;

[0031] ③ Continue to discharge in a constant current or constant power manner. Stop discharging when the BMS shows about 10% SOC, let it stand for 2 h, and calculate the total discharge capacity Q by integrating the discharge current over the discharge time 3, extract the voltage U at the end of the static state aged,3 ;

[0032] ④ Continue the constant current or constant power discharge until the discharge reaches U min and then continuously reduce the discharge current or power to continue the discharge until the battery reaches about 0% SOC shown by the BMS. Calculate the total discharge capacity Q by integrating the discharge time according to the discharge current aged .

[0033] Further, the method for calculating the calibrated OCV - SOC points is as follows:

[0034] According to the three groups of (Q i , U aged,i ) data combinations, perform the following conversion to calculate the corresponding SOC: i

[0035]

[0036] where Q 0 is the capacity measured according to the OCV curve at the initial stage of the battery life; thus, three groups of measured OCV - SOC points can be obtained, namely (z aged,i , U aged,i ).

[0037] Further, the method for establishing the OCV - SOC curve model of the aged battery is as follows:

[0038] The OCV - SOC curve model of the aged battery is:

[0039]

[0040] where N is the fitting order of the high - order polynomial used in step S1, a k is the fitting coefficient in step S1, and α is the undetermined coefficient.

[0041] Further, the method for fitting and calculating the OCV - SOC curve of the aged battery is as follows:

[0042] Use a function fitting algorithm or a parameter optimization algorithm to determine the undetermined parameter α; according to the three groups of measured OCV - SOC points of the aged battery in step S3 and the OCV - SOC curve model of the aged battery in step S3, perform the identification of α to obtain the optimal value of α, so as to obtain the OCV - SOC curve of the aged battery;

[0043] The obtained curve is the SOC calculated based on the capacity Q 0 measured according to the OCV curve at the initial stage of the battery life, and is further converted to the SOC based on the capacity Q aged measured according to the OCV curve in the current aging state of the batteryThe calculated SOC, with the conversion formula being:

[0044]

[0045] The final OCV - SOC curve of the aged battery is U aged,i ~z' aged,i 。

[0046] The advantages of the present invention compared with the prior art are as follows:

[0047] The online calibration method for the OCV - SOC curve of the energy - storage lithium - ion battery provided by the present invention can achieve high - precision calculation of the OCV - SOC curve of the aged battery through a small number of online calibration tests, without the use of complex equipment and models, is simple and easy to operate, and can be truly used online.

[0048] The online calibration method for the OCV - SOC curve of the energy - storage lithium - ion battery provided by the present invention deduces the mathematical models before and after aging from the mechanism theory of the OCV - SOC curve, has strong theoreticality, high accuracy, and a wide range of applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 is a flowchart of an online calibration method for the OCV - SOC curve of an energy - storage lithium - ion battery according to the present invention.

[0050] Figure 2 is a flowchart of step 1 of an online calibration method for the OCV - SOC curve of an energy - storage lithium - ion battery according to the present invention.

[0051] Figure 3 is a flowchart of step 2 of an online calibration method for the OCV - SOC curve of an energy - storage lithium - ion battery according to the present invention.

[0052] Figure 4 is a flowchart of step 3 of an online calibration method for the OCV - SOC curve of an energy - storage lithium - ion battery according to the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0053] Hereinafter, various exemplary embodiments of the present invention will be described in detail with reference to the drawings. It should be noted that: unless otherwise specifically stated, the relative arrangements of components and steps, numerical expressions and values set forth in these embodiments do not limit the scope of the present invention.

[0054] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended as a limitation on the present invention and its application or use.

[0055] Technologies, methods, and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, such technologies, methods, and devices should be regarded as part of the specification.

[0056] In all the examples shown and discussed here, any specific value should be construed as merely exemplary and not as a limitation. Thus, other examples of the exemplary embodiments may have different values.

[0057] Combined with the attached Figures 1-4 , the specific implementation process of an online calibration method for the OCV-SOC curve of an energy storage lithium-ion battery according to the present invention is as follows:

[0058] An online calibration method for the OCV-SOC curve of an energy storage lithium-ion battery includes the following steps:

[0059] Step 1: Fit the existing OCV-SOC curve at the initial stage of the life of the energy storage lithium-ion battery;

[0060] This process in the present invention mainly consists of the following two sub-steps:

[0061] Step 11: Determine the upper limit voltage U max and the lower limit voltage U min of the energy storage lithium-ion battery during operation in the energy storage power station. Obtain the OCV-SOC test data (U~z') provided by the battery supplier, and perform linear scaling on the SOC range according to U max and U min to ensure that the SOC is 1 when the OCV is at U max and the SOC is 0 when the OCV is at U min . The scaling formula is:

[0062]

[0063] where z is the scaled SOC, z ’ is the SOC in the OCV-SOC test data provided by the battery supplier, z 1 is the SOC corresponding to U max among them, and z 0 is the SOC corresponding to U min among them.

[0064] Step 12: Fit the obtained OCV-SOC curve (U~z) with a high-order polynomial, and the fitting formula is:

[0065]

[0066] where U i and z i respectively represent the OCV and SOC of the i-th point participating in the fitting, N is the fitting order, selected as 25, a k is the fitting coefficient, and all the fitting coefficients form the fitting coefficient vector A = {a k, k = 0, 1, 2, …, 25}。

[0067] Step 2: Online calibrate and test a small amount of OCV of the aged energy storage lithium-ion battery;

[0068] This process in the present invention mainly consists of the following 2 sub-steps:

[0069] Step 21: Use a constant current of 1C to charge the aged battery in the energy storage power station online to U max , then continuously reduce the charging current and continue charging until the battery reaches 100% SOC shown by the BMS, and let it stand for 1 h.

[0070] Step 22: Conduct an online calibration test on the aged battery according to the following process:

[0071] ① Start discharging at a constant current of 1C from 100% SOC, stop discharging when the BMS shows 90% SOC, let it stand for 2 h, calculate the total discharge capacity Q by integrating the discharge time according to the discharge current 1 , and extract the voltage U at the end of the standing aged,1 .

[0072] ② Continue to discharge at a constant current of 1C, stop discharging when the BMS shows 50% SOC, let it stand for 2 h, calculate the total discharge capacity Q by integrating the discharge time according to the discharge current 2 , and extract the voltage U at the end of the standing aged,2 .

[0073] ③ Continue to discharge at a constant current of 1C, stop discharging when the BMS shows 10% SOC, let it stand for 2 h, calculate the total discharge capacity Q by integrating the discharge time according to the discharge current 3 , and extract the voltage U at the end of the standing aged,3 .

[0074] ④ Continue to discharge at a constant current of 1C, continuously reduce the discharge current and continue discharging until the battery reaches 0% SOC shown by the BMS, calculate the total discharge capacity Q by integrating the discharge time according to the discharge current min , aged .

[0075] Step 3: Calculate the OCV-SOC curve after aging according to the calibration test results;

[0076] This process in the present invention mainly consists of the following 3 sub-steps:

[0077] Step 31: According to the measured three groups of (Q i , U aged,i ) data combinations, perform the following conversion to calculate the corresponding SOC: i

[0078]

[0079] Among them, Q 0 is the capacity measured according to the OCV curve at the initial stage of the battery life. From this, three sets of measured OCV-SOC points can be obtained, namely (z aged,i , U aged,i ).

[0080] Step 32: The OCV-SOC curve model of the aged battery is:

[0081]

[0082] Among them, N is the order 25 adopted in Step 12, a k is the corresponding element in the coefficient vector A obtained by fitting in Step 12, and α is a coefficient to be determined.

[0083] Step 33: According to the three sets of measured OCV-SOC points of the aged battery and the OCV-SOC curve model of the aged battery, use the genetic algorithm to identify α and obtain the optimal value of α. The specific process is as follows:

[0084] (1) Generate the initial population

[0085] Set the population size to 100, the number of iterations to 5000, the mutation probability to 0.05, and the crossover probability to 0.7, and generate the initial chromosome population in binary coding.

[0086] (2) Genetic and mutation

[0087] Calculate the fitness of each individual in the population according to the curve model function. Use the roulette wheel method to select individuals with high fitness with a certain probability, and then use the single-point crossover method and the single-point mutation method in turn with the established probability among the selected individuals to generate new individuals and form a new population.

[0088] (3) Loop iteration

[0089] If the set cut-off condition is not reached, calculate the individual fitness and perform genetic mutation iteratively in the new population. When the cut-off condition is reached, output the gene coding of the individual with the current maximum fitness, and obtain the optimal value of α after decoding.

[0090] Substitute α into the OCV-SOC curve model of the aged battery to obtain the OCV-SOC curve U aged,i ~z aged,i .

[0091] According to the following, further convert to the SOC calculated based on the capacity Q aged measured according to the OCV curve of the battery in the current aging state:

[0092]

[0093] Therefore, the final OCV-SOC curve of the aged battery is U aged,i ~z' aged,i 。

[0094] The present invention and its embodiments have been described above. Such description is not restrictive. What is shown in the drawings is only one of the embodiments of the present invention, and the actual structure is not limited thereto. In general, if those of ordinary skill in the art are inspired by it and, without departing from the gist of the present invention, design similar structural modes and embodiments to this technical solution without creative efforts, they shall fall within the protection scope of the present invention.

Claims

1. An online calibration method for OCV-SOC curve of energy storage lithium-ion battery, characterized by: The following steps are included S1. Fit the existing OCV-SOC curve at the early stage of the life of the energy storage lithium-ion battery: (1) Determine the upper and lower voltage limits of energy storage lithium-ion batteries during operation of energy storage power stations; (2) fitting the obtained OCV-SOC curve using a high-order polynomial; S2. Perform a small amount of OCV online calibration test on the aged energy storage lithium-ion battery: (1) Online adjustment of the aged battery SOC to 100%; (2) Online calibration of the aged battery: starting from 100% SOC, perform multi-stage discharge at constant current or constant power, and stop after discharging until the BMS displays 90%, 50%, 10%, and 0% SOC. After each stage of discharge is completed, let it stand for 2 hours, calculate the total discharge capacity based on the integral of the discharge current and the discharge time, and extract the voltage at the end of the standing period; S3. Calculate the OCV-SOC curve after aging based on the calibration test results: (1) Calculate the calibrated OCV-SOC point; (2) Establish an OCV-SOC curve model for aged batteries; (3) Fit and calculate the OCV-SOC curve of the aged battery.

2. The method for online calibration of OCV-SOC curve of energy storage lithium-ion battery according to claim 1, characterized in that: The method for determining the upper and lower voltage limits of energy storage lithium-ion batteries in the operation of energy storage power stations is as follows: According to the upper limit voltage U set by the energy storage lithium-ion battery in the operation of the energy storage power station max And the lower limit voltage U min , the OCV-SOC test data provided by the battery supplier at the factory for energy storage lithium-ion batteries is linearly scaled to ensure that when OCV is equal to U max When SOC is 1, SOC is 1; when OCV is equal to U min When , SOC is 0; In the OCV-SOC test data provided by the battery supplier, let OCV be U and SOC be z ’ , corresponding to U max The SOC is z1, corresponding to U min The SOC of is z0, then the scaled SOC is recorded as z, and is calculated as:

3. The method for online calibration of OCV-SOC curve of energy storage lithium-ion battery according to claim 2, characterized in that: The method of fitting the obtained OCV-SOC curve using a high-order polynomial is as follows: The fitting formula is: Among them U i and z i They represent the OCV and SOC of the i-th point involved in the fitting, N is the fitting order, and a suitable number is selected according to the actual data volume and fitting effect. k is the fitting coefficient, and all the fitting coefficients constitute the fitting coefficient vector A = {a k ,k=0,1,2,…,N}.

4. The method for online calibration of OCV-SOC curve of energy storage lithium-ion battery according to claim 3, characterized in that: The method to adjust the SOC of an aged battery to 100% online is as follows: Use constant current or constant power to charge the aged batteries in the energy storage power station online to U max , then continue charging by continuously reducing the charging current or power until the battery reaches 100% SOC as indicated on the BMS, and let it sit for 1 hour.

5. The method for online calibration of OCV-SOC curve of energy storage lithium-ion battery according to claim 4, characterized in that: The detailed process of online calibration of aged batteries is as follows: ① Start constant current or constant power discharge from 100% SOC, stop discharging when the BMS displays 90% SOC, let it stand for 2 hours, calculate the total discharge capacity Q1 based on the integral of the discharge current and discharge time, and extract the voltage U at the end of the standstill aged,1 ; ② Continue to discharge at constant current or constant power until the BMS displays 50% SOC, then stop and let it stand for 2 hours. Calculate the total discharge capacity Q2 based on the integral of the discharge current and discharge time, and extract the voltage U at the end of the standstill. aged,2 ; ③ Continue to discharge at constant current or constant power until the BMS displays 10% SOC, then stop and let it stand for 2 hours. Calculate the total discharge capacity Q3 based on the integral of the discharge current and discharge time, and extract the voltage U at the end of the standstill. aged,3 ; ④Continue to discharge at constant current or constant power until U min Then continue to discharge by reducing the discharge current or power until the battery reaches about 0% SOC displayed on the BMS. The total discharge capacity Q is calculated by integrating the discharge current with the discharge time. aged .

6. The method for online calibration of OCV-SOC curve of energy storage lithium-ion battery according to claim 5, characterized in that: The method to calculate the calibrated OCV-SOC point is as follows: According to the three groups (Q i , U aged,i ) Capacity Q in data combination i Perform the following conversion to calculate the corresponding SOC: Among them, Q0 is the capacity of the battery measured according to the OCV curve at the beginning of its life; thus, three sets of measured OCV-SOC points can be obtained, namely (z aged,i , U aged,i ).

7. The method for online calibration of OCV-SOC curve of energy storage lithium-ion battery according to claim 6, characterized in that: The method for establishing the aged battery OCV-SOC curve model is as follows: The OCV-SOC curve model of the aged battery is: Where N is the fitting order of the high-order polynomial used in step S1, a k is the fitting coefficient in step S1, and α is the coefficient to be determined.

8. The method for online calibration of OCV-SOC curve of energy storage lithium-ion battery according to claim 7, characterized in that: The method for fitting and calculating the aged battery OCV-SOC curve is as follows: Determine the undetermined parameter α by using a function fitting algorithm or a parameter optimization algorithm; identify α according to the three groups of OCV-SOC points of the aged battery measured in step S3 and the OCV-SOC curve model of the aged battery in step S3, obtain the optimal value of α, and thus obtain the OCV-SOC curve of the aged battery; The obtained curve is the SOC calculated based on the capacity Q0 measured by the OCV curve at the beginning of the battery life, and further converted to the capacity Q measured by the OCV curve in the current aging state of the battery. aged The calculated SOC is calculated as follows: The final OCV-SOC curve of the aged battery is U aged,i ~z′ aged,i .

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