A method for rapidly predicting cycle life of lithium batteries
By establishing the relationship between lithium battery cycle data and fitting key parameters using 50-100 cycles of data, the problem of long prediction time and poor applicability of lithium battery cycle life in existing technologies has been solved. This has enabled rapid and accurate prediction of lithium battery cycle life, which is adaptable to different preparation methods and electrolyte types, with an error of less than 2%.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- EVE ENERGY CO LTD
- Filing Date
- 2022-11-30
- Publication Date
- 2026-05-12
AI Technical Summary
Existing methods for predicting the cycle life of lithium batteries require long-term actual testing and have poor model applicability, failing to adapt to changes in preparation methods and electrolyte types.
By establishing the cycle data relationship of lithium batteries of the same system, using data from 50-100 cycles to fit key parameters, the performance change trend of higher cycle counts is predicted. The relationship ln(Qloss) = A + Zln(x) is used to calculate the values of A and Z. Combined with the test data of the battery under test, the future cycle life is predicted.
It achieves rapid and accurate prediction of lithium battery cycle life, is highly adaptable, has an error of less than 2%, is applicable to different preparation methods and electrolyte types, and simplifies the testing process.
Smart Images
Figure CN115792676B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of lithium battery performance prediction methods, and specifically to a method for rapidly predicting the cycle life of lithium batteries. Background Technology
[0002] Lithium-ion batteries are a type of battery that uses lithium metal or lithium alloys as positive / negative electrode materials and a non-aqueous electrolyte solution. They possess high energy density and power density and are widely used in various fields. Improving the cycle life of lithium-ion batteries is crucial for their development. However, the actual testing process for lithium-ion battery cycle life is lengthy and cumbersome. Therefore, developing a rapid and accurate method for predicting the cycle life of lithium-ion batteries is of great significance.
[0003] The most common method for predicting the cycle performance of lithium batteries is to fit actual test data to establish a semi-empirical cycle life prediction model. The disadvantage of this method is that the test data of the battery to be predicted must be at least several hundred cycles to ensure the accuracy of the prediction. In addition, it can only predict the cycle life state of lithium batteries that are exactly the same as the battery to be predicted. If the preparation method, site or electrolyte of the lithium battery to be predicted changes, the model will not be applicable. The general applicability of this method is poor. Summary of the Invention
[0004] The purpose of this invention is to address the problems of existing methods for predicting the cycle life of lithium batteries, which require long-term actual testing of the lithium batteries to be predicted and whose prediction models are affected by the preparation method, site, or electrolyte type, resulting in poor adaptability. This invention provides a method for rapidly predicting the cycle life of lithium-ion batteries. This method utilizes cycle data from batteries of the same system to establish the relationship between key data affecting battery cycle life and the number of cycles. Only 50-100 cycles of data are needed to quickly and accurately predict the performance change trend of lithium batteries at higher cycle counts. Furthermore, it is unaffected by the preparation method, site, or electrolyte type, exhibiting strong adaptability and high prediction accuracy.
[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:
[0006] A method for rapidly predicting the cycle life of lithium batteries includes the following steps:
[0007] Step 1: Determine the test temperature T of the lithium battery to be predicted; then, obtain the capacity loss rate data of the first lithium battery at different cycle numbers under temperature T, and establish linear relationship curves of ln(Qloss) and ln(x) at different cycle numbers: ln(Qloss)=A+Zln(x), calculate the A value and Z value of the first lithium battery at different cycle numbers; x is the cycle number, and Qloss is the capacity loss rate; the first lithium battery is a battery with the same system as the lithium battery to be predicted;
[0008] Obtain the fitted relationship between A and x: A = ax 2 +bx+c; a, b, and c are constants, and x is the number of cycles;
[0009] Obtain the fitted relationship between Z and x: Z = dx 2 +ex+f; d, e, and f are constants, and x is the number of cycles;
[0010] Step 2: Under temperature T, perform cycle tests on the lithium battery to be predicted; calculate the A1 and Z1 values of the lithium battery to be predicted after m cycles; m is a positive integer from 50 to 100;
[0011] Then substitute the values of A1 and m into A = ax 2 In Z = bx + g, a and b are known constants, x is the number of cycles, and g is an unknown constant. Substitute the values of Z1 and m into Z = dx. 2 +ex+h; d and e are known constants, x is the number of cycles, and h is an unknown constant;
[0012] The values of g and h were calculated.
[0013] Step 3: Assume x is n, where n is a positive integer ≥ 200; substitute n into A = ax 2 In the equation +bx+g, we obtain A2; substituting n into Z = dx 2 In +ex+h, we get Z2;
[0014] The predicted cycle life relationship of the lithium battery to be predicted is obtained as follows: ln(Qloss)=A2+Z2ln(x); Qloss is the capacity loss rate; x is the number of cycles.
[0015] This invention discloses a method for rapidly predicting the cycle life of a lithium battery. Step 1: Determine the test temperature T of the lithium battery to be predicted; then, acquire the capacity loss rate data of the first lithium battery at different cycle numbers under temperature T, and establish linear relationship curves between ln(Qloss) and ln(x) at different cycle numbers: ln(Qloss) = A + Zln(x), and calculate the A and Z values of the first lithium battery at different cycle numbers; x is the cycle number, and Qloss is the capacity loss rate; the first lithium battery is a battery with the same system as the lithium battery to be predicted; obtain the fitting relationship between A and x: A = ax 2 +bx+c; a, b, and c are constants, and x is the number of cycles; obtain the fitting relationship between Z and x: Z = dx 2 +ex+f; d, e, and f are constants, and x is the number of cycles; Step 2: Under temperature T, perform a cycle test on the lithium battery to be predicted; Calculate the A1 and Z1 values of the lithium battery to be predicted for m cycles; m is a positive integer from 50 to 100; Then substitute the A1 and m values into A = ax 2 In Z = bx + g, a and b are known constants, x is the number of cycles, and g is an unknown constant. Substitute the values of Z1 and m into Z = dx. 2 +ex+h; d and e are known constants, x is the number of cycles, and h is an unknown constant; calculate the values of g and h; Step 3: Assume x is n, where n is a positive integer ≥ 200; substitute n into A = ax 2 In the equation +bx+g, we obtain A2; substituting n into Z = dx 2 In +ex+h, we obtain Z2; that is, the predicted cycle life relationship of the lithium battery to be predicted is: ln(Qloss)=A2+Z2ln(x); Qloss is the capacity loss rate; x is the number of cycles. This method uses battery cycle data of the same system to establish the relationship between key data affecting battery cycle life and the number of cycles. Only 50-100 cycles of data are needed to quickly and accurately predict the performance change trend of lithium batteries at higher cycle numbers. It is not affected by the preparation method, site or electrolyte type, has strong adaptability, high prediction accuracy, and is easy to promote.
[0016] Furthermore, in step 1, "the same system as the lithium battery to be predicted" means that it is the same as the positive electrode system and the negative electrode system of the lithium battery to be predicted.
[0017] Furthermore, in step 1, capacity loss rate data of the first lithium battery within at least six different cycle counts under temperature T are obtained, with the difference between adjacent cycle counts ≥ 50. These can be within six, seven, eight, nine, ten, eleven, twelve, or more different cycle counts. Appropriate values for the different cycle counts ensure the fitting effect of the subsequent fitted relationship and the accuracy of the prediction method. Preferably, capacity loss rate data of the first lithium battery within at least seven different cycle counts under temperature T are obtained, with the difference between adjacent cycle counts ≥ 50. More preferably, the difference between adjacent cycle counts is ≤ 150. The difference between adjacent cycle counts can be 50, 55, 60, 66, 70, 80, 90, 100, 110, 120, 130, 140, or 150 cycles.
[0018] Furthermore, in step 1, the temperature T is -10℃ to 60℃. Preferably, when the lithium battery to be predicted is a pouch battery, the temperature T in step 1 is -10℃ to 45℃.
[0019] Furthermore, in step 2, when -10℃≤T≤30℃, m is a positive integer from 80 to 100; m can be 80, 85, 90, 95, or 100. When 30℃<T≤60℃, m is a positive integer from 50 to 100; m can be 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100.
[0020] Furthermore, in step 2, by acquiring the capacity loss rate data of the lithium battery to be predicted within m cycles, a linear relationship curve of ln(Qloss) and ln(x) is established within m cycles: ln(Qloss)=A+Zln(x), and the A1 value and Z1 value of the lithium battery to be predicted within m cycles are calculated; x is the number of cycles, and Qloss is the capacity loss rate.
[0021] Furthermore, in step 3, n is a positive integer ≥ 250. Preferably, in step 3, n is a positive integer ≥ 300. More preferably, in step 3, n is a positive integer between 300 and 600.
[0022] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:
[0023] This invention discloses a method for rapidly predicting the cycle life of a lithium battery. Step 1: Determine the test temperature T of the lithium battery to be predicted; then, acquire the capacity loss rate data of the first lithium battery at different cycle numbers under temperature T, and establish linear relationship curves between ln(Qloss) and ln(x) at different cycle numbers: ln(Qloss) = A + Zln(x), and calculate the A and Z values of the first lithium battery at different cycle numbers; x is the cycle number, and Qloss is the capacity loss rate; the first lithium battery is a battery with the same system as the lithium battery to be predicted; obtain the fitting relationship between A and x: A = ax 2 +bx+c; a, b, and c are constants, and x is the number of cycles; obtain the fitting relationship between Z and x: Z = dx 2 +ex+f; d, e, and f are constants, and x is the number of cycles; Step 2: Under temperature T, perform a cycle test on the lithium battery to be predicted; Calculate the A1 and Z1 values of the lithium battery to be predicted for m cycles; m is a positive integer from 50 to 100; Then substitute the A1 and m values into A = ax 2 In Z = bx + g, a and b are known constants, x is the number of cycles, and g is an unknown constant. Substitute the values of Z1 and m into Z = dx. 2 +ex+h; d and e are known constants, x is the number of cycles, and h is an unknown constant; calculate the values of g and h; Step 3: Assume x is n, where n is a positive integer ≥ 200; substitute n into A = ax 2 In the equation +bx+g, we obtain A2; substituting n into Z = dx 2 In +ex+h, we obtain Z2; that is, the predicted cycle life relationship of the lithium battery to be predicted is: ln(Qloss)=A2+Z2ln(x); Qloss is the capacity loss rate; x is the number of cycles. This method uses battery cycle data of the same system to establish the relationship between key data affecting battery cycle life and the number of cycles. Only 50-100 cycles of data are needed to quickly and accurately predict the performance change trend of lithium batteries at higher cycle numbers. It is not affected by the preparation method, site or electrolyte type, has strong adaptability, high prediction accuracy, and the error value can be controlled within 2%, making it easy to promote. Attached Figure Description
[0024] Figure 1 This is a trend graph of the A value and the number of cycles in Example 1.
[0025] Figure 2 This is a trend graph of Z value versus number of cycles in Example 1.
[0026] Figure 3 This is a comparison chart of the measured trend curve and the predicted trend curve of the lithium battery capacity retention rate and cycle number in Example 1.
[0027] Figure 4 This is an error analysis graph showing the actual and predicted values of battery capacity loss for different cycle numbers in Example 1.
[0028] Figure 5 This is a trend graph of the A value and the number of cycles in Example 2.
[0029] Figure 6 This is a trend graph of Z value and number of cycles in Example 2.
[0030] Figure 7 This is a comparison chart of the measured trend curve and the predicted trend curve of the lithium battery capacity retention rate and cycle number in Example 2.
[0031] Figure 8 This is an error analysis graph showing the actual and predicted values of battery capacity loss for different cycle numbers in Example 2. Detailed Implementation
[0032] The present invention will now be described in detail with reference to the accompanying drawings.
[0033] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.
[0034] Using the formula Qloss=B*exp(-Ea / RT)*nZ, let G=LnB, A=G-Ea / RT, we get Qloss=exp(A)*nZ. Then, taking the logarithm of both sides, we get ln(Qloss=A+Zln(n). A and Z change with the number of iterations n.
[0035] Wherein, Qloss is the capacity loss rate of the battery under test, B is a constant, A is a function related to temperature, Z is a function related to the number of cycles, n is the number of cycles, R is a gas constant, T is temperature, and Ea is activation energy.
[0036] Example 1
[0037] The lithium battery to be predicted: The 801444 battery using the lithium cobalt oxide system is used as the representative model. The raw material ratio is 98wt% LiCoO2, 0.5wt% conductive agent 1, 0.5% conductive agent 2 and 1% PVDF. The negative electrode uses 97wt% graphite, 1wt% conductive agent 1, 1% CMC and 1% SBR. After the positive and negative electrodes and separator are wound, packaged and injected with electrolyte to make the battery, the battery pressure formation parameters are: 80℃ 1.1Mpa 96min 1C. The electrolyte type is LBC440F80. After capacity grading and standing at room temperature for 72h, the cycle performance test is carried out.
[0038] The objective is to predict the cycle life of the lithium battery to be predicted under 25°C conditions.
[0039] The first lithium battery with the same system as the lithium battery to be predicted was selected.
[0040] The first lithium battery: The 801444 battery, which uses the lithium cobalt oxide system, is used as the representative model. The raw material ratio is 98wt% LiCoO2, 0.5wt% conductive agent 1, 0.5% conductive agent 2 and 1% PVDF. The negative electrode uses 97wt% graphite, 1wt% conductive agent 1, 1% CMC and 1% SBR. After the positive and negative electrodes and separator are wound, packaged and injected with electrolyte to make the battery, the battery pressure formation parameters are: 80℃ 1.1Mpa 120min 1C. The electrolyte type is LBC440G16. After capacity testing and standing at room temperature for 72h, the cycle performance test is carried out.
[0041] The specific process of loop testing:
[0042] Step 1: Let the first lithium battery stand for 30 minutes;
[0043] Step 2, Constant Current and Constant Voltage Charging: After charging the first lithium battery with constant current to the charging limit voltage, switch to constant voltage charging until the charging current is less than or equal to 0.05C.
[0044] Step 3: Let it stand: Let the fully charged battery to be evaluated stand for 10 minutes;
[0045] Step 4, Constant Current Discharge: Discharge the battery to be evaluated under constant current to the termination voltage after it has been left to stand.
[0046] Step 5: Let stand: Let the discharged battery to be evaluated stand for 10 minutes.
[0047] The first lithium battery was subjected to charge-discharge cycle operations in steps 2-5, 600 cycles, and the battery cycle capacity was calculated.
[0048] Calculate the A and Z values for the 100th lap, the 250th lap, the 300th lap, the 350th lap, the 400th lap, the 450th lap, the 500th lap, the 550th lap, and the 600th lap.
[0049] The specific calculation process for the A and Z values of the 100th lap is as follows:
[0050] Obtain the capacity loss rate data of the first lithium battery within 100 cycles at 25℃, and establish a linear relationship curve between ln(Qloss) and ln(x) within 100 cycles: ln(Qloss)=A+Zln(x). Calculate the A value and Z value for 100 cycles using the intercept and slope of the curve.
[0051] The specific calculation process for the A and Z values of lap 250 is as follows:
[0052] Obtain the capacity loss rate data of the first lithium battery within 250 cycles at 25℃, and establish a linear relationship curve between ln(Qloss) and ln(x) within 250 cycles: ln(Qloss)=A+Zln(x). Calculate the A value and Z value for 250 cycles using the intercept and slope of the curve.
[0053] The A and Z values for other different number of cycles were calculated using the same method described above.
[0054] like Figure 1 and Figure 2 As shown, the calculation relationship between A and x is fitted; the calculation relationship between Z and x is fitted.
[0055] Cyclic tests were conducted on the lithium battery to be tested at 25°C.
[0056] The process of loop testing:
[0057] Step 1: Let the lithium battery to be predicted stand for 30 minutes;
[0058] Step 2, Constant Current and Constant Voltage Charging: Charge the lithium battery to be predicted with constant current until the charging limit voltage is reached, then switch to constant voltage charging until the charging current is less than or equal to 0.05C.
[0059] Step 3: Let it stand: Let the fully charged lithium battery to be predicted stand for 10 minutes;
[0060] Step 4, Constant Current Discharge: Discharge the lithium battery to be predicted under constant current until the termination voltage is reached after the resting period.
[0061] Step 5: Let stand: Let the discharged lithium battery to be predicted stand for 10 minutes.
[0062] Perform charge-discharge cycles of steps 2-5 on the lithium battery to be tested, for a total of 100 cycles.
[0063] After 100 iterations, the values of A1 and Z1 are calculated to be -9.6189 and 1.3152, respectively.
[0064] Substituting x = 100 and A1 into A = ax 2 +bx+g; This gives the value of g.
[0065] Substituting x = 100 and Z1 into Z = dx 2 +ex+h; This gives the value of h.
[0066] Assume x is 550. Substitute x = 550 into A = ax 2 In the expression +bx+g, we obtain A2 as 8.584.
[0067] Substituting x = 550 into Z = dx 2 In +ex+h, Z2 is 1.059.
[0068] The cycle prediction curve of the lithium battery to be predicted is obtained: ln(Qloss) = A² + Z²ln(x). Specifically, as shown below... Figure 3 As shown.
[0069] Simultaneously, the percentage error between the actual capacity loss and the predicted capacity loss was calculated during 600 cycles, specifically as follows: Figure 4 As shown.
[0070] from Figure 4 The test results show that when the formation process and electrolyte are changed, by establishing a corresponding relationship using data from the same system and adding relevant data from 100 discharge cycles, the trend at 600 cycles can be predicted with an error controlled within 5%, which has high reference value.
[0071] Example 2
[0072] The lithium battery to be predicted: The 801444 battery using the lithium cobalt oxide system is used as the representative model. The raw material ratio is 98wt% LiCoO2, 0.5wt% conductive agent 1, 0.5% conductive agent 2 and 1% PVDF. The negative electrode uses 97wt% graphite, 1wt% conductive agent 1, 1% CMC and 1% SBR. After the positive and negative electrodes and separator are wound, packaged and injected with electrolyte to make the battery, the battery pressure formation parameters are: 80℃ 1.1Mpa 96min 1C. The electrolyte type is LBC440F80. After capacity grading and standing at room temperature for 72h, the cycle performance test is carried out.
[0073] The objective is to predict the cycle life of the lithium battery to be predicted under 45°C conditions.
[0074] The first lithium battery with the same system as the lithium battery to be predicted was selected.
[0075] The first lithium battery: The 801444 battery, which uses the lithium cobalt oxide system, is used as the representative model. The raw material ratio is 98wt% LiCoO2, 0.5wt% conductive agent 1, 0.5% conductive agent 2 and 1% PVDF. The negative electrode uses 97wt% graphite, 1wt% conductive agent 1, 1% CMC and 1% SBR. After the positive and negative electrodes and separator are wound, packaged and injected with electrolyte to make the battery, the battery pressure formation parameters are: 80℃ 1.1Mpa 120min 1C. The electrolyte type is LBC440G16. After capacity testing and standing at room temperature for 72h, the cycle performance test is carried out.
[0076] The specific process of loop testing:
[0077] Step 1: Let the first lithium battery stand for 60 minutes;
[0078] Step 2, Constant Current and Constant Voltage Charging: After charging the first lithium battery with constant current to the charging limit voltage, switch to constant voltage charging until the charging current is less than or equal to 0.05C.
[0079] Step 3: Let it stand: Let the fully charged battery to be evaluated stand for 10 minutes;
[0080] Step 4, Constant Current Discharge: Discharge the battery to be evaluated under constant current to the termination voltage after it has been left to stand.
[0081] Step 5: Let stand: Let the discharged battery to be evaluated stand for 10 minutes.
[0082] Perform charge-discharge cycle operations (steps 2-5) on the first lithium battery, cycling 300 times, and then calculate the battery cycle capacity.
[0083] Calculate the A and Z values after the 50th lap, the 100th lap, the 150th lap, the 200th lap, the 250th lap, and the 300th lap.
[0084] The specific calculation process for the A and Z values of lap 50 is as follows:
[0085] Obtain the capacity loss rate data of the first lithium battery within 50 cycles at 45℃, and establish a linear relationship curve between ln(Qloss) and ln(x) within 50 cycles: ln(Qloss)=A+Zln(x). Calculate the A value and Z value for 50 cycles using the intercept and slope of the curve.
[0086] The specific calculation process for the A and Z values of the 100th lap is as follows:
[0087] Obtain the capacity loss rate data of the first lithium battery within 100 cycles at 45℃, and establish a linear relationship curve between ln(Qloss) and ln(x) within 100 cycles: ln(Qloss)=A+Zln(x). Calculate the A value and Z value for 100 cycles using the intercept and slope of the curve.
[0088] The A and Z values for other different number of cycles were calculated using the same method described above.
[0089] like Figure 5 and Figure 6 As shown, the calculation relationship between A and x is fitted; the calculation relationship between Z and x is fitted.
[0090] Cyclic tests were conducted on the lithium battery to be tested at 45°C.
[0091] The process of loop testing:
[0092] Step 1: Let the lithium battery to be predicted stand for 60 minutes;
[0093] Step 2, Constant Current and Constant Voltage Charging: Charge the lithium battery to be predicted with constant current until the charging limit voltage is reached, then switch to constant voltage charging until the charging current is less than or equal to 0.05C.
[0094] Step 3: Let it stand: Let the fully charged lithium battery to be predicted stand for 10 minutes;
[0095] Step 4, Constant Current Discharge: Discharge the lithium battery to be predicted under constant current until the termination voltage is reached after the resting period.
[0096] Step 5: Let stand: Let the discharged lithium battery to be predicted stand for 10 minutes.
[0097] Perform the charge-discharge cycle operation of steps 2-5 on the lithium battery to be predicted, for a total of 50 cycles.
[0098] After 50 iterations, the values of A1 and Z1 are calculated to be 1.095 and 8.7008, respectively.
[0099] Substituting x = 50 and A1 into A = ax^2 + bx + g, we obtain the value of g.
[0100] Substituting x = 50 and Z1 into Z = dx^2 + ex + h, we obtain the value of h.
[0101] Assuming x is 250, substituting x = 250 into A = ax^2 + bx + g, we get A^2 = 0.845.
[0102] Substituting x = 250 into Z = dx^2 + ex + h, we get Z^2 as 7.701.
[0103] The cycle prediction curve of the lithium battery to be predicted is obtained: ln(Qloss) = A² + Z²ln(x). Specifically, as shown below... Figure 7 As shown.
[0104] Simultaneously, the percentage error between the actual capacity loss and the predicted capacity loss was calculated during 600 cycles, specifically as follows: Figure 8 As shown. From Figure 8 The test results show that the trend of the lithium battery to be predicted can be predicted by combining the data of the first 50 weeks. The error is less than 2% within 300 weeks. The method is simple and easy to implement and has high accuracy.
[0105] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A method for rapidly predicting the cycle life of a lithium battery, characterized in that, Includes the following steps: Step 1: Determine the test temperature T of the lithium battery to be predicted; then, obtain the capacity loss rate data of the first lithium battery at different cycle numbers under temperature T, and establish linear relationship curves of ln(Qloss) and ln(x) at different cycle numbers: ln(Qloss)=A+Zln(x), calculate the A value and Z value of the first lithium battery at different cycle numbers; x is the cycle number, and Qloss is the capacity loss rate; the first lithium battery is a battery with the same system as the lithium battery to be predicted; Obtain the fitted relationship between A and x: A = ax 2 +bx+c; a, b, and c are constants, and x is the number of cycles; Obtain the fitted relationship between Z and x: Z = dx 2 +ex+f; d, e, and f are constants, and x is the number of cycles; Step 2: Under temperature T, perform cycle tests on the lithium battery to be predicted; calculate the A1 and Z1 values of the lithium battery to be predicted after m cycles; m is a positive integer from 50 to 100. Then substitute the values of A1 and m into A = ax 2 In Z = bx + g, a and b are known constants, x is the number of cycles, and g is an unknown constant. Substitute the values of Z1 and m into Z = dx. 2 +ex+h; d and e are known constants, x is the number of cycles, and h is an unknown constant; The values of g and h were calculated. Step 3: Assume x is n, where n is a positive integer ≥ 200; substitute n into A = ax 2 In the equation +bx+g, we obtain A2; substituting n into Z = dx 2 In +ex+h, we get Z2; The predicted cycle life relationship of the lithium battery to be predicted is obtained as follows: ln(Qloss)=A2+Z2ln(x); Qloss is the capacity loss rate; x is the number of cycles.
2. The method for rapidly predicting the cycle life of a lithium battery according to claim 1, characterized in that, In step 1, "the same system as the lithium battery to be predicted" means that the positive electrode system and the negative electrode system are the same as those of the lithium battery to be predicted.
3. The method for rapidly predicting the cycle life of a lithium battery according to claim 1, characterized in that, In step 1, capacity loss rate data of the first lithium battery at least six different cycle numbers are obtained under temperature T, with the difference between adjacent cycle numbers ≥ 50.
4. The method for rapidly predicting the cycle life of a lithium battery according to claim 3, characterized in that, In step 1, capacity loss rate data of the first lithium battery at least seven different cycle numbers are obtained under temperature T, with the difference between adjacent cycle numbers ≥ 50.
5. The method for rapidly predicting the cycle life of a lithium battery according to claim 3 or 4, characterized in that, The difference in the number of adjacent cycles is ≤150.
6. The method for rapidly predicting the cycle life of a lithium battery according to claim 1, characterized in that, In step 1, the temperature T is -10℃ to 60℃.
7. The method for rapidly predicting the cycle life of a lithium battery according to claim 6, characterized in that, In step 2, when -10℃≤T≤30℃, m is a positive integer from 80 to 100; when 30℃<T≤60℃, m is a positive integer from 50 to 100.
8. The method for rapidly predicting the cycle life of a lithium battery according to claim 1, characterized in that, In step 2, by acquiring the capacity loss rate data of the lithium battery to be predicted within m cycles, a linear relationship curve of ln(Qloss) and ln(x) is established within m cycles: ln(Qloss) = A + Zln(x), and the A1 value and Z1 value of the lithium battery to be predicted within m cycles are calculated; x is the number of cycles, and Qloss is the capacity loss rate.
9. The method for rapidly predicting the cycle life of a lithium battery according to any one of claims 1, characterized in that, In step 3, n is a positive integer ≥ 250.
10. The method for rapidly predicting the cycle life of a lithium battery according to claim 9, characterized in that, In step 3, n is a positive integer ≥ 300.