Method for constructing lithium ion battery SOH life model based on activation factors

Abstract

The invention discloses a construction method of a lithium ion battery SOH life model based on an activation factor, and relates to the field of lithium ion batteries. According to the specific implementation scheme, the method comprises the steps of obtaining test data of the lithium ion battery under different temperatures, different SOCs and different cycle working conditions through testing; based on the test data, acquiring the current relative capacity retention ratio of the battery cell; and based on the current relative capacity retention rate and the test data and by introducing an activation factor, constructing an SOH life model. According to the method, the activation factor item is introduced to correct the SOH life model, so that the'rising 'behavior of the relative capacity retention ratio of the energy storage lithium ion battery in the early storage and circulation stages can be accurately captured, and the prediction precision of the SOH is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium-ion batteries, and in particular to a method for constructing a lithium-ion battery SOH life model based on an activation factor. Background Art

[0002] The maximum relative capacity retention rate allowed by the existing semi-empirical SOH life model is 100%, that is, during the entire life decay process of the lithium-ion battery, the relative capacity retention rate will decrease from the initial 100% to a certain value, and the relative capacity retention rate will not exceed 100%. However, experiments have repeatedly found that energy storage lithium-ion batteries often experience a "rising" relative capacity retention rate over a relatively long period of time in the early stages of storage and circulation, that is, the relative capacity retention rate will increase from 100% to a certain value, and then the relative capacity retention rate will begin to decrease. This phenomenon of the relative capacity retention of energy storage lithium-ion batteries first increasing and then decreasing obviously cannot be described by the existing semi-empirical SOH life model. Summary of the invention

[0003] Based on this, the present invention provides a method for constructing a SOH life model of a lithium-ion battery based on an activation factor in order to solve the problem of being unable to accurately capture the "increasing" behavior of the relative capacity retention rate of energy storage lithium-ion batteries in the early stages of storage and cycling.

[0004] The present invention provides a method for constructing a lithium-ion battery SOH life model based on an activation factor, comprising:

[0005] Through testing, the test data of lithium-ion batteries under different temperatures, different SOCs, and different cycle conditions are obtained;

[0006] Based on the test data, obtaining a current relative capacity retention rate of the battery cell;

[0007] Based on the current relative capacity retention rate and the test data and by introducing an activation factor, a SOH life model is constructed.

[0008] Before obtaining the relative capacity retention rate of the battery cell under the current test conditions, the test data is cleaned using a Gaussian kernel function to remove abnormal test data;

[0009] The test data includes battery storage days, cycle number, battery capacity, SOC and real-time temperature of the battery cell.

[0010] The acquisition of the relative capacity retention rate of the battery cell under the current test conditions includes:

[0011] Let the battery discharge from 100% SOC to 0% SOC or the lower limit voltage to obtain the discharge capacity of the battery;

[0012] Repeat the above discharge process multiple times and take the average value as the current capacity of the battery;

[0013]

[0014] The SOH life model includes a storage SOH life model and a cycle SOH life model.

[0015] For the storage SOH life model, an activation factor term based on real-time temperature, SOC and storage cycles is introduced to correct the SOH increase region of the energy storage lithium-ion battery in the early storage stage.

[0016] The calculation of the storage SOH life model includes:

[0017] SOH calendar,new =1-CapFade calendar,new ;

[0018]

[0019] Among them, SOH calendar,new Indicates the relative capacity retention rate during storage, CapFade calendar,new represents the capacity decay caused by the activation term during storage, A1 represents the pre-exponential factor, E a1 represents activation energy, Time z1 Indicates battery aging time, SOC y1 Indicates the effect of state of charge on battery aging. SOC indicates the SOC value during storage. ΔSOH calendar,ative represents the storage activation term of the energy storage cell, z1 and y1 represent the fitting parameters;

[0020] The energy storage cell storage activation term ΔSOH calendar,ative The calculation includes,

[0021]

[0022] Among them, α1 and β1 represent fitting parameters, T represents the temperature of the energy storage cell during storage, Time represents the storage time, and Time threshold Indicates the storage time threshold;

[0023] The storage time threshold Time threshold The calculation includes,

[0024] The storage time threshold Time is calculated based on the temperature T of the energy storage cell during storage and the SOC value during storage. threshold ,

[0025]

[0026] Among them, a1, b1, c1, and d1 all represent fitting parameters.

[0027] For the cycle SOH life model, the SOH increase area of ​​the energy storage lithium-ion battery in the early cycle is corrected by introducing an activation factor based on the real-time temperature and the number of cycles.

[0028] The calculation of the cyclic SOH life model includes,

[0029]

[0030] Among them, SOH cycling,new Indicates the relative capacity retention rate during the cycle, CapFade calendar@ESS represents the capacity decay caused by storage during the cycle based on the activation term, A2 represents the pre-exponential factor, and E a2 represents the activation energy, T′ represents the temperature of the energy storage cell during the cycle, Cycle represents the number of cycles, ΔSOH cycling,active Energy storage cell cycle activation term, a2 and z2 represent fitting parameters;

[0031]

[0032] Among them, α2 and β2 represent fitting parameters, Cycle threshold Indicates the cycle number threshold;

[0033] Cycle threshold =a2×(T′) b2 +c2;

[0034] Among them, b2 and c2 represent fitting parameters.

[0035] The SOH life model is calculated based on the storage SOH life model and the cycle SOH life model. cell The calculation includes,

[0036] SOH cell =1-SOH calendar,new -SOH cycling,new .

[0037] Beneficial effect: The present invention modifies the SOH life model by introducing an activation factor term, which can accurately capture the "increasing" behavior of the relative capacity retention rate of energy storage lithium-ion batteries in the early stages of storage and cycling, thereby greatly improving the prediction accuracy of SOH.

[0038] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings are used to better understand the present invention and do not constitute a limitation of the present invention.

[0040] Figure 1 is a flow chart provided according to the present invention;

[0041] Figure 2 Schematic diagram of relative capacity retention rate between simulation curve and measured data at 25 degrees;

[0042] Figure 3 Schematic diagram of the error between the simulation curve and the measured data at 25 degrees;

[0043] Figure 4 Schematic diagram of relative capacity retention rate between simulation curve and measured data at 35 degrees;

[0044] Figure 5 Schematic diagram of the error between the simulation curve and the measured data at 35 degrees. DETAILED DESCRIPTION

[0045] The following is a description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0046] like Figure 1 As shown, the present invention provides a method for constructing a lithium-ion battery SOH life model based on an activation factor, comprising:

[0047] S1: Obtain test data of lithium-ion batteries under different temperatures, different SOCs, and different cycle conditions through testing.

[0048] It should be noted that:

[0049] Before obtaining the relative capacity retention rate of the battery cell under the current test conditions, the test data is cleaned using a Gaussian kernel function to remove abnormal test data;

[0050] Furthermore, removing abnormal test data before obtaining the relative capacity retention rate of the battery cell under the current test conditions can improve the quality of the test data. The Gaussian kernel function can smooth the data distribution and reduce the weight of outliers, thereby making the test data closer to the actual distribution pattern, thereby improving the accuracy of the SOH life model.

[0051] The test data includes battery storage days, cycle number, battery capacity, SOC and real-time temperature of the battery cell.

[0052] S2: Based on the test data, obtain the current relative capacity retention rate of the battery cell. It should be noted that:

[0053] The acquisition of the relative capacity retention rate of the battery cell under the current test conditions includes:

[0054] Let the battery discharge from 100% SOC to 0% SOC or the lower limit voltage to obtain the discharge capacity of the battery;

[0055] Repeat the above discharge process multiple times and take the average value as the current capacity of the battery;

[0056]

[0057] Furthermore, by repeating the discharge process multiple times and taking the average value, the influence of random errors can be reduced and the accuracy of the measurement results can be improved. Multiple discharges and taking the average value can check the consistency of the battery. The consistency of the battery refers to the ability of the battery to show similar performance under the same conditions. By comparing the results of multiple discharges, it can be judged whether the consistency of the battery is good.

[0058] S3: Based on the current relative capacity retention rate and the test data and by introducing an activation factor, a SOH life model is constructed. It should be noted that:

[0059] The SOH life model includes a storage SOH life model and a cycle SOH life model.

[0060] For the storage SOH life model, an activation factor term based on real-time temperature, SOC and storage cycles is introduced to correct the SOH increase region of the energy storage lithium-ion battery in the early storage stage.

[0061] For the storage SOH life model, by introducing the activation factor term based on real-time temperature, SOC and storage cycles, the SOH increase area of ​​the energy storage lithium-ion battery in the early stage of storage is corrected, that is, the "increase" part of the relative capacity retention rate of the energy storage lithium-ion battery in the early stage of storage is corrected.

[0062] The calculation of the storage SOH life model includes, so that the storage SOH life model can accurately estimate the SOH change of the energy storage lithium-ion battery after long-term storage.

[0063] SOH calendar,new =1-CapFade calendar,new ;

[0064]

[0065] Among them, SOH calendar,new Indicates the relative capacity retention rate during storage, CapFadecalendar,new represents the capacity decay caused by the activation term during storage, A1 represents the pre-exponential factor, E a1 represents activation energy, Time z1 Indicates battery aging time, SOC y1 Indicates the effect of state of charge on battery aging. SOC indicates the SOC value during storage. ΔSOH calendar,ative represents the storage activation term of the energy storage cell, z1 and y1 represent the fitting parameters;

[0066] The energy storage cell storage activation term ΔSOH calendar,ative The calculation includes,

[0067]

[0068] Among them, α1 and β1 represent fitting parameters, T represents the temperature of the energy storage cell during storage, Time represents the storage time, and Time threshold Indicates the storage time threshold;

[0069] The storage time threshold Time threshold The calculation includes,

[0070] The storage time threshold Time is calculated based on the temperature T of the energy storage cell during storage and the SOC value during storage. threshold ,

[0071]

[0072] Among them, a1, b1, c1, and d1 all represent fitting parameters.

[0073] For the cycle SOH life model, the SOH increase area of ​​the energy storage lithium-ion battery in the early cycle is corrected by introducing an activation factor based on the real-time temperature and the number of cycles.

[0074] For the cyclic SOH life model,

[0075] The calculation of the cyclic SOH life model includes correcting the SOH increase area of ​​the energy storage lithium-ion battery in the early stage of the cycle, that is, correcting the "increasing" part of the relative capacity retention rate of the energy storage lithium-ion battery in the early stage of the cycle by introducing an activation factor term based on the real-time temperature and the number of cycles, and capturing the phenomenon of "zero" attenuation of the cycle life capacity within a certain period of time from the beginning, so that the cyclic SOH life model can accurately estimate the SOH change of the energy storage lithium-ion battery under cycling.

[0076]

[0077] Among them, SOH cycling,new Indicates the relative capacity retention rate during the cycle, CapFadecalendar@ESS represents the capacity decay caused by storage during the cycle based on the activation term, A2 represents the pre-exponential factor, and E a2 represents the activation energy, T′ represents the temperature of the energy storage cell during the cycle, Cycle represents the number of cycles, ΔSOH cycling,active Energy storage cell cycle activation term, a2 and z2 represent fitting parameters;

[0078]

[0079] Among them, α2 and β2 represent fitting parameters, Cycle threshold Indicates the cycle number threshold;

[0080] Cycle threshold =a2×(T′) b2 +c2;

[0081] Among them, b2 and c2 represent fitting parameters.

[0082] The SOH life model is calculated based on the storage SOH life model and the cycle SOH life model. cell The calculation includes,

[0083] SOH cell =1-SOH calendar,new -SOH cycling,new .

[0084] At present, a certain energy storage project of the company requires to predict the future trend of battery SOH based on the existing N-cycle data. Now, the life test data is fitted based on the traditional SOH life formula and the SOH life formula proposed by the present invention.

[0085] The specific implementation steps are as follows:

[0086] Step 1: At the test center, collect battery data at different temperatures, different SOCs, and different cycle conditions, including battery cell storage days, number of cycles, battery capacity, real-time battery cell temperature, and other data.

[0087] Step 2: Based on the test data obtained in step 1, obtain the relative capacity retention rate of the battery under the current test conditions, that is, the relative capacity percentage (this is the actual value measured by the test). The test method is: let the battery discharge from 100% SOC to 0% SOC or the lower limit voltage to obtain the discharge capacity of the battery. Repeat the above operation 3 times, and take the average value of the 3 times as the current capacity of the battery. Use current capacity / initial battery capacity = current relative capacity retention rate.

[0088] Step 3: Based on the relative capacity retention rate, battery cell storage days, number of cycles, battery capacity, real-time temperature and other data obtained in step 2, the traditional SOH life formula is used to fit the data to obtain the corresponding fitting parameters;

[0089] Among them, the traditional SOH life formula is as follows:

[0090]

[0091] Among them, SOH calendar represents the relative capacity retention rate during storage, A represents the pre-exponential factor, E a represents activation energy, T represents the cell temperature during storage, Time represents the storage time, SOC represents the state of charge value during storage, and z and y represent fitting parameters;

[0092] The relative capacity decay during cycling can usually be described by the traditional semi-empirical cycle SOH life model:

[0093]

[0094] Among them, SOH cycling represents the relative capacity retention rate during the cycle, B represents the pre-exponential factor; E b represents activation energy, T represents the cell temperature during cycling, Cycle represents the number of cycles at an equivalent 100% DOD (Depth of Discharge), and x represents the fitting parameter.

[0095] In summary, the total SOH model of the battery is SOH cell The losses caused by circulation and storage need to be subtracted separately:

[0096]

[0097] Step 4: Based on the relative capacity retention rate, battery cell storage days, number of cycles, battery capacity, real-time temperature and other data obtained in step 2, the SOH life formula based on the activation factor proposed in the present invention is used to fit the data to obtain corresponding fitting parameters.

[0098] Step 5: Draw the cycle life prediction curve and compare the fitting results based on step 3 and step 4. The results are as follows Figures 2 to 5 shown.

[0099] like Figure 2As shown in the figure, at room temperature, the traditional SOH life model (dotted line part) cannot fit the part of the capacity increase, which starts to decrease from 1, so it will cause a lot of deviations. The SOH life model (dashed line part) proposed in the present invention can fit the original data well, so that the prediction accuracy of SOH is greatly improved. At the same time, we can predict the duration of SOH "zero" attenuation. For example, the number of SOH "zero" attenuation cycles under 25 degrees is about 0.3N cycles.

[0100] like Figure 3 As shown in the figure, the fitting error based on the traditional SOH life model (black line part) is large, and at different numbers of turns, its error fluctuates in a large range (the maximum error is greater than 4%). However, the fitting error of the SOH life model (blue line part) proposed in the present invention is very small, and basically fluctuates around 0, indicating that no matter how many turns, its fitting accuracy is high.

[0101] Figure 4 The fitting curve at 35 degrees shows that the capacity increase is much smaller than that at 25 degrees, indicating that the capacity increase is strongly related to temperature, which also confirms the accuracy of the formula proposed in the present invention. At the same time, we can predict the duration of SOH "zero" attenuation. For example, the number of SOH "zero" attenuation cycles at 35 degrees is about 0.05N cycles.

[0102] Figure 5 It is shown that when the capacity increase is not large, the fitting error of the SOH life model proposed in the present invention is only slightly smaller than that of the traditional SOH life model. Even under high temperature conditions (45 degrees or 60 degrees), there is no capacity increase part of the battery, and the fitting effects of the traditional SOH life model and the SOH life model proposed in the present invention are completely consistent.

[0103] comprehensive Figure 2-5 It can be seen that the SOH life model proposed in the present invention has a significantly better fitting effect than the traditional SOH life model when the initial capacity of the battery rises more (25 degrees). At the same time, it can be seen that the traditional SOH life model cannot capture the area where the cycle capacity "rises", while the SOH life model proposed in the present invention can capture this area very well. More importantly, the SOH life model proposed in the present invention can estimate the time of "zero" attenuation of the battery SOH life.

[0104] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for constructing a lithium-ion battery SOH life model based on an activation factor, characterized in that: include: Through testing, the test data of lithium-ion batteries under different temperatures, different SOCs, and different cycle conditions are obtained; Based on the test data, obtaining a current relative capacity retention rate of the battery cell; Based on the current relative capacity retention rate and the test data and by introducing an activation factor, a SOH life model is constructed.

2. The method for constructing a lithium-ion battery SOH life model based on an activation factor according to claim 1, characterized in that: include, Before obtaining the relative capacity retention rate of the battery cell under the current test conditions, the test data is cleaned using a Gaussian kernel function to remove abnormal test data; The test data includes battery storage days, cycle number, battery capacity, SOC and real-time temperature of the battery cell.

3. The method for constructing a lithium-ion battery SOH life model based on an activation factor according to claim 2, characterized in that: The acquisition of the relative capacity retention rate of the battery cell under the current test conditions includes: Let the battery discharge from 100% SOC to 0% SOC or the lower limit voltage to obtain the discharge capacity of the battery; Repeat the above discharge process multiple times and take the average value as the current capacity of the battery; 4. The method for constructing a lithium-ion battery SOH life model based on an activation factor according to claim 3, characterized in that: The SOH life model includes a storage SOH life model and a cycle SOH life model.

5. The method for constructing a lithium-ion battery SOH life model based on an activation factor according to claim 4, characterized in that: For the storage SOH life model, an activation factor term based on real-time temperature, SOC and storage cycles is introduced to correct the SOH increase region of the energy storage lithium-ion battery in the early storage stage.

6. The method for constructing a lithium-ion battery SOH life model based on activation factor according to claim 5, characterized in that: The calculation of the storage SOH life model includes: SOH calendar,new =1-CapFade calendar,new ; Among them, SOH calendar,new Indicates the relative capacity retention rate during storage, CapFade calendar,new represents the capacity decay caused by the activation term during storage, A1 represents the pre-exponential factor, E a1 represents activation energy, Time z1 Indicates battery aging time, SOC y1 Indicates the effect of state of charge on battery aging. SOC indicates the SOC value during storage. ΔSOH calendar,ative represents the storage activation term of the energy storage cell, z1 and y1 represent the fitting parameters; The energy storage cell storage activation term ΔSOH calendar,ative The calculation includes, Among them, α1 and β1 represent fitting parameters, T represents the temperature of the energy storage cell during storage, Time represents the storage time, and Time threshold Indicates the storage time threshold; The storage time threshold Time threshold The calculation includes, The storage time threshold Time is calculated based on the temperature T of the energy storage cell during storage and the SOC value during storage. threshold , Among them, a1, b1, c1, and d1 all represent fitting parameters.

7. The method for constructing a lithium-ion battery SOH life model based on activation factor according to claim 6, characterized in that: For the cycle SOH life model, the SOH increase area of ​​the energy storage lithium-ion battery in the early cycle is corrected by introducing an activation factor based on the real-time temperature and the number of cycles.

8. The method for constructing a lithium-ion battery SOH life model based on activation factor according to claim 7, characterized in that: The calculation of the cyclic SOH life model includes, Among them, SOH cycling,new Indicates the relative capacity retention rate during the cycle, CapFade calendar@ESS represents the capacity decay caused by storage during the cycle based on the activation term, A2 represents the pre-exponential factor, and E a2 represents the activation energy, T′ represents the temperature of the energy storage cell during the cycle, Cycle represents the number of cycles, ΔSOH cycling,active Energy storage cell cycle activation term, a2 and z2 represent fitting parameters; Among them, α2 and β2 represent fitting parameters, Cycle threshold Indicates the cycle number threshold; Cycle threshold =a2×(T′) b2 +c2; Among them, b2 and c2 represent fitting parameters.

9. The method for constructing a lithium-ion battery SOH life model based on activation factor according to claim 8, characterized in that: The SOH life model is calculated based on the storage SOH life model and the cycle SOH life model. cell The calculation includes, SOH cell =1-SOH calendar,new -SOH cycling,new 。

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