Lithium-ion battery cycle life prediction method

Abstract

The present invention discloses a method for predicting the cycle life of a lithium-ion battery, belonging to the technical field of lithium-ion batteries. The method comprises: placing a battery to be tested in a constant temperature chamber for cycle testing, subjecting the battery to test to charge and discharge cycles every set number of cycles T, and performing capacity calibration; performing voltage differential fitting on the charge capacity-voltage curve obtained from the low-rate charge and discharge cycles to calculate the loss of positive electrode active material and the positive electrode offset; and predicting the cycle life of the lithium-ion battery based on the capacity calibration results, the loss of positive electrode active material, and the positive electrode offset. The present invention obtains the attenuation trend of the active lithium and active material at the positive and negative electrodes during the lithium-ion battery cycle by performing voltage differential fitting on the capacity-voltage curve, thereby obtaining the attenuation proportions of the positive and negative electrodes, analyzing the causes of attenuation, and thus achieving a qualitative prediction of the battery cycle life.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium-ion batteries, and in particular to a method for predicting the cycle life of a lithium-ion battery. Background Art

[0002] Lithium-ion batteries are widely used in our daily lives and production due to their high energy density and long cycle life. As energy systems, lithium-ion batteries often have service cycles of several years or even more than a decade, placing high demands on their lifespan. However, in actual testing, lithium-ion battery life tests often require a significant time delay, which cannot meet product development requirements. Therefore, research on accelerated life degradation experiments for lithium-ion batteries has become extremely important and urgent.

[0003] The charging and discharging process of a lithium-ion battery is the extraction and insertion of lithium ions into the positive and negative electrode materials of the battery. The amount of lithium inserted between the positive and negative electrodes is the main factor affecting the cycle capacity of the lithium-ion battery. In the related art, the Chinese invention patent application with publication number CN113884929A discloses a method for predicting the cycle life of a lithium iron phosphate battery. The steps include proving that there is no obvious structural change in the normal area of ​​the negative electrode of the lithium iron phosphate battery after cycling; ICP testing lithium loss after fresh and aging: ICP testing the lithium concentration of the empty negative electrode after formation and the lithium concentration of the empty negative electrode after different cycle cycles; establishing a cycle-active lithium loss ICP model F(x); calculating the active lithium concentration of the lithium iron phosphate battery to be tested, and inputting the ideal curve model F(x) for calculation to obtain the predicted cycle life of the lithium iron phosphate battery. However, this solution determines the battery attenuation rate by analyzing the lithium content of the battery electrode, which involves battery disassembly and is a destructive test.

[0004] A Chinese invention patent application with publication number CN113533988A discloses a capacity decay analysis method suitable for long-term cycle batteries. The method performs capacity differentiation (dQ / dV) on the voltage (V)-capacity (Q) curve. The intensity and position changes of the peaks in the curve obtained by capacity differentiation can only qualitatively obtain information on battery decay.

[0005] Chinese invention patent application publication number CN110133527A discloses a method for analyzing capacity decay in three-electrode lithium-ion batteries. This method allows for non-destructive testing of lithium-ion batteries. Without disassembling the battery, the method analyzes the discharge voltage curve (VQ) and the voltage differential curve (dV / dQ-Q) at different cycle times during aging to determine the cause of capacity decay. However, this method qualitatively captures battery decay information by analyzing the peak intensity and position changes in the curve obtained by voltage differentiation. This method is limited in that it can only roughly determine the cause of decay and cannot determine the magnitude of decay in each component. Summary of the Invention

[0006] The technical problem to be solved by the present invention is how to quantitatively predict the changing trend of the cycle life of a lithium-ion battery.

[0007] The present invention solves the above technical problems through the following technical means:

[0008] The present invention proposes a method for predicting the cycle life of a lithium-ion battery, the method comprising:

[0009] The battery to be tested is placed in a constant temperature box for cycle testing. The battery to be tested is charged and discharged every set number of cycles T to calibrate its capacity.

[0010] The voltage differential fitting is performed based on the charge capacity-voltage curve obtained from the low-rate charge-discharge cycle to calculate the positive electrode active material loss and positive electrode offset;

[0011] Predict the cycle life of lithium-ion batteries based on capacity calibration results, positive electrode active material loss and positive electrode offset.

[0012] The present invention obtains the attenuation trend of the active lithium and active material at the positive and negative electrodes during the cycle of the lithium-ion battery by performing voltage differential fitting on the capacity-voltage curve, obtains the attenuation proportions of the positive and negative electrodes, analyzes the causes of attenuation, and then constructs a lithium-ion battery cycle life attenuation model based on the attenuation trends of the active lithium and active material at the positive electrode and the capacity calibration results, thereby quantitatively predicting the battery cycle life. In contrast, the traditional battery cycle capacity attenuation analysis process does not distinguish between the calculation of the active lithium loss at the positive electrode and the active lithium loss at the negative electrode, making it impossible to achieve quantitative prediction. The present solution is simple and easy to implement, and accurately predicts the changing trend of the battery's later cycle life based only on data from the first 500 or so cycles of the battery, showing good application prospects in lithium-ion battery life testing.

[0013] Furthermore, the battery to be tested is placed in a constant temperature box for cycle testing, and the battery to be tested is charged and discharged every set number of cycles T to perform capacity calibration, including:

[0014] The battery to be tested is placed in a constant temperature box for cycle testing. The battery to be tested is cycled at a low rate for two weeks every set number of weeks T, and the capacity of the battery to be tested at a normal rate cycle is recorded as A. n , the capacity of the small rate cycle is Q n , n=1,T,2T,.......NT, N is a positive integer.

[0015] Furthermore, the charge capacity-voltage curve obtained based on the low-rate charge-discharge cycle is subjected to voltage differential fitting to calculate the positive electrode active material loss and the positive electrode offset, including:

[0016] Performing voltage differential processing based on the low-rate charging capacity-voltage curve of the battery to be tested in the ath T cycle to obtain a voltage differential curve;

[0017] The voltage differential curve is fitted to calculate the positive electrode active material loss and the positive electrode offset.

[0018] Furthermore, the method of predicting the cycle life of the lithium-ion battery based on the capacity calibration results, the loss of positive electrode active material and the positive electrode offset includes:

[0019] The capacity loss caused by the positive electrode active material and active lithium every T weeks is measured as C n , n=T,2T,.......NT, N is a positive integer;

[0020] Calculate the percentage W of capacity loss caused by positive electrode active material and active lithium every T weeks as a percentage of the cycle capacity of the previous T weeks n =C n / Q n-T , and calculate the average loss percentage of capacity in the first a cycles T weeks as

[0021] According to the normal rate cycle capacity of the battery to be tested is A n and the capacity of the small rate cycle is Q n , calculate the cycle capacity difference when the same cycle number is D n =Q n -A n ;

[0022] The average capacity difference during the first a cycles T is calculated as

[0023] Based on the stated average loss percentage The trend of the later low-rate cycle of the battery to be tested is predicted to be

[0024] Based on the average capacity difference The trend of the normal rate cycle of the battery to be tested is predicted to be

[0025] Furthermore, the cycle number T is 50-200.

[0026] Furthermore, the value range of a is [4, 5].

[0027] Furthermore, the mass loss m of the positive electrode active material and the loss u of the positive electrode active lithium satisfy C n =u+m*Q0, Q0 is the gram capacity of the positive electrode material.

[0028] Furthermore, the recording condition of the low-rate cycle test of the battery to be tested is t≤1s, and the recording condition of the normal-rate cycle test is 10s≤t≤30s.

[0029] Furthermore, the low rate cycle test rate A of the battery to be tested rate ≤0.1C; the high rate cycle test rate A of the battery to be tested rate ≤2C.

[0030] Furthermore, the charging mechanism for the low-rate cycle test of the battery to be tested is constant-current charging, and the charging mechanism for the high-rate cycle test of the battery to be tested is constant-current and constant-voltage charging.

[0031] The advantages of the present invention are:

[0032] (1) The present invention obtains the attenuation trend of the active lithium and active material of the positive and negative electrodes during the cycle of the lithium-ion battery by performing voltage differential fitting on the capacity-voltage curve, and can obtain the attenuation ratio of the positive and negative electrodes, analyze the attenuation cause, and then construct a lithium-ion battery cycle life attenuation model based on the attenuation trend of the active lithium and active material of the positive electrode and the capacity calibration result, and quantitatively predict the battery cycle life; while the traditional battery cycle capacity attenuation analysis process does not distinguish between the calculation of the active lithium loss of the positive electrode and the active lithium loss of the negative electrode, and cannot achieve quantitative prediction; this scheme is simple and easy to implement, and can accurately predict the changing trend of the battery's later cycle life only through the data of about 500 weeks of battery cycle, and has good application prospects in lithium-ion battery life testing.

[0033] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 The figure is a flow chart of a method for predicting the cycle life of a lithium-ion battery proposed in one embodiment of the present invention. DETAILED DESCRIPTION

[0035] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0036] Reference Figure 1 An embodiment of the present invention provides a method for predicting the cycle life of a lithium-ion battery, the method comprising the following steps:

[0037] S10, placing the battery to be tested in a constant temperature box for cycle testing, charging and discharging the battery to be tested every set number of cycles T, and performing capacity calibration;

[0038] S20, performing voltage differential fitting based on the charge capacity-voltage curve obtained by the low-rate charge-discharge cycle to calculate the positive electrode active material loss and the positive electrode offset;

[0039] S30. Predicting the cycle life of the lithium-ion battery based on the capacity calibration results, the loss of positive electrode active material, and the positive electrode offset.

[0040] This embodiment performs voltage differential fitting on the capacity-voltage curve to obtain the attenuation trend of the active lithium and active material at the positive and negative electrodes during the lithium-ion battery cycle. The attenuation ratios of the positive and negative electrodes can be obtained, and the causes of attenuation can be analyzed. Then, based on the attenuation trends of the active lithium and active material at the positive electrode and the capacity calibration results, a lithium-ion battery cycle life attenuation model is constructed to quantitatively predict the battery cycle life. However, in the traditional battery cycle capacity attenuation analysis process, the loss of active lithium at the positive electrode and the loss of active lithium at the negative electrode are not distinguished and calculated, making quantitative prediction impossible.

[0041] It should be noted that the charging and discharging process of lithium-ion batteries is the extraction and embedding of lithium ions in the positive and negative electrode materials of the battery, and the amount of lithium embedded between the positive and negative electrodes is the main reason affecting the cycle capacity of the lithium-ion battery. The amount of lithium embedded between the positive and negative electrodes is not only related to the amount of active lithium provided by the positive electrode, but also related to the active materials of the battery electrodes. However, in the battery design process, the N / P of the battery is often greater than 1. Therefore, the degree of destruction of the negative electrode active material in the cycle process is often less than the degree of destruction of the positive electrode active material. Therefore, in the process of the battery cycling to 80% of the capacity, it is believed that the contribution of the destruction of the negative electrode active material to the loss of battery capacity is small. Accordingly, this embodiment constructs a lithium-ion battery cycle life attenuation model based on the attenuation trend of the positive electrode active material and the active lithium during the cycle of the lithium-ion battery. Experimental results show that this scheme is simple and easy to implement. It can accurately predict the changing trend of the battery's later cycle life only through the data of the first 400-500 weeks of battery cycling. It has good application prospects in lithium-ion battery life testing.

[0042] It should be noted that the charge and discharge cycle rate is set according to the test requirements, the capacity calibration uses a small rate of no more than 0.1C, and the capacity differentiation and fitting analysis are performed using small rate data.

[0043] In one embodiment, step S10 specifically includes the following steps:

[0044] The battery to be tested is placed in a constant temperature box for cycle testing. The battery to be tested is cycled for two weeks at a low rate every set cycle number T, and the capacity of the battery to be tested at a normal rate cycle is recorded as A. n, the capacity of the small rate cycle is Q n , n=1,T,2T,.......NT, N is a positive integer.

[0045] Furthermore, the value range of the cycle number T is 50-200.

[0046] In one embodiment, the mass loss m of the positive electrode active material and the positive electrode active lithium loss u satisfy C n =u+m*Q0, Q0 is the gram capacity of the positive electrode material.

[0047] In one embodiment, the recording condition of the low-rate cycle test of the battery to be tested is t≤1s, preferably 1s; the recording condition of the normal-rate cycle test is 10s≤t≤30s, preferably 30s.

[0048] In one embodiment, the low rate cycle test rate A of the battery to be tested is rate ≤0.1C; the high rate cycle test rate A of the battery to be tested rate ≤2C.

[0049] In one embodiment, the low-rate cycle test charging mechanism of the battery to be tested is constant current charging, and the high-rate cycle test charging mechanism of the battery to be tested is constant current and constant voltage charging.

[0050] In one embodiment, step S20 includes the following steps:

[0051] S21, performing voltage differential processing based on the low-rate charging capacity-voltage curve of the battery to be tested during the first a cycles T weeks to obtain a voltage differential curve;

[0052] S22. Fit the voltage differential curve to calculate the positive electrode active material loss and the positive electrode offset.

[0053] It should be noted that, in this embodiment, dV / dQ fitting software may be used to perform fitting processing on the voltage differential curve to obtain fitting parameters.

[0054] Furthermore, the value range of a is [4, 5], and preferably the value of a is 5.

[0055] In one embodiment, step S30 includes the following steps:

[0056] S31, measure the capacity loss caused by the positive electrode active material and active lithium every T weeks as C n , n=T,2T,.......NT, N is a positive integer;

[0057] S32. Calculate the percentage W of capacity loss caused by positive electrode active material and active lithium every T weeks to the capacity of the previous T weeks. n=C n / Q n-T , and calculate the average loss percentage of capacity in the first a cycles T weeks as

[0058] S33, according to the normal rate cycle capacity of the battery to be tested is A n and the capacity of the small rate cycle is Q n , calculate the cycle capacity difference when the same cycle number is D n =Q n -A n ;

[0059] S34, calculate the average capacity difference of the first a cycles T weeks as

[0060] S35. Based on the average loss percentage The trend of the later low-rate cycle of the battery to be tested is predicted to be

[0061] S36, based on the average capacity difference The trend of the normal rate cycle of the battery to be tested is predicted to be

[0062] It should be noted that this embodiment accurately predicts the changing trend of the battery's later cycle life only through data from the first 400-500 cycles of the battery. Compared with the existing battery cycle life prediction time, it is shortened by more than half, which is of great significance to the research on accelerated attenuation experiments of lithium-ion battery life.

[0063] The following describes this solution in detail through two specific embodiments:

[0064] Example 1

[0065] (1) Place the battery under test in a 25°C constant temperature box and perform a 1C cycle test. Perform two cycles of 0.05C cycle test every 100 cycles, and take the capacity-voltage curve of the 0.05C charge in the last week;

[0066] (2) Perform voltage differential fitting based on the low-rate cycling curve of the first 400 cycles of the battery to calculate the changes in the positive and negative electrode active materials and the positive and negative electrode active lithium;

[0067] (3) Based on the changing trend of this parameter, a capacity prediction model is established to predict the trend of the late cycle of the lithium-ion battery. The prediction results are shown in Table 1 below.

[0068] Table 1

[0069]

[0070] The relative error formula is: relative error = (A n Predicted Value-A n Actual value) / A n *100%, and the relative deviation is within about 5%. It can be considered that this method can accurately predict the cycle capacity of lithium-ion batteries.

[0071] As can be seen from the data in the table below, during the battery cycle to a capacity retention rate of 80%, the maximum error between the predicted and measured capacity of the battery is 1.37%. This phenomenon shows that the present invention can accurately predict the capacity change trend of lithium-ion batteries in the later stages of cycling based on the data of the early stages of lithium-ion battery cycling.

[0072] Example 2

[0073] (1) Place the battery under test in a constant temperature chamber and perform a 1C cycle test. Perform two cycles of 0.05C cycle test every 100 cycles, and take the capacity-voltage curve of the 0.05C charge in the last week.

[0074] (2) Calculate the changes in active materials and active lithium based on the low-rate cycling curves of the first 400 cycles of the battery;

[0075] (3) Based on the changing trend of this parameter, a capacity prediction model is established to predict the trend of the late cycle of the lithium-ion battery. The prediction results are shown in Table 2 below.

[0076] Table 2

[0077]

[0078] The relative error formula is: relative error = (A n Predicted Value-A n Actual value) / A n *100%, and the relative deviation is within about 5%. It can be considered that this method can accurately predict the cycle capacity of lithium-ion batteries.

[0079] As can be seen from the data in the table below, during the battery cycle to a capacity retention rate of 80%, the maximum error between the predicted and measured capacity of the battery is -0.81%. This phenomenon shows that the present invention can accurately predict the change trend of the capacity in the later stage of lithium-ion cycling based on the data of the early cycle of lithium-ion batteries.

[0080] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0081] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0082] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for predicting the cycle life of a lithium-ion battery, characterized in that: The method comprises: The battery to be tested is placed in a constant temperature box for cycle testing. The battery to be tested is charged and discharged every set number of cycles T to calibrate its capacity. The voltage differential fitting is performed based on the charge capacity-voltage curve obtained from the low-rate charge-discharge cycle to calculate the positive electrode active material loss and positive electrode offset; Predict the cycle life of lithium-ion batteries based on capacity calibration results, positive electrode active material loss and positive electrode offset.

2. The method for predicting the cycle life of a lithium-ion battery according to claim 1, wherein: The battery to be tested is placed in a constant temperature box for cycle testing, and the battery to be tested is charged and discharged every set cycle number T to perform capacity calibration, including: The battery to be tested is placed in a constant temperature box for cycle testing. The battery to be tested is cycled at a low rate for two weeks every set number of weeks T, and the capacity of the battery to be tested at a normal rate cycle is recorded as A. n , the capacity of the small rate cycle is Q n , n=1,T,2T,.......NT, N is a positive integer.

3. The method for predicting the cycle life of a lithium-ion battery according to claim 1, wherein: The voltage differential fitting of the charge capacity-voltage curve obtained based on the charge-discharge cycle to calculate the positive electrode active material loss and the positive electrode offset includes: Performing voltage differential processing on the charging capacity-voltage curve of the battery to be tested at a-th T cycle at a low rate to obtain a voltage differential curve; The voltage differential curve is fitted to calculate the positive electrode active material loss and the positive electrode offset.

4. The method for predicting the cycle life of a lithium-ion battery according to claim 2, wherein: The method of predicting the cycle life of a lithium-ion battery based on the capacity calibration results, the loss of positive electrode active material and the positive electrode offset includes: The capacity loss caused by the positive electrode active material and active lithium every T weeks is measured as C n , n=T,2T,.......NT, N is a positive integer; Calculate the percentage W of capacity loss due to positive electrode active material and active lithium in the previous cycle capacity every T weeks n =C n / Q n-T , and calculate the average loss percentage of capacity in the first a cycles T weeks as =(W T +W 2T +W 3T +…) / a; According to the normal rate cycle capacity of the battery to be tested is A n and the capacity of the small rate cycle is Q n , calculate the cycle capacity difference when the same cycle number is D n =Q n -A n ; The average capacity difference during the first a cycles T is calculated as Based on the stated average loss percentage The trend of the later low-rate cycle of the battery to be tested is predicted to be Based on the average capacity difference The trend of the normal rate cycle of the battery to be tested is predicted to be 5. The method for predicting the cycle life of a lithium-ion battery according to claim 1, wherein: The cycle number T is set to be 50-200.

6. The method for predicting the cycle life of a lithium-ion battery according to claim 3, wherein: The value range of a is [4, 5].

7. The method for predicting the cycle life of a lithium-ion battery according to claim 1, wherein: The positive electrode active material mass loss m and the positive electrode active lithium loss u satisfy C n =u+m*Q0, Q0 is the gram capacity of the positive electrode material.

8. The method for predicting the cycle life of a lithium-ion battery according to claim 2, wherein: The recording conditions for the low-rate cycle test of the battery to be tested are t≤1s, and the recording conditions for the normal-rate cycle test are 10s≤t≤30s.

9. The method for predicting the cycle life of a lithium-ion battery according to claim 2, wherein: The low rate cycle test rate A of the battery to be tested rate ≤0.1C; the high rate cycle test rate A of the battery to be tested rate ≤2C.

10. The method for predicting the cycle life of a lithium-ion battery according to claim 1, wherein: The charging mechanism of the low-rate cycle test of the battery to be tested is constant-current charging, and the charging mechanism of the high-rate cycle test of the battery to be tested is constant-current and constant-voltage charging.

Images