Prediction Method and System for the Cycling Dive of Lithium-Ion Batteries

Abstract

The present invention discloses a method and system for predicting the cycling dive of a lithium-ion battery, belonging to the technical field of lithium-ion batteries. The method includes: performing a cycling test on the battery under test after normal formation on a test cabinet, performing charge and discharge cycling on the battery under test using a current every set number of cycling turns T, and performing capacity calibration; selecting the charging curve of the last week of capacity calibration for voltage differential fitting to obtain the fitting parameters of the battery under test, and the fitting parameters include the loss of positive active material, the loss of negative active material, the positive offset, and the negative offset; plotting the fitting parameters and the number of cycling turns to obtain a graph of the change of the fitting parameters with the cycling period; based on the change graph, when at least two of the fitting parameters show an increasing phenomenon, it is determined that the battery under test will undergo a cycling dive. The present invention can effectively and timely predict the sudden decrease of the battery cycling capacity, with a small capacity prediction error and high accuracy, and has good application prospects.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium-ion batteries, and particularly to a method and system for predicting the cyclic diving of lithium-ion batteries. Background Art

[0002] As a carrier for energy storage, the cycle life of lithium batteries has always been one of the performance issues that have received much attention. During the product development process, it is necessary to evaluate the cycle life of the battery, so as to formulate corresponding improvement measures and improve the cycle performance of the product. However, the traditional cycle evaluation test method for lithium-ion batteries has a long cycle, and the cycle capacity usually suddenly decreases at the end of the cycle, that is, the cycle diving phenomenon, making it difficult to predict the battery capacity.

[0003] At present, there are many studies on the prediction of the full battery cycle. The mainstream method is to establish an accelerated model of battery attenuation. The accelerated model can provide a method for quickly evaluating the battery capacity, but it cannot predict the battery capacity diving.

[0004] In the related art, the Chinese patent application with the publication number CN109143078A discloses a method for identifying and predicting the "diving" fault of a lithium iron phosphate power battery. The charging capacity Q and terminal voltage V of the battery during the constant current charging process are recorded at a preset sampling period to obtain the Q-V curve during the charging process; the Q-V curve during the charging process is numerically differentiated to obtain the dQ / dV-SOC curve; it is compared with the characteristics of the standard dQ / dV-SOC curve of the battery, and the changes in the characteristic peaks corresponding to the vicinity of SOC = 50% and SOC = 80% in its dQ / dV-SOC curve are analyzed to obtain the information of LLI. This method can reflect the internal state of the battery without damage, intuitively judge whether the battery has a "diving" fault and the cause of the "diving", and can realize the pre-judgment and identification of 400 cycles before the occurrence of "diving", reducing the risk of sudden capacity failure of new energy vehicle lithium-ion power storage batteries during vehicle operation and retired cascade utilization. This scheme performs capacity differentiation (dQ / dV) on the voltage (V)-capacity (Q) curve, and judges whether the battery has a "diving" fault based on the changes in the intensity and position of the peaks in the curve obtained by capacity differentiation. However, in practical applications, when the intensity and position of the peaks in the capacity differentiation curve change, the "diving" fault has already occurred, and it is impossible to make a qualitative prediction before the occurrence of the "diving" fault. Summary of the Invention

[0005] The technical problem to be solved by the present invention is how to predict the cyclic diving of lithium-ion batteries in advance.

[0006] The present invention solves the above technical problem by the following technical means:

[0007] The present invention provides a method for predicting the cycling dive of a lithium-ion battery, and the method includes the following steps:

[0008] Perform a cycling test on the battery under test after normalization on a test cabinet. Every set number of cycling turns T, charge and discharge the battery under test with a current to perform capacity calibration.

[0009] Select the charging curve of the last week of capacity calibration for voltage differential fitting to obtain the fitting parameters of the battery under test, and the fitting parameters include the loss of positive active material, the loss of negative active material, the positive offset, and the negative offset.

[0010] Plot the fitting parameters and the number of cycling turns to obtain a graph of the change of the fitting parameters with the cycling period.

[0011] Based on the change graph, when at least two of the fitting parameters show an increasing phenomenon, it is determined that the battery under test will undergo a cycling dive.

[0012] The present invention performs voltage differential fitting on the charging curve to obtain four fitting parameters, namely the loss of active materials at the positive and negative electrodes and the offsets at the positive and negative electrodes. Plot the fitting parameters and the number of cycling turns. When it is monitored that at least two of the four parameters show an increasing phenomenon, it is determined that the battery capacity will suddenly decrease, that is, a cycling dive. The model of the present invention is convenient to establish. Only a small current capacity needs to be added during cycling for data analysis and model establishment. It can effectively and timely predict the sudden decrease of the battery cycling capacity, with a small capacity prediction error and high accuracy, and has a good application prospect.

[0013] Further, the value range of the number of cycling turns T is 50-200.

[0014] Further, the method includes:

[0015] Charge and discharge the battery under test with a current less than or equal to 0.1C.

[0016] Further, the test cabinet samples according to time sampling or voltage sampling. The accuracy of time sampling is higher than 1 second, and the accuracy of voltage sampling is higher than 10 mV.

[0017] Further, for the step of selecting the charging curve of the last week of capacity calibration for voltage differential fitting to obtain the fitting parameters of the battery under test, it includes:

[0018] Select the charging curve of the last week of capacity calibration for voltage differential to obtain a voltage differential curve;

[0019] Use dV / dQ fitting software to perform fitting processing on the voltage differential curve to obtain the fitting parameters.

[0020] In addition, the present invention also provides a prediction system for the cyclic diving of a lithium-ion battery, and the system includes:

[0021] A cyclic test module, configured to perform a cyclic test on a battery to be tested after normalization on a test cabinet, and perform charge and discharge cycling on the battery to be tested using a current every set number of cycles T, and perform capacity calibration;

[0022] A differential fitting module, configured to select the charging curve of the last week of capacity calibration for voltage differential fitting to obtain the fitting parameters of the battery to be tested, and the fitting parameters include the loss of positive active material, the loss of negative active material, the positive offset and the negative offset;

[0023] A plotting module, configured to plot the fitting parameters and the number of cycles to obtain a graph of the change of the fitting parameters with the cycle period;

[0024] A determination module, configured to determine that the battery to be tested will undergo cyclic diving based on the change graph when at least two of the fitting parameters show an increasing phenomenon.

[0025] Further, the value range of the number of cycles T is 50 to 200.

[0026] Further, the current used for charge and discharge cycling of the battery to be tested is less than or equal to 0.1C.

[0027] Further, the test cabinet samples according to time sampling or voltage sampling, the accuracy of time sampling is higher than 1 second, and the accuracy of voltage sampling is higher than 10 mV.

[0028] Further, the differential fitting module includes:

[0029] A differential unit, configured to select the charging curve of the last week of capacity calibration for voltage differentiation to obtain a voltage differentiation curve;

[0030] A fitting unit, configured to perform fitting processing on the voltage differentiation curve using dV / dQ fitting software to obtain the fitting parameters.

[0031] The advantages of the present invention are as follows:

[0032] (1) By performing voltage differential fitting on the charging curve, the present invention obtains four fitting parameters, namely the loss of positive and negative active materials and the positive and negative offsets. Plotting the fitting parameters and the number of cycles, when it is monitored that at least two of the four parameters show an increasing phenomenon, it is determined that the battery will experience a sudden decrease in cyclic capacity, that is, cyclic diving. The model of the present invention is convenient to establish, and only a small current capacity needs to be added during the cycle for data analysis and model establishment. It can effectively and timely predict the sudden decrease in the cyclic capacity of the battery, with small capacity prediction error and high accuracy, and has good application prospects.

[0033] Additional aspects and advantages of the present invention will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 is a schematic flowchart of a method for predicting the cycling dive of a lithium-ion battery according to an embodiment of the present invention;

[0035] Figure 2 is a schematic diagram showing the variation of the capacity retention rate, the attenuation amount of the active material, and the electrode offset amount of a certain lithium iron phosphate-graphite system lithium-ion battery with the number of cycles in an embodiment of the present invention;

[0036] Figure 3 is a schematic diagram showing the variation of the capacity retention rate, the attenuation amount of the active material, and the electrode offset amount of a certain lithium iron phosphate-graphite system lithium-ion battery with the number of cycles in an embodiment of the present invention;

[0037] Figure 4 is a schematic diagram showing the variation of the capacity retention rate, the attenuation amount of the active material, and the electrode offset amount of a certain ternary material-graphite system lithium-ion battery with the number of cycles in an embodiment of the present invention;

[0038] Figure 5 is a schematic structural diagram of a prediction system for the cycling dive of a lithium-ion battery in another embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts fall within the scope of protection of the present invention.

[0040] As Figure 1 shown, a first embodiment of the present invention proposes a method for predicting the cycling dive of a lithium-ion battery, and the method includes the following steps:

[0041] S10. Perform a cycling test on the battery under test after normalization on a test cabinet, and perform charge and discharge cycling on the battery under test using a current every set number of cycling turns T, and perform capacity calibration;

[0042] It should be noted that the charge and discharge current and the ambient temperature of the battery cycling test can be set according to actual needs. The set number of turns for capacity calibration can be selected according to the actual situation of the battery cycle, and usually an integer number of weeks is selected for convenience of prediction.

[0043] S20. Select the charging curve of the last week for capacity calibration and perform voltage differential fitting to obtain the fitting parameters of the battery under test. The fitting parameters include the loss of positive active material, the loss of negative active material, the positive offset, and the negative offset.

[0044] S30. Plot the fitting parameters against the number of cycles to obtain a graph of the change of the fitting parameters with the cycle period.

[0045] S40. Based on the change graph, when at least two of the fitting parameters show an increasing phenomenon, determine that the battery under test will experience cycle dive.

[0046] It should be noted that in this embodiment, by performing voltage differential fitting on the charging curve, four fitting parameters are obtained, namely the loss of active materials of the positive and negative electrodes and the offsets of the positive and negative electrodes. Plot the fitting parameters against the number of cycles. When it is monitored that at least two of the four parameters show an increasing phenomenon, it is determined that the battery is about to experience a sudden decrease in cycle capacity, that is, cycle dive. The model of the present invention is convenient to establish. Only by adding a small current capacity during the cycle can data analysis and model establishment be carried out. It can effectively and timely predict the sudden decrease in the cycle capacity of the battery, with small capacity prediction error and high accuracy, and has good application prospects.

[0047] In one embodiment, the value range of the number of cycles T is 50 - 200.

[0048] It should be noted that within a certain range of the number of cycles, the attenuation rate of the battery can be regarded as constant, that is, stable attenuation. Therefore, this solution uses a fixed number of cycles to calibrate the battery capacity, determine the attenuation rate of the battery at this stage, and predict the attenuation trend of the next stage.

[0049] Generally, the number of cycles when the battery capacity decays to 80% of the initial capacity is the battery life. In practical applications, if it is monitored that the battery attenuation speed is too fast in the initial stage, for example: the capacity decays by 1% every 10 cycles. The interval can be selected to calibrate the capacity every 20 or 50 cycles for prediction. If 100 cycles are selected for calibration, it is possible that the battery has already decayed to about 90% at the first calibration point, losing the meaning of prediction. If the battery cycle attenuation speed is slow, such as only 0.1% attenuation in the initial 10 cycles, at this time, the capacity can be calibrated every 100 cycles. Through the data of 300 - 400 cycles, the attenuation rate of the battery can be predicted.

[0050] In one embodiment, the method includes:

[0051] Charge and discharge the battery under test in a cycle with a current less than or equal to 0.1C.

[0052] In this embodiment, a small current, that is, a current not exceeding one-tenth of the battery capacity, is adopted mainly to avoid the polarization effect generated during the charge and discharge process of the battery, obtain the steady-state data of the battery, and improve the prediction accuracy.

[0053] In one embodiment, the test cabinet samples according to time sampling or voltage sampling. The accuracy of time sampling is higher than 1 second, and that of voltage sampling is higher than 10 mV.

[0054] In one embodiment, step S20 includes the following steps:

[0055] S21. Select the charging curve of the last week of capacity calibration for voltage differentiation to obtain a voltage differentiation curve;

[0056] S22. Use dV / dQ fitting software to perform fitting processing on the voltage differentiation curve to obtain the fitting parameters.

[0057] In one embodiment, in step S40, the increase of at least two fitting parameters means that it can be the simultaneous increase of at least two fitting parameters, or the increase of one fitting parameter and the subsequent increase of another fitting parameter after maintaining a certain cycle period.

[0058] The following details this solution through three specific embodiments:

[0059] Embodiment 1

[0060] As Figure 2 shown, for a certain lithium iron phosphate-graphite system battery, a diving phenomenon starts to occur after 1100 cycles. The attenuation rate of the cycle capacity intensifies. Around 900 cycles, it is found that the loss of negative active material accelerates. At the same time, the negative electrode offset starts to increase. Around 1000 cycles, the loss of negative active material and the offset continue to increase. Therefore, through the method criterion proposed by the present invention, the diving prediction time can be advanced by 100 - 200 cycles.

[0061] Embodiment 2

[0062] As Figure 3 shown, for a certain lithium iron phosphate-graphite battery, a diving phenomenon starts to occur after 1600 cycles. The attenuation rate of the cycle capacity intensifies. Around 1400 cycles, it is found that the loss of negative active material accelerates. At the same time, the negative electrode offset starts to increase. Around 1500 cycles, the loss of negative active material and the offset continue to increase. Therefore, through the method criterion proposed by the present invention, the diving prediction time can be advanced by 100 - 200 cycles.

[0063] Embodiment 3

[0064] As Figure 4As shown in the figure, the cycling of a certain ternary material-graphite battery starts to show a diving phenomenon after 1000 cycles, the cycling capacity attenuation rate increases, around 900 cycles, it is found that the loss of negative electrode active material accelerates, and at the same time, the negative electrode offset starts to increase. Around 1000 cycles, the loss and offset of the negative electrode active material continue to increase. Therefore, through the method criterion proposed in the present invention, the diving prediction time can be advanced by 100 - 200 cycles.

[0065] Based on the voltage differential, the present invention monitors the changes in the properties of the positive and negative electrodes of the battery during the cycling process in real time. After being verified by multiple systems, a criterion for cycling diving is proposed. Currently, the prediction of cycling diving can be advanced to around 100 cycles. Therefore, this embodiment proposes a simple and effective method for evaluating the cycling diving of lithium-ion batteries, which has strong practical significance.

[0066] In addition, as Figure 5 shown, the second embodiment of the present invention also proposes a prediction system for the cycling diving of lithium-ion batteries, and the system includes:

[0067] A cycling test module 10, configured to perform a cycling test on the battery under test after normalization on a test cabinet, and perform charge and discharge cycling on the battery under test using a current every set number of cycling turns T for capacity calibration;

[0068] A differential fitting module 20, configured to select the charging curve of the last week of capacity calibration for voltage differential fitting to obtain the fitting parameters of the battery under test, and the fitting parameters include the loss of positive electrode active material, the loss of negative electrode active material, the positive electrode offset, and the negative electrode offset;

[0069] A plotting module 30, configured to plot the fitting parameters and the number of cycling turns to obtain a graph of the change of the fitting parameters with the cycling period;

[0070] A determination module 40, configured to determine that the battery under test will undergo cycling diving based on the change graph when at least two of the fitting parameters show an increasing phenomenon.

[0071] In one embodiment, the value range of the number of cycling turns T is 50 - 200.

[0072] In one embodiment, the current used for charge and discharge cycling of the battery under test is less than or equal to 0.1C.

[0073] In one embodiment, the test cabinet samples according to time sampling or voltage sampling, the accuracy of time sampling is higher than 1 second, and the accuracy of voltage sampling is higher than 10mV.

[0074] In one embodiment, the differential fitting module includes:

[0075] A differential unit, configured to select the charging curve of the last week for capacity calibration to perform voltage differentiation, so as to obtain a voltage differentiation curve;

[0076] A fitting unit, configured to perform fitting processing on the voltage differentiation curve by using dV / dQ fitting software to obtain the fitting parameters.

[0077] In this embodiment, by performing voltage differentiation fitting on the charging curve, four fitting parameters are obtained, which are the loss of active materials at the positive and negative electrodes and the offsets of the positive and negative electrodes respectively. Plot the fitting parameters and the number of cycles. When it is monitored that at least two of the four parameters increase, it is determined that the cycle capacity of the battery will suddenly decrease, that is, cycle diving occurs. The model of the present invention is convenient to establish. Only a small current capacity needs to be added during the cycle for data analysis and model establishment. It can effectively and timely predict the sudden decrease of the battery cycle capacity, with small capacity prediction error and high accuracy, and has good application prospects.

[0078] It should be noted that other embodiments or implementation methods of the prediction system for lithium-ion battery cycle diving of the present invention can refer to the above method embodiments, and will not be repeated here.

[0079] In the description of this specification, the descriptions referring to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0080] In addition, the terms "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" can explicitly or implicitly include at least one of these features. In the description of the present invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0081] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A prediction method for cyclic diving of a lithium-ion battery, characterized in that, the method includes: Performing a cyclic test on the battery under test after normalization on a test cabinet, performing charge and discharge cycles on the battery under test using a current every set number of cycles T, and performing capacity calibration, where the value range of the number of cycles T is 50 - 200; Selecting the charging curve of the last week of capacity calibration for voltage differential fitting to obtain the fitting parameters of the battery under test, where the fitting parameters include loss of positive active material, loss of negative active material, positive offset, and negative offset; Plotting the fitting parameters and the number of cycles to obtain a graph of the change of the fitting parameters with the cycle period; Based on the change graph, when at least two of the fitting parameters show an increasing phenomenon, it is determined that the battery under test will undergo cyclic diving.

2. The prediction method for cyclic diving of a lithium-ion battery according to claim 1, characterized in that, the method includes: Performing charge and discharge cycles on the battery under test using a current less than or equal to 0.1C.

3. The prediction method for cyclic diving of a lithium-ion battery according to claim 1, characterized in that, The test cabinet samples according to time sampling or voltage sampling, the accuracy of time sampling is higher than 1 second, and the accuracy of voltage sampling is higher than 10 mV.

4. The prediction method for cyclic diving of a lithium-ion battery according to claim 1, characterized in that, The selection of the charging curve of the last week of capacity calibration for voltage differential fitting to obtain the fitting parameters of the battery under test includes: Selecting the charging curve of the last week of capacity calibration for voltage differential to obtain a voltage differential curve; Using dV / dQ fitting software to perform fitting processing on the voltage differential curve to obtain the fitting parameters.

5. A prediction system for cyclic diving of a lithium-ion battery, characterized in that, the system includes: A cyclic test module for performing a cyclic test on the battery under test after normalization on a test cabinet, performing charge and discharge cycles on the battery under test using a current every set number of cycles T, and performing capacity calibration, where the value range of the number of cycles T is 50 - 200; A differential fitting module for selecting the charging curve of the last week of capacity calibration for voltage differential fitting to obtain the fitting parameters of the battery under test, where the fitting parameters include loss of positive active material, loss of negative active material, positive offset, and negative offset; A plotting module for plotting the fitting parameters and the number of cycles to obtain a graph of the change of the fitting parameters with the cycle period; A determination module for, based on the change graph, when at least two of the fitting parameters show an increasing phenomenon, determining that the battery under test will undergo cyclic diving.

6. The prediction system for cyclic diving of a lithium-ion battery according to claim 5, characterized in that, The current used for performing charge and discharge cycles on the battery under test is less than or equal to 0.1C.

7. The prediction system for cyclic diving of a lithium-ion battery according to claim 5, characterized in that, The test cabinet samples according to time sampling or voltage sampling, the accuracy of time sampling is higher than 1 second, and the accuracy of voltage sampling is higher than 10 mV.

8. The prediction system for cyclic diving of a lithium-ion battery according to claim 5, characterized in that, The differential fitting module includes: A differential unit for selecting the charging curve of the last week of capacity calibration to perform voltage differentiation, obtaining a voltage differentiation curve; A fitting unit for using dV / dQ fitting software to perform fitting processing on the voltage differentiation curve to obtain the fitting parameters.

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