Battery life prediction method, apparatus, device, and medium
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- LIYANG HINA BATTERY TECH CO LTD
- Filing Date
- 2024-12-13
- Publication Date
- 2026-06-16
Smart Images
Figure CN122218518A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of battery life prediction technology, and in particular to a battery life prediction method, apparatus, device and medium. Background Technology
[0002] Sodium-ion batteries have become a highly anticipated energy storage technology in recent years, and their performance evaluation, especially cycle life testing, is a crucial step in the research and development process. Existing methods for testing the cycle life of sodium-ion batteries typically require long charge-discharge cycles. This testing process is not only time-consuming but also occupies a significant amount of testing equipment, severely impacting the overall development progress of battery products. Summary of the Invention
[0003] To address the aforementioned technical problems, embodiments of this application provide a battery life prediction method, apparatus, device, and medium.
[0004] In a first aspect, embodiments of this application provide a battery life prediction method, the method comprising:
[0005] Perform cyclic charge-discharge tests on the battery under test, and obtain basic test data based on the cyclic charge-discharge tests.
[0006] A test curve is generated based on the basic test data, the test curve is differentiated, and multiple differential curves are obtained. Multiple health factors are obtained based on each differential curve.
[0007] A comprehensive health factor is obtained based on each of the described health factors;
[0008] The cycle life of the battery under test is determined based on the basic test data and the comprehensive health factors.
[0009] In one embodiment, after performing a first preset charge-discharge test on the battery under test at a first preset rate according to a first preset number of cycles, a second preset rate charge-discharge test is performed on the battery under test at a second preset number of cycles to obtain the basic test data.
[0010] In one embodiment, the test curve is subjected to voltage differentiation processing to obtain a voltage differentiation curve;
[0011] The peak shift rate of the first characteristic peak of the voltage differential curve is calculated based on the voltage differential curve to obtain the first health factor;
[0012] The peak shift rate of the second characteristic peak of the voltage differential curve is calculated based on the voltage differential curve to obtain the second health factor;
[0013] The test curve is processed by capacitance differentiation to obtain a capacitance differentiation curve;
[0014] The peak shift rate of the first characteristic peak of the capacitance differential curve is calculated based on the capacitance differential curve to obtain the third health factor.
[0015] In one embodiment, the product of the first health factor, the second health factor, and the third health factor is obtained, and the product is used as the comprehensive health factor.
[0016] In one embodiment, multiple capacity decay rates are obtained based on the basic test data, and a first relationship model is established based on each capacity decay rate and the comprehensive health factor;
[0017] Based on the basic test data, multiple cycle counts are obtained, and a second relationship model is established based on each cycle count and the comprehensive health factor.
[0018] The cycle life of the battery under test is determined based on the first relational model and the second relational model.
[0019] In one embodiment, a target comprehensive health factor corresponding to a preset capacity decay rate is calculated based on the first relationship model;
[0020] The cycle life of the battery under test is determined based on the target comprehensive health factors and the second relationship model.
[0021] In one embodiment, the target comprehensive health factor is substituted into the second relational model to determine the target number of cycles of the battery under test, and the target number of cycles is used as the cycle life of the battery under test.
[0022] Secondly, embodiments of this application provide a battery life prediction device, the battery life prediction device comprising:
[0023] The test module is used to perform cyclic charge-discharge tests on the battery under test and obtain basic test data based on the cyclic charge-discharge tests.
[0024] The processing module is used to generate a test curve based on the basic test data, perform differential processing on the test curve, obtain multiple differential curves, and obtain multiple health factors based on each differential curve.
[0025] The acquisition module is used to acquire a comprehensive health factor based on each of the described health factors;
[0026] The determination module is used to determine the cycle life of the battery under test based on the basic test data and the comprehensive health factors.
[0027] Thirdly, embodiments of this application provide an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the computer program executes the battery life prediction method provided in the first aspect when the processor is running.
[0028] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when run on a processor, executes the battery life prediction method provided in the first aspect.
[0029] The battery life prediction method, apparatus, equipment, and medium provided in this application include the following steps: performing a cyclic charge-discharge test on the battery under test; obtaining basic test data based on the cyclic charge-discharge test; generating a test curve based on the basic test data; performing differential processing on the test curve to obtain multiple differential curves; obtaining multiple health factors based on each differential curve; obtaining a comprehensive health factor based on each health factor; and determining the cycle life of the battery under test based on the basic test data and the comprehensive health factor. This application increases the prediction accuracy, reduces the battery cycle life testing time, and improves the R&D efficiency of battery products by obtaining multiple health factors from the differential curves and determining the cycle life of the battery under test based on each health factor and the basic test data. Attached Figure Description
[0030] To more clearly illustrate the technical solutions of this application, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of this application and should not be considered as a limitation on the scope of protection of this application. In the various drawings, similar components are numbered similarly.
[0031] Figure 1 A flowchart illustrating a battery life prediction method provided in an embodiment of this application is shown.
[0032] Figure 2 A schematic diagram of the first relational model provided in an embodiment of this application is shown;
[0033] Figure 3 A schematic diagram of the second relational model provided in an embodiment of this application is shown;
[0034] Figure 4 A schematic diagram of the first comparative model provided in an embodiment of this application is shown;
[0035] Figure 5 A schematic diagram of the second comparative model provided in an embodiment of this application is shown;
[0036] Figure 6 A schematic diagram of the third comparative model provided in the embodiments of this application is shown;
[0037] Figure 7 A schematic diagram of the fourth comparative model provided in the embodiments of this application is shown;
[0038] Figure 8 Another schematic diagram of the first relational model provided in the embodiments of this application is shown;
[0039] Figure 9 Another schematic diagram of the second relational model provided in the embodiments of this application is shown;
[0040] Figure 10 A schematic diagram of the battery life prediction device provided in an embodiment of this application is shown.
[0041] Figure 11 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown.
[0042] Icons: 1000 - Battery life prediction device, 1001 - Test module, 1002 - Processing module, 1003 - Acquisition module, 1004 - Determination module, 1100 - Electronic device, 1101 - Transceiver, 1102 - Processor, 1103 - Memory. Detailed Implementation
[0043] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.
[0044] The components of the embodiments of this application described and illustrated in the accompanying drawings can be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of this application provided in the drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.
[0045] In the following, the terms “comprising,” “having,” and their cognates, which may be used in various embodiments of this application, are intended only to indicate a particular feature, number, step, operation, element, component, or combination thereof, and should not be construed as excluding, firstly, the presence of one or more other features, numbers, steps, operations, elements, components, or combinations thereof, or adding the possibility of one or more features, numbers, steps, operations, elements, components, or combinations thereof.
[0046] Furthermore, the terms "first," "second," and "third" are used only to distinguish descriptions and should not be interpreted as indicating or implying relative importance.
[0047] Unless otherwise specified, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which the various embodiments of this application pertain. Terms (such as those defined in commonly used dictionaries) shall be interpreted as having the same meaning as in their contextual meaning in the relevant technical field and shall not be construed as having an idealized or overly formal meaning, unless clearly defined in the various embodiments of this application.
[0048] Example 1
[0049] This application provides a method for predicting battery life.
[0050] See Figure 1 Battery life prediction methods include:
[0051] S101, Perform a cyclic charge-discharge test on the battery under test, and obtain basic test data based on the cyclic charge-discharge test.
[0052] In one embodiment, after performing a first preset charge-discharge test on the battery under test at a first preset rate according to a first preset number of cycles, a second preset rate charge-discharge test is performed on the battery under test at a second preset number of cycles to obtain the basic test data.
[0053] In this embodiment, the battery under test is placed in a constant temperature chamber and subjected to cyclic testing according to a first preset number of cycles, with the capacity recorded for each cycle. Every second preset number of cycles, a low-rate charge-discharge test is performed on the battery to calibrate its capacity. The peak shift rate of the voltage differential curve is then calculated based on the calibrated capacity. During the testing process, it must be ensured that the constant temperature chamber has been calibrated to the required temperature. The constant temperature chamber is typically used to test the battery under test at room temperature.
[0054] For example, the battery under test is placed in a constant temperature chamber for 3C cycle testing, and the battery under test is subjected to two weeks of 0.1C cycle testing every 100 cycles, and the discharge capacity and voltage-capacity curve under the 0.1C cycle test are recorded.
[0055] It should be noted that the first preset number of cycles can be any positive integer value within 600 cycles, and the second preset number of cycles can be any positive integer value between 50 and 200. The first preset rate should be less than or equal to 3C, and the second preset rate should be less than or equal to 0.1C. Controlling the battery under test to perform a cycle test at the first preset rate according to the first preset number of cycles is constant current charging, and controlling the battery under test to perform a cycle test at the second preset rate according to the second preset number of cycles is constant current and constant voltage charging.
[0056] S102, generate a test curve based on the basic test data, perform differential processing on the test curve, obtain multiple differential curves, and obtain multiple health factors based on each differential curve.
[0057] In one embodiment, the test curve is subjected to voltage differentiation processing to obtain a voltage differentiation curve; the peak offset rate of the first characteristic peak of the voltage differentiation curve is calculated based on the voltage differentiation curve to obtain a first health factor; the peak offset rate of the second characteristic peak of the voltage differentiation curve is calculated based on the voltage differentiation curve to obtain a second health factor; the test curve is subjected to capacitance differentiation processing to obtain a capacitance differentiation curve; the peak offset rate of the first characteristic peak of the capacitance differentiation curve is calculated based on the capacitance differentiation curve to obtain a third health factor.
[0058] It should be noted that the test curve obtained by performing a second preset charge-discharge test on the battery under test at a second preset rate based on a second preset number of cycles is a discharge capacity-voltage curve. By differentiating the voltage of the discharge capacity-voltage curve and calculating the peak-to-peak shift rate of the two characteristic peaks of the voltage differential curve at the corresponding number of cycles compared to the initial state, the first health factor and the second health factor are obtained.
[0059] For example, if the two characteristic peaks of the voltage differential curve are located at positions P1 and P2 in the initial state, and the corresponding characteristic peak positions are P1n and P2n when the number of cycles is n, then the first health factor Mn is... The second health factor Ln is Where N is a positive integer and T is the second preset number of cycles.
[0060] The third health factor is obtained by differentiating the discharge capacity-voltage curve by capacitance and calculating the characteristic peak shift rate of the differential capacitance curve at the corresponding number of cycles compared to the initial state.
[0061] For example, if the characteristic peak position of the capacitance differential curve is P3 in the initial state, the corresponding characteristic peak position is P3n when the number of cycles is n. The third health factor Pn is... Where N is a positive integer and T is the second preset number of cycles.
[0062] This application quantifies the battery's health status using health factors. As batteries are cycled, electrode materials age and undergo structural changes, which can cause a shift in the characteristic peak position of the discharge capacity-voltage curve. By monitoring the values of multiple health factors, the degree of change in the battery's health status can be understood in a timely manner, increasing the efficiency of cycle life detection for the battery under test.
[0063] S103, Obtain comprehensive health factors based on each of the described health factors.
[0064] In one embodiment, the product of the first health factor, the second health factor, and the third health factor is obtained, and the product is used as the comprehensive health factor.
[0065] In this embodiment, the first, second and third health factors calculated with the same number of cycles are combined, that is, the peak offset rates of the two characteristic peaks of the voltage differential curve and the peak offset rates of the characteristic peaks of the capacity differential curve are multiplied to obtain the comprehensive health factor Qn, Qn=Mn*Ln*Pn, n=T,2T,……NT, where N is a positive integer and T is the second preset number of cycles.
[0066] Based on the characteristics of the voltage differential curve of a sodium-ion battery, this application extracts two health factors from the peak-to-peak offset rates of two characteristic peaks of the voltage differential curve, and a third health factor from the peak-to-peak offset rate of the capacity differential curve. These three health factors are then combined to construct a cycle life degradation model. This model simultaneously considers the impact of irreversible capacity loss and polarization capacity loss on capacity degradation, improving prediction accuracy and eliminating the need for internal resistance testing, thus simplifying the detection process.
[0067] S104, determine the cycle life of the battery under test based on the basic test data and the comprehensive health factors.
[0068] The first and second health factors obtained in this application are associated with irreversible capacity loss, and the third health factor is associated with polarization capacity loss. The comprehensive health factor obtained by combining the three can characterize the capacity decay trend. Compared with the existing technology that only uses either the voltage differential curve or the capacity differential curve as a health factor for lifetime prediction, the accuracy of lifetime prediction in this application is higher.
[0069] In one embodiment, multiple capacity decay rates are obtained based on the basic test data, and a first relationship model is established based on each capacity decay rate and the comprehensive health factor; multiple cycle counts are obtained based on the basic test data, and a second relationship model is established based on each cycle count and the comprehensive health factor; the cycle life of the battery under test is determined based on the first relationship model and the second relationship model.
[0070] In this embodiment, the capacity decay rate is calculated using basic test data, and a first relationship model between the capacity decay rate and the comprehensive health factor is established. A second relationship model between the number of cycles and the comprehensive health factor is also established. The capacity decay rate is the percentage of battery capacity loss relative to the initial capacity during cycling, which can be obtained from the initial capacity value and the discharge capacity value after n cycles in the basic test data.
[0071] Furthermore, this application fits a curve with the capacity decay rate on the horizontal axis and the comprehensive health factor on the vertical axis to generate a first relationship model, and fits a curve with the comprehensive health factor on the horizontal axis and the number of cycles on the vertical axis to generate a second relationship model.
[0072] In one embodiment, a target comprehensive health factor corresponding to a preset capacity decay rate is calculated based on the first relationship model; the cycle life of the battery under test is determined based on the target comprehensive health factor and the second relationship model.
[0073] In this embodiment, by substituting the comprehensive health factor values obtained from the first relational model into the second relational model, the corresponding battery cycle number is calculated, thus completing the battery cycle life prediction based on the established first and second relational models.
[0074] In one embodiment, the target comprehensive health factor is substituted into the second relational model to determine the target number of cycles of the battery under test, and the target number of cycles is used as the cycle life of the battery under test.
[0075] For example, as one embodiment, the battery under test is placed in a 35°C constant temperature chamber for 1C, 2-4V cycle testing. Two 0.05C cycle tests are performed every 100 cycles, and the discharge capacity value and discharge point capacity-voltage curve are recorded for the last cycle. Based on the battery's first 600 cycles of 35°C cycle testing data and 0.05C cycle data, the first health factor, second health factor, and third health factor are calculated. The combined health factor, cycle capacity decay rate, and number of cycles are shown in Table 1.
[0076] Table 1. Summary of Cycle Life Prediction Data
[0077]
[0078]
[0079] Based on the calculated parameters of cycle number, capacity decay rate, and comprehensive health factor, a first relationship model and a second relationship model are established to predict the later cycle trend of sodium-ion batteries.
[0080] like Figure 2 As shown, a curve is fitted with the capacity decay rate Wn on the horizontal axis and the comprehensive health factor Qn on the vertical axis to generate the first relationship model. The formula for the first relationship model is: Q=a+b*W+c*W^2, a=1.18688, b=22.36501, c=5.39581, where a, b, and c are constants that can be changed according to the specific cyclic test conditions.
[0081] like Figure 3As shown, a curve is fitted with the comprehensive health factor Qn as the horizontal axis and the number of cycles Cn as the vertical axis to generate a second relationship model. The formula for the second relationship model is: C=a+b*Q^c, where a=31.0018, b=55.40622, c=0.44664, where a, b, and c are constants that can be changed according to the specific cycle test conditions.
[0082] It should be noted that, according to the first relational model, when the capacity loss rate is 20% (i.e., the capacity retention rate is 80%), the corresponding comprehensive health factor Q = 2606.81108. Substituting the comprehensive health factor into the second relational model, the predicted cycle life (the number of cycles when the capacity retention rate is 80%) is obtained as 1890 cycles, which is only 60 cycles different from the actual cycle life of 1950 cycles. The relative error is only 3.07%, and the accuracy rate is as high as 96.93%.
[0083] like Figure 4 As shown, when the capacity decay rate Wn is used as the horizontal axis and the third health factor Pn is used as the vertical axis to fit the curve, the first comparison model is generated. The formula of the first comparison model is: P=ab*ln(W+c), where a, b, and c are constants, a=-0.82105, b=-0.88169, c=3.55546;
[0084] like Figure 5 As shown, a curve was fitted with the third health factor Pn as the horizontal axis and the number of cycles Cn as the vertical axis to generate a second comparison model. The formula for the second comparison model is: C=a+b*P^c, where a=-192.53093, b=801.93041, c=0.8146;
[0085] It should be noted that, according to the first comparative model, when the capacity loss rate is calculated to be 20% (i.e., the capacity retention rate is 80%), the corresponding third health factor P = 1.96452. Substituting the third health factor into the second comparative model, the predicted cycle life (the number of cycles when the capacity retention rate is 80%) is 1197 cycles, which differs from the actual cycle life of 1950 cycles by 752 cycles, with a relative error as high as 38.59% and an accuracy rate of only 61.41%.
[0086] like Figure 6 As shown, when the capacity decay rate Wn is used as the horizontal axis and the comprehensive health factor qn is used as the vertical axis to fit the curve, a third comparison model is generated. The formula of the third comparison model is: q=ab*ln(W+c), where a, b, and c are constants, a=-1448.45196, b=-593.57163, c=11.58264;
[0087] like Figure 7As shown, a curve was fitted with the comprehensive health factor qn as the horizontal axis and the number of cycles Cn as the vertical axis to generate the fourth comparison model. The formula for the fourth comparison model is: C=a+b*P^c, where a=58.20252, b=12.23376, c=0.72308;
[0088] To further clarify, when the capacity loss rate is calculated to be 20% (i.e., the capacity retention rate is 80%) according to the third comparison model, the corresponding comprehensive health factor q = 600.91796. Substituting the comprehensive health factor into the fourth comparison model, the predicted cycle life (the number of cycles when the capacity retention rate is 80%) is 1308 cycles, which differs from the actual cycle life of 1950 cycles by 642 cycles, with a relative error as high as 32.92% and an accuracy rate of only 67.08%.
[0089] Comparative analysis revealed that constructing a lifetime degradation model solely based on voltage differential curves or capacity differential curves has limited accuracy in predicting cycle life. In contrast, this application uses a comprehensive health factor obtained by combining voltage and capacity differential curves to predict lifetime, taking into account both irreversible capacity loss and polarization capacity loss. This results in higher accuracy, enhanced reliability of lifetime prediction results, and broad applicability, making it suitable for batteries of different types, specifications, and application scenarios.
[0090] For example, as a second embodiment, the battery under test is placed in a 45°C constant temperature chamber for 1C, 2-3.95V cycle testing. Two 0.05C cycle tests are performed every 100 cycles. The discharge capacity value and discharge point capacity-voltage curve are recorded for the last cycle. Based on the battery's first 600 cycles of 45°C cycle testing data and 0.05C cycle data, the first health factor, second health factor, and third health factor are calculated. The combined health factor, cycle capacity decay rate, and number of cycles are shown in Table 2.
[0091] Table 2. Summary of Cycle Life Prediction Data
[0092]
[0093] Based on the calculated parameters of cycle number, capacity decay rate, and comprehensive health factor, a first relationship model and a second relationship model are established to predict the later cycle trend of sodium-ion batteries.
[0094] like Figure 8 As shown, a curve is fitted with the capacity decay rate Wn on the horizontal axis and the comprehensive health factor Qn on the vertical axis to generate the first relationship model. The formula for the first relationship model is: Q=a+b*W+c*W^2, a=15.77949, b=-20.63299, c=6.71613, where a, b, and c are constants that can be changed according to the specific cyclic test conditions.
[0095] like Figure 9 As shown, a curve is fitted with the comprehensive health factor Qn as the horizontal axis and the number of cycles Cn as the vertical axis to generate a second relationship model. The formula for the second relationship model is: C=a+b*Q^c, where a=9.28821, b=143.88293, c=0.35734, where a, b, and c are constants that can be changed according to the specific cycle test conditions.
[0096] It should be noted that, according to the first relational model, when the capacity loss rate is 20% (i.e., the capacity retention rate is 80%), the corresponding comprehensive health factor Q = 2289.57169. Substituting the comprehensive health factor into the second relational model, the predicted cycle life (the number of cycles when the capacity retention rate is 80%) is 2292 cycles, which is only 58 cycles different from the actual cycle life of 2350 cycles. The relative error is only 2.44%, and the accuracy rate is as high as 97.56%.
[0097] The battery life prediction method provided in this embodiment increases the prediction accuracy, reduces the battery cycle life test time, and improves the R&D efficiency of battery products by obtaining multiple health factors in the differential curve and determining the cycle life of the battery under test based on each health factor and basic test data.
[0098] Example 2
[0099] In addition, embodiments of this application provide a battery life prediction device.
[0100] like Figure 10 As shown, the battery life prediction device 1000 includes:
[0101] Test module 1001 is used to perform cyclic charge-discharge tests on the battery under test and obtain basic test data based on the cyclic charge-discharge tests.
[0102] The processing module 1002 is used to generate a test curve based on the basic test data, perform differential processing on the test curve, obtain multiple differential curves, and obtain multiple health factors based on each differential curve.
[0103] The acquisition module 1003 is used to acquire a comprehensive health factor based on each of the health factors.
[0104] The determination module 1004 is used to determine the cycle life of the battery under test based on the basic test data and the comprehensive health factors.
[0105] The battery life prediction device 1000 provided in this embodiment can implement the battery life prediction method provided in Embodiment 1. To avoid repetition, it will not be described again here.
[0106] The battery life prediction device provided in this embodiment increases the prediction accuracy, reduces the battery cycle life test time, and improves the R&D efficiency of battery products by acquiring multiple health factors in the differential curve and determining the cycle life of the battery under test based on each health factor and basic test data.
[0107] Example 3
[0108] Furthermore, this application provides an electronic device including a memory and a processor. The memory stores a computer program, which executes the battery life prediction method provided in Embodiment 1 when the computer program is run on the processor.
[0109] For details, see Figure 11 The electronic device 1100 includes a transceiver 1101, a bus interface, and a processor 1102. The processor 1102 is used for: performing cyclic charge-discharge tests on the battery under test; obtaining basic test data based on the cyclic charge-discharge tests; generating test curves based on the basic test data; performing differential processing on the test curves and obtaining multiple differential curves; obtaining multiple health factors based on each differential curve; obtaining a comprehensive health factor based on each health factor; and determining the cycle life of the battery under test based on the basic test data and the comprehensive health factor.
[0110] In one embodiment, the processor 1102 is further configured to: perform a first preset charge-discharge test on the battery under test at a first preset rate according to a first preset number of cycles, and then perform a second preset rate charge-discharge test on the battery under test at a second preset number of cycles to obtain the basic test data.
[0111] In one embodiment, the processor 1102 is further configured to: perform voltage differentiation processing on the test curve to obtain a voltage differentiation curve; calculate the peak offset rate of the first characteristic peak of the voltage differentiation curve based on the voltage differentiation curve to obtain a first health factor; calculate the peak offset rate of the second characteristic peak of the voltage differentiation curve based on the voltage differentiation curve to obtain a second health factor; perform capacitance differentiation processing on the test curve to obtain a capacitance differentiation curve; and calculate the peak offset rate of the first characteristic peak of the capacitance differentiation curve based on the capacitance differentiation curve to obtain a third health factor.
[0112] In one embodiment, the processor 1102 is further configured to: obtain the product of the first health factor, the second health factor, and the third health factor, and use the product as the comprehensive health factor.
[0113] In one embodiment, the processor 1102 is further configured to: obtain multiple capacity decay rates based on the basic test data; establish a first relationship model based on each capacity decay rate and the comprehensive health factor; obtain multiple cycle counts based on the basic test data; establish a second relationship model based on each cycle count and the comprehensive health factor; and determine the cycle life of the battery under test based on the first relationship model and the second relationship model.
[0114] In one embodiment, the processor 1102 is further configured to: calculate a target comprehensive health factor corresponding to a preset capacity decay rate based on the first relational model; and determine the cycle life of the battery under test based on the target comprehensive health factor and the second relational model.
[0115] In one embodiment, the processor 1102 is further configured to: substitute the target comprehensive health factor into the second relational model to determine the target number of cycles of the battery under test, and use the target number of cycles as the cycle life of the battery under test.
[0116] In this embodiment of the application, the electronic device 1100 further includes a memory 1103. Figure 11 In this context, the bus architecture may include any number of interconnected buses and bridges, specifically connecting various circuits of one or more processors represented by processor 1102 and memory represented by memory 1103. The bus architecture may also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. The bus interface provides an interface. Transceiver 1101 may be multiple elements, including transmitters and receivers, providing a unit for communicating with various other devices over a transmission medium. Processor 1102 is responsible for managing the bus architecture and general processing, and memory 1103 may store data used by processor 1102 during operation.
[0117] The electronic device 1100 provided in this application embodiment can execute the steps of the battery life prediction method provided in the above method embodiment 1. To avoid repetition, it will not be described again here.
[0118] The electronic device provided in this embodiment obtains multiple health factors in the differential curve and determines the cycle life of the battery under test based on each health factor and basic test data, thereby increasing the prediction accuracy, reducing the test time for battery cycle life, and improving the R&D efficiency of battery products.
[0119] Example 4
[0120] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the battery life prediction method provided in Embodiment 1.
[0121] In this embodiment, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.
[0122] The computer-readable storage medium provided in this embodiment can implement the battery life prediction method provided in Embodiment 1. To avoid repetition, it will not be described again here.
[0123] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal that includes that element.
[0124] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.
[0125] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.
Claims
1. A method for predicting battery life, characterized in that, The method includes: Perform cyclic charge-discharge tests on the battery under test, and obtain basic test data based on the cyclic charge-discharge tests. A test curve is generated based on the basic test data, the test curve is differentiated, and multiple differential curves are obtained. Multiple health factors are obtained based on each differential curve. A comprehensive health factor is obtained based on each of the described health factors; The cycle life of the battery under test is determined based on the basic test data and the comprehensive health factors.
2. The battery life prediction method according to claim 1, characterized in that, The battery under test undergoes a cyclic charge-discharge test, and basic test data is obtained based on the cyclic charge-discharge test, including: After performing a first preset charge-discharge test on the battery under test at a first preset rate according to a first preset number of cycles, a second preset rate charge-discharge test is performed on the battery under test at a second preset number of cycles to obtain the basic test data.
3. The battery life prediction method according to claim 2, characterized in that, The process involves generating a test curve based on the basic test data, performing differentiation on the test curve to obtain multiple differential curves, and obtaining multiple health factors based on each differential curve, including: The test curve is subjected to voltage differentiation processing to obtain a voltage differentiation curve; The peak shift rate of the first characteristic peak of the voltage differential curve is calculated based on the voltage differential curve to obtain the first health factor; The peak shift rate of the second characteristic peak of the voltage differential curve is calculated based on the voltage differential curve to obtain the second health factor; The test curve is processed by capacitance differentiation to obtain a capacitance differentiation curve; The peak shift rate of the first characteristic peak of the capacitance differential curve is calculated based on the capacitance differential curve to obtain the third health factor.
4. The battery life prediction method according to claim 3, characterized in that, The process of obtaining a comprehensive health factor based on each of the aforementioned health factors includes: Obtain the product of the first health factor, the second health factor, and the third health factor, and use the product as the comprehensive health factor.
5. The battery life prediction method according to claim 1, characterized in that, The process of determining the cycle life of the battery under test based on the basic test data and the comprehensive health factors includes: Based on the basic test data, multiple capacity decay rates are obtained, and a first relationship model is established based on each capacity decay rate and the comprehensive health factor. Based on the basic test data, multiple cycle counts are obtained, and a second relationship model is established based on each cycle count and the comprehensive health factor. The cycle life of the battery under test is determined based on the first relational model and the second relational model.
6. The battery life prediction method according to claim 5, characterized in that, Determining the cycle life of the battery under test based on the first relational model and the second relational model includes: Calculate the target comprehensive health factor corresponding to the preset capacity decay rate based on the first relationship model; The cycle life of the battery under test is determined based on the target comprehensive health factors and the second relationship model.
7. The battery life prediction method according to claim 6, characterized in that, The step of determining the cycle life of the battery under test based on the target comprehensive health factor and the second relationship model includes: The target comprehensive health factor is substituted into the second relationship model to determine the target number of cycles of the battery under test, and the target number of cycles is taken as the cycle life of the battery under test.
8. A battery life prediction device, characterized in that, The device includes: The test module is used to perform cyclic charge-discharge tests on the battery under test and obtain basic test data based on the cyclic charge-discharge tests. The processing module is used to generate a test curve based on the basic test data, perform differential processing on the test curve, obtain multiple differential curves, and obtain multiple health factors based on each differential curve. The acquisition module is used to acquire a comprehensive health factor based on each of the described health factors; The determination module is used to determine the cycle life of the battery under test based on the basic test data and the comprehensive health factors.
9. An electronic device, characterized in that, The device includes a memory and a processor, the memory storing a computer program that executes the battery life prediction method according to any one of claims 1 to 7 when the processor is running.
10. A computer-readable storage medium, characterized in that, It stores a computer program that, when run on a processor, executes the battery life prediction method according to any one of claims 1 to 7.