Battery capacity diving prediction method and device, intelligent equipment and storage medium
By analyzing the voltage change data of lithium batteries during charging and static, a relationship curve is generated to predict capacity dip, which solves the problem of complex and inaccurate existing detection methods, and achieves a fast and accurate prediction of battery capacity dip.
Patent Information
- Application Number
- CN202510123486.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-26
- Publication Date
- 2025-05-09
AI Technical Summary
The existing battery capacity diving detection methods are complex and have low accuracy, making it difficult to effectively predict the lithium battery capacity diving phenomenon.
By obtaining the voltage and time data of the battery's standstill phase after the charging of different rounds, a first relationship curve between the voltage change rate and time is determined, and a second relationship curve is generated based on the characteristic values of the curve. When the slope of the second relation curve changes suddenly, it is predicted that the battery will experience a capacity dip after multiple rounds of charge and discharge at the beginning of the round.
It realizes the simple, fast and accurate prediction of the battery capacity diving point, improves detection efficiency, and can predict during the battery charging process, and prevents dangerous situations in a timely manner.
Smart Images
Figure CN119959771A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of battery capacity dive detection, and in particular to a battery capacity dive prediction method, apparatus, intelligent device and storage medium. Background Art
[0002] With the development of lithium batteries, higher requirements have been placed on the safety performance of lithium batteries. The capacity diving phenomenon of lithium batteries refers to the phenomenon that the active lithium in lithium batteries is continuously lost, resulting in a sudden acceleration of battery decay and the deviation of the capacity curve from the normal track. Capacity diving will greatly reduce the safety of battery use. Therefore, it is of great significance to predict battery capacity diving. The commonly used capacity diving detection method is mainly to indirectly calculate through capacity retention rate and battery aging data. The test process is complicated and the accuracy is not high.
[0003] Accordingly, the art needs a new battery capacity drop prediction solution to solve the above problem. Summary of the invention
[0004] In order to overcome the above-mentioned defects, the present application is proposed to solve or at least partially solve the technical problem that the current method for predicting battery capacity drop is complicated and has low accuracy.
[0005] In a first aspect, a method for predicting battery capacity drop is provided, comprising: obtaining the battery capacity drop at a1, a2, ..., a n The voltage and time data of the static phase after the round test charging is completed, where a n =a1+(n-1)×d, a1, n, d are all positive integers, n=1, 2, 3…, any round of testing includes charging, standing after charging, discharging, and standing after discharging; determining a first relationship curve between the voltage change rate and time corresponding to each round of testing according to the voltage and time data obtained; obtaining a characteristic value of each of the first relationship curves, and generating a second relationship curve according to the obtained multiple characteristic values and the number of test rounds corresponding to the multiple characteristic values; when a sudden change in the slope of the second relationship curve is detected, predicting that the capacity of the battery will drop after multiple rounds of charging and discharging starting from the number of test rounds where the slope suddenly changes.
[0006] In a technical solution of the above-mentioned method for predicting battery capacity dive, when a sudden change in the slope of the second relationship curve is detected, predicting that the battery will experience a capacity dive after multiple rounds of charge and discharge starting from the number of test rounds in which the slope suddenly changes, includes: when the slope of the second characteristic value on the second relationship curve changes with the slope of the first characteristic value on the second relationship curve by more than a preset threshold, predicting that the battery will experience a capacity dive after 50 to 60 rounds of charge and discharge starting from the number of test rounds corresponding to the second characteristic value, wherein the first characteristic value and the second characteristic value are characteristic values corresponding to two adjacent test rounds on the second relationship curve.
[0007] In a technical solution of the above-mentioned battery capacity drop prediction method, any round of testing includes: charging the battery to 100% SOC, leaving it to rest for a first preset time, discharging the battery to 0% SOC, and leaving it to rest for a second preset time.
[0008] In one technical solution of the above-mentioned method for predicting battery capacity drop, any round of testing charges and discharges the battery at a constant current / power at a constant temperature.
[0009] In a technical solution of the above-mentioned battery capacity drop prediction method, d=5, and the battery capacity drop prediction method of the battery is obtained at a1, a2, ..., a n The voltage and time data of the static phase after the round test charging is completed, where a n =a1+(n-1)×d, a1, n, d are all positive integers, n=1, 2, 3…, including: obtaining the voltage and time data of the battery within the first preset time period after the a1, a1+5, a1+10… rounds of test charging are completed, wherein a1 is a positive integer.
[0010] In a technical solution of the above-mentioned battery capacity dive prediction method, when the number of test rounds is equal to a1, a1+5, a1+10..., the first preset time length is greater than the second preset time length; when the number of test rounds is not equal to a1, a1+5, a1+10..., the first preset time length is equal to the second preset time length.
[0011] In a technical solution of the above-mentioned battery capacity drop prediction method, a1=5, the battery capacity drop prediction method of ... n The voltage and time data of the static stage after the round test charging is completed include: obtaining the voltage and time data of the battery within the first preset time length of the 5th, 10th, 15th... round test at the first sampling interval.
[0012] In a second aspect, a battery capacity drop prediction device is provided, comprising: a data acquisition module for acquiring the battery capacity drop prediction device at a1, a2, ..., a nThe voltage and time data of the static phase after the round test charging is completed, where a n =a1+(n-1)×d, a1, n, d are all positive integers, n=1, 2, 3…, any round of testing includes charging, standing after charging, discharging, and standing after discharging; a first curve fitting module, used to determine a first relationship curve between the voltage change rate and time according to the voltage and time data of each round of testing obtained; a second curve fitting module, used to obtain a characteristic value of each of the first relationship curves, and generate a second relationship curve according to the obtained multiple characteristic values and the number of test rounds corresponding to the multiple characteristic values; a diving prediction module, used to predict that the capacity of the battery will dive after multiple rounds of charging and discharging starting from the number of test rounds where the slope suddenly changes when a sudden change in the slope of the second relationship curve is detected.
[0013] In a technical solution of the above-mentioned battery capacity dive prediction device, the dive prediction module includes: a prediction unit, which is used to predict that the battery will experience a capacity dive after 50 to 60 rounds of charge and discharge starting from the number of test rounds corresponding to the second characteristic value when the slope of the second characteristic value on the second relationship curve changes from the slope of the first characteristic value on the second relationship curve by more than a preset threshold, wherein the first characteristic value and the second characteristic value are characteristic values corresponding to two adjacent test rounds on the second relationship curve.
[0014] In a technical solution of the above-mentioned battery capacity drop prediction device, any round of testing includes: charging the battery to 100% SOC, standing for a first preset time, discharging the battery to 0% SOC, and standing for a second preset time.
[0015] In a technical solution of the above-mentioned device for predicting battery capacity drop, any round of testing charges and discharges the battery at a constant current / power at a constant temperature.
[0016] In a technical solution of the above-mentioned battery capacity diving prediction device, d=5, and the data acquisition module includes: a first acquisition unit, used to obtain the voltage and time data of the battery within the first preset time period after the a1, a1+5, a1+10... rounds of test charging are completed, where a1 is a positive integer.
[0017] In a technical solution of the above-mentioned battery capacity dive prediction device, when the number of test rounds is equal to a1, a1+5, a1+10..., the first preset time length is greater than the second preset time length; when the number of test rounds is not equal to a1, a1+5, a1+10..., the first preset time length is equal to the second preset time length.
[0018] In a technical solution of the above-mentioned battery capacity diving prediction device, a1=5, the data acquisition module includes: a second acquisition unit, used to acquire the voltage and time data of the battery within the first preset time length of the 5th, 10th, 15th... rounds of testing at a first sampling point interval.
[0019] In a third aspect, a smart device is provided, comprising at least one processor; and a memory communicatively connected to the at least one processor; wherein a computer program is stored in the memory, and when the computer program is executed by the at least one processor, the method described in any one of the technical solutions of the above-mentioned method for predicting a battery capacity dive is implemented.
[0020] In a fourth aspect, a computer-readable storage medium is provided, wherein a plurality of program codes are stored therein, wherein the program codes are suitable for being loaded and run by a processor to execute the method described in any one of the technical solutions of the above-mentioned battery capacity dive prediction method.
[0021] The above one or more technical solutions of the present application have at least one or more of the following beneficial effects:
[0022] In the technical solution of the present application, the battery is subjected to charge and discharge cycle test, and the voltage-time data of the static stage after the battery is fully charged in different rounds of test is obtained to determine the first relationship curve between the voltage change rate and time; the characteristic value of the first relationship curve is obtained to determine the second relationship curve between the characteristic value of multiple rounds of test and the number of test rounds; and it is judged whether the battery has a sudden increase in the amount of reversible lithium precipitation during the charge and discharge process according to whether the slope of the second relationship curve has a sudden change. The purpose of simply, quickly and accurately predicting the battery diving point is achieved. This method does not require changing the battery's charge and discharge system, and does not require adding battery auxiliary detection equipment, which greatly improves the detection efficiency. In addition, the prediction method can be carried out during the battery charging process, and charging can be stopped in time according to the predicted capacity diving point to avoid the occurrence of dangerous situations. BRIEF DESCRIPTION OF THE DRAWINGS
[0023] The disclosure of the present application will become easier to understand with reference to the accompanying drawings. It is easy for those skilled in the art to understand that these drawings are only for illustrative purposes and are not intended to limit the scope of protection of the present application. Among them:
[0024] Figure 1 This is a schematic flow chart of the main steps of a method for predicting battery capacity drop according to an embodiment of the present application;
[0025] Figure 2 This is a schematic diagram of the relationship between the positive and negative electrode potentials and time during the charging and resting stages of an LFP lithium battery according to an embodiment of the present application;
[0026] Figure 3 is a schematic diagram of a voltage differential curve during a battery cycle according to Example 1 of the present application;
[0027] Figure 4 is a schematic diagram of the relationship between the characteristic value and the number of cycles in the battery cycle process according to the first embodiment of the present application;
[0028] Figure 5 It is a schematic diagram of the relationship between the battery capacity retention rate and the number of cycles according to the first embodiment of the present application;
[0029] Figure 6 is a schematic diagram of a voltage differential curve during a battery cycle according to Example 2 of the present application;
[0030] Figure 7 is a schematic diagram of the relationship between the characteristic value and the number of cycles in the battery cycle process according to the second embodiment of the present application;
[0031] Figure 8 This is a schematic diagram of the relationship between the battery capacity retention rate and the number of cycles according to the second embodiment of the present application;
[0032] Fig. 9 It is a schematic diagram of the main structural block diagram of a device for predicting battery capacity drop according to an embodiment of the present application;
[0033] Fig.10 The figure is a schematic diagram of the connection relationship between the processor and the memory of a smart device according to an embodiment of the present application. DETAILED DESCRIPTION
[0034] Some embodiments of the present application are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present application and are not intended to limit the protection scope of the present application.
[0035] In the description of the present application, the terms "first", "second", etc. are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. The terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected, it can be indirectly connected through an intermediate medium, it can also be the internal communication of two elements, it can be a wireless connection, or it can be a wired connection.
[0036] In addition, "module" and "processor" may include hardware, software or a combination of the two. A module may include hardware circuits, various suitable sensors, communication ports, and memories, and may also include software parts, such as program codes, or a combination of software and hardware. The processor may be a central processing unit, a microprocessor, an image processor, a digital signal processor, or any other suitable processor. The processor has data and / or signal processing functions. The processor may be implemented in software, hardware, or a combination of the two. Computer-readable storage media include any suitable media that can store program codes, such as magnetic disks, hard disks, optical disks, flash memory, read-only memory, random access memory, etc.
[0037] In addition, if the meaning of "and / or" appears in this application, it includes three parallel solutions. Taking "A and / or B" as an example, it includes solution A, solution B, or a solution that satisfies both A and B. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in this field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by this application. The term "at least one A or B" or "at least one of A and B" has a similar meaning to "A and / or B" and can include only A, only B, or A and B. The singular terms "one" and "this" can also include plural forms.
[0038] Lithium batteries have become the mainstream energy storage battery due to their unparalleled advantages in terms of safety, stability, and low cost. However, due to factors such as side reactions at the interface between the electrolyte and the negative electrode, and repeated expansion of graphite due to lithium deintercalation, the SEI film (Solid Electrolyte Interface) becomes thicker and the loss of active lithium increases. When the loss of active lithium continues to increase and the impedance continues to rise, capacity diving may occur. Capacity diving not only makes the battery unable to achieve the designed cycle life, but also greatly reduces the safety of battery use.
[0039] At present, there is no universal detection process for the prediction method of capacity diving. The existing detection methods are difficult, complicated and have low accuracy. In one example, the diving point can be predicted by measuring the electrolyte consumption rate and the minimum threshold during the battery charge and discharge cycle, but this method can only predict the capacity diving caused by electrolyte loss, and may not be applicable to other forms of capacity attenuation. In another example, a small rate cycle can be periodically added during the battery charge and discharge cycle, and the discharge curve can be differentiated as the target curve, and the peak area change of the differential curve can be calculated to determine whether the capacity diving occurs. This method reduces the complexity of the diving prediction to a certain extent, but the periodic small rate charge and discharge will greatly extend the test time of the normal cycle of the battery, and will also have a certain impact on the cycle performance. Therefore, a fast and accurate battery capacity diving prediction method is needed to predict the battery diving point, confirm the safe service life of the battery, and speed up the evaluation test cycle in the development stage.
[0040] In view of the above problems, the present application provides a method for predicting battery capacity drop, which is mainly aimed at lithium-ion batteries, such as lithium iron phosphate (LiFePO4, LFP) lithium-ion batteries. Figure 1 , Figure 1 FIG. 1 is a flow chart of the main steps of a method for predicting battery capacity drop according to an embodiment of the present application. Figure 1 As shown, the method mainly includes the following steps S1 to S4:
[0041] Step S1, obtaining the battery at a1, a2...a n The voltage and time data of the static phase after the round test charging is completed, where a n =a1+(n-1)×d, a1, n, d are all positive integers, n=1, 2, 3…, any round of testing includes charging, standing still after charging, discharging, and standing still after discharging.
[0042] In this embodiment, the battery is subjected to multiple rounds of (charge and discharge) tests, wherein any round of testing includes the following stages: fully charging the battery, leaving it to rest for a period of time, and then discharging the battery and leaving it to rest for a period of time. When obtaining voltage and time data, the voltage and time data can be obtained in the resting stage after each round of charging, or the voltage and time data can be obtained in the resting stage after charging is completed every few rounds. Specifically, the following expression can be used: n =a1+(n-1)×dGet the battery in a1, a2…a n The voltage and time data of the static stage after the round of test charging is completed. As an example, assuming a1=5, d=5, the voltage and time data are obtained in the static stage after the 5th, 10th, 15th, 20th... rounds of test charging are completed; assuming a1=1, d=3, the voltage and time data are obtained in the static stage after the 1st, 4th, 7th, 10th... rounds of test charging are completed. Among them, a1 and d can be set according to the test requirements, and this embodiment does not impose specific restrictions on this.
[0043] In one embodiment, the process of a round of charge and discharge test may specifically include: charging the battery to 100% SOC (State of Charge), leaving it to rest for a first preset time, discharging the battery to 0% SOC, and leaving it to rest for a second preset time. Among them, 100% SOC means that the battery is fully charged (charging is complete), and 0% SOC means that the battery is completely exhausted. The first preset time and the second preset time of rest may be equal or unequal, and can be set according to the test requirements. This embodiment does not impose specific restrictions on this.
[0044] In one embodiment, the voltage and time data of the battery in the static stage after any round of charge and discharge test is completed are obtained, for example, the voltage of the battery at multiple time points within a first preset time period is obtained.
[0045] In one implementation, the first preset duration ranges from 10 to 30 minutes, and the second preset duration is 5 minutes.
[0046] In one embodiment, each round of charge and discharge testing may be performed at a constant temperature, and a constant current / power may be used to charge and discharge the battery.
[0047] Step S2, determining a first relationship curve between the corresponding voltage change rate and time according to the acquired voltage and time data of each round of testing.
[0048] In this embodiment, for a1, a2...a nIn any round of testing, the voltage change rate dV / dt is calculated according to the obtained voltage V and time t data, and then the calculated voltage change rate dV / dt is used as the ordinate and the corresponding time t is used as the abscissa to determine the first relationship curve between the voltage change rate and time.
[0049] It should be noted that since the positive electrode potential of lithium iron phosphate is relatively stable, the potential change of the graphite negative electrode can be reflected by the change of the full battery voltage. Figure 2 As shown, the dotted line represents the positive electrode potential of the lithium iron phosphate lithium battery, the dotted line represents the negative electrode potential, and the solid line represents the potential difference between the positive electrode potential and the negative electrode potential, that is, the full battery voltage. It can be seen that the positive electrode potential is relatively stable during the charging stage and the static stage after charging. Therefore, the change in potential difference can be used to reflect the change in the potential of the negative electrode. It is understandable that the method in the embodiment of the present application can also be used to predict the diving of other batteries with this characteristic.
[0050] In a preferred embodiment, a1 = 5, d = 5. This embodiment obtains the voltage and time data of the static stage after the 5th, 10th, 15th, 20th, ... rounds of test charging are completed, and determines the first relationship curve between the voltage change rate and time based on the above data.
[0051] In one embodiment, for test rounds requiring acquisition of voltage and time data, the first preset duration is greater than the second preset duration, and for test rounds not requiring acquisition of voltage and time data, the first preset duration is equal to the second preset duration.
[0052] In this embodiment, the first preset time length is the time length of the rest stage after the charging is completed, and the second preset time length is the time length of the rest stage after the discharging is completed. When obtaining the voltage and time data of the rest stage after the charging is completed for the 5th, 10th, 15th, 20th, ... rounds of testing, the first preset time length is greater than the second preset time length. For other rounds of charging and discharging tests, such as the 1st, 2nd, 3rd, 4th, 6th, 7th, 8th, 9th, 11th, ... rounds of testing, the first preset time length may be equal to the second preset time length.
[0053] In one embodiment, when the number of test rounds is equal to a1+(n-1)×d, that is, when voltage and time data need to be obtained, the first preset time length can be 20 minutes and the second preset time length can be 5 minutes; when the number of test rounds is not equal to a1+(n-1)×d, that is, when voltage and time data do not need to be obtained, the first preset time length and the second preset time length can both be 5 minutes.
[0054] In this embodiment, the voltage and time data of the static stage after charging is completed are obtained every few rounds. For the test rounds that do not require data acquisition, the first preset static time after charging is completed can be shortened, thereby achieving the purpose of reducing the test time and improving the test efficiency.
[0055] In one embodiment, when the number of test rounds is equal to a1+(n-1)×d, the voltage and time data of the static stage after charging is completed in the nth round of charging and discharging are obtained at the first sampling interval; when the number of test rounds is not equal to a1+(n-1)×d, the voltage and time data of the static stage after charging is completed in the nth round of charging and discharging are obtained at the second sampling interval. Specifically, for example, the voltage and time data of the static stage (within the first preset time length) after the 5th, 10th, 15th, 20th, etc. rounds of charging are obtained based on the first sampling interval, and for other rounds of testing, the voltage and time data of the static stage after charging is completed are obtained based on the second sampling interval.
[0056] In one implementation, the first sampling interval is recorded as t1, and the second sampling interval is recorded as t2. The ranges of the first sampling interval and the second sampling interval are as follows: t1≤10s, 10s≤t2≤30s. As an example, t1=10s, t2=30s.
[0057] In this embodiment, the voltage and time data for generating the first relationship curve are acquired at a smaller sampling interval, which can improve the smoothness and accuracy of the subsequent fitted curve, thereby more accurately analyzing the trends and rules of the data, and further improving the accuracy of the dive prediction.
[0058] Step S3, obtaining a characteristic value of each first relationship curve, and generating a second relationship curve according to the obtained multiple characteristic values and the number of test rounds corresponding to the multiple characteristic values.
[0059] In this embodiment, the characteristic value of the first relationship curve refers to the minimum value after the trend of the first relationship curve changes. It should be noted that the characteristic value reflects the amount of lithium deposition in the battery. The later the time corresponding to the characteristic value is, the more lithium deposition there is. Assuming that the characteristic value is recorded as F, the ath n The characteristic value of the first relationship curve corresponding to the round test is recorded as Fa n In this embodiment, the characteristic values of each first relationship curve determined in step S2 such as Fa1, Fa2...Fa are obtained. n , and then according to the obtained multiple eigenvalues Fa1, Fa2…Fa n And the number of test rounds corresponding to multiple eigenvalues a1, a2…a n Generate a second relationship curve, that is, a correlation curve between the characteristic value and the number of test rounds.
[0060] Step S4, when a sudden change in the slope of the second relationship curve is detected, it is predicted that the battery will experience a capacity drop after multiple rounds of charge and discharge starting from the test round number at which the slope suddenly changes.
[0061] In this embodiment, if the slope of the second relationship curve suddenly changes, it is predicted that the battery will experience a capacity drop after multiple rounds of charge and discharge starting from the slope sudden change point.
[0062] In one implementation, the sudden change in slope may be measured using features such as the slope difference or change amplitude between two adjacent points on the curve, and this embodiment does not impose any specific limitation on this.
[0063] In one implementation of the embodiment of the present application, the above step S4 may further include the following step S41:
[0064] Step S41, when the slope of the second characteristic value on the second relationship curve changes more than the slope of the first characteristic value on the second relationship curve than a preset threshold, it is predicted that the battery will experience a capacity dive after 50 to 60 rounds of charge and discharge starting from the number of test rounds corresponding to the second characteristic value, wherein the first characteristic value and the second characteristic value are characteristic values corresponding to two adjacent test rounds on the second relationship curve.
[0065] In this embodiment, whether the slope of the second relationship curve has a sudden change is determined by calculating the change range of the slope of the first eigenvalue and the second eigenvalue corresponding to two adjacent test rounds on the second relationship curve. Specifically, the change range of the slope is recorded as R n , R can be calculated using the following formula n :
[0066] R n =(k n -k n-1 ) / k n
[0067] Among them, k n Indicates the first n The slope of the characteristic value of the round test on the second relationship curve, k n-1 Indicates the first n-1 The slope of the characteristic value of the round test on the second relationship curve. If R n If R n If the value is less than or equal to the preset threshold, the battery is judged to be in a stable cycle state.
[0068] In one embodiment, according to multiple experiments, when R n When it is greater than the preset threshold, the battery's capacity will drop after 50 to 60 cycles of charge and discharge.
[0069] In one embodiment, the a n The slope k of the characteristic value of the round test on the second relationship curve n The slope can be calculated based on the characteristic values of the previous round of tests, or multiple characteristic values of previous rounds of tests can be used to fit the slope. The embodiment of the present application does not impose any specific limitation on the method of calculating the slope of the curve.
[0070] In the technical solution of the present application, the battery is subjected to charge and discharge cycle test, and the voltage-time data of the static stage after the battery is fully charged in different rounds of test is obtained to determine the first relationship curve between the voltage change rate and time; the characteristic value of the first relationship curve is obtained to determine the second relationship curve between the characteristic value of multiple rounds of test and the number of test rounds; and it is judged whether the battery has a sudden increase in the amount of reversible lithium precipitation during the charge and discharge process according to whether the slope of the second relationship curve has a sudden change. The purpose of simply, quickly and accurately predicting the battery diving point is achieved. This method does not require changing the battery's charge and discharge system, and does not require adding battery auxiliary detection equipment, which greatly improves the detection efficiency. In addition, the prediction method can be carried out during the battery charging process, and charging can be stopped in time according to the predicted capacity diving point to avoid the occurrence of dangerous situations.
[0071] The overall process of the above-mentioned battery capacity drop prediction method is explained below through two embodiments:
[0072] Embodiment 1
[0073] A lithium iron phosphate-graphite lithium-ion battery with a theoretical capacity of 2Ah was placed in a 25°C constant temperature box for constant temperature (charge and discharge) cycles. The cycle steps are as follows: 1.5P constant power charging to 3.65V, standing for 20 minutes, 1.5P constant power discharge to 2.0V, standing for 5 minutes. The voltage-time curve of the standing stage after each cycle (round) of charging is obtained, and the curve is differentiated to obtain the dV / dt-t voltage differential curve (first relationship curve) as shown in Figure 3 As shown, 1cls, 50cls, and 100cls represent the 1st week, the 50th week, and the 100th week respectively, and the corresponding curves are the first relationship curves of the 1st week, the 50th week, and the 100th week respectively. Figure 3 The corresponding vertical lines are used to mark the locations of the characteristic values of the first relationship curve. It should be noted that: Figure 3 Only three first relationship curves are selected as examples, and actually more first relationship curves tested for a certain number of weeks can be included. Assuming that the characteristic value of the first relationship curve after every 5 cycles is plotted against the number of cycles, a characteristic value variation curve (second relationship curve) is obtained as shown in FIG. Figure 4 As shown. Figure 4 It can be seen that the characteristic value is relatively stable in the first 50 cycles, and the characteristic value after 50 cycles shows an accelerating upward trend, indicating that a capacity dive is about to occur. Figure 5 Schematic diagram of the relationship between battery capacity retention rate and cycle number according to Example 1 of the present application. Figure 5 It can be seen that the battery capacity drops at around 100 weeks. Therefore, according to the method proposed in this embodiment, a warning of battery capacity drop can be issued 50 weeks in advance.
[0074] Embodiment 2
[0075] The lithium iron phosphate-graphite system lithium-ion battery with a theoretical capacity of 2Ah and better negative electrode kinetic performance was placed in a 25°C constant temperature box for constant temperature (charge and discharge) cycles. The cycle steps are as follows: 1.5P constant power charging to 3.65V, standing for 20 minutes, 1.5P constant power discharge to 2.0V, standing for 5 minutes. Obtain the voltage-time curve of the standing stage after each cycle (round) of charging, differentiate the curve, and obtain the dV / dt-t voltage differential curve (first relationship curve) as shown Figure 6 As shown, 1cls, 50cls, and 100cls represent the 1st week, the 50th week, and the 100th week respectively, and the corresponding curves are the first relationship curves of the 1st week, the 50th week, and the 100th week respectively. Figure 6 The corresponding vertical lines are used to mark the locations of the characteristic values of the first relationship curve. It should be noted that: Figure 6 Only three first relationship curves are selected as examples, and actually more first relationship curves tested for a certain number of weeks can be included. Assuming that the characteristic value of the first relationship curve after every 5 cycles is plotted against the number of cycles, a characteristic value variation curve (second relationship curve) is obtained as shown in FIG. Figure 7 As shown. Figure 7 It can be seen that the characteristic value is relatively stable in the first 75 cycles, and the characteristic value after 75 cycles shows an accelerating upward trend, indicating that a capacity dive is about to occur. Figure 8 Schematic diagram of the relationship between battery capacity retention rate and cycle number according to Example 2 of the present application. Figure 8 It can be seen that the battery capacity drops at around 135 weeks. Therefore, according to the method proposed in this embodiment, a warning of battery capacity drop can be issued 60 weeks in advance.
[0076] The embodiment of the present application calculates the value of the lithium reinsertion amount during the charge-discharge cycle based on the detection of the amount of lithium reinsertion in the graphite negative electrode, and obtains the change of the characteristic value with the number of test rounds. When the characteristic value rises rapidly, it is predicted that the capacity is about to drop. This embodiment is simple and convenient, and can warn of battery diving in advance, which helps to shorten the battery R&D evaluation cycle and improve the safety of the battery during use.
[0077] It should be pointed out that although the various steps in the above embodiments are described in a specific order, those skilled in the art can understand that in order to achieve the effect of the present application, different steps do not have to be executed in such an order. They can be executed simultaneously (in parallel) or in other orders. These adjusted schemes are equivalent to the technical schemes described in this application, and therefore will also fall within the scope of protection of this application.
[0078] Another aspect of the present application also provides a battery capacity drop prediction device, such as Fig. 9 As shown, it includes: a data acquisition module 1, used to obtain the battery at a1, a2...a n The voltage and time data of the static phase after the round test charging is completed, where a n =a1+(n-1)×d, a1, n, d are all positive integers, n=1, 2, 3…, any round of testing includes charging, standing after charging, discharging, and standing after discharging; a first curve fitting module 2 is used to determine a first relationship curve between the voltage change rate and time according to the voltage and time data of each round of testing obtained; a second curve fitting module 3 is used to obtain a characteristic value of each first relationship curve, and generate a second relationship curve according to the obtained multiple characteristic values and the number of test rounds corresponding to the multiple characteristic values; a diving prediction module 4 is used to predict that the battery will have a capacity diving after multiple rounds of charging and discharging starting from the number of test rounds where the slope suddenly changes when a sudden change in the slope of the second relationship curve is detected.
[0079] In a technical solution of the above-mentioned battery capacity dive prediction device, the dive prediction module includes: a prediction unit, which is used to predict that the battery will experience a capacity dive after 50 to 60 rounds of charge and discharge starting from the number of test rounds corresponding to the second characteristic value when the slope of the second characteristic value on the second relationship curve changes from the slope of the first characteristic value on the second relationship curve by more than a preset threshold, wherein the first characteristic value and the second characteristic value are characteristic values corresponding to two adjacent test rounds on the second relationship curve.
[0080] In a technical solution of the above-mentioned battery capacity drop prediction device, any round of testing includes: charging the battery to 100% SOC, leaving it to rest for a first preset time, discharging the battery to 0% SOC, and leaving it to rest for a second preset time.
[0081] In one technical solution of the above-mentioned device for predicting battery capacity drop, in any round of testing, the battery is charged and discharged at a constant current / power at a constant temperature.
[0082] In a technical solution of the above-mentioned battery capacity diving prediction device, d=5, and the data acquisition module includes: a first acquisition unit, used to obtain the voltage and time data of the battery within the first preset time period after the a1, a1+5, a1+10... rounds of test charging are completed, where a1 is a positive integer.
[0083] In a technical solution of the above-mentioned battery capacity diving prediction device, when the number of test rounds is equal to a1, a1+5, a1+10..., the first preset time length is greater than the second preset time length; when the number of test rounds is not equal to a1, a1+5, a1+10..., the first preset time length is equal to the second preset time length.
[0084] In a technical solution of the above-mentioned battery capacity diving prediction device, a1=5, the data acquisition module includes: a second acquisition unit, used to acquire the voltage and time data of the battery within a first preset time length of the 5th, 10th, 15th... rounds of testing at a first sampling interval.
[0085] It should be noted that the above battery capacity drop prediction device is used to execute Figure 1 The battery capacity diving prediction method embodiment shown in the figure has similar technical principles, technical problems solved and technical effects produced. Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working process and related instructions of the battery capacity diving prediction device can refer to the contents described in the embodiment of the battery capacity diving prediction method, which will not be repeated here.
[0086] It is understood by those skilled in the art that all or part of the processes in the method for implementing the above-mentioned embodiment of the present application can also be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may include: any entity or device, medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal and software distribution medium, etc. that can carry the computer program code.
[0087] Another aspect of the present application also provides a computer-readable storage medium.
[0088] In an embodiment of a computer-readable storage medium according to the present application, the computer-readable storage medium may be configured to store a program for executing the battery capacity dive prediction method of the above method embodiment, and the program may be loaded and run by a processor to implement the above battery capacity dive prediction method. For ease of explanation, only the parts related to the embodiment of the present application are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present application. The computer-readable storage medium may be a storage device formed by various electronic devices. Optionally, the computer-readable storage medium in the embodiment of the present application is a non-temporary computer-readable storage medium.
[0089] Another aspect of the present application also provides a smart device.
[0090] In an embodiment of an intelligent device according to the present application, the intelligent device may include at least one processor; and a memory connected to the at least one processor in communication; wherein a computer program is stored in the memory, and when the computer program is executed by the at least one processor, the method described in any of the above embodiments is implemented. The intelligent device described in the present application may include a driving device, a smart car, a robot, and the like. Fig.10 , Fig.10 FIG. 4 exemplarily shows that the memory 11 and the processor 12 are communicatively connected via a bus.
[0091] In some embodiments of the present application, the smart device may further include at least one sensor, and the sensor is used to sense information. The sensor is communicatively connected to any type of processor mentioned in the present application. Optionally, the smart device described in the present application may be, but is not limited to, a mobile phone, a tablet computer, a desktop, a laptop, a handheld computer, a notebook computer, a vehicle-mounted device, etc., and the embodiments of the present application are not limited to this.
[0092] So far, the technical solution of the present application has been described in conjunction with an embodiment shown in the accompanying drawings, but it is easy for those skilled in the art to understand that the protection scope of the present application is obviously not limited to these specific embodiments. Without departing from the principles of the present application, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present application.
Claims
1. A method for predicting battery capacity drop, characterized in that: include: Get the battery in a1, a2…a n The voltage and time data of the static phase after the round test charging is completed, where a n =a1+(n-1)×d, a1, n, d are all positive integers, n=1,2,3…, any round of testing includes charging, resting after charging, discharging, and resting after discharging; Determine a first relationship curve between the voltage change rate and time corresponding to each round of test according to the acquired voltage and time data; Acquire a characteristic value of each of the first relationship curves, and generate a second relationship curve according to the acquired multiple characteristic values and the number of test rounds corresponding to the multiple characteristic values; When a sudden change in the slope of the second relationship curve is detected, it is predicted that the battery will experience a capacity drop after multiple rounds of charge and discharge starting from the test round number at which the slope suddenly changes.
2. The method according to claim 1, characterized in that When a sudden change in the slope of the second relationship curve is detected, predicting that the battery will have a capacity drop after multiple rounds of charge and discharge starting from the test round number at which the slope suddenly changes, includes: When the slope of the second characteristic value on the second relationship curve changes more than the slope of the first characteristic value on the second relationship curve than a preset threshold, it is predicted that the capacity of the battery will drop after 50 to 60 rounds of charge and discharge starting from the number of test rounds corresponding to the second characteristic value, wherein the first characteristic value and the second characteristic value are characteristic values corresponding to two adjacent test rounds on the second relationship curve.
3. The method according to claim 1 or 2, characterized in that: Any round of testing includes: The battery is charged to 100% SOC and left to stand for a first preset time, and then the battery is discharged to 0% SOC and left to stand for a second preset time.
4. The method according to claim 1, characterized in that: Any of the test rounds charges and discharges the battery at a constant current / power at a constant temperature.
5. The method according to claim 3, characterized in that: d=5, the battery is obtained in a1, a2...a n The voltage and time data of the static phase after the round test charging is completed, where a n =a1+(n-1)×d, where a1, n, d are all positive integers, n=1, 2, 3…, including: Obtain the voltage and time data of the battery within the first preset time period after the a1, a1+5, a1+10... rounds of test charging are completed, where a1 is a positive integer.
6. The method according to claim 5, characterized in that When the number of test rounds is equal to a1, a1+5, a1+10, ..., the first preset duration is greater than the second preset duration; When the number of test rounds is not equal to a1, a1+5, a1+10, ..., the first preset time length is equal to the second preset time length.
7. The method according to claim 5, characterized in that a1=5, the battery is obtained at a1, a2...a n The voltage and time data of the static phase after the round test charging is completed, including: The voltage and time data of the battery within the first preset time period of the 5th, 10th, 15th ... rounds of testing are obtained at a first sampling interval.
8. A battery capacity drop prediction device, characterized in that: include: The data acquisition module is used to obtain the battery data in a1, a2…a n The voltage and time data of the static phase after the round test charging is completed, where a n =a1+(n-1)×d, a1, n, d are all positive integers, n=1,2,3…, any round of testing includes charging, resting after charging, discharging, and resting after discharging; A first curve fitting module, used to determine a first relationship curve between voltage change rate and time according to the voltage and time data acquired in each round of testing; A second curve fitting module, used for obtaining a characteristic value of each of the first relationship curves, and generating a second relationship curve according to the obtained multiple characteristic values and the number of test rounds corresponding to the multiple characteristic values; A capacity drop prediction module is used to predict that the battery will experience a capacity drop after multiple rounds of charge and discharge starting from the test round number at which the slope suddenly changes when a sudden change in the slope of the second relationship curve is detected.
9. A smart device, characterized in that: include: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores a computer program, and when the computer program is executed by the at least one processor, the method according to any one of claims 1 to 7 is implemented.
10. A computer-readable storage medium storing a plurality of program codes, characterized in that: The program code is suitable for being loaded and executed by a processor to execute the method according to any one of claims 1 to 7.