State estimation method and battery pack

Abstract

The present invention provides a state estimation method and a battery pack, the method comprising: obtaining an aging coefficient data, a preset discharge data and a preset dissipated capacity change data. Recording a measured discharge data of a battery cell to be tested. Calculating a discharge voltage difference data based on the preset discharge data and the measured discharge data. Determining a specific interval Sa based on the discharge voltage difference data, and obtaining a voltage difference statistical value △V within the specific interval Sa. 2，Stats Based on the aging coefficient data and the voltage difference statistical value △V 2，Stats , calculate the estimated dissipated capacity △Q of the battery cell to be tested D And, according to the preset dissipation capacity change data and the estimated dissipation capacity ΔQ D , determine whether the battery cell to be tested is abnormal.

Description

Technical Field

[0001] The present invention relates to a state estimation method and a battery pack, and more particularly to a state estimation method and a battery pack capable of detecting abnormal conditions. Background Art

[0002] In recent years, lithium-ion batteries have been widely used in many mobile devices, such as mobile phones, tablets, laptops, etc. As environmental awareness gradually increases, many countries have set a time limit for banning the sale of fuel-powered motorcycles, which will lead to the popularization of electric motorcycles in the future. In order to ensure that electric motorcycles have a certain endurance, batteries need to have high-capacity storage requirements. Therefore, the number of batteries required will continue to increase, causing batteries to play a very critical role in devices.

[0003] During the use of batteries, improper operation and environmental conditions, as well as problems in the manufacturing process, may cause abnormalities in the batteries. For example, the batteries may experience accelerated aging, changes in internal structure, detachment, and metal deposition. When these situations occur, the milder ones are the loss of voltage and capacity. In the most serious cases, it will trigger battery safety issues. Therefore, how to detect battery abnormalities in real time is a very important issue.

[0004] Chinese patent application publication No. CN108459272A discloses a battery pack state estimation device, which can estimate the state of a battery pack having multiple storage elements. The state estimation device first obtains a low-change region where the change in OCV of the storage element relative to the remaining capacity is small and a high-change region where the change in OCV relative to the remaining capacity is larger than the low-change region, and then estimates the state of the battery pack based on the change position of the high-change region of the storage element relative to the actual capacity.

[0005] Another existing method is to use the rate of change of the battery's internal resistance over time to make a judgment. However, this method requires additional operations to calculate the internal resistance. For example, a load needs to be added in a short period of time to use the voltage difference and current to calculate. Furthermore, this method can only calculate the battery's internal resistance at one point in time. If you want to calculate the internal resistance at another point in time, you need to perform another operation, because to calculate the rate of change of resistance over time, you need to have the resistance values ​​measured at two points in time, and the interval between the two measurements cannot be too large, so that you can approximate the differential rate of change by differential calculation. Therefore, for actual battery applications, using this method to measure whether the battery has fallen off will not only interrupt the user's original operating conditions, but also require an extremely short measurement interval. Summary of the invention

[0006] According to one embodiment of the present invention, it is an object to provide a state estimation method, which can determine whether a battery cell to be tested is abnormal. According to another embodiment of the present invention, it is an object to provide a state estimation method, which can detect the abnormality of the battery cell during the use of the battery cell. According to another embodiment of the present invention, it is an object to provide a battery pack that can execute the above-mentioned state estimation method.

[0007] According to one embodiment of the present invention, a state estimation method is provided, which includes the following steps. A plurality of preset data are obtained from a storage unit, wherein the preset data include an aging coefficient data, a preset discharge data, and a preset dissipated capacity change data. A measured discharge data of a battery cell to be tested is recorded. Based on the preset discharge data and the measured discharge data, a discharge voltage difference data is calculated. Based on the discharge voltage difference data, a specific interval Sa is determined, and a voltage difference statistical value △V within the specific interval Sa is obtained. 2，Stats Based on the aging coefficient data and the voltage difference statistical value △V 2，Stats , calculate the estimated dissipated capacity △Q of the battery cell to be tested D And, according to the preset dissipation capacity change data and the estimated dissipation capacity ΔQ D , determine whether the battery cell to be tested is abnormal.

[0008] In one embodiment, the preset discharge data is a discharge curve V1, which is a relationship curve between voltage and capacity. The measured discharge data of the battery cell to be tested is a discharge curve V, and the discharge curve V4 is a relationship curve between voltage and capacity. The discharge voltage difference data is a discharge voltage difference curve V obtained based on the discharge curve V1 and the discharge curve V3. 14 Moreover, the voltage difference statistical value △V 2，Stats It is obtained by the voltage difference between the discharge curve V1 and the discharge curve V4 in the specific interval.

[0009] In one embodiment, the step of determining a specific interval Sa according to the discharge voltage difference data includes: using a calculation method to calculate the discharge voltage difference curve V 14 The area is divided into an inclined area and a flat area, and the specific interval Sa is set as the flat area.

[0010] In one embodiment, the calculation method comprises the following steps. The discharge voltage difference curve V 14 It is divided into a plurality of secants, which include a first secant and a second secant. The slope of the first secant is less than a first threshold value TH1, indicating the beginning of the flat area, and the slope of the second secant is greater than a second threshold value TH2, indicating the end of the flat area.

[0011] In one embodiment, the calculation method comprises the following steps. The discharge voltage difference curve V14 The data is divided into a plurality of intervals, and a plurality of variances of the intervals are calculated, the variances include a first variance and a second variance, the intervals include a first interval and a second interval, the variance of the first interval is the first variance, and the variance of the second interval is the second variance. The first variance is less than a first threshold value TH3, indicating the beginning of the flat area, and the second variance is greater than a second threshold value TH4, indicating the end of the flat area.

[0012] In one embodiment, the preset dissipated capacity change data is a preset dissipated capacity curve ΔCQ P The preset data also include: corresponding to the preset loss capacity curve ΔCQ P An upper limit curve ΔUQ P And a lower limit curve △LQ P The step of determining whether the battery cell to be tested is abnormal based on the preset dissipated capacity change data includes: determining the estimated dissipated capacity △Q D Is it located in the corresponding preset dissipation capacity curve ΔCQ? P The upper limit curve ΔUQ P And the lower limit curve ΔLQ P When the estimated dissipated capacity △Q D Located at the corresponding preset dissipation capacity curve △CQ p The upper limit curve ΔUQ P And the lower limit curve ΔLQ P It is determined that the cell under test is aging normally. D Not located in the corresponding preset dissipation capacity curve ΔCQ P The upper limit curve ΔUQ P And the lower limit curve ΔLQ P It is determined that the cell under test is abnormal.

[0013] In one embodiment, the step of determining whether the battery cell to be tested is abnormal based on the preset dissipated capacity change data further includes: obtaining a current cycle number Ncy, wherein a preset dissipated capacity ΔQ is obtained based on the preset dissipated capacity change data and the cycle number Ncy. P ; and compare the estimated dissipated capacity △Q D And the preset dissipation capacity ΔQ P , and determine the estimated dissipated capacity ΔQ D Is it located in the corresponding preset dissipation capacity curve ΔCQ? P The upper limit curve ΔUQ P And the lower limit curve ΔLQ P In one embodiment, preferably, the upper limit curve ΔUQ PThe preset dissipation capacity curve ΔCQ P , plus an upper limit value γ1, the lower limit curve ΔLQ P The preset dissipation capacity curve ΔCQ P , minus a limit value γ2, and compare the estimated dissipated capacity △Q D And the preset dissipation capacity ΔQ P The steps include: judging |ΔQ D -ΔQ P |Whether it exceeds the upper limit γ1 or the lower limit γ 2 .

[0014] In one embodiment, the aging coefficient data is at least one aging coefficient β, and the estimated dissipated capacity ΔQ D =β×ΔV 2,Stats In one embodiment, the at least one aging coefficient β is obtained by using a preset dissipation capacity ΔQ1 and a first statistical value ΔV 1,Stats Calculate, and β=△Q1 / ΔV 1,Stats The preset dissipated capacity ΔQ1 is obtained based on the discharge curve V1 and a discharge curve V2, and the first statistical value ΔV 1，Stats A discharge voltage difference curve V is formed based on the discharge curve V1 and the discharge curve V2. 12 It is obtained by the specific interval Sa of .

[0015] In one embodiment, the voltage difference statistical value ΔV 2,stats The discharge voltage difference curve V is the discharge curve V1 and the discharge curve V4. 14 The average voltage difference ΔV in this specific interval Sa 2,avg , the first statistical value △V 1,Stats is the discharge voltage difference curve V between the discharge curve V1 and the discharge curve V2 12 The average voltage difference V in this specific interval Sa 1，avg In one embodiment, at least one of the upper limit value γ1 and the lower limit value γ2 is a fixed value or a variable value obtained by a function.

[0016] In one embodiment, the aforementioned state estimation method further includes: when it is determined that the battery cell to be tested is abnormal, a warning signal needs to be issued.

[0017] According to an embodiment of the present invention, a battery pack is provided, which includes a battery cell and a control device. The control device is electrically connected to the battery cell and executes the aforementioned state estimation method.

[0018] As described above, according to the state estimation method of one embodiment of the present invention, after measuring the discharge voltage difference curve related to the voltage difference and the capacitance, a specific interval Sa is obtained according to the discharge voltage difference curve, and the dissipated capacity ΔQ of the battery cell to be tested is obtained by using the specific interval Sa and the pre-established aging coefficient β. D -, finally determine the dissipated capacity △Q D - Whether it is abnormal, to detect whether there is any abnormal situation in the battery cell. In this way, the abnormality of the battery cell can be detected during the use of the battery cell. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A functional block diagram of a battery pack according to an embodiment of the present invention is shown.

[0020] Figure 2 A flow chart showing the preset data of a state estimation method according to an embodiment of the present invention.

[0021] Figure 3A The figure shows a plurality of discharge curves with different cycle numbers according to an embodiment of the present invention.

[0022] Figure 3B A discharge voltage difference curve showing the voltage difference and capacitance according to an embodiment of the present invention.

[0023] Figure 4A A graph showing the variation of β for different aging conditions according to an embodiment of the present invention.

[0024] Figure 4B A graph showing the variation of β for different aging conditions according to another embodiment of the present invention.

[0025] Figure 5 A curve showing the number of cycles versus lost capacity.

[0026] Figure 6 A flow chart showing a state estimation method according to another embodiment of the present invention.

[0027] Figure 7 A discharge voltage difference curve showing the voltage difference and capacitance of another embodiment of the present invention.

[0028] Fig. 8A The diagram is used to explain a method for calculating a specific interval according to an embodiment of the present invention.

[0029] Figure 8B This is a diagram for explaining a method for calculating a specific interval according to another embodiment of the present invention.

[0030] Explanation of symbols

[0031] 300: battery pack;

[0032] 310: battery device;

[0033] 311: battery cell;

[0034] 320: control device;

[0035] 321: storage unit;

[0036] 322: detection unit;

[0037] 323: Processing unit. DETAILED DESCRIPTION

[0038] According to an embodiment of the present invention, a state estimation method is provided, which can use a specific interval Sa of the voltage difference and capacitance curve, and then use the specific interval Sa; and a pre-established aging coefficient β to calculate the dissipated capacity ΔQ D _, finally determine the dissipated capacity ΔQ D _Whether it is abnormal, to detect whether there is any abnormality in the battery cell.

[0039] Figure 1 The functional block diagram of a battery pack according to an embodiment of the present invention is shown. The aforementioned state estimation method can be applied to the battery pack 300. Figure 1 As shown, the battery pack 300 includes a battery device 310 and a control device 320. The battery device 310 includes at least one battery cell 311. The control device 320 can execute the aforementioned state estimation method, and includes a storage unit 321, a detection unit 322, and a processing unit 323. The storage unit 321 can be, for example, a memory for storing a variety of preset data. For example, it is used to store detection data such as a preset aging coefficient β, a preset discharge curve V, and a preset dissipated capacity change. The detection unit 322 is connected to the battery cell 311 of the battery device 310 to obtain battery information such as the voltage of the battery cell 311. The processing unit 323 obtains the data required by the state estimation method from the storage unit 321 and the detection unit 322 to detect whether the battery cells 311 have abnormal phenomena. In addition, those skilled in the art can determine the structure of the battery device 310 and the control device 320 according to the disclosure of the present invention, the characteristics of the circuit elements used in the implementation of the present invention, and / or the effect to be achieved in the implementation of the present invention. Moreover, those skilled in the art can equally change and implement the present invention according to the above disclosure. In the following, a state estimation method according to an embodiment of the present invention will be described in more detail.

[0040] Figure 2 A flow chart showing the preset data of a state estimation method according to an embodiment of the present invention. Figure 3AA plurality of discharge curves with different cycle numbers Ncy (Cycle number) according to an embodiment of the present invention are shown. Figure 3B The discharge voltage difference curve of the voltage difference and capacitance of an embodiment of the present invention is displayed. Before detecting anomalies, it is necessary to establish information for detecting anomalies in advance. The cycle aging experiment of the preset battery cell can be performed in advance, and the battery discharge curves under different cycle times can be obtained, and then the aging coefficient β can be obtained by using these battery discharge curves. The detailed description is as follows, Figures 2 to 3B As shown, a method for obtaining preset data of a state estimation method according to an embodiment of the present invention includes the following steps.

[0041] Step S02: Obtain two discharge curves V1 and V2 of different aging states. Figure 3B As shown, V1 is the discharge curve of the preset battery cell. Preferably, the preset battery cell is a brand new battery cell. V2 is the discharge curve of the aged battery cell. In addition, V3 is another discharge curve of the aged battery cell. Since the more times of discharge, the greater the loss of capacity, it can be known that the number of cycles Ncy (i.e., the number of discharges) of the discharge curve V3 is greater than the number of cycles Ncy of the discharge curve V2.

[0042] Step S04: Calculate the dissipated capacity ΔQ1 by using the fully discharged capacities FDC1 and FDC2 of the discharge curves V1 and V2, respectively. Figure 3A As shown, the dissipated capacity ΔQ1 can be obtained by deducting the full-charge capacity FDC2 of the aged cell from the full-charge capacity FDC1 of the preset cell.

[0043] Step S06: Subtract the discharge curves to obtain the voltage difference, and obtain the discharge voltage difference curve V corresponding to the capacitance of the voltage difference. 12 In one embodiment, Figure 3A The voltage difference can be obtained by subtracting the voltage of the same discharge capacity Q in the discharge curves V1 and V2, and the voltage difference and the discharge voltage difference curve V of the capacitance can be obtained. 12 (like Figure 3B shown).

[0044] Step S08: Determine a specific interval Sa, and calculate the first statistical value △V of the voltage difference in the specific interval Sa 1,Stats In one embodiment, the first statistical value △V 1，Stats It is the average voltage difference ΔV within a specific interval Sa 1，avg Preferably, if Figure 3B As shown, the specific interval Sa is the discharge voltage difference curve V 12 More specifically, the discharge voltage difference curve V 12The invention comprises at least one tilted region tilt and a flat region flat, wherein the overall slope of the flat region flat is smaller than the overall slope of the at least one tilted region tilt. It is particularly noted that in this embodiment, although the first statistical value is an average value V 1，avg As an example, the first statistical value may also be the median, mode or other statistical value that can be used as an indicator or feature of the voltage difference in the specific interval Sa. The present invention is not limited to the average. In one embodiment, a calculation method can be used to calculate the discharge voltage difference curve V 12 The area is divided into at least one tilted area tilt and one flat area flat. The calculation method can be appropriately designed according to the requirements. For example, a statistical method can be used to find out the distinguishing features of the tilted area tilt and the flat area flat, and the distinguishing features can be used to design the calculation method.

[0045] Step S10: Using the dissipated capacity ΔQ1 and the average ΔV 1，avg , the aging coefficient β is calculated. In one embodiment, the average ΔV 1，avg Dividing ΔQ1 can obtain the aging coefficient β of the discharge curve V2. The present invention is not limited to the average value, and can also use the dissipated capacity ΔQ1 and the first statistical value ΔV 1,Stats , the aging coefficient β is calculated. In one embodiment, the first statistical value ΔV 1，Stats Dividing ΔQ1 can obtain the aging coefficient β of the discharge curve V2.

[0046] Figure 4A A graph showing the change of β under different aging conditions of an embodiment of the present invention. Generally speaking, the main mechanism causing battery pack aging is the growth of the SEI film. When the SEI continues to thicken, the electron consumption will increase, causing the battery to lose more capacity, and it will cause energy loss when lithium ions are inserted or removed, causing the battery pack voltage to drop more. Since the growth of the SEI film will cause the battery voltage to drop and the capacity to be lost at the same time, and the growth relationship is linear, the voltage drop divided by the lost capacity, that is, ΔV / ΔQ, can be obtained as a constant used as the aging coefficient β. If this constant is plotted against the number of cycles Ncy, it can be obtained Figure 4A The result is Figure 4A As shown, the aging coefficient β does not change with the number of cycles Ncy, that is, it is a constant relative to the number of cycles Ncy.

[0047] Figure 4BA graph showing the change of β under different aging conditions of another embodiment of the present invention. In addition, in many battery pack aging conditions, due to changes in environmental conditions, such as extremely high or low temperatures, or the battery pack is subjected to extremely high current operation, the battery pack will additionally produce rapid capacity drop characteristics, as well as the growth of lithium metal. These aging mechanisms will affect the proportional relationship between voltage drop and lost capacity, and will cause changes in the β value. In one embodiment, in order to obtain the aging phenomenon reflecting the aforementioned conditions, the aging coefficient β is changed according to the number of cycles Ncy. Figure 4B It shows that in the early stage of aging, the aging mechanism is dominated by the SEI film, so the ratio can be calculated to be β1. After a number of cycles, the ratio of voltage drop to lost capacity will change due to the influence of other aging mechanisms, making the ratio become β2 or β3, etc.

[0048] As mentioned above, when the aging mechanism is dominated by SEI film, the change of β will be as follows Figure 4A As shown in Figure 2, at this time, only a β value needs to be established. For the case where the aging mechanism is not dominated by SEI alone, the β value changes will be as follows: Figure 4B As shown, at this time, a table showing the change of β value with the number of cycles Ncy can be established. As mentioned above, depending on the product, the aging coefficient data can be an aging coefficient β and a constant, or the aging coefficient data can include multiple aging coefficients β2 or β3, etc. This is determined by relevant technicians in this technical field and can be determined according to different products.

[0049] Figure 5 Displays the preset loss capacity curve of the number of cycles and loss capacity obtained from the experiment; and the upper limit curve and lower limit curve corresponding to the preset loss capacity curve. Figure 5 As shown, then, the full charge capacity change obtained from the previous aging test is used to deduct the full charge capacity of the aged cells with different cycle numbers Ncy from the full charge capacity of the preset cell 311, and multiple sets of cycle numbers Ncy and their corresponding dissipated capacities can be obtained, and the preset dissipated capacity curve ΔCQ can be obtained. P ; and the corresponding preset dissipation capacity curve ΔCQ P The upper limit curve ΔUQ P And the lower limit curve △LQ P .

[0050] After completing the above process and establishing the information required for abnormality detection, the abnormality detection of the battery cell can be started, as described in detail below. Figure 6 A flow chart showing a state estimation method according to another embodiment of the present invention. Figure 6 As shown, a state estimation method according to an embodiment of the present invention includes the following steps.

[0051] Step S11: Obtain multiple preset data from a storage unit 321, the preset data including an aging coefficient data, a preset discharge data, and a preset dissipated capacity change data. The preset discharge data is the relationship data between voltage and capacity, and the preset dissipated capacity change data is the relationship data between the number of cycles Ncy and the dissipated capacity. In one embodiment, the aging coefficient data includes at least one pre-established aging coefficient β, and the preset discharge data includes the preset battery cell 311 such as Figure 3A The discharge curve V1 and the preset dissipated capacity change data include Figure 5 The preset loss capacity curve △CQ P In one embodiment, the preset dissipated capacity change data preferably further includes a pre-established corresponding preset dissipated capacity curve ΔCQ P An upper limit curve ΔUQ P And a lower limit curve ΔLQ P .

[0052] Step S12: Record a measured discharge data of a cell to be tested 311. In one embodiment, record the discharge curve V4 of the cell to be tested 311. During the discharge process of the cell to be tested 311, record the voltage and capacity, and obtain the discharge curve V4 of the cell to be tested 311.

[0053] Step S14: Calculate a discharge voltage difference data according to the preset discharge data and the measured discharge data. In one embodiment, a discharge voltage difference curve V is calculated according to the discharge curve V1 and the discharge curve V4. 14 . Figure 7 The discharge voltage difference curve showing the voltage difference and capacitance of an embodiment of the present invention. Figure 7 As shown, the voltage difference ΔV2 can be obtained by subtracting the voltage of the same discharge capacity Q in the discharge curves V1 and V4. Based on multiple groups of discharge capacities Q and voltage differences ΔV2, the discharge voltage difference curve V of the voltage difference and capacitance can be obtained. 14 .

[0054] Step S16: Determine a specific interval Sa according to the discharge voltage difference data, and obtain a voltage difference statistical value ΔV within the specific interval Sa. 2,stats In one embodiment, according to the discharge voltage difference curve V 14 A specific interval Sa is determined. In one embodiment, the voltage difference statistical value ΔV 2，stats It can be a mean, a median, a mode, etc. Preferably, the voltage difference statistical value ΔV 2，Stats In one embodiment, the voltage difference statistical value △V 2，stats According to the discharge voltage difference curve V 14 The average voltage difference ΔV within a specific interval Sa 2,avg .

[0055] Step S18: Record the current cycle number Ncy.

[0056] Step S20: Based on the aging coefficient data and the voltage difference statistical value ΔV 2,Stats , calculate an estimated dissipated capacity ΔQ of the battery cell 311 to be tested D In one embodiment, the corresponding aging coefficient β is multiplied by the voltage difference statistical value ΔV 2,stats , we can get the estimated dissipated capacity, that is, the estimated dissipated capacity ΔQ D =β×ΔV 2，Stats In one embodiment, the corresponding aging coefficient β is multiplied by the average voltage difference ΔV 2,avg , we can get the estimated dissipated capacity, that is, the estimated dissipated capacity ΔQ D =β×ΔV 2,avg .

[0057] Step S22: Based on the preset dissipation capacity change data and the estimated dissipation capacity ΔQ D , determine whether the battery cell 311 to be tested is abnormal. Figure 5 As shown, in one embodiment, step S22 is to determine the estimated dissipated capacity ΔQ D Is it in accordance with the preset dissipation capacity curve ΔCQ? P An upper limit curve ΔUQ P And a lower limit curve ΔLQ P To determine whether the battery cell 311 to be tested is abnormal.

[0058] In one embodiment, the step S22 includes the following steps.

[0059] Step S42: Obtain a preset dissipation capacity ΔQ based on the preset dissipation capacity change data and the number of cycles Ncy P In one embodiment, using Figure 5 The preset dissipation capacity curve ΔCQ P And the current number of cycles Ncy, find the preset dissipation capacity ΔQ corresponding to the current number of cycles Ncy P .

[0060] Step S44: Compare the estimated dissipated capacity ΔQ D And the preset dissipation capacity ΔQ P , to determine whether the battery cell 311 to be tested is abnormal. When it is determined to be abnormal, the process goes to step S26, and when it is determined to be abnormal, the process goes to step S12.

[0061] In one embodiment, if Figure 5 As shown, ΔCQ PThe lines represent the number of cycles Ncy established by the preset battery cell 311 and the number of preset dissipated capacities ΔQ that it should have. P The upper limit curve ΔUQ P And the lower limit curve △LQ P Based on the preset dissipation capacity curve ΔCQ P The loss capacity of Ncy at the current cycle number is obtained. n If the test result falls within the normal range, as shown by point P1, it means that the cell to be tested is a normal cell. If the test result falls outside the normal range, as shown by point P2, it means that the cell to be tested is an abnormal cell.

[0062] Step S24: Issue an alarm. If the detection result falls outside the normal range, it is considered that the battery is abnormal. At this time, an alarm signal needs to be issued. The alarm signal can be a sound, an LED light or a simple data signal for the processing unit 323 to perform an alarm procedure.

[0063] In one embodiment, the dissipated capacity curve ΔCQ P , upper limit curve ΔUQ P And the lower limit curve ΔLQ P Conforms to Y=axNcy b The function of γ is the dissipation capacity ΔQ, Ncy is the number of cycles, and a and b are constants. P After finding the constants a and b, the upper limit curve ΔUQ can be set according to the product specifications. P And the lower limit curve ΔLQ P The values ​​of constants a and b.

[0064] In one embodiment, the dissipated capacity curve ΔCQ P In line with γ=a×Ncy b function, this dissipated capacity curve ΔCQ P Adding the upper limit value γ1 or subtracting the lower limit value γ2 can obtain the upper limit curve ΔUQ P (For example, a×Ncy b +γ1) and the lower limit curve ΔLQ P (For example, a×Ncy b -γ2). In one embodiment, the upper limit value γ1 or the lower limit value γ2 may be a fixed value, and the upper limit value γ1 may be the same as or different from the lower limit value γ2. In one embodiment, the upper limit value γ1 or the lower limit value γ2 may be a fixed value, and a×Ncy b +γ1≥0 or a×Ncy b-γ2≥0. γ1 or γ2 can be both fixed values, both variable values, or one can be a fixed value and the other a variable value. Preferably, the variable value can be obtained by a function.

[0065] In one embodiment, γ1 may be γ1=a1×Ncy b1 function, or γ2 can be γ2=a2×Ncy b2 When the constant a in these functions is equal to 0, the ΔQ at the same cycle number Ncy is compared. D Whether it falls within ΔQ P In one embodiment, the determination of |△Q D -△Q P Whether the upper limit value γ1 or the lower limit value γ2 is exceeded determines whether the tested battery cell 311 is abnormal. It should be noted that the constants a and b of the aforementioned curves may be different depending on the characteristics of each curve. It should be understood that the above embodiments are only examples, and those skilled in the art can arbitrarily combine the calculation methods of the upper limit value γ1 or the lower limit value γ2 according to the above embodiments.

[0066] The specific interval Sa can be obtained by various calculation methods. Preferably, the discharge voltage difference curve V 14 The specific interval Sa is set as the flat area flat. In one embodiment, the overall slope is determined by the discharge voltage difference curve V 14 Determined or composed of a number of secants. Fig. 8A The diagram is used to explain a method for calculating a specific interval according to an embodiment of the present invention. Fig. 8A is a slope method, which uses the slope to obtain the specific interval Sa. In one embodiment, Fig. 8A As shown, for each interval △Q, the voltage difference curve V 14 Divide into multiple secant lines and calculate the secant slope s of each secant line i , taking the secant slope s1 as an example, s1 = (ΔV2-ΔV1) / (Q2-Q1). i When it is less than (<) the critical value TH1, it indicates the beginning of the flat zone, and when the secant slope s i is greater than (>) the threshold value TH2, indicating that the flat region ends. In one embodiment, the overall slope is determined by the discharge voltage difference curve V 14 is determined or composed of multiple intervals. Figure 8B This is a diagram for explaining a method for calculating a specific interval according to another embodiment of the present invention. Figure 8Bis a variance method, which uses variance to obtain a specific interval Sa. In one embodiment, Figure 8B As shown, for each interval △Q, the voltage difference curve V 14 Divide the data into multiple intervals and calculate the variance σ of each interval i , and the calculation method is from the position of 0 to the current position. When the variance σ i When it is less than (<) the critical value TH3, it indicates the beginning of the flat zone, and when the variance σ i When it is greater than (>) the critical value TH4, it indicates that the flat zone ends. Regarding the calculation of the variance, taking σ1 as an example, assuming that there are N voltage differences in the interval, the variance σ1 can be calculated using the following formula 1.

[0067]

[0068] Here, μ can be obtained using the following formula 2.

[0069]

[0070] It should be noted that the data mentioned above may be multiple sets of discrete values, and the curve may be obtained by calculating these discrete values ​​using mathematical or statistical algorithms.

[0071] In summary, according to an embodiment of the present invention, a state estimation method is provided, which can measure the discharge voltage difference curve related to the voltage difference and the capacitance, and then obtain a specific interval Sa according to the discharge voltage difference curve, and use the specific interval Sa and the pre-established aging coefficient β to obtain the dissipated capacity △Q of the battery cell to be tested. D -, finally determine the dissipated capacity △Q D - Whether it is abnormal, to detect whether there is any abnormal situation in the battery cell. In this way, there is no need to perform other operations or there is no need for extremely short measurement intervals. The abnormality of the battery cell can be detected during the use of the battery cell.

Claims

1. A state estimation method, characterized in that: Include: Acquire a plurality of preset data from a storage unit, wherein the preset data include an aging coefficient data, a preset discharge data and a preset dissipated capacity change data, wherein the preset discharge data is data capable of forming a discharge curve V1; Obtaining measured discharge data of a battery cell to be tested, wherein the measured discharge data is data capable of forming a discharge curve V4; Calculating a discharge voltage difference data according to the preset discharge data and the measured discharge data; According to the discharge voltage difference data, a specific interval Sa is determined, and a voltage difference statistical value ΔV in the specific interval Sa is obtained. 2，Stats , the voltage difference statistical value ΔV 2，Stats Obtained according to the voltage difference between the preset discharge data and the measured discharge data within the specific interval; According to the aging coefficient data and the voltage difference statistical value ΔV 2，Stats , calculate an estimated dissipated capacity ΔQ of the battery cell to be tested D ; as well as According to the preset dissipation capacity change data and the estimated dissipation capacity ΔQ D , determine whether the battery cell to be tested is abnormal.

2. The state estimation method according to claim 1, characterized in that: The discharge curve V1 is a curve showing the relationship between voltage and capacitance. The discharge curve V4 is a curve showing the relationship between voltage and capacitance. The discharge voltage difference data is a discharge voltage difference curve V obtained based on the discharge curve V1 and the discharge curve V4. 14 ,and The voltage difference statistical value ΔV 2，Stats It is obtained according to the voltage difference between the discharge curve V1 and the discharge curve V4 in the specific interval.

3. The state estimation method according to claim 2, characterized in that: The step of determining a specific interval Sa according to the discharge voltage difference data includes: using a calculation method to convert the discharge voltage difference curve V 14 The area is divided into an inclined area and a flat area, and the specific section Sa is set as the flat area, wherein the overall slope of the flat area is smaller than the overall slope of the inclined area.

4. The state estimation method according to claim 3, characterized in that: The calculation method comprises: The discharge voltage difference curve V 14 is divided into a plurality of secants, the secants comprising a first secant and a second secant, The slope of the first secant line is less than the first critical value TH1, indicating the beginning of the flat area, and The slope of the second secant line is greater than a second threshold value TH2, indicating the end of the flat area.

5. The state estimation method according to claim 3, characterized in that: The calculation method comprises: The discharge voltage difference curve V 14 is divided into multiple intervals, and multiple variances of these intervals are calculated. The intervals include a first interval and a second interval, the variance of the first interval is a first variance, and the variance of the second interval is a second variance, The first variance is less than the first critical value TH3, indicating the beginning of the flat area, and The second variance is greater than a second threshold value TH4, indicating the end of the flat area.

6. The state estimation method according to any one of claims 2 to 5, characterized in that: The preset dissipated capacity change data is a preset dissipated capacity curve ΔCQ P , The preset data also include: corresponding to the preset dissipation capacity curve ΔCQ P An upper limit curve ΔUQ P And a lower limit curve ΔLQ P ,and The step of determining whether the battery cell to be tested is abnormal based on the preset dissipated capacity change data comprises: determining the estimated dissipated capacity ΔQ D Is it located at the corresponding preset dissipation capacity curve ΔCQ P The upper limit curve ΔUQ P and the lower limit curve ΔLQ P between; in When the estimated dissipated capacity ΔQ D Located corresponding to the preset dissipation capacity curve ΔCQ P The upper limit curve ΔUQ P and the lower limit curve ΔLQ P It is determined that the battery cell under test is normally aged. When the estimated dissipated capacity ΔQ D Not located at the corresponding preset dissipation capacity curve ΔCQ P The upper limit curve ΔUQ P and the lower limit curve ΔLQ P It is determined that the battery cell to be tested is abnormal.

7. The state estimation method according to claim 6, characterized in that: The step of determining whether the battery cell to be tested is abnormal based on the preset dissipated capacity change data further includes: Get the current number of cycles Ncy; in, According to the preset dissipation capacity change data and the number of cycles Ncy, a preset dissipation capacity ΔQ is obtained. P ;as well as Compare the estimated dissipated capacity ΔQ D And the preset dissipation capacity ΔQ P , and determine the estimated dissipated capacity ΔQ D Is it located at the corresponding preset dissipation capacity curve ΔCQ P The upper limit curve ΔUQ P and the lower limit curve ΔLQ P between.

8. The state estimation method according to claim 7, characterized in that: The upper limit curve ΔUQ P The preset dissipation capacity curve ΔCQ P , plus an upper limit value γ1, The lower limit curve ΔLQ P The preset dissipation capacity curve ΔCQ P , minus the limit value γ2, Furthermore, the comparison of the estimated dissipated capacity ΔQ D And the preset dissipation capacity ΔQ P The steps include: judging |ΔQ D -ΔQ P |Whether it exceeds the upper limit value γ1 or the lower limit value γ2.

9. The state estimation method according to claim 7, characterized in that: The aging coefficient data is at least one aging coefficient β, and The estimated dissipated capacity ΔQ D =β×ΔV 2，Stats .

10. The state estimation method according to claim 9, characterized in that: The at least one aging coefficient β is obtained by using a preset dissipation capacity ΔQ1 and a first statistical value ΔV 1，Stats Calculate, and β = ΔQ1 / ΔV 1，Stats , The preset dissipation capacity ΔQ1 is obtained based on the discharge curve V1 and a discharge curve V2, and The first statistical value ΔV 1，Stats is a discharge voltage difference curve V according to the discharge curve V1 and the discharge curve V2 12 It is obtained by the specific interval Sa of .

11. The state estimation method according to claim 10, characterized in that: The voltage difference statistical value ΔV 2，Stats The discharge voltage difference curve V is the discharge curve V1 and the discharge curve V4. 14 The average voltage difference ΔV in the specific interval Sa 2，avg , The first statistical value ΔV 1，Stats is the discharge voltage difference curve V between the discharge curve V1 and the discharge curve V2 12 The average voltage difference V in the specific interval Sa 1，avg .

12. The state estimation method according to claim 8, characterized in that: At least one of the upper limit value γ1 and the lower limit value γ2 is a fixed value or a variable value obtained by a function.

13. The state estimation method according to claim 1, characterized in that: The method also includes: when it is determined that the battery cell to be tested is abnormal, a warning signal needs to be issued.

14. A battery pack, characterized in that: Include: a battery cell; and A control device is electrically connected to the battery cell and executes the state estimation method according to any one of claims 1-13.

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