CN115144775A - State estimation method and battery pack - Google Patents

Abstract

The present invention provides a state estimation method and a battery pack. The method includes: obtaining an aging coefficient data, a preset discharge data and a preset loss capacity variation data. A measured discharge data of a cell to be tested is recorded. According to the preset discharge data and the measured discharge data, a discharge voltage difference data is calculated and obtained. According to the discharge voltage difference data, a specific interval Sa is determined, and a statistical value ΔV _2,Stats of the voltage difference in the specific area Sa is obtained. According to the aging coefficient data and the statistical value of the voltage difference ΔV _2,Stats , an estimated dissipation capacity ΔQ _D of the cell to be tested is calculated. And, it is determined whether the cell to be tested is abnormal or not according to the preset change data of the dissipation capacity and the estimated dissipation capacity ΔQ _D .

Description

State estimation method and battery pack

technical field

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 technique

In recent years, lithium-ion batteries have been widely used in many mobile devices, such as mobile phones, tablets, laptops and so on. Due to the increasing awareness of environmental protection, many countries have set a period for prohibiting the sale of gasoline-powered locomotives, which will lead to the popularity of electric locomotives in the future. In order to ensure a certain range of electric vehicles, batteries need to have high-capacity storage requirements. Therefore, the number of batteries required will continue to increase, resulting in batteries playing a key role in the device.

During the use of the battery, due to improper operation and environmental conditions, as well as the problems of its own production, the battery may have abnormal conditions, such as the battery may have accelerated aging, internal structure changes, shedding, metal deposition problems, When these situations occur, the minor situation is the loss of voltage and capacity, and the most serious situation is that it will trigger the safety problem of the battery. Therefore, how to detect the abnormality of the battery in real time is a very important issue.

Chinese Patent Application Publication No. CN108459272A discloses a state estimation device for a battery pack, which can estimate the state of a battery pack having a plurality of storage elements. The state estimating device first obtains a low-change region where the change in OCV of the storage element with respect to the remaining capacity is small and a high-change region where the change in OCV with respect to the remaining capacity is larger than the low-change region, and then based on the The position of the high variation region relative to the actual capacity variation to estimate the state of the battery pack.

Another existing method is to use detection of the rate of change of the battery internal resistance with time to determine, however, this method requires additional operation methods to calculate the internal resistance. For example, the load needs to be added in a short time to use the voltage difference and current to calculate. Furthermore, this method can only calculate the internal resistance of the battery at one time point. If you want to calculate the internal resistance of the battery at another time point, you need to perform another operation, because to calculate the rate of change of the resistance with time, two The resistance value measured at the time point, and the interval between the two measurements should not be too large, the differential change rate can be calculated by the differential approach. Therefore, for practical battery applications, this method is used to measure Whether the battery is detached will not only interrupt the user's original operating conditions, but also require a very short measurement interval.

SUMMARY OF THE INVENTION

An object of an embodiment of the present invention is to provide a state estimation method capable of judging whether a cell to be tested is abnormal. According to another embodiment of the present invention, the purpose is to provide a state estimation method, which can detect the abnormality of the battery during the use of the battery. Another embodiment of the present invention aims to provide a battery pack capable of implementing the aforementioned state estimation method.

According to an 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, and the preset data includes an aging coefficient data, a preset discharge data, and a preset dissipation capacity change data. A measured discharge data of a cell to be tested is recorded. According to the preset discharge data and the measured discharge data, a discharge voltage difference data is calculated and obtained. According to the discharge voltage difference data, a specific interval Sa is determined, and a statistical value ΔV _2,Stats of the voltage difference in the specific area Sa is obtained. According to the aging coefficient data and the statistical value of the voltage difference ΔV _2,Stats , an estimated dissipation capacity ΔQ _D of the cell to be tested is calculated. And, it is determined whether the cell to be tested is abnormal or not according to the preset change data of the dissipation capacity and the estimated dissipation capacity ΔQ _D .

In one embodiment, the preset discharge data is a discharge curve V ₁ , and the discharge curve V ₁ is a relationship curve between voltage and capacitance. The measured discharge data of the cell to be tested is a discharge curve V ₄ , and the discharge curve V ₄ is a relationship curve between voltage and capacitance. The discharge voltage difference data is a discharge voltage difference curve V ₁₄ obtained according to the discharge curve V ₁ and the discharge curve V ₃ . Moreover, the statistical value of the voltage difference ΔV _2,Stats is obtained from the voltage difference between the discharge curve V ¹ and the discharge curve V ₄ in the specific interval.

In one embodiment, the step of determining a specific interval Sa according to the discharge voltage difference data includes: using a calculation method to divide the discharge voltage difference curve V ₁₄ into a sloped area and a flat area, and the specific interval Sa is set to this flat area.

In one embodiment, the calculation method includes the following steps. The discharge voltage difference curve V ₁₄ is divided into a plurality of secant lines. The secant lines include a first secant line and a second secant line. The slope of the first secant line is smaller than the first threshold value TH ₁ , indicating the flatness The beginning of the region, and the slope of the second secant line is greater than the second threshold TH ₂ , indicating the end of the flat region.

In one embodiment, the calculation method includes the following steps. The discharge voltage difference curve _V14 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, and the intervals include a first interval and a second interval, the variation of the first interval is the first variation, and the variation of the second interval is the second variation. The first variance is smaller than the first threshold TH ₃ , indicating the beginning of the flat region, and the second variance is greater than the second threshold TH ₄ , indicating the end of the flat region.

In one embodiment, the preset dissipation capacity change data is a preset dissipation capacity curve ΔCQ _P . The preset data also include: an upper limit curve _ΔUQP and a lower limit curve ΔLQP corresponding to the preset dissipation capacity curve _{ΔCQ P .} The step of determining whether the cell to be tested is abnormal according to the preset dissipation capacity change data includes: judging whether the estimated dissipation capacity _ΔQD is located in the upper limit curve _ΔUQP corresponding to the preset dissipation capacity curve _ΔCQP and The lower limit curve is between ΔLQ _P. When the estimated dissipation capacity ΔQ _D is located between the upper limit curve ΔUQ _P and the lower limit curve _{ΔLQP corresponding to the preset dissipation capacity curve ΔCQ P ,} it is determined that the cell to be tested is aging normally. When the estimated dissipation capacity ΔQ _D is not between the upper limit curve _ΔUQP and the lower limit curve _{ΔLQP corresponding to the preset dissipation capacity curve ΔCQ P ,} it is determined that the cell to be tested is abnormal.

In one embodiment, the step of determining whether the battery cell to be tested is abnormal according to the preset change data of stray capacity further includes: obtaining a current number of cycles Ncy, wherein according to the preset change data of stray capacity and the The cycle number Ncy obtains a preset dissipation capacity ΔQ _P ; and compares the estimated dissipation capacity ΔQ _D with the preset dissipation capacity ΔQ _P , and determines whether the estimated dissipation capacity ΔQ _D is located in the curve corresponding to the preset dissipation capacity Between the upper limit curve ΔUQ _P of ΔCQ _P and the lower limit curve ΔLQ _P. In an embodiment, preferably, the upper limit curve ΔUQ _P is the preset dissipation capacity curve ΔCQ _P , plus an upper limit γ ₁ , the lower limit curve _ΔLQP is the preset dissipation capacity curve ΔCQ _P , minus the lower limit value γ ₂ , and the step of comparing the estimated dissipation capacity ΔQ _D with the preset dissipation capacity ΔQ _P includes: judging whether |ΔQ _D -ΔQ _P | exceeds the upper _The limit value γ1 or the lower limit value γ2.

In one embodiment, the aging coefficient data is at least one aging coefficient β, and the estimated dissipation capacity ΔQ _D =β×ΔV _2,Stats . In one embodiment, the at least one aging coefficient β is obtained by using a predetermined dissipation capacity ΔQ ¹ and a first statistical value ΔV _1,Stats , and β=ΔQ ₁ /ΔV _1,Stats , the predetermined Let the dissipation capacity ΔQ ₁ be obtained according to the discharge curve V ₁ and a discharge curve V ₂ , and the first statistical value ΔV _{1, Stats} is a discharge voltage according to the discharge curve V ₁ and the discharge curve V ₂ The specific interval Sa of the difference curve V ₁₂ is obtained.

In one embodiment, the statistical value of the voltage difference ΔV _{2 , Stats} is the average number ΔV 2 of the voltage difference in the specific interval Sa of the discharge curve V ¹ and the discharge voltage difference curve V ₁₄ of the discharge curve V ⁴ _{, avg} , the first statistical value ΔV _1,Stats is the average value V _1,avg of the voltage difference in the specific interval Sa of the discharge voltage difference curve V ₁₂ of the discharge curve V ₁ and the discharge curve V ₂ . In one embodiment, at least one of the upper limit value γ ¹ and the lower limit value γ ² is a fixed value or a variable value obtained by a function.

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

According to an embodiment of the present invention, there is provided a battery pack including a cell and a control device, and the control device is electrically connected to the cell and executes the aforementioned state estimation method.

As described above, according to the state estimation method of an embodiment of the present invention, after the discharge voltage difference curve related to the voltage difference and the capacitance can be measured, a specific interval Sa can be obtained according to the discharge voltage difference curve, and the The specific interval Sa and the pre-established aging coefficient β are used to obtain the loss capacity △Q _D- of the battery to be tested, and finally determine whether the loss capacity △Q _D- is abnormal to detect whether there is any abnormal situation in the battery cell. During the use of the core, an abnormality of the battery core is detected.

Description of drawings

FIG. 1 shows a functional block diagram of a battery pack according to an embodiment of the present invention.

FIG. 2 shows a flow chart of obtaining preset data of a state estimation method according to an embodiment of the present invention.

FIG. 3A shows a plurality of discharge curves with different cycle numbers according to an embodiment of the present invention.

FIG. 3B shows the discharge voltage difference curve of the voltage difference and the capacitance according to an embodiment of the present invention.

FIG. 4A is a graph showing the change in β for different aging conditions according to an embodiment of the present invention.

FIG. 4B is a graph showing the change in β for different aging conditions for another embodiment of the present invention.

Figure 5 shows a plot of cycle number versus dissipation capacity.

FIG. 6 shows a flowchart of a state estimation method according to another embodiment of the present invention.

FIG. 7 shows the discharge voltage difference curves of the voltage difference and the capacitance according to another embodiment of the present invention.

FIG. 8A is a diagram for explaining a calculation method of a specific interval according to an embodiment of the present invention.

FIG. 8B is a diagram for explaining a calculation method of a specific interval according to another embodiment of the present invention.

Symbol Description

300: battery pack;

310: battery device;

311: battery cell;

320: control device;

321: storage unit;

322: detection unit;

323: Processing unit.

Detailed ways

According to an embodiment of the present invention, a state estimation method is provided, which can utilize a specific interval Sa of a curve of voltage difference and capacitance (Capacity), and then utilize the specific interval Sa; and a pre-established aging coefficient β Obtain the dissipation capacity △Q _D- , and finally judge whether the dissipation capacity ΔQ _D- is abnormal to detect whether there is an abnormal situation in the battery cell.

FIG. 1 shows a functional block diagram of a battery pack according to an embodiment of the present invention. The aforementioned state estimation method can be applied to the battery pack 300 . As shown in FIG. 1 , 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 . A storage unit 321 can be, for example, a memory for storing various preset data. For example, it is used to store the pre-acquired preset aging coefficient β, the preset discharge curve V ₁ , and the detection data such as the preset dissipation capacity change. The detection unit 322 is connected to the battery cell 311 of the battery device 310 , and is used to obtain battery information such as 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 batteries 311 are abnormal. In addition, those skilled in the art can determine 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 effects to be achieved in the implementation of the present invention. Structure. Moreover, those skilled in the art can implement the present invention with equal variations based on the foregoing disclosure. Hereinafter, a state estimation method according to an embodiment of the present invention will be described in more detail.

FIG. 2 shows a flow chart of obtaining preset data of a state estimation method according to an embodiment of the present invention. FIG. 3A shows a plurality of discharge curves of different cycle numbers Ncy (Cycle number) according to an embodiment of the present invention. FIG. 3B shows the discharge voltage difference curve of the voltage difference and the capacitance according to an embodiment of the present invention. Before detecting anomalies, information for detecting anomalies needs to be created in advance. The cycle aging experiment of the preset battery cells can be carried out in advance, and the battery discharge curves under different cycle times can be obtained, and the aging coefficient β can be obtained by using the battery discharge curves. The detailed description is as follows. As shown in FIG. 2 to FIG. 3B , the method for obtaining the preset data of the state estimation method according to an embodiment of the present invention includes the following steps.

Step S02: Obtain two discharge curves V ₁ and V ₂ in different aging states. As shown in FIG. 3B , V ₁ is the discharge curve of the preset battery cell, and preferably the preset battery cell is a brand-new battery cell. V ₂ is the discharge curve of the aging cell. In addition, V ₃ is another discharge curve of the aging cell. Since the more times of discharge, the larger the dissipation capacity will be. Therefore, it can be known that the cycle number Ncy (ie the number of discharges) of the discharge curve V ₃ is greater than that of the discharge curve V ₂ for the number of cycles Ncy.

Step S04 : Calculate the dissipation capacity ΔQ ₁ by using the fully discharge capacities FDC1 and FDC2 of the discharge curves V ₁ and V ₂ respectively. In one embodiment, as shown in FIG. 3A , the dissipation capacity ΔQ ₁ can be obtained by deducting the saturated capacitance FDC1 of the preset battery cell from the saturated capacitance FDC2 of the aging battery cell.

Step S06: Subtract these discharge curves to obtain a voltage difference, and obtain a discharge voltage difference curve V ₁₂ of the capacitance corresponding to the voltage difference. In one embodiment, the voltage difference can be obtained by subtracting the voltages of the same discharge capacity Q in the discharge curves V ₁ and V ₂ of FIG. 3A , so as to obtain the discharge voltage difference curve V ₁₂ of the voltage difference and the capacity (as shown in FIG. 3B ) shown).

Step S08: Determine a specific interval Sa, and calculate the first statistical value ΔV _1,Stats of the voltage difference in the specific interval Sa. In one embodiment, the first statistic value ΔV _1,Stats is the average value ΔV _1,avg of the voltage differences in the specific interval Sa. Preferably, as shown in FIG. 3B , the specific interval Sa is a flat area flat in the discharge voltage difference curve V ₁₂ . More specifically, the discharge voltage difference curve V ₁₂ includes at least one inclined 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 inclined region tilt. In particular, although the first statistical value in this embodiment is described by taking the average V _1,avg as an example, the first statistical value may also be the median and mode of the voltage difference in the specific interval Sa or other statistical values that can be used as indicators or characteristics, the present invention is not limited to the average. In one embodiment, a calculation method can be used to divide the discharge voltage difference curve V ₁₂ into at least one inclined region tilt and one flat region flat, and the calculation method can be appropriately designed according to the requirements, for example, a statistical method can be used to find the tilt The distinguishing feature of the region tilt and the flat region flat, and using the distinguishing feature to design the calculation method.

Step S10: Using the dissipation capacity ΔQ ₁ and the average value ΔV _1,avg to calculate the aging coefficient β. In one embodiment, the aging coefficient β of the discharge curve V ₂ can be obtained by dividing the average ΔV _1,avg by ΔQ ₁ . The present invention is not limited to the average number, and the aging coefficient β can also be calculated by using the dissipation capacity ΔQ ₁ and the first statistical value ΔV _1,Stats . In one embodiment, the aging coefficient β of the discharge curve V ₂ can be obtained by dividing the first statistical value ΔV _1,Stats by ΔQ ₁ .

FIG. 4A is a graph showing the change in β for different aging conditions according to an embodiment of the present invention. Generally speaking, the most important mechanism that causes the aging of the battery pack is the growth of the SEI film. When the SEI continues to thicken, the consumption of electrons will increase, resulting in more battery loss capacity, and will cause energy loss when lithium ions are inserted or removed. The more the voltage of the battery pack drops. Since the growth of the SEI film will cause the battery voltage drop and the dissipation capacity at the same time, and the growth relationship is linear, the voltage drop divided by the dissipation capacity can be obtained, that is, ΔV/ΔQ is a constant used as the aging coefficient β. The result of FIG. 4A can be obtained by plotting this constant against the number of cycles Ncy. As shown in FIG. 4A , the aging coefficient β does not change with the number of cycles Ncy, that is, it is constant with respect to the number of cycles Ncy.

FIG. 4B is a graph showing the change in β for different aging conditions for another embodiment of the present invention. In addition, in the case of many battery packs aging, due to changes in environmental conditions, such as extremely high or low temperature, or the battery pack is subjected to extremely high current operation, the battery pack will additionally produce rapid capacity degradation characteristics, and lithium metal growth, these aging mechanisms affect the proportional relationship between voltage drop and dissipated capacity, resulting in changes in the β value. In one embodiment, in order to obtain an aging phenomenon reflecting the aforementioned conditions, the aging coefficient β is changed according to the number of cycles Ncy. As shown in Fig. 4B, in the early stage of aging, the aging mechanism is dominated by the SEI film. Therefore, the ratio can be calculated as β ₁ , and after several cycles are added, the ratio of voltage drop to dissipation capacity will be caused by the influence of other aging mechanisms. Change so that the ratio becomes β ₂ or β ₃ , etc.

As mentioned above, for the case where the aging mechanism is dominated by the SEI film, the β change will be as shown in Figure 4A. At this time, only one β value needs to be established. For the case where the aging mechanism is not simply dominated by SEI, the change of the β value will be shown in Figure 4B. At this time, a table of the change of the β value with the number of cycles Ncy can be established. As described above, depending on the product, the aging coefficient data may be a constant aging coefficient β, or the aging coefficient data may include a plurality of aging coefficients β ₂ or β ₃ and the like. This is a person skilled in the art, and can be determined according to different products.

FIG. 5 shows a preset dissipation capacity curve of the number of cycles and dissipation capacity obtained by experiments; and an upper limit curve and a lower limit curve corresponding to the preset dissipation capacity curve. As shown in FIG. 5 , then, using the saturated capacitance change obtained in the previous aging test, the saturated capacitance of the preset battery cell 311 is deducted from the saturated capacitance of the aging battery cells with different cycle times Ncy, so as to obtain more According to the number of cycles Ncy and its corresponding dissipation capacity, the preset dissipation capacity curve ΔCQ _P is obtained _; and the upper limit curve _ΔUQP and the lower limit curve ΔLQP corresponding to the preset dissipation capacity curve _ΔCQP .

After the above process is completed and the information required for abnormality detection is established, the abnormality detection of the battery cell can be started, which will be described in detail as follows. FIG. 6 shows a flowchart of a state estimation method according to another embodiment of the present invention. As shown in FIG. 6 , a state estimation method according to an embodiment of the present invention includes the following steps.

Step S11 : Acquire a variety of preset data from a storage unit 321 , the preset data includes an aging coefficient data, a preset discharge data, and a preset dissipation capacity change data. The preset discharge data is relational data of voltage and capacitance, and the preset dissipated capacity change data is relational data of cycle number Ncy and dissipated capacity. In one embodiment, the aging coefficient data includes at least one pre-established aging coefficient β, the preset discharge data includes the preset discharge curve V ₁ of the battery cell 311 as shown in FIG. Set the dissipation capacity curve ΔCQ _P . In an embodiment, preferably, the preset dissipation capacity change data further includes a pre-established upper limit curve _ΔUQP and a lower limit curve _ΔLQP corresponding to the preset dissipation capacity curve _ΔCQP .

Step S12 : recording a measured discharge data of a cell to be tested 311 . In one embodiment, the discharge curve V ₄ of the cell to be tested 311 is recorded. The voltage and capacitance are recorded during the discharge process of the cell to be tested 311 , and the discharge curve V ₄ of the cell to be tested 311 is obtained.

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 ₁₄ is calculated according to the discharge curve V ₁ and the discharge curve V ₄ . FIG. 7 shows the discharge voltage difference curves of the voltage difference and the capacitance according to an embodiment of the present invention. In one embodiment, as shown in FIG. 7 , the voltage difference ΔV 2 can be obtained by subtracting the voltages of the same discharge capacity Q in the discharge curves V ₁ and V ₄ , so as to obtain the voltage difference ΔV ₂ according to the discharge capacity Q and the voltage difference ΔV ₂ of the multiple groups , and obtain the discharge voltage difference curve V ₁₄ of the voltage difference and the capacitance.

Step S16: Determine a specific interval Sa according to the discharge voltage difference data, and obtain a voltage difference statistical value ΔV _2,Stats in the specific interval Sa. In one embodiment, a specific interval Sa is determined according to the discharge voltage difference curve V ₁₄ . In one embodiment, the statistical value of voltage difference ΔV _2,Stats can be mean, median, mode, etc. Preferably, the statistical value of voltage difference ΔV _2,Stats is the average. In one embodiment, the voltage difference statistic value ΔV _2,Stats is preferably based on the average voltage difference ΔV _2,avg in the specific interval Sa of the discharge voltage difference curve V ₁₄ .

Step S18: Record the current number of cycles Ncy.

Step S20 : According to the aging coefficient data and the statistical value of the voltage difference ΔV _2,Stats , calculate an estimated dissipation capacity ΔQ _D of the cell to be tested 311 . In one embodiment, the corresponding aging coefficient β is multiplied by the voltage difference statistical value ΔV _2,Stats to obtain the estimated dissipation capacity, that is, the estimated dissipation 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 to obtain the estimated dissipation capacity, that is, the estimated dissipation capacity ΔQ _D =β×ΔV _2,avg .

Step S22: Determine whether the battery cell 311 to be tested is abnormal according to the preset change data of the lost capacity and the estimated lost capacity ΔQ _D . As shown in FIG. 5 , in an embodiment, step S22 is to determine whether the estimated dissipation capacity ΔQ _D is between an upper limit curve ΔUQ _P and a lower limit curve _{ΔLQP corresponding to the preset dissipation capacity curve ΔCQ P ,} to determine whether the cell to be tested 311 is abnormal.

In one embodiment, the step S22 includes the following steps.

Step S42 : According to the preset dissipation capacity change data, and use the cycle number Ncy to obtain a preset dissipation capacity ΔQ _P . In an embodiment, the preset dissipation capacity ΔQ _P corresponding to the current cycle number Ncy is found by using the preset dissipation capacity curve ΔCQP in FIG. 5 and the current cycle number _Ncy .

Step S44 : Compare the estimated dissipation capacity ΔQ _D with the preset dissipation capacity ΔQ _P to determine whether the battery cell 311 to be tested is abnormal, if yes, go to step S26 , if no, go to step S12 .

In an embodiment, as shown in FIG. 5 , the ΔCQ _P line represents the multiple cycle times Ncy established by the preset cell 311 and the relationship between the multiple preset dissipation capacities ΔQ _P , and the upper limit The curve _ΔUQP and the lower limit curve _ΔLQP are obtained according to the preset dissipation capacity curve _ΔCQP . Detect the dissipated capacity ΔQ _D of the current number of cycles Ncy. If the detected result falls within the normal area, as indicated by point P1, it means that the cell to be tested is a normal cell. If the detection result falls outside the normal area, such as point P2, it represents an abnormal cell.

Step S24: issue a warning. If the detection result falls outside the normal area, the battery is regarded as abnormal, and a warning signal needs to be issued at this time. The warning signal can be a sound, an LED light signal or a simple data signal for the processing unit 323 to perform the warning procedure.

In one embodiment, the dissipation capacity curve ΔCQ _P , the upper limit curve ΔUQ _P and the lower limit curve ΔLQ _P conform to the function of γ=a×Ncy ^b , where γ is the dissipation capacity ΔQ, Ncy is the number of cycles, and a and b are constant. After obtaining the values of the constants a and b of the dissipation capacity curve _ΔCQP , the values of the constants a and b of the upper limit curve _ΔUQP and the lower limit curve _ΔLQP can be set respectively according to the product specifications.

In one embodiment, the dissipation capacity curve ΔCQ _P conforms to the function of γ=a×Ncy ^b , and the upper limit curve ΔCQ _P can be obtained by adding the upper limit value γ ₁ or subtracting the lower limit value γ ₂ to this dissipation capacity curve ΔCQ P _UQP (for example, a×Ncy ^b +γ ₁ ) and the lower limit curve _ΔLQP (for example, a×Ncy ^b −γ ₂ ). In one embodiment, the upper limit value γ ₁ or the lower limit value γ ₂ may be a fixed value, and the upper limit value γ ₁ may be the same as or different from the lower limit value γ ₂ . In one embodiment, the upper limit value γ ₁ or the lower limit value γ ₂ may be a fixed value, and a×Ncy ^b +γ ₁ ≧0 or a×Ncy ^b −γ ₂ ≧0. γ ₁ or γ ₂ may be both a fixed value and a variable value, or one may be a fixed value and the other may be a variable value. Preferably, the variable value can be obtained by a function.

In one embodiment, γ ₁ can be a function of γ ₁ =a ₁ ×Ncy ^b1 , or γ ₂ can be a function of γ ₂ =a ₂ ×Ncy ^b2 , and when the constant a in these functions is equal to 0, then Compare whether ΔQ _D at the same cycle number Ncy falls on the point of ΔQ _P. In one embodiment, it is determined whether |ΔQ _D -ΔQ _P | exceeds the upper limit value γ ₁ or the lower limit value γ ₂ , to determine whether the battery cell 311 to be tested is abnormal. It should be noted that the constants a and b of the aforementioned curves may vary according to the characteristics of the respective curves. 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 γ ₁ or the lower limit value γ ₂ according to the foregoing embodiments.

The specific interval Sa can be obtained by various calculation methods. Preferably, the discharge voltage difference curve V ₁₄ includes an inclined region tilt and a flat region flat, wherein the overall slope of the flat region flat is smaller than the overall slope of the inclined region tilt , and the specific interval Sa is set as a flat area flat. In one embodiment, the overall slope is determined or formed by dividing the discharge voltage difference curve V ₁₄ into a plurality of secant lines. FIG. 8A is a diagram for explaining a calculation method of a specific interval according to an embodiment of the present invention. FIG. 8A shows the slope method, which uses the slope to obtain the specific interval Sa. In one embodiment, as shown in FIG. 8A , for each interval ΔQ, the discharge voltage difference curve V ₁₄ is divided into a plurality of secant lines, and the secant line slope _si of each secant line is calculated, and the secant line slope s is calculated as the secant line slope s ₁ as an example, s ₁ =(ΔV ₂ -ΔV ₁ )/(Q ₂ -Q ₁ ). When the secant slope s _i is less than (<) the critical value TH ₁ , it indicates the beginning of the flat region, and when the secant slope _si is greater than (>) the critical value TH ₂ , it indicates the end of the flat region. In one embodiment, the overall slope is determined or composed of a plurality of intervals of the discharge voltage difference curve V ₁₄ . FIG. 8B is a diagram for explaining a calculation method of a specific interval according to another embodiment of the present invention. FIG. 8B shows the variance method, which uses the variance to obtain the specific interval Sa. In an embodiment, as shown in FIG. 8B , for each interval ΔQ, the discharge voltage difference curve V ₁₄ is divided into a plurality of intervals, and the variation number σ _i of each interval is calculated, and the calculation method is from the position of 0 Count to the current position. When the variance σ _i is less than (<) the critical value TH ₃ , the flat region begins, and when the variance σ _i is greater than (>) the critical value TH ₄ , the flat region ends. Regarding the calculation of the variance, taking σ ₁ as an example, assuming that there are N voltage differences in the interval, the variance σ ₁ can be obtained by the following formula 1.

Among them, μ can be obtained by the following formula 2.

It should be noted that the data in the above can be multiple sets of discrete values, and the curve can be obtained by performing operations on these discrete values using mathematical or statistical algorithms.

To sum up, 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 ΔQ _D- of the cell to be tested, and finally determine whether the dissipated capacity ΔQ _D- is abnormal to detect whether there is an abnormal situation in the cell, so , the abnormality of the cell can be detected during the use of the cell without the need for additional operations, or without a very short measurement interval.

Claims (14)

1. A method of state estimation, comprising:

obtaining a plurality of preset data from a storage unit, wherein the preset data comprises an aging coefficient data, a preset discharge data and a preset loss capacity change data;

obtaining measured discharge data of a cell to be measured;

calculating to obtain discharge pressure difference data according to the preset discharge data and the measured discharge data;

determining a specific interval Sa according to the discharge voltage difference data,and obtaining a statistical value of voltage difference DeltaV in the specific area Sa _2,Stats ；

According to the aging coefficient data and the voltage difference statistic value delta V _2,Stats Calculating an estimated loss capacity delta Q of the cell to be tested _D (ii) a And

according to the preset scattering capacity change data and the estimated scattering capacity delta Q _D And determining whether the battery cell to be tested is abnormal or not.

2. The state estimation method according to claim 1,

the preset discharge data is a discharge curve V ₁ Said discharge curve V ₁ Is a curve relating voltage to capacitance,

the measured discharge data of the battery cell to be tested is a discharge curve V ₄ Said discharge curve V ₄ Is a curve relating voltage to capacitance,

the discharge voltage difference data is based on the discharge curve V ₁ And said discharge curve V ₄ A discharge voltage difference curve V obtained ₁₄ And is also

Said statistical value of voltage difference Δ V _2,Stats According to the discharge curve V in the specific interval ¹ And the discharge curve V ₄ The voltage difference of the voltage is obtained.

3. The state estimation method according to claim 2,

the step of determining a specific interval Sa according to the discharge voltage difference data includes: using a calculation method to obtain the discharge voltage difference curve V ₁₄ The specific interval Sa is set as the flat area.

4. The state estimation method according to claim 3, wherein the calculation method includes:

the discharge pressure difference curve V ₁₄ Is divided into a plurality of secant linesComprises a first cutting line and a second cutting line,

the slope of the first secant is less than a first critical value TH ₁ Denotes the start of said flat area, and

the slope of the second secant is greater than a second critical value TH ₂ Indicating the end of the flat area.

5. The state estimation method according to claim 3, wherein the calculation method includes:

the discharge voltage difference curve V ₁₄ Is divided into a plurality of sections, and a plurality of variance numbers of the sections are calculated,

the intervals comprise a first interval and a second interval, the variation number of the first interval is a first variation number, and the variation number of the second interval is a second variation number,

the first variance is smaller than a first critical value TH ₃ Denotes the start of said flat area, and

the second variation number is greater than a second critical value TH ₄ Indicating the end of the flat area.

6. The state estimation method according to any one of claims 2 to 5,

the preset dispersion capacity change data is a preset dispersion capacity curve delta CQ _P ，

The preset data further comprises: corresponding to the preset dispersion capacity curve delta CQ _P An upper limit curve of (Δ UQ) _P And a lower limit curve Delta LQ _P And is also

The step of determining whether the electric core to be tested is abnormal or not according to the preset dispersion capacity change data comprises the following steps: judging the estimated loss capacity Delta Q _D Whether the curve is located corresponding to the preset dissipating capacity curve delta CQ _P Said upper limit curve Δ UQ _P And the lower limit curve DeltaLQ _P To (c) to (d); wherein

When said estimated loss capacity is DeltaQ _D Is located corresponding to the preset scattering and scatteringCapacity curve Δ CQ _P Said upper limit curve Δ UQ _P And the lower limit curve Δ LQ _P Determining that the cell to be tested is normally aged,

when said estimated loss capacity is DeltaQ _D Is not located at the curve corresponding to the preset scattering capacity delta CQ _P Said upper limit curve Δ UQ _P And the lower limit curve DeltaLQ _P And determining that the electric core to be tested is abnormal.

7. The state estimation method according to claim 6, wherein the step of determining whether the electrical core to be tested is abnormal according to the preset dissipation capacity change data further includes:

obtaining a current cycle number Ncy;

wherein,

obtaining a preset dissipating capacity delta Q according to the preset dissipating capacity change data and the cycle number Ncy _P (ii) a And

comparing said estimated loss capacities DeltaQ _D And said predetermined dissipation capacity Δ Q _P And determining said estimated capacity to dissipate Δ Q _D Whether the curve is located corresponding to the preset scattering capacity curve delta CQ _P Said upper limit curve Δ UQ _P And the lower limit curve DeltaLQ _P In between.

8. The state estimation method according to claim 7,

the upper limit curve DeltaUQ _P For said predetermined dispersion capacity curve Δ CQ _P Adding an upper limit value gamma ₁ ，

The lower limit curve Δ LQ _P For said predetermined dispersion capacity curve Δ CQ _P Minus a lower limit value gamma ₂ ，

Moreover, said comparison of said estimated loss capacities Δ Q _D And said predetermined dissipation capacity Δ Q _P Comprises the following steps: judgment of |. DELTA.Q _D -△Q _P Whether | exceeds the upper limit value γ ₁ Or the lower limit value γ ₂ 。

9. The state estimation method according to claim 7,

the aging coefficient data is at least one aging coefficient beta, and

said estimated capacity to dissipate Δ Q _D ＝β×△V _2,Stats 。

10. The state estimation method according to claim 9,

the at least one aging coefficient beta is a predetermined dissipation capacity DeltaQ ¹ And a first statistical value DeltaV _1,Stats Obtained, and β =:. DELTA.Q ¹ /△V _1,Stats ，

The preset dissipation capacity DeltaQ ₁ According to the discharge curve V ₁ And a discharge curve V ₂ Is obtained and

the first statistical value Δ V _1,Stats According to the discharge curve V ₁ And the discharge curve V ₂ A discharge voltage difference curve V ₁₂ Is obtained for the specific section Sa.

11. The state estimation method according to claim 10,

the statistical value of the voltage difference DeltaV _2,Stats Is the discharge curve V ¹ And the discharge curve V ⁴ Said discharge voltage difference curve V ₁₄ Is the average number Δ V of voltage differences within said specific interval Sa _2,avg ，

Said first statistical value Δ V _1,Stats Is the discharge curve V ₁ And the discharge curve V ₂ The discharge voltage difference curve V ₁₂ Of the specific interval Sa, an average number V of voltage differences within the specific interval Sa _1,avg 。

12. The state estimation method according to claim 8,

the upper limit value gamma ₁ And the lower limit value gamma ₂ At least one of which is a fixed value or is derived from a functionA variable value.

13. The state estimation method according to claim 1, further comprising: when the battery cell to be tested is determined to be abnormal, a warning signal needs to be sent out.

14. A battery pack, comprising:

an electric core; and

a control device electrically connected to the cell and executing the state estimation method according to any one of claims 1 to 13.

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