Lithium battery pack thermal runaway prediction device and method based on online impedance measurement

Abstract

The invention discloses a lithium battery pack thermal runaway prediction device and method based on online impedance measurement, and relates to the field of lithium battery safety management. The invention aims to solve the problem of thermal runaway false alarm caused by impedance measurement fluctuation when a traditional thermal runaway prediction method based on battery impedance is used for predicting a high-capacity battery cell. The method comprises the following steps: collecting impedance values of each battery cell in a detected lithium battery pack, selecting a minimum value and impedance values of two battery cells closest to the battery cell corresponding to the minimum value, respectively calculating a characteristic value representing a deviation degree and a characteristic value representing an overall change trend, carrying out fuzzy calculation on the two characteristic values, and reasoning according to expert experience to obtain an output value. The larger the output value is, the higher the thermal runaway risk of the tested lithium battery pack is.

Description

Technical Field

[0001] The present invention belongs to the field of lithium battery safety management. Background Art

[0002] As an energy storage carrier, lithium-ion batteries have been widely used in electric vehicles, energy storage power stations and other places due to their high power density, high energy density and strong cycle stability. However, problems such as overcharging, over-discharging, and different heat dissipation environments exist during the use of lithium-ion battery packs, which may lead to thermal runaway safety risks. The initial manifestation of thermal runaway is overheating of the battery cell. During the entire thermal runaway process, the internal resistance of the lithium-ion battery will change. By online detecting the internal resistance, the internal resistance characteristics of early thermal runaway can be identified, and early warning can be made in advance to effectively prevent the occurrence of thermal runaway. Traditional thermal runaway prediction methods based on battery impedance only judge thermal runaway events according to the impedance change of the battery cell itself, such as according to the real-axis impedance value, imaginary-axis impedance value and impedance angle of the battery cell. However, the impedance value of large-capacity battery cells is small and the impedance information of the battery cell changes slightly during the thermal runaway process. It is easy to cause false alarms of thermal runaway due to impedance measurement fluctuations. Summary of the Invention

[0003] The present invention is to solve the problem that traditional thermal runaway prediction methods based on battery impedance will cause false alarms of thermal runaway due to impedance measurement fluctuations when predicting large-capacity battery cells, and now provides a thermal runaway prediction device and method for lithium battery packs based on online impedance measurement.

[0004] A thermal runaway prediction device for lithium battery packs based on online impedance measurement includes: a battery cell impedance measurement device and a controller, and the controller includes an impedance feature extraction unit and a fuzzy controller;

[0005] The battery cell impedance measurement device is used to collect the impedance values of each battery cell in the lithium battery pack to be measured;

[0006] The impedance feature extraction unit is used to select the minimum value from the impedance values of each battery cell in the lithium battery pack to be measured, and the impedance values of the two battery cells closest to the battery cell corresponding to the minimum value, and respectively calculate the characteristic value representing the deviation degree and the characteristic value representing the overall change trend, and input them to the fuzzy controller;

[0007] The fuzzy controller is used to obtain an output value through expert experience reasoning. The larger the output value is, the higher the thermal runaway risk of the lithium battery pack to be measured is.

[0008] Further, the above-mentioned characteristic value K1 representing the deviation degree includes:

[0009] K1 = (Z min2 - Z initial ) 2 +(Zmin -Z initial ) 2 +(Z min1 -Z initial ) 2 ,

[0010] where Z initial is the average value of the impedance of the initial lithium battery pack under test; Z min is the minimum value of the impedance of the battery cells in the lithium battery pack under test, and Z min1 and Z min2 are the impedance values of the two battery cells closest to the battery cell corresponding to Z min .

[0011] Furthermore, the eigenvalue K2 representing the overall change trend in the above calculation includes:

[0012]

[0013] where Z initial is the average value of the impedance of the initial lithium battery pack under test, and Z real is the real-time average value of the overall impedance of the lithium battery pack under test.

[0014] Furthermore, the average value of the impedance Z initial of the initial lithium battery pack under test is the average value of the impedance measured when the lithium battery pack under test leaves the factory or is used for the first time.

[0015] Furthermore, the lithium battery pack thermal runaway prediction device based on online impedance measurement further includes a thermal runaway warning classification unit;

[0016] The thermal runaway warning classification unit divides the output value of the fuzzy controller and determines the warning level according to the divided interval;

[0017] The warning levels include:

[0018] When the range of the output value of the fuzzy controller is 0 to 0.2, it is a no-warning state;

[0019] When the range of the output value of the fuzzy controller is 0.2 to 0.3, it is a first-level warning;

[0020] When the range of the output value of the fuzzy controller is 0.3 to 0.5, it is a second-level warning;

[0021] When the range of the output value of the fuzzy controller is >0.5, it is a third-level warning.

[0022] The lithium battery pack thermal runaway prediction method based on online impedance measurement includes:

[0023] Collect the impedance values of each cell in the lithium battery pack under test, and select the minimum value and the impedance values of the two cells closest to the cell corresponding to the minimum value.

[0024] Calculate the characteristic value representing the degree of deviation and the characteristic value representing the overall change trend respectively.

[0025] Perform fuzzy calculation on the two characteristic values, and obtain the output value according to the expert experience reasoning. The larger the output value, the higher the risk of thermal runaway of the lithium battery pack under test.

[0026] Further, the above-mentioned calculation of the characteristic value K1 representing the degree of deviation includes:

[0027] K1 = (Z min2 - Z initial ) 2 + (Z min - Z initial ) 2 + (Z min1 - Z initial ) 2 ,

[0028] In the formula, Z initial is the average impedance value of the initial lithium battery pack under test; Z min is the minimum value of the impedance of the cells in the lithium battery pack under test, Z min1 and Z min2 are the impedance values of the two cells closest to the cell corresponding to Z min .

[0029] Further, the above-mentioned calculation of the characteristic value K2 representing the overall change trend includes:

[0030]

[0031] In the formula, Z initial is the average impedance value of the initial lithium battery pack under test, and Z real is the real-time average value of the overall impedance of the lithium battery pack under test.

[0032] Further, the above-mentioned average impedance value Z initial of the initial lithium battery pack under test is the average impedance value measured when the lithium battery pack leaves the factory or is used for the first time.

[0033] Further, the above-mentioned method for predicting thermal runaway of a lithium battery pack based on online impedance measurement further includes: dividing the output value of the fuzzy controller, and determining the warning level according to the divided interval;

[0034] The warning levels include:

[0035] When the range of the output value of the fuzzy controller is 0 to 0.2, it is a non-warning state;

[0036] The range of the output value of the fuzzy controller is from 0.2 to 0.3, which is the first-level warning;

[0037] The range of the output value of the fuzzy controller is from 0.3 to 0.5, which is the second-level warning;

[0038] The range of the output value of the fuzzy controller is >0.5, which is the third-level warning.

[0039] The present invention provides a lithium battery pack thermal runaway prediction device and method based on online impedance measurement. By analyzing the impedance distribution law of the battery cells in the battery module, the thermal runaway event is judged, the reliability of thermal runaway prediction is improved, the error rate is reduced, and the fuzzy controller is combined to realize the hierarchical prediction of thermal runaway risk and multi-level warning. It has the characteristics of high prediction reliability and low error rate, and provides a reliable thermal runaway hierarchical prediction method for the safety management of lithium batteries. Compared with the prior art, the beneficial effects of the present invention are:

[0040] 1. By judging the variance of the difference curve and the overall decline rate of the impedance of each battery cell in the battery module, the thermal runaway event of the problematic battery cell is judged, which has the characteristics of high prediction reliability and low error rate.

[0041] 2. Introducing the fuzzy controller into the thermal runaway prediction algorithm can realize the hierarchical prediction of thermal runaway risk, multi-level warning, and high stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic structural diagram of a lithium battery pack thermal runaway prediction device based on online impedance measurement;

[0043] Figure 2 It is a schematic diagram of the measurement principle of the impedance distribution law when the temperature programmable heating aluminum plate simulates the thermal runaway of the battery cell;

[0044] Figure 3 It is a curve graph of the impedance of the battery cell changing with the temperature of the heating aluminum plate;

[0045] Figure 4 It is a curve graph of the impedance distribution law of the battery cell under different temperatures of the heating aluminum plate;

[0046] Figure 5 It is a curve graph of the change law of the eigenvalue K1 with the temperature of the heating plate;

[0047] Figure 6 It is a curve graph of the change law of the eigenvalue K2 with the temperature of the heating plate;

[0048] Figure 7 It is a flowchart of the fuzzy controller;

[0049] Figure 8 It is a flowchart of the lithium battery pack thermal runaway prediction method based on online impedance measurement. DETAILED DESCRIPTION OF THE INVENTION

[0050] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without any creative work belong to the scope of protection of the present invention. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments may be combined with each other.

[0051] The thermal runaway of a lithium battery module is generally caused by a single problematic cell, and then the thermal runaway spreads to the nearest cells, ultimately leading to the thermal runaway of the entire battery module. During the use of the lithium battery module, the heat generation of the problematic cell will be greater than that of the normal cells, resulting in an increase in its own temperature and ultimately leading to thermal runaway. During the process of the problematic cell triggering thermal runaway, its temperature will be higher than that of the surrounding cells. Therefore, the heat will spread to the surrounding cells, forming a temperature distribution, and the temperature value will be higher the closer it is to the problematic cell. The change in the cell temperature will cause a change in impedance. Due to the existence of the temperature distribution, the impedances of the cells in the battery module will form a corresponding impedance distribution. According to the characteristics of this impedance distribution, the thermal runaway situation of the problematic cell can be judged. Thus, the present invention provides the following specific embodiments to solve the problem that when the present invention predicts large-capacity cells, impedance measurement fluctuations will cause false alarms of thermal runaway.

[0052] Specific Embodiment 1: Refer to Figure 1 This specific embodiment will be specifically described. The thermal runaway prediction device for a lithium battery pack based on online impedance measurement described in this embodiment includes: a cell impedance measurement device and a controller, where the thermal runaway prediction is implemented in the controller.

[0053] First, the cell impedance measurement device performs real-time impedance measurement on each cell of the battery module to be measured, and then transmits the cell impedance value to the controller. The controller includes an impedance feature extraction unit, a fuzzy controller, and a thermal runaway warning classification unit. First, the impedance feature extraction unit extracts the feature information of the cell impedance, then inputs the feature information into the fuzzy controller for fuzzy reasoning, and finally the thermal runaway warning classification unit performs hierarchical warning output for thermal runaway according to the output value of the fuzzy controller. The above methods for extracting the cell impedance feature information, the expert library of the fuzzy reasoning process, and the hierarchical warning method are all obtained through experiments.

[0054] As Figure 2As shown, a cell thermal runaway experiment is carried out. In the figure, cell Bn is the simulated thermal runaway cell, and the simulated thermal runaway cell Bn is clamped between the temperature-programmable heating aluminum plates. The temperature of the heating aluminum plates is adjusted, and the impedance of each cell under this temperature condition is recorded after the temperature is stable. Example: An experiment is carried out on 10 25Ah lithium iron phosphate batteries. The 5th cell is clamped between the temperature-programmable heating aluminum plates, and the curve of the cell impedance changing with the temperature of the heating aluminum plate is as Figure 3 shown. It can be seen that the cell impedance shows a downward trend with the increase of the aluminum plate temperature, and the closer to the heating aluminum plate, the more obvious the decrease. When the 5th cell is heated by an external heat source, the heat not only acts on the cell itself, but also diffuses to adjacent cells through the heat conduction effect. This heat transfer process causes the temperature of the battery cells closer to the heat source to be higher, showing an obvious temperature gradient distribution, which is consistent with the actual thermal runaway process. The impedance distribution law of the cells under different heating aluminum plate temperatures is as Figure 4 shown (@ represents the temperature of the heating plate). It can be seen that the closer to the 5th cell, that is, the closer to the heat source, the more obvious the decrease in the battery impedance. In this way, two characteristic quantities can be used to describe the impedance change law of the cells as shown in Figure 3 and Figure 4 shown.

[0055] In this embodiment, two characteristic quantities are designed, namely the characteristic value K1 representing the deviation degree and the characteristic value K2 representing the overall change trend.

[0056] When a local thermal runaway occurs in the battery pack, the impedance of the thermal runaway cell will decrease. Due to the heat transfer effect, the impedance of the cell closest to the thermal runaway cell will also decrease. Therefore, in this embodiment, the square value of the subtraction of the average value of the impedance of the cell with the lowest impedance and the impedance of the two adjacent cells of this cell from the initial impedance of the battery pack is selected as the characteristic value K1, as shown in Equation (1):

[0057] K1=(Z min2 -Z initial ) 2 +(Z min -Z initial ) 2 +(Z min1 -Z initial ) 2 (1),

[0058] In the formula, Z initial is the average value of the initial battery pack impedance, which is the average value of the battery pack impedance measured at the time of factory or the first use; Z min is the minimum value of the cell impedance in the battery pack, and Z min1 , Z min2 are the impedances of the two cells closest to the cell with the minimum impedance.

[0059] A set of characteristic value K1 curves obtained in the experiment is as Figure 5 shown. The larger the K1 value, the higher the risk of thermal runaway.

[0060] When thermal runaway occurs in the battery pack, the overall impedance of the battery pack will decrease with the increase in temperature. The more the overall impedance decreases, it indicates that the temperature of the battery pack rises faster and the trend of thermal runaway is more obvious. And the overall impedance decrease rate is an index to measure the impedance change speed. It reflects the overall change trend of the impedance of the battery pack during thermal runaway. Therefore, in this embodiment, the average value of the overall impedance is subtracted from the average value of the initial impedance of the battery pack, and then divided by the average value of the initial impedance of the battery pack as the characteristic value K2, as shown in Equation (2):

[0061]

[0062] In the formula, Z real is the real-time average value of the overall impedance of the battery pack.

[0063] A set of characteristic value K2 curves obtained in the experiment is as Figure 6 shown. The larger the K2 value, the higher the risk of thermal runaway.

[0064] The battery impedance changes with the battery life. As the battery life decreases, the battery impedance increases. The battery impedance changes with the ambient temperature. The higher the ambient temperature, the lower the average value of the battery pack impedance. It is not possible to predict thermal runaway relying solely on the single change of K1 or K2. It is necessary to combine the two and let them act together. Therefore, as Figure 7 shown, in this embodiment, the characteristic values K1 and K2 are used as the inputs of the fuzzy controller. After the output of the fuzzy controller is graded, the larger the output value, the higher the risk of thermal runaway.

[0065] Specific Embodiment 2: Refer to Figure 8 to specifically describe this embodiment. The method for predicting thermal runaway of a lithium battery pack based on online impedance measurement described in this embodiment includes:

[0066] Online measure the impedance values of the battery cells in the battery pack to be measured, and then calculate the characteristic value K1 representing the deviation degree and the parameter K2 representing the overall change trend respectively,

[0067] K1 = (Z min2 - Z initial ) 2 + (Z min - Z initial ) 2 + (Z min1 - Z initial ) 2 (1),

[0068]

[0069] Where Z initial is the average value of the initial battery pack impedance, which is the average value of the battery pack impedance measured at the time of factory shipment or the first use; Z min is the minimum value of the impedance of the battery cells in the battery pack, Z min1 and Z min2 are the impedances of the two nearest battery cells to the minimum impedance battery cell, Z real is the real-time average value of the overall impedance of the battery pack.

[0070] Input the characteristic parameters K1 and K2 into the fuzzy controller, and calculate the output result according to the expert experience in the fuzzy controller. The output value of the fuzzy controller is classified for early warning, and the greater the output value of the fuzzy controller, the higher the early warning level. The thermal runaway prediction process is carried out in a loop until the termination condition is met and then it ends.

[0071] The termination condition set in this embodiment is: there is no early warning for the battery pack and the battery module is idle for more than 1 hour. If this condition is met, the program ends; otherwise, the program will run in a loop, continuously predicting the thermal runaway state of the battery pack.

[0072] Example:

[0073] According to the flowchart of the lithium battery pack thermal runaway prediction method based on online impedance measurement as shown in Figure 8 , an implementation method is as follows:

[0074] (1) First, measure the impedance values of the battery cells in the battery pack online. In this embodiment, the AC current injection method is used to measure the impedance values of the battery cells at a frequency of 20 Hz.

[0075] (2) Calculate the characteristic values K1 and K2 according to formulas (1) and (2). Among them, Z initial adopts the average value of the battery pack impedance measured at the first use; Z min1 and Z min2 are the impedances of the two nearest battery cells to the minimum impedance battery cell. If the minimum impedance battery cell appears at both ends of the battery pack, the impedances of the two consecutive battery cells close to this battery cell are selected; if the minimum impedance battery cell does not appear at both ends of the battery pack, the impedances of the two battery cells before and after close to this battery cell are selected.

[0076] (3) Send the characteristic values K1 and K2 into the fuzzy controller and perform reasoning and calculation according to expert experience. Among them, the method for obtaining expert experience is based on the impedance distribution law measurement scheme of battery cell thermal runaway as shown in Figure 2 . Start testing from room temperature, measure a set of battery cell parameters every 5 °C until the heated battery cell experiences thermal runaway, and summarize the expert experience based on the test results.

[0077] (4) Perform hierarchical thermal runaway early warning on the output of the fuzzy controller. Three-level early warning can be performed:

[0078] The output range of the fuzzy controller is from 0 to 0.2, which is the no-warning state;

[0079] The output range of the fuzzy controller is from 0.2 to 0.3, which is the first-level warning, indicating that the temperature is overheated and there is a risk of thermal runaway;

[0080] The output range of the fuzzy controller is from 0.3 to 0.5, which is the second-level warning, indicating that the temperature is high and the risk of thermal runaway is relatively high, and measures such as heat dissipation and derating operation need to be taken;

[0081] The output range of the fuzzy controller is >0.5, which is the third-level warning, indicating that early thermal runaway has occurred and the fire protection system needs to be activated to suppress the battery thermal runaway.

[0082] During the use of the lithium battery energy storage system, the thermal runaway prediction program of the lithium battery pack based on online impedance measurement is continuously executed until the stop condition is met, that is, the battery module is idle for more than 1 hour without warning. When the lithium battery is used again, that is, during charging or discharging, the thermal runaway prediction program is restarted.

[0083] Although the present invention has been described herein with reference to specific embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the present invention. Therefore, it should be understood that many modifications can be made to the exemplary embodiments, and other arrangements can be designed, as long as they do not depart from the spirit and scope of the present invention as defined by the appended claims. It should be understood that the different dependent claims and the features described herein can be combined in a manner different from that described in the original claims. It should also be understood that the features described in connection with a single embodiment can be used in other described embodiments.

Claims

1. A lithium battery pack thermal runaway prediction device based on online impedance measurement, characterized in that, Including: A battery cell impedance measurement device and a controller, the controller including an impedance feature extraction unit and a fuzzy controller; The battery cell impedance measurement device is used to collect the impedance values of each battery cell in the measured lithium battery pack; The impedance feature extraction unit is used to select the minimum value among the impedance values of each battery cell in the measured lithium battery pack, and the impedance values of the two battery cells closest to the battery cell corresponding to the minimum value, and respectively calculate the feature value representing the deviation degree and the feature value representing the overall change trend, and input them to the fuzzy controller; The fuzzy controller is used to obtain an output value through expert experience reasoning. The larger the output value, the higher the thermal runaway risk of the measured lithium battery pack.

2. The lithium battery pack thermal runaway prediction device based on online impedance measurement according to claim 1, wherein, Calculating the feature value K1 representing the deviation degree includes: K1 = (Z min2 - Z initial ) 2 + (Z min - Z initial ) 2 + (Z min1 - Z initial ) 2 , Where, Z initial is the average value of the impedance of the initial lithium battery pack under test; Z min is the minimum value of the impedance of the battery cells in the lithium battery pack under test, Z min1 and Z min2 are the impedance values of the two battery cells closest to the battery cell corresponding to Z min respectively.

3. The thermal runaway prediction device for a lithium battery pack based on online impedance measurement according to claim 1, wherein Calculating the feature value K2 representing the overall change trend includes: Where Z initial is the average value of the impedance of the initial lithium battery pack under test, and Z real is the real-time average value of the overall impedance of the lithium battery pack under test.

4. The device for predicting thermal runaway of a lithium battery pack based on on-line impedance measurement according to claim 2 or 3, characterized in that The average impedance Z of the initial lithium battery pack under test initial is the average impedance measured when the lithium battery pack under test leaves the factory or is used for the first time.

5. The device for predicting thermal runaway of a lithium battery pack based on on-line impedance measurement according to claim 1, 2 or 3, characterized in that It further includes a thermal runaway warning classification unit; The thermal runaway warning classification unit divides the output value of the fuzzy controller and determines the warning level according to the divided interval; The warning levels include: When the range of the output value of the fuzzy controller is 0 - 0.2, it is a no-warning state; When the range of the output value of the fuzzy controller is 0.2 - 0.3, it is a first-level warning; When the range of the output value of the fuzzy controller is 0.3 - 0.5, it is a second-level warning; When the range of the output value of the fuzzy controller is >0.5, it is a third-level warning.

6. A method for predicting thermal runaway of a lithium battery pack based on online impedance measurement, characterized in that, Including: Collecting the impedance values of each battery cell in the measured lithium battery pack, and selecting the minimum value and the impedance values of the two battery cells closest to the battery cell corresponding to the minimum value, Respectively calculating the feature value representing the deviation degree and the feature value representing the overall change trend, Performing fuzzy calculation on the two feature values, and obtaining an output value through expert experience reasoning. The larger the output value, the higher the thermal runaway risk of the measured lithium battery pack.

7. The method for predicting thermal runaway of a lithium battery pack based on online impedance measurement according to claim 6, wherein Calculating the feature value K1 representing the deviation degree includes: K1 = (Z min2 - Z initial ) 2 + (Z min - Z initial ) 2 + (Z min1 - Z initial ) 2 , where Z initial is the average value of the impedance of the initial lithium battery pack under test; Z min is the minimum value of the impedance of the battery cells in the lithium battery pack under test, Z min1 and Z min2 are the impedance values of the two battery cells closest to the battery cell corresponding to Z min .

8. The method for predicting thermal runaway of a lithium battery pack based on online impedance measurement according to claim 6, wherein Calculating the feature value K2 representing the overall change trend includes: where Z initial is the average value of the initial impedance of the lithium battery pack under test, and Z real is the real-time average value of the overall impedance of the lithium battery pack under test.

9. The method for predicting thermal runaway of a lithium battery pack based on online impedance measurement according to claim 7 or 8, characterized in that The average impedance Z of the initial lithium battery pack under test initial is the average impedance measured when the lithium battery pack under test leaves the factory or is used for the first time.

10. The method for predicting thermal runaway of a lithium battery pack based on online impedance measurement according to claim 6, 7 or 8, characterized in that, Dividing the output value of the fuzzy controller and determining the warning level according to the divided interval; The warning levels include: When the range of the output value of the fuzzy controller is 0 - 0.2, it is a no-warning state; When the range of the output value of the fuzzy controller is 0.2 - 0.3, it is a first-level warning; When the range of the output value of the fuzzy controller is 0.3 - 0.5, it is a second-level warning; When the range of the output value of the fuzzy controller is >0.5, it is a third-level warning.

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