A method for preparing battery diaphragm material for electrical energy storage

Abstract

The present application relates to the technical field of preparation of battery diaphragm materials, and specifically to a preparation method of battery diaphragm materials for electrical energy storage, the method comprising: adding LDPE and xylene to a reaction vessel for mixing, heating and adding DCP, adding ethanol and stirring, and after the product is precipitated and treated, obtaining a maleic anhydride grafted polyethylene product, which is completely dissolved in butyl acetate, and sequentially adding dried magnesium hydroxide fine powder, and performing a first-stage and a second-stage grafting reaction to form magnesium hydroxide grafted coated particles; melt blending and extruding a polyolefin resin, magnesium hydroxide grafted coated particles, and a pore-forming agent to obtain a film; stretching the film; adaptively adjusting the temperature of the longitudinal and transverse synchronous stretching to obtain a stretched film and heat-setting to obtain a diaphragm for an electrical energy storage battery. The present application aims to adjust the temperature according to the stretched state of the battery diaphragm to improve the quality of the battery diaphragm.

Description

Technical Field

[0001] The present application relates to the technical field of battery diaphragm material preparation, and in particular to a method for preparing a battery diaphragm material for electrical energy storage. Background Art

[0002] With the development of science and technology, the application of energy storage batteries has become increasingly widespread. Electrical energy storage batteries are mainly composed of positive electrodes, negative electrodes, electrolytes and battery separators. The battery separator is a layer of separator material between the positive and negative electrodes of the battery. It can prevent the positive and negative electrodes of the battery from directly contacting each other and causing a short circuit. It is one of the important battery components and can directly affect the battery's capacity, cycle and safety performance. Therefore, excellent separators play an important role in improving the overall performance of batteries.

[0003] At present, the battery diaphragm material is mainly made by grafting polyethylene maleic anhydride on the surface of magnesium hydroxide, then melt-blending the resin-grafted modified magnesium hydroxide and the matrix resin, and finally by synchronously stretching the diaphragm material in the longitudinal and transverse directions to ensure that the shape and pore size of the micropores on the diaphragm do not deform. Usually in the process of stretching the battery diaphragm material, the temperature of the stretching process is usually a preset parameter. However, in the actual production process, there is a certain deviation in the state of the battery diaphragm material. Too high longitudinal and transverse synchronous stretching temperatures may lead to poor uniformity of the diaphragm, and may cause irreversible damage to the grid structure of the diaphragm under the action of strong tensile stress, ultimately resulting in poor diaphragm performance and affecting product quality. Summary of the invention

[0004] In view of the above, it is necessary to provide a method for preparing battery diaphragm materials for electrical energy storage to achieve adaptive temperature adjustment of the stretched state of the battery diaphragm.

[0005] One embodiment of the present application provides a method for preparing a battery separator material for electrical energy storage, the method comprising:

[0006] Step 1: low-density polyethylene and xylene are added into a reaction vessel, and after they are completely dissolved, the temperature is raised and dicumyl peroxide is added, the temperature is kept to stop the reaction, and then the temperature is lowered to room temperature, ethanol is added and stirred, and the product is precipitated and filtered, washed, and dried to constant weight to obtain maleic anhydride grafted polyethylene;

[0007] Step 2: completely dissolving the maleic anhydride grafted polyethylene in butyl acetate to obtain a maleic anhydride grafted polyethylene solution;

[0008] Step 3: adding the dried magnesium hydroxide fine powder to the maleic anhydride grafted polyethylene solution to carry out a grafting reaction;

[0009] Step 4: adding the dried magnesium hydroxide fine powder again to carry out a two-stage grafting reaction to form magnesium hydroxide grafted coated particles on the surface of the magnesium hydroxide;

[0010] Step 5: melt-blending the polyolefin resin, the magnesium hydroxide grafted coated particles and the pore-forming agent, extruding the mixture, and pulling the mixture into a film to obtain a film;

[0011] Step 6: sequentially stretching the film longitudinally, extracting, and stretching the film longitudinally and transversely simultaneously; adaptively adjusting the temperature of the longitudinal and transversely synchronous stretching to obtain a stretched film;

[0012] The specific process of adaptively adjusting the temperature of the longitudinal and transverse synchronous stretching is as follows:

[0013] The sequence of Raman spectrum data of all wavelengths collected at each moment is used as the Raman spectrum intensity sequence of each membrane; the stretching noise point is confirmed according to the intensity fluctuation characteristics and intensity difference characteristics of the Raman spectrum intensity sequence of each membrane; the stretching noise confirmation point is confirmed according to the intensity distribution trend characteristics and shape characteristics of each stretching noise point; according to the membrane Raman spectrum data after eliminating the stretching noise confirmation point, combined with the spectral angle matching, the membrane stretching temperature at the next moment is adjusted;

[0014] Step 7: heat-set the stretched film to obtain a separator for an electrical energy storage battery.

[0015] In some of the embodiments, the specific operation process of heating and adding dicumyl peroxide in step 1 is to heat to 115-125°C, add dicumyl peroxide at 0.055-0.065% of the mass of low-density polyethylene; the insulation time is 4.5-5.5h; the stirring time after adding ethanol is 35-55min; the number of cleaning times is 2-4 times; and the drying temperature is 65-75°C.

[0016] In some of the embodiments, the dicumyl peroxide is a solution prepared by dissolving dicumyl peroxide in xylene.

[0017] In some of the embodiments, in step 3, the dried magnesium hydroxide fine powder is added to the maleic anhydride grafted polyethylene solution, and the mass ratio of the dried magnesium hydroxide fine powder to the maleic anhydride grafted polyethylene is 1.5~2:1; the specific process of the grafting reaction is to heat the temperature to 55~65℃ at 5℃ / min in a microwave, stir and keep the reaction for 35~45min.

[0018] In some of the embodiments, the mass ratio of maleic anhydride grafted polyethylene to magnesium hydroxide for the two-stage grafting reaction in step 4 is 35-45:1, and the temperature is raised to 125-135°C by microwave heating at a rate of 10°C / min, and the reaction is stirred and kept warm for 25-35 minutes.

[0019] In some of the embodiments, the temperature of the longitudinal stretching in step six is ​​55-75° C., and the ratio is 1.5-3; in the longitudinal and transverse simultaneous stretching, the longitudinal stretching ratio is 1.3-1.5, and the transverse stretching ratio is 1.4-1.9.

[0020] In some of the embodiments, the specific process of confirming the stretching noise point is:

[0021] Fitting the Raman spectrum intensity sequence of each membrane to obtain the Raman spectrum curve of each membrane, and obtaining all the peak points on the Raman spectrum intensity sequence of each membrane; for each membrane Raman spectrum intensity sequence, each peak point is removed in turn, and the remaining data in the membrane Raman spectrum intensity sequence constitutes a removal sequence of each peak point; each peak point and N consecutive data points on its left and right sides constitute a peak change sequence of each peak point;

[0022] The absolute value of the difference between the intensity value of each peak point and the maximum intensity value on the Raman spectrum curve of the membrane is recorded as the peak intensity difference of each peak point; the absolute value of the difference between the intensity mean of the membrane Raman spectrum intensity sequence and the intensity mean of the elimination sequence of each peak point is recorded as the intensity distribution difference; the influence smoothing index of each peak point is in an increasing relationship with the peak intensity difference and in a decreasing relationship with the intensity distribution difference;

[0023] The absolute value of the difference between the intensity value of each peak point and the intensity mean of the corresponding peak change sequence is taken as the prominence coefficient of each peak point, and the peak points are clustered according to the prominence coefficients of all peak points to obtain each clustering cluster; the ratio of the number of all data in the cluster where each peak point is located to the total number of elements in the corresponding membrane Raman spectrum intensity sequence is taken as the occurrence probability; the absolute value of the difference between the element variance value of the peak change sequence of each peak point and the element variance value of the remaining peak change sequences on the membrane Raman spectrum curve is recorded as the change distribution difference, and the neighboring specificity of each peak point is in an increasing relationship with the occurrence probability of each peak point and the cumulative sum of all the change distribution differences;

[0024] The stretching noise confidence of each peak point is in an increasing relationship with the influence smoothing index and the proximity specificity of each peak point; the peak points are clustered according to the stretching noise confidence of all peak points on the Raman spectrum curve of each diaphragm, and the peak point in the cluster with the largest average value of the stretching noise confidence is taken as the stretching noise point.

[0025] In some of the embodiments, the specific process of confirming the stretch noise confidence point is:

[0026] The sequence consisting of the Raman spectrum data of the diaphragm corresponding to all the stretching noise points is used as the noise point judgment sequence, and the noise point judgment curve is obtained by fitting;

[0027] The absolute value of the difference between the intensity value of any stretched noise point and the element mean of the noise point judgment sequence is recorded as the noise point intensity difference; the absolute value of the difference between the peak width values ​​of any stretched noise point and the remaining stretched noise points is recorded as the peak width difference value; the material spectrum significance index of any stretched noise point and the noise intensity difference and the sum of all the peak width difference values ​​of any stretched noise point are in increasing relationship;

[0028] For each stretched noise point, a preset number of data points on the left are taken from the noise point judgment sequence to construct a climbing feature sequence of each stretched noise point, and the absolute value of the difference between the slope value of each data point in the climbing feature sequence and the slope value of the previous data point in the noise point judgment curve is calculated, and recorded as the slope difference value; the trend strength and trend statistics of the climbing feature sequence of each stretched noise point are obtained, and the product of the trend strength and the trend statistics is recorded as the trend fusion term; the peak body sharpness coefficient of each stretched noise point is in an increasing relationship with the slope difference value and the trend fusion term;

[0029] The product of the material spectrum significance index of each stretched noise point and the peak body sharpness coefficient is used as the peak point judgment deviation index of each stretched noise point; threshold segmentation is performed according to the peak point judgment deviation index of all stretched noise points, and the stretched noise points with peak point judgment deviation index less than the division threshold are used as stretched noise confidence points.

[0030] In some of the embodiments, the process of adjusting the stretching temperature of the diaphragm at the next moment includes:

[0031] The sequence of Raman spectrum data corresponding to the confident points after removing the stretching noise is taken as the clean data sequence of Raman spectrum; the standard intensity of the wavelength corresponding to all data points in the clean data sequence of diaphragm Raman spectrum in the Raman spectrum database of standard battery diaphragm is obtained to form a standard diaphragm Raman spectrum data sequence;

[0032] The clean Raman spectrum data sequence and the standard diaphragm Raman spectrum data sequence are used as input, and the spectral angle matching value is obtained by using the matching algorithm; the normalized value of the spectral angle matching value is used as the diaphragm stretching temperature adaptation index; the diaphragm stretching temperature adaptation index at all moments is arranged in ascending time order to obtain the diaphragm stretching temperature adaptation index sequence, and the sequence prediction algorithm is used to obtain the diaphragm stretching temperature adaptation index at the next moment;

[0033] When the diaphragm stretching temperature adaptation index at the next moment is greater than or equal to the preset threshold, the longitudinal and transverse synchronous stretching temperature remains unchanged; otherwise, the difference between the diaphragm stretching temperature adaptation index at the next moment and the preset threshold is calculated and recorded as the adaptation adjustment factor; the sum of the extreme value of the longitudinal and transverse synchronous stretching temperature before the current moment, the adaptation adjustment factor and the stretching temperature of the battery diaphragm equipment at the current moment is taken as the stretching temperature of the battery diaphragm equipment at the next moment.

[0034] In some embodiments, the heat setting temperature is 110-115° C., and the duration is 30-40 minutes.

[0035] This application has at least the following beneficial effects:

[0036] This application mainly analyzes the Raman spectral data of the battery diaphragm to construct the stretching noise confidence degree, characterizes the difference between the noise data points and the normal data points in the diaphragm stretching Raman spectral data and the influence of noise on the Raman spectral data, and then accurately divides the normal points and the stretching noise points; further constructs the peak point judgment deviation index to characterize the special diagnostic difference between the material Raman peak and the noise data in the diaphragm Raman spectral data, and further effectively divides the stretching noise confidence points and the normal points; finally, constructs the diaphragm stretching temperature adaptation index, which is used to adaptively adjust the longitudinal and transverse synchronous stretching temperature of the battery diaphragm stretching equipment, so as to solve the problem that the battery diaphragm material cannot perform adaptive temperature adjustment according to the stretching state of the battery diaphragm during the longitudinal and transverse synchronous stretching process, and improve the quality of the battery diaphragm. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 A flow chart of a method for preparing a battery diaphragm material for electrical energy storage provided in this application;

[0038] Figure 2 Schematic diagram of data collection provided for this application;

[0039] Figure 3 A schematic diagram of Raman spectrum data of the central area of ​​the diaphragm during the temperature adjustment process provided in Example 1 of the present application at a temperature of 105° C. during stretching;

[0040] Figure 4 A schematic diagram of Raman spectrum data of the central area of ​​the diaphragm during the temperature adjustment process provided in Example 1 of the present application at a temperature of 111° C. during the stretching process;

[0041] Figure 5 A schematic diagram of Raman spectrum data of the central area of ​​the diaphragm during the temperature adjustment process provided in Example 2 of the present application at a temperature of 108° C. during the stretching process;

[0042] Figure 6 A schematic diagram of Raman spectrum data of the central area of ​​the diaphragm during the temperature adjustment process provided in Example 2 of the present application at a temperature of 114° C. during the stretching process;

[0043] Figure 7 A schematic diagram of Raman spectrum data of the central area of ​​the diaphragm during the temperature adjustment process provided in Example 3 of the present application at a temperature of 117° C. during the stretching process;

[0044] Figure 8A schematic diagram of Raman spectrum data of the central area of ​​the diaphragm during the temperature adjustment process provided in Example 3 of the present application at a temperature of 123° C. during the stretching process;

[0045] Fig. 9 This is a schematic diagram of the K-means clustering effect of the center area of ​​the diaphragm during the temperature adjustment process provided in Example 1 of the present application, when the temperature is 105°C and the stretching process;

[0046] Fig.10 A schematic diagram of the K-means clustering effect of the center area of ​​the diaphragm during the temperature adjustment process provided in Example 1 of the present application at a temperature of 111°C during the stretching process;

[0047] Fig.11 A schematic diagram of the K-means clustering effect of the center area of ​​the diaphragm during the temperature adjustment process provided in Example 2 of the present application at a temperature of 108° C. during the stretching process;

[0048] Fig.12 A schematic diagram of the K-means clustering effect of the center area of ​​the diaphragm during the temperature adjustment process provided in Example 2 of the present application at a temperature of 114°C during stretching;

[0049] Fig.13 A schematic diagram of the K-means clustering effect of the center area of ​​the diaphragm during the temperature adjustment process provided in Example 3 of the present application at a temperature of 117° C. during the stretching process;

[0050] Fig.14 A schematic diagram of the K-means clustering effect of the center area of ​​the diaphragm during the temperature adjustment process provided in Example 3 of the present application at a temperature of 123°C during the stretching process;

[0051] Fig.15 A flow chart of the steps for adjusting the stretching temperature of the diaphragm provided in this application. DETAILED DESCRIPTION

[0052] In the description of the embodiments of the present application, words such as "exemplary", "or", "for example" and the like are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "or", "for example" and the like is intended to present related concepts in a concrete manner.

[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art in the present application. The terms used in the specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application.

[0054] It should also be noted that the terms "first" and "second" in the specification, claims and drawings of this application are used to distinguish similar objects, rather than to describe a specific order or sequence. The method disclosed in the embodiments of this application or the method shown in the flowchart includes one or more steps for implementing the method. Without departing from the scope of the claims, the execution order of multiple steps can be interchanged with each other, and some steps can also be deleted.

[0055] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0056] The present application embodiment first proposes a method for preparing a battery diaphragm material for electrical energy storage, which is applied to the technical field of battery diaphragm material preparation. Figure 1 , the method comprises the following steps:

[0057] Step 1: Add low-density polyethylene (LDPE) and xylene into a reaction container, heat and stir to completely dissolve LDPE, then heat and add diisopropylbenzene peroxide (DCP), keep warm and then stop the reaction. When it cools to room temperature, add ethanol and stir. After the product is precipitated, filter it, wash it, and dry it to constant weight to obtain maleic anhydride grafted polyethylene.

[0058] In Example 1 of the present application, the temperature was raised to 115° C. after complete dissolution, and DCP was added at 0.065% of the mass of LDPE. DCP was preliminarily dissolved in xylene to prepare a solution. After adding DCP, the temperature was kept for 4.5 hours to stop the reaction. When the temperature dropped to room temperature, ethanol was added and stirred for 35 minutes. After the product was precipitated, it was filtered and washed with acetone 4 times, and dried at 65° C. to constant weight;

[0059] In Example 2 of the present application, after complete dissolution, the temperature was raised to 125°C, and DCP was added at 0.055% of the mass of LDPE. DCP was preliminarily dissolved in xylene to prepare a solution. After adding DCP, the temperature was kept for 5 hours to stop the reaction. When it cooled to room temperature, ethanol was added and stirred for 46 minutes. After the product was precipitated, it was filtered and washed with acetone 3 times, and dried at 75°C to constant weight;

[0060] In Example 3 of the present application, the temperature was raised to 122°C after complete dissolution, and DCP was added at 0.058% of the mass of LDPE. DCP was preliminarily dissolved in xylene to prepare a solution. After adding DCP, the temperature was kept for 5.5 hours to stop the reaction. When it cooled to room temperature, ethanol was added and stirred for 55 minutes. After the product was precipitated, it was filtered and washed twice with acetone, and dried at 65°C to constant weight.

[0061] Step 2: Completely dissolving the maleic anhydride grafted polyethylene in butyl acetate to obtain a maleic anhydride grafted polyethylene solution.

[0062] Step 3: Add the dried magnesium hydroxide fine powder into the maleic anhydride grafted polyethylene solution, heat it with a microwave, stir and keep it warm to react, and perform a grafting reaction.

[0063] In Example 1 of the present application, the mass ratio of maleic anhydride grafted polyethylene to dried magnesium hydroxide fine powder is 1.5:1; the heating rate is 5°C / min, and the temperature is raised to 55°C; the insulation reaction time is 35min;

[0064] In Example 2 of the present application, the mass ratio of maleic anhydride grafted polyethylene to dried magnesium hydroxide fine powder is 2:1; the heating rate is 5°C / min, and the temperature is raised to 60°C; the insulation reaction time is 40min;

[0065] In Example 3 of the present application, the mass ratio of maleic anhydride grafted polyethylene to dried magnesium hydroxide fine powder is 1.5:1; the heating rate is 5°C / min, and the temperature is raised to 65°C; and the insulation reaction time is 45 minutes.

[0066] Step 4: After the first-stage grafting reaction, the maleic anhydride grafted polyethylene solution is added again for the second-stage grafting, and the butyl acetate solvent is evaporated by microwave heating, stirring and keeping warm to form magnesium hydroxide grafted polyethylene particles.

[0067] In Example 1 of the present application, the mass ratio of maleic anhydride grafted polyethylene to magnesium hydroxide is 35:1; microwave heating is specifically carried out by heating the temperature to 130° C. at a rate of 10° C. / min; and the heat preservation reaction time is 30 min;

[0068] In Example 2 of the present application, the mass ratio of maleic anhydride grafted polyethylene to magnesium hydroxide is 45:1; microwave heating is specifically carried out by heating the temperature to 125° C. at a rate of 10° C. / min; and the heat preservation reaction time is 25 min;

[0069] In Example 3 of the present application, the mass ratio of maleic anhydride grafted polyethylene to magnesium hydroxide is 40:1; the microwave heating is specifically to increase the temperature to 135° C. at a rate of 10° C. / min; and the insulation reaction time is 35 min.

[0070] Step 5: Add polyolefin resin, magnesium hydroxide grafted coated particles prepared in step 4, and pore-forming agent into a twin-screw extruder for melt blending and extrusion, and draw to form a film to obtain a film in which magnesium hydroxide grafted coated particles are uniformly distributed and tightly combined in the polyolefin resin, and air-cool the film.

[0071] Step 6: sequentially subjecting the film to longitudinal stretching, extraction, and longitudinal and transverse synchronous stretching, and adaptively adjusting the longitudinal and transverse synchronous stretching temperatures to obtain a stretched film.

[0072] In Example 1 of the present application, the film is sequentially stretched longitudinally and extracted at a temperature of 55° C. and a stretching ratio of 1.5; when the longitudinal and transverse stretching is performed simultaneously, the stretching parameters are set as follows: the longitudinal stretching ratio is 1.3 and the transverse stretching ratio is 1.4;

[0073] In Example 2 of the present application, the film is sequentially stretched longitudinally and extracted at a temperature of 60° C. and a stretching ratio of 3.0; when the longitudinal and transverse stretching is performed simultaneously, the stretching parameters are set as follows: the longitudinal stretching ratio is 1.3 and the transverse stretching ratio is 1.9;

[0074] In Example 3 of the present application, the film is sequentially stretched longitudinally and extracted at a temperature of 75°C and a stretching ratio of 3.0; when the longitudinal and transverse stretching is performed simultaneously, the stretching parameters are set as follows: the longitudinal stretching ratio is 1.5, and the transverse stretching ratio is 1.5.

[0075] In some embodiments of the present application, the steps of adaptively adjusting the temperature of the longitudinal and transverse synchronous stretching are:

[0076] The first sub-step: taking the sequence composed of Raman spectrum data of all wavelengths collected at each moment as the Raman spectrum intensity sequence of each diaphragm.

[0077] The present application obtains the stretching condition of the battery diaphragm material in the battery diaphragm stretching equipment, collects data by connecting a Raman spectrometer with a high temperature resistant probe, and pre-processes the data.

[0078] In some embodiments of the present application, a high temperature resistant probe is connected to a Raman spectrometer by using a collection optical fiber, and the high temperature resistant probe is set above the workbench in the battery diaphragm stretching equipment, and the Raman spectrum data of the battery diaphragm material is collected once every time interval T; wherein, the data collection schematic diagram, such as Figure 2 The sequence of Raman spectrum data of all wavelengths collected at each moment is taken as the Raman spectrum intensity sequence of each diaphragm.

[0079] In some embodiments of the present application, in order to avoid data loss during data transmission, a mean filling method is used to fill data; wherein mean filling is a well-known technology, and its process is not described in detail; T is artificially set to 1s;

[0080] It should be noted that, regarding the method of data filling, the implementer can choose a suitable method according to the actual situation, and this application does not impose any restrictions on this.

[0081] The second sub-step: confirm the stretching noise points based on the intensity fluctuation characteristics and intensity difference characteristics of the Raman spectrum intensity sequence of each membrane.

[0082] During the Raman spectrum data collection process of the battery diaphragm material, the biaxial stretching equipment needs to heat the battery diaphragm material, and the internal environment of the equipment changes to a certain extent, which will cause certain stretching noise in the Raman spectrum. These stretching noises exist in various forms, resulting in misjudgment when using Raman spectroscopy to judge the stretching condition of the battery diaphragm material. Therefore, it is necessary to eliminate the stretching noise in the Raman spectrum.

[0083] When there is stretching noise in the Raman spectrum, many small peaks and burrs will be formed in the spectrum of the Raman spectrum, and the peak characteristics in each band are more similar. The Raman spectrum intensity sequence of each membrane is fitted to obtain the Raman spectrum curve of each membrane, and all the peak points on the Raman spectrum intensity sequence of each membrane are obtained. Among them, the temperature in the temperature adjustment process of Example 1 is 105°C and 111°C respectively. The schematic diagram of the Raman spectrum data of the central area of ​​the membrane during the stretching process is as follows, Figure 3 , 4 As shown; Example 2 in the temperature adjustment process, the temperature was 108 ℃, 114 ℃ during the stretching process of the central area of ​​the diaphragm Raman spectrum data schematic diagram, as Figure 5 , 6 As shown; Example 1 in the temperature adjustment process, the temperature was 117 ℃, 123 ℃ during the stretching process of the central area of ​​the diaphragm Raman spectrum data schematic diagram, as Figure 7 , 8 As shown; among them, Figure 3-8 In the equation, the horizontal axis is the band length and the vertical axis is the intensity. Figure 3-8 It can be seen that as the temperature changes, the more burrs there are in the Raman spectrum image, that is, the more obvious the thermal noise interference is.

[0084] In some embodiments of the present application, the least square method is used to fit the Raman spectrum intensity sequence of the diaphragm; it should be noted that the implementer may choose other methods for fitting, and the present application does not impose any limitation on this.

[0085] For each membrane Raman spectrum intensity sequence, each peak point is eliminated in turn, and the remaining data in the membrane Raman spectrum intensity sequence constitutes a elimination sequence of each peak point; each peak point and the continuous N data points on its left and right side constitute a peak change sequence of each peak point.

[0086] In some embodiments of the present application, the elements in the elimination sequence of each peak point are arranged from small to large according to the wavelength; N is artificially set to 5; when sufficient data points cannot be obtained, the Lagrange interpolation method is used to supplement the data, wherein the Lagrange interpolation method is a well-known technology and the present application will not repeat its process; in addition, the implementer may also choose other data supplement methods, and the present application does not limit this.

[0087] The absolute value of the difference between the intensity value of each peak point and the maximum intensity value on the Raman spectrum curve of the diaphragm is recorded as the peak intensity difference of each peak point; the absolute value of the difference between the intensity mean of the diaphragm Raman spectrum intensity sequence and the intensity mean of the elimination sequence of each peak point is recorded as the intensity distribution difference; the influence smoothing index of each peak point is in an increasing relationship with the peak intensity difference and in a decreasing relationship with the intensity distribution difference.

[0088] It should be noted that an increasing relationship is when the value of one variable increases as another variable increases; a decreasing relationship is when the value of one variable decreases as another variable increases. Common increasing (decreasing) relationships include: linear increasing (decreasing) relationship, exponential increasing (decreasing) relationship, logarithmic increasing (decreasing) relationship, power function increasing (decreasing) relationship, periodic increasing (decreasing) relationship, gradual increasing (decreasing) relationship, etc., and this application does not limit this.

[0089] In some embodiments of the present application, when the intensity distribution difference is not equal to 0, the ratio of the peak intensity difference to the intensity distribution difference is used as the influence smoothing index of each peak point; the ratio of the peak intensity difference to the preset adjustment parameter greater than zero is used as the influence smoothing index of each peak point. The adjustment parameter is artificially set to 0.01.

[0090] The influence smoothness index is used to reflect the influence of each peak point on the Raman spectrum data of the membrane. The greater the difference between the intensity of each peak point and the maximum intensity on the curve, and the smaller the difference between the mean intensity when the peak point is not removed and the mean Raman spectrum data of the membrane after removing the peak point, it means that the influence of each peak point on the overall data of the membrane Raman spectrum data is smaller.

[0091] The absolute value of the difference between the intensity value of each peak point and the intensity mean of the corresponding peak change sequence is taken as the prominence coefficient of each peak point, and the peak points are clustered according to the prominence coefficients of all peak points to obtain each cluster cluster; the ratio of the number of all data in the cluster where each peak point is located to the total number of elements in the corresponding membrane Raman spectrum intensity sequence is taken as the occurrence probability; the absolute value of the difference between the element variance value of the peak change sequence of each peak point and the element variance value of the remaining peak change sequences on the membrane Raman spectrum curve is recorded as the change distribution difference, and the neighboring specificity of each peak point is in an increasing relationship with the occurrence probability of each peak point and the cumulative sum of all the change distribution differences.

[0092] In some embodiments of the present application, clustering adopts density peaks clustering (DPC); the DPC clustering algorithm is a well-known technology, and the present application will not describe its process in detail; implementers can select a suitable clustering algorithm on their own, and the present application does not impose any restrictions on this.

[0093] In some embodiments of the present application, the cumulative sum of all the change distribution differences of any peak point is calculated, and the sum of the cumulative sum and the occurrence probability is used as the proximity specificity of each peak point.

[0094] It should be noted that the greater the probability of occurrence of the data in the cluster where each peak point is located, and the greater the absolute value of the difference between the variance of the peak change sequence where each peak point is located and the variance of other peak change sequences, it means that the data adjacent to the peak point and the data in the data cluster where the peak point is located are more specific in the membrane Raman spectrum data.

[0095] The stretch noise confidence of each peak point is in an increasing relationship with the impact smoothing index and the neighboring specificity of each peak point. The peak points are clustered according to the stretch noise confidence of all peak points on the Raman spectrum curve of each membrane, and the peak points in the cluster with the largest average stretch noise confidence are taken as stretch noise points, and the peak points in the remaining clusters are normal points.

[0096] In some embodiments of the present application, the sum of the impact smoothing index of each peak point and the adjacent specificity is used as the stretching noise confidence of each peak point; the K-means clustering algorithm is used, which is a well-known technology and will not be repeated in this application; wherein, in the temperature adjustment process of Example 1, the temperature is 105°C and 111°C respectively, and the schematic diagram of the K-means clustering effect of the central area of ​​the diaphragm during the stretching process is shown as follows Fig. 9 , 10 As shown; Example 2 in the temperature adjustment process, the temperature was 108 ℃, 114 ℃ respectively during the stretching process of the center area of ​​the diaphragm K-means clustering effect schematic diagram, as shown Fig.11 , 12 As shown; Example 3, the temperature during the temperature adjustment process was 117 ° C, 123 ° C respectively. The schematic diagram of the K-means clustering effect of the central area of ​​the diaphragm during the stretching process is as shown Fig.13 , 14 shown. Figure 9-14 In the figure, triangles represent stretching noise points, and circles represent normal points; the horizontal axis of the image is the Raman spectrum intensity, and the vertical axis is the stretching noise confidence.

[0097] It should be noted that, in addition to clustering algorithms, threshold segmentation can also be used to classify data. Implementers can choose other reasonable methods to classify data, and this application does not impose any restrictions on this.

[0098] The third sub-step: confirm the stretch noise confidence points according to the intensity distribution trend characteristics and shape characteristics of each stretch noise point.

[0099] The raw materials for preparing battery separator materials are usually complex mixtures of compounds, which may contain many different substances. Due to the sensitivity of Raman spectroscopy, even substances with small contents may show up in the spectrum. However, if the concentration of certain substances is very low, their characteristic peaks in the spectrum may be very weak or even difficult to distinguish. This may result in some peaks with small intensities being observed in the Raman spectrum. When obtaining the stretching noise points through the second sub-step, the peak points of substances with small contents may also be divided into stretching noise points. Finally, after the stretching noise points are eliminated, the characteristics of such substances are lost, which in turn leads to deviations in the judgment of the battery separator material situation. Therefore, the stretching noise points need to be further divided.

[0100] Specifically, for the peak of the Raman spectrum, the peak of the material is sharper and the overall width of the peak is narrower, while the peak width caused by stretching noise is relatively wide. First, all normal points are removed, and the sequence composed of the diaphragm Raman spectrum data after removing the normal points is used as the noise point judgment sequence, and the noise point judgment curve is obtained by fitting.

[0101] In some embodiments of the present application, a linear regression curve fitting algorithm is used to obtain a noise point judgment curve; the linear regression curve fitting algorithm is a well-known technology, and the present application will not repeat its process; in addition, the implementer may choose other suitable methods for fitting.

[0102] The absolute value of the difference between the intensity value of any stretched noise point and the element mean of the noise point judgment sequence is recorded as the noise point intensity difference; the absolute value of the difference between the peak width values ​​of any stretched noise point and the remaining stretched noise points is recorded as the peak width difference value; the material spectrum significance index of any stretched noise point and the noise intensity difference and the sum of all the peak width difference values ​​of any stretched noise point are in an increasing relationship.

[0103] In some embodiments of the present application, the sum of the peak width difference values ​​between any stretched noise point and all other stretched noise points is calculated and recorded as the total peak width difference; the product of the total peak width difference and the noise point intensity difference is taken as the material spectrum significance index of any stretched noise point.

[0104] It should be noted that, when the difference between the peak intensity value of the stretched noise point and the data mean of the noise point judgment sequence is greater, and the difference between the peak width value of the stretched noise point and the peak width value of the other stretched noise points is greater, it means that the difference between the stretched noise point and the stretched noise points in the other stretched noise point judgment sequence is greater, and the Raman spectrum material characteristics contained in the stretched noise point are more significant. Among them, the peak width value is an existing known technology and will not be repeated in this application.

[0105] For each stretched noise point, a preset number of data points on the left are taken from the noise point judgment sequence to construct a climbing feature sequence for each stretched noise point, and the absolute value of the difference between the slope value of each data point in the climbing feature sequence and the slope value of the previous data point in the noise point judgment curve is calculated, which is recorded as the slope difference value; the trend intensity and trend statistics of the climbing feature sequence of each stretched noise point are obtained, and the product of the trend intensity and the trend statistics is recorded as the trend fusion term; the peak sharpness coefficient of each stretched noise point is in an increasing relationship with the slope difference value and the trend fusion term.

[0106] In some embodiments of the present application, the preset value is 10. If sufficient data points cannot be obtained, the mean filling method is used to fill the data. The preset value and mean filling can be adjusted by the implementer, and the present application does not impose any restrictions on this. The acquisition of trend strength and trend statistics are both well-known technologies and will not be repeated in the present application. The trend statistics adopt the Mann-Kendall trend statistics, and the implementer can select the trend statistic calculation method according to the actual situation, and the present application does not impose any restrictions on this.

[0107] In some embodiments of the present application, the mean of the slope difference values ​​of all data points in the climbing feature sequence is calculated and recorded as the slope difference mean; and the sum of the slope difference mean and the trend fusion term is used as the peak sharpness coefficient of each stretched noise point.

[0108] It should be noted that, when the trend intensity and trend statistics of the climbing feature sequence of the stretched noise point are greater, and the slope change difference of the data points in the climbing feature sequence is greater, it means that the peak at the location of the stretched noise point is more prominent and sharper.

[0109] The product of the material spectrum significance index and the peak body sharpness coefficient of each stretched noise point is used as the peak point judgment deviation index of each stretched noise point; threshold segmentation is used to obtain the division threshold according to the peak point judgment deviation index of all stretched noise points, and the stretched noise points with peak point judgment deviation index greater than or equal to the division threshold are divided into normal points, otherwise, they are stretched noise confirmation points.

[0110] In some embodiments of the present application, the threshold segmentation algorithm adopts the Otsu threshold algorithm; the implementer may also select other classification algorithms to further classify the stretched noise points according to the peak point judgment deviation index of each stretched noise point, and the present application does not impose any restrictions on this.

[0111] It should be noted that, when the material spectrum significance index and peak sharpness coefficient of the stretched noise point are larger, the possibility that the stretched noise point is misjudged is greater.

[0112] The fourth sub-step: According to the Raman spectrum data of the diaphragm after removing the stretching noise confirmation points, combined with the spectral angle matching, the stretching temperature of the diaphragm at the next moment is adjusted.

[0113] The peak points in the Raman spectrum data of the diaphragm are divided into normal points and stretch noise confidence points, and the remaining data points are divided into ordinary points. The stretch noise confidence points are eliminated, and the sequence composed of normal points and ordinary points is used as the Raman spectrum clean data sequence; the standard intensity of the corresponding wavelengths of all data points in the Raman spectrum clean data sequence in the Raman spectrum database of the standard battery diaphragm is obtained to form the standard diaphragm Raman spectrum data sequence. Based on this, the wavelengths corresponding to the data points at the same position in the Raman spectrum clean data sequence and the standard diaphragm Raman spectrum data sequence are equal.

[0114] In some embodiments of the present application, the order of elements of the Raman spectrum clean data sequence and the standard diaphragm Raman spectrum data sequence is arranged from small to large according to the wavelength corresponding to each data point; the selection of the arrangement method is not limited in this application.

[0115] The clean Raman spectrum data sequence of the diaphragm and the standard Raman spectrum data sequence of the diaphragm are used as input, and the spectral angle matching algorithm is used to output the spectral angle matching value; the normalized value of the spectral angle matching value is used as the diaphragm stretching temperature adaptation index. The larger the diaphragm stretching temperature adaptation index is, the better the stretching condition of the current battery diaphragm is.

[0116] It should be noted that the spectral angle matching algorithm is a well-known technology, and this application will not describe its process in detail.

[0117] The diaphragm stretching temperature adaptation indexes at all moments are arranged in ascending order of time to obtain a diaphragm stretching temperature adaptation index sequence, and the diaphragm stretching temperature adaptation index at the next moment is obtained by using the ARIMA prediction algorithm.

[0118] In some embodiments of the present application, the number of autoregressive terms p in the ARIMA prediction algorithm is 2, the number of moving average terms q is 1, and the order d is 1; the ARIMA prediction algorithm is a well-known technology, and the present application will not repeat its process.

[0119] When the membrane stretching temperature adaptation index at the next moment is greater than or equal to the preset threshold, it means that the battery membrane stretching temperature is relatively suitable at this time, and there is no need to adjust the longitudinal and transverse synchronous stretching temperature; when the membrane stretching temperature adaptation index at the next moment is less than the preset threshold, it is judged that the current battery diaphragm material stretching temperature is more likely to be overheated, and the temperature of the battery diaphragm stretching equipment needs to be adjusted within a collection time interval: the difference between the membrane stretching temperature adaptation index at the next moment and the preset threshold is calculated, and recorded as the adaptation adjustment factor; the sum of the extreme value of the longitudinal and transverse synchronous stretching temperature, the adaptation adjustment factor and the stretching temperature of the battery diaphragm equipment at the current moment is taken as the stretching temperature of the battery diaphragm equipment at the next moment.

[0120] It should be noted that the stretching temperature of the battery diaphragm device at the next moment is the synchronous stretching temperature in the longitudinal and transverse directions at the next moment.

[0121] In some embodiments of the present application, the preset threshold is 0.7 and the value range is [0, 1].

[0122] It should be noted that the larger the preset threshold, the stricter the stretching effect requirement in the battery diaphragm stretching device. The implementer can adjust the threshold value according to the actual situation. Among them, the flow chart of the steps for adjusting the diaphragm stretching temperature is as follows: Fig.15 shown.

[0123] Step 7: heat-set the stretched film to obtain a separator for an electrical energy storage battery.

[0124] In Example 1 of the present application, the temperature for heat setting is 110° C. and the time is 40 min;

[0125] In Example 2 of the present application, the temperature for heat setting is 115° C. and the time is 30 min;

[0126] In Example 3 of the present application, the temperature for heat setting is 112° C. and the time is 30 min;

[0127] The embodiment of the present application provides a method for preparing a battery diaphragm material for electrical energy storage, the method comprising: constructing a stretching noise confidence degree by analyzing the Raman spectral data of the battery diaphragm, characterizing the difference between the noise data points and the normal data points in the diaphragm stretching Raman spectral data and the influence of the noise on the Raman spectral data, and then accurately dividing the normal points and the stretching noise points; further constructing a peak point judgment deviation index to characterize the special diagnostic difference between the material Raman peak and the noise data in the diaphragm Raman spectral data, and further effectively dividing the stretching noise confidence points and the normal points; finally constructing a diaphragm stretching temperature adaptation index for adaptively adjusting the longitudinal and transverse synchronous stretching temperature of the battery diaphragm stretching equipment, so as to solve the problem that the battery diaphragm material cannot perform adaptive temperature adjustment according to the stretching state of the battery diaphragm during the longitudinal and transverse synchronous stretching process, and improve the quality of the battery diaphragm.

[0128] The flowchart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to the embodiment of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowchart and the block diagram in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in a different order from the order disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

[0129] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the spirit or basic features of the present application. Therefore, no matter from which point of view, the above embodiments of the present application should be regarded as exemplary and non-restrictive, and the scope of the present application is defined by the attached claims rather than the above description, and therefore it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present application.

Claims

1. A method for preparing a battery diaphragm material for electrical energy storage, characterized in that: The method comprises the following steps: Step 1: low-density polyethylene and xylene are added into a reaction vessel, and after they are completely dissolved, the temperature is raised and dicumyl peroxide is added, the temperature is kept to stop the reaction, and then the temperature is lowered to room temperature, ethanol is added and stirred, and the product is precipitated and filtered, washed, and dried to constant weight to obtain maleic anhydride grafted polyethylene; Step 2: completely dissolving the maleic anhydride grafted polyethylene in butyl acetate to obtain a maleic anhydride grafted polyethylene solution; Step 3: adding the dried magnesium hydroxide fine powder to the maleic anhydride grafted polyethylene solution to carry out a grafting reaction; Step 4: adding the dried magnesium hydroxide fine powder again to carry out a two-stage grafting reaction to form magnesium hydroxide grafted coated particles on the surface of the magnesium hydroxide; Step 5: melt-blending the polyolefin resin, the magnesium hydroxide grafted coated particles and the pore-forming agent, extruding the mixture, and pulling the mixture into a film to obtain a film; Step 6: sequentially stretching the film longitudinally, extracting, and stretching the film longitudinally and transversely simultaneously; adaptively adjusting the temperature of the longitudinal and transversely synchronous stretching to obtain a stretched film; The specific process of adaptively adjusting the temperature of the longitudinal and transverse synchronous stretching is as follows: The first sub-step: taking the sequence of Raman spectrum data of all wavelengths collected at each moment as the Raman spectrum intensity sequence of each diaphragm; The second sub-step: confirm the stretching noise points according to the intensity fluctuation characteristics and intensity difference characteristics of the Raman spectrum intensity sequence of each diaphragm; The third sub-step: confirming the stretch noise confidence points according to the intensity distribution trend characteristics and shape characteristics of each stretch noise point; The fourth sub-step: according to the Raman spectrum data of the diaphragm after removing the stretching noise confidence point, combined with the spectral angle matching, the stretching temperature of the diaphragm at the next moment is adjusted; Step 7: heat-setting the stretched film to obtain a separator for an electrical energy storage battery; The specific process of confirming the stretching noise point is: Fitting the Raman spectrum intensity sequence of each membrane to obtain the Raman spectrum curve of each membrane, and obtaining all the peak points on the Raman spectrum intensity sequence of each membrane; for each membrane Raman spectrum intensity sequence, each peak point is removed in turn, and the remaining data in the membrane Raman spectrum intensity sequence constitutes a removal sequence of each peak point; each peak point and N consecutive data points on its left and right sides constitute a peak change sequence of each peak point; The absolute value of the difference between the intensity value of each peak point and the maximum intensity value on the Raman spectrum curve of the membrane is recorded as the peak intensity difference of each peak point; the absolute value of the difference between the intensity mean of the membrane Raman spectrum intensity sequence and the intensity mean of the elimination sequence of each peak point is recorded as the intensity distribution difference; the influence smoothing index of each peak point is in an increasing relationship with the peak intensity difference and in a decreasing relationship with the intensity distribution difference; The absolute value of the difference between the intensity value of each peak point and the intensity mean of the corresponding peak change sequence is taken as the prominence coefficient of each peak point, and the peak points are clustered according to the prominence coefficients of all peak points to obtain each clustering cluster; the ratio of the number of all data in the cluster where each peak point is located to the total number of elements in the corresponding membrane Raman spectrum intensity sequence is taken as the occurrence probability; the absolute value of the difference between the element variance value of the peak change sequence of each peak point and the element variance value of the remaining peak change sequences on the membrane Raman spectrum curve is recorded as the change distribution difference, and the neighboring specificity of each peak point is in an increasing relationship with the occurrence probability of each peak point and the cumulative sum of all the change distribution differences; The stretch noise confidence of each peak point is in an increasing relationship with the impact smoothing index and the neighboring specificity of each peak point; the peak points are clustered according to the stretch noise confidence of all peak points on the Raman spectrum curve of each membrane, and the peak point in the cluster with the largest average value of the stretch noise confidence is taken as the stretch noise point; The specific process of confirming the stretch noise confidence point is as follows: The sequence consisting of the Raman spectrum data of the diaphragm corresponding to all the stretched noise points is used as the noise point judgment sequence, and the noise point judgment curve is obtained by fitting; The absolute value of the difference between the intensity value of any stretched noise point and the element mean of the noise point judgment sequence is recorded as the noise point intensity difference; the absolute value of the difference between the peak width values ​​of any stretched noise point and the remaining stretched noise points is recorded as the peak width difference value; the material spectrum significance index of any stretched noise point and the noise point intensity difference and the sum of all the peak width difference values ​​of any stretched noise point are in increasing relationship; For each stretched noise point, a preset number of data points on the left are taken from the noise point judgment sequence to construct a climbing feature sequence of each stretched noise point, and the absolute value of the difference between the slope value of each data point in the climbing feature sequence and the slope value of the previous data point in the noise point judgment curve is calculated, and recorded as the slope difference value; the trend strength and trend statistics of the climbing feature sequence of each stretched noise point are obtained, and the product of the trend strength and the trend statistics is recorded as the trend fusion term; the peak body sharpness coefficient of each stretched noise point is in an increasing relationship with the slope difference value and the trend fusion term; The product of the material spectrum significance index of each stretched noise point and the peak body sharpness coefficient is used as the peak point judgment deviation index of each stretched noise point; threshold segmentation is performed according to the peak point judgment deviation index of all stretched noise points, and the stretched noise point whose peak point judgment deviation index is less than the segmentation threshold is used as the stretched noise confidence point; The process of adjusting the stretching temperature of the diaphragm at the next moment includes: The sequence of Raman spectrum data corresponding to the confident points after removing the stretching noise is taken as the clean data sequence of Raman spectrum; the standard intensity of the wavelength corresponding to all data points in the clean data sequence of diaphragm Raman spectrum in the Raman spectrum database of standard battery diaphragm is obtained to form a standard diaphragm Raman spectrum data sequence; The clean Raman spectrum data sequence and the standard diaphragm Raman spectrum data sequence are used as input, and the spectral angle matching value is obtained by using the matching algorithm; the normalized value of the spectral angle matching value is used as the diaphragm stretching temperature adaptation index; the diaphragm stretching temperature adaptation index at all moments is arranged in ascending time order to obtain the diaphragm stretching temperature adaptation index sequence, and the sequence prediction algorithm is used to obtain the diaphragm stretching temperature adaptation index at the next moment; When the diaphragm stretching temperature adaptation index at the next moment is greater than or equal to the preset threshold, the longitudinal and transverse synchronous stretching temperature remains unchanged; otherwise, the difference between the diaphragm stretching temperature adaptation index at the next moment and the preset threshold is calculated and recorded as the adaptation adjustment factor; the sum of the extreme value of the longitudinal and transverse synchronous stretching temperature before the current moment, the adaptation adjustment factor and the stretching temperature of the battery diaphragm equipment at the current moment is taken as the stretching temperature of the battery diaphragm equipment at the next moment.

2. A method for preparing a battery separator material for electrical energy storage according to claim 1, characterized in that: The specific operation process of heating and adding dicumyl peroxide in step 1 is to heat to 115-125° C., add dicumyl peroxide at 0.055-0.065% of the mass of low-density polyethylene; the insulation time is 4.5-5.5 hours; the stirring time after adding ethanol is 35-55 minutes; the number of cleaning times is 2-4 times; and the drying temperature is 65-75° C.

3. A method for preparing a battery separator material for electrical energy storage according to claim 2, characterized in that: The dicumyl peroxide is a solution prepared by dissolving xylene.

4. A method for preparing a battery separator material for electrical energy storage according to claim 1, characterized in that: In step 3, the dried magnesium hydroxide fine powder is added to the maleic anhydride grafted polyethylene solution, and the mass ratio of the dried magnesium hydroxide fine powder to the maleic anhydride grafted polyethylene is 1.5~2:1; the specific process of the grafting reaction is to heat the temperature to 55~65℃ at 5℃ / min microwave heating, stir and keep the reaction for 35~45min.

5. A method for preparing a battery separator material for electrical energy storage according to claim 1, characterized in that: The mass ratio of maleic anhydride grafted polyethylene to magnesium hydroxide for the two-stage grafting reaction in step 4 is 35-45:1, and the temperature is raised to 125-135° C. by microwave heating at a rate of 10° C. / min, and the mixture is stirred and kept warm for 25-35 minutes.

6. A method for preparing a battery separator material for electrical energy storage according to claim 1, characterized in that: The temperature of the longitudinal stretching in step six is ​​55-75° C., and the ratio is 1.5-3; in the longitudinal and transverse synchronous stretching, the longitudinal stretching ratio is 1.3-1.5, and the transverse stretching ratio is 1.4-1.

9.

7. A method for preparing a battery separator material for electrical energy storage according to claim 1, characterized in that: The temperature of the heat setting is 110-115° C., and the duration is 30-40 minutes.

Images