Battery cell life prediction method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of batteries, and discloses a battery cell life prediction method and device, computer equipment and a storage medium, a battery module comprises a battery cell and a cooling belt for fixing the battery cell, the cooling belt contains a cooling liquid, the cooling belt and the battery cell are connected through a structural adhesive, and the method comprises the following steps: obtaining the contact area of the structural adhesive and the side surface of the battery cell, determining the area variation of the contact area and the initial contact area; determining the temperature variation of the cooling liquid corresponding to the area variation in a preset area temperature association table; the pressure difference between a water inlet and a water outlet of cooling liquid in the cooling zone and the flow of the cooling liquid are obtained; and according to the battery cell attribute factor, determining the residual cycle index of the battery cell, the residual cycle index representing the service life of the battery cell. Through the technical scheme of the invention, the problem that the accuracy of battery life prediction is low under the condition that the number of sensing devices deployed in the battery module is small is solved, and the accuracy of battery life prediction is improved.

Description

Technical Field

[0001] The present invention relates to the field of battery technology, and in particular to a method, device, computer equipment and storage medium for predicting battery cell life. Background Art

[0002] During use, the battery cell will naturally expand, causing the battery cell shell to deform, and then causing the battery module to deform and displace, affecting the performance and safety of the battery pack, and even damaging the structural frame, shortening the service life of the entire module or battery pack.

[0003] Related technologies use the changes in the cross-sectional area of ​​the coolant channels caused by the squeezing force generated by the expansion of the battery cells and the numerous cooling plates deployed within the battery module to reflect the extent of cell expansion. Using a large number of cooling plates to measure cell attribute factors for predicting cell lifespan results in low accuracy when fewer sensing devices are deployed within the battery module. Summary of the Invention

[0004] In view of this, the present invention provides a method, apparatus, computer device and storage medium for predicting battery cell life to solve the problem of low accuracy in battery cell life prediction when there are few sensing devices inside the battery module.

[0005] In a first aspect, the present invention provides a method for predicting the life of a battery cell. The battery module includes a battery cell and a cooling belt for fixing the battery cell. The cooling belt contains a coolant, and the cooling belt and the battery cell are connected by a structural adhesive. The prediction method includes:

[0006] Obtain the contact area between the structural adhesive and the side of the battery cell, and determine the area change between the contact area and the initial contact area;

[0007] Determining the temperature change of the coolant corresponding to the area change using a preset area-temperature correlation table, wherein the preset area-temperature correlation table indicates a one-to-one correspondence between a plurality of preset area changes and a plurality of temperature changes;

[0008] Obtain the pressure difference between the coolant inlet and outlet in the cooling zone and the coolant flow rate;

[0009] The remaining number of cycles of the battery cell is determined based on the battery cell property factors. The remaining number of cycles represents the service life of the battery cell. The battery cell property factors include temperature change, pressure difference and flow rate.

[0010] In an optional embodiment, the cooling belt is arranged on the side and / or bottom of the battery cell.

[0011] In an optional embodiment, determining the remaining number of cycles of the battery cell according to the battery cell property factor includes:

[0012] The remaining number of cycles is determined based on the pre-established correlation between target attribute factors of the sample cell and the number of cycles of the cell. The target attribute factors include the temperature change, pressure difference and flow rate of the sample cell.

[0013] In an optional embodiment, before determining the remaining number of cycles based on the pre-established correlation between the target attribute factor of the sample battery cell and the number of cycles of the battery cell, the method further includes:

[0014] Collect the temperature change, pressure difference, flow rate and sample cycle number corresponding to the expansion of the sample cell as the sample coordinates;

[0015] All collected sample coordinates are fitted, and the fitted curve is obtained as the target correlation curve between the target attribute factor and the number of battery cell cycles.

[0016] In an optional embodiment, determining the remaining number of cycles based on a pre-established correlation between a target attribute factor of a sample battery cell and the number of cycles of the battery cell includes:

[0017] Find the number of cell cycles corresponding to the cell attribute factor in the target correlation curve;

[0018] Determine the remaining number of cycles based on the number of battery cell cycles.

[0019] In an optional embodiment, determining the remaining number of cycles of the battery cell according to the battery cell property factor includes:

[0020] When the surface of the battery cell expands or concave, the remaining number of cycles of the battery cell is determined based on the battery cell property factor.

[0021] In a second aspect, the present invention provides a device for predicting the life of a battery cell. The battery module includes a battery cell and a cooling belt for fixing the battery cell. The cooling belt contains a coolant, and the cooling belt and the battery cell are connected by structural adhesive. The prediction device includes:

[0022] An area change determination module is used to obtain the contact area between the structural adhesive and the side of the battery cell and determine the area change between the contact area and the initial contact area;

[0023] a temperature change determination module, configured to determine a temperature change of the coolant corresponding to the area change using a preset area-temperature association table, wherein the preset area-temperature association table indicates a one-to-one correspondence between a plurality of preset area changes and a plurality of temperature changes;

[0024] A pressure difference and flow acquisition module is used to obtain the pressure difference between the water inlet and the water outlet of the coolant in the cooling zone and the flow rate of the coolant;

[0025] The life determination module is used to determine the remaining number of cycles of the battery cell based on the battery cell attribute factors, which include temperature change, pressure difference and flow rate.

[0026] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the battery cell life prediction method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0027] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the cell life prediction method of the first aspect or any corresponding embodiment thereof.

[0028] In a fifth aspect, the present invention provides a computer program product, comprising computer instructions for causing a computer to execute the battery cell life prediction method of the first aspect or any corresponding embodiment thereof.

[0029] The battery cell life prediction method provided by the present invention achieves the following beneficial technical effects: a battery module includes a battery cell and a cooling belt for securing the battery cell, the cooling belt containing coolant, and the cooling belt and the battery cell connected by structural adhesive. The prediction method includes: obtaining the contact area between the structural adhesive and the side of the battery cell and determining the area change between the contact area and the initial contact area; determining the temperature change of the coolant corresponding to the area change using a preset area-temperature correlation table, wherein the preset area-temperature correlation table indicates a one-to-one correspondence between multiple preset area changes and multiple temperature changes; obtaining the pressure difference between the coolant inlet and outlet and the coolant flow rate within the cooling belt; and allowing the cooling belt to not only secure the battery cell but also collect the area change, coolant pressure difference, and flow rate. The remaining number of cycles of the battery cell is determined based on the battery cell attribute factors, which represent the battery cell's service life. The battery cell attribute factors include temperature change, pressure difference, and flow rate. This improves the accuracy of battery life prediction when fewer sensing devices are deployed within the battery module. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0031] Figure 1is a schematic diagram of a battery cell according to an embodiment of the present invention;

[0032] Figure 2 is a flow chart of a method for predicting battery cell life according to an embodiment of the present invention;

[0033] Figure 3 is a schematic diagram of a battery module according to an embodiment of the present invention;

[0034] Figure 4 A partial schematic diagram of a battery module according to an embodiment of the present invention;

[0035] Figure 5 is a flow chart of a method for determining the remaining number of cycles of a battery cell based on a battery cell attribute factor according to an embodiment of the present invention;

[0036] Figure 6 is a flow chart of another method for predicting battery cell life according to an embodiment of the present invention;

[0037] Figure 7 is a flow chart of another method for predicting battery cell life according to an embodiment of the present invention;

[0038] Figure 8 is a structural block diagram of a battery cell life prediction device according to an embodiment of the present invention;

[0039] Figure 9 is a structural diagram of a computer device provided by an optional embodiment of the present invention;

[0040] Reference numerals:

[0041] 10: Positive pole; 11: Negative pole; 12: Front of battery cell; 13: Side of battery cell; 31: Battery cell; 32: Tab; 33: Long bolt; 34a: End plate A; 34b: End plate B; 35: Cooling belt; 41: Structural adhesive; 42: Coolant inlet; 43: Coolant outlet; 44: Channel for coolant flow; 45: Bolt. DETAILED DESCRIPTION

[0042] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0043] Amidst the rapid development of new energy vehicles, consumer electronics, and energy storage systems, demand for battery cells, a key component, has exploded, and the industry is evolving rapidly. Improving energy density, extending cycle life, enhancing safety, enabling fast charging, and meeting environmental protection requirements have become the primary development directions for battery cell technology.

[0044] During the use of battery cells, a problem that cannot be ignored is the expansion phenomenon. During charging, lithium ions are deintercalated from the positive electrode and embedded in the negative electrode, causing the lattice structure of the negative electrode graphite material to change, resulting in an increase in the interlayer spacing and volume expansion; the opposite is true during discharge. As the number of charge and discharge cycles increases, the microstructure of the electrode material deteriorates, such as particle agglomeration and crack formation, which exacerbate expansion. In addition, during the first charge, the negative electrode material reacts with the electrolyte to form a solid electrolyte interface (SEI). This process consumes lithium ions and generates gas, increasing internal pressure. The continuous repair of the SEI in subsequent cycles will lead to localized uneven lithium ion concentrations, causing uneven expansion forces.

[0045] Cell expansion causes deformation of the cell casing, which in turn causes deformation and displacement of the module, impacting battery pack performance and safety, and even damaging the structural frame, shortening the life of the entire module or battery pack. To address cell expansion, efforts are underway to improve the structure and composition of electrode materials and optimize the SEI formation process to reduce gas generation and its impact on lithium-ion transport. In modules or battery packs, gaps should be reserved and appropriate preload structures should be implemented to counteract expansion forces, ensuring the safety and stability of the cells during use and extending their lifespan.

[0046] However, battery cells typically expand during use. Collecting this information, analyzing the cell status, and flexibly adjusting the charge and discharge strategy to extend the cell lifespan are of great practical significance and commercial value. Related technologies use cooling plates fixed around the battery cells in the battery pack to detect this expansion and predict their lifespan. Because cooling plates occupy a significant amount of space within the battery module, they reduce the energy density of the battery pack. Reducing the number of cell sensors, such as cooling plates, can reduce the accuracy of cell lifespan predictions.

[0047] Figure 1 Schematic diagram of the battery cell according to an embodiment of the present invention. Figure 1 As shown, the cell has a cubic structure. Cubic structures include rectangular parallelepiped and square structures. When the cell has a rectangular parallelepiped structure, a positive electrode column 10 and a negative electrode column 11 are provided on the top of the cell. Of the four exterior surfaces, excluding the top and bottom, the larger exterior surface is the front surface 12, and the smaller exterior surface is the side surface 11.

[0048] According to an embodiment of the present invention, an embodiment of a method for predicting the life of a battery cell is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0049] In this embodiment, a method for predicting the life of a battery cell is provided, which can be used for a battery control device of a battery module. Figure 2 is a flow chart of a method for predicting the life of a battery cell according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0050] Step S201 , obtaining the contact area between the structural adhesive and the side surface of the battery cell, and determining the area change between the contact area and the initial contact area.

[0051] In this embodiment, structural adhesive refers to the adhesive that bonds the outer surface of the battery cell to the cooling tape. In the battery module, the structural adhesive bonds the sides of the battery cell to the cooling tape, forming a bonded contact surface. The contact surface between the structural adhesive and the sides of the battery cell is measured periodically and compared with the initial contact area to determine the change in contact area.

[0052] Step S202 : determining the temperature change of the coolant corresponding to the area change using a preset area-temperature correlation table, wherein the preset area-temperature correlation table indicates a one-to-one correspondence between a plurality of preset area changes and a plurality of temperature changes.

[0053] In this embodiment, the preset area-temperature association table is used to store the correspondence between area changes and temperature changes, indicating a one-to-one correspondence between multiple preset area changes and multiple temperature changes. Using the preset area-temperature association table, the corresponding coolant temperature change can be found.

[0054] Step S203: obtaining the pressure difference between the water inlet and the water outlet of the coolant in the cooling zone and the flow rate of the coolant.

[0055] In this embodiment, the cooling belt refers to the strip adhered to the outer surface of the battery cells in the battery module to secure them in place. The coolant refers to the liquid contained in the cooling belt, used to cool the battery cells. The pressure difference between the pressure at the coolant inlet and the pressure at the coolant outlet within the cooling belt is the pressure differential. The coolant flow rate refers to the volume of coolant flowing through the cooling belt per unit time. The pressure difference between the coolant inlet and outlet within the cooling belt, as well as the coolant flow rate, are measured at regular intervals.

[0056] Step S204 : determining the remaining number of cycles of the battery cell according to the battery cell attribute factors, where the remaining number of cycles represents the service life of the battery cell. The battery cell attribute factors include temperature variation, pressure difference, and flow rate.

[0057] In this embodiment, the battery cell attribute factor refers to a variable or factor that reflects the deformation of the battery cell, including temperature change, pressure difference and flow rate. The remaining number of cycles of the battery cell refers to the total number of charge and discharge cycles completed by the battery within its full life cycle, which is used to characterize the service life of the battery cell. According to the battery cell attribute factor, a mapping relationship between the battery cell attribute factor and the number of cycles of the battery cell can be established through machine learning, or the number of cycles of the battery cell can be mathematically modeled through the battery cell attribute factor. The remaining number of cycles of the battery cell can be obtained through the mapping relationship or mathematical model. The remaining number of cycles reflects the service life of the battery cell. The more remaining cycles, the longer the service life of the battery cell. Conversely, the shorter the service life of the battery cell.

[0058] The method of this embodiment not only utilizes the cooling belt to secure the battery cells, but also measures the pressure difference and flow rate of the coolant, which reflects the expansion or contraction of the battery cells. This achieves a dual purpose. Compared to related art methods that use a large number of cooling plates within the battery module to measure the expansion of the battery cells to predict battery life, the cooling belt saves a significant amount of space within the battery module while accurately measuring the parameters of the coolant, which can reflect the expansion or contraction of the battery cells. Combined with the coolant parameters, the remaining number of cycles in the battery is predicted, resulting in the battery's service life.

[0059] In one embodiment provided in this example, cooling belts are provided on the sides and / or bottom of the battery cells. Compared to the related art method of providing cooling plates within the battery module, this significantly reduces the space occupied by the battery module, saves internal space within the battery module, and helps improve the energy density of the battery.

[0060] Figure 3 Schematic diagram of a battery module according to an embodiment of the present invention. Figure 3 As shown, the battery module includes battery cells 31, tabs 32, long bolts 33, end plates A 34a, end plates B 34b, and cooling strips 35. Tabs 32 are welded to the positive and negative posts at the top of the stacked battery cells 31. Gaps are reserved between the front faces of adjacent battery cells 31, and these gaps are filled with compressible, uniformly thick insulating pads. The battery cells 31 are secured by end plates A 34a, end plates B 34b, and two cooling strips 35. The battery module is fixed to the battery pack via long bolts 33, and the cooling strips 35 are adhered to the outer surfaces of the battery cells 31 using structural adhesive.

[0061] In an example of this embodiment, the ratio of the contact area between the end plate A34a and the end plate B34b and the front face 12 of the battery cell to the front face area of ​​the battery cell is 0.8:1 to 1.2:1, preferably 1:1. The number of battery cells is 4 to 20, and 8 to 16 can be selected according to the capacitance and voltage arrangement required by the battery module or battery pack, preferably 11 to 16. An expansion gap of 0.3 to 1 mm is reserved, which is set according to the expansion amount of the battery cell, and can be selected from 0.4 to 0.8 mm, preferably 0.5 to 0.6 mm. The thickness of the insulating pad before compression is 0.5 to 1.4 mm, and can be selected from 0.6 to 1.2 mm, preferably 0.7 to 1.0 mm. The external force for compressing the insulating pad ranges from 2000 to 10000 N. This preload force is determined according to the actual battery cell and operating conditions, preferably 3000 to 8000 N.

[0062] Figure 4 This is a partial schematic diagram of a battery module according to an embodiment of the present invention. Figure 4 As shown, the cooling belt 35 is adhered to the battery cell 31 by structural adhesive 41. One side of the structural adhesive 41 contacts the side of the battery module shell and the other side contacts the cooling belt 35. The coolant inlet (water inlet) 42 and outlet (water outlet) 43 are relatively connected to ensure the circulation of the coolant. The shape of the channel 44 for the flow of coolant in the cooling belt 35 is not limited to circular, square, etc., and the number of channels is at least one. The coolant outlet 43 and inlet 42 are fixed by bolts 45 to tighten the cooling belt 35.

[0063] In this embodiment, a method for predicting the life of a battery cell is provided. Figure 5 is a flow chart of a method for determining the remaining number of cycles of a battery cell according to a battery cell attribute factor according to an embodiment of the present invention. Figure 5 As shown, the process includes the following steps:

[0064] Step S501 : obtaining the contact area between the structural adhesive and the side surface of the battery cell, and determining the area change between the contact area and the initial contact area.

[0065] For details, please see Figure 1 Step S201 of the illustrated embodiment will not be described in detail here.

[0066] Step S502 : determining the temperature change of the coolant corresponding to the area change using a preset area-temperature correlation table, wherein the preset area-temperature correlation table indicates a one-to-one correspondence between a plurality of preset area changes and a plurality of temperature changes.

[0067] For details, please see Figure 2 Step S202 of the illustrated embodiment will not be described in detail here.

[0068] Step S503 , obtaining the pressure difference between the water inlet and the water outlet of the coolant in the cooling zone and the flow rate of the coolant.

[0069] For details, please see Figure 2 Step S203 of the illustrated embodiment will not be described in detail here.

[0070] Step S504 : determining the remaining number of cycles of the battery cell according to the battery cell attribute factors, where the remaining number of cycles represents the service life of the battery cell. The battery cell attribute factors include temperature variation, pressure difference, and flow rate.

[0071] Specifically, the above step S504 includes:

[0072] Step S5041 : determining the remaining number of cycles based on a pre-established correlation between target attribute factors of the sample cell and the number of cycles of the cell, wherein the target attribute factors include the temperature variation, pressure difference, and flow rate of the sample cell.

[0073] In this embodiment, the sample cell is a test cell used to construct a cell attribute factor and the number of cycles of the cell. The target attribute factor refers to the cell attribute factor of the sample cell, including the temperature change, pressure difference, and flow rate of the sample cell. By collecting the target attribute factors and corresponding cycle numbers of a large number of sample cells, a correlation between the target attribute factors of the sample cells and the number of cycles of the cells is constructed. This correlation can be a mathematical model, a lookup table storing the target attribute factors and the corresponding cycle numbers, or a neural network model trained using the target attribute factors and cycle numbers of the sample cells. Through this correlation, the remaining number of cycles can be determined, thereby obtaining the battery's service life.

[0074] In this embodiment, a method for predicting the life of a battery cell is provided. Figure 6 is a flow chart of another method for predicting the life of a battery cell according to an embodiment of the present invention. Figure 6 As shown, the process includes the following steps:

[0075] Step S601 : collecting a combination of a temperature change, a pressure difference, and a flow rate of a sample cell and a sample cycle number corresponding to an expansion amount of the sample cell as a sample coordinate.

[0076] In this embodiment, the expansion of a sample cell is the amount of expansion of the outer surface of the sample cell due to expansion. It can be calculated based on the coolant flow rate in the cooling zone and the change in the contact area between the structural adhesive and the cell. For example, if the coolant flow rate increases by 1 ml per minute, the expansion of the sample cell is set to 1. Alternatively, if the area change indicates an increase of 1 square centimeter per unit time, the expansion of the sample cell is set to 2. The number of sample cycles refers to the number of cycles the sample cell has undergone. The expansion of the sample cell has a one-to-one correspondence with the number of cycles that the sample cell can cycle. For example, if the expansion of the sample cell is 1, the number of sample cycles is 100, and if the expansion of the sample cell is 2, the number of sample cycles is 80. The temperature change determined by the area change, the pressure difference of the coolant in the cooling zone, the flow rate of the cooling zone, and the sample cycle number corresponding to the expansion of the sample cell are combined to form the sample coordinates. In one example of this embodiment, a charge and discharge experiment is performed on the cell, and the cell expansion is recorded as the sample cell expansion, along with the sample cycle number at each sample cell expansion value. The expansion of a sample battery cell and the corresponding number of sample cycles are used as a set of sample coordinates. Several sets of sample coordinates can be collected through the battery cell expansion experiment. For example, the sample coordinates (x, y, z, m) are formed by the temperature change x, pressure difference y, flow rate z, and sample cycle number m.

[0077] Step S602 : Fitting all collected sample coordinates to obtain a fitted curve as a target correlation curve between the target attribute factor and the number of battery cell cycles.

[0078] In this embodiment, after collecting several sets of sample coordinates, all sample coordinates are fitted to obtain a fitted curve, which is used as the target correlation curve between the sample cell expansion amount and the sample cycle number. If the sample cycle number in the collected sample coordinates is the number of completed cell cycles, then the target correlation curve obtained by fitting is a correlation curve between the sample cell expansion amount and the number of completed cell cycles. For example, after obtaining a correlation curve between the cell expansion amount and the number of completed cell cycles, in practical applications, the cell cycle number corresponding to the cell expansion amount can be queried from the correlation curve, and then the remaining number of cell cycles can be determined based on the cell cycle number. If the correlation curve is a correlation curve between the cell expansion amount and the number of completed cell cycles, the cell cycle number corresponding to the cell expansion amount queried from the correlation curve is the number of completed cell cycles. Then, using the pre-recorded total number of cell cycles, the difference between the total number of cell cycles and the queried number of completed cell cycles is calculated to obtain the remaining number of cell cycles, and the remaining number of cell cycles is used as the cell lifespan. When the correlation curve is a correlation curve between cell expansion and the remaining number of cell cycles, the number of cell cycles corresponding to the cell expansion amount queried from the correlation curve is the remaining number of cell cycles, and the remaining number of cell cycles is directly used as the cell lifespan. For example, the above sample coordinates (x, y, z, m) are fitted using a regression algorithm to form a curve representing the relationship between the cell attribute factors (x, y, z) and the number of cell cycles m, which serves as the target correlation curve.

[0079] In some optional implementations, the above step S603 includes:

[0080] Step a1: Find the number of cell cycles corresponding to the cell attribute factor in the target association curve.

[0081] Step a2: Determine the remaining number of cycles based on the number of cycles of the battery cell.

[0082] Through this embodiment, based on the collected parameters reflecting the expansion of the sample battery cell and the corresponding number of cycles, a target correlation curve is constructed between the expansion of the sample battery cell and the remaining number of cycles of the battery cell. The number of cycles corresponding to the sample battery cell expansion associated with the battery cell attribute factor can be directly queried. For example, by using (x, y, z) as the input of the expression of the target correlation curve, the value of the number of cycles m corresponding to the battery cell at the point (x, y, z) of the target correlation curve can be obtained. The number of cycles m of the battery cell is subtracted from the total number of cycles M of the battery cell to obtain the remaining number of cycles Mm of the battery cell as the battery cell life.

[0083] In some optional embodiments, the above step S204 includes: when the surface of the battery cell expands or concaves, determining the remaining number of cycles of the battery cell according to the battery cell property factor.

[0084] Because the structural adhesive is tightly bonded to the surface of the battery cell, when the cell surface is not expanding or shrinking, the contact surface between the structural adhesive and the cell is a flat plane, and the area of ​​this contact surface is minimized. However, when the cell surface expands, a bulge forms on the cell surface. Because the structural adhesive is tightly bonded to the cell surface, as the height of the bulge increases, the contact surface between the cell and the structural adhesive becomes curved, and the area of ​​this curved contact surface is greater than that of the flat contact surface. Therefore, when the cell surface expands, the cell increases its thermal contact area with the structural adhesive. This increase in thermal contact area increases heat absorption, resulting in increased heat transfer capacity and changes in the cooling zone and internal coolant temperature. When the cell produces abnormal gas and experiences significant expansion, the side bulges significantly, the structural adhesive and cooling zone deform significantly, and the flow cross-sectional area decreases, causing significant changes in the coolant flow rate and pressure differential.

[0085] When the surface of the battery cell is concave, the cooling plate in the related art will lose contact with the battery cell, and the temperature change of the current battery cell cannot be obtained, and thus the battery cell life cannot be predicted. In this embodiment, when the surface of the battery cell is concave, due to the strong adhesion of the structural adhesive to the cooling belt and the surface of the battery cell, as the depth of the concave of the battery cell surface becomes deeper, the contact surface between the battery cell and the structural adhesive is a curved surface, and the area of ​​the curved contact surface is larger than the area of ​​the flat contact surface. According to the area of ​​the curved contact surface and the area of ​​the flat contact surface, the area change of the battery cell surface can be obtained, and the area change of the cooling belt and the battery cell surface bonded by the structural adhesive, as well as the change in the flow rate of the coolant and the pressure difference between the outlet and the inlet of the cooling belt, and the pre-constructed association rules of temperature, flow rate and pressure difference with the battery cell expansion amount, and the association rules of the battery cell expansion amount with the number of battery cell cycles, the battery cell life can be predicted. This method for predicting the life of a battery cell is similar to Figure 2 The method for predicting battery cell life is the same as that of [1], so I will not go into details here.

[0086] In one example of this embodiment, during use, if the side of the battery cell becomes concave, the contact area between the structural adhesive and the side of the battery cell increases, increasing the heat transfer area of ​​the structural adhesive, which increases heat transfer and changes the coolant temperature. If the side of the battery cell convexes, the contact area also increases, increasing the heat transfer area of ​​the structural adhesive, which increases heat transfer and changes the coolant temperature. If the side of the battery cell convexes significantly, it will cause the cooling zone to deform, causing changes in the coolant pressure difference and flow rate within the cooling zone.

[0087] Based on the above three situations, the remaining number of cycles of the battery cell is determined by comprehensively considering battery cell property factors such as temperature, pressure difference and flow rate change, thereby predicting the battery cell life.

[0088] Figure 7 FIG. 1 is a flow chart of another method for predicting the life of a battery cell according to an embodiment of the present invention. Figure 7As shown, when the side surface of the battery cell is concave, as the depth of the concave on the surface of the battery cell becomes deeper, the contact surface between the battery cell and the structural adhesive is a curved surface, which causes the curved surface area of ​​the contact between the side surface of the battery cell and the structural adhesive to increase, resulting in increased heat transfer between the battery cell and the cooling belt; the temperature, pressure difference and flow rate of the coolant are obtained through the cooling belt, and the expansion amount of the battery cell is confirmed based on the temperature, pressure difference and flow rate of the coolant, as well as the pre-constructed relationship between the temperature, pressure difference and flow rate and the expansion amount of the battery cell; based on the expansion amount of the battery cell and the pre-constructed relationship between the expansion amount of the battery cell and the battery life, the service life of the battery cell corresponding to the expansion amount of the battery cell is determined. When the side of the battery cell bulges outward, that is, when the battery cell expands, as the height of the bulge on the battery cell surface increases, the contact surface between the battery cell and the structural adhesive becomes a curved surface, causing the curved surface area of ​​the contact between the side of the battery cell and the structural adhesive to increase, resulting in increased heat transfer between the battery cell and the cooling belt. At the same time, the side bulges outward, squeezing the structural adhesive and the cooling belt, causing the cooling belt to deform; the temperature, pressure difference and flow rate of the coolant are obtained through the cooling belt; the expansion of the battery cell is confirmed based on the temperature, pressure difference, flow rate of the coolant, and the pre-constructed relationship between the temperature, pressure difference, flow rate and the expansion of the battery cell; the service life of the battery cell is determined based on the expansion of the battery cell and the pre-constructed relationship between the expansion of the battery cell and the battery life.

[0089] In this embodiment, a cell life prediction device is also provided, which is used to implement the above-mentioned embodiments and preferred embodiments. The details that have been described will not be repeated here. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and conceivable.

[0090] This embodiment provides a device for predicting the life of a battery cell. Figure 8 is a structural block diagram of a cell life prediction device according to an embodiment of the present invention. Figure 8 Shown, including:

[0091] An area change determination module 801 is used to obtain the contact area between the structural adhesive and the side surface of the battery cell, and determine the area change between the contact area and the initial contact area;

[0092] a temperature change determination module 802 for determining a temperature change of the coolant corresponding to the area change using a preset area-temperature association table, wherein the preset area-temperature association table indicates a one-to-one correspondence between a plurality of preset area changes and a plurality of temperature changes;

[0093] The pressure difference and flow acquisition module 803 is used to obtain the pressure difference between the water inlet and the water outlet of the coolant in the cooling zone and the flow rate of the coolant;

[0094] The life determination module 804 is used to determine the remaining number of cycles of the battery cell according to the battery cell attribute factors, where the battery cell attribute factors include temperature change, pressure difference, and flow rate.

[0095] In some optional embodiments, the cooling belt of the battery life prediction device is arranged on the side and / or bottom of the battery cell.

[0096] In some optional implementations, the lifespan determination module 804 includes:

[0097] The remaining cycle number determination unit is used to determine the remaining cycle number based on the pre-established correlation between the target attribute factors of the sample battery cell and the cycle number of the battery cell. The target attribute factors include the temperature change, pressure difference and flow rate of the sample battery cell.

[0098] In some optional embodiments, the remaining cycle number determination unit determines the remaining cycle number based on the pre-established association between the target attribute factor of the sample battery cell and the cycle number of the battery cell, and before determining the remaining cycle number, further includes:

[0099] A collection subunit, used to collect the temperature change, pressure difference and flow rate of the sample cell and the number of sample cycles corresponding to the expansion of the sample cell as sample coordinates;

[0100] The fitting subunit is used to fit all the sample coordinates collected to obtain the fitted curve as the target correlation curve between the target attribute factor and the number of battery cell cycles.

[0101] In some optional embodiments, the remaining cycle number determination unit includes:

[0102] A search subunit is used to search the number of cell cycles corresponding to the cell attribute factor in the target association curve;

[0103] The cell remaining cycle number determination subunit is used to determine the remaining cycle number based on the cell cycle number.

[0104] In some optional embodiments, the remaining cycle number determination unit includes:

[0105] The target remaining cycle number determination subunit is used to determine the remaining cycle number of the battery cell based on the battery cell property factor when the surface of the battery cell expands or concave.

[0106] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0107] The battery cell life prediction device in this embodiment is presented in the form of a functional unit, where the unit refers to an application specific integrated circuit (ASIC) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0108] The embodiment of the present invention also provides a computer device having the above Figure 8 The battery cell life prediction device shown.

[0109] See also Figure 9 , Figure 9 is a structural diagram of a computer device provided by an optional embodiment of the present invention, such as Figure 9 As shown, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in the memory or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 9 A processor 10 is taken as an example.

[0110] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0111] The memory 20 stores instructions that can be executed by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0112] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0113] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0114] The computer device further includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 9 The bus connection is taken as an example.

[0115] The input device 30 can receive input digital or character information and generate key signal input related to user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a trackpad, a touch pad, an indicator stick, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 can include a display device, an auxiliary lighting device (e.g., an LED), and a tactile feedback device (e.g., a vibration motor). The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display, and a plasma display. In some optional embodiments, the display device can be a touch screen.

[0116] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0117] A portion of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the form in which the computer program instruction exists in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc. Accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium that can be accessed by the computer.

[0118] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for predicting the life of a battery cell, characterized in that: The battery module includes a battery cell and a cooling belt for fixing the battery cell, wherein the cooling belt contains a coolant, and the cooling belt and the battery cell are connected by structural adhesive. The method includes: Obtaining a contact area between the structural adhesive and the side surface of the battery cell, and determining an area change between the contact area and an initial contact area; Determining the temperature change of the coolant corresponding to the area change using a preset area-temperature association table, wherein the preset area-temperature association table indicates a one-to-one correspondence between a plurality of preset area changes and a plurality of temperature changes; Obtaining the pressure difference between the water inlet and the water outlet of the coolant in the cooling zone and the flow rate of the coolant; The remaining number of cycles of the battery cell is determined according to a battery cell property factor, where the remaining number of cycles represents the service life of the battery cell. The battery cell property factor includes the temperature change, the pressure difference, and the flow rate.

2. The method according to claim 1, characterized in that The cooling belt is arranged on the side and / or bottom of the battery core.

3. The method according to claim 1, characterized in that The determining the remaining number of cycles of the battery cell according to the battery cell property factor includes: The remaining number of cycles is determined based on a pre-established correlation between target attribute factors of the sample battery cell and the number of cycles of the battery cell, wherein the target attribute factors include a temperature change, a pressure difference, and a flow rate of the sample battery cell.

4. The method according to claim 3, characterized in that Before determining the remaining number of cycles based on the association between the pre-established target attribute factor of the sample battery cell and the number of cycles of the battery cell, the method further includes: Collecting a combination of a temperature change, a pressure difference, and a flow rate of a sample cell and a number of sample cycles corresponding to an expansion amount of the sample cell as a sample coordinate; All collected sample coordinates are fitted, and the fitted curve is obtained as the target correlation curve between the target attribute factor and the number of battery cell cycles.

5. The method according to claim 4, characterized in that The determining the remaining number of cycles based on the pre-established correlation between the target attribute factor of the sample battery cell and the number of cycles of the battery cell includes: Searching the target correlation curve for the number of cell cycles corresponding to the cell attribute factor; The remaining number of cycles is determined according to the number of cycles of the battery cell.

6. The method according to claim 1, characterized in that The determining the remaining number of cycles of the battery cell according to the battery cell property factor includes: When the surface of the battery cell expands or concaves, the remaining number of cycles of the battery cell is determined according to the battery cell property factor.

7. A battery cell life prediction device, characterized in that: The battery module includes a battery cell and a cooling belt for fixing the battery cell. The cooling belt contains a coolant and is connected to the battery cell via structural adhesive. The device includes: an area change determination module, configured to obtain a contact area between the structural adhesive and the side surface of the battery cell, and determine an area change between the contact area and an initial contact area; a temperature change determination module, configured to determine a temperature change of the coolant corresponding to the area change using a preset area-temperature association table, wherein the preset area-temperature association table indicates a one-to-one correspondence between a plurality of preset area changes and a plurality of temperature changes; A pressure difference and flow acquisition module, used to acquire the pressure difference between the water inlet and the water outlet of the coolant in the cooling zone and the flow rate of the coolant; The life determination module is used to determine the remaining number of cycles of the battery cell according to the battery cell property factors, wherein the battery cell property factors include the temperature change, the pressure difference and the flow rate.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the battery cell life prediction method according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the battery cell life prediction method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer instructions for causing a computer to execute the battery cell life prediction method according to any one of claims 1 to 6.

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