Design method, design device, computer readable storage medium and computer program product

Abstract

The invention discloses a battery energy design method, a battery energy design device, a computer readable storage medium and a computer program product. The design method comprises the steps that a preset attenuation prediction model, nominal energy of a to-be-designed battery and transportation conditions are obtained, the transportation conditions at least comprise the transportation duration and the transportation temperature of the to-be-designed battery, the attenuation prediction model is related to a preset period T, and the preset period T is smaller than the transportation duration; and determining a factory energy value of the battery to be designed according to the attenuation prediction model, the nominal energy and the transportation condition.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of batteries, in particular to a battery energy design method, a battery energy design device, a computer readable storage medium and a computer program product. BACKGROUND

[0002] In the process of transporting lithium batteries, in order to cope with calendar fading, a certain amount of surplus, i.e. a supplementary energy value, is usually set on the basis of the nominal energy of the battery in the design process of the factory energy value of the battery, so as to ensure that the product standard can still be met in the case of calendar fading when the battery is delivered. However, in long-period transportation, if a measurement method is used to simulate the transportation process to determine the supplementary energy value, a large amount of time and labor cost needs to be invested, and there are problems of long period, large consumption of manpower and material resources, which cannot efficiently cope with the design of the factory energy value in long-period transportation. SUMMARY

[0003] In order to solve the problems of long period and large consumption of manpower and material resources in the design of the factory energy value, the embodiments of the present application provide a battery energy design method, a battery energy design device, a computer readable storage medium and a computer program product.

[0004] The present application provides a battery energy design method. The design method comprises obtaining a preset fading prediction model, a nominal energy of a battery to be designed and transportation conditions, the transportation conditions at least comprising a transportation duration and a transportation temperature of the battery to be designed, the fading prediction model being related to a preset period T, the preset period T being less than the transportation duration; and determining a factory energy value of the battery to be designed according to the fading prediction model, the nominal energy and the transportation conditions.

[0005] In some embodiments, the determination of the factory energy value of the battery to be designed according to the fading prediction model, the nominal energy and the transportation conditions comprises inputting the transportation conditions of the battery to be designed into the fading prediction model to obtain a transportation fading rate of the battery to be designed under the transportation conditions; determining a supplementary energy value according to the transportation fading rate and the nominal energy; and determining the factory energy value according to the supplementary energy value and the nominal energy.

[0006] In some embodiments, the design method further comprises establishing a test matrix comprising a plurality of test batteries under different test working conditions, the test working conditions at least comprising a test temperature, the number of the test batteries under each test working condition being at least two; performing a fading test on the test matrix with the preset period T, and obtaining a test fading rate of each test battery under the corresponding test working condition; and establishing a fading prediction model according to the test fading rate and a preset energy fading algorithm.

[0007] In some embodiments, the step of performing a decay test on the test matrix at a preset period T and obtaining the decay rate of each test battery under the corresponding test conditions includes alternatingly performing static storage and at least two first charge-discharge cycles on each test battery within the preset period T to obtain a first decay rate of the test battery; cyclically performing at least two second charge-discharge cycles on different test batteries under the same test conditions to obtain a second decay rate of the test battery, wherein the parameters of the first charge-discharge and the parameters of the second charge-discharge are exactly the same; and obtaining the test decay rate based on the first decay rate and the second decay rate.

[0008] In some embodiments, the charge / discharge temperature, charging current rate, discharging current rate, and depth of discharge of the first charge / discharge are the same as those of the second charge / discharge.

[0009] In some embodiments, the step of alternately performing static storage and at least two first charge-discharge cycles on each of the test batteries within the preset period T to obtain the first degradation rate of the test batteries includes, within the preset period T, alternately performing static storage for a preset test duration N and first charge-discharge cycles of a second preset number K on each of the test batteries, the number of cycles being a first preset number L, and the preset period T being greater than the product of the first preset number L and the preset test duration N; obtaining a first energy value that the test battery can release during the discharge process of the last first charge-discharge cycle; and obtaining the first degradation rate based on the first energy value and the initial energy of the test battery.

[0010] In some embodiments, at least two second charge-discharge cycles are performed on different test batteries under the same test conditions to obtain a second degradation rate of the test battery. The parameters of the first charge-discharge are exactly the same as those of the second charge-discharge. This includes performing a third preset number of second charge-discharge cycles M on different test batteries under the same test conditions, where the third preset number of cycles M is the product of the first preset number of cycles L and the second preset number of cycles K; obtaining a second energy value that the test battery can release during the discharge process of the last second charge-discharge; and obtaining the second degradation rate based on the second energy value and the initial energy of the test battery.

[0011] In some embodiments, establishing an attenuation prediction model based on the test attenuation rate and energy attenuation algorithm includes inputting the test attenuation rate and the preset period T under each test condition into the energy attenuation algorithm to determine the attenuation prediction sub-model under that test condition; and integrating the attenuation prediction sub-models under each test condition to obtain the attenuation prediction model.

[0012] This application provides a battery energy design apparatus, which includes a memory and a processor. The memory stores instructions. The instructions stored in the memory are executed by the processor to implement the design method described in any of the above embodiments.

[0013] This application provides a computer-readable storage medium including a computer program. When the computer program is run on a processor, the computer program is used to cause the processor to perform the design method described in any of the above embodiments.

[0014] This application provides a computer program product, which includes a computer program stored in a computer-readable storage medium. A processor reads from the computer-readable storage medium and executes the computer program to implement the design method described in any of the above embodiments.

[0015] In the battery energy design method, battery energy design device, computer-readable storage medium, and computer program product of this application, the nominal energy, transportation time, and transportation temperature of the battery to be designed are input into a preset degradation prediction model to determine the factory energy value of the battery to be designed. The degradation prediction model is related to a preset period T, and the preset period T is shorter than the transportation time. Therefore, there is no need to use actual measurement methods to simulate the transportation process; instead, the factory energy value of the battery to be designed can be accurately and efficiently determined by a degradation prediction model related to a shorter preset period T, greatly saving time and labor costs.

[0016] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0017] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, wherein: Figure 1 This is a flowchart illustrating the battery energy design method of certain embodiments of this application; Figure 2 This is a schematic diagram of the design device for battery energy in some embodiments of this application; Figure 3 This is a flowchart illustrating the battery energy design method of certain embodiments of this application; Figure 4 This is a flowchart illustrating the battery energy design method of certain embodiments of this application; Figure 5 This is a flowchart illustrating the battery energy design method of certain embodiments of this application; Figure 6 This is a flowchart illustrating the battery energy design method of certain embodiments of this application; Figure 7 This is a flowchart illustrating the battery energy design method of certain embodiments of this application; Figure 8 This is a flowchart illustrating the battery energy design method of certain embodiments of this application; Figure 9 This is a schematic diagram showing the connection state of a computer-readable storage medium and a processor according to certain embodiments of this application; Figure 10 This is a schematic diagram showing the connection state of a computer program product and a processor according to certain embodiments of this application.

[0018] The reference numerals in the detailed embodiments are as follows: Design device 100; memory 10; processor 30. Detailed Implementation

[0019] To make the above-mentioned objectives, features, and advantages of this application more apparent and understandable, the specific embodiments of this application are described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of this application. However, this application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of this application. Therefore, this application is not limited to the specific embodiments disclosed below.

[0020] In the description of this application, it should be understood that the terms "center", "length", "upper", "lower", "front", "rear", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0021] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0022] In this application, unless otherwise expressly specified and limited, the terms "installation," "connection," "joining," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise expressly limited. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0023] In this application, unless otherwise expressly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "on top of," and "over" the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0024] During the transportation of lithium batteries, to address calendar degradation, a certain margin, or supplementary energy value, is typically set in the battery's design process beyond its nominal energy value. This ensures that the battery still meets product standards upon delivery, even with calendar degradation. However, in long-term transportation, simulating the transportation process using experimental methods to determine the supplementary energy value requires significant time and manpower, resulting in long cycles and high resource consumption. This approach is inefficient for addressing the design of factory energy values ​​during long-term transportation. To solve these problems, this application provides a battery energy design method (…). Figure 1 , Figures 3 to 8 As shown), a battery energy design device 100 ( Figure 2 As shown), computer-readable storage medium ( Figure 9 (as shown) and computer program products ( Figure 10 (As shown).

[0025] Please refer to Figure 1 This application provides a method for designing battery energy. The design method includes: 07: Obtain the preset degradation prediction model, the nominal energy of the battery to be designed, and the transportation conditions. The transportation conditions must include at least the transportation duration and temperature of the battery to be designed. The degradation prediction model is related to a preset period T, and the preset period T is less than the transportation duration; and 09: Determine the factory energy value of the battery to be designed based on the attenuation prediction model, nominal energy, and transportation conditions.

[0026] Correspondingly, please combine Figure 2 This application provides a battery energy design apparatus 100. The design apparatus 100 includes a memory 10 and a processor 30. The memory 10 is used to store instructions. The instructions stored in the memory 10 are executed by the processor 30 to implement the anomaly detection methods in [specific methods]. More specifically, the processor 30 is used to: acquire a preset attenuation prediction model, the nominal energy of the battery to be designed, and transportation conditions, wherein the transportation conditions include at least the transportation time and transportation temperature of the battery to be designed, and the attenuation prediction model is related to a preset period T, wherein the preset period T is less than the transportation time; and determine the factory energy value of the battery to be designed based on the attenuation prediction model, the nominal energy, and the transportation conditions.

[0027] Specifically, a battery is an electrochemical device that directly converts internally stored chemical energy into direct current electrical energy. Through a controlled electrochemical reaction, a potential difference is established between the positive and negative electrodes, allowing the battery to continuously supply power to devices connected to an external circuit. During the design and manufacturing process, the battery's energy is determined by factors such as energy density and processing technology. However, due to calendar decay—the irreversible decrease in energy over time even when not in use—the energy value of a battery at the factory may differ from its actual energy value after transportation, such as from the manufacturer to the downstream customer. In such cases, the customer may receive a battery whose actual energy value does not meet product standards.

[0028] Therefore, during battery design, a certain margin, or supplementary energy value, is typically set on top of the nominal energy level to ensure that the battery still meets standards even after calendar degradation occurs upon delivery. It should be noted that the supplementary energy value cannot be infinitely large to avoid performance waste or degradation in battery power, fast charging, or other electrical performance aspects.

[0029] Traditional testing methods suffer from long cycles and high manpower and material costs. For example, if transporting batteries to customer A's warehouse takes three months at a temperature of 29 degrees Celsius, then designing the battery's factory energy value requires conducting a three-month static storage experiment at 29 degrees Celsius to determine the supplementary energy value and factory energy value for this batch of products (or other batches under the same transportation conditions). Therefore, this application provides a battery energy design method to solve the problems of long cycles and high manpower and material costs in designing factory energy values.

[0030] The battery energy design device 100 is used to determine the factory energy value of a battery to be designed. The memory 10 stores instructions corresponding to the battery energy design method, and the processor 30 executes the instructions stored in the memory 10 to enable the design device 100 to implement the battery energy design method. The memory 10 and the processor 30 are electrically connected and connected to various parts of the entire design device 100 via various interfaces and lines. The processor 30 executes the methods in 07 and 09 by running or loading the instructions stored in the memory 10 and by calling the data stored in the memory 10, thereby realizing the design of the factory energy value of the battery to be designed.

[0031] The battery to be designed has a known nominal energy and transportation conditions, and requires design for its factory-delivered energy value. The transportation conditions include at least the transportation time from the manufacturer to the customer, and the transportation temperature during this process. The degradation prediction model is a model that can determine the energy loss due to calendar degradation during transportation based on information about the battery, such as transportation conditions. The degradation model can be a preset, fixed model, or a variable model that can be optimized and adjusted by technicians during application; this application does not impose any restrictions on this.

[0032] In some implementations, transportation conditions also include the State of Charge (SOC) during transportation. To minimize the negative impact of transportation on the battery, the battery is typically set to a lower SOC during transportation. The degree of calendar degradation varies at different SOCs; therefore, the SOC during transportation can be input into the degradation prediction model to obtain a more accurate factory-set energy value.

[0033] The processor 30 acquires the nominal energy, transportation conditions, and degradation prediction model of the battery to be designed. Based on the transportation conditions, it inputs the output of the degradation prediction model and uses it, along with the nominal energy, to determine the factory energy value of the battery to be designed. It can be understood that because the degradation prediction model is related to a preset period T, and the preset period T is a fixed value shorter than the transportation time, the time required to establish the degradation prediction model is relatively short. Furthermore, once established, the degradation prediction model can be applied to various batteries to be designed with different transportation conditions, greatly saving time and manpower costs.

[0034] In the battery energy design method of this application, the nominal energy, transportation time, and transportation temperature of the battery to be designed are input into a preset degradation prediction model to determine the factory energy value of the battery to be designed. The degradation prediction model is related to a preset period T, and the preset period T is shorter than the transportation time. Therefore, there is no need to simulate the transportation process using actual measurement methods. Instead, the factory energy value of the battery to be designed can be accurately and efficiently determined by a degradation prediction model related to a shorter preset period T, greatly saving time and labor costs.

[0035] Please refer to Figure 1 and Figure 3 In some implementations, 09: Determine the factory energy value of the battery to be designed based on the degradation prediction model, nominal energy, and transportation conditions, including: 091: Input the transportation conditions of the battery to be designed into the attenuation prediction model to obtain the transportation attenuation rate of the battery to be designed under transportation conditions; 093: Determine the supplementary energy value based on the transportation attenuation rate and nominal energy; and 095: The factory energy value is determined based on the replenished energy value and the nominal energy value.

[0036] Furthermore, please combine Figure 2 The processor 30 is also used to execute the methods in 091, 093, and 095. Specifically, the processor 30 is configured to: input the transportation conditions of the battery to be designed into the attenuation prediction model to obtain the transportation attenuation rate of the battery to be designed under the transportation conditions; determine the replenishment energy value based on the transportation attenuation rate and the nominal energy; and determine the factory energy value based on the replenishment energy value and the nominal energy.

[0037] Specifically, the transport degradation rate is the ratio of energy loss due to calendar degradation to the nominal energy of the battery under known transport conditions. The processor 30 inputs the transport duration and temperature (and SOC during transport) of the battery under design into the degradation prediction model, and the model outputs the transport degradation rate. This transport degradation rate corresponds to the ratio of energy loss to the nominal energy of the battery under the input transport duration and temperature (and SOC during transport).

[0038] Furthermore, the processor 30 calculates the supplementary energy value by multiplying the transportation degradation rate by the nominal energy. The supplementary energy value is the extra energy exceeding the nominal energy reserved to cope with calendar degradation, and is referred to as the "margin" mentioned earlier. The supplementary energy value corresponds to the "margin" of the battery under design at its current nominal energy level under current transportation conditions. The processor 30 sums the supplementary energy value with the nominal energy to obtain the factory energy value, ensuring that the product standards are still met even if calendar degradation occurs upon battery delivery.

[0039] It should be noted that when transportation conditions are complex, such as when the transportation temperature changes in multiple stages throughout the transportation process, the processor 30 can obtain the corresponding supplementary energy value for each stage of transportation, and then obtain the factory-delivered energy value by summing all the supplementary energy values ​​with the nominal energy. Therefore, the battery energy design method of this application has good flexibility and can cope with various complex transportation conditions.

[0040] Therefore, in the battery energy design method of this application, the nominal energy and transportation conditions of the battery to be designed are input into a preset attenuation prediction model to obtain the transportation attenuation rate, thereby obtaining the supplementary energy value and determining the factory energy value. At this time, the attenuation prediction model can accurately and efficiently determine the factory energy value of the battery to be designed, greatly saving time and manpower costs.

[0041] Please refer to Figure 1 and Figure 4 In some implementations, the design method further includes: 01: Establish a test matrix that includes multiple test batteries under different test conditions. The test conditions include at least the test temperature, and the number of test batteries under each test condition is at least two. 03: Perform attenuation tests on the test matrix at a preset period T, and obtain the attenuation rate of each test cell under the corresponding test conditions; and 05: Establish an attenuation prediction model based on the test attenuation rate and the preset energy attenuation algorithm.

[0042] Furthermore, please combine Figure 2 The processor 30 is also used to execute the methods in 01, 03, and 05. Specifically, the processor 30 is configured to: establish a test matrix including multiple test batteries under different test conditions, wherein the test conditions include at least a test temperature, and the number of test batteries under each test condition is at least two; perform a decay test on the test matrix at a preset period T, and obtain the test decay rate of each test battery under the corresponding test condition; and establish a decay prediction model based on the test decay rate and a preset energy decay algorithm.

[0043] Specifically, the test battery is the battery used for testing during the process of establishing the degradation prediction model. It can be understood that the test battery is the same as the battery to be designed, to ensure that the degradation prediction model can be effectively applied to the design of the battery to be designed. The test conditions are the experimental conditions under which the test battery is tested. Test conditions include at least the test temperature; in some implementations, the test conditions also include the test state of charge (SOC), in which case the degradation prediction model is more accurate. Correspondingly, the transportation conditions used as input during the design process of the battery to be tested also include the SOC during transportation.

[0044] The test matrix is ​​a set of test cells under different test conditions. Each test condition requires at least two test cells to be used in two separate steps of obtaining the degradation rate (described below), ensuring the accuracy and reliability of the test results. It should be noted that establishing the test matrix here does not refer to physically arranging or classifying the test cells in a specific order, but rather to the processor 30 acquiring and storing data related to the multiple test cells under different test conditions.

[0045] The preset period T is the required duration of the test experiment. The preset period T is a fixed and relatively short duration, which is less than the transportation time. It should be noted that the preset period T being less than the transportation time here means that, in the initial design phase for battery A, the time spent establishing the degradation prediction model (preset period T) is shorter than the time spent using traditional experimental methods (actual transportation time). This application does not exclude the possibility that the transportation time for the manufacturer's other battery B is shorter and longer than the preset period T. However, in this case, battery B reuses the degradation prediction model established for battery A, without the need to establish a new degradation prediction model or use traditional experimental methods. In this case, it also achieves the effect of saving time and labor costs.

[0046] At this time, the processor 30 performs a decay test on the test matrix at a preset period T. Since the test battery is the same as the battery to be designed, the decay test can determine the calendar decay of this type of battery with this nominal energy under the corresponding test conditions within the preset period T, that is, determine the test decay rate.

[0047] Furthermore, the energy decay algorithm is based on the Arrhenius equation to determine the calendar decay rate under specific operating conditions (temperature and / or SOC). The processor 30 can establish a decay prediction model based on the test decay rate obtained from the test batteries under various test conditions and the energy decay algorithm. This decay prediction model can then be used to determine the proportion of energy loss for batteries of the same type as the test battery (such as the battery under design) under specific operating conditions when stored statically for a certain number of days, i.e., the transportation decay rate mentioned earlier. These operating conditions correspond to the transportation conditions in the design process.

[0048] Therefore, in the battery energy design method of this application, a test matrix composed of multiple test batteries under different test conditions is used. A decay prediction model is established using the test decay rate obtained from a decay test over a preset period T and an energy decay algorithm to determine the transportation decay rate corresponding to each transportation condition. The decay prediction model establishment process is relatively short and covers multiple transportation conditions, exhibiting good flexibility and versatility. It can be applied to the design process of various batteries under different transportation conditions, greatly saving time and manpower costs.

[0049] Please refer to Figure 1 , Figure 4 and Figure 5 In some implementations, 03: A decay test is performed on the test matrix at a preset period T, and the decay rate of each test cell under the corresponding test conditions is obtained, including: 031: Within a preset period T, each test battery is alternately subjected to static storage and at least two first charge-discharge cycles to obtain the first degradation rate of the test battery; 033: Perform at least two second charge-discharge cycles on different test batteries under the same test conditions to obtain the second degradation rate of the test battery. The parameters of the first charge-discharge and the second charge-discharge are exactly the same. 035: Obtain the test attenuation rate based on the first attenuation rate and the second attenuation rate.

[0050] Furthermore, please combine Figure 2 The processor 30 is also configured to execute the methods in 031, 033, and 035. Specifically, the processor 30 is configured to: alternately perform static storage and at least two first charge-discharge cycles on each test battery within a preset period T to obtain a first degradation rate of the test battery; cyclically perform at least two second charge-discharge cycles on different test batteries under the same test conditions to obtain a second degradation rate of the test battery, wherein the parameters of the first charge-discharge and the parameters of the second charge-discharge are exactly the same; and obtain a test degradation rate based on the first degradation rate and the second degradation rate.

[0051] Specifically, cycle degradation refers to the irreversible decrease in energy of a battery as the number of charge-discharge cycles increases during repeated use. It can be understood that cycle degradation is a phenomenon that occurs in a used battery. In the above embodiments, the degradation test includes at least a calendar degradation test, which simulates energy loss due to static storage during transportation. However, during the calendar degradation test, to obtain test data, it is unavoidable to charge and discharge the test battery. Therefore, the cycle degradation introduced by the charge-discharge process cannot be ignored. Thus, the degradation test also includes a cyclic degradation test, which is used to remove errors caused by the charge-discharge process from the results obtained from the calendar degradation test.

[0052] More specifically, within a preset period T, each test battery sequentially undergoes a calendar decay test consisting of static storage, at least two first charge-discharge cycles, static storage, at least two first charge-discharge cycles, and so on. This calendar decay test can be understood as starting with static storage and ending with at least two first charge-discharge cycles; the total time for each of these operations is the preset period T. The processor 30 obtains the ratio of the energy decay of the test battery to its initial energy based on the test data from the calendar decay test, which is the first decay rate. The first decay rate includes the calendar decay rate corresponding to the calendar decay within the preset period T, and the cyclic decay rate corresponding to the cyclic decay caused by each first charge-discharge cycle within the preset period T.

[0053] Furthermore, different test batteries operating under the same test conditions as those in the calendar degradation test undergo a second charge-discharge cycle to simulate the cyclic degradation caused by each first charge-discharge within a preset period T. It is understood that the parameters of the first and second charge-discharge cycles are identical, and the total number of first and second charge-discharge cycles is the same, so that the cyclic degradation test can be used to correct the calendar degradation test. At this time, the ratio of the energy decay of the test battery obtained by the processor 30 based on the test data from the aforementioned cyclic degradation test to the initial energy of the test battery is the second degradation rate. The second degradation rate is equal to the cyclic degradation rate corresponding to the cyclic degradation caused by each first charge-discharge within the preset period T.

[0054] Therefore, the processor 30 obtains the test attenuation rate by subtracting the first attenuation rate from the second attenuation rate. This test attenuation rate is the corrected calendar attenuation rate, eliminating the influence of multiple charge-discharge cycles in the calendar attenuation test on the test results. This yields a purer test attenuation rate that more closely reflects the attenuation situation in actual transportation. Consequently, the attenuation prediction model subsequently established by the processor 30 using the test attenuation rate is more accurate.

[0055] Therefore, in the battery energy design method of this application, a first attenuation rate is obtained by alternately performing static storage and at least two first charge-discharge cycles, and a second attenuation rate is obtained by cyclically performing the same number of second charge-discharge cycles. The test attenuation rate is then obtained based on the difference between the first and second attenuation rates. The test attenuation rate closely approximates the pure calendar attenuation that occurs during actual transportation and can be used in the process of establishing an attenuation prediction model. The attenuation prediction model is flexible, versatile, and accurate, capable of accurately and efficiently handling the design of various batteries under different transportation conditions, significantly saving time and manpower costs.

[0056] Please refer to Figure 1 , Figure 4 and Figure 5 In some embodiments, the charge / discharge temperature, charging current rate, discharging current rate, and depth of discharge of the first charge / discharge are the same as those of the second charge / discharge.

[0057] Specifically, in the above embodiments, to ensure that the second attenuation rate obtained from the cyclic attenuation test can accurately correct the first attenuation rate obtained from the calendar attenuation test, the active charge-discharge process occurring in the cyclic attenuation test and the calendar attenuation test must be set to be exactly the same process, that is, the parameters of the first charge-discharge and the second charge-discharge must be exactly the same. These parameters include at least the charge-discharge temperature, charging current rate, discharging current rate, and depth of discharge. For example, both the first and second charge-discharge cycles are 25℃-0.2C / 0.2C-100% DOD, or 25℃-0.5C / 0.5C-100% DOD, or 25℃-1C / 1C-100% DOD. In this case, the test attenuation rate obtained by the processor 30 is more accurate.

[0058] Therefore, in the battery energy design method of this application, the charging and discharging temperatures, charging current rates, discharging current rates, and depths of discharge are the same for the first and second charge / discharge cycles. This results in highly accurate test degradation rates that closely approximate the pure calendar degradation that occurs during actual transportation, making it better suited for establishing degradation prediction models. These degradation prediction models offer high flexibility and versatility while also maintaining high accuracy, enabling them to accurately and efficiently address the design of various batteries under different transportation conditions, significantly saving time and manpower costs.

[0059] Please refer to Figure 1 , Figures 4 to 6 In some embodiments, 031: Within a preset period T, each test battery is alternately subjected to static storage and at least two first charge-discharge cycles to obtain the first degradation rate of the test battery, including: 0311: Within a preset period T, each test battery is alternately subjected to a preset test duration N of static storage and a second preset number of first charge-discharge cycles K, with the number of cycles being the first preset number L. The preset period T is greater than the product of the first preset number L and the preset test duration N. 0313: Obtain the first energy value that the test battery can release during the final charge-discharge process; and 0315: Obtain the first degradation rate based on the first energy value and the initial energy of the test battery.

[0060] Furthermore, please combine Figure 2 The processor 30 is also configured to execute the methods in 0311, 0313, and 0315. Specifically, the processor 30 is configured to: alternately perform a preset test duration N of static storage and a second preset number of first charge-discharge cycles K on each test battery within a preset period T, wherein the number of executions is a first preset number L, and the preset period T is the product of the first preset number L and the preset test duration N; obtain a first energy value that the test battery can release during the discharge process of the last first charge-discharge; and obtain a first decay rate based on the first energy value and the initial energy of the test battery.

[0061] Specifically, in the calendar decay test, the processor 30 controls the execution of a static storage period and a first charge-discharge cycle of a second preset number of cycles (K) to be performed a first preset number of cycles (L), where the static storage duration is a preset test duration (N). The total duration of the above process is the preset period (T), which is the sum of the total static storage duration and the total duration of the first charge-discharge cycle, where the total static storage duration is the product of the first preset number of cycles (L) and the preset test duration (N).

[0062] For example, if the preset test duration N is 7 days, the second preset number of cycles K is 3, and the first preset number of cycles L is 4, then the calendar decay test process is as follows: each test battery is left to stand for 7 days, undergoes 3 first charge-discharge cycles, is left to stand for 7 days, undergoes 3 first charge-discharge cycles, is left to stand for 7 days, undergoes 3 first charge-discharge cycles, is left to stand for 7 days, and undergoes 3 first charge-discharge cycles. In this case, the preset period T is 28 days plus the duration of 12 first charge-discharge cycles.

[0063] It should be noted that the first charge / discharge cycle executes the discharge process first, followed by the charging process. The energy released by the test battery during the final discharge of the first charge / discharge cycle represents the actual energy of the test battery at that time, i.e., the first energy value. Therefore, the processor 30 calculates the difference between the ratio of the first energy value to the initial energy of the test battery and 1, which is the first degradation rate. For example, if the initial energy of the test battery is 100Wh and the first energy value is 90Wh, then the first degradation rate is (1-90 / 100)%, i.e., 10%. In this case, the first degradation rate is the sum of the calendar degradation rate corresponding to the preset period T and the cycle degradation rate corresponding to 12 first charge / discharge cycles.

[0064] Therefore, in the battery energy design method of this application, within a preset period T, each test battery is alternately subjected to a first preset number of times L for a preset test duration N of static storage and a second preset number of times K of first charge and discharge cycles to obtain a first energy value and calculate a first degradation rate. The process of obtaining the first degradation rate at this time is well-regulated, and the accuracy of the obtained first degradation rate is high. Therefore, the subsequently obtained test degradation rate is closer to the pure calendar degradation that occurs in actual transportation, and the established degradation prediction model is more accurate and reliable.

[0065] Please refer to Figure 1 , Figure 4 , Figure 5 and Figure 7 In some embodiments, 033: At least two second charge-discharge cycles are performed on different test batteries under the same test conditions to obtain a second degradation rate of the test battery. The parameters of the first charge-discharge are exactly the same as those of the second charge-discharge, including: 0331: Perform a second charge and discharge cycle of a third preset number of times M on different test batteries under the same test conditions. The third preset number of times M is the product of the first preset number of times L and the second preset number of times K. 0333: Obtain the second energy value that the test battery can release during the final second charge-discharge process; and 0335: The second decay rate is obtained based on the second energy value and the initial energy of the test battery.

[0066] Furthermore, please combine Figure 2 The processor 30 is also configured to execute the methods in 0331, 0333, and 0335. Specifically, the processor 30 is configured to: perform a third preset number of M second charge-discharge cycles on different test batteries under the same test conditions, where the third preset number of M is the product of the first preset number of L and the second preset number of K; obtain a second energy value that the test battery can release during the discharge process of the last second charge-discharge cycle; and obtain a second decay rate based on the second energy value and the initial energy of the test battery.

[0067] Specifically, in the cyclic degradation test, the processor 30 controls another test battery that is under the same operating conditions as the test batteries in the calendar degradation test to undergo a third preset number of second charge-discharge cycles M. Here, the third preset number of cycles M is the product of the first preset number of cycles L and the second preset number of cycles K. For example, if the first preset number of cycles L is 4 and the second preset number of cycles K is 3, then the calendar degradation test involves 12 first charge-discharge cycles, and the corresponding third preset number of cycles M is 12. Therefore, in the cyclic degradation test, each test battery undergoes 12 second charge-discharge cycles.

[0068] Similarly, in the second charge / discharge cycle, the discharge process is executed first, followed by the charging process. The energy released by the test battery during the final discharge of the second charge / discharge cycle represents the actual energy of the test battery, i.e., the second energy value. Therefore, the processor 30 calculates the difference between the ratio of the second energy value to the initial energy of the test battery and 1, which is the second degradation rate. For example, if the initial energy of the test battery is 100Wh and the second energy value is 98Wh, then the second degradation rate is (1-98 / 100)%, or 2%. At this point, the second degradation rate is the sum of the cycle degradation rates corresponding to the third preset number of M second charge / discharge cycles. It can be understood that the test degradation rate at this point is 10%-2%=8%, which is the calendar degradation rate corresponding to the preset period T is 8%.

[0069] Therefore, in the battery energy design method of this application, each test battery undergoes a third preset number of second charge-discharge cycles (M times) to obtain a second energy value and calculate a second decay rate. The process for obtaining the second decay rate at this point is well-regulated, and the accuracy of the obtained second decay rate is high, making it better suited for correcting the first decay rate. Consequently, the subsequently obtained test decay rate is closer to the pure calendar decay that occurs during actual transportation, and the established decay prediction model is more accurate and reliable.

[0070] Please refer to Figure 1 , Figure 4 and Figure 8 In some implementations, 05: Establishing an attenuation prediction model based on the test attenuation rate and energy attenuation algorithm, including: 051: Input the test attenuation rate and preset period T under each test condition into the energy attenuation algorithm to determine the attenuation prediction sub-model under that test condition; and 053: Integrate the attenuation prediction sub-models under various test conditions to obtain the attenuation prediction model.

[0071] Furthermore, please combine Figure 2The processor 30 is also used to execute the methods in 051 and 053. Specifically, the processor 30 is configured to: input the test attenuation rate and preset period T under each test condition into the energy attenuation algorithm to determine the attenuation prediction sub-model under that test condition; and integrate the attenuation prediction sub-models under each test condition to obtain the attenuation prediction model.

[0072] Specifically, as mentioned above, the energy decay algorithm is an algorithm for determining the calendar decay rate under specific operating conditions (temperature and / or SOC) based on the Arrhenius equation. According to the Arrhenius equation, the calendar decay rate... The model is:

[0073] In the above model, t represents the number of storage days. To store the calendar decay rate corresponding to day t under specific operating conditions, i.e., the test decay rate obtained from the test experiment, A and z are constant coefficients. Taking the logarithm to the base e of both sides of the equation yields:

[0074] It can be understood that the above formula corresponds to the energy decay algorithm, where the logarithm of the calendar decay rate to the base e has a linear relationship with ln(t), with a slope of z and an intercept of A. Therefore, by inputting the test decay rate obtained from the test experiment under each test condition and the preset period T into the energy decay algorithm, the processor 30 can obtain the values ​​of z and A under each test condition. Substituting the values ​​of z and A corresponding to each test condition into the Arrhenius equation, the processor 30 can determine the decay prediction sub-model under that test condition. It can be understood that the decay prediction sub-model is the Arrhenius equation with coefficients corresponding to each test condition and having known values.

[0075] Furthermore, the processor 30 integrates the attenuation prediction sub-models under various test conditions to obtain an attenuation prediction model that covers all types of test conditions. For example, the application process of the attenuation prediction model is as follows: The manufacturer determines that the transportation time for a battery to be designed is one year, the transportation temperature is 25°C, and the nominal energy is 100Wh. The manufacturer inputs the one-year transportation time, the 25°C transportation temperature, and the 100Wh nominal energy into the design device 100. The processor 30 retrieves the attenuation prediction sub-model corresponding to the 25°C transportation temperature from the attenuation prediction model, and then inputs the one-year transportation time into this attenuation prediction sub-model to obtain the transportation attenuation rate, assuming it is 15%. The processor 30 then determines the replenishment energy value to be 15% × 100Wh = 15Wh, and the factory energy value to be delivered is 100Wh + 15Wh = 115Wh. Therefore, the manufacturer designs the energy of the battery to be designed to be 115Wh to ensure that the battery to be tested has an energy of 100Wh when it arrives at the customer after one year of transportation, thus meeting the customer's needs.

[0076] Therefore, in the battery energy design method of this application, the attenuation prediction sub-model under each test condition is determined based on the test attenuation rate, preset period T and energy attenuation algorithm, and the attenuation prediction model is obtained by summarizing. At this time, the attenuation model can be used to predict the transportation attenuation rate of the battery under each condition, so as to determine the factory energy value of the battery.

[0077] Therefore, there is no need to use actual measurement methods to simulate the transportation process. Instead, the energy value of the battery to be designed can be accurately and efficiently determined through the attenuation prediction model. The time and manpower cost of establishing the attenuation prediction model are relatively small, and it can be used in the design process of batteries under various transportation conditions after it is established, which greatly saves time and manpower costs.

[0078] Please refer to Figure 9 This application provides a computer-readable storage medium including a computer program. When the computer program is run on a processor, it causes the processor to perform the design method described in any of the above embodiments.

[0079] Specifically, the processor in this application embodiment may be the same as or different from the processor 30 in the design device 100, and this application does not impose any restrictions on this. For example, when a computer program is executed by the processor, the following method is implemented: 07: Obtain the preset degradation prediction model, the nominal energy of the battery to be designed, and the transportation conditions. The transportation conditions must include at least the transportation duration and temperature of the battery to be designed. The degradation prediction model is related to a preset period T, and the preset period T is less than the transportation duration; and 09: Determine the factory energy value of the battery to be designed based on the attenuation prediction model, nominal energy, and transportation conditions.

[0080] For example, when a computer program is executed by a processor, the following method is implemented: 091: Input the transportation conditions of the battery to be designed into the attenuation prediction model to obtain the transportation attenuation rate of the battery to be designed under transportation conditions; 093: Determine the supplementary energy value based on the transportation attenuation rate and nominal energy; and 095: Determine the factory energy value based on the replenished energy value.

[0081] For example, when a computer program is executed by a processor, it can also implement the methods in 01, 03, 05, 031, 033, 035, 0311, 0313, 0315, 0331, 0333, 0335, 051, and 053.

[0082] In the computer-readable storage medium of this application, the nominal energy, transportation time, and transportation temperature of the battery to be designed are input into a preset degradation prediction model to determine the factory energy value of the battery to be designed. The degradation prediction model is related to a preset period T, and the preset period T is shorter than the transportation time. Therefore, there is no need to simulate the transportation process using actual measurement methods. Instead, the factory energy value of the battery to be designed can be accurately and efficiently determined using a degradation prediction model related to a shorter preset period T, significantly saving time and manpower costs.

[0083] Please refer to Figure 10 This application provides a computer program product, which includes a computer program stored in a computer-readable storage medium. A processor reads from the computer-readable storage medium and executes the computer program to implement the design method described in any of the above embodiments.

[0084] Specifically, the processor in this embodiment may be the same as or different from the processor 30 in 100, and this application does not impose any restrictions on this. For example, when a computer program is executed by the processor, the following method is implemented: 07: Obtain the preset degradation prediction model, the nominal energy of the battery to be designed, and the transportation conditions. The transportation conditions must include at least the transportation duration and temperature of the battery to be designed. The degradation prediction model is related to a preset period T, and the preset period T is less than the transportation duration; and 09: Determine the factory energy value of the battery to be designed based on the attenuation prediction model, nominal energy, and transportation conditions.

[0085] For example, when a computer program is executed by a processor, the following method is implemented: 091: Input the transportation conditions of the battery to be designed into the attenuation prediction model to obtain the transportation attenuation rate of the battery to be designed under transportation conditions; 093: Determine the supplementary energy value based on the transportation attenuation rate and nominal energy; and 095: Determine the factory energy value based on the replenished energy value.

[0086] For example, when a computer program is executed by a processor, it can also implement the methods in 01, 03, 05, 031, 033, 035, 0311, 0313, 0315, 0331, 0333, 0335, 051, and 053.

[0087] In the computer program product of this application, the nominal energy, transportation time, and transportation temperature of the battery to be designed are input into a preset degradation prediction model to determine the factory energy value of the battery. The degradation prediction model is related to a preset period T, and the preset period T is shorter than the transportation time. Therefore, there is no need to simulate the transportation process using actual measurement methods. Instead, the factory energy value of the battery to be designed can be accurately and efficiently determined using a degradation prediction model related to a shorter preset period T, significantly saving time and labor costs.

[0088] The technical features of the embodiments described above can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification. Furthermore, other implementation methods can be derived from the above embodiments, allowing for structural and logical substitutions and changes without departing from the scope of this disclosure.

[0089] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for designing battery energy, characterized in that, include: Obtain a preset attenuation prediction model, the nominal energy of the battery to be designed, and transportation conditions. The transportation conditions include at least the transportation time and transportation temperature of the battery to be designed. The attenuation prediction model is related to a preset period T, and the preset period T is less than the transportation time. and The factory energy value of the battery to be designed is determined based on the attenuation prediction model, the nominal energy, and the transportation conditions.

2. The design method according to claim 1, characterized in that, The step of determining the factory energy value of the battery to be designed based on the attenuation prediction model, the nominal energy, and the transportation conditions includes: The transportation conditions of the battery to be designed are input into the attenuation prediction model to obtain the transportation attenuation rate of the battery to be designed under the transportation conditions. The supplementary energy value is determined based on the transport attenuation rate and the nominal energy; and The factory energy value is determined based on the replenished energy value and the nominal energy value.

3. The design method according to claim 1, characterized in that, Also includes: Establish a test matrix comprising multiple test batteries under different test conditions, wherein the test conditions include at least a test temperature, and the number of test batteries under each test condition is at least two. The test matrix is ​​subjected to attenuation test at the preset period T, and the test attenuation rate of each test battery under the corresponding test conditions is obtained. and An attenuation prediction model is established based on the test attenuation rate and the preset energy attenuation algorithm.

4. The design method according to claim 3, characterized in that, The step of performing attenuation testing on the test matrix at the preset period T and obtaining the attenuation rate of each test battery under the corresponding test conditions includes: Within the preset period T, each of the test batteries is alternately subjected to static storage and at least two first charge-discharge cycles to obtain the first degradation rate of the test battery. At least two second charge-discharge cycles are performed on different test batteries under the same test conditions to obtain a second degradation rate of the test battery, wherein the parameters of the first charge-discharge are exactly the same as the parameters of the second charge-discharge; and The test attenuation rate is obtained based on the first attenuation rate and the second attenuation rate.

5. The design method according to claim 4, characterized in that, The charging and discharging temperature, charging current rate, discharging current rate, and depth of discharge of the first charging and discharging are the same as those of the second charging and discharging.

6. The design method according to claim 4, characterized in that, The step of alternately performing static storage and at least two first charge-discharge cycles on each of the test batteries within the preset period T to obtain the first degradation rate of the test batteries includes: Within the preset period T, each of the test batteries is alternately subjected to a preset test duration N of static storage and a second preset number of first charge-discharge cycles K, the number of cycles being the first preset number L. The preset period T is greater than the product of the first preset number L and the preset test duration N. During the final discharge of the first charge-discharge cycle, the test battery was able to release a first energy value; and The first decay rate is obtained based on the first energy value and the initial energy of the test battery.

7. The design method according to claim 6, characterized in that, The test batteries under the same test conditions are subjected to at least two second charge-discharge cycles to obtain a second degradation rate of the test batteries. The parameters of the first charge-discharge cycle are exactly the same as those of the second charge-discharge cycle, including: Perform a second charge-discharge cycle of a third preset number M times on different test batteries under the same test conditions, wherein the third preset number M is the product of the first preset number L and the second preset number K; During the final discharge process of the second charge-discharge cycle, the second energy value that the test battery can release is obtained; and The second decay rate is obtained based on the second energy value and the initial energy of the test battery.

8. The design method according to claim 3, characterized in that, The step of establishing an attenuation prediction model based on the test attenuation rate and energy attenuation algorithm includes: The test attenuation rate and the preset period T under each test condition are input into the energy attenuation algorithm to determine the attenuation prediction sub-model under that test condition; and The attenuation prediction sub-models under each of the aforementioned test conditions are integrated to obtain the attenuation prediction model.

9. A battery energy design device, comprising a memory and a processor, wherein the memory is used to store instructions, characterized in that, The instructions stored in the memory are executed by the processor to implement the design method of any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, Includes a computer program, which, when run on a processor, causes the processor to perform the design method according to any one of claims 1-8.

11. A computer program product, characterized in that, The computer program product includes a computer program stored in a computer-readable storage medium, and a processor reads from and executes the computer program to implement the design method of any one of claims 1-8.

Images