Battery life prediction method and apparatus, and program product

Abstract

The present invention relates to the technical field of battery life management. Disclosed are a battery life prediction method and apparatus, and a program product. In the present invention, a target fast charging strategy at a current temperature for controlling constant current charging of a battery between specific states of charge is acquired, so that stable charging conditions are provided for inflection point identification, thereby reducing interferences caused by current instability. Furthermore, charging and inflection point identification are performed under the target fast charging strategy, and a second full charge capacity value and a second inflection point capacity value of a battery under test under the same target fast charging strategy are acquired, so that the relative state of health and remaining life of a battery to be predicted can be more accurately estimated, thereby improving the accuracy and reliability of prediction. Finally, the battery life is determined on the basis of the obtained plurality of parameters of the battery under test and the battery to be predicted, so that the actual situation of the battery can be more comprehensively considered, thereby more accurately predicting the battery life.

Description

Battery life prediction methods, devices and procedures

[0001] Cross-references to related applications

[0002] This application claims priority to Chinese Patent Application No. 202510365450.9, filed on March 25, 2025, entitled "Battery Life Prediction Method, Apparatus and Procedure Product", the entire contents of which are incorporated herein by reference. Technical Field

[0003] This invention relates to the field of battery life management technology, specifically to a battery life prediction method, device, and program product. Background Technology

[0004] Accurate prediction of battery state of health (SOH) is crucial for ensuring the safe and efficient operation of electric vehicles. It is also a significant factor influencing battery state of energy (SOE) and state of charge (SOC), directly impacting the precise calculation of battery capacity and energy levels. However, in practical applications, calculating SOH through full charge and discharge cycles is complex and time-consuming, making it difficult to implement effectively. Therefore, developing a reasonable SOH prediction method is of great importance for achieving precise battery control.

[0005] Currently, there are two main technical solutions: First, based on the required temperature and cycle capacity, actual measurements are taken to obtain the variation of SOH with cycle capacity and cycle temperature, and then an aging curve is generated for the corresponding state based on the actual temperature and cycle capacity. However, this method has limitations; the static data measured in the laboratory often fails to accurately reflect the dynamic condition of the battery in actual use. Second, the inflection point method is used to obtain the inflection point position and correct the SOH. Its core principle is that certain characteristic parameters of the battery will change significantly under specific conditions during charging. By capturing the inflection points formed by these changes, the health status of the battery can be inferred more accurately.

[0006] Ideally, the inflection point can be easily identified using algorithms based on the curve formed by a relatively stable charging current. However, in actual fast charging, the charging strategy is often complex in pursuit of rapid charging speeds, leading to frequent changes in current. Furthermore, these frequent current fluctuations disrupt the originally clear battery charging characteristic curve, significantly increasing the difficulty of inflection point identification. Simultaneously, inflection point identification algorithms, originally suitable for stable current conditions, are prone to misjudgment or missed detection when faced with such complex and variable current conditions.

[0007] Furthermore, the reduced accuracy of inflection point identification directly affects the accuracy and reliability of SOH correction based on the inflection point method. Consequently, due to the correction deviation of SOH, the accuracy of subsequent battery life prediction based on SOH is low. Summary of the Invention

[0008] In view of this, the present invention provides a battery life prediction method, device and program product to solve the problem that in the current actual fast charging process, frequent current fluctuations reduce the accuracy of inflection point identification, which in turn reduces the accuracy of battery life prediction results based on SOH.

[0009] In a first aspect, the present invention provides a battery life prediction method, the method comprising:

[0010] A target fast charging strategy for the battery to be predicted is obtained at the current temperature. The target fast charging strategy is used to control the battery to be predicted to be charged at a constant current between a first state of charge and a second state of charge. The battery to be predicted is charged using the target fast charging strategy and the inflection point is identified to obtain the first full-charge capacity value and the first inflection point capacity value of the battery to be predicted. The second full-charge capacity value and the second inflection point capacity value of the test battery under the target fast charging strategy are obtained. The battery life of the battery to be predicted is determined based on the first full-charge capacity value, the first inflection point capacity value, the second full-charge capacity value, and the second inflection point capacity value.

[0011] The battery life prediction method provided by this invention obtains a target fast-charging strategy at the current temperature, enabling the charging process to adapt to the battery's current temperature characteristics. This avoids unreasonable charging strategies caused by temperature factors, improving charging efficiency and battery safety. Simultaneously, this target fast-charging strategy controls the battery to charge at a constant current between specific states of charge, providing stable charging conditions for subsequent inflection point identification and capacity measurement. This reduces interference caused by current instability and helps to predict battery life more accurately. Furthermore, charging and inflection point identification under the target fast-charging strategy can obtain accurate first full-charge capacity and first inflection point capacity values ​​reflecting the battery's current state. This helps to understand the health status and performance degradation of the battery to be predicted, providing a reliable data foundation for subsequent battery life prediction. Furthermore, obtaining the second full-charge capacity and second inflection point capacity values ​​of the test battery under the same target fast-charging strategy establishes a correlation between the test battery and the battery to be predicted. By comparing the parameters of the two under the same conditions, the relative health status and remaining life of the battery to be predicted can be assessed more accurately, improving the accuracy and reliability of the prediction. Finally, by using multiple parameters of the obtained test battery and the battery to be predicted, the battery life can be determined. Compared with a single parameter or a simple estimation method, this method can take into account the actual situation of the battery more comprehensively, thereby predicting the battery life more accurately and providing strong support for battery management and safe operation of equipment.

[0012] In one alternative implementation, before obtaining the target fast charging strategy for the battery to be predicted at the current temperature, the method further includes:

[0013] For any given first temperature, a preset fast charging strategy for the test battery at the first temperature is obtained, and a first charging current for the test battery in the second state of charge is determined based on the preset fast charging strategy. When the test battery meets the charging conditions, the first charging current is used to perform constant current charging on the test battery, and inflection point identification is performed during the charging process. When a target inflection point is identified, the first temperature is determined as the target temperature. The charging current of the test battery between the first and second states of charge in the preset fast charging strategy corresponding to the target temperature is updated to the first charging current, and an updated fast charging strategy is obtained, generating a first mapping relationship between the target temperature and the updated fast charging strategy.

[0014] The battery life prediction method provided by this invention determines the first charging current of the test battery in its second state of charge by obtaining a preset fast charging strategy. This allows for setting an appropriate charging current based on the battery's characteristics under different states of charge, avoiding low charging efficiency or battery damage caused by improper current settings, thus improving charging safety and efficiency. Furthermore, when charging conditions are met, constant current charging is performed using the first charging current, and inflection point identification is performed. This allows for the acquisition of the target temperature of the inflection point, which is a crucial indicator of battery characteristics, thereby accurately reflecting the battery's state changes during charging. Furthermore, updating the charging current in the preset fast charging strategy corresponding to the target temperature to the first charging current yields a more optimized updated fast charging strategy and generates a mapping relationship between temperature and the fast charging strategy. This better adapts to the battery's characteristics at different temperatures, increasing the likelihood of identifying inflection points and correcting SOH (State of Health) during actual fast charging, providing a more reliable fast charging strategy basis for accurate battery life prediction.

[0015] In one alternative implementation, the method further includes:

[0016] At the target temperature, the test battery is charged using the updated fast charging strategy corresponding to the target temperature, and the third full charge capacity value of the test battery is determined. During the charging process, the inflection point of the test battery is identified, and the third inflection point capacity value of the target inflection point is determined. A second mapping relationship is generated between the target temperature, the third full charge capacity value, and the third inflection point capacity value.

[0017] The battery life prediction method provided by this invention charges the test battery using an updated fast-charging strategy at a target temperature and determines a third full-charge capacity value. This allows for the acquisition of accurate full-charge capacity data of the battery under the optimized fast-charging strategy, thus better reflecting the battery's performance under suitable actual conditions. Furthermore, identifying the inflection point during the charging process and determining the third inflection point capacity value provides further insight into the battery's important state parameters under this temperature and fast-charging strategy. Finally, establishing a second mapping relationship between the target temperature, the third full-charge capacity value, and the third inflection point capacity value facilitates the rapid and accurate acquisition of relevant capacity parameters based on the battery's temperature, contributing to more efficient battery life prediction and management.

[0018] In one optional implementation, obtaining the target fast charging strategy for the battery to be predicted at the current temperature includes: using a first mapping relationship to determine the target fast charging strategy for the battery to be predicted at the current temperature.

[0019] The battery life prediction method provided by this invention can quickly and accurately determine the target fast charging strategy of the battery to be predicted at the current temperature by utilizing the previously established first mapping relationship. This avoids the risks of blindly selecting a fast charging strategy at different temperatures, and enables the charging process to be optimized according to the current temperature characteristics of the battery, thereby improving charging efficiency and battery safety. It also provides suitable charging conditions for subsequent accurate inflection point identification and battery life prediction.

[0020] In one optional implementation, obtaining the second full-charge capacity value and the second inflection point capacity value of the test battery under the target fast charging strategy includes: using a second mapping relationship to determine the second full-charge capacity value and the second inflection point capacity value of the test battery under the target fast charging strategy.

[0021] The battery life prediction method provided by this invention can quickly and accurately obtain the second full-charge capacity value and the second inflection point capacity value of the test battery under the target fast charging strategy by utilizing the second mapping relationship. This reduces the workload of actual testing and ensures the accuracy and reliability of the obtained capacity parameters. It provides reliable reference data for subsequent accurate prediction of the battery life, thereby improving the efficiency and accuracy of battery life prediction.

[0022] In one optional implementation, a target fast charging strategy is used to charge the battery to be predicted and inflection point identification is performed to obtain the first full-charge capacity value and the first inflection point capacity value of the battery to be predicted, including:

[0023] The target fast charging strategy is used to charge the battery to be predicted and obtain the first full charge capacity value of the battery to be predicted; the first voltage-capacity differential curve is obtained, which is used to characterize the relationship between the voltage and capacity of the battery to be predicted; multiple voltage-capacity ratios are determined based on the first voltage-capacity differential curve; each voltage-capacity ratio is compared with a preset threshold to determine the inflection point of the battery to be predicted and the first inflection point capacity value of the battery to be predicted is determined.

[0024] The battery life prediction method provided by this invention charges the battery under a target fast charging strategy and obtains a first full-charge capacity value, which can accurately reflect the full-charge capacity data reflecting the current state of the battery. Furthermore, by obtaining a first voltage-capacity differential curve, the relationship between battery voltage and capacity can be intuitively reflected, and the charging characteristics of the battery can be quantified. This helps to analyze the performance changes of the battery during charging in more depth and provides richer information for accurately identifying inflection points. Furthermore, by determining multiple voltage-capacity ratios based on the first voltage-capacity differential curve, the voltage-capacity change relationship of the battery can be further quantified, thus transforming inflection point identification from qualitative analysis to quantitative analysis. This enhances the accuracy and scientific rigor of the analysis, avoids errors caused by subjective judgment, and improves the reliability of inflection point identification. Finally, by comparing the voltage-capacity ratio with a preset threshold to determine the inflection point and obtaining the first inflection point capacity value, the location where the battery state undergoes a significant change during charging can be objectively and accurately determined, which helps to predict battery life more accurately.

[0025] In one optional implementation, determining the battery life of the battery to be predicted based on a first full-charge capacity value, a first inflection point capacity value, a second full-charge capacity value, and a second inflection point capacity value includes:

[0026] The actual capacity of the battery to be predicted is calculated based on the first full charge capacity value, the first inflection point capacity value, and the second inflection point capacity value; the battery life of the battery to be predicted is determined based on the actual capacity value and the second full charge capacity value.

[0027] The battery life prediction method provided by this invention calculates the actual capacity value of the battery to be predicted by combining relevant parameters of the test battery and the battery to be predicted. This method can more accurately reflect the true capacity of the battery to be predicted. Compared with calculation based on a single parameter, it considers more factors affecting battery capacity, thus improving the accuracy of the actual capacity value calculation. Furthermore, life prediction based on accurate actual capacity values ​​makes the battery life prediction results more reliable.

[0028] In one optional implementation, the battery to be tested is a lithium iron phosphate battery, the first state of charge ranges from [50%, 60%], and the second state of charge ranges from [65%, 75%].

[0029] The battery life prediction method provided by this invention, by specifying that the battery under test is a lithium iron phosphate battery, enables more targeted optimization of charging strategies and battery life prediction, thus improving the applicability and accuracy of the life prediction method. Simultaneously, by defining the value ranges of the first and second states of charge, it helps to perform charging and analysis within a reasonable range, avoiding errors and risks caused by inappropriate states of charge, and improving the reliability of battery testing and life prediction.

[0030] In a second aspect, the present invention provides a battery life prediction device, the device comprising:

[0031] The first acquisition module is used to acquire the target fast charging strategy of the battery to be predicted at the current temperature, wherein the target fast charging strategy is used to control the battery to be predicted to be charged at a constant current between the first state of charge and the second state of charge; the first identification module is used to charge the battery to be predicted using the target fast charging strategy and identify the inflection point to obtain the first full charge capacity value and the first inflection point capacity value of the battery to be predicted; the second acquisition module is used to acquire the second full charge capacity value and the second inflection point capacity value of the test battery under the target fast charging strategy; the first determination module is used to determine the battery life of the battery to be predicted based on the first full charge capacity value, the first inflection point capacity value, the second full charge capacity value, and the second inflection point capacity value.

[0032] Thirdly, the present invention provides a computer program product, including computer instructions for causing a computer to execute the battery life prediction method of the first aspect or any corresponding embodiment thereof. Attached Figure Description

[0033] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0034] Figure 1 is a flowchart illustrating a battery life prediction method according to an embodiment of the present invention;

[0035] Figure 2 is a flowchart illustrating another battery life prediction method according to an embodiment of the present invention;

[0036] Figure 3 is a flowchart illustrating another battery life prediction method according to an embodiment of the present invention;

[0037] Figure 4 is a structural block diagram of a battery life prediction device according to an embodiment of the present invention;

[0038] Figure 5 is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] Accurate prediction of battery state of health (SOH) is an important foundation for ensuring the safe and efficient operation of electric vehicles. It is also an important factor affecting battery state of energy (SOE) and battery state of charge (SOC), and is directly related to the accurate calculation of electric vehicle charge and energy.

[0041] For example, during the daily charging process of electric vehicles, by accurately predicting battery life, the system can provide owners with more accurate battery health information, such as estimating the remaining driving range and reminding owners when to perform maintenance or replace the battery. This prevents vehicle breakdowns due to sudden battery life degradation, ensuring travel safety and convenience. Therefore, developing a reasonable SOH prediction method is of great significance for achieving precise battery control.

[0042] According to an embodiment of the present invention, a battery life prediction method embodiment is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0043] This embodiment provides a battery life prediction method, which can be used in electronic devices such as computers, mobile phones, and tablets. Figure 1 is a flowchart of the battery life prediction method according to an embodiment of the present invention. As shown in Figure 1, the process includes the following steps:

[0044] Step S101: Obtain the target fast charging strategy for the battery to be predicted at the current temperature.

[0045] In this context, "battery to be predicted" refers to a battery whose future performance, lifespan, or other relevant characteristics need to be estimated through certain methods or tests. In this embodiment, "battery to be predicted" refers to a battery whose lifespan needs to be predicted. For example, a battery currently in use in a vehicle.

[0046] The target fast charging strategy is used to represent the relationship between the battery's state of charge (SOC) and the charging current during the charging process. Under a typical fast charging strategy, the battery charging current decreases continuously as the SOC increases. This application does not restrict the magnitude or variation of the charging current when the SOC is less than the first state of charge and greater than the second state of charge in the target fast charging strategy, as long as the battery to be predicted is charged with constant current between the first and second states of charge, where the first state of charge is less than the second state of charge.

[0047] In this embodiment, the first state of charge (SBC) and the second state of charge (SOC) are two pre-set different SOC values, which can be pre-set according to the type of battery to be predicted. The values ​​of the first SOC and the second SOC can be determined based on the SOC range where the battery to be predicted reaches its inflection point. The first SOC is less than the lower limit of the SOC range, and the second SOC is greater than the upper limit of the SOC range. In an optional embodiment, when the battery to be predicted is a lithium iron phosphate battery, the range of the first SOC is [50%, 60%], and the range of the second SOC is [65%, 75%].

[0048] Specifically, different temperatures correspond to different target fast charging strategies. Therefore, by obtaining and determining the corresponding target fast charging strategy based on the current temperature of the battery to be predicted, the subsequent charging process can be adapted to the current temperature characteristics of the battery, avoiding unreasonable charging strategies caused by temperature factors, and improving charging efficiency and battery safety.

[0049] Step S102: Use the target fast charging strategy to charge the battery to be predicted and identify the inflection point to obtain the first full charge capacity value and the first inflection point capacity value of the battery to be predicted.

[0050] The first full charge capacity value represents the total capacity of the battery charged from the initial charging state to the full charge state during the charging process of the battery to be predicted according to the target fast charging strategy.

[0051] The first inflection point capacity value represents the capacity value charged from the initial charging state of the battery until the inflection point is identified during the process of charging the predicted battery using the target fast charging strategy and identifying the inflection point.

[0052] Specifically, the corresponding charging current can be determined through the target fast charging strategy. Furthermore, the charging current is used to charge the battery to be predicted. When the battery to be predicted reaches a fully charged state, the corresponding first full charge capacity value can be obtained.

[0053] Furthermore, the target fast charging strategy can control the battery to be predicted to perform constant current charging between the first state of charge and the second state of charge. When the battery is in the first state of charge, the inflection point identification can be performed to ensure that the battery is charged under relatively stable current conditions during the inflection point identification process, thereby improving the reliability of the inflection point identification.

[0054] Furthermore, during the inflection point identification process, the voltage, current, and other parameters of the battery to be predicted change with capacity. Therefore, by observing the changes in voltage, current, and other parameters with capacity, the turning point, i.e., the inflection point, where a significant change occurs in the internal chemical reaction or physical state of the battery, can be further determined. Then, based on the identified inflection point, the corresponding first inflection point capacity value can be obtained and determined.

[0055] Step S103: Obtain the second full-charge capacity value and the second inflection point capacity value of the test battery under the target fast charging strategy.

[0056] The test battery is used to obtain relevant data and information by performing various performance tests on it, and then to infer the condition of the battery to be predicted. In this embodiment, the test battery is used to obtain relevant data and determine the charging strategy.

[0057] The second full charge capacity value represents the total capacity of the battery charged from its initial state (i.e., SOC=0) until it reaches full charge when the test battery is charged according to the target fast charging strategy.

[0058] The second inflection point capacity value represents the capacity value charged from the initial state of the battery to the point when the battery is identified as an inflection point during the process of charging the test battery using the target fast charging strategy and identifying the inflection point.

[0059] Specifically, by testing the relevant historical test data and information of the battery, the second full-charge capacity value and the second inflection point capacity value of the test battery can be directly obtained.

[0060] Step S104: Determine the battery life of the battery to be predicted based on the first full charge capacity value, the first inflection point capacity value, the second full charge capacity value, and the second inflection point capacity value.

[0061] Specifically, by comprehensively comparing and analyzing the capacity data changes of the battery to be predicted and the test battery under the same target fast charging strategy, the health status and remaining life of the battery to be predicted can be assessed more comprehensively and accurately. Compared with a single parameter or a simple estimation method, it can take into account the actual situation of the battery more comprehensively, thereby predicting the battery life more accurately.

[0062] The battery life prediction method provided in this embodiment obtains a target fast-charging strategy at the current temperature, enabling the charging process to adapt to the battery's current temperature characteristics. This avoids unreasonable charging strategies caused by temperature factors, improving charging efficiency and battery safety. Simultaneously, the target fast-charging strategy controls the battery to charge at a constant current between specific states of charge, providing stable charging conditions for subsequent inflection point identification and capacity measurement. This reduces interference caused by current instability and helps to predict battery life more accurately. Furthermore, charging and inflection point identification under the target fast-charging strategy can obtain accurate first full-charge capacity and first inflection point capacity values ​​reflecting the battery's current state. This helps to understand the health status and performance degradation of the battery to be predicted, providing a reliable data foundation for subsequent battery life prediction. Furthermore, obtaining the second full-charge capacity and second inflection point capacity values ​​of the test battery under the same target fast-charging strategy establishes a correlation between the test battery and the battery to be predicted. By comparing the parameters of the two under the same conditions, the relative health status and remaining life of the battery to be predicted can be assessed more accurately, improving the accuracy and reliability of the prediction. Finally, by using multiple parameters of the obtained test battery and the battery to be predicted, the battery life can be determined. Compared with a single parameter or a simple estimation method, this method can take into account the actual situation of the battery more comprehensively, thereby predicting the battery life more accurately and providing strong support for battery management and safe operation of equipment.

[0063] This embodiment provides a battery life prediction method, which can be used in electronic devices such as computers, mobile phones, and tablets. Figure 2 is a flowchart of the battery life prediction method according to an embodiment of the present invention. As shown in Figure 2, the process includes the following steps:

[0064] Step S201: Obtain the target fast charging strategy for the battery to be predicted at the current temperature. For details, please refer to step S101 of the embodiment shown in Figure 1, which will not be repeated here.

[0065] Step S202: Use the target fast charging strategy to charge the battery to be predicted and identify the inflection point to obtain the first full charge capacity value and the first inflection point capacity value of the battery to be predicted.

[0066] Specifically, step S202 includes:

[0067] Step S2021: Charge the battery to be predicted using the target fast charging strategy and obtain the first full charge capacity value of the battery to be predicted.

[0068] Specifically, the corresponding charging current can be determined through the target fast charging strategy. Furthermore, the charging current is used to charge the battery to be predicted. When the battery to be predicted reaches a fully charged state, the corresponding first full charge capacity value can be obtained.

[0069] Step S2022: Obtain the first voltage-capacity differential curve.

[0070] The first voltage-capacity differential curve (dV / dQ) is used to characterize the relationship between the voltage and capacity of the battery to be predicted. It can be obtained by calculating the ratio of the voltage change (dV) to the capacity change (dQ) at two adjacent moments during the charging process and arranging these ratios in order of charging capacity (Q).

[0071] Furthermore, dV represents the difference between the voltage value at the current moment and the voltage value at the previous moment; dQ represents the difference between the battery capacity at the current moment (obtained by integrating the charging current over time) and the battery capacity at the previous moment.

[0072] Specifically, during the charging process, high-precision voltage sensors, current sensors, and timing devices can be used to collect the battery's voltage, current, and corresponding charging time in real time. The sampling frequency can be set to collect data once per second (e.g., a sampling frequency of 0.1 seconds).

[0073] Furthermore, by using the capacity value corresponding to the charging time (calculated by integrating the current over time, i.e., the capacity value at each time point is the sum of the current values ​​at all previous collection times multiplied by the time interval, such as the capacity at the 2nd second = the current at the 1st second × 1 second + the current at the 2nd second × 1 second, and so on) as the horizontal axis and the collected voltage value as the vertical axis, and connecting the data points in sequence, the corresponding charging curve can be plotted and obtained.

[0074] Furthermore, a sliding filter window is set, and the charging curve is subjected to a sliding average filter to obtain a filtered charging curve, which can effectively remove noise and fluctuations in the original charging curve data.

[0075] In this context, a sliding filter window represents an interval of fixed or variable length that slides across a data sequence with a certain step size. Specific filtering operations are performed on the data within the window to achieve purposes such as smoothing the data, removing noise, and extracting features. For example, in one instance, the size of the sliding filter window can be set to 3.

[0076] Furthermore, by sequentially selecting two adjacent data points along the data point sequence of the filtered charging curve and calculating the voltage change (dV) and capacity change (dQ), the voltage change and capacity change between adjacent data points of the entire second charging curve can be calculated pairwise.

[0077] Furthermore, with battery capacity (Q) as the horizontal axis and voltage-capacity differential ratio (dV / dQ) as the vertical axis, the points are connected sequentially to form the corresponding first voltage-capacity differential curve.

[0078] Step S2023: Determine multiple voltage-capacity ratios based on the first voltage-capacity differential curve.

[0079] Specifically, multiple data points can be selected from the first voltage-capacity differential curve according to certain rules. For example, they can be selected at equal intervals, which ensures that enough sample data is obtained to comprehensively analyze the trend of the curve, while avoiding the problem of excessive subsequent calculations and potential redundancy caused by overly dense selection of points.

[0080] Furthermore, for each selected data point, its corresponding ordinate value (dV / dQ) is the voltage-capacity ratio at that point.

[0081] The above method can be used to obtain multiple voltage-capacity ratios for multiple data points.

[0082] Step S2024: Compare each voltage-capacity ratio with a preset threshold to determine the inflection point of the battery to be predicted, and determine the first inflection point capacity value of the battery to be predicted.

[0083] Specifically, the voltage-capacity ratio of each data point can be compared with a preset threshold (e.g., 0.05V / Ah).

[0084] Furthermore, if the voltage-capacity ratio dV / dQ is greater than the preset threshold of 0.05V / Ah, the target inflection point is considered to have occurred. Further, based on the identified inflection point, the first inflection point capacity value of the battery to be predicted can be obtained and determined.

[0085] Step S203: Obtain the second full-charge capacity value and the second inflection point capacity value of the test battery under the target fast charging strategy. For details, please refer to step S103 of the embodiment shown in Figure 1, which will not be repeated here.

[0086] Step S204: Determine the battery life of the battery to be predicted based on the first full charge capacity value, the first inflection point capacity value, the second full charge capacity value, and the second inflection point capacity value.

[0087] Specifically, step S204 includes:

[0088] Step S2041: Calculate the actual capacity value of the battery to be predicted based on the first full charge capacity value, the first inflection point capacity value, and the second inflection point capacity value.

[0089] Specifically, the actual capacity of the battery to be predicted can be calculated using the following relationship (1): C=C1-C2+C3

[0090] In the formula: C represents the actual capacity value; C1 represents the first fully charged capacity value; C2 represents the first inflection point capacity value; C3 represents the second inflection point capacity value.

[0091] Since the battery under test does not start charging at SOC=0 during fast charging, experiments have shown that the battery capacity is basically the same when the inflection point is identified. Therefore, the capacity value of the battery under test from the identification of the inflection point to full charge can be calculated using C1-C2. Then, C3 (i.e., the total capacity of the battery when the inflection point is identified) is added to obtain the actual capacity value of the battery under test.

[0092] Step S2042: Determine the battery life of the battery to be predicted based on the actual capacity value and the second full charge capacity value.

[0093] Specifically, the battery life of the battery to be predicted can be calculated using the following relationship (2):

[0094] In the formula: S0H represents the battery life of the battery to be predicted; C4 represents the second full charge capacity value.

[0095] The battery life prediction method provided in this embodiment charges the battery under a target fast charging strategy and obtains a first full-charge capacity value, which can accurately reflect the full-charge capacity data reflecting the current state of the battery. Furthermore, by obtaining a first voltage-capacity differential curve, the relationship between battery voltage and capacity can be intuitively reflected, and the battery charging characteristics can be quantified. This helps to analyze the performance changes of the battery during charging in more depth and provides richer information for accurately identifying inflection points. Furthermore, by determining multiple voltage-capacity ratios based on the first voltage-capacity differential curve, the voltage-capacity change relationship of the battery can be further quantified, thus transforming inflection point identification from qualitative analysis to quantitative analysis. This enhances the accuracy and scientific rigor of the analysis, avoids errors caused by subjective judgment, and improves the reliability of inflection point identification. Finally, by comparing the voltage-capacity ratio with a preset threshold to determine the inflection point and obtaining the first inflection point capacity value, the location where the battery state undergoes a significant change during charging can be objectively and accurately determined. Furthermore, by combining relevant parameters of the test battery and the battery to be predicted, the actual capacity value of the battery to be predicted can be calculated, which can more accurately reflect the true capacity of the battery to be predicted. Compared with calculation based on a single parameter, more factors affecting battery capacity are considered, thus improving the accuracy of the actual capacity value calculation. Furthermore, lifespan prediction based on accurate actual capacity values ​​makes the battery lifespan prediction results more reliable.

[0096] This embodiment provides a battery life prediction method, which can be used in electronic devices such as computers, mobile phones, and tablets. Figure 3 is a flowchart of the battery life prediction method according to an embodiment of the present invention. As shown in Figure 3, the process includes the following steps:

[0097] Step S301: Obtain the target fast charging strategy for the battery to be predicted at the current temperature.

[0098] Specifically, step S301 includes:

[0099] Step S3011: Using the first mapping relationship, determine the target fast charging strategy for the battery to be predicted at the current temperature.

[0100] The first mapping relationship is used to characterize the correspondence between different temperatures and different fast charging strategies, and can be obtained through the following steps:

[0101] Step a1: For any first temperature, obtain the preset fast charging strategy of the test battery at the first temperature and determine the first charging current of the test battery in the second state of charge based on the preset fast charging strategy.

[0102] Different temperatures correspond to different preset fast charging strategies. The preset fast charging strategy refers to the fast charging strategy that the battery itself is pre-set before battery life prediction. It can be formulated by the battery manufacturer based on factors such as the battery's chemical composition, physical structure, performance characteristics and safety requirements, and is used to reflect the different charging currents of the test battery under different states of charge (SOC).

[0103] Specifically, during fast charging, different currents are used to charge the battery when it is at different states of charge (SOC). Generally, the charging current is larger when the battery SOC is relatively small, and smaller when the battery SOC is relatively large, in order to improve the charging speed of the battery while ensuring the charging safety of the battery.

[0104] Therefore, a pre-set fast-charging strategy for the test battery at any given first temperature can be obtained from the battery manufacturer. Furthermore, the first charging current of the test battery in its second state of charge can be determined based on the pre-set fast-charging strategy.

[0105] This application uses a preset fast charging strategy at 25°C, with a first state of charge of 60% and a second state of charge of 70% as an example for illustrative purposes.

[0106] The charging current at SOC=70% in the preset fast charging strategy at 25℃ is taken as the first charging current I. 70％,T .

[0107] Step a2: When the test battery meets the charging conditions, the test battery is charged with constant current using the first charging current, and the inflection point is identified during the charging process.

[0108] The charging conditions are used to ensure that the test battery can be safely and stably charged with a constant current. The test battery is discharged to the cutoff voltage and placed in an environment of 25°C to stabilize the battery at the first temperature. For example, in this embodiment, the test battery may also be subjected to several charge-discharge cycles and left to stand before charging to ensure battery stability. This embodiment does not limit this, and for example, the test battery is processed by the following steps (1) to (6):

[0109] (1) Place the test battery in an environment of 25°C until thermal equilibrium is reached;

[0110] (2) After constant current discharge at 0.2C to discharge cutoff voltage of 2.5V, let stand for 1 hour;

[0111] (3) Charge at 0.2C constant current and constant voltage until the charging cutoff voltage is 3.65V, then cut off current at 0.05C and let stand for 1 hour;

[0112] (4) Repeat (2)-(3) 3 times;

[0113] (5) Place the test battery at different temperatures until thermal equilibrium is reached;

[0114] (6) After discharging at a constant current of 0.2C to the discharge cutoff voltage of 2.5V, let it stand for 1 hour.

[0115] Specifically, once the test battery meets the first charging condition, the first charging current (I0) can be used. 70％,T The test battery was charged with a constant current. Because, under ideal conditions, a curve formed by a relatively stable charging current is more conducive to identifying inflection points through algorithms, constant current charging avoids the interference of complex and variable currents on the charging characteristic curve in actual fast charging, making the curve relatively clear and facilitating subsequent inflection point identification.

[0116] Meanwhile, during the charging process, inflection points can be identified and the temperature value of the test battery corresponding to the identified inflection point, i.e., the first inflection point temperature value, can be obtained.

[0117] The inflection point identification process can be referred to in step S202, and will not be repeated here.

[0118] Step a3: When the target inflection point is identified, the first temperature is determined as the target temperature.

[0119] Specifically, if the target inflection point is identified, it means that the inflection point can be successfully identified by using the first charging current to perform constant current charging on the test battery. That is, at this time, the combination of the first temperature of the preset fast charging strategy corresponding to the first charging current and the first charging current is conducive to accurately identifying the inflection point, and can overcome the problems caused by complex currents in actual fast charging.

[0120] Therefore, the first temperature corresponding to the preset fast charging strategy that can identify the inflection point is taken as the target temperature, which can then provide suitable temperature conditions for establishing a more accurate fast charging strategy.

[0121] It should be noted that if the target inflection point cannot be identified at the current first temperature, then the current first temperature will not be used as the target temperature, and the preset fast charging strategy will not be updated. In subsequent life prediction of the battery under test, the temperature at which the target inflection point cannot be identified will be avoided. That is, if the current temperature of the battery to be predicted is the first temperature at which the target inflection point cannot be identified, then the life prediction of the battery to be predicted will not be performed, so as to ensure the accuracy of the life prediction of the battery to be predicted.

[0122] Step a4: In the preset fast charging strategy corresponding to the target temperature, the charging current of the test battery between the first state of charge and the second state of charge is updated to the first charging current, and the updated fast charging strategy is obtained, generating the first mapping relationship between the target temperature and the updated fast charging strategy.

[0123] Specifically, in the preset fast charging strategy corresponding to the target temperature, the charging current of the test battery between the first state of charge and the second state of charge is updated to the first charging current to obtain the updated fast charging strategy. This allows the updated fast charging strategy to use the first charging current that can successfully identify the inflection point between the first state of charge and the second state of charge, thereby improving the accuracy of inflection point identification in the actual fast charging process, and further improving the accuracy and reliability of SOH correction based on the inflection point method.

[0124] For example, in the preset fast charging strategy corresponding to 25℃, the charging current of the test battery between the first state of charge (SOC) of 60% and the second state of charge (SOC) of 70% is updated to the first charging current I. 70％,T The charging current in other states of charge ranges of the battery remains unchanged from the current in the preset fast charging strategy. At this time, the updated preset fast charging strategy is the updated fast charging strategy corresponding to 25℃.

[0125] Furthermore, a mapping relationship can be established between the target temperature and the updated fast charging strategy to generate the first mapping relationship, which can then be stored for subsequent use.

[0126] Finally, when determining the current temperature of the battery to be predicted, the corresponding updated fast charging strategy can be found in the first mapping relationship based on the current temperature, and used as the target fast charging strategy for the battery to be predicted at the current temperature.

[0127] If a corresponding updated fast charging strategy can be found in the first mapping relationship, then the found updated fast charging strategy will be used as the target fast charging strategy for the battery to be predicted at the current temperature.

[0128] Furthermore, if no updated fast charging strategy is found in the first mapping relationship, the preset fast charging strategy of the battery to be predicted at the current temperature will continue to be used for charging, and the lifespan prediction of the battery to be predicted will not be performed.

[0129] Step S302: The target fast charging strategy is used to charge the battery to be predicted and inflection point identification is performed to obtain the first full charge capacity value and the first inflection point capacity value of the battery to be predicted. For details, please refer to step S202 of the embodiment shown in Figure 2, which will not be repeated here.

[0130] Step S303: Obtain the second full-charge capacity value and the second inflection point capacity value of the test battery under the target fast charging strategy.

[0131] Specifically, step S303 includes:

[0132] Step S3031: Using the second mapping relationship, determine the second full-charge capacity value and the second inflection point capacity value of the test battery under the target fast charging strategy.

[0133] The second mapping relationship is used to characterize the correspondence between the target temperature, full charge capacity and inflection point capacity for each updated fast charging strategy.

[0134] Specifically, after obtaining the first mapping relationship between the target temperature and the updated fast charging strategy, the following steps may also be included:

[0135] Step a5: At the target temperature, the test battery is charged using the updated fast charging strategy corresponding to the target temperature, and the third full charge capacity value of the test battery is determined.

[0136] The third full charge capacity value represents the total capacity of the battery charged from its initial state to when it reaches full charge, during the charging process of the test battery according to the updated fast charging strategy corresponding to the target temperature.

[0137] When the test battery meets the charging conditions at the target temperature, the test battery is charged using the updated fast charging strategy corresponding to the target temperature. When the test battery reaches full charge, the third full charge capacity value corresponding to the test battery can be obtained.

[0138] Step a6: During the charging process, identify the inflection point of the test battery and determine the third inflection point capacity value of the target inflection point.

[0139] The third inflection point capacity value represents the capacity value at the inflection point when the test battery is charged using the updated fast charging strategy corresponding to the target temperature and the inflection point is identified.

[0140] Specifically, as can be seen from steps a1 to a4 above, the updated fast charging strategy corresponding to the target temperature can control the test battery to perform constant current charging between the first state of charge and the second state of charge, that is, the updated fast charging strategy can successfully identify the inflection point between the first state of charge and the second state of charge. The inflection point identification process can be referred to in step S202, and will not be repeated here.

[0141] Furthermore, based on the identified inflection points, the corresponding third inflection point capacity value can be obtained and determined.

[0142] Step a7 generates a second mapping relationship between the target temperature, the third full charge capacity value, and the third inflection point capacity value.

[0143] Specifically, a correspondence can be established between the target temperature, the third full charge capacity value, and the third inflection point capacity value to generate a second mapping relationship.

[0144] Finally, after determining the target fast charging strategy for the battery to be predicted at the current temperature, the third full-charge capacity value and the third inflection point capacity value of the corresponding test battery (i.e., the battery to be predicted before leaving the factory) can be directly found in the second mapping relationship based on the target temperature of the target fast charging strategy.

[0145] Step S304: Determine the battery life of the battery to be predicted based on the first full-charge capacity value, the first inflection point capacity value, the second full-charge capacity value, and the second inflection point capacity value. For details, please refer to step S204 of the embodiment shown in Figure 2, which will not be repeated here.

[0146] The battery life prediction method provided in this embodiment determines the first charging current of the test battery in the second state of charge by obtaining the preset fast charging strategy of the test battery. This allows for setting an appropriate charging current based on the characteristics of the battery in different states of charge, avoiding low charging efficiency or battery damage caused by improper current settings, and improving charging safety and efficiency. Furthermore, when charging conditions are met, constant current charging is performed using the first charging current, and inflection point identification is performed. This allows for obtaining the target temperature of the target inflection point, which is an important indicator of battery characteristics, and thus accurately reflects the state changes of the battery during charging. Furthermore, updating the charging current in the preset fast charging strategy corresponding to the target temperature to the first charging current yields a more optimized updated fast charging strategy and generates a mapping relationship between temperature and the fast charging strategy. This better adapts to the characteristics of the battery at different temperatures, increasing the possibility of identifying inflection points and correcting SOH (State of Health) during actual fast charging. Furthermore, charging the test battery using the updated fast charging strategy at the target temperature and determining the third full-charge capacity value allows for obtaining accurate full-charge capacity data of the battery under the optimized fast charging strategy, thus better reflecting the battery performance under suitable actual conditions. Furthermore, identifying the inflection point and determining the third inflection point capacity value during the charging process allows for a deeper understanding of the battery's key state parameters under this temperature and fast-charging strategy. Finally, a second mapping relationship is established between the target temperature, the third full-charge capacity value, and the third inflection point capacity value. Furthermore, by utilizing the previously established first mapping relationship, the target fast-charging strategy for the battery under test at the current temperature can be quickly and accurately determined, avoiding the risks of blindly selecting fast-charging strategies at different temperatures. This allows the charging process to be optimized based on the battery's current temperature characteristics, improving charging efficiency and battery safety, while also providing suitable charging conditions for subsequent accurate inflection point identification and battery life prediction. Furthermore, the second mapping relationship enables the rapid and accurate acquisition of the second full-charge capacity value and the second inflection point capacity value of the test battery under the target fast-charging strategy, reducing the workload of actual testing while ensuring the accuracy and reliability of the acquired capacity parameters. This provides reliable reference data for subsequent accurate prediction of the battery's lifespan, improving the efficiency and accuracy of battery life prediction.

[0147] This embodiment also provides a battery life prediction device, which is used to implement the above embodiments and preferred embodiments, and will not be repeated as already described. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0148] This embodiment provides a battery life prediction device, as shown in Figure 4. The device includes:

[0149] The first acquisition module 401 is used to acquire the target fast charging strategy of the battery to be predicted at the current temperature, wherein the target fast charging strategy is used to control the battery to be predicted to be charged at a constant current between the first state of charge and the second state of charge.

[0150] The first identification module 402 is used to perform charging processing on the battery to be predicted using the target fast charging strategy and to identify the inflection point, so as to obtain the first full charge capacity value and the first inflection point capacity value of the battery to be predicted.

[0151] The second acquisition module 403 is used to acquire the second full-charge capacity value and the second inflection point capacity value of the test battery under the target fast charging strategy.

[0152] The first determining module 404 is used to determine the battery life of the battery to be predicted based on the first full charge capacity value, the first inflection point capacity value, the second full charge capacity value, and the second inflection point capacity value.

[0153] In some alternative embodiments, the device further includes:

[0154] The third acquisition module is used to acquire a preset fast charging strategy for the test battery at any given first temperature and determine the first charging current of the test battery in the second state of charge based on the preset fast charging strategy.

[0155] The second identification module is used to perform constant current charging on the test battery using the first charging current and to identify the inflection point during the charging process when the test battery meets the charging conditions.

[0156] The second determining module is used to determine the first temperature as the target temperature when the target inflection point is identified.

[0157] The update and generation module is used to update the charging current of the test battery between the first state of charge and the second state of charge in the preset fast charging strategy corresponding to the target temperature to the first charging current, and obtain the updated fast charging strategy to generate the first mapping relationship between the target temperature and the updated fast charging strategy.

[0158] In some alternative embodiments, the device further includes:

[0159] The charging and determination module is used to charge the test battery at the target temperature using the updated fast charging strategy corresponding to the target temperature and determine the third full charge capacity value of the test battery.

[0160] The third identification module is used to identify the inflection point of the test battery during the charging process and determine the third inflection point capacity value of the target inflection point.

[0161] The generation module is used to generate a second mapping relationship between the target temperature, the third full-charge capacity value, and the third inflection point capacity value.

[0162] In some optional implementations, the first acquisition module 401 includes:

[0163] The first determining submodule is used to determine the target fast charging strategy of the battery to be predicted at the current temperature using the first mapping relationship.

[0164] In some optional implementations, the second acquisition module 403 includes:

[0165] The second determining submodule is used to determine the second full-charge capacity value and the second inflection point capacity value of the test battery under the target fast charging strategy by utilizing the second mapping relationship.

[0166] In some alternative implementations, the first identification module 402 includes:

[0167] The charging and acquisition submodule is used to charge the battery to be predicted using the target fast charging strategy and acquire the first full charge capacity value of the battery to be predicted.

[0168] The acquisition submodule is used to acquire the first voltage-capacity differential curve, which is used to characterize the relationship between the voltage and capacity of the battery to be predicted.

[0169] The third determining submodule is used to determine multiple voltage-capacity ratios based on the first voltage-capacity differential curve.

[0170] The comparison and determination submodule is used to compare each voltage-capacity ratio with a preset threshold to determine the inflection point of the battery to be predicted and to determine the first inflection point capacity value of the battery to be predicted.

[0171] In some alternative implementations, the first determining module 404 includes:

[0172] The calculation submodule is used to calculate the actual capacity value of the battery to be predicted based on the first full charge capacity value, the first inflection point capacity value, and the second inflection point capacity value.

[0173] The fourth determination submodule is used to determine the battery life of the battery to be predicted based on the actual capacity value and the second full charge capacity value.

[0174] In some optional implementations, the battery to be tested is a lithium iron phosphate battery, the first state of charge ranges from [50%, 60%], and the second state of charge ranges from [65%, 75%].

[0175] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0176] In this embodiment, the battery life prediction device is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0177] This invention also provides a computer device having the battery life prediction device shown in FIG4 above.

[0178] Please refer to Figure 5, which is a schematic diagram of a computer device according to an optional embodiment of the present invention. As shown in Figure 5, the computer device includes one or more processors 10, a memory 20, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components communicate with each other using different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, multiple processors and / or multiple buses can be used with multiple memories, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 5 uses one processor 10 as an example.

[0179] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0180] The memory 20 stores instructions executable by at least one processor 10 to cause at least one processor 10 to perform the method shown in the above embodiments.

[0181] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0182] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0183] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0184] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0185] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0186] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for predicting battery life, characterized in that, The method includes: Obtain a target fast charging strategy for the battery to be predicted at the current temperature, wherein the target fast charging strategy is used to control the battery to be predicted to be charged at a constant current between a first state of charge and a second state of charge. The target fast charging strategy is used to charge the battery to be predicted and inflection point identification is performed to obtain the first full charge capacity value and the first inflection point capacity value of the battery to be predicted. Obtain the second full-charge capacity value and the second inflection point capacity value of the test battery under the target fast charging strategy; The battery life of the battery to be predicted is determined based on the first full charge capacity value, the first inflection point capacity value, the second full charge capacity value, and the second inflection point capacity value.

2. The method according to claim 1, characterized in that, Before obtaining the target fast charging strategy for the battery to be predicted at the current temperature, the method further includes: For any given first temperature, obtain a preset fast charging strategy for the test battery at the first temperature and determine the first charging current of the test battery in the second state of charge based on the preset fast charging strategy; When the test battery meets the charging conditions, the test battery is charged with constant current using the first charging current and inflection point is identified during the charging process. Once the target inflection point is identified, the first temperature is determined to be the target temperature; In the preset fast charging strategy corresponding to the target temperature, the charging current of the test battery between the first state of charge and the second state of charge is updated to the first charging current, and an updated fast charging strategy is obtained, generating a first mapping relationship between the target temperature and the updated fast charging strategy.

3. The method according to claim 2, characterized in that, The method further includes: At the target temperature, the test battery is charged using the updated fast charging strategy corresponding to the target temperature, and the third full charge capacity value of the test battery is determined. During the charging process, the test battery is subjected to inflection point identification, and the third inflection point capacity value of the target inflection point is determined. A second mapping relationship is generated between the target temperature, the third full charge capacity value, and the third inflection point capacity value.

4. The method according to claim 2, characterized in that, Obtain the target fast charging strategy for the battery under the current temperature, including: Using the first mapping relationship, the target fast charging strategy for the battery to be predicted at the current temperature is determined.

5. The method according to claim 3, characterized in that, Obtaining the second full-charge capacity value and the second inflection point capacity value of the test battery under the target fast charging strategy includes: Using the second mapping relationship, the second full-charge capacity value and the second inflection point capacity value of the test battery under the target fast charging strategy are determined.

6. The method according to claim 1, characterized in that, The target fast charging strategy is used to charge the battery to be predicted and inflection point identification is performed to obtain the first full-charge capacity value and the first inflection point capacity value of the battery to be predicted, including: The target fast charging strategy is used to charge the battery to be predicted, and the first full charge capacity value of the battery to be predicted is obtained. Obtain a first voltage-capacity differential curve, which is used to characterize the relationship between the voltage and capacity of the battery to be predicted; Multiple voltage-capacity ratios are determined based on the first voltage-capacity differential curve; Each voltage-capacity ratio is compared with a preset threshold to determine the inflection point of the battery to be predicted, and the first inflection point capacity value of the battery to be predicted is determined.

7. The method according to claim 1, characterized in that, The battery life of the battery to be predicted is determined based on the first full-charge capacity value, the first inflection point capacity value, the second full-charge capacity value, and the second inflection point capacity value, including: The actual capacity value of the battery to be predicted is calculated based on the first full charge capacity value, the first inflection point capacity value, and the second inflection point capacity value. The battery life of the battery to be predicted is determined based on the actual capacity value and the second full charge capacity value.

8. The method according to claim 1, characterized in that, The battery under test is a lithium iron phosphate battery, and the first state of charge ranges from [50%, 60%], while the second state of charge ranges from [65%, 75%].

9. A battery life prediction device, characterized in that, The device includes: The first acquisition module is used to acquire the target fast charging strategy of the battery to be predicted at the current temperature, wherein the target fast charging strategy is used to control the battery to be predicted to be charged at a constant current between a first state of charge and a second state of charge. The first identification module is used to charge the battery to be predicted using the target fast charging strategy and identify the inflection point to obtain the first full charge capacity value and the first inflection point capacity value of the battery to be predicted. The second acquisition module is used to acquire the second full-charge capacity value and the second inflection point capacity value of the test battery under the target fast charging strategy. The first determining module is used to determine the battery life of the battery to be predicted based on the first full charge capacity value, the first inflection point capacity value, the second full charge capacity value, and the second inflection point capacity value.

10. A computer program product, characterized in that, Includes computer instructions for causing a computer to perform the battery life prediction method according to any one of claims 1 to 8.

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