Battery life prediction method and apparatus, and program product

Abstract

The invention relates to the technical field of battery life management, and discloses a battery life prediction method and device and a program product, and provides a stable charging condition for inflection point identification by obtaining a target fast charging strategy for controlling a battery to be charged at a constant current between specific charge states at a current temperature. And interference caused by unstable current is reduced. Furthermore, charging and inflection point identification are carried out under the target fast charging strategy, and a second full charging capacity value and a second inflection point capacity value of the tested battery under the same target fast charging strategy are acquired, so that the relative health state and the residual life of the to-be-predicted battery can be evaluated more accurately, and the prediction accuracy and reliability are improved. And finally, determining the service life of the battery by using the obtained multiple parameters of the tested battery and the to-be-predicted battery, so that the actual condition of the battery can be considered more comprehensively, and the service life of the battery can be predicted more accurately.

Description

Technical Field

[0001] The present invention relates to the technical field of battery life management, and particularly to a battery life prediction method, device and program product. Background Art

[0002] Accurate prediction of the state of health (SOH) of a battery is an important basis for ensuring the safe and efficient operation of electric vehicles, and is also an important influencing factor for the state of energy (SOE) and state of charge (SOC) of the battery, which is directly related to the accurate calculation of the electric quantity and energy of electric vehicles. However, in actual use scenarios, the method of calculating SOH by fully charging and discharging is difficult to be truly implemented due to its complex operation and time-consuming nature. Therefore, developing a reasonable SOH prediction method is of great significance for achieving precise battery control.

[0003] Currently, the existing technical solutions mainly include the following two: One is to conduct actual measurements based on the required temperature and cycle capacity to obtain the variation law of SOH with cycle capacity and cycle temperature, and then generate an aging curve corresponding to the actual state based on the actual temperature and cycle capacity. However, this method has limitations, and the static data measured in the laboratory often cannot accurately reflect the dynamic situation of the battery in actual use. The other is to obtain the inflection point position based on the inflection point method to correct SOH. Its core principle is that some characteristic parameters of the battery during the charging process will change significantly under specific conditions. By capturing the inflection points formed by these changes, the health status of the battery can be inferred more accurately.

[0004] Under ideal conditions, based on the curve formed by a relatively stable charging current, it is relatively easy to identify the inflection point through an algorithm. However, during actual fast charging, due to the pursuit of a fast charging rate in fast charging technology, the charging strategy is often complex, resulting in frequent changes in the current. Furthermore, the frequent current fluctuations make the originally relatively clear battery charging characteristic curve become disordered, greatly increasing the difficulty of inflection point identification. At the same time, the inflection point identification algorithm originally applicable to stable current conditions is extremely prone to misjudgment or missed judgment when facing such complex and variable current conditions.

[0005] Furthermore, the reduction in the accuracy of inflection point identification directly affects the accuracy and reliability of correcting SOH 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 relatively low. Summary of the Invention

[0006] In view of this, the present invention provides a battery life prediction method, device and program product to solve the problem that during the actual fast charging process at the present stage, due to frequent current fluctuations, the accuracy of inflection point identification is reduced, and consequently the accuracy of the battery life prediction result based on SOH is reduced.

[0007] In a first aspect, the present invention provides a method for predicting battery life, the method comprising:

[0008] Obtain a 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 charge at a constant current between a first state of charge and a second state of charge; use the target fast charging strategy to charge the battery to be predicted and perform inflection point identification to obtain a first full charge capacity value and a first inflection point capacity value of the battery to be predicted; obtain a second full charge capacity value and a second inflection point capacity value of a test battery under the target fast charging strategy; determine the battery life of the battery to be predicted according to 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.

[0009] The method for predicting battery life provided by the present invention, by obtaining the target fast charging strategy at the current temperature, enables the charging process to be adapted according to the current temperature characteristics of the battery, avoids unreasonable charging strategies caused by temperature factors, and improves charging efficiency and battery safety. At the same time, 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, reducing interference caused by unstable current, and helping to more accurately predict battery life. Further, charging and inflection point identification are performed under the target fast charging strategy, and accurate first full charge capacity value and first inflection point capacity value reflecting the current state of the battery can be obtained, which helps to understand the health status and performance degradation of the battery to be predicted and provides a reliable data basis for subsequent battery life prediction. Further, obtaining the second full charge capacity value and the second inflection point capacity value of the test battery under the same target fast charging strategy establishes the association between the test battery and the battery to be predicted, and then 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 more accurately evaluated, improving the accuracy and reliability of the prediction. Finally, using multiple parameters of the obtained test battery and the battery to be predicted to determine the battery life can more comprehensively consider the actual situation of the battery compared with a single parameter or a simple estimation method, thus more accurately predicting the battery life and providing strong support for battery management and the safe operation of the device.

[0010] In an optional implementation manner, before obtaining the target fast charging strategy of the battery to be predicted at the current temperature, the method further comprises:

[0011] 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 at the second state of charge based on the preset fast charging strategy; when the test battery meets the charging conditions, use the first charging current to perform constant current charging on the test battery and perform inflection point identification during the charging process; when the target inflection point is identified, determine the first temperature as the target temperature; update the charging current 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, obtain the updated fast charging strategy, and generate the first mapping relationship between the target temperature and the updated fast charging strategy.

[0012] The battery life prediction method provided by the present invention can determine the first charging current of the test battery at the second state of charge by obtaining the preset fast charging strategy of the test battery, and can set appropriate charging currents according to the characteristics of the battery in different states of charge, avoiding low charging efficiency or battery damage caused by improper current setting, and improving the safety and efficiency of charging. Further, when the charging conditions are met, constant current charging is performed using the first charging current and inflection point identification is performed, so that the target temperature of the target inflection point, which has an important indication effect on the battery characteristics, can be obtained, and thus the state change of the battery during the charging process can be accurately reflected. Further, the charging current in the preset fast charging strategy corresponding to the target temperature is updated to the first charging current, a more optimized updated fast charging strategy is obtained, and the mapping relationship between the temperature and the fast charging strategy is generated, which can better adapt to the characteristics of the battery at different temperatures, improve the possibility of identifying inflection points and correcting SOH (State of Health) during actual fast charging, and provide a more reliable fast charging strategy basis for accurately predicting the battery life.

[0013] In an alternative embodiment, the method further includes:

[0014] At the target temperature, use the updated fast charging strategy corresponding to the target temperature to charge the test battery and determine the third full charge capacity value of the test battery; during the charging process, perform inflection point identification on the test battery and determine the third inflection point capacity value of the target inflection point; generate the second mapping relationship between the target temperature, the third full charge capacity value and the third inflection point capacity value.

[0015] The battery life prediction method provided by the present invention charges a test battery using an updated fast charging strategy at a target temperature and determines a third full charge capacity value, which can obtain accurate full charge capacity data of the battery under the optimized fast charging strategy, and thus can better reflect the performance of the battery under actual suitable conditions. Further, by identifying the inflection point during the charging process and determining the third inflection point capacity value, the important state parameters of the battery under this temperature and fast charging strategy can be further understood. Finally, establishing the second mapping relationship between the target temperature, the third full charge capacity value, and the third inflection point capacity value provides convenience for quickly and accurately obtaining relevant capacity parameters according to the temperature of the battery subsequently, which helps to more efficiently predict and manage the battery life.

[0016] In an alternative embodiment, obtaining the target fast charging strategy of the battery to be predicted at the current temperature includes: using the first mapping relationship to determine the target fast charging strategy of the battery to be predicted at the current temperature.

[0017] The battery life prediction method provided by the present invention can quickly and accurately determine the target fast charging strategy of the battery to be predicted at the current temperature by using the previously established first mapping relationship, avoiding the risks brought by blindly selecting fast charging strategies at different temperatures, enabling the charging process to be optimized according to the current temperature characteristics of the battery, improving the charging efficiency and battery safety, and at the same time providing suitable charging conditions for subsequent accurate inflection point identification and battery life prediction.

[0018] In an alternative embodiment, 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 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.

[0019] The battery life prediction method provided by the present 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 using the second mapping relationship, reducing the actual test workload, and at the same time ensuring the accuracy and reliability of the obtained capacity parameters, providing reliable reference data for accurately predicting the life of the battery to be predicted subsequently, and improving the efficiency and accuracy of battery life prediction.

[0020] In an alternative embodiment, charging the battery to be predicted using the target fast charging strategy and performing inflection point identification to obtain the first full charge capacity value and the first inflection point capacity value of the battery to be predicted includes:

[0021] 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; obtain the first voltage-capacity differential curve, which is used to characterize the change relationship between the voltage and capacity of the battery to be predicted; determine a plurality of voltage-capacity ratios according to the first voltage-capacity differential curve; compare each voltage-capacity ratio with a preset threshold value 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.

[0022] The battery life prediction method provided by the present invention charges the battery under the target fast charging strategy and obtains the first full charge capacity value, and can obtain full charge capacity data that accurately reflects the current state of the battery. Further, by obtaining the first voltage-capacity differential curve, the change relationship between the battery voltage and capacity can be intuitively reflected, and at the same time, the charging characteristics of the battery are quantified, which helps to more deeply analyze the performance changes of the battery during the charging process and provides richer information for accurately identifying the inflection point. Further, determining a plurality of voltage-capacity ratios according to the first voltage-capacity differential curve can further quantify the voltage-capacity change relationship of the battery, and then make the inflection point identification change from qualitative analysis to quantitative analysis, enhancing the accuracy and scientificity of the analysis, avoiding the errors caused by subjective judgment, and improving the reliability of the inflection point identification. Finally, by comparing the voltage-capacity ratio with the preset threshold value to determine the inflection point and obtaining the first inflection point capacity value, the position where the state of the battery changes significantly during the charging process can be objectively and accurately judged, which helps to more accurately predict the battery life.

[0023] In an optional implementation manner, determining the battery life of the battery to be predicted according to 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 includes:

[0024] Calculate the actual capacity value of the battery to be predicted according to the first full charge capacity value, the first inflection point capacity value, and the second inflection point capacity value; determine the battery life of the battery to be predicted according to the actual capacity value and the second full charge capacity value.

[0025] The battery life prediction method provided by the present invention calculates the actual capacity value of the battery to be predicted by combining the relevant parameters of the test battery and the battery to be predicted, and can more accurately reflect the true capacity of the battery to be predicted. Compared with single-parameter calculation, more factors affecting the battery capacity are considered, and the accuracy of the actual capacity value calculation is improved. Further, based on the accurate actual capacity value for life prediction, the battery life prediction result is more reliable.

[0026] In an optional implementation manner, the battery to be predicted is a lithium iron phosphate battery, the value range of the first state of charge is [50%, 60%], and the value range of the second state of charge is [65%, 75%].

[0027] The battery life prediction method provided by the present invention can more specifically optimize the charging strategy and predict the battery life by identifying the battery to be predicted as a lithium iron phosphate battery, improving the applicability and accuracy of the life prediction method. At the same time, by defining the value ranges of the first state of charge and the second state of charge, it helps to charge and analyze within a reasonable range, avoiding errors and risks caused by improper state of charge, and improving the reliability of battery testing and life prediction.

[0028] In a second aspect, the present invention provides a battery life prediction device, which includes:

[0029] A first acquisition module, configured to acquire a target fast charging strategy of the battery to be predicted at the current temperature, where the target fast charging strategy is used to control the battery to be predicted to perform constant current charging between the first state of charge and the second state of charge; a first identification module, configured to perform charging processing on the battery to be predicted using the target fast charging strategy and perform inflection point identification to obtain a first full charge capacity value and a first inflection point capacity value of the battery to be predicted; a second acquisition module, configured to acquire a second full charge capacity value and a second inflection point capacity value of the test battery under the target fast charging strategy; a first determination module, configured to determine the battery life of the battery to be predicted according to 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.

[0030] In a third aspect, the present invention provides a computer program product, including computer instructions for causing a computer to execute the battery life prediction method according to the first aspect or any corresponding embodiment thereof. Description of the Drawings

[0031] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0032] Figure 1 is a flowchart of the battery life prediction method according to an embodiment of the present invention;

[0033] Figure 2 is a flowchart of another battery life prediction method according to an embodiment of the present invention;

[0034] Figure 3 is a flowchart of yet another battery life prediction method according to an embodiment of the present invention;

[0035] Figure 4It is a structural block diagram of a battery life prediction device according to an embodiment of the present invention;

[0036] Figure 5 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed implementation manners

[0037] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0038] Accurate prediction of the state of health (SOH) of a battery is an important basis for ensuring the safe and efficient driving of an electric vehicle, and is also an important influencing factor for the state of energy (SOE) and state of charge (SOC) of the battery, which is directly related to the accurate calculation of the electric quantity and energy of the electric vehicle.

[0039] For example, during the daily charging process of an electric vehicle, by accurately predicting the battery life, the system can provide the vehicle owner with more accurate battery health status information, such as estimating the remaining mileage that can be traveled, reminding the vehicle owner when to perform maintenance or replace the battery, and avoiding vehicle breakdowns caused by sudden attenuation of the battery life, thus ensuring travel safety and convenience. Therefore, developing a reasonable SOH prediction method is of great significance for realizing accurate battery control.

[0040] According to an embodiment of the present invention, an embodiment of a battery life prediction method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0041] In this embodiment, a battery life prediction method is provided, which can be used in electronic devices such as computers, mobile phones, and tablet computers. Figure 1 It is a flowchart of the battery life prediction method according to an embodiment of the present invention. As Figure 1 shown, the process includes the following steps:

[0042] Step S101, obtain the target fast charging strategy of the battery to be predicted at the current temperature.

[0043] Among them, the battery to be predicted refers to a battery whose future performance, life, or other related characteristics need to be estimated through a certain method or test. In this embodiment, the battery to be predicted refers to a battery whose battery life needs to be predicted. For example, a battery that is currently in use in a vehicle.

[0044] The target fast charging strategy is used to represent the relationship between the battery SOC and the charging current during the charging process of the battery. Under the normal fast charging strategy, the battery charging current continuously decreases as the SOC increases. In this application, the magnitude and change of the charging current when it is less than the first state of charge and greater than the second state of charge in the target fast charging strategy are not restricted, as long as it satisfies the condition that the battery to be predicted is charged at a constant current between the first state of charge and the second state of charge, where the first state of charge is less than the second state of charge.

[0045] In this embodiment, the first state of charge and the second state of charge are two preset different state-of-charge values, which can be preset according to the type of the battery to be predicted. The value of the first state of charge and the value of the second state of charge can be determined according to the state-of-charge interval where the battery to be predicted has an inflection point. The first state of charge is less than the lower limit value of the state-of-charge interval, and the second state-of-charge interval is greater than the upper limit value of the state-of-charge interval. In an optional embodiment, when the battery to be predicted is a lithium iron phosphate battery, the value range of the first state of charge is [50%, 60%], and the value range of the second state of charge is [65%, 75%].

[0046] Specifically, different temperatures correspond to different target fast charging strategies. Therefore, obtaining and determining the corresponding target fast charging strategy according to the current temperature of the battery to be predicted can enable the subsequent charging process to be adapted according to the current temperature characteristics of the battery, avoiding unreasonable charging strategies caused by temperature factors, and improving the charging efficiency and battery safety.

[0047] Step S102: Use the target fast charging strategy to charge the battery to be predicted and perform inflection point identification to obtain the first full charge capacity value and the first inflection point capacity value of the battery to be predicted.

[0048] Among them, the first full charge capacity value represents the total capacity charged into the battery when charging the battery to be predicted according to the target fast charging strategy from the initial charging state of the battery until the battery reaches the full charge state.

[0049] The first inflection point capacity value represents the capacity value charged into the battery when charging the battery to be predicted using the target fast charging strategy and performing inflection point identification from the initial charging state of the battery until the inflection point is identified.

[0050] Specifically, the corresponding charging current can be determined through the target fast charging strategy. Further, use this charging current to charge the battery to be predicted, and when the battery to be predicted reaches the full charge state, the corresponding first full charge capacity value can be further obtained.

[0051] Further, 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 starts to identify the inflection point at the first state of charge, it can ensure that during the inflection point identification process, the battery is charged under relatively stable current conditions, improving the reliability of the inflection point identification.

[0052] Further, during the inflection point identification process, parameters such as the voltage and current of the battery to be predicted change with the capacity. Therefore, by observing the changes in parameters such as voltage and current with the capacity, the turning point where a large change occurs in the chemical reaction or physical state inside the battery, that is, the inflection point, can be further determined. Then, according to the identified inflection point, the corresponding first inflection point capacity value can be obtained and determined.

[0053] 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.

[0054] Among them, the test battery is used to obtain relevant data and information through various performance tests on it, and then infer the situation of the battery to be predicted. In this embodiment, the test battery is used to obtain relevant data and determine the charging strategy.

[0055] The second full charge capacity value represents the total capacity charged into the battery when charging the test battery according to the target fast charging strategy from the initial state of the battery (i.e., SOC = 0) until the battery reaches the full charge state.

[0056] The second inflection point capacity value represents the capacity value charged into the battery when charging the test battery using the target fast charging strategy and during the inflection point identification process from the initial state of the battery to the point where the inflection point is identified.

[0057] Specifically, through the relevant historical test data and information of the test battery, the second full charge capacity value and the second inflection point capacity value of the test battery can be directly obtained.

[0058] Step S104, determine the battery life of the battery to be predicted according to 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.

[0059] 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 evaluated more comprehensively and accurately. Compared with single parameters or simple estimation methods, it can consider the actual situation of the battery more comprehensively, thus predicting the battery life more accurately.

[0060] The battery life prediction method provided in this embodiment obtains the target fast charging strategy at the current temperature, enabling the charging process to be adapted according to the current temperature characteristics of the battery, avoiding unreasonable charging strategies caused by temperature factors, improving charging efficiency and battery safety. At the same time, the target fast charging strategy controls the battery to charge at a constant current between specific state of charge, providing stable charging conditions for subsequent inflection point identification and capacity measurement, reducing interference caused by unstable current, and helping to predict battery life more accurately. Further, charging and inflection point identification are performed under the target fast charging strategy, and the first full charge capacity value and the first inflection point capacity value that accurately reflect the current state of the battery can be obtained, which helps to understand the health status and performance degradation of the battery to be predicted, providing a reliable data basis for subsequent battery life prediction. Further, the second full charge capacity value and the second inflection point capacity value of the test battery under the same target fast charging strategy are obtained, establishing the association between the test battery and the battery to be predicted. Then, 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 evaluated more accurately, improving the accuracy and reliability of the prediction. Finally, using multiple parameters of the test battery and the battery to be predicted to determine the battery life can consider the actual situation of the battery more comprehensively compared with single parameter or simple estimation methods, thus predicting the battery life more accurately and providing strong support for battery management and the safe operation of the device.

[0061] In this embodiment, a battery life prediction method is provided, which can be used in electronic devices such as computers, mobile phones, and tablet computers. Figure 2 It is a flowchart of the battery life prediction method according to an embodiment of the present invention, as Figure 2 shown. The process includes the following steps:

[0062] Step S201, obtain the target fast charging strategy of the battery to be predicted at the current temperature. For details, please refer to Figure 1 Step S101 of the embodiment shown, which will not be elaborated here.

[0063] Step S202, use the target fast charging strategy to charge the battery to be predicted and perform inflection point identification to obtain the first full charge capacity value and the first inflection point capacity value of the battery to be predicted.

[0064] Specifically, the above step S202 includes:

[0065] Step S2021, use the target fast charging strategy to charge the battery to be predicted and obtain the first full charge capacity value of the battery to be predicted.

[0066] Specifically, the corresponding charging current can be determined through the target fast charging strategy. Further, the battery to be predicted is charged using this charging current, and when the battery to be predicted reaches the fully charged state, the corresponding first fully charged capacity value can be further obtained.

[0067] Step S2022, obtain the first voltage-capacity differential curve.

[0068] Among them, the first voltage-capacity differential curve (dV / dQ) is used to characterize the change relationship between the voltage and capacity of the battery to be predicted. It can be obtained by calculating the ratio of the voltage change amount (dV) and the capacity change amount (dQ) at two adjacent moments during the charging process and arranging these ratios in the order of the charging capacity (Q).

[0069] Further, 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.

[0070] Specifically, during the charging process, a high-precision voltage sensor, current sensor, and timing device can be used to collect the voltage value, current value, and corresponding charging time of the battery in real time. Among them, the sampling frequency can be set to collect data once per second (such as the sampling frequency is 0.1 s).

[0071] Further, using the capacity value corresponding to the charging time (calculated by integrating the current over time, that is, the capacity value at each time point is the cumulative result of multiplying the current values at all previous sampling moments 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 abscissa, and taking the collected voltage value as the ordinate, and connecting each data point in sequence, the corresponding charging curve can be plotted and obtained.

[0072] Further, set a sliding filter window and perform a moving average filter on the charging curve to obtain a filtered charging curve, which can effectively remove the noise and fluctuations in the original charging curve data.

[0073] Among them, the sliding filter window represents an interval with a fixed length or variable length, which slides on a set of data sequences at a certain step size and performs specific filtering operations on the data within the window to achieve the purposes of smoothing data, removing noise, and extracting features. For example, in one instance, the size of the sliding filter window can be set to 3.

[0074] Further, along the order of the data points of the filtered charging curve, two adjacent data points can be sequentially selected and the voltage change amount (dV) and the capacity change amount (dQ) can be calculated, and then the voltage change amount and the capacity change amount between adjacent data points of the entire second charging curve can be calculated pair by pair.

[0075] Further, with the battery capacity (Q) as the abscissa and the voltage-capacity differential ratio (dV / dQ) as the ordinate, connect the points in sequence to form a corresponding first voltage-capacity differential curve.

[0076] Step S2023: Determine multiple voltage-capacity ratios according to the first voltage-capacity differential curve.

[0077] Specifically, multiple data points can be selected in the first voltage-capacity differential curve according to a certain rule. For example, they can be selected at equal intervals, which can not only ensure obtaining enough sample data to comprehensively analyze the change trend of the curve, but also avoid the problem of excessive subsequent calculation amount and possible redundant information caused by too dense selected points.

[0078] Further, for each selected data point, the corresponding ordinate value (dV / dQ) is the voltage-capacity ratio of this point.

[0079] In the above way, multiple voltage-capacity ratios of multiple data points can be obtained.

[0080] Step S2024: Compare each voltage-capacity ratio with a preset threshold respectively 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.

[0081] Specifically, the voltage-capacity ratio of each obtained data point can be compared with a preset threshold (such as 0.05 V / Ah).

[0082] Further, if the voltage-capacity ratio dV / dQ is greater than the preset threshold of 0.05 V / Ah, it is considered that the target inflection point has occurred. Further, according to the identified inflection point, the first inflection point capacity value of the battery to be predicted can be obtained and determined.

[0083] 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 Figure 1 Step S103 of the illustrated embodiment, which will not be elaborated here.

[0084] Step S204: Determine the battery life of the battery to be predicted according to 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.

[0085] Specifically, the above step S204 includes:

[0086] Step S2041: Calculate the actual capacity value of the battery to be predicted according to the first full charge capacity value, the first inflection point capacity value and the second inflection point capacity value.

[0087] Specifically, the actual capacity value of the battery to be predicted can be calculated using the following relationship:

[0088] C = C1 - C2 + C3

[0089] Where: C represents the actual capacity value; C1 represents the first full charge capacity value; C2 represents the first inflection point capacity value; C3 represents the second inflection point capacity value.

[0090] Since the battery to be tested does not start charging at SOC = 0 during fast charging, it is known from experiments that the capacity of the battery at the identified inflection point is basically the same. Therefore, the capacity value of the battery to be tested from the identified inflection point to full charge can be calculated using C1 - C2, and then adding C3 (i.e., the total capacity of the battery when the inflection point is identified) to obtain the actual capacity value of the battery to be tested.

[0091] Step S2042, determine the battery life of the battery to be predicted according to the actual capacity value and the second full charge capacity value.

[0092] Specifically, the battery life of the battery to be predicted can be calculated using the following relationship:

[0093]

[0094] Where: 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 the target fast charging strategy and obtains the first full charge capacity value, and can obtain the full charge capacity data that accurately reflects the current state of the battery. Further, by obtaining the first voltage-capacity differential curve, the change relationship between the battery voltage and capacity can be intuitively reflected, and at the same time, the quantization of the battery charging characteristics is realized, which helps to more deeply analyze the performance changes of the battery during charging and provides richer information for accurately identifying the inflection point. Further, determining multiple voltage-capacity ratios according to the first voltage-capacity differential curve can further quantify the voltage-capacity change relationship of the battery, and then transform the inflection point identification from qualitative analysis to quantitative analysis, enhancing the accuracy and scientificity of the analysis, avoiding the errors caused by subjective judgment, and improving the reliability of the inflection point identification. Finally, by comparing the voltage-capacity ratio with the preset threshold to determine the inflection point and obtaining the first inflection point capacity value, the position where the state of the battery changes significantly during charging can be objectively and accurately judged. Further, by combining the relevant parameters of the test battery and the battery to be predicted and calculating the actual capacity value of the battery to be predicted, the true capacity situation of the battery to be predicted can be more accurately reflected. Compared with single-parameter calculation, more factors affecting the battery capacity are considered, and the accuracy of the actual capacity value calculation is improved. Further, based on the accurate actual capacity value for life prediction, the battery life prediction result is more reliable.

[0096] In this embodiment, a battery life prediction method is provided, which can be used in electronic devices such as computers, mobile phones, tablet computers, etc. Figure 3 It is a flowchart of the battery life prediction method according to the embodiment of the present invention, as Figure 3 shown, the process includes the following steps:

[0097] Step S301, obtain the target fast charging strategy of the battery to be predicted at the current temperature.

[0098] Specifically, the above step S301 includes:

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

[0100] Among them, the first mapping relationship is used to represent the corresponding relationship 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 at the second state of charge based on the preset fast charging strategy.

[0102] Among them, different temperatures respectively correspond to different preset fast charging strategies. The preset fast charging strategy represents the fast charging strategy preset by the battery itself before battery life prediction, and can be comprehensively formulated by the battery manufacturer according to factors such as the chemical composition, physical structure, performance characteristics, and safety requirements of the battery, and is used to reflect the different charging currents of the test battery at different states of charge (SOC).

[0103] Specifically, during the fast charging process of the battery, generally, when the battery is in different SOCs, different currents are used to charge the battery. Generally, when the battery SOC is relatively small, the charging current is larger, and when the battery SOC is larger, the charging current is smaller, so as to improve the charging speed of the battery while ensuring the charging safety of the battery.

[0104] Therefore, the preset fast charging strategy of the test battery at any first temperature can be obtained from the battery manufacturer. Further, the first charging current of the test battery at the second state of charge can be found according to the preset fast charging strategy.

[0105] In the embodiment of the present application, the preset fast charging strategy at 25°C, the first state of charge is 60%, and the second state of charge is 70% are taken as examples for illustrative description.

[0106] Take the charging current when SOC = 70% in the preset fast charging strategy at 25°C as the first charging current I 70％,T .

[0107] Step a2, when the test battery meets the charging conditions, charge the test battery with a first charging current in constant current mode and identify the inflection point during the charging process.

[0108] Among them, the charging conditions are used to ensure that the test battery can be charged in constant current mode safely and stably. Discharge the test battery to the cut-off voltage and place it in an environment of 25°C to make the battery stable at a first temperature. Exemplarily, in the embodiments of the present application, the test battery can also be subjected to several charge-discharge cycles and standing before charging to ensure battery stability. The embodiments of the present application do not limit this. Exemplarily, the test battery is processed through 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) Discharge at a constant current of 0.2C to the discharge cut-off voltage of 2.5V, and then stand for 1h;

[0111] (3) Charge at a constant current and constant voltage of 0.2C to the charge cut-off voltage of 3.65V, then the cut-off current is 0.05C, and stand for 1h;

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

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

[0114] (6) Discharge at a constant current of 0.2C to the discharge cut-off voltage of 2.5V, and then stand for 1h.

[0115] Specifically, when the test battery meets the first charging conditions, the test battery can be charged with a first charging current (I 70％,0 ) in constant current mode. Because in an ideal state, a curve formed by a relatively stable charging current is more conducive to identifying the inflection point through an algorithm. Therefore, constant current charging can avoid the interference of complex and variable currents in actual fast charging on the charging characteristic curve, making the curve relatively clear and facilitating subsequent inflection point identification.

[0116] At the same time, the inflection point can be identified during the charging process, and the temperature value of the test battery corresponding to the identified inflection point, that is, the first inflection point temperature value, can be obtained.

[0117] Among them, the inflection point identification can refer to the inflection point identification process in step S202, which will not be elaborated here.

[0118] Step a3, when the target inflection point is identified, determine the first temperature as the target temperature.

[0119] Specifically, if the target inflection point is identified, it means that the constant current charging of the test battery with the first charging current can successfully identify the inflection point. That is, at this time, the combination of the first temperature and the first charging current of the preset fast charging strategy corresponding to the first charging current is conducive to accurately identifying the inflection point and can overcome the problems brought 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 used as the target temperature, which can provide appropriate temperature conditions for establishing a more accurate fast charging strategy subsequently.

[0121] It should be noted that if the target inflection point cannot be identified at the current first temperature, the current first temperature is not used as the target temperature, and the preset fast charging strategy is not updated either. When predicting the life of the battery to be tested subsequently, avoid the temperature at which the target inflection point cannot be identified. 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 for the test battery, the life prediction of the battery to be predicted is not performed to ensure the accuracy of the life prediction of the battery to be predicted.

[0122] Step a4: 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, obtain the updated fast charging strategy, and generate the first mapping relationship between the target temperature and the updated fast charging strategy.

[0123] Specifically, updating 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 obtaining the updated fast charging strategy can enable the updated fast charging strategy to charge with the first charging current that can successfully identify the inflection point between the first state of charge and the second state of charge. Thus, the accuracy of inflection point identification of the updated fast charging strategy in the actual fast charging process is improved, and further, the accuracy and reliability of SOH correction based on the inflection point method are improved.

[0124] For example, in the preset fast charging strategy corresponding to 25°C, update the charging current of the test battery between the first state of charge of 60% SOC and the second state of charge of 70% SOC to the first charging current I 70％,T , and keep the charging current in other state-of-charge intervals of the battery unchanged as that in the preset fast charging strategy. At this time, the updated preset fast charging strategy is the updated fast charging strategy corresponding to 25°C.

[0125] Furthermore, a corresponding relationship can be established between the target temperature and the updated fast charging strategy to generate the first mapping relationship and store it for subsequent calling.

[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 according to the current temperature, and used as the target fast charging strategy of the battery to be predicted at the current temperature.

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

[0128] Furthermore, if no updated fast charging strategy is found in the first mapping relationship, continue to charge the battery to be predicted using the preset fast charging strategy at the current temperature, and do not perform the life prediction of the battery to be predicted.

[0129] Step S302, charge the battery to be predicted using the target fast charging strategy and perform inflection point identification 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 Figure 2 Step S202 of the illustrated embodiment, which will not be elaborated 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, the above step S303 includes:

[0132] Step S3031, use the 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.

[0133] Among them, the second mapping relationship is used to represent the corresponding relationship between the target temperature, full charge capacity, and inflection point capacity corresponding to 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, charge the test battery using the updated fast charging strategy corresponding to the target temperature and determine the third full charge capacity value of the test battery.

[0136] Among them, the third full charge capacity value represents the total capacity charged into the battery when charging the test battery from the initial state to the full charge state 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, charge the test battery using the updated fast charging strategy corresponding to the target temperature, and when the test battery reaches the full charge state, the corresponding third full charge capacity value of the test battery can be further 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 refers to the capacity value when the inflection point is identified during the charging of the test battery using the updated fast charging strategy corresponding to the target temperature and the inflection point identification process.

[0140] Specifically, according to the above steps a1 to a4, 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 can refer to the inflection point identification process in step S202, which will not be elaborated here.

[0141] Furthermore, according to the identified inflection point, the corresponding third inflection point capacity value can be obtained and determined.

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

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

[0144] Finally, after determining the target fast charging strategy of 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 this second mapping relationship according to the target temperature of the target fast charging strategy.

[0145] Step S304: Determine the battery life of the battery to be predicted according to 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 Figure 2 Step S204 of the illustrated embodiment, which will not be elaborated here.

[0146] The battery life prediction method provided in this embodiment can determine the first charging current of a test battery at a second state of charge by obtaining a preset fast charging strategy of the test battery. It can set an appropriate charging current according to the characteristics of the battery at different states of charge, avoiding low charging efficiency or battery damage caused by improper current setting, and improving the safety and efficiency of charging. Further, when the charging conditions are met, constant current charging is performed using the first charging current and inflection point identification is carried out, and the target temperature of the target inflection point, which has an important indicating effect on the battery characteristics, can be obtained. Furthermore, the state change of the battery during charging can be accurately reflected. Further, the charging current in the preset fast charging strategy corresponding to the target temperature is updated to the first charging current to obtain a more optimized updated fast charging strategy, and a mapping relationship between temperature and fast charging strategy is generated, which can better adapt to the characteristics of the battery at different temperatures, and improve the possibility of identifying inflection points and correcting SOH (State of Health) during actual fast charging. Further, charging the test battery using the updated fast charging strategy at the target temperature and determining the third full charge capacity value can obtain accurate full charge capacity data of the battery under the optimized fast charging strategy, and further can better reflect the performance of the battery under actual suitable conditions. Further, inflection point identification is carried out during charging and the third inflection point capacity value is determined, which can further understand the important state parameters of the battery at this temperature and fast charging strategy. Finally, a second mapping relationship between the target temperature, the third full charge capacity value, and the third inflection point capacity value is established. Further, by using the first mapping relationship established previously, the target fast charging strategy of the battery to be predicted at the current temperature can be quickly and accurately determined, avoiding the risk of blindly selecting a fast charging strategy at different temperatures, enabling the charging process to be optimized according to the current temperature characteristics of the battery, improving the charging efficiency and battery safety, and at the same time providing suitable charging conditions for subsequent accurate inflection point identification and battery life prediction. Further, using the second mapping relationship 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, reducing the workload of actual testing, while ensuring the accuracy and reliability of the obtained capacity parameters, providing reliable reference data for subsequent accurate prediction of the life of the battery to be predicted, and improving the efficiency and accuracy of battery life prediction.

[0147] In this embodiment, a battery life prediction device is also provided. This device is used to implement the above-mentioned embodiment and preferred implementation manners, and those that have been described will not be repeated. As used hereinafter, the term "module" can be a combination of software and / or hardware that can achieve a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

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

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

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

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

[0152] The first determination module 404 is configured to determine the battery life of the battery to be predicted according to 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 apparatus further includes:

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

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

[0156] The second determination module is configured to, when the target inflection point is identified, determine the first temperature as the target temperature.

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

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

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

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

[0161] The generation module is configured 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 alternative embodiments, the first acquisition module 401 includes:

[0163] A first determination sub-module, configured to determine a target fast charging strategy of the battery to be predicted at the current temperature by using the first mapping relationship.

[0164] In some alternative embodiments, the second acquisition module 403 includes:

[0165] A second determination sub-module, configured to determine a second full charge capacity value and a second inflection point capacity value of the test battery under the target fast charging strategy by using the second mapping relationship.

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

[0167] A charging and acquisition sub-module, configured to charge the battery to be predicted by using the target fast charging strategy and acquire a first full charge capacity value of the battery to be predicted.

[0168] An acquisition sub-module, configured to acquire a first voltage-capacity differential curve, where the first voltage-capacity differential curve is used to characterize the change relationship between the voltage and the capacity of the battery to be predicted.

[0169] A third determination sub-module, configured to determine a plurality of voltage-capacity ratios according to the first voltage-capacity differential curve.

[0170] A comparison and determination sub-module, configured to compare each voltage-capacity ratio with a preset threshold respectively to determine the inflection point of the battery to be predicted and determine a first inflection point capacity value of the battery to be predicted.

[0171] In some alternative embodiments, the first determination module 404 includes:

[0172] A calculation sub-module, configured to calculate an actual capacity value of the battery to be predicted according to the first full charge capacity value, the first inflection point capacity value, and the second inflection point capacity value.

[0173] A fourth determination sub-module, configured to determine the battery life of the battery to be predicted according to the actual capacity value and the second full charge capacity value.

[0174] In some alternative embodiments, the battery to be predicted is a lithium iron phosphate battery, the value range of the first state of charge is [50%, 60%], and the value range of the second state of charge is [65%, 75%].

[0175] The further function descriptions of the above-mentioned modules and units are the same as those in the corresponding above embodiments, and will not be elaborated herein.

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

[0177] An embodiment of the present invention further provides a computer device having the above Figure 4 battery life prediction device.

[0178] Please refer to Figure 5 , Figure 5 which is a schematic structural diagram of a computer device provided by an alternative 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 various components, including high-speed interfaces and low-speed interfaces. Each component communicates with each other using different buses and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed within the computer device, including instructions stored in the memory or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some alternative embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (such as an array of servers, a set of blade servers, or a multi-processor system). Figure 5 One processor 10 is taken as an example in

[0179] The processor 10 can be a central processing unit, a network processor, or a combination thereof. Among them, the processor 10 can further include a hardware chip. The above hardware chip can be an application specific integrated circuit, a programmable logic device, or a combination thereof. The above programmable logic device can be a complex programmable logic device, a field programmable gate array, a general array logic, or any combination thereof.

[0180] Among them, the memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiment.

[0181] The memory 20 may include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the computer device and the like. In addition, the memory 20 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some alternative embodiments, the memory 20 may optionally include a memory remotely provided with respect to the processor 10, and these remote memories can be connected to the computer device through a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[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, a hard disk, or a solid-state drive; the memory 20 may further include a combination of the above types of memory.

[0183] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0184] Embodiments of the present invention also provide a computer-readable storage medium. The methods according to the embodiments of the present invention can be implemented in hardware, firmware, or be implemented as computer code that can be recorded on a storage medium, or be implemented as computer code originally stored in a remote storage medium or a non-transitory machine-readable storage medium and to be downloaded through a network and stored in a local storage medium, so that the methods described herein can be stored in such software processed on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid-state drive, etc.; further, the storage medium may further include a combination of the above types of memory. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code, and when the software or computer code is accessed and executed by the computer, the processor, or the hardware, the methods shown in the above embodiments are implemented.

[0185] A part of the present 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 present invention through the operations of the computer. Those skilled in the art should 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 executes the instructions, or the computer compiles the instructions and then executes the corresponding compiled program, or the computer reads and executes the instructions, or the computer reads and installs the instructions and then executes the corresponding installed program. Herein, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible by the computer.

[0186] Although embodiments of the present 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 present invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A battery life prediction method, characterized in that, The method includes: Obtaining a target fast charging strategy for the battery to be predicted at the current temperature, where the target fast charging strategy is used to control the battery to be predicted to charge at a constant current between a first state of charge and a second state of charge; Charging the battery to be predicted using the target fast charging strategy and performing inflection point identification to obtain a first full charge capacity value and a first inflection point capacity value of the battery to be predicted; Obtaining a second full charge capacity value and a second inflection point capacity value of a test battery under the target fast charging strategy; Determining the battery life of the battery to be predicted according to 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 first temperature, obtaining a preset fast charging strategy of the test battery at the first temperature and determining a first charging current of the test battery at the second state of charge based on the preset fast charging strategy; When the test battery meets the charging conditions, charging the test battery at a constant current using the first charging current and performing inflection point identification during the charging process; When the target inflection point is identified, determining the first temperature as the target temperature; Updating the charging current between the first state of charge and the second state of charge of the test battery in the preset fast charging strategy corresponding to the target temperature to the first charging current, obtaining an updated fast charging strategy, and generating a first mapping relationship between the target temperature and the updated fast charging strategy.

3. The method according to claim 2, wherein The method further includes: At the target temperature, charging the test battery using the updated fast charging strategy corresponding to the target temperature and determining a third full charge capacity value of the test battery; During the charging process, performing inflection point identification on the test battery and determining a third inflection point capacity value of the target inflection point; Generating a second mapping relationship between the target temperature, the third full charge capacity value, and the third inflection point capacity value.

4. The method according to claim 2, wherein Obtaining the target fast charging strategy for the battery to be predicted at the current temperature includes: Using the first mapping relationship to determine the target fast charging strategy of the battery to be predicted at the current temperature.

5. The method according to claim 3, wherein 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 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.

6. The method according to claim 1, characterized in that Charging the battery to be predicted using the target fast charging strategy and performing inflection point identification to obtain the first full charge capacity value and the first inflection point capacity value of the battery to be predicted includes: Charging the battery to be predicted using the target fast charging strategy and obtaining the first full charge capacity value of the battery to be predicted; Obtaining a first voltage-capacity differential curve, where the first voltage-capacity differential curve is used to characterize the change relationship between the voltage and the capacity of the battery to be predicted; Determining a plurality of voltage-capacity ratios according to the first voltage-capacity differential curve; Compare each voltage capacity ratio with a preset threshold respectively 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.

7. The method according to claim 1, characterized in that, Determine the battery life of the battery to be predicted according to 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: Calculate the actual capacity value of the battery to be predicted according to the first full charge capacity value, the first inflection point capacity value, and the second inflection point capacity value; Determine the battery life of the battery to be predicted according to the actual capacity value and the second full charge capacity value.

8. The method according to claim 1, characterized in that, The battery to be predicted is a lithium iron phosphate battery, the value range of the first state of charge is [50%, 60%], and the value range of the second state of charge is [65%, 75%].

9. A battery life prediction device, characterized in that, The device includes: A first acquisition module, configured to acquire a target fast charging strategy of the battery to be predicted at the current temperature, where the target fast charging strategy is used to control the battery to be predicted to charge at a constant current between the first state of charge and the second state of charge; A first identification module, configured to perform charging processing and inflection point identification on the battery to be predicted by using the target fast charging strategy, and obtain the first full charge capacity value and the first inflection point capacity value of the battery to be predicted; A second acquisition module, configured to acquire a second full charge capacity value and a second inflection point capacity value of the test battery under the target fast charging strategy; A first determination module, configured to determine the battery life of the battery to be predicted according to 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, Including computer instructions for causing a computer to execute the battery life prediction method according to any one of claims 1 to 8.

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