A battery throughput prediction method and device, electronic equipment and storage medium

Abstract

The application discloses a battery throughput prediction method and device, electronic equipment and storage medium. The battery throughput prediction method comprises the following steps: fitting a decay model of a battery to be tested according to measured data of a battery life prediction test; determining working condition data of each unit time in a prediction period of the battery life test; according to the sequence of the unit time in the prediction period, the decay model and the working condition data corresponding to each unit time, the total energy attenuation of the battery to be tested at the end of each unit time is determined in a cycle; after the total energy attenuation of the battery to be tested at the end of each unit time is determined, the energy throughput in the corresponding unit time is calculated according to the working condition data; and in the case that the total energy attenuation meets a life termination condition, the cycle determination of the total energy attenuation of the battery to be tested at the end of each unit time is stopped, and the total throughput of the battery to be tested is determined according to the energy throughput in each unit time. The application can improve the comprehensiveness and quality inspection effect of battery performance testing.

Description

Technical Field

[0001] This invention relates to battery testing technology, and more particularly to a method, apparatus, electronic device, and storage medium for predicting battery throughput. Background Technology

[0002] Lithium-ion batteries, with their high energy and power density, have become an indispensable part of modern life. A thorough understanding of lithium-ion battery performance is crucial for maximizing their actual capabilities and protecting them, both during product development and usage.

[0003] In existing technologies, evaluating battery cell performance based on user operating conditions is essential when assessing battery capacity. Most methods calculate the end-of-life time using a combination of cycle and calendar degradation models to determine if the battery meets usage requirements. In practical applications, it's crucial to consider not only usage duration but also whether the energy throughput throughout the battery's lifespan meets operational needs. However, as battery cells degrade with use, accurately calculating their energy throughput under different degradation conditions is challenging. Therefore, a method is needed to predict the energy throughput of a battery cell under specific operating conditions. Summary of the Invention

[0004] This invention provides a method, apparatus, electronic device, and storage medium for predicting battery throughput, thereby improving the comprehensiveness of battery performance testing and the effectiveness of quality inspection.

[0005] In a first aspect, embodiments of the present invention provide a method for predicting battery throughput, the method comprising:

[0006] The degradation model of the battery under test is fitted based on the measured data of the battery life pretest.

[0007] The operating condition data for each unit time in the predicted cycle of battery life testing is determined based on the cell operating condition data.

[0008] According to the order of the unit time in the prediction period, the total energy decay of the battery under test at the end of each unit time is determined cyclically based on the decay model and the operating condition data corresponding to each unit time.

[0009] After determining the total energy decay of the battery under test at the end of each unit time, the energy throughput within the corresponding unit time is calculated based on the operating condition data.

[0010] When the total energy decay meets the end-of-life condition, the cyclic determination of the total energy decay of the battery under test at the end of each unit time is stopped, and the total throughput of the battery under test is determined based on the energy throughput within each unit time.

[0011] Optionally, the battery life pre-test includes a cycle pre-test; the degradation model includes a cycle degradation model; and the measured data of the cycle pre-test includes various cycle test conditions and their corresponding cycle degradation amounts.

[0012] A degradation model for the battery under test is fitted based on the measured data from the battery life pre-test, including:

[0013] Measure the cyclic decay under different cyclic test conditions during the cyclic pre-test;

[0014] Based on the cycle test conditions and their corresponding cycle decay, the cycle decay model of the battery under test is fitted using the Arrhenius formula.

[0015] Optionally, the battery life pre-test also includes a calendar pre-test; the degradation model also includes a calendar degradation model; the measured data of the calendar pre-test includes various calendar test conditions and their corresponding calendar degradation amounts;

[0016] The degradation model of the battery under test is fitted based on the measured data of the battery life pretest, and also includes:

[0017] Measure the calendar decay amount under different calendar test conditions during the calendar pre-test;

[0018] Based on the calendar test conditions and their corresponding calendar decay values, the calendar decay model of the battery under test is fitted using the Arrhenius formula.

[0019] Optionally, the total energy decay of the battery under test at the end of each unit time is determined cyclically according to the decay model and the operating condition data corresponding to each unit time in the prediction period, following the order of the unit time in the prediction period, including:

[0020] Based on the total energy decay of the battery under test at the end of the previous unit time, the operating data of the current unit time, and the decay model, the initial parameters of the current unit time are determined. The total decay of the battery life pretest is used as the total energy decay of the battery under test at the end of the previous unit time corresponding to the first unit time. The initial parameters include the initial number of cycles or the initial placement time.

[0021] Based on the operating data of the current unit time, the initial parameters, and the decay model, the total energy decay of the battery under test at the end of the current unit time is determined.

[0022] Optionally, the unit time includes multiple forecasting phases, and the forecasting phases include a cyclic forecasting phase or a calendar forecasting phase;

[0023] Based on the operating data of the current unit time, the initial parameters, and the attenuation model, the total energy attenuation of the battery under test at the end of the current unit time is determined, including:

[0024] Based on the operating data of the first prediction phase, the initial parameters, and the attenuation model, the total energy attenuation of the battery under test at the end of the first prediction phase is determined, wherein the initial parameters of the first prediction phase per unit time are the initial parameters of the unit time.

[0025] Based on the total energy decay of the battery under test at the end of the previous prediction stage, the operating condition data of the current prediction stage, and the decay model, the initial parameters of the current prediction stage are determined; based on the operating condition data of the current prediction stage, the initial parameters of the current prediction stage, and the decay model, the total energy decay of the battery under test at the end of the current prediction stage is determined.

[0026] Optionally, calculating the energy throughput per unit time based on the operating condition data includes:

[0027] The depth of charge / discharge and number of cycles per unit time are determined based on the operating data per unit time.

[0028] The energy throughput per unit time is determined based on the total energy decay of the battery under test at the end of the unit time, the nominal energy of the battery under test, the depth of charge and discharge per unit time, and the number of cycles.

[0029] Optionally, the total throughput of the battery under test is determined based on the energy throughput per unit time, including:

[0030] The total throughput of the battery under test is determined by summing the energy throughput of each unit time period.

[0031] Secondly, embodiments of the present invention also provide a battery throughput prediction device, which includes a model fitting module, an operating condition determination module, an attenuation determination module, a throughput calculation module, and a total throughput determination module. The model fitting module is used to fit an attenuation model of the battery under test based on the measured data of the battery life pre-test. The operating condition determination module is used to determine the operating condition data of each unit time in the prediction cycle of the battery life test based on the cell operating condition data. The attenuation determination module is used to cyclically determine the total energy attenuation of the battery under test at the end of each unit time according to the order of the unit time in the prediction cycle, based on the attenuation model and the operating condition data corresponding to each unit time. The throughput calculation module is used to calculate the energy throughput of the corresponding unit time based on the operating condition data after determining the total energy attenuation of the battery under test at the end of each unit time. The total throughput determination module is used to stop the cyclic determination of the total energy attenuation of the battery under test at the end of each unit time and determine the total throughput of the battery under test based on the energy throughput of each unit time when the total energy attenuation meets the life end condition.

[0032] Thirdly, embodiments of the present invention also provide an electronic device, the electronic device comprising:

[0033] At least one processor; and a memory communicatively connected to said at least one processor; wherein,

[0034] The memory stores a computer program that can be executed by the at least one processor to enable the at least one processor to perform the battery throughput prediction method described in any of the first aspects.

[0035] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing computer instructions for causing a processor to execute the battery throughput prediction method described in any of the first aspects.

[0036] The battery throughput prediction method, apparatus, electronic device, and storage medium provided in this invention, based on user usage data and influencing factors of the battery under test, design multiple sets of test conditions in the battery life pre-test. A degradation model of the battery under test is fitted based on the measured data of the battery life pre-test under these multiple test conditions. Then, following the order of the unit time in the prediction cycle, the total energy degradation and energy throughput of the battery under test at the end of each unit time are cyclically determined according to the degradation model and the operating condition data corresponding to each unit time, until the total energy degradation meets the life end condition. Finally, the energy throughput of each unit time is accumulated to determine the total throughput of the battery under test, realizing the estimation and prediction of the throughput at the end of the battery's life, and improving the comprehensiveness and quality inspection effect of battery performance testing. Attached Figure Description

[0037] Figure 1 This is a flowchart illustrating a battery throughput prediction method proposed in an embodiment of the present invention.

[0038] Figure 2 This is a flowchart illustrating another method for predicting battery throughput proposed in an embodiment of the present invention.

[0039] Figure 3 A schematic diagram of the structure of a battery throughput prediction device provided in an embodiment of the present invention;

[0040] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0041] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0042] As described in the background section, manufacturers predict battery life to determine battery performance and assess whether it meets customer needs. Specifically, life prediction involves subjecting the battery to at least one of charge-discharge cycles and storage under preset operating conditions until the battery's state parameters reach the end-of-life condition. At least one of the cumulative number of cycles and cumulative storage time at the end of the battery's life can be used to describe its lifespan. Furthermore, the relative relationship between the battery life and the preset lifespan (at least one of the preset number of cycles and preset storage time) can determine whether the battery meets customer needs. In addition, the battery lifespan obtained from testing represents the battery's lifespan under normal usage conditions and can be used to determine the battery's warranty period, facilitating better warranty and repair services for users. However, the inventors have discovered that battery quality not only affects battery lifespan but also the total energy throughput during degradation. Therefore, predicting battery lifespan alone cannot comprehensively assess battery quality and performance. Moreover, more and more users are now concerned not only with how long the battery lasts (i.e., battery lifespan) but also with its durability (battery throughput). Therefore, during quality inspection or actual use, when the battery reaches the end of its lifespan, it is also necessary to pay attention to whether the battery's energy throughput can meet the usage requirements.

[0043] To address the aforementioned problems, this invention proposes a method for predicting battery throughput. Figure 1 This is a flowchart illustrating a battery throughput prediction method proposed in an embodiment of the present invention. (Refer to...) Figure 1 Methods for predicting battery throughput include:

[0044] S101. Fit the degradation model of the battery under test based on the measured data of the battery life pretest.

[0045] Battery life pre-testing refers to the actual degradation test stage and / or actual calendar test stage, which requires measured data to fit the degradation model of the battery under test. Multiple sets of energy degradation amounts under different test conditions can be obtained during battery life pre-testing. Measured data can include test conditions and their corresponding energy degradation amounts. The degradation model is a mathematical model that can represent the relationship between various test conditions and energy degradation amounts.

[0046] Specifically, before conducting battery life pre-testing, the influencing factors of battery degradation under cyclic conditions and under calendar conditions are determined based on empirical or experimental data. For example, the battery degradation under cyclic conditions is related to temperature, depth of charge / discharge, charge / discharge rate, and number of cycles, respectively; the battery degradation under calendar conditions is related to temperature, battery state of charge, and storage time, respectively.

[0047] Furthermore, based on user habits and influencing factors of the battery under test, multiple sets of test conditions were designed for the battery life pre-test. Influencing factors such as temperature, charge / discharge rate, and battery state of charge (SOC) must cover commonly used ranges. For example, the temperature gradient can be 10-20℃, the charge / discharge rate gradient can be 1-2C, and the SOC gradient can be 10% or more. The test conditions include cyclic test conditions and calendar test conditions. One set of cyclic test conditions includes four influencing factors: temperature, depth of charge / discharge, charge / discharge rate, and number of cycles. One set of calendar test conditions includes three influencing factors: temperature, battery SOC, and storage time. For example, in the cyclic test conditions, the temperature of the cyclic test may include -30℃, -20℃, -10℃, 0℃, 10℃, 25℃, 45℃, and 60℃; the charge-discharge depth of the cyclic test may include 60%, 70%, 80%, and 90%; the charge-discharge rate of the cyclic test may include 1C, 2C, 3C, and 4C; the number of cycles may include any number of cycles from 200 to 2000, and the setting of the number of cycles is related to the preset charge-discharge depth. If the charge-discharge depth is in the range of 60%-100%, the number of cycles may be set to 250 cycles; if the charge-discharge depth is in the range of 10-30%, the number of cycles may be set to 1000 to 2000 cycles. The amount of electricity discharged or charged into the preset charge-discharge depth is one cycle. In calendar testing conditions, the temperature at which the calendar is placed can include -30℃, -20℃, -10℃, 0℃, 10℃, 25℃, 45℃, and 60℃; the state of charge of the battery during calendar placement can include 30%, 40%, 50%, 60%, 70%, 80%, and 90%; the placement time can include 7 days and 30 days, and the placement test generally lasts for six months to one year. In the first month, the energy decay of the battery under test is measured every 7 days, and then every 30 days thereafter.

[0048] According to multiple preset conditions, a battery life pre-test is conducted on the battery under test. For example, the battery life pre-test may include cycle pre-testing and / or calendar pre-testing. Cycle pre-testing refers to conducting charge-discharge cycle tests on the battery under test under different cycle test conditions to measure the energy decay of the battery under different cycle test conditions. Calendar pre-testing refers to conducting placement tests on the battery under test under different calendar test conditions to measure the energy decay of the battery under different calendar test conditions. In the battery life pre-testing, measured data under multiple test conditions can be obtained. With other influencing factors remaining constant, the battery decay is tested sequentially when an influencing factor equals different preset values ​​in a gradient, to study the effect of that influencing factor on battery decay.

[0049] Furthermore, the measured data from the battery life pre-test are substituted into an empirical equation to fit the relationship between the battery degradation and various influencing factors. For example, the empirical equation can be the Arrhenius equation. On one hand, using multiple sets of cyclic test conditions from the battery life pre-test and the corresponding energy degradation of the battery under test, a cyclic degradation model is fitted in the form of a semi-empirical Arrhenius equation. This cyclic degradation model can represent the influence of each influencing factor in the cyclic test conditions on the energy degradation of the battery under test. On the other hand, using multiple sets of calendar test conditions from the battery life pre-test and the corresponding energy degradation of the battery under test, a calendar degradation model is fitted in the form of a semi-empirical Arrhenius equation. This calendar degradation model can represent the influence of each influencing factor in the calendar test conditions on the energy degradation of the battery under test.

[0050] S102. Determine the operating condition data for each unit time in the predicted cycle of battery life testing based on the cell operating condition data.

[0051] Battery life testing refers to the degradation test used to determine the lifespan of a battery under test. This test involves subjecting the battery to cyclic charging and discharging and / or storage under different test conditions. The test conditions in battery life testing change periodically; this period is called the prediction period. The prediction period can be divided into multiple unit times, and the test conditions in each unit time can differ depending on the testing requirements. Operating condition data includes the order of each unit time within the prediction period, the order of each prediction stage within each unit time, and the test conditions corresponding to each prediction stage. Cell operating condition data refers to battery operating condition data determined based on an unlimited number (at least 100 sets) of user usage habit data. The cell operating condition data conforms to the usage patterns of the target user. The meaning of cell operating condition data differs from that of unit time operating condition data; in this article, "operating condition data" refers specifically to unit time operating condition data.

[0052] Specifically, user cell operating condition data can be obtained from the cloud service platform or database of electric vehicle companies. This data reflects user battery usage habits. Based on these habits, operating condition data for each unit of time within the prediction cycle can be determined. A single unit of time can include at least one prediction stage, corresponding to a set of test conditions. This stage can be either a cyclic charge-discharge test or a calendar placement test. For example, unit time 1 may include only one cyclic charge-discharge prediction stage, with test conditions of a cyclic test temperature of 10°C, a depth of charge / discharge of 60%, a charge / discharge rate of 1C, and 1.5 cycles. Unit time 2 may include only one calendar placement prediction stage, with test conditions of a placement test temperature of 15°C, a calendar placement battery state of charge of 30% (equal to the state of charge of the battery under test at the end of unit time 1), and a placement time of 5 hours. The third unit of time can sequentially include one cycle charge / discharge phase, one calendar placement prediction phase, and another cycle charge / discharge prediction phase. The test conditions for the first cycle charge / discharge prediction phase are: a test temperature of 20℃, a depth of charge / discharge of 80%, a charge / discharge rate of 2C, and 1.2 cycles. The test conditions for the calendar placement prediction phase are: a test temperature of 20℃, a battery state of charge of 29.9% (equal to the state of charge of the battery at the end of the previous prediction phase), and a placement time of 12 hours. The test conditions for the second cycle charge / discharge prediction phase are: a test temperature of 20℃, a depth of charge / discharge of 80%, a charge / discharge rate of 2C, and 1 cycle. It is important to note that the test conditions are identical across multiple calendar placement prediction phases within the same unit of time, except for the placement time; similarly, the test conditions are identical across multiple cycle charge / discharge prediction phases within the same time unit, except for the number of cycles. Following this method, and similarly, operating condition data for each unit of time within the prediction cycle can be designed based on the user's usage habits. The length of a unit of time can include a preset time length; for example, a unit of time can be 500 seconds.

[0053] S103. Based on the order of unit time in the prediction cycle, determine the total energy decay of the battery under test at the end of each unit time in a cyclical manner according to the decay model and the operating condition data corresponding to each unit time.

[0054] Specifically, determining the total energy decay of the battery under test at the end of each unit time is a predictive calculation process based on pre-designed operating condition data, not an actual charge-discharge decay or placement decay test process. When determining the total energy decay of the battery under test at the end of a unit time, the total energy decay of the battery before that unit time and the test conditions of the first prediction stage within that unit time are combined with the decay model to determine the total number of cycles or total placement time before the first prediction stage. Then, based on the total number of cycles or total placement time before the first prediction stage, combined with the test conditions of the first prediction stage within that unit time, the number of cycles or placement time at the end of the first prediction stage can be determined. Then, the number of cycles or placement time at the end of the first prediction stage, along with the test conditions of the first prediction stage, are substituted into the decay model to determine the total energy decay of the battery under test at the end of the first prediction stage. This process is then repeated to determine the total energy decay of the battery under test at the end of each subsequent prediction stage within that unit time. The total energy decay of the battery under test at the end of the last prediction stage is the total energy decay of the battery under test at the end of that unit time. After determining the total energy decay of the battery under test at the end of all unit time within a prediction cycle, the total energy decay of the battery under test at the end of all unit time within the next cycle is determined sequentially, and this process is repeated until the total energy decay meets the end-of-life condition.

[0055] For example, if the total energy decay before a certain unit of time is A1, and the first prediction stage is a cyclic charge-discharge test with test conditions of 15°C, 60% depth of charge / discharge, 1C charge / discharge rate, and 1.2 cycles, then A1, 15°C, 60%, and 1C can be substituted into the decay model to determine the number of cycles A2 required to achieve the A1 decay under the test conditions of the first prediction stage. This number is then used as the total number of cycles before the first prediction stage. The total number of cycles A2 before the first prediction stage is then added to 1.2 to obtain the number of cycles at the end of the first prediction stage: A2 + 1.2. Finally, A2 + 1.2, 15°C, 60%, and 1C are substituted into the decay model to determine the total energy decay of the battery at the end of the first stage (the sum of the decay in the first stage and the total decay A1 before the unit of time). The total energy decay of the battery at the end of each subsequent prediction stage within the same unit of time is then determined in the same manner. The total energy decay of the battery under test at the end of the last prediction stage is the total energy decay of the battery under test at the end of that unit of time.

[0056] S104. After determining the total energy decay of the battery under test at the end of each unit of time, calculate the energy throughput per unit of time based on the operating condition data.

[0057] Specifically, when calculating the energy throughput per unit time, the number of cycles and depth of charge / discharge within that unit time can be determined based on the operating data. Then, based on the nominal energy of the battery under test, the total degradation of the battery at the end of that unit time, the number of cycles, and the depth of charge / discharge within that unit time, the energy throughput per unit time can be calculated. For example, if the nominal energy of the battery under test is 71.3Wh, and the depth of charge / discharge in each predicted cycle phase within that unit time can remain consistent (e.g., 80%), and the total number of cycles in all predicted cycles within that unit time is 5, and the total degradation of the battery at the end of that unit time is 1.5%, then the energy throughput per unit time can be equal to 71.3 * 80% * 5 * (1 - 1.5%). It should be noted that the unit time is generally set to several hundred seconds or several minutes. For example, the length of the unit time can be 500 seconds, so the attenuation is not obvious. When calculating the rated throughput per unit time, the attenuation at each moment of the unit time can be approximated to the total attenuation at the end of the unit time. The calculated throughput error is extremely small, less than 0.0001 watt-hours, and does not affect the accuracy of the overall prediction result.

[0058] S105. When the total energy decay meets the end-of-life condition, stop the cyclic determination of the total energy decay of the battery under test at the end of each unit time and determine the total throughput of the battery under test based on the energy throughput of each unit time.

[0059] Specifically, the end-of-life condition can be a preset condition determined based on user usage data, such as the amount of battery degradation affecting normal user use. For example, the end-of-life condition can be a total energy degradation of 20% or more. When the total energy degradation meets the end-of-life condition, it can be determined that the battery under test no longer meets the user's normal power needs and has reached the end of its life. At this point, the cyclic determination in steps S103 and S104 is stopped, and the energy throughput at the end of all currently determined unit times is cumulatively calculated. The total throughput of the battery under test is determined based on the cumulative result. It should be noted that the total throughput, based on the cumulative calculation result, can further consider the throughput in the battery pre-test, thus obtaining a more accurate total throughput that is closer to the actual throughput. For example, the total throughput can represent how much electricity the battery under test can handle during use. The predicted total throughput can be used to determine whether the quality of the battery under test meets the standards (i.e., whether the battery under test is durable enough) in conjunction with the preset throughput, and can also be used as one of the conditions for determining the end of the battery under test's life in actual use. Users can determine how long the battery can remain stable by comparing the current battery throughput with the total throughput predicted by this method. This information can help users plan their vehicle usage accordingly, seek battery replacement services promptly, and avoid disruptions to their lives caused by the end of battery life.

[0060] The battery throughput prediction method provided in this invention designs multiple sets of test conditions in the battery life pre-test based on user usage data and influencing factors of the battery under test. A degradation model for the battery under test is fitted based on the measured data from the battery life pre-test under these multiple test conditions. Then, following the order of unit time within the prediction cycle, the total energy degradation and energy throughput of the battery under test at the end of each unit time are determined iteratively based on the degradation model and the corresponding operating data for each unit time, until the total energy degradation meets the life-end condition. Finally, the energy throughput of each unit time is accumulated to determine the total throughput of the battery under test, realizing the estimation and prediction of throughput at the end of the battery's life, thus improving the comprehensiveness of battery performance testing and quality inspection effectiveness.

[0061] Figure 2 This is a flowchart illustrating another battery throughput prediction method proposed in an embodiment of the present invention, referred to... Figure 2 Based on the foregoing embodiments, the battery throughput prediction method may further include:

[0062] S201. Measure the cyclic decay under different cyclic test conditions during cyclic pre-testing.

[0063] Cyclic pre-testing refers to the cyclic charge-discharge test performed on the battery under test, and is a form of lifespan pre-testing. Cyclic test conditions refer to the preset conditions for conducting cyclic pre-testing; a set of cyclic test conditions may include temperature, depth of charge / discharge, charge / discharge rate, and number of cycles. Cyclic degradation refers to the amount of energy loss of the battery under test during cyclic pre-testing.

[0064] Specifically, lifetime pre-testing includes cycle pre-testing. The degradation model includes a cycle degradation model. The measured data from cycle pre-testing includes various cycle test conditions and the corresponding cycle degradation amount for each set of cycle test conditions. Cycle pre-testing is performed on the battery under test under each set of cycle test conditions. During cycle pre-testing, the battery under test needs to be cycle-charged and discharged using a charge-discharge instrument according to the cycle test conditions, and the cycle degradation amount of the battery under test after the test under each cycle test condition is recorded.

[0065] S202. Based on the cyclic test conditions and their corresponding cyclic decay, the cyclic decay model of the battery under test is fitted using the Arrhenius formula.

[0066] Specifically, a set of cyclic test conditions includes four influencing factors: temperature, depth of charge / discharge, charge / discharge rate, and number of cycles. To determine the impact of a single influencing factor on cycle degradation, multiple sets of cyclic test conditions and their corresponding cycle degradation values ​​are substituted into the Arrhenius equation. The only difference between the multiple sets of cyclic test conditions is the setting of the influencing factor to be determined; all other influencing factors are set identically. This analysis establishes the functional relationship between a single influencing factor and cycle degradation. After determining the functional relationships between all influencing factors and cycle degradation, the cycle degradation model of the battery under test is thus determined. Where E1 refers to the total cycle degradation of the battery under test, representing the total degradation from the initial state to the current state during the cyclic charge-discharge test. The initial state here refers to the state where the battery's energy equals its nominal energy, i.e., the state where the degradation is 0. A(C,DOD) is a function of the charge / discharge rate C and the depth of charge / discharge DOD. B(T1) is a function of the temperature T1 in the cyclic test conditions. e is the natural constant. N is the total number of cycles, which is the sum of the number of cycles the battery under test undergoes from the initial state to the current state during the cyclic charge-discharge test. Z1 is the first constant, which can be 0.5.

[0067] S203. Measure the calendar decay under different calendar test conditions during calendar pre-testing.

[0068] Calendar pre-testing refers to placement tests performed on the battery under test, and is a form of lifespan pre-testing. Calendar test conditions refer to the preset conditions for performing cycle pre-testing; a set of calendar test conditions may include temperature, battery state of charge, and placement time. Calendar degradation refers to the amount of energy decay of the battery under test during calendar pre-testing.

[0069] Specifically, lifetime pre-testing includes calendar pre-testing. The degradation model includes a calendar degradation model. The measured data from calendar pre-testing includes various calendar test conditions and the corresponding calendar degradation amount for each set of calendar test conditions. Calendar pre-testing is performed on the battery under test under each set of calendar test conditions. During calendar pre-testing, the battery under test needs to be charged and discharged according to the calendar test conditions using a charge-discharge instrument, and the calendar degradation amount of the battery under test after the test under each calendar test condition is recorded.

[0070] S204. Based on the calendar test conditions and their corresponding calendar decay, the calendar decay model of the battery under test is fitted using the Arrhenius formula.

[0071] Specifically, a set of calendar test conditions includes three influencing factors: temperature, battery state of charge, and storage time. To determine the impact of a single factor on cycle degradation, multiple sets of calendar test conditions and their corresponding calendar degradation values ​​are substituted into the Arrhenius equation. The only difference between the multiple sets of calendar test conditions is the setting of the factor to be determined; all other influencing factors are set identically. This analysis establishes the functional relationship between a single influencing factor and the calendar degradation value. After determining the functional relationships between all influencing factors and the calendar degradation value, the calendar degradation model for the battery under test is determined as follows: Where E2 refers to the total calendar decay of the battery under test, representing the total decay from the initial state to the current state during the calendar placement test. The initial state here refers to the state when the battery's energy equals its nominal energy, i.e., when the decay is 0. A′(SOC) is a function of the battery's state of charge (SOC). B′(T2) is a function of the temperature T2 in the calendar test conditions. t is the total placement time in the calendar pre-test, which is the sum of the placement times experienced by the battery under test from the initial state to the current state during the cyclic charge-discharge test. Z2 is the second constant. Steps S201, S202, S203, and S204 can be used as alternatives to step S101 in the previous embodiment.

[0072] S205. Determine the operating condition data for each unit time in the predicted cycle of battery life testing based on the cell operating condition data.

[0073] Step S205 is the same as step S102 described above, and will not be repeated here.

[0074] S206. Based on the total energy decay of the battery under test at the end of the previous unit time, the operating data and decay model of the current unit time, determine the initial parameters for the current unit time.

[0075] The total energy decay in the battery life pre-test is defined as the total energy decay of the battery under test at the end of the previous unit time corresponding to the first unit time. Initial parameters refer to the test parameters before the start of the unit time or prediction phase, including the initial number of cycles or the initial placement time.

[0076] Specifically, the type of initial parameter is determined based on the initial operating condition data of the current unit time. For example, if the first prediction stage in the current unit time is a cyclic charge-discharge test, the initial parameter is the initial number of cycles. If the first prediction stage in the current unit time is a calendar placement test, the initial parameter is the initial placement time. Further, the test conditions of the first prediction stage in the current unit time (excluding the number of cycles and placement time) and the total energy decay of the battery under test at the end of the previous unit time are substituted into the decay model to calculate the initial number of cycles or the initial placement time per unit time. This initial number of cycles refers to the number of cycles required to obtain the same amount of decay as the total energy decay of the battery under test at the end of the previous unit time, assuming the test conditions of the first prediction stage in this unit time (excluding the number of cycles), and can be used as the starting number of cycles for calculating the decay per unit time. The initial placement time refers to the placement time required to obtain the same amount of energy decay as the battery under test at the end of the previous unit time, assuming the test conditions of the first prediction phase of this unit time (excluding the placement time) are used for calendar placement testing. It can be used as the starting placement time for calculating the decay per unit time.

[0077] For example, on the one hand, the total energy decay of the battery under test at the end of the previous unit time is A1. The first prediction stage in the current unit time is a cycle charge-discharge test, and its test conditions include a temperature of 15°C, a depth of charge / discharge of 80%, and a charge / discharge rate of 1.5C. Then, substituting E1 = A1, T1 = 15°C, DOD = 80%, and C = 1.5C into the cycle decay model of the battery under test, we get: The value of N can be calculated, which is the initial number of cycles N0 per unit time. On the other hand, the total energy decay of the battery under test at the end of the previous unit time is A1. The first prediction stage in the current unit time is a calendar placement test, whose test conditions include a temperature of 15℃ and a state of charge (SOC) of 75%. Substituting E2 = A1, T2 = 15℃, and SOC = 75% into the calendar decay model of the battery under test, we get... The value of t can be calculated, and this value of t is the initial placement time per unit time.

[0078] S207. Based on the operating data, initial parameters, and attenuation model of the current unit time, determine the total energy attenuation of the battery under test at the end of the current unit time.

[0079] Specifically, the operating condition data per unit time includes the order of the various prediction stages within the unit time and the test conditions corresponding to each prediction stage, wherein each unit time includes at least one prediction stage.

[0080] First, based on the operating conditions, initial parameters, and degradation model of the first prediction phase per unit time, the total energy degradation of the battery under test at the end of the first prediction phase can be determined. The initial parameters of the first prediction phase are the initial parameters for that unit time. For example, if the first prediction phase in the previous unit time is a cyclic charge-discharge test, then the initial parameters obtained in the previous step are the initial number of cycles N0. In this case, the number of cycles N at the end of the first prediction phase is equal to the initial number of cycles N0 plus the number of cycles ΔN1 within the first prediction phase, i.e., N = N0 + ΔN1. Substituting the number of cycles N, temperature T1, depth of charge / discharge DOD, and charge / discharge rate C of the first prediction phase into... The total energy decay E1 of the battery under test at the end of the first prediction phase within a unit of time can be calculated. If the first prediction phase within the previous unit of time is a calendar placement test, then the initial parameter obtained in the previous step is the initial placement time t0. In this case, the placement time t at the end of the first prediction phase is equal to the initial placement time t0 plus the placement time Δt1 within the first prediction phase, i.e., t = t0 + Δt1. Substituting the placement time t, temperature T2, and state of charge (SOC) of the battery under test into the calculation... The total energy decay E2 of the battery under test at the end of the first prediction stage per unit time can be obtained.

[0081] Then, based on the total energy decay of the battery under test at the end of the previous prediction stage, the operating data of the current prediction stage, and the decay model, the initial parameters for the current prediction stage can be determined. The method for calculating the initial parameters for the prediction stage is the same as the method for calculating the initial parameters per unit time. Specifically, first, the type of initial parameters is determined based on the operating data of the current prediction stage. Further, the test conditions of the current prediction stage (excluding the number of cycles and placement time) and the total energy decay of the battery under test at the end of the previous prediction stage are substituted into the decay model to calculate the initial number of cycles or the initial placement time for the prediction stage. Further examples are not provided here.

[0082] Furthermore, based on the operating data, initial parameters, and degradation model of the current prediction phase, the total energy degradation of the battery under test at the end of the current prediction phase is determined. The method for determining the total energy degradation of the battery under test at the end of the current prediction phase is the same as the method for determining the total energy degradation of the battery under test at the end of the first prediction phase. The only difference is that the initial parameters of the first prediction phase per unit time are the initial parameters of that unit time, while the initial parameters of other prediction phases need to be calculated based on the total energy degradation of the battery under test at the end of the previous prediction phase, the operating data of the current prediction phase, and the degradation model.

[0083] Following the above method, the total energy decay of the battery under test at the end of each prediction stage within a unit of time is determined sequentially. The total energy decay of the battery under test at the end of the last prediction stage within a unit of time is the total energy decay of the battery under test at the end of that unit of time.

[0084] It is important to note that if the operating data differs between the aforementioned steps and prediction stages, a conversion between different prediction stages is necessary during the calculation process. This conversion can be achieved by calculating the corresponding energy decay amounts and connecting them. For example, the previous prediction stage might be a cyclic charge-discharge test, while this prediction stage is a calendar placement test. Thus, the operating data of the current prediction stage can be used to calculate the time required for the calendar decay to reach the same level (the same level of decay as the battery under test at the end of the previous prediction stage), which serves as the start time for the calendar placement test in the current prediction stage. The placement time in the current prediction stage is then used as a continuation time, and adding it to the start time gives the total placement time at the end of this prediction stage, used to calculate the total energy decay of the battery under test at the end of the current prediction stage. Similarly, when switching from calendar placement test to cyclic charge-discharge test, the number of cycles required for cyclic decay to reach the same level (the same level of decay as the battery under test at the end of the previous prediction stage) is calculated, and this number serves as the starting number of cycles for the current prediction stage. The number of cycles in the current prediction phase is the number of cycles continuing forward. Adding this to the initial number of cycles gives the total number of cycles at the end of the current prediction phase, used to calculate the total energy decay of the battery under test at the end of the current prediction phase. This conversion can be implemented not only when the test types (cycle charge / discharge test and / or calendar placement test) differ between the two prediction phases, but also when the specific test conditions (any of temperature, state of charge, depth of charge / discharge, and charge / discharge rate) differ between the two prediction phases. Steps S206 and S207 can replace step S103 in the previous embodiment.

[0085] S208. Determine the depth of charge / discharge and number of cycles per unit time based on the operating data per unit time.

[0086] Specifically, the unit time includes multiple prediction stages, which can be either calendar placement testing or cyclic charge-discharge testing. The battery under test has no energy throughput during calendar placement testing, but it does during cyclic charge-discharge testing. Before calculating the energy throughput per unit time, the depth of charge / discharge and the number of cycles for each cyclic charge-discharge prediction stage are determined based on the test conditions of each stage within the unit time. The sum of the number of cycles for each cyclic charge-discharge prediction stage within the unit time is the number of cycles within the unit time. Since the depth of charge / discharge is consistent across all cyclic charge-discharge prediction stages within the unit time, the depth of charge / discharge for any given cyclic charge-discharge prediction stage within the unit time is the depth of charge / discharge for the unit time.

[0087] S209. Determine the energy throughput per unit time based on the total energy decay of the battery under test at the end of the unit time, the nominal energy of the battery under test, the depth of charge and discharge per unit time, and the number of cycles.

[0088] Specifically, by substituting the total energy decay of the battery under test at the end of a unit time, the nominal energy of the battery under test, the depth of charge / discharge and the number of cycles per unit time into the energy throughput calculation formula, the energy throughput within the current time unit can be calculated. The energy throughput calculation formula is: Wn=Q*DOD*(1-E n Wn, where Wn is the energy throughput per unit time, Q is the nominal energy of the cell, En is the total energy decay of the battery under test at the end of the current unit time, Nn is the number of cycles per unit time, and DOD is the depth of charge and discharge tested per cycle per unit time. n is the label of the current unit time, and n can be equal to 1, 2, 3..., and n is a natural number other than 0. Steps S208 and S209 can be used as alternatives to S104 in the aforementioned embodiments.

[0089] S210. When the total energy decay meets the end-of-life condition, stop the cyclic determination of the total energy decay of the battery under test at the end of each unit time and sum the energy throughput of each unit time to determine the total throughput of the battery under test.

[0090] Specifically, when the total energy decay meets the end-of-life condition, the cyclic determination of the total energy decay of the battery under test at the end of each unit time is stopped. The energy throughput of each unit time is summed, and the calculation formula is W = ∑Wn. In addition, the total throughput of the battery life pre-test can also be added. Since the battery life pre-test is an actual test, the total throughput of the battery life pre-test can be measured by a charge-discharge cycle test instrument. Step S210 can be used as an alternative to S105 in the aforementioned embodiment.

[0091] In the battery throughput prediction method proposed in this embodiment, the operating condition data differs between the various prediction stages. The calculation process incorporates a conversion between placement time and cycle count for different prediction stages. This conversion connects the two prediction stages by calculating the corresponding energy decay, facilitating step-by-step calculations and enabling the prediction of battery throughput. The predicted energy throughput can be used to rationally plan battery usage, extending the lifespan of the tested battery as much as possible without affecting user experience.

[0092] This invention also provides a battery throughput prediction device. Figure 3 This is a schematic diagram of a battery throughput prediction device provided in an embodiment of the present invention, with reference to... Figure 3The battery throughput prediction device 300 includes a model fitting module 301, a working condition determination module 302, a degradation amount determination module 303, a throughput calculation module 304, and a total throughput determination module 305. The model fitting module 301 is used to fit a degradation model of the battery under test based on measured data from a battery life pre-test. The working condition determination module 302 is used to design working condition data for each unit time in the prediction cycle of the battery life test. The degradation amount determination module 303 is used to cyclically determine the total energy degradation amount of the battery under test at the end of each unit time according to the order of unit time in the prediction cycle, based on the degradation model and the working condition data corresponding to each unit time. The throughput calculation module 304 is used to calculate the energy throughput within the corresponding unit time after determining the total energy degradation amount of the battery under test at the end of each unit time, based on the working condition data. The total throughput determination module 305 stops the cyclic determination of the total energy degradation amount of the battery under test at the end of each unit time and determines the total throughput of the battery under test based on the energy throughput within each unit time when the total energy degradation amount meets the life end condition.

[0093] The battery throughput prediction method and apparatus provided in this invention, based on user usage data and influencing factors of the battery under test, designs multiple sets of test conditions for battery life pre-testing. Based on the measured data of the battery life pre-testing under these multiple test conditions, a degradation model of the battery under test is fitted. Then, following the order of unit time within the prediction cycle, the total energy degradation and energy throughput of the battery under test at the end of each unit time are determined iteratively according to the degradation model and the corresponding operating data for each unit time, until the total energy degradation meets the life-end condition. Finally, the energy throughput of each unit time is accumulated to determine the total throughput of the battery under test, realizing the estimation and prediction of throughput at the end of the battery's life, thus improving the comprehensiveness and accuracy of battery performance testing.

[0094] This invention also provides an electronic device. Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention, with reference to... Figure 4 The electronic device 400 includes: at least one processor 401 (only three are shown in the figure); and a memory 402 communicatively connected to at least one processor 401; wherein the memory 402 stores a computer program that can be executed by at least one processor 401, the computer program being executed by at least one processor 401 to enable at least one processor 401 to execute any battery throughput prediction method in the embodiments of the present invention.

[0095] This invention also provides a computer-readable storage medium. The computer-readable storage medium stores computer instructions that, when executed by a processor, can implement any of the battery throughput prediction methods described in this invention.

[0096] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0097] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0098] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0099] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0100] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0101] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0102] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0103] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for predicting battery throughput, characterized in that, include: The degradation model of the battery under test is fitted based on the measured data of the battery life pretest. The operating condition data for each unit time in the predicted cycle of battery life testing is determined based on the cell operating condition data. According to the order of the unit time in the prediction period, the total energy decay of the battery under test at the end of each unit time is determined cyclically based on the decay model and the operating condition data corresponding to each unit time. After determining the total energy decay of the battery under test at the end of each unit time, the energy throughput within the corresponding unit time is calculated based on the operating condition data. When the total energy decay meets the end-of-life condition, the cyclic determination of the total energy decay of the battery under test at the end of each unit time is stopped, and the total throughput of the battery under test is determined based on the energy throughput in each unit time. Calculating the energy throughput per unit time based on the operating data includes: The depth of charge / discharge and number of cycles per unit time are determined based on the operating data per unit time. The energy throughput per unit time is determined based on the total energy decay of the battery under test at the end of the unit time, the nominal energy of the battery under test, the charge / discharge depth per unit time, and the number of cycles. The formula for calculating the energy throughput is W. n =Q*DOD*(1-E n )*N n W n E represents the energy throughput per unit time, where Q is the nominal energy of the battery cell, and E is the energy throughput per unit time. n N represents the total energy decay of the battery under test at the end of the current unit time. n The value is the number of cycles per unit time, DOD is the depth of charge / discharge tested per unit time, n is the label of the current unit time, and n is a natural number other than 0.

2. The battery throughput prediction method according to claim 1, characterized in that, The battery life pre-test includes a cycle pre-test; the degradation model includes a cycle degradation model; the measured data of the cycle pre-test includes various cycle test conditions and their corresponding cycle degradation amounts; A degradation model for the battery under test is fitted based on the measured data from the battery life pre-test, including: Measure the cyclic decay under different cyclic test conditions during the cyclic pre-test; Based on the cycle test conditions and their corresponding cycle decay, the cycle decay model of the battery under test is fitted using the Arrhenius formula.

3. The battery throughput prediction method according to claim 2, characterized in that, The battery life pre-test also includes calendar pre-test; the degradation model also includes a calendar degradation model; the measured data of the calendar pre-test includes various calendar test conditions and their corresponding calendar degradation amounts; The degradation model of the battery under test is fitted based on the measured data of the battery life pretest, and also includes: Measure the calendar decay amount under different calendar test conditions during the calendar pre-test; Based on the calendar test conditions and their corresponding calendar decay values, the calendar decay model of the battery under test is fitted using the Arrhenius formula.

4. The method for predicting battery throughput according to any one of claims 1-3, characterized in that, According to the order of the unit time in the prediction period, the total energy decay of the battery under test at the end of each unit time is determined cyclically based on the decay model and the operating condition data corresponding to each unit time, including: Based on the total energy decay of the battery under test at the end of the previous unit time, the operating data of the current unit time, and the decay model, the initial parameters of the current unit time are determined. The total decay of the battery life pretest is used as the total energy decay of the battery under test at the end of the previous unit time corresponding to the first unit time. The initial parameters include the initial number of cycles or the initial placement time. Based on the operating data of the current unit time, the initial parameters, and the decay model, the total energy decay of the battery under test at the end of the current unit time is determined.

5. The method for predicting battery throughput according to claim 4, characterized in that, The unit of time includes multiple forecasting phases, which include cyclic forecasting phases or calendar forecasting phases; Based on the operating data of the current unit time, the initial parameters, and the attenuation model, the total energy attenuation of the battery under test at the end of the current unit time is determined, including: Based on the operating data of the first prediction phase, the initial parameters, and the attenuation model, the total energy attenuation of the battery under test at the end of the first prediction phase is determined, wherein the initial parameters of the first prediction phase per unit time are the initial parameters of the unit time. Based on the total energy decay of the battery under test at the end of the previous prediction stage, the operating data of the current prediction stage, and the decay model, the initial parameters of the current prediction stage are determined. Based on the operating data of the current prediction phase, the initial parameters of the current prediction phase, and the attenuation model, the total energy attenuation of the battery under test at the end of the current prediction phase is determined.

6. The method for predicting battery throughput according to claim 1, characterized in that, The total throughput of the battery under test is determined based on the energy throughput per unit time, including: The total throughput of the battery under test is determined by summing the energy throughput of each unit time period.

7. A battery throughput prediction device, controlled by the battery throughput prediction method as described in any one of claims 1-6, characterized in that, The model fitting module is used to fit the degradation model of the battery under test based on the measured data of the battery life pretest. The operating condition determination module is used to determine the operating condition data for each unit time in the predicted cycle of battery life testing based on the cell operating condition data. The attenuation determination module is used to determine the total energy attenuation of the battery under test at the end of each unit time in the prediction period according to the order of the unit time in the prediction period, based on the attenuation model and the operating condition data corresponding to each unit time. The throughput calculation module is used to calculate the energy throughput within the corresponding unit time based on the operating condition data after determining the total energy decay of the battery under test at the end of each unit time. The total throughput determination module is used to stop the cyclic determination of the total energy decay of the battery under test at the end of each unit time when the total energy decay meets the end-of-life condition, and to determine the total throughput of the battery under test based on the energy throughput in each unit time.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor to enable the at least one processor to perform the battery throughput prediction method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the method for predicting battery throughput according to any one of claims 1-6.

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