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WO2024120075A1 - 寿命预测方法、电池管理系统、用电设备及存储介质 - Google Patents

寿命预测方法、电池管理系统、用电设备及存储介质 Download PDF

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Publication number
WO2024120075A1
WO2024120075A1 PCT/CN2023/128637 CN2023128637W WO2024120075A1 WO 2024120075 A1 WO2024120075 A1 WO 2024120075A1 CN 2023128637 W CN2023128637 W CN 2023128637W WO 2024120075 A1 WO2024120075 A1 WO 2024120075A1
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capacity
fitting
multiple sets
battery
data
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French (fr)
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蔡翔
吴长风
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Xiamen Hithium Energy Storage Technology Co Ltd
Shenzhen Hithium Energy Storage Technology Co Ltd
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Xiamen Hithium Energy Storage Technology Co Ltd
Shenzhen Hithium Energy Storage Technology Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/392—Determining battery ageing or deterioration, e.g. state of health
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/10—Energy storage using batteries

Definitions

  • the present application relates to the technical field of energy storage batteries, and in particular to a battery life prediction method, a battery management system, an electrical device and a storage medium.
  • the remaining service life of energy storage batteries refers to the number of charge and discharge cycles that the energy storage battery experiences from the current moment to the failure threshold when the output power cannot meet the normal operation of the machine or equipment under certain working conditions.
  • the long service life of energy storage batteries when the number of cycles is large, the relationship between capacity and the number of cycles is no longer close to linear, and it is impossible to guarantee that the remaining service life of long-life energy storage batteries has a high accuracy rate.
  • the present application provides a battery life prediction method, a battery management system, an electrical device and a storage medium, which are at least used to solve the problem of low accuracy in predicting the remaining service life of a long-life energy storage battery.
  • the present application provides a method for predicting the life of a battery.
  • the method comprises: obtaining multiple groups of cycle numbers of the battery and multiple groups of capacity data corresponding to the multiple groups of cycle numbers; wherein the multiple groups of cycle numbers are multiple groups of cycle numbers of the battery at multiple time nodes in a historical time period; fitting the multiple groups of cycle numbers and the multiple groups of capacity data using a double exponential empirical model to obtain multiple groups of first fitting data; fitting the multiple groups of cycle numbers and the multiple groups of capacity data using a Box-cox transformation method to obtain multiple groups of second fitting data; determining the first capacity error corresponding to the multiple groups of cycle numbers according to the multiple groups of first fitting data and the multiple groups of capacity data; determining the second capacity error corresponding to the multiple groups of cycle numbers according to the multiple groups of second fitting data and the multiple groups of capacity data; generating a battery life prediction model according to the first capacity error, the second capacity error, the multiple groups of first fitting data and the multiple groups of second fitting data; determining the remaining service life of the battery according to the
  • the multiple sets of first fitting data include multiple sets of first fitting capacities, and determining the first capacity errors corresponding to the multiple sets of cycle numbers according to the multiple sets of first fitting data and the multiple sets of capacity data includes:
  • a square root process is performed on the sum of squares of the first differences to obtain the first capacity error.
  • the plurality of sets of second fitting data include a plurality of sets of second fitting capacities. Determining the second capacity errors corresponding to the multiple sets of cycle numbers using the multiple sets of second fitting data and the multiple sets of capacity data includes:
  • the second capacity error is obtained by performing a square root process on the sum of squares of the second differences.
  • the multiple sets of first fitting data include multiple sets of first fitting capacities and multiple sets of first fitting laps corresponding to the multiple sets of first fitting capacities
  • the multiple sets of second fitting data include multiple sets of second fitting capacities
  • generating a battery life prediction model according to the first capacity error, the second capacity error, the multiple sets of first fitting data, and the multiple sets of second fitting data includes:
  • the ratio of the first capacity error to the second capacity error is greater than or equal to the preset threshold, the number of fitting failure cycles in the multiple groups of first fitting cycles is obtained, and the battery life prediction model is generated according to the fitting failure cycle number and a cubic polynomial algorithm.
  • generating the battery life prediction model according to the first capacity error, the second capacity error, the multiple groups of first fitting capacities, and the multiple groups of second fitting capacities includes:
  • the battery life prediction model is generated according to the sum of the product of each group of the first fitting capacity and the first weight and the product of each group of the second fitting capacity and the second weight.
  • the battery life prediction model directly outputs the predicted capacity data corresponding to the predicted number of failure cycles, reducing the amount of calculation and improving the prediction efficiency, and then determines the current capacity data of the battery according to the current number of cycles of the battery, and uses the difference between the predicted capacity data and the current capacity data to determine the remaining service life of the battery.
  • obtaining the number of fitting failure cycles in the multiple sets of first fitting data, and generating the battery life prediction model according to the number of fitting failure cycles and a cubic polynomial algorithm includes:
  • the battery life prediction model is generated according to the multiple groups of effective capacities and the cubic polynomial algorithm.
  • the multiple groups of first fitting capacities before the fitting failure cycles are fitted with the cubic polynomial algorithm to ensure that the data used to fit the cubic polynomial algorithm is the data closest to the actual capacity of the battery, thereby improving the accuracy of the battery life prediction model.
  • the effective capacity obtained by using the double exponential empirical model to fit the multiple sets of effective capacity includes part of the first fitting capacity between the acquired number of cycles and the fitted failure number of cycles, making the data for fitting the cubic polynomial richer, thereby improving the accuracy of the battery life prediction model.
  • determining the remaining service life of the battery according to the predetermined battery capacity and the battery life prediction model includes:
  • the remaining service life of the battery is determined according to the cycle number difference.
  • the present application provides a battery management system.
  • the battery management system includes a first acquisition module, a first fitting module, a second fitting module, a second acquisition module, a third acquisition module, a first determination module and a second determination module.
  • the first acquisition module is used to acquire multiple groups of cycle numbers and multiple groups of capacity data of the battery; wherein the multiple groups of cycle numbers are multiple groups of cycle numbers of the battery at multiple time nodes in a historical time period.
  • the first fitting module is used to fit the multiple groups of cycle numbers and the multiple groups of capacity data using a double exponential empirical model to obtain multiple groups of first fitting data.
  • the second fitting module is used to fit the multiple groups of cycle numbers and the multiple groups of capacity data using a Box-cox transformation method to obtain multiple groups of second fitting data.
  • the second acquisition module is used to determine the first capacity error corresponding to the multiple groups of cycle numbers according to the multiple groups of first fitting data and the multiple groups of capacity data.
  • the third acquisition module is used to determine the second capacity error corresponding to the multiple groups of cycle numbers according to the multiple groups of second fitting data and the multiple groups of capacity data.
  • the first determination module is used to generate a battery life prediction model according to the first capacity error, the second capacity error, the multiple groups of first fitting data and the multiple groups of second fitting data.
  • the second determination module is used to determine the remaining service life of the battery according to the predetermined capacity of the battery and the battery life prediction model.
  • the present application provides an electrical device.
  • the electrical device includes a processor and a memory, the processor and the memory are connected to each other, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the life prediction method as described in the first aspect.
  • the present application provides a computer-readable storage medium.
  • the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes the life prediction method as described in the first aspect.
  • a double exponential empirical model is used to fit multiple groups of battery cycle numbers and capacity data corresponding to each group of cycle numbers to obtain multiple groups of first fitting data
  • the multiple groups of cycle numbers are multiple groups of cycle numbers of the battery at multiple time nodes in a historical time period, and the historical time period refers to the entire time period from the first charge and discharge of the battery to multiple charge and discharge
  • the Box-cox transformation method is used to fit multiple groups of battery cycle numbers and capacity data corresponding to each group of cycle numbers to obtain multiple groups of second fitting data, and the first capacity error between the multiple groups of first fitting data and the multiple groups of capacity data, and the second capacity error between the multiple groups of second fitting data and the multiple groups of capacity data are calculated respectively, and the first capacity error and the second capacity error, the multiple groups of first fitting data and the multiple groups of second fitting data are combined to generate a battery life prediction model, so as to obtain the remaining service life of the battery according to the predetermined capacity
  • the present application couples the double exponential empirical model and the Box-cox transformation method to avoid prediction errors caused by the double exponential empirical model or the Box-cox transformation method alone.
  • the overly large problem is being able to accurately predict the remaining useful life of long-life batteries, such as energy storage batteries.
  • FIG1 is a schematic flow chart of a battery life prediction method provided in an embodiment of the present application.
  • 2 to 6 are diagrams showing the relationship between a predicted curve obtained by a battery life prediction model provided by an embodiment of the present application and an actual curve during actual use;
  • FIG7 is a schematic diagram of the structure of a battery management system provided in an embodiment of the present application.
  • FIG8 is a schematic diagram of the structure of an electrical device provided in an embodiment of the present application.
  • a battery management system 700 a first acquisition module 701, a first fitting module 702, a second fitting module 703, a second acquisition module 704, a third acquisition module 705, a first determination module 706 and a second determination module 707;
  • Power-consuming device 800 bus 20 , processor 30 , memory 50 , and communication interface 70 .
  • connection and “coupling” mentioned in this application, unless otherwise specified, include direct and indirect connections (couplings).
  • the remaining service life of energy storage batteries refers to the number of charge and discharge cycles that the energy storage battery experiences from the current moment to the failure threshold when the output power cannot meet the normal operation of the machine or equipment under certain working conditions.
  • the long service life of energy storage batteries when the number of cycles is large, the relationship between capacity and the number of cycles is no longer close to linear, and it is impossible to guarantee that the remaining service life of long-life energy storage batteries has a high accuracy rate.
  • the present application provides a battery life prediction method.
  • a double exponential empirical model is used to fit multiple groups of battery cycle numbers and capacity data corresponding to each group of cycle numbers to obtain multiple groups of first fitting data, wherein the multiple groups of cycle numbers are multiple groups of cycle numbers of the battery at multiple time nodes in a historical time period, and the historical time period refers to the entire time period from the first charge and discharge of the battery to multiple charge and discharges
  • the Box-cox transformation method is used to fit multiple groups of battery cycle numbers and capacity data corresponding to each group of cycle numbers to obtain multiple groups of second fitting data, and the first capacity error between the multiple groups of first fitting data and the multiple groups of capacity data, and the second capacity error between the multiple groups of second fitting data and the multiple groups of capacity data are calculated respectively, combined with the first The capacity error and the second capacity error, multiple sets of first fitting data and multiple sets of second fitting data generate a battery life prediction model, so as to obtain the remaining service life of the battery according to the predetermined capacity
  • the batteries of the present application may include energy storage batteries and power batteries.
  • the cycle life of the power battery is relatively short, and the capacity data and the number of cycles are in a simple linear relationship.
  • the power battery predicts the remaining service life of the power battery through the battery life prediction model in the life prediction method provided by the present application, the error between the predicted result and the actual result can be made smaller, thereby improving the accuracy of the prediction.
  • the early capacity data and the number of cycles are in a linear relationship.
  • the capacity data and the number of cycles are in an exponentially decreasing relationship.
  • the double exponential empirical model and the Box-cox transformation method are coupled to reduce the error between the predicted result and the actual result, thereby improving the accuracy of the life prediction.
  • FIG1 is a schematic diagram of a flow chart of a battery life prediction method provided by the present application.
  • the life prediction method includes the following steps S101-S107, wherein:
  • S101 Acquire multiple groups of cycle numbers of a battery and multiple groups of capacity data corresponding to the multiple groups of cycle numbers; wherein the multiple groups of cycle numbers are multiple groups of cycle numbers of the battery at multiple time nodes within a historical time period.
  • multiple groups of cycle numbers refer to multiple cycle numbers at multiple time nodes within the historical time period of the battery.
  • the historical time period refers to the entire time period from the first charge and discharge of the battery to multiple charge and discharge.
  • each time node corresponds to a cycle number and a capacity data.
  • the cycle number and capacity data of the battery correspond one to one.
  • the multiple groups of cycle numbers are the historical cycle numbers of the battery, and the multiple groups of capacity data are the historical capacity data of the battery.
  • the current cycle number of the battery is 3000, and the capacity data corresponding to the current cycle number is Q1.
  • the cycle number is 3000 groups, and the capacity data also corresponds to 3000 groups.
  • the cycle number input to the double exponential empirical model and the Box-cox transformation method is 1 to 3000, and correspondingly, the capacity data is the capacity data corresponding to 1 to 3000 cycles.
  • the obtained multiple sets of cycle numbers and multiple sets of capacity data are input into the double exponential empirical model for simulation to obtain multiple sets of first fitting data.
  • Each set of first fitting data includes the first fitting number of cycles and the first fitting capacity, wherein a set of first fitting capacity is a set of fitting data obtained after a set of capacity data is fitted by the double exponential empirical model. If the obtained number of cycles is smaller than the number of failure cycles, the number of sets of first fitting capacity is greater than the number of sets of capacity data. For example, when the number of failure cycles is 4000 and the obtained number of cycles is 3000, the number of first fitting capacities can be 4000 and the number of capacity data is 3000.
  • the obtained multiple sets of cycle numbers and multiple sets of capacity data are fitted by Box-cox transformation method to obtain multiple sets of second fitting data.
  • Each set of second fitting data includes the second fitting number of cycles and the second fitting capacity. If the obtained number of cycles is smaller than the number of failure cycles, the number of sets of second fitting capacity is greater than the number of sets of capacity data. For example, when the number of failure cycles is 4000 and the obtained number of cycles is 3000, the number of second fitting capacities can be 4000 and the number of capacity data is 3000.
  • S104 Determine first capacity errors corresponding to multiple sets of cycle numbers according to multiple sets of first fitting data and multiple sets of capacity data.
  • a method for determining the first capacity errors corresponding to multiple sets of cycle numbers according to multiple sets of first fitting data and multiple sets of capacity data may be: calculating the first fitting capacity corresponding to each set of cycle numbers and the capacity data. first difference; performing summation processing on the squares of the first differences of multiple groups of cycle numbers to obtain a first sum of squares of differences; performing square root processing on the first sum of squares of differences to obtain a first capacity error.
  • the first difference between the first fitting capacity and the capacity data corresponding to each group of cycle numbers is calculated respectively, wherein the number of groups of the first fitting capacity is the same as the number of groups of the capacity data, and both are equal to the number of groups of cycle numbers.
  • multiple groups of first differences are obtained, and the number of groups of first differences is equal to the number of groups of cycle numbers.
  • the first differences of multiple groups of cycle numbers are squared, and the squares of each first difference are accumulated and summed to obtain the first difference square sum, and the square root of the first difference square sum is performed to obtain the first capacity error.
  • the calculation formula of the first capacity error is shown in Formula 1 below:
  • RSE bi-exp represents the first capacity error
  • bipred i represents the first fitting capacity corresponding to the i-th group of cycles (i.e., the i-th cycle)
  • exp i represents the capacity data corresponding to the i-th group of cycles
  • N represents the number of groups of cycles
  • i is a positive integer.
  • the standard error of the residual (Root Square Error, RSE) (here RSE bi-exp ) represents the standard error value of the residual calculated using the first fitting capacity and capacity data, that is, the present application uses the standard error value of the residual to characterize the first capacity error, and combines the impact of the first capacity error analysis on the battery life prediction to reduce the occurrence of situations where the error between the predicted results output by the battery life prediction model and the actual results is too large, thereby improving the accuracy of the remaining service life prediction.
  • RSE Root Square Error
  • S105 Determine second capacity errors corresponding to multiple sets of cycle numbers according to multiple sets of second fitting data and multiple sets of capacity data.
  • a method for determining the second capacity error corresponding to multiple groups of cycle numbers based on multiple groups of second fitting data and multiple groups of capacity data may be: calculating the second difference between the second fitting capacity and the capacity data corresponding to each group of cycle numbers; performing a summation process on the squares of the second differences of the multiple groups of cycle numbers to obtain a second sum of squares of differences; and performing a square root process on the second sum of squares of differences to obtain a second capacity error.
  • the second difference between the second fitting capacity and the capacity data corresponding to each group of cycle numbers is calculated respectively, wherein the number of groups of the second fitting capacity is the same as the number of groups of the capacity data, and is equal to the number of groups of cycle numbers.
  • multiple groups of second differences are obtained, and the number of groups of second differences is equal to the number of groups of cycle numbers.
  • the second differences of multiple groups of cycle numbers are squared, and the squares of each second difference are accumulated and summed to obtain the sum of the squares of the second differences, and the square root of the sum of the squares of the second differences is performed to obtain the second capacity error.
  • the calculation formula for the second capacity error is shown in Formula 2 below:
  • RSE box-cox represents the second capacity error
  • boxpred i represents the second fitting capacity corresponding to the i-th group of cycles (i.e., the i-th cycle)
  • exp i represents the capacity data corresponding to the i-th group of cycles
  • N represents the number of groups of cycles
  • i is a positive integer.
  • RSE box-cox represents the standard error value of the residual calculated using the second fitting capacity and capacity data, that is, the present application uses the standard error of the residual to characterize the second capacity error, and comprehensively analyzes the impact of the first capacity error and the second capacity error on the battery life prediction, reduces the occurrence of situations where the error between the predicted results output by the battery life prediction model and the actual results is too large, and improves the accuracy of the remaining service life prediction.
  • S106 Generate a battery life prediction model according to the first capacity error, the second capacity error, multiple groups of first fitting data, and multiple groups of second fitting data.
  • the method for generating a battery life prediction model based on the first capacity error, the second capacity error, multiple groups of first fitting data, and multiple groups of second fitting data may be: when the ratio of the first capacity error to the second capacity error is less than a preset threshold, the battery life prediction model is generated based on the first capacity error, the second capacity error, multiple groups of first fitting capacities, and multiple groups of second fitting capacities.
  • the ratio of the first capacity error to the second capacity error is greater than or equal to the preset threshold, the number of fitting failure cycles in the multiple groups of first fitting cycles is obtained, and the battery life prediction model is generated based on the number of fitting failure cycles and a cubic polynomial algorithm.
  • the preset thresholds for the same type of batteries are the same, and the preset thresholds can be determined based on multiple curves of the battery life prediction model for training the same type of batteries.
  • the slope of the linear decline phase in the curve with the smallest error is selected as the preset threshold.
  • the number of groups of the first fitting data is the number of groups of the first fitting cycles obtained by fitting the double exponential empirical model; the number of groups of the second fitting data is the number of groups of the second fitting cycles obtained by fitting the Box-cox transformation method.
  • the multiple groups of first fitting data obtained by the double exponential empirical model and the multiple groups of second fitting data obtained by the Box-cox transformation method are weighted averaged according to the first capacity error and the second capacity error, and the number of cycles when the battery reaches the predetermined capacity of the battery (i.e., the predicted number of failure cycles) is predicted by weighted average, so as to obtain the remaining service life of the battery according to the predicted number of failure cycles and the obtained number of cycles (i.e., the current number of cycles of the battery).
  • multiple groups of first fitting capacities before reaching the fitted number of failure cycles in the multiple groups of first fitting data obtained by calculating the double exponential empirical model are fitted using a cubic polynomial algorithm, thereby determining multiple parameters of the cubic polynomial algorithm, and obtaining the fitted cubic polynomial algorithm to predict the predicted number of failure cycles when the battery reaches a predetermined capacity of the battery, thereby determining the remaining service life of the battery based on the predicted number of failure cycles and the current number of cycles of the battery.
  • a battery life prediction model is generated according to the first capacity error, the second capacity error, the plurality of groups of first fitting capacities and the plurality of groups of second fitting capacities, including:
  • a battery life prediction model is generated according to the sum of the product of each group's first fitting capacity and the first weight and the product of each group's second fitting capacity and the second weight.
  • the ratio of the first capacity error to the second capacity error is less than a preset threshold
  • the first capacity error and the second capacity error are summed to obtain a first value
  • the ratio of the first capacity error to the first value is used as The first weight
  • the ratio of the second capacity error and the first value is used as the second weight.
  • Each group of first fitting capacity is multiplied by the first weight respectively to obtain a product about the first fitting capacity
  • each group of second fitting capacity is multiplied by the second weight respectively to obtain a product about the second fitting capacity.
  • the battery life prediction model directly outputs the predicted capacity data corresponding to the predicted number of failure cycles of the battery.
  • the relationship of the battery life prediction model is shown in the following formula:
  • pred represents the predicted capacity data corresponding to the predicted number of failure cycles
  • pred bi-exp represents multiple groups of first fitting capacity predicted by the double exponential empirical model
  • pred box-cox represents multiple groups of second fitting capacity predicted by the Box-cox transformation method
  • error bi-exp represents the first capacity error, that is, the RSE bi-exp calculated in formula 1
  • error box-cox represents the second capacity error, that is, the RSE box-cox calculated in formula 2.
  • the first capacity error and the second capacity error are both determined values, and the calculated first weight and second weight are both determined values.
  • the battery life prediction model directly outputs the predicted capacity data corresponding to the predicted number of failure cycles, thereby reducing the amount of calculation and improving the prediction efficiency.
  • the current capacity data of the battery is then determined based on the current number of cycles of the battery, and the remaining service life of the battery is determined using the difference between the predicted capacity data and the current capacity data.
  • a battery life prediction model is generated based on multiple sets of effective capacities and a cubic polynomial algorithm.
  • the ratio of the first capacity error to the second capacity error is greater than or equal to the preset threshold, the number of fitting failure cycles predicted in the double exponential empirical model is obtained, and multiple groups of first fitting capacities before the battery reaches the number of fitting failure cycles in the first fitting cycles are obtained as multiple groups of effective capacities.
  • the multiple groups of effective capacities are input into the cubic polynomial algorithm with undetermined parameters, and multiple parameters of the cubic polynomial algorithm can be calculated, thereby obtaining a cubic polynomial about the capacity data and the number of cycles, that is, a battery life prediction model.
  • the cubic polynomial algorithm is fitted with multiple groups of first fitting capacities before the number of fitting failure cycles to ensure that the data used to fit the cubic polynomial algorithm is the data closest to the actual capacity of the battery, thereby improving the accuracy of the battery life prediction model.
  • the effective capacity of the cubic polynomial algorithm fitting multiple groups of effective capacities obtained by the double exponential empirical model includes part of the first fitting capacity between the number of cycles obtained and the number of fitting failure cycles, making the data of the cubic polynomial fitting more abundant, thereby improving the accuracy of the battery life prediction model prediction.
  • the battery life prediction model is a relationship between capacity data and the number of cycles, as shown in the following formula 4:
  • a, b, c, d are all parameters of the cubic polynomial (battery life prediction model), x represents the number of cycles, and y represents the capacity data.
  • the capacity data corresponding to any number of cycles can be calculated, and then the relationship curve between the capacity data and the number of cycles can be obtained.
  • the relationship curve between the capacity data and the number of cycles the predicted number of failure cycles corresponding to the battery reaching the predetermined capacity can be obtained, and then the predicted number of failure cycles and the current number of cycles of the battery can be subtracted to obtain the difference in the number of cycles, and the remaining service life of the battery can be determined according to the difference in the number of cycles.
  • S107 Determine the remaining service life of the battery according to the predetermined battery capacity and the battery life prediction model.
  • the battery's predetermined capacity may be the failure capacity of the battery, for example, the capacity when the battery's predetermined capacity is 70% of the initial capacity. After determining the battery's predetermined capacity, the predicted failure cycles corresponding to the battery's predetermined capacity in the battery life prediction model can be obtained, and the predicted failure cycles are subtracted from the current cycle number to obtain the cycle number difference, and the remaining service life of the battery is determined based on the cycle number difference.
  • the remaining service life of a battery is determined based on a predetermined battery capacity and a battery life prediction model, including: determining a predicted number of failure cycles corresponding to when the battery reaches the predetermined battery capacity based on the battery life prediction model; determining the current cycle number of the battery; calculating a cycle difference between the predicted number of failure cycles and the current number of cycles; and determining the remaining service life of the battery based on the cycle difference.
  • the battery life prediction model in this case directly outputs the predicted capacity data corresponding to the predicted number of failure cycles.
  • the predicted number of failure cycles can be obtained based on the predicted capacity data.
  • the predicted number of failure cycles is subtracted from the current number of cycles of the battery to obtain the cycle difference, and the remaining service life of the battery is determined based on the cycle difference.
  • the battery life prediction model in this case is a cubic polynomial about the number of cycles and capacity data.
  • the predicted capacity data of the battery's predetermined capacity of the battery of the battery life prediction model is obtained, and the corresponding predicted number of failure cycles is obtained based on the predicted capacity data, and the predicted number of failure cycles is subtracted from the current number of cycles of the battery to obtain the difference in the number of cycles, and then the remaining service life of the battery is determined based on the difference in the number of cycles.
  • Figures 2 to 6 are relationship diagrams of a prediction curve obtained by a battery life prediction model provided in an embodiment of the present application and an actual curve during actual use, wherein the horizontal axis of each figure represents the number of cycles, and the vertical axis represents the capacity data. The intersection of the three lines in each figure is the predetermined capacity.
  • the actual curve represents the actual relationship curve between the capacity data and the number of cycles during the actual charging and discharging process of the battery.
  • the prediction curve represents the prediction relationship curve obtained by the battery life prediction model.
  • the obtained number of cycles is multiple groups of cycle numbers of the battery obtained in S101.
  • the error number of cycles represents the error between the predicted number of failure cycles and the actual number of failure cycles when the battery reaches the predetermined capacity of the battery.
  • Figure 2 is a schematic diagram of the predicted curve of the battery life prediction model obtained when the number of cycles obtained is 6000 and the actual curve during the actual charging and discharging process of the battery, with an error of 9 cycles
  • Figure 3 is a schematic diagram of the predicted curve of the battery life prediction model obtained when the number of cycles obtained is 7000 and the actual curve during the actual charging and discharging process of the battery, with an error of 84 cycles
  • Figure 4 is a schematic diagram of the predicted curve of the battery life prediction model obtained when the number of cycles obtained is 8000 and the actual curve during the actual charging and discharging process of the battery, with an error of 115 cycles
  • Figure 5 is a schematic diagram of the predicted curve of the battery life prediction model obtained when the number of cycles obtained is 9000 and the actual curve during the actual charging and discharging process of the battery, with an error of 91 cycles
  • Figure 6 is a schematic diagram of the predicted curve of the battery life prediction model obtained when the number of cycles obtained is 10000 and the actual curve during the actual charging and discharging process of the battery
  • the method designed in the embodiment of the present application is described in detail above.
  • the battery management system 700 designed in the embodiment of the present application is provided below.
  • FIG. 7 is a schematic diagram of a battery management system 700 provided in an embodiment of the present application.
  • the battery life prediction method provided in the present application is applied to the battery management system 700.
  • the battery management system 700 includes a first acquisition module 701, a first fitting module 702, a second fitting module 703, a second acquisition module 704, a third acquisition module 705, a first determination module 706 and a second determination module 707.
  • the first acquisition module 701 is used to obtain multiple groups of cycle numbers and multiple groups of capacity data of the battery; wherein the multiple groups of cycle numbers are multiple groups of cycle numbers of the battery at multiple time nodes within a historical time period.
  • the fitting module 702 is used to fit multiple groups of cycle numbers and multiple groups of capacity data using a double exponential empirical model to obtain multiple groups of first fitting data.
  • the second fitting module 703 is used to fit multiple groups of cycle numbers and multiple groups of capacity data using a Box-cox transformation method to obtain multiple groups of second fitting data.
  • the second acquisition module 704 is used to determine the first capacity error corresponding to the multiple groups of cycle numbers based on the multiple groups of first fitting data and the multiple groups of capacity data.
  • the third acquisition module 705 is used to determine the second capacity error corresponding to the multiple groups of cycle numbers based on the multiple groups of second fitting data and the multiple groups of capacity data.
  • the first determination module 706 is used to generate a battery life prediction model based on the first capacity error, the second capacity error, the multiple groups of first fitting data and the multiple groups of second fitting data.
  • the second determination module 707 is used to determine the remaining service life of the battery based on the predetermined capacity of the battery and the battery life prediction model.
  • the battery management system 700 can record the capacity data and cycle numbers corresponding to multiple batteries in real time, and the battery life prediction model for the remaining battery life has a small amount of calculation and can output the remaining service life of each battery in real time. There is no need to transmit data remotely or wait for data transmission, thereby improving prediction efficiency.
  • the multiple groups of first fitting data include multiple groups of first fitting capacities; the second acquisition module 704 is also used to: calculate the first difference between the first fitting capacity and the capacity data corresponding to each group of cycle numbers; perform summation processing on the squares of the first differences of the multiple groups of cycle numbers to obtain a first sum of squares of the differences; perform square root processing on the first sum of squares of the differences to obtain a first capacity error.
  • the multiple groups of second fitting data include multiple groups of second fitting capacities; the third acquisition module 705 is also used to: calculate the second difference between the second fitting capacity and the capacity data corresponding to each group of cycle numbers; perform summation processing on the squares of the second differences of the multiple groups of cycle numbers to obtain the second sum of squares of the differences; perform square root processing on the second sum of squares of the differences to obtain the second capacity error.
  • the multiple groups of first fitting data include multiple groups of first fitting capacities and multiple groups of first fitting cycles corresponding to the multiple groups of first fitting capacities
  • the multiple groups of second fitting data include multiple groups of second fitting capacities
  • the first determination module 706 is also used to: when the ratio of the first capacity error to the second capacity error is less than a preset threshold, generate a battery life prediction model according to the first capacity error, the second capacity error, the multiple groups of first fitting capacities and the multiple groups of second fitting capacities; when the ratio of the first capacity error to the second capacity error is greater than or equal to the preset threshold, obtain the number of fitting failure cycles in the multiple groups of first fitting cycles, and generate a battery life prediction model according to the number of fitting failure cycles and a cubic polynomial algorithm.
  • the first determination module 706 is further used to: take the sum of the first capacity error and the second capacity error as a first value; obtain the ratio of the first capacity error to the first value as a first weight; obtain the ratio of the second capacity error to the first value as a second weight; and generate a battery life prediction model according to the sum of the product of each group of first fitting capacity and the first weight and the product of each group of second fitting capacity and the second weight.
  • the first determination module 706 is further used to: obtain multiple groups of first fitting capacities before the batteries in multiple groups of first fitting cycles reach the fitting failure number of cycles as multiple groups of effective capacities; and generate a battery life prediction model based on the multiple groups of effective capacities and a cubic polynomial algorithm.
  • the second determination module 707 is also used to: determine the predicted number of failure cycles corresponding to when the battery reaches a predetermined capacity according to a battery life prediction model; determine the current number of cycles of the battery; calculate the difference between the predicted number of failure cycles and the current number of cycles; and determine the remaining service life of the battery according to the difference in the number of cycles.
  • each module of the battery management system 700 can also correspond to the corresponding description of the life prediction method embodiment described in any embodiment of the present application.
  • FIG8 is a schematic diagram of the composition of an electric device 800 provided in an embodiment of the present application.
  • the electric device 800 may include a processor, a memory, and a communication interface, wherein the processor, the memory, and the communication interface are connected via a bus, the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions to implement The life prediction method described in any embodiment of the present application.
  • the technical solutions of the embodiments of the present application are applicable to various electrical equipment 800 that use energy storage batteries or power batteries, such as electric vehicles, electric toys, electric tools, electric vehicles, ships and spacecraft, mobile phones, portable devices, PDAs, laptops, etc.
  • the function of the communication interface 70 may be implemented by a transceiver circuit or a dedicated transceiver chip.
  • the processor 30 may be implemented by a dedicated processing chip, a processing circuit, a processor or a general-purpose chip.
  • a computer-readable storage medium is provided, on which instructions are stored. When the instructions are executed, the method described in any embodiment of the present application is performed.
  • a computer program product including instructions is provided, and when the instructions are executed, the method described in any embodiment of the present application is performed.
  • FIG8 shows only one memory 50 and processor 30. In an actual terminal or server, there may be multiple processors 30 and memories 50.
  • the memory 50 may also be referred to as a storage medium or a storage device, etc., which is not limited in the present embodiment of the application.
  • the processor 30 can be a central processing unit (CPU), and the processor 30 can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc.
  • CPU central processing unit
  • DSP digital signal processors
  • ASIC application specific integrated circuits
  • FPGA field-programmable gate arrays
  • the memory 50 mentioned in the embodiment of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories.
  • the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory.
  • the volatile memory may be a random access memory (RAM), which is used as an external cache.
  • RAM static random access memory
  • DRAM dynamic random access memory
  • SDRAM synchronous dynamic random access memory
  • DDR SDRAM double data rate synchronous dynamic random access memory
  • ESDRAM enhanced synchronous dynamic random access memory
  • Synchlink DRAM SLDRAM
  • Direct Rambus RAM Direct Rambus RAM
  • processor 30 is a general-purpose processor, DSP, ASIC, FPGA or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, the memory (storage module) is integrated in the processor.
  • memory 50 described herein is intended to include, but is not limited to, these and any other suitable types of memory.
  • the bus 20 may include, in addition to the data bus, a power bus, a control bus, a status signal bus, etc. However, for the sake of clarity, various buses are all labeled as the bus 20 in the figure.
  • each step of the above method can be completed by an integrated logic circuit of hardware in the processor 30 or an instruction in the form of software.
  • the steps of the method disclosed in conjunction with the embodiment of the present application can be directly embodied as a hardware processor for execution, or a combination of hardware and software modules in the processor 30 for execution.
  • the software module can be located in a mature storage medium in the field such as a random access memory 50, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc.
  • the storage medium is located in the memory 50, and the processor 30 reads the information in the memory 50 and completes the steps of the above method in conjunction with its hardware. To avoid repetition, it is not described in detail here.
  • the size of the serial numbers of the above-mentioned processes does not mean the order of execution.
  • the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.
  • the disclosed systems, devices and methods can be implemented in other ways.
  • the device embodiments described above are only schematic, for example, the division of modules is only a logical function division, and there may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed.
  • Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.
  • modules described as separate components of the above units may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
  • each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.
  • the computer program product includes one or more computer instructions.
  • the computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.
  • the computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium.
  • the computer instructions can be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center.
  • the computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server, a data center, etc. that contains one or more available media integration. Available media can be magnetic media, (e.g., floppy disk, hard disk, tape), optical media (e.g., DVD), or semiconductor media (e.g., solid-state hard disk), etc.

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Abstract

一种寿命预测方法、电池管理系统、用电设备及存储介质。寿命预测方法包括获取电池的多组循环圈数和与多组循环圈数对应的多组容量数据（S101）；利用双指数经验模型对多组循环圈数和多组容量数据进行拟合，得到多组第一拟合数据（S102）；利用Box-cox转换方法对多组循环圈数和多组容量数据进行拟合，得到多组第二拟合数据（S103）；根据多组第一拟合数据和多组容量数据确定多组循环圈数对应的第一容量误差（S104）；根据多组第二拟合数据和多组容量数据确定多组循环圈数对应的第二容量误差（S105）；根据第一容量误差、第二容量误差、多组第一拟合数据和多组第二拟合数据生成电池寿命预测模型（S106）；根据电池预定容量和电池寿命预测模型确定电池的剩余使用寿命（S107）。

Description

寿命预测方法、电池管理系统、用电设备及存储介质
本申请要求于2022年12月09日提交中国专利局、申请号为202211576245.X、申请名称为“寿命预测方法、电池管理系统、用电设备及存储介质”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及储能电池技术领域，具体涉及一种电池的寿命预测方法、电池管理系统、用电设备及存储介质。
背景技术
储能电池在实际的应用过程中，储能电池的容量会随着充放电循环圈数的增加而下降，性能逐渐衰退，产生电池寿命失效问题可能会导致安全事故，因此，储能电池的剩余使用寿命预测显得尤为重要。其中，储能电池的剩余使用寿命是指在一定的工作条件下，储能电池从当前时刻开始到输出功率无法满足机器或设备正常工作时，失效阈值所经历的充放电循环圈数。但由于储能电池的使用寿命较长，在循环圈数较大的情况下，容量与循环圈数的关系不再趋近于线性，无法保证预测长寿命储能电池的剩余使用寿命具有较高的准确率。
发明内容
本申请提供了一种电池的寿命预测方法、电池管理系统、用电设备及存储介质，至少用于解决预测长寿命储能电池时剩余使用寿命准确度较低的问题。
第一方面，本申请提供一种电池的寿命预测方法。所述寿命预测方法包括：获取所述电池的多组循环圈数和与所述多组循环圈数对应的多组容量数据；其中，所述多组循环圈数为所述电池在历史时间段内的多个时间节点下的多组循环圈数；利用双指数经验模型对所述多组循环圈数和所述多组容量数据进行拟合，得到多组第一拟合数据；利用Box-cox转换方法对所述多组循环圈数和所述多组容量数据进行拟合，得到多组第二拟合数据；根据所述多组第一拟合数据和所述多组容量数据确定所述多组循环圈数对应的第一容量误差；根据所述多组第二拟合数据和所述多组容量数据确定所述多组循环圈数对应的第二容量误差；根据所述第一容量误差、所述第二容量误差、所述多组第一拟合数据和所述多组第二拟合数据生成电池寿命预测模型；根据电池预定容量和所述电池寿命预测模型确定所述电池的剩余使用寿命。
在一种可能的实施方式中，所述多组第一拟合数据包括多组第一拟合容量，所述根据所述多组第一拟合数据和所述多组容量数据确定所述多组循环圈数对应的第一容量误差，包括：
计算每组所述循环圈数对应的所述第一拟合容量与所述容量数据的第一差值；
对所述多组循环圈数的第一差值的平方执行求和处理，得到第一差值平方和；
对所述第一差值平方和执行开方处理，得到所述第一容量误差。
可以看出，通过计算多组第一拟合容量和多组容量数据之间的第一容量误差，结合第一容量误差分析对电池寿命预测的影响，减少电池寿命预测模型输出的预测结果与实际结果误差过大的情况的出现，提高剩余使用寿命预测的准确性。
在一种可能的实施方式中，所述多组第二拟合数据包括多组第二拟合容量，所述根据所 述多组第二拟合数据和所述多组容量数据确定所述多组循环圈数对应的第二容量误差，包括：
计算每组所述循环圈数对应的所述第二拟合容量与所述容量数据的第二差值；
对所述多组循环圈数的第二差值的平方执行求和处理，得到第二差值平方和；
对所述第二差值平方和执行开平方处理得到所述第二容量误差。
可以看出，通过计算多组第二拟合容量和多组容量数据之间的第二容量误差，综合分析第一容量误差和第二容量误差对电池寿命预测的影响，减少电池寿命预测模型输出的预测结果与实际结果误差过大的情况的出现，提高剩余使用寿命预测的准确性。
在一种可能的实施方式中，所述多组第一拟合数据包括多组第一拟合容量及与所述多组第一拟合容量对应的多组第一拟合圈数，所述多组第二拟合数据包括多组第二拟合容量，所述根据所述第一容量误差、所述第二容量误差、所述多组第一拟合数据和所述多组第二拟合数据生成电池寿命预测模型，包括：
在所述第一容量误差与所述第二容量误差的比值小于预设阈值的情况下，根据所述第一容量误差、所述第二容量误差、所述多组第一拟合容量和所述多组第二拟合容量生成所述电池寿命预测模型；
在所述第一容量误差与所述第二容量误差的比值大于等于所述预设阈值的情况下，获取所述多组第一拟合圈数中的拟合失效圈数，根据所述拟合失效圈数及三次多项式算法生成所述电池寿命预测模型。
可以看出，综合考虑第一容量误差和第二容量误差的比值是否小于预设阈值，对不同情况分别拟合不同的电池寿命预测模型，并对不同情况下的电池寿命预测模型通过将不同模型或算法进行耦合的方式实现拟合，使得获取得到的电池寿命预测模型能够在预测长寿命电池的情况下更加准确地预测长寿命电池的剩余使用寿命。
在一种可能的实施方式中，所述在所述第一容量误差与所述第二容量误差的比值小于预设阈值的情况下，根据所述第一容量误差、所述第二容量误差、所述多组第一拟合容量和所述多组第二拟合容量生成所述电池寿命预测模型，包括：
将所述第一容量误差和所述第二容量误差之和作为第一数值；
获取所述第一容量误差与所述第一数值的比值，以作为第一权值；
获取所述第二容量误差与所述第一数值的比值，以作为第二权值；
根据各组所述第一拟合容量和所述第一权值的乘积、与各组所述第二拟合容量和所述第二权值的乘积的和值生成所述电池寿命预测模型。
可以看出，在第一容量误差和第二容量误差的比值小于预设阈值的情况下，电池寿命预测模型直接输出预测失效圈数对应的预测容量数据，减少运算量，提高预测效率，再根据电池的当前循环圈数确定电池的当前容量数据，利用预测容量数据和当前容量数据的差值确定电池的剩余使用寿命。
在一种可能的实施方式中，所述在所述第一容量误差与所述第二容量误差的比值大于等于所述预设阈值的情况下，获取所述多组第一拟合数据中的拟合失效圈数，根据所述拟合失效圈数及三次多项式算法生成所述电池寿命预测模型，包括：
获取所述多组第一拟合圈数中的所述电池达到所述拟合失效圈数之前的多组所述第一拟合容量，以作为多组有效容量；
根据所述多组有效容量和所述三次多项式算法生成所述电池寿命预测模型。
可以看出，将拟合失效圈数之前的多组第一拟合容量拟合三次多项式算法，保证用于拟合三次多项式算法的数据为最接近电池实际容量的数据，提高电池寿命预测模型预测的准确 性。且，采用双指数经验模型得到的多组有效容量拟合三次多项式算法相较于采用获取得到的多组容量数据拟合三次多项式而言，有效容量中包含了获取得到的循环圈数到拟合失效圈数之间的部分第一拟合容量，使得拟合三次多项式的数据更加丰富，从而提高电池寿命预测模型预测的精确度。
在一种可能的实施方式中，所述根据电池预定容量和所述电池寿命预测模型确定所述电池的剩余使用寿命，包括：
根据所述电池寿命预测模型确定所述电池达到所述电池预定容量时对应的预测失效圈数；
确定所述电池的当前循环圈数；
计算所述预测失效圈数和所述当前循环圈数的循环圈数差值；
根据所述循环圈数差值确定所述电池的剩余使用寿命。
可以看出，结合第一容量误差和第二容量误差确定不同的电池寿命预测模型，不同情况下的电池寿命预测模型由不同的模型的数据耦合得到，可以有效避免预测误差过大的问题，能够准确地预测长寿命电池的剩余使用寿命。
第二方面，本申请提供一种电池管理系统。所述电池管理系统包括第一获取模块、第一拟合模块、第二拟合模块、第二获取模块、第三获取模块、第一确定模块及第二确定模块。所述第一获取模块用于获取电池的多组循环圈数和多组容量数据；其中，所述多组循环圈数为所述电池在历史时间段内的多个时间节点下的多组循环圈数。所述第一拟合模块用于利用双指数经验模型对所述多组循环圈数和所述多组容量数据进行拟合，得到多组第一拟合数据。所述第二拟合模块用于利用Box-cox转换方法对所述多组循环圈数和所述多组容量数据进行拟合，得到多组第二拟合数据。所述第二获取模块用于根据所述多组第一拟合数据和所述多组容量数据确定所述多组循环圈数对应的第一容量误差。所述第三获取模块用于根据所述多组第二拟合数据和所述多组容量数据确定所述多组循环圈数对应的第二容量误差。所述第一确定模块用于根据所述第一容量误差、所述第二容量误差、所述多组第一拟合数据和所述多组第二拟合数据生成电池寿命预测模型。所述第二确定模块用于根据电池预定容量和所述电池寿命预测模型确定所述电池的剩余使用寿命。
第三方面，本申请提供一种用电设备。所述用电设备包括处理器和存储器，所述处理器和所述存储器相互连接，其中，所述存储器用于存储计算机程序，所述计算机程序包括程序指令，所述处理器被配置用于调用所述程序指令，执行如第一方面所述的寿命预测方法。
第四方面，本申请提供一种计算机可读存储介质。所述计算机可读存储介质存储有计算机程序，所述计算机程序包括程序指令，所述程序指令被处理器执行时使所述处理器执行如第一方面所述的寿命预测方法。
本申请的电池的寿命预测方法、电池管理系统、用电设备及计算机可读存储介质中，利用双指数经验模型对电池的多组循环圈数和与各组循环圈数一一对应的容量数据进行拟合，得到多组第一拟合数据，其中，多组循环圈数为电池在历史时间段内的多个时间节点下的多组循环圈数，历史时间段是指电池从开始使用的第一次充放电到经历多次充放电的整个时间段内，及利用Box-cox转换方法对电池的多组循环圈数和与各组循环圈数一一对应的容量数据进行拟合，得到多组第二拟合数据，并分别计算多组第一拟合数据与多组容量数据之间的第一容量误差，及多组第二拟合数据与多组容量数据之间的第二容量误差，结合第一容量误差与第二容量误差、多组第一拟合数据和多组第二拟合数据生成电池寿命预测模型，从而根据电池预定容量和电池寿命预测模型获取电池的剩余使用寿命。本申请将双指数经验模型和Box-cox转换方法进行耦合，避免单独通过双指数经验模型或Box-cox转换方法导致预测误差 过大的问题，能够准确地预测长寿命电池(如储能电池)的剩余使用寿命。
附图说明
为了更清楚地说明本申请实施例的技术方案，下面将对实施例中所需要使用的附图作简单地介绍。
图1是本申请实施例提供的一种电池的寿命预测方法的流程示意图；
图2至图6是本申请实施例提供的一种电池寿命预测模型得到预测曲线与实际使用过程的实际曲线的关系图；
图7是本申请实施例提供的一种电池管理系统的结构示意图；
图8是本申请实施例提供的一种用电设备的结构示意图。
附图标记：
电池管理系统700、第一获取模块701、第一拟合模块702、第二拟合模块703、第二获
取模块704、第三获取模块705、第一确定模块706和第二确定模块707；
用电设备800、总线20、处理器30、存储器50、通信接口70。
具体实施方式
下面将结合本申请实施方式中的附图，对本申请实施方式中的技术方案进行清楚、完整地描述，显然，所描述的实施方式仅仅是本申请一部分实施方式，而不是全部的实施方式。基于本申请中的实施方式，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施方式，都属于本申请保护的范围。
以下各实施例的说明是参考附加的图示，用以例示本申请可用以实施的特定实施例。本申请中所提到的方向用语，例如，“上”、“下”、“前”、“后”、“左”、“右”、“内”、“外”、“侧面”等，仅是参考附加图式的方向，因此，使用的方向用语是为了更好、更清楚地说明及理解本申请，而不是指示或暗指所指的装置或元件必须具有特定的方位、以特定的方位构造和操作，因此不能理解为对本申请的限制。
此外，本文中为部件所编序号本身，例如“第一”、“第二”等，仅用于区分所描述的对象，不具有任何顺序或技术含义。而本申请所说“连接”、“联接”，如无特别说明，均包括直接和间接连接(联接)。
储能电池在实际的应用过程中，储能电池的容量会随着充放电循环圈数的增加而下降，性能逐渐衰退，产生电池寿命失效问题可能会导致安全事故，因此，储能电池的剩余使用寿命预测显得尤为重要。其中，储能电池的剩余使用寿命是指在一定的工作条件下，储能电池从当前时刻开始到输出功率无法满足机器或设备正常工作时，失效阈值所经历的充放电循环圈数。但由于储能电池的使用寿命较长，在循环圈数较大的情况下，容量与循环圈数的关系不再趋近于线性，无法保证预测长寿命储能电池的剩余使用寿命具有较高的准确率。
为解决上述问题，本申请提供一种电池的寿命预测方法。本申请的电池的寿命预测方法中，利用双指数经验模型对电池的多组循环圈数和与各组循环圈数一一对应的容量数据进行拟合，得到多组第一拟合数据，其中，多组循环圈数为电池在历史时间段内的多个时间节点下的多组循环圈数，历史时间段是指电池从开始使用的第一次充放电到经历多次充放电的整个时间段内，及利用Box-cox转换方法对电池的多组循环圈数和与各组循环圈数一一对应的容量数据进行拟合，得到多组第二拟合数据，并分别计算多组第一拟合数据与多组容量数据之间的第一容量误差，及多组第二拟合数据与多组容量数据之间的第二容量误差，结合第一 容量误差与第二容量误差、多组第一拟合数据和多组第二拟合数据生成电池寿命预测模型，从而根据电池预定容量和电池寿命预测模型获取电池的剩余使用寿命。本申请将双指数经验模型和Box-cox转换方法进行耦合，避免单独通过双指数经验模型或Box-cox转换方法导致预测误差过大的问题，能够准确地预测长寿命电池(如储能电池)的剩余使用寿命。
需要说明的是，本申请的电池可以包括储能电池和动力电池。其中，动力电池的循环寿命较短，容量数据和循环圈数呈简单的线性关系，当动力电池通过本申请提供的寿命预测方法中的电池寿命预测模型预测动力电池的剩余使用寿命时，能够使得预测结果与实际结果之间的误差更小，提升预测的准确性。而对于寿命较长的储能电池而言，前期容量数据与循环圈数呈线性关系，后期因为储能电池存在析锂、孔隙率减小等问题，导致容量数据与循环圈数呈指数下降关系，通过本申请提供的寿命预测方法中的电池寿命预测模型，将双指数经验模型和Box-cox转换方法进行耦合，减小预测结果和实际结果之间的误差，提高寿命预测的准确性。
请参阅图1，图1为本申请提供的一种电池的寿命预测方法的流程示意图。寿命预测方法包括以下步骤S101-S107，其中：
S101：获取电池的多组循环圈数和与多组循环圈数对应的多组容量数据；其中，多组循环圈数为电池在历史时间段内的多个时间节点下的多组循环圈数。
可以理解，多组循环圈数是指在电池的历史时间段内的多个时间节点下的多个循环圈数，历史时间段是指电池从开始使用的第一次充放电到经历多次充放电的整个时间段，在该历史时间段内，每个时间节点对应一个循环圈数和一个容量数据。电池的循环圈数和容量数据一一对应，多组循环圈数为电池的历史循环圈数，多组容量数据为电池的历史容量数据。例如，电池当前的循环圈数为3000，与当前的循环圈数对应的容量数据为Q1，循环圈数则为3000组，容量数据也对应为3000组，则输入双指数经验模型和Box-cox转换方法的循环圈数为1至3000圈，对应地，容量数据分别是与1至3000圈一一对应的容量数据。
S102：利用双指数经验模型对多组循环圈数和多组容量数据进行拟合，得到多组第一拟合数据。
将获取得到的多组循环圈数和多组容量数据输入双指数经验模型中进行模拟，得到多组第一拟合数据。每组第一拟合数据中包括第一拟合圈数和第一拟合容量，其中，一组第一拟合容量为一组容量数据经双指数经验模型拟合后得到的一组拟合数据，若获取的循环圈数比失效圈数小，则第一拟合容量的组数大于容量数据的组数，例如，当失效圈数为4000圈，而获取得到的循环圈数为3000圈时，则第一拟合容量的个数可以是4000个，而容量数据则为3000个。
S103：利用Box-cox转换方法对多组循环圈数和多组容量数据进行拟合，得到多组第二拟合数据。
类似地，将获取得到的多组循环圈数和多组容量数据通过Box-cox转换方法进行拟合，得到多组第二拟合数据。每组第二拟合数据中包括第二拟合圈数和第二拟合容量，若获取的循环圈数比失效圈数小，则第二拟合容量的组数大于容量数据的组数，例如，当失效圈数为4000圈，而获取得到的循环圈数为3000圈时，则第二拟合容量的个数可以是4000个，而容量数据则为3000个。
S104：根据多组第一拟合数据和多组容量数据确定多组循环圈数对应的第一容量误差。
在一种可能的实施方式中，根据多组第一拟合数据和多组容量数据确定多组循环圈数对应的第一容量误差的实现方法可以是：计算每组循环圈数对应的第一拟合容量与容量数据的 第一差值；对多组循环圈数的第一差值的平方执行求和处理，得到第一差值平方和；对第一差值平方和执行开方处理，得到第一容量误差。
获取多组循环圈数后，分别计算每组循环圈数对应的第一拟合容量和容量数据之间的第一差值，其中，第一拟合容量的组数与容量数据的组数相同，均等于循环圈数的组数。计算完多组循环圈数对应的第一拟合容量和容量数据之间的第一差值之后，得到多组第一差值，第一差值的组数等于循环圈数的组数。再对多组循环圈数的第一差值进行求平方，再将每个第一差值的平方进行累加求和，得到第一差值平方和，并对第一差值平方和执行开平方处理，得到第一容量误差，第一容量误差的计算公式如下公式一所示：
公式一：
其中，RSEbi-exp表示第一容量误差，bipredi表示第i组循环圈数(即第i圈)对应的第一拟合容量，expi表示第i组循环圈数对应的容量数据，N表示循环圈数的组数，i为正整数。
基于上述公式一，可以理解的是，针对双指数经验模型拟合得到的第一拟合数据，残差的标准误差(Root Square Error，RSE)(此处为RSEbi-exp)代表的是利用第一拟合容量和容量数据计算的残差的标准误差值，即，本申请利用残差的标准误差值来表征第一容量误差，并结合第一容量误差分析对电池寿命预测的影响，减少电池寿命预测模型输出的预测结果与实际结果误差过大的情况的出现，提高剩余使用寿命预测的准确性。
S105：根据多组第二拟合数据和多组容量数据确定多组循环圈数对应的第二容量误差。
在一种可能的实施方式中，根据多组第二拟合数据和多组容量数据确定多组循环圈数对应的第二容量误差的实现方法可以是：计算每组循环圈数对应的第二拟合容量与容量数据的第二差值；对多组循环圈数的第二差值的平方执行求和处理，得到第二差值平方和；对第二差值平方和执行开平方处理得到第二容量误差。
类似地，获取多组循环圈数后，分别计算每组循环圈数对应的第二拟合容量和容量数据之间的第二差值，其中，第二拟合容量的组数与容量数据的组数相同，均等于循环圈数的组数。计算完多组循环圈数对应的第二拟合容量和容量数据之间的第二差值之后，得到多组第二差值，第二差值的组数等于循环圈数的组数。再对多组循环圈数的第二差值进行求平方，再将每个第二差值的平方进行累加求和，得到第二差值平方和，并对第二差值平方和执行开平方处理，得到第二容量误差，第二容量误差的计算公式如下公式二所示：
公式二：
其中，RSEbox-cox表示第二容量误差，boxpredi表示第i组循环圈数(即第i圈)对应的第二拟合容量，expi表示第i组循环圈数对应的容量数据，N表示循环圈数的组数，i为正整数。
基于上述公式二，可以理解的是，针对Box-cox转换方法拟合得到的第二拟合数据，RSEbox-cox代表的是利用第二拟合容量和容量数据计算的残差的标准误差值，即，本申请利用残差的标准误差来表征第二容量误差，并综合分析第一容量误差和第二容量误差对电池寿命预测的影响，减少电池寿命预测模型输出的预测结果与实际结果误差过大的情况的出现，提高剩余使用寿命预测的准确性。
S106：根据第一容量误差、第二容量误差、多组第一拟合数据和多组第二拟合数据生成电池寿命预测模型。
在一种可能的实施方式中，根据第一容量误差、第二容量误差、多组第一拟合数据和多组第二拟合数据生成电池寿命预测模型的实现方法可以是：在第一容量误差与第二容量误差的比值小于预设阈值的情况下，根据第一容量误差、第二容量误差、多组第一拟合容量和多组第二拟合容量生成电池寿命预测模型。在第一容量误差与第二容量误差的比值大于等于预设阈值的情况下，获取多组第一拟合圈数中的拟合失效圈数，根据拟合失效圈数及三次多项式算法生成电池寿命预测模型。
需要说明的是，同一类型的电池的预设阈值相同，预设阈值可根据训练同一类型电池的电池寿命预测模型的多条曲线中确定，优选地，选取误差最小的曲线中线性下降阶段的斜率作为预设阈值。第一拟合数据的组数为双指数经验模型拟合得到的第一拟合圈数的组数；第二拟合数据的组数为Box-cox转换方法拟合得到的第二拟合圈数的组数。
当第一容量误差和第二容量误差的比值小于预设阈值的情况下，将双指数经验模型获得的多组第一拟合数据和Box-cox转换方法获得的多组第二拟合数据根据第一容量误差和第二容量误差进行加权平均，采用加权平均的方式预测电池达到电池预定容量时的循环圈数(即预测失效圈数)，从而根据预测失效圈数和获取得到的循环圈数(即电池的当前循环圈数)获取电池的剩余使用寿命。避免单独采用双指数经验模型或Box-cox转换方法预测电池的剩余使用寿命时，出现预测结果和实际结果误差过大的情况，影响电池的安全使用。
当第一容量误差和第二容量误差的比值大于等于预设阈值的情况下，计算双指数经验模型得到的多组第一拟合数据中达到拟合失效圈数之前的多组第一拟合容量采用三次多项式算法拟合，从而确定三次多项式算法的多个参数，得到拟合好的三次多项式算法预测电池达到电池预定容量时的预测失效圈数，从而根据预测失效圈数和电池的当前循环圈数确定电池的剩余使用寿命。
本申请中，综合考虑第一容量误差和第二容量误差的比值是否小于预设阈值，对不同情况分别拟合不同的电池寿命预测模型，并对不同情况下的电池寿命预测模型通过将不同模型或算法进行耦合的方式实现拟合，使得获取得到的电池寿命预测模型能够在预测长寿命电池的情况下更加准确地预测长寿命电池的剩余使用寿命。
其中，在第一容量误差与第二容量误差的比值小于预设阈值的情况下，根据第一容量误差、第二容量误差、多组第一拟合容量和多组第二拟合容量生成电池寿命预测模型，包括：
将第一容量误差和第二容量误差之和作为第一数值；
获取第一容量误差与第一数值的比值，以作为第一权值；
获取第二容量误差与第一数值的比值，以作为第二权值；
根据各组第一拟合容量和第一权值的乘积、与各组第二拟合容量和第二权值的乘积的和值生成电池寿命预测模型。
具体地，在第一容量误差和第二容量误差的比值小于预设阈值的情况下，将第一容量误差和第二容量误差进行求和计算，得到第一数值，并将第一容量误差和第一数值的比值作为 第一权值；将第二容量误差和第一数值的比值作为第二权值。并将每组第一拟合容量分别和第一权值相乘，得到一个关于第一拟合容量乘积，将每组第二拟合容量分别和第二权值相乘，得到一个关于第二拟合容量的乘积，将两个乘积进行求和，得到预测失效圈数对应的预测容量数据，即第一误差和第二误差的比值小于预设阈值的情况下的电池寿命预测模型直接输出电池在预测失效圈数对应的预测容量数据，电池寿命预测模型的关系式如下公式所示：
公式三：
pred＝predbi-exp*[errorbox-cox/(errorbi-exp+errorbox-cox)]+predbox-cox*[errorbi-exp/(errorbi-exp+errorbox-cox)]
其中，pred表示预测失效圈数对应的预测容量数据，predbi-exp表示双指数经验模型预测的多组第一拟合容量；predbox-cox表示Box-cox转换方法预测的多组第二拟合容量；errorbi-exp表示第一容量误差，即公式一中计算得到的RSEbi-exp；errorbox-cox示第二容量误差，即公式二计算得到的RSEbox-cox。第一容量误差和第二容量误差均为确定的数值，计算得到的第一权值和第二权值均是确定的数值。
在第一容量误差和第二容量误差的比值小于预设阈值的情况下，电池寿命预测模型直接输出预测失效圈数对应的预测容量数据，减少运算量，提高预测效率，再根据电池的当前循环圈数确定电池的当前容量数据，利用预测容量数据和当前容量数据的差值确定电池的剩余使用寿命。
在第一容量误差与第二容量误差的比值大于等于预设阈值的情况下，获取多组第一拟合数据中的拟合失效圈数，根据拟合失效圈数及三次多项式算法生成电池寿命预测模型，包括：
获取多组第一拟合圈数中电池达到拟合失效圈数之前的多组第一拟合容量，以作为多组有效容量；
根据多组有效容量和三次多项式算法生成电池寿命预测模型。
第一容量误差和第二容量误差的比值大于等于预设阈值时，获取双指数经验模型中预测得到的拟合失效圈数，并获取第一拟合圈数中电池达到拟合失效圈数之前的多组第一拟合容量，以作为多组有效容量，将多组有效容量输入到参数未定的三次多项式算法中，即可计算得到三次多项式算法的多个参数，从而得到关于容量数据和循环圈数的三次多项式，即电池寿命预测模型。本申请中，将拟合失效圈数之前的多组第一拟合容量拟合三次多项式算法，保证用于拟合三次多项式算法的数据为最接近电池实际容量的数据，提高电池寿命预测模型预测的准确性。且，采用双指数经验模型得到的多组有效容量拟合三次多项式算法相较于采用获取得到的多组容量数据拟合三次多项式而言，有效容量中包含了获取得到的循环圈数到拟合失效圈数之间的部分第一拟合容量，使得拟合三次多项式的数据更加丰富，从而提高电池寿命预测模型预测的精确度。
其中，在第一容量误差和第二容量误差的比值大于等于预设阈值的情况下，电池寿命预测模型是关于容量数据和循环圈数的关系式，如下公式四所示：
公式四：
y＝ax3+bx2+cx+d
其中，a、b、c、d均是三次多项式(电池寿命预测模型)的参数，x表示循环圈数，y表示容量数据。
根据公式四可计算得到任一循环圈数对应的容量数据，进而获取容量数据和循环圈数的关系曲线。根据容量数据和循环圈数的关系曲线可获取得到电池达到电池预定容量对应的预测失效圈数，从而将预测失效圈数和电池的当前循环圈数进行相减得到循环圈数差值，根据循环圈数差值确定电池的剩余使用寿命。
S107：根据电池预定容量和电池寿命预测模型确定电池的剩余使用寿命。
电池预定容量可以是电池的失效容量，例如，电池预定容量为初始容量的70％时的容量。确定电池预定容量后，即可得到电池寿命预测模型中与电池预定容量对应的预测失效圈数，将预测失效圈数与当前循环圈数进行相减，得到循环圈数差值，根据循环圈数差值确定电池的剩余使用寿命。
在一种可能的实施方式中，根据电池预定容量和电池寿命预测模型确定电池的剩余使用寿命，包括：根据电池寿命预测模型确定电池达到电池预定容量时对应的预测失效圈数；确定电池的当前循环圈数；计算预测失效圈数和当前循环圈数的循环圈数差值；根据循环圈数差值确定电池的剩余使用寿命。
例如，当第一容量误差和第二容量误差的比值小于预设阈值的情况下，该情况下的电池寿命预测模型直接输出预测失效圈数对应的预测容量数据，根据该预测容量数据可得到预测失效圈数，将预测失效圈数减去电池的当前循环圈数，得到循环圈数差值，进而根据循环圈数差值确定电池的剩余使用寿命。
例如，当第一容量误差和第二容量误差的比值大于等于预设阈值的情况下，该情况下的电池寿命预测模型是关于循环圈数和容量数据的三次多项式，获取该电池寿命预测模型电池的电池预定容量的预测容量数据，并根据该预测容量数据得到对应的预测失效圈数，并将预测失效圈数与电池的当前循环圈数进行相减，得到循环圈数差值，进而根据循环圈数差值确定电池的剩余使用寿命。
请结合图2至图6，图2至图6为本申请实施例提供的一种电池寿命预测模型得到的预测曲线与实际使用过程的实际曲线的关系图，其中，各图的横坐标表示循环圈数，纵坐标表示容量数据，各图中三条线的交点处即为预定容量，实际曲线表示电池实际充放电过程中的容量数据与循环圈数的实际关系曲线，预测曲线表示电池寿命预测模型得到的预测关系曲线，获取得到的循环圈数为S101中获取的电池的多组循环圈数，误差圈数表示电池达到电池预定容量时预测失效圈数与实际失效圈数之间的误差。其中，图2为获取的循环圈数为6000圈时，得到的电池寿命预测模型的预测曲线和电池实际充放电过程中的实际曲线的示意图，误差圈数为9圈；图3为获取的循环圈数为7000圈时，得到的电池寿命预测模型的预测曲线和电池实际充放电过程中的实际曲线的示意图，误差圈数为84圈；图4为获取的循环圈数为8000圈时，得到的电池寿命预测模型的预测曲线和电池实际充放电过程中的实际曲线的示意图，误差圈数为115圈；图5为获取的循环圈数为9000圈时，得到的电池寿命预测模型的预测曲线和电池实际充放电过程中的实际曲线的示意图，误差圈数为91圈；图6为获取的循环圈数为10000圈时，得到的电池寿命预测模型的预测曲线和电池实际充放电过程中的实际曲线的示意图，误差圈数为36圈。从图2图6可以看出,电池实际充放电过程中的循环圈数和容量数据的实际曲线与电池寿命预测模型得到的预测曲线偏差较小，剩余使用寿命的预测准确性高。
上述详细阐述了本申请实施例设计的方法，下面提供本申请实施例设计的电池管理系统700。
请参阅图7，图7是本申请实施例提供的一种电池管理系统700的示意图。本申请提供的电池的寿命预测方法应用于电池管理系统700。电池管理系统700包括第一获取模块701、第一拟合模块702、第二拟合模块703、第二获取模块704、第三获取模块705、第一确定模块706和第二确定模块707。第一获取模块701用于获取电池的多组循环圈数和多组容量数据；其中，多组循环圈数为电池在历史时间段内的多个时间节点下的多组循环圈数。第一拟 合模块702用于利用双指数经验模型对多组循环圈数和多组容量数据进行拟合，得到多组第一拟合数据。第二拟合模块703用于利用Box-cox转换方法对多组循环圈数和多组容量数据进行拟合，得到多组第二拟合数据。第二获取模块704用于根据多组第一拟合数据和多组容量数据确定多组循环圈数对应的第一容量误差。第三获取模块705用于根据多组第二拟合数据和多组容量数据确定多组循环圈数对应的第二容量误差。第一确定模块706用于根据第一容量误差、第二容量误差、多组第一拟合数据和多组第二拟合数据生成电池寿命预测模型。第二确定模块707用于根据电池预定容量和电池寿命预测模型确定电池的剩余使用寿命。
需要说明的是，电池管理系统700可以实时记录多个电池各自对应的容量数据和循环圈数，且电池剩余使用寿命的电池寿命预测模型计算量小，且可以实时输出各个电池的剩余使用寿命，无需远程传输数据，无需等待数据传输，提高预测效率。
在一种可能的实施方式中，多组第一拟合数据包括多组第一拟合容量；第二获取模块704还用于：计算每组循环圈数对应的第一拟合容量与容量数据的第一差值；对多组循环圈数的第一差值的平方执行求和处理，得到第一差值平方和；对第一差值平方和执行开方处理，得到第一容量误差。
在一种可能的实施方式中，多组第二拟合数据包括多组第二拟合容量；第三获取模块705还用于：计算每组循环圈数对应的第二拟合容量与容量数据的第二差值；对多组循环圈数的第二差值的平方执行求和处理，得到第二差值平方和；对第二差值平方和执行开平方处理得到第二容量误差。
在一种可能的实施方式中，多组第一拟合数据包括多组第一拟合容量及与多组第一拟合容量对应的多组第一拟合圈数，多组第二拟合数据包括多组第二拟合容量；第一确定模块706还用于：在第一容量误差与第二容量误差的比值小于预设阈值的情况下，根据第一容量误差、第二容量误差、多组第一拟合容量和多组第二拟合容量生成电池寿命预测模型；在第一容量误差与第二容量误差的比值大于等于预设阈值的情况下，获取多组第一拟合圈数中的拟合失效圈数，根据拟合失效圈数及三次多项式算法生成电池寿命预测模型。
在一种可能的实施方式中，在第一容量误差与第二容量误差的比值小于预设阈值的情况下，第一确定模块706还用于：将第一容量误差和第二容量误差之和作为第一数值；获取第一容量误差与第一数值的比值，以作为第一权值；获取第二容量误差与第一数值的比值，以作为第二权值；根据各组第一拟合容量和第一权值的乘积、与各组第二拟合容量和第二权值的乘积的和值生成电池寿命预测模型。
在一种可能的实施方式中，在第一容量误差与第二容量误差的比值大于等于预设阈值的情况下；第一确定模块706还用于：获取多组第一拟合圈数中的电池达到拟合失效圈数之前的多组第一拟合容量，以作为多组有效容量；根据多组有效容量和三次多项式算法生成电池寿命预测模型。
在一种可能的实施方式中，第二确定模块707还用于：根据电池寿命预测模型确定电池达到电池预定容量时对应的预测失效圈数；确定电池的当前循环圈数；计算预测失效圈数和当前循环圈数的循环圈数差值；根据循环圈数差值确定电池的剩余使用寿命。
其中，该电池管理系统700的各个模块的实现还可以对应本申请任一实施方式所述的寿命预测方法实施例的相应描述。
请参阅图8，图8是本申请实施例提供的一种用电设备800的组成示意图。用电设备800可包括处理器、存储器和通信接口，其中，处理器、存储器和通信接口通过总线连接，该存储器用于存储计算机程序，计算机程序包括程序指令，该处理器用于执行程序指令，以实现 本申请任一实施方式所述的寿命预测方法。
本申请实施例技术方案均适用于各种使用储能电池或动力电池的用电设备800，例如，电瓶车、电动玩具、电动工具、电动车辆、船舶和航天器、手机、便携式设备、掌上电脑、笔记本电脑等。
作为一种实现方式，通信接口70的功能可以考虑通过收发电路或者收发的专用芯片实现。处理器30可以考虑通过专用处理芯片、处理电路、处理器或者通用芯片实现。
该用电设备800所涉及的与本申请实施例提供的技术方案相关的概念，解释和详细说明及其他步骤请参见前述方法或其他实施例中关于装置执行的方法步骤的内容的描述，此处不做赘述。
作为本实施例的另一种实现方式，提供一种计算机可读存储介质，其上存储有指令，该指令被执行时执行本申请任一实施方式所述的方法。
作为本实施例的另一种实现方式，提供一种包含指令的计算机程序产品，该指令被执行时执行本申请任一实施方式所述的方法。
本领域技术人员可以理解，为了便于说明，图8中仅示出了一个存储器50和处理器30。在实际的终端或服务器中，可以存在多个处理器30和存储器50。存储器50也可以称为存储介质或者存储设备等，本申请实施例对此不做限制。
应理解，在本申请实施例中，处理器30可以是中央处理单元(Central Processing Unit，简称CPU)，该处理器30还可以是其他通用处理器、数字信号处理器(Digital Signal Processing，简称DSP)、专用集成电路(Application Specific Integrated Circuit，简称ASIC)、现成可编程门阵列(Field－Programmable Gate Array，简称FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。
还应理解，本申请实施例中提及的存储器50可以是易失性存储器或非易失性存储器，或可包括易失性和非易失性存储器两者。其中，非易失性存储器可以是只读存储器(Read-Only Memory，简称ROM)、可编程只读存储器(Programmable ROM，简称PROM)、可擦除可编程只读存储器(Erasable PROM，简称EPROM)、电可擦除可编程只读存储器(Electrically EPROM，简称EEPROM)或闪存。易失性存储器可以是随机存取存储器(Random Access Memory，简称RAM)，其用作外部高速缓存。通过示例性但不是限制性说明，许多形式的RAM可用，例如静态随机存取存储器(Static RAM，简称SRAM)、动态随机存取存储器(Dynamic RAM，简称DRAM)、同步动态随机存取存储器(Synchronous DRAM，简称SDRAM)、双倍数据速率同步动态随机存取存储器(Double Data Rate SDRAM，简称DDR SDRAM)、增强型同步动态随机存取存储器(Enhanced SDRAM，简称ESDRAM)、同步连接动态随机存取存储器(Synchlink DRAM，简称SLDRAM)和直接内存总线随机存取存储器(Direct Rambus RAM，简称DR RAM)。
需要说明的是，当处理器30为通用处理器、DSP、ASIC、FPGA或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件时，存储器(存储模块)集成在处理器中。
应注意，本文描述的存储器50旨在包括但不限于这些和任意其它适合类型的存储器。
该总线20除包括数据总线之外，还可以包括电源总线、控制总线和状态信号总线等。但是为了清楚说明起见，在图中将各种总线都标为总线20。
还应理解，本文中涉及的第一、第二、第三、第四以及各种数字编号仅为描述方便进行的区分，并不用来限制本申请的范围。
应理解，本文中术语“和/或”，仅仅是一种描述关联对象的关联关系，表示可以存在三 种关系，例如，A和/或B，可以表示：单独存在A，同时存在A和B，单独存在B这三种情况。另外，本文中字符“/”，一般表示前后关联对象是一种“或”的关系。
在实现过程中，上述方法的各步骤可以通过处理器30中的硬件的集成逻辑电路或者软件形式的指令完成。结合本申请实施例所公开的方法的步骤可以直接体现为硬件处理器执行完成，或者用处理器30中的硬件及软件模块组合执行完成。软件模块可以位于随机存储器50，闪存、只读存储器，可编程只读存储器或者电可擦写可编程存储器、寄存器等本领域成熟的存储介质中。该存储介质位于存储器50，处理器30读取存储器50中的信息，结合其硬件完成上述方法的步骤。为避免重复，这里不再详细描述。
在本申请的各种实施例中，上述各过程的序号的大小并不意味着执行顺序的先后，各过程的执行顺序应以其功能和内在逻辑确定，而不应对本申请实施例的实施过程构成任何限定。
本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的各种说明性逻辑块(illustrative logical block，简称ILB)和步骤，能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本申请的范围。
在本申请所提供的几个实施例中，应该理解到，所揭露的系统、设备和方法，可以通过其它的方式实现。例如，以上所描述的设备实施例仅仅是示意性的，例如，模块的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个模块或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，设备或模块的间接耦合或通信连接，可以是电性，机械或其它的形式。
上述单元作为分离部件说明的模块可以是或者也可以不是物理上分开的，作为模块显示的部件可以是或者也可以不是物理模块，即可以位于一个地方，或者也可以分布到多个网络模块上。可以根据实际的需要选择其中的部分或者全部模块来实现本实施例方案的目的。
另外，在本申请各个实施例中的各功能模块可以集成在一个处理模块中，也可以是各个模块单独物理存在，也可以两个或两个以上模块集成在一个模块中。
在上述实施例中，可以全部或部分地通过软件、硬件、固件或者其任意组合来实现。当使用软件实现时，可以全部或部分地以计算机程序产品的形式实现。计算机程序产品包括一个或多个计算机指令。在计算机上加载和执行所述计算机程序指令时，全部或部分地产生按照本申请实施例所述的流程或功能。计算机可以是通用计算机、专用计算机、计算机网络、或者其他可编程装置。所述计算机指令可以存储在计算机可读存储介质中，或者从一个计算机可读存储介质向另一个计算机可读存储介质传输，例如，所述计算机指令可以从一个网站站点、计算机、服务器或数据中心通过有线(例如同轴电缆、光纤、数字用户线)或无线(例如红外、无线、微波等)方式向另一个网站站点、计算机、服务器或数据中心进行传输。计算机可读存储介质可以是计算机能够存取的任何可用介质或者是包含一个或多个可用介质集成的服务器、数据中心等数据存储设备。可用介质可以是磁性介质，(例如，软盘、硬盘、磁带)、光介质(例如，DVD)、或者半导体介质(例如固态硬盘)等。
以上是本申请的部分实施方式，应当指出，对于本技术领域的普通技术人员来说，在不脱离本申请原理的前提下，还可以做出若干改进和润饰，这些改进和润饰也视为本申请的保护范围。

Claims (10)

  1. 一种电池的寿命预测方法，包括：
    获取所述电池的多组循环圈数和与所述多组循环圈数对应的多组容量数据；其中，所述多组循环圈数为所述电池在历史时间段内的多个时间节点下的多组循环圈数；
    利用双指数经验模型对所述多组循环圈数和所述多组容量数据进行拟合，得到多组第一拟合数据；
    利用Box-cox转换方法对所述多组循环圈数和所述多组容量数据进行拟合，得到多组第二拟合数据；
    根据所述多组第一拟合数据和所述多组容量数据确定所述多组循环圈数对应的第一容量误差；
    根据所述多组第二拟合数据和所述多组容量数据确定所述多组循环圈数对应的第二容量误差；
    根据所述第一容量误差、所述第二容量误差、所述多组第一拟合数据和所述多组第二拟合数据生成电池寿命预测模型；
    根据电池预定容量和所述电池寿命预测模型确定所述电池的剩余使用寿命。
  2. 根据权利要求1所述的寿命预测方法，其中，所述多组第一拟合数据包括多组第一拟合容量，所述根据所述多组第一拟合数据和所述多组容量数据确定所述多组循环圈数对应的第一容量误差，包括：
    计算每组所述循环圈数对应的所述第一拟合容量与所述容量数据的第一差值；
    对所述多组循环圈数的第一差值的平方执行求和处理，得到第一差值平方和；
    对所述第一差值平方和执行开方处理，得到所述第一容量误差。
  3. 根据权利要求1所述的寿命预测方法，其中，所述多组第二拟合数据包括多组第二拟合容量，所述根据所述多组第二拟合数据和所述多组容量数据确定所述多组循环圈数对应的第二容量误差，包括：
    计算每组所述循环圈数对应的所述第二拟合容量与所述容量数据的第二差值；
    对所述多组循环圈数的第二差值的平方执行求和处理，得到第二差值平方和；
    对所述第二差值平方和执行开平方处理得到所述第二容量误差。
  4. 根据权利要求1所述的寿命预测方法，其中，所述多组第一拟合数据包括多组第一拟合容量及与所述多组第一拟合容量对应的多组第一拟合圈数，所述多组第二拟合数据包括多组第二拟合容量，所述根据所述第一容量误差、所述第二容量误差、所述多组第一拟合数据和所述多组第二拟合数据生成电池寿命预测模型，包括：
    在所述第一容量误差与所述第二容量误差的比值小于预设阈值的情况下，根据所述第一容量误差、所述第二容量误差、所述多组第一拟合容量和所述多组第二拟合容量生成所述电池寿命预测模型；
    在所述第一容量误差与所述第二容量误差的比值大于等于所述预设阈值的情况下，获取所述多组第一拟合圈数中的拟合失效圈数，根据所述拟合失效圈数及三次多项式算法生成所述电池寿命预测模型。
  5. 根据权利要求4所述的寿命预测方法，其中，所述在所述第一容量误差与所述第二容量误差的比值小于预设阈值的情况下，根据所述第一容量误差、所述第二容量误差、所述多组第一拟合容量和所述多组第二拟合容量生成所述电池寿命预测模型，包括：
    将所述第一容量误差和所述第二容量误差之和作为第一数值；
    获取所述第一容量误差与所述第一数值的比值，以作为第一权值；
    获取所述第二容量误差与所述第一数值的比值，以作为第二权值；
    根据各组所述第一拟合容量和所述第一权值的乘积、与各组所述第二拟合容量和所述第二权值的乘积的和值生成所述电池寿命预测模型。
  6. 根据权利要求4所述的寿命预测方法，其中，所述在所述第一容量误差与所述第二容量误差的比值大于等于所述预设阈值的情况下，获取所述多组第一拟合数据中的拟合失效圈数，根据所述拟合失效圈数及三次多项式算法生成所述电池寿命预测模型，包括：
    获取所述多组第一拟合圈数中的所述电池达到所述拟合失效圈数之前的多组所述第一拟合容量，以作为多组有效容量；
    根据所述多组有效容量和所述三次多项式算法生成所述电池寿命预测模型。
  7. 根据权利要求1所述的寿命预测方法，其中，所述根据电池预定容量和所述电池寿命预测模型确定所述电池的剩余使用寿命，包括：
    根据所述电池寿命预测模型确定所述电池达到所述电池预定容量时对应的预测失效圈数；
    确定所述电池的当前循环圈数；
    计算所述预测失效圈数和所述当前循环圈数的循环圈数差值；
    根据所述循环圈数差值确定所述电池的剩余使用寿命。
  8. 一种电池管理系统，包括：
    第一获取模块，用于获取电池的多组循环圈数和多组容量数据；其中，所述多组循环圈数为所述电池在历史时间段内的多个时间节点下的多组循环圈数；
    第一拟合模块，用于利用双指数经验模型对所述多组循环圈数和所述多组容量数据进行拟合，得到多组第一拟合数据；
    第二拟合模块，用于利用Box-cox转换方法对所述多组循环圈数和所述多组容量数据进行拟合，得到多组第二拟合数据；
    第二获取模块，用于根据所述多组第一拟合数据和所述多组容量数据确定所述多组循环圈数对应的第一容量误差；
    第三获取模块，用于根据所述多组第二拟合数据和所述多组容量数据确定所述多组循环圈数对应的第二容量误差；
    第一确定模块，用于根据所述第一容量误差、所述第二容量误差、所述多组第一拟合数据和所述多组第二拟合数据生成电池寿命预测模型；
    第二确定模块，用于根据电池预定容量和所述电池寿命预测模型确定所述电池的剩余使用寿命。
  9. 一种用电设备，包括处理器和存储器，所述处理器和所述存储器相互连接，其中，所 述存储器用于存储计算机程序，所述计算机程序包括程序指令，所述处理器被配置用于调用所述程序指令，执行如权利要求1至7任一项所述的寿命预测方法。
  10. 一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，所述计算机程序包括程序指令，所述程序指令被处理器执行时使所述处理器执行如权利要求1至7任一项所述的寿命预测方法。
PCT/CN2023/128637 2022-12-09 2023-10-31 寿命预测方法、电池管理系统、用电设备及存储介质 Ceased WO2024120075A1 (zh)

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