Method and device for predicting service life of single lithium battery

Abstract

The invention discloses a method and a device for predicting the service life of a single lithium battery. Relates to the technical field of lithium batteries, and comprises the following steps: obtaining historical working condition data of a to-be-predicted single lithium battery, the historical working condition data at least comprising historical charging and discharging parameter information of the single lithium battery; obtaining the current attenuation amount of the electric quantity of the single lithium battery; according to the current attenuation, the historical working condition data and a target attenuation prediction model, calculating the attenuation of the single lithium battery in a fixed time interval to obtain an accumulative attenuation; and when the accumulative attenuation amount is greater than or equal to a preset threshold value, determining the residual life information of the single lithium battery according to the number of the fixed time intervals. According to the method and the device, the problem that the service life prediction efficiency is relatively low due to the fact that the service life of the single lithium battery is predicted by constructing an electrochemical model in related technologies is solved.

Description

Technical Field

[0001] The present application relates to the technical field of lithium batteries, and in particular to a method and device for predicting the life of a single lithium battery. Background Art

[0002] Lithium-ion batteries, with their high energy, high power, long life, and low cost, are considered effective units in energy storage systems. With the increasing popularity of electric vehicles and the large-scale deployment of energy storage systems, lithium battery applications have reached unprecedented breadth, and the prospects for lithium battery development are enormous. However, over long-term use, batteries can experience capacity decay and increased internal resistance due to factors such as chemical side reactions, material structural degradation, and SEI film growth, ultimately leading to performance degradation and even failure. Therefore, accurately predicting battery life is crucial for optimizing battery management, reducing maintenance costs, providing early warning, improving safety, and enabling gradual utilization.

[0003] In the existing technology, battery life prediction methods mainly use electrochemical, physical and statistical principles, aiming to infer the future performance of the battery through limited test data. Among them, the electrochemical model method has attracted much attention because it can simulate the internal reactions of the battery in detail. Although the electrochemical model method provides a detailed analysis of the battery aging mechanism at the theoretical level, in practical applications, the accurate solution of the electrochemical model often requires advanced numerical methods and computing resources, which poses a severe test to the computing efficiency and real-time performance, especially in situations where the battery operating conditions are changeable and the prediction results need to be updated frequently. In addition, the calibration and updating of the model parameters also rely on a large amount of detailed experimental data, which further increases the complexity and cost of the prediction process.

[0004] Currently, no effective solution has been proposed to the problem that the service life of lithium battery cells is predicted by constructing electrochemical models in related technologies, resulting in relatively low efficiency in life prediction. Summary of the Invention

[0005] The main purpose of this application is to provide a method and device for predicting the life of a single lithium battery, so as to solve the problem in the related art of predicting the service life of a lithium battery single cell by constructing an electrochemical model, resulting in relatively low efficiency in life prediction.

[0006] To achieve the above objectives, according to one aspect of the present application, a method for predicting the lifespan of a single lithium battery is provided. The method comprises: obtaining historical operating condition data of a single lithium battery to be predicted, wherein the historical operating condition data includes at least historical charge and discharge parameter information of the single lithium battery; obtaining the current attenuation of the power of the single lithium battery; calculating the attenuation of the single lithium battery within a fixed time interval based on the current attenuation, the historical operating condition data, and a target attenuation prediction model to obtain a cumulative attenuation; and when the cumulative attenuation is greater than or equal to a preset threshold, determining the remaining lifespan of the single lithium battery based on the number of fixed time intervals.

[0007] Furthermore, before obtaining the historical operating condition data of the single lithium battery to be predicted, the method also includes: collecting first test data of the historical cycle aging of the single lithium battery and second test data of the storage aging of the single lithium battery; based on the first test data, constructing a cycle attenuation prediction model of the historical single lithium battery regarding the relationship between the cycle attenuation and the cumulative throughput of the battery; based on the second test data, constructing a calendar attenuation prediction model of the historical single lithium battery regarding the relationship between the calendar attenuation and the storage time; and determining the target attenuation prediction model based on the cycle attenuation prediction model and the calendar attenuation prediction model.

[0008] Furthermore, based on the first test data, constructing a cycle attenuation prediction model for the historical single lithium battery regarding the relationship between the cycle attenuation and the battery cumulative throughput includes: constructing a discharge depth prediction model for the historical single lithium battery regarding the relationship between the discharge depth and the state of charge cycle interval based on the state of charge data in the first test data; constructing the cycle attenuation prediction model based on the charge and discharge rate data in the first test data and the discharge depth prediction model.

[0009] Furthermore, the attenuation of the single lithium battery within a fixed time interval is calculated based on the current attenuation, the historical operating condition data and the target attenuation prediction model to obtain the cumulative attenuation, including: for a first fixed time interval, calculating based on the current attenuation, the historical operating condition data and the cyclic attenuation prediction model in the target attenuation prediction model to obtain the first cyclic attenuation for the first fixed time interval; calculating based on the first cyclic attenuation, the historical operating condition data and the calendar attenuation prediction model in the target attenuation prediction model to obtain the first calendar attenuation corresponding to the first fixed time interval; and obtaining the cumulative attenuation based on the first cyclic attenuation and the first calendar attenuation.

[0010] Furthermore, the first cyclic attenuation for the first fixed time interval is obtained by performing calculations based on the current attenuation, the historical operating condition data, and the cyclic attenuation prediction model in the target attenuation prediction model, including: performing calculations based on the current attenuation, the historical operating condition data, and the cyclic attenuation prediction model to obtain a cyclic attenuation rate for the first fixed time interval; determining the number of cycles of the single lithium battery within the first fixed time interval based on the historical operating condition data; and performing calculations based on the cyclic attenuation rate and the number of cycles to obtain a first cyclic attenuation corresponding to the first fixed time interval.

[0011] Furthermore, calculating based on the current attenuation, the historical operating condition data, and the cyclic attenuation prediction model to obtain the cyclic attenuation rate for the first fixed time interval includes: obtaining a current cumulative throughput corresponding to the current attenuation based on the current attenuation and the cyclic attenuation prediction model; predicting a predicted throughput of the single lithium battery after the first fixed time interval based on the historical operating condition data, and obtaining a predicted cumulative throughput based on the predicted throughput and the current cumulative throughput; obtaining a predicted cyclic attenuation based on the predicted cumulative throughput and the cyclic attenuation prediction model; and obtaining the cyclic attenuation rate by calculating based on the current attenuation, the current cumulative throughput, the predicted cumulative throughput, and the predicted cyclic attenuation.

[0012] Furthermore, obtaining the current cumulative throughput corresponding to the current attenuation based on the current attenuation and the cyclic attenuation prediction model includes: determining the current state of charge cycle interval of the single lithium battery based on the historical operating condition data; calculating the current state of charge cycle interval through a discharge depth prediction model to obtain the current discharge depth; and calculating the current discharge depth and the current attenuation through the cyclic attenuation prediction model to obtain the current cumulative throughput.

[0013] Furthermore, calculation is performed based on the first cyclic attenuation, the historical operating condition data, and the calendar attenuation prediction model in the target attenuation prediction model to obtain the first calendar attenuation corresponding to the first fixed time interval, including: calculating based on the first cyclic attenuation and the calendar attenuation prediction model to obtain the calendar attenuation rate of the first fixed time interval; predicting the storage time of the single lithium battery in the first fixed time interval based on the historical operating condition data; and calculating based on the calendar attenuation rate and the storage time to obtain the first calendar attenuation corresponding to the first fixed time interval.

[0014] In order to achieve the above object, according to another aspect of the present application, a life prediction device of a single lithium battery is provided. The device comprises: a first acquisition unit configured to acquire historical working condition data of a single lithium battery to be predicted, wherein the historical working condition data at least comprises historical charging and discharging parameter information of the single lithium battery; a second acquisition unit configured to acquire a current attenuation amount of an electric quantity of the single lithium battery; a calculation unit configured to calculate an attenuation amount of the single lithium battery in a fixed time interval according to the current attenuation amount, the historical working condition data and a target attenuation amount prediction model, to obtain an accumulated attenuation amount; and a first determination unit configured to, when the accumulated attenuation amount is greater than or equal to a preset threshold, determine remaining life information of the single lithium battery according to a number of the fixed time intervals.

[0015] Further, the device further comprises: an acquisition unit configured to, before acquiring the historical working condition data of the single lithium battery to be predicted, acquire first test data of cycle aging of a historical single lithium battery and second test data of storage aging of the single lithium battery; a first construction unit configured to construct a cycle attenuation amount prediction model of the historical single lithium battery about a relationship between cycle attenuation amount and battery accumulated throughput according to the first test data; a second construction unit configured to construct a calendar attenuation amount prediction model of the historical single lithium battery about a relationship between calendar attenuation amount and storage duration according to the second test data; and a second determination unit configured to determine the target attenuation amount prediction model according to the cycle attenuation amount prediction model and the calendar attenuation amount prediction model.

[0016] Further, the first construction unit comprises: a first construction sub-unit configured to construct a discharge depth prediction model of the historical single lithium battery about a relationship between discharge depth and state of charge cycle interval according to state of charge data in the first test data; and a second construction sub-unit configured to construct the cycle attenuation amount prediction model according to charging and discharging rate data in the first test data and the discharge depth prediction model.

[0017] Further, the calculation unit comprises: a first calculation sub-unit configured to, for a first fixed time interval, calculate according to the current attenuation amount, the historical working condition data and a cycle attenuation amount prediction model in the target attenuation amount prediction model, to obtain a first cycle attenuation amount of the first fixed time interval; a second calculation sub-unit configured to calculate according to the first cycle attenuation amount, the historical working condition data and a calendar attenuation amount prediction model in the target attenuation amount prediction model, to obtain a first calendar attenuation amount corresponding to the first fixed time interval; and a determination sub-unit configured to obtain the accumulated attenuation amount according to the first cycle attenuation amount and the first calendar attenuation amount.

[0018] Furthermore, the first calculation subunit includes: a first calculation module, used to calculate based on the current attenuation, the historical operating condition data and the cycle attenuation prediction model to obtain the cycle attenuation rate of the first fixed time interval; a determination module, used to determine the number of cycles of the single lithium battery within the first fixed time interval based on the historical operating condition data; a second calculation module, used to calculate based on the cycle attenuation rate and the number of cycles to obtain the first cycle attenuation corresponding to the first fixed time interval.

[0019] Furthermore, the first calculation module includes: a first determination submodule, used to obtain a current cumulative throughput corresponding to the current attenuation based on the current attenuation and the cyclic attenuation prediction model; a second determination submodule, used to predict the predicted throughput of the single lithium battery after the first fixed time interval based on the historical operating condition data, and obtain a predicted cumulative throughput based on the predicted throughput and the current cumulative throughput; a third determination submodule, used to obtain a predicted cyclic attenuation based on the predicted cumulative throughput and the cyclic attenuation prediction model; and a first calculation submodule, used to calculate based on the current attenuation, the current cumulative throughput, the predicted cumulative throughput, and the predicted cyclic attenuation to obtain the cyclic attenuation rate.

[0020] Furthermore, the first determination submodule includes: a determination submodule, used to determine the current state of charge cycle interval of the single lithium battery based on the historical operating condition data; a first calculation submodule, used to calculate the current state of charge cycle interval through a discharge depth prediction model to obtain the current discharge depth; and a second calculation submodule, used to calculate the current discharge depth and the current attenuation through the cycle attenuation prediction model to obtain the current cumulative throughput.

[0021] Furthermore, the second calculation subunit includes: a second calculation submodule, used to calculate based on the first cycle attenuation and the calendar attenuation prediction model to obtain the calendar attenuation rate of the first fixed time interval; a prediction submodule, used to predict the storage time of the single lithium battery in the first fixed time interval based on the historical operating condition data; a calculation submodule, used to calculate based on the calendar attenuation rate and the storage time to obtain the first calendar attenuation corresponding to the first fixed time interval.

[0022] According to another aspect of an embodiment of the present invention, an electronic device is provided, comprising: a memory storing an executable program; and a processor for running the program, wherein when the program is run, any one of the above-mentioned methods for predicting the life of a single lithium battery is executed.

[0023] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided, which stores a program, wherein when the program is running, the device where the storage medium is located is controlled to execute any of the above-mentioned single-cell lithium battery life prediction methods.

[0024] According to another aspect of an embodiment of the present invention, a computer program product is provided, including a computer program or instructions. When the computer program or instructions are executed by a processor, any one of the above methods for predicting the life of a single lithium battery is implemented.

[0025] In an embodiment of the present application, the following steps are adopted: obtaining historical operating condition data of a single lithium battery to be predicted, wherein the historical operating condition data includes at least: historical charge and discharge parameter information of the single lithium battery; obtaining the current attenuation of the power of the single lithium battery; calculating the attenuation of the single lithium battery within a fixed time interval based on the current attenuation, historical operating condition data and a target attenuation prediction model to obtain a cumulative attenuation; when the cumulative attenuation is greater than or equal to a preset threshold, determining the remaining life information of the single lithium battery based on the number of fixed time intervals, thereby solving the technical problem in related technologies of predicting the service life of lithium battery cells by constructing an electrochemical model, resulting in relatively low efficiency in life prediction.

[0026] In this solution, historical operating data of the lithium-ion battery to be predicted is collected, along with the battery's current charge decay. Then, based on the collected historical operating data and the measured current decay, a target decay prediction model is used to calculate the expected decay of the battery within a fixed time interval. The predicted decay over multiple future time intervals is then accumulated until the cumulative decay reaches a preset decay threshold. When the cumulative decay equals or exceeds this threshold, the battery's remaining life is determined based on the number of fixed time intervals that have elapsed. Compared to existing electrochemical models that require modeling and solving complex internal battery reactions, this solution's prediction method based on historical data and current state reduces computation time, improves prediction efficiency, and meets the battery management system's demand for real-time prediction. By analyzing historical operating data, this solution can more accurately capture the battery's aging patterns under specific charge and discharge conditions. Combined with the decay of the current battery state, the prediction model can dynamically adapt to individual battery differences and environmental changes, providing more accurate battery life predictions and ultimately improving the accuracy of battery life predictions. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of this application. The exemplary embodiments and descriptions of this application are intended to explain this application and do not constitute an improper limitation on this application. In the accompanying drawings:

[0028] Figure 1A hardware structure block diagram of a computer terminal for implementing a method for predicting the life of a single lithium battery is shown;

[0029] Figure 2 This is a flow chart of a method for predicting the life of a single lithium battery provided in an embodiment of the present application;

[0030] Figure 3 This is a schematic diagram of the power attenuation curve of a single lithium battery provided in an embodiment of the present application. Figure 1 ;

[0031] Figure 4 This is a schematic diagram of the power attenuation curve of a single lithium battery provided in an embodiment of the present application. Figure 2 ;

[0032] Figure 5 This is a schematic diagram of the power attenuation curve of a single lithium battery provided in an embodiment of the present application. Figure 3 ;

[0033] Figure 6 This is a schematic diagram of the power attenuation curve of a single lithium battery provided in an embodiment of the present application. Figure 4 ;

[0034] Figure 7 This is a schematic diagram of a depth of discharge curve of a single lithium battery provided in an embodiment of the present application:

[0035] Figure 8 This is a schematic diagram of the power attenuation curve of a single lithium battery provided in an embodiment of the present application. Figure 5 ;

[0036] Figure 9 Schematic diagram of a life prediction device for a single lithium battery provided in an embodiment of the present application;

[0037] Figure 10 This is a structural block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0038] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0039] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0040] First, some of the nouns or terms appearing in the description of the embodiments of the present application are applicable to the following explanations:

[0041] SOC, State of Charge, battery state of charge;

[0042] Q loss , Cumulative capacity loss battery capacity loss;

[0043] Q loss,cal , Calendar capacity loss storage capacity loss;

[0044] Q loss,cyc , Cycle capacity loss cycle capacity loss;

[0045] DOD, Depth of Discharge, discharge depth.

[0046] It should be noted that the information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, analyzed data, etc.) involved in the present application are information and data authorized by the user or authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of relevant data comply with relevant laws, regulations and standards, necessary security measures are taken, do not violate public order and good customs, and provide corresponding operation portal for user to choose authorization or refusal. For example, the system and related users or institutions are provided with an interface to provide the user with a corresponding operation portal for the user to choose to agree or refuse the automatic decision result; if the user chooses to refuse, the expert decision process is entered.

[0047] Embodiment 1

[0048] According to an embodiment of the present application, an embodiment of a method for predicting the life of a single lithium battery is also provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0049] The method embodiment provided in the first embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 The hardware structure block diagram of a computer terminal (or mobile device) for implementing a method for predicting the life of a single lithium battery is shown. Figure 1 As shown, the computer terminal 10 (or mobile device) may include one or more (illustrated as 102a, 102b, ..., 102n in the figure) processors 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0050] It should be noted that the one or more processors 102 and / or other data processing circuits described above may generally be referred to herein as "data processing circuitry". The data processing circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuitry may be a single independent processing module, or may be incorporated in whole or in part into any of the other components of the computer terminal 10 (or mobile device). As described in the embodiments of the present application, the data processing circuitry serves as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0051] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the life prediction method of a single lithium battery in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, implementing the above-mentioned life prediction method of a single lithium battery. The memory 104 may include a high-speed random access memory and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely located relative to the processor 102, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0052] The transmission device 106 is configured to receive or transmit data via a network. A specific example of the aforementioned network may include a wireless network provided by the communications provider of the computer terminal 10. In one embodiment, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, the transmission device 106 may be a radio frequency (RF) module, which is configured to communicate with the Internet wirelessly.

[0053] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 10 (or mobile device).

[0054] Under the above operating environment, this application provides Figure 2 The life prediction method of a single lithium battery is shown. Figure 2 This is a flow chart of a method for predicting the life of a single lithium battery according to the first embodiment of the present application. The method for predicting the life of a single lithium battery includes:

[0055] Step S201 : acquiring historical operating condition data of a single lithium battery to be predicted, wherein the historical operating condition data at least includes historical charge and discharge parameter information of the single lithium battery.

[0056] Optionally, the battery management system can be used to obtain historical operating data for single lithium batteries. This data includes, but is not limited to, operating time characteristics, charge and discharge rates, duration, temperature, SOC, discharge capacity, and cycle count. These parameters directly reflect the battery's operating intensity and operating environment and are the primary factors affecting battery life.

[0057] It should be noted that if the single lithium battery to be predicted is a fresh battery, that is, an unused battery, the operating condition data of the single lithium battery to be predicted can be determined based on the operating condition data of batteries with similar operating conditions to the single lithium battery.

[0058] Step S202: obtaining the current attenuation of the power of the single lithium battery.

[0059] Optionally, the amount of charge attenuation, also known as capacity loss, refers to the maximum amount of charge or capacity that can be released by the battery during use, relative to the reduction in initial capacity. It is usually expressed as a percentage, that is, the percentage by which the current battery capacity has decreased compared to the brand new state of the battery. If the single lithium battery to be predicted is a used lithium battery, the battery management system can indirectly calculate the current capacity attenuation by monitoring the charging and discharging process of the battery, and the current attenuation can also be calculated using the target attenuation prediction model proposed in this application. If the single lithium battery to be predicted is a fresh, unused battery, the corresponding current attenuation is 0. When predicting battery life, the current attenuation serves as a starting point and reference benchmark, which can help the prediction model evaluate the subsequent aging rate.

[0060] Step S203 , calculating the attenuation of the single lithium battery within a fixed time interval based on the current attenuation, historical operating condition data, and the target attenuation prediction model to obtain a cumulative attenuation.

[0061] Optionally, the target attenuation prediction model can be obtained by fitting the laboratory aging history data of a single lithium battery. For example, by collecting cycle aging test data of a single lithium battery, including but not limited to battery, temperature, charge and discharge rate, SOC (state of charge), SOH (state of health), depth of discharge, SOC cycle interval, and charge and discharge capacity. Then, the target attenuation prediction model is obtained through a data fitting tool. For example, the target attenuation prediction model is shown in Formula (1).

[0062]

[0063] Among them, Q loss represents the cycle attenuation of a single lithium battery, T1 is the thermodynamic temperature, i.e., the thermodynamic temperature of the battery cycle aging test; Ea1 is the first activation energy (which can be obtained by fitting in laboratory tests); R is the ideal gas constant (constant value); C rateis the charge / discharge rate, k is the weight of the charge / discharge rate, which can be obtained by fitting laboratory data; Ah is the cumulative battery throughput; cumulative battery throughput refers to the total amount of battery charge and discharge accumulated over the battery's life cycle. b1 and z1 are the parameters to be fitted, the first parameter and the second parameter, respectively. The parameters to be fitted in formula (1) above are solved using lithium battery cell cycle aging test data to obtain the final target attenuation prediction model.

[0064] In an optional embodiment, calculating the attenuation of a single lithium battery within a fixed time interval using a target attenuation prediction model includes the following steps: first, dividing the time into multiple small time periods, such as a small time period every few hours, the smaller the time period, the smaller the prediction error and the higher the accuracy, that is, determining a fixed time interval, for example, the fixed time interval is 48 hours. Then, the battery cumulative throughput corresponding to the current attenuation is calculated based on the current attenuation and formula (1), and the predicted battery throughput of the single lithium battery in the next 48 hours is predicted based on historical operating condition data, and the attenuation corresponding to the predicted battery throughput is calculated based on the predicted battery throughput and formula (1). Then, the power attenuation rate in 48 hours is obtained based on the current attenuation, the battery cumulative throughput corresponding to the current attenuation, the predicted battery throughput, and the attenuation corresponding to the predicted battery throughput. Finally, the attenuation is obtained based on the power attenuation rate.

[0065] As time goes by, the above prediction process is repeated at fixed time intervals, and the attenuation obtained from each prediction is accumulated to obtain the cumulative attenuation so far.

[0066] Step S204 : when the accumulated attenuation is greater than or equal to a preset threshold, the remaining life information of the single lithium battery is determined according to the number of fixed time intervals.

[0067] Optionally, the preset threshold is a standard value pre-set by the battery manufacturer or maintainer based on the type, purpose and performance requirements of the battery. When the cumulative attenuation exceeds or equals the preset threshold (for example, 30%), it means that the capacity of the battery has dropped to a level that cannot meet its original function, or the performance of the battery has seriously degraded and has reached the end of its application or technical life. Therefore, after calculating the above-mentioned cumulative attenuation, the cumulative attenuation is compared with the preset threshold. If the cumulative attenuation exceeds the preset threshold, this indicates that the battery is approaching or has reached the end of its life.

[0068] When the accumulated attenuation is greater than or equal to a preset threshold, the remaining life of the single lithium battery is determined based on the number of fixed time intervals. It should be noted that the number of fixed time intervals refers to the number of predefined fixed time intervals (such as hours, days, weeks, or months) that have passed since the battery began to be used or since the most recent life prediction. The total time of these fixed time intervals is the remaining life of the single lithium battery.

[0069] In summary, historical operating data of the lithium-ion battery to be predicted is collected, along with the current battery charge decay. Then, based on the collected historical operating data and the measured current decay, a target decay prediction model is used to calculate the expected decay of the battery within a fixed time interval. The predicted decay over multiple future time intervals is then accumulated until the cumulative decay reaches a preset decay threshold. When the cumulative decay equals or exceeds this threshold, the remaining life of the battery is determined based on the number of fixed time intervals that have elapsed. Compared to existing electrochemical models that require modeling and solving complex internal battery reactions, this prediction method based on historical data and current state reduces computation time, improves prediction efficiency, and meets the battery management system's demand for real-time prediction. By analyzing historical operating data, this solution can more accurately capture the battery's aging patterns under specific charge and discharge conditions. Combined with the decay of the current battery state, the prediction model can dynamically adapt to individual battery differences and environmental changes, providing more accurate battery life predictions and ultimately achieving the technical effect of improving the accuracy of battery life predictions.

[0070] Optionally, in the life prediction method of a single lithium battery provided in an embodiment of the present application, before obtaining the historical operating condition data of the single lithium battery to be predicted, the method further includes: collecting first test data of the cycle aging of the historical single lithium battery and second test data of the storage aging of the single lithium battery; based on the first test data, constructing a cycle attenuation prediction model for the historical single lithium battery regarding the relationship between the cycle attenuation and the cumulative throughput of the battery; based on the second test data, constructing a calendar attenuation prediction model for the historical single lithium battery regarding the relationship between the calendar attenuation and the storage time; and determining a target attenuation prediction model based on the cycle attenuation prediction model and the calendar attenuation prediction model.

[0071] In an optional embodiment, the battery life is divided into battery cycle life, battery storage life and battery operating life. The cycle life refers to the life of the battery from production to continuous charging and discharging cycles without storage in between. The storage life refers to the calendar life of the battery from production to continuous storage state without charging or discharging. The operating life refers to the life of the battery during operation, which has both cycle process and storage process, and the loss is the result of calendar life and storage life together, also known as service life. In order to accurately predict the life of the single lithium battery under the working condition, in the life prediction method of the single lithium battery provided in the embodiment of the application, the following steps can be used to obtain the above-mentioned target attenuation amount prediction model:

[0072] First, laboratory aging history data collection: collect single lithium battery cycle aging test data (i.e. the first test data) and calendar aging test data (i.e. the second test data), including but not limited to battery voltage, temperature, charge-discharge rate, SOC (state of charge), SOH (state of health), discharge depth, SOC cycle interval, charge-discharge capacity and other parameters.

[0073] Then, according to the cycle aging test data, i.e. the first test data, a cycle attenuation prediction model about the relationship between cycle attenuation and battery cumulative throughput is constructed. For example, a model that can represent the relationship between cycle attenuation and cumulative throughput is fitted by using statistical or machine learning methods (such as linear regression), i.e. a cycle attenuation prediction model. The model can describe the relationship between the cumulative throughput of the battery and the cycle attenuation.

[0074] In an optional embodiment, the cycle attenuation prediction model is shown in formula (2):

[0075]

[0076] In an optional embodiment, the cumulative throughput can be equivalent to the number of charge-discharge cycles. In the case of equivalent to the number of cycles, the schematic diagram of the cycle life fitted by the cycle attenuation prediction model is shown in FIG. 2. Figure 3 Figure 3 Cycle NUM in FIG. 2 is the number of cycles. Figure 3 FIG. 3 shows the cycle life curve at different temperatures.

[0077] Further, according to the calendar aging test data, i.e. the second test data, a calendar attenuation prediction model about the relationship between calendar attenuation and storage time is constructed.

[0078] In an optional embodiment, the calendar attenuation prediction model is shown in formula (3):

[0079] ​

[0080] wherein Q loss,cal represents the monobloc battery calendar degradation amount, T2 is the thermodynamic temperature, i.e. the thermodynamic temperature of the monobloc battery calendar aging test environment; Ea2 is the second activation energy; R is the ideal gas constant (constant value); t is the standing time (i.e. storage time) in seconds; f(SOC) is a function related to the standing SOC; b2, z2 are to-be-fitted parameters, i.e. the third parameter and the fourth parameter respectively.

[0081] It should be noted that f1(SOC) and f2(SOC) are both functions related to the standing SOC, i.e. the function of the influence of different SOCs on the storage capacity loss, which can be obtained by fitting the laboratory test data. For example, f1(SOC) and f2(SOC) are functions after fitting two different curves, for example, f1(SOC) is a first-order curve and f2(SOC) is a second-order curve.

[0082] In an optional embodiment, the storage life of the battery is calculated by the calendar degradation amount prediction model using early storage aging data, and a schematic diagram of the storage life is shown in Figure 4 . Figure 4 The calendar life curve at different temperatures is shown in

[0083] The monobloc lithium battery is simultaneously affected by cycle aging and calendar aging in actual application scenarios. Therefore, a comprehensive model, i.e. a target degradation amount prediction model, is created by combining the cycle degradation amount prediction model and the calendar degradation amount prediction model to comprehensively predict the aging of the battery under real use conditions. Therefore, the cycle degradation amount prediction model and the calendar degradation amount prediction model are determined as the target degradation amount prediction model.

[0084] In an optional embodiment, the superimposition of the cycle life curve obtained by the cycle degradation amount prediction model and the calendar life curve obtained by the calendar degradation amount prediction model is shown in Figure 5 . Figure 5 In , Calendar loss is the calendar degradation curve, Cycleloss is the cycle degradation curve, and Condition loss is the condition life aging degradation curve. The cycle degradation amount prediction model and the calendar degradation amount prediction model can more accurately predict the monobloc life of the lithium battery.

[0085] Since cycle aging and calendar aging are considered respectively, the target degradation amount prediction model can reduce the prediction error caused by insufficient evaluation of a single aging effect. The target degradation amount prediction model can better simulate the aging behavior of the battery under complex conditions (such as temperature variation, SOC change, etc.), thereby improving the accuracy of life prediction.

[0086] Optionally, in the life prediction method of a single lithium battery provided in an embodiment of the present application, a cycle attenuation prediction model for a historical single lithium battery regarding the relationship between the cycle attenuation and the cumulative throughput of the battery is constructed based on the first test data, including: constructing a discharge depth prediction model for a historical single lithium battery regarding the relationship between the discharge depth and the state of charge cycle interval based on the state of charge data in the first test data; and constructing a cycle attenuation prediction model based on the charge and discharge rate data and the discharge depth prediction model in the first test data.

[0087] In an optional embodiment, since different depths of discharge can quantify the impact of DOD on battery cycle aging in different SOC cycle intervals, when constructing a cycle decay prediction model for a historical single lithium battery based on the relationship between cycle decay and battery cumulative throughput, it is also necessary to construct a discharge depth prediction model for the historical relationship between discharge depth and state of charge cycle interval.

[0088] First, the state of charge (SOC) data is extracted from the first test data. The SOC data reflects the percentage of charge remaining in the battery during the charge and discharge cycle. A depth of discharge prediction model is then constructed based on the SOC data.

[0089] For example, the discharge depth prediction model is shown in formula (4):

[0090]

[0091] Where m is the depth of discharge, SOC_min and SOC_max are the discharge-end SOC and charge-end SOC of the single battery during use; a and b are parameters to be fitted, which can be obtained by fitting aging data in different SOC cycle intervals; X is the SOC.

[0092] In an optional embodiment, the cycle aging data in different SOC cycle intervals is used. Figure 6 As shown, refer to formula (4) to calculate m. It should be noted that the higher the cycle SOC, the faster the aging loss, which can be converted to different cycle numbers, as shown in Table 1. In an optional embodiment, the relationship between m and different SOC cycle intervals is as follows: Figure 7 As shown in Figure 2, with the increase of SOC cycle interval, the m value shows an upward trend.

[0093] Table 1

[0094]

[0095] After obtaining the discharge depth prediction model, a cycle decay prediction model is constructed based on the charge and discharge rate data in the first test data and the discharge depth prediction model. For example, the cycle decay prediction model is shown in formula (5).

[0096]

[0097] Among them, T1 is the thermodynamic temperature, that is, the thermodynamic temperature of the battery cycle aging test; Ea1 is the first activation energy; R is the ideal gas constant (constant value); Crate is the charge and discharge rate; m is a parameter related to the discharge depth and SOC cycle range. The m value is related to the negative electrode SEI film formation side reaction. In the high SOC cycle range, the negative electrode potential is low, the film formation side reaction rate occurs faster, and the m value is large. The specific calculation method can be used using formula (4); Ah is the cumulative throughput of the battery (which can be equivalent to the number of cycles); b1 and z1 are parameters to be fitted, that is, the first parameter and the second parameter respectively.

[0098] By constructing a discharge depth prediction model and a cycle attenuation prediction model, the embodiments of the present application can more carefully evaluate the aging rate of the battery in various SOC ranges, thereby providing a more accurate life prediction.

[0099] Optionally, in the life prediction method of a single lithium battery provided in an embodiment of the present application, the attenuation of the single lithium battery within a fixed time interval is calculated based on the current attenuation, historical operating condition data and a target attenuation prediction model, and the cumulative attenuation is obtained, including: for a first fixed time interval, calculating based on the current attenuation, historical operating condition data and a cyclic attenuation prediction model in the target attenuation prediction model to obtain the first cyclic attenuation of the first fixed time interval; calculating based on the first cyclic attenuation, historical operating condition data and a calendar attenuation prediction model in the target attenuation prediction model to obtain the first calendar attenuation corresponding to the first fixed time interval; and obtaining the cumulative attenuation based on the first cyclic attenuation and the first calendar attenuation.

[0100] In an optional embodiment, for a first fixed time interval (such as a working day, a week, etc.), first, the current attenuation and the cyclic attenuation prediction model constructed based on historical operating condition data are used to calculate and obtain the first cyclic attenuation caused by the charge and discharge cycle in the first fixed time interval. Then, based on the first cyclic attenuation, historical operating condition data and the calendar attenuation prediction model, the first calendar attenuation of the first fixed time interval caused by static or storage is calculated. Finally, the first cyclic attenuation and the first calendar attenuation are added to obtain the cumulative attenuation in the first fixed time interval. This calculation method comprehensively considers the capacity loss of the battery under two operating conditions: charge and discharge cycles and static storage during use, and provides a basis for a comprehensive assessment of the battery's health status.

[0101] By distinguishing between cycle attenuation and calendar attenuation, the attenuation rate of the battery under actual working conditions can be evaluated more accurately, thereby improving the accuracy of predicting battery life.

[0102] Optionally, in the life prediction method of a single lithium battery provided in an embodiment of the present application, calculation is performed based on the current attenuation, historical operating condition data and the cyclic attenuation prediction model in the target attenuation prediction model to obtain the first cyclic attenuation for the first fixed time interval, including: calculating based on the current attenuation, historical operating condition data and the cyclic attenuation prediction model to obtain the cyclic attenuation rate for the first fixed time interval; determining the number of cycles of the single lithium battery within the first fixed time interval based on the historical operating condition data; and calculating based on the cyclic attenuation rate and the number of cycles to obtain the first cyclic attenuation corresponding to the first fixed time interval.

[0103] Calculating based on the current attenuation, historical operating data, and a cyclic attenuation prediction model to obtain a cyclic attenuation rate for a first fixed time interval includes: obtaining a current cumulative throughput corresponding to the current attenuation based on the current attenuation and the cyclic attenuation prediction model; predicting a predicted throughput of a single lithium battery after the first fixed time interval based on the historical operating data, and obtaining a predicted cumulative throughput based on the predicted throughput and the current cumulative throughput; obtaining a predicted cyclic attenuation based on the predicted cumulative throughput and the cyclic attenuation prediction model; and obtaining a cyclic attenuation rate by calculating based on the current attenuation, the current cumulative throughput, the predicted cumulative throughput, and the predicted cyclic attenuation.

[0104] In an optional embodiment, the cycle attenuation rate is a proportional factor that describes the degree of capacity loss after each cycle of the battery, and is the percentage of battery capacity reduction after each charge and discharge cycle. This ratio is affected by various factors, including the depth of charge and discharge (DOD), charge and discharge rate (C-rate), battery temperature, etc. In the life prediction method of a single lithium battery provided in the embodiment of the present application, the following steps can be used to calculate the first cycle attenuation at the first fixed time interval:

[0105] During a first fixed time interval, the cyclic decay rate for that time interval can be calculated based on the current decay, historical operating condition data, and the cyclic decay prediction model. For example, by inputting the current decay into the cyclic decay prediction model, the battery's cumulative throughput corresponding to that decay (i.e., the current cumulative throughput described above) can be reversely calculated, i.e., the total charge and discharge volume experienced by the battery to date.

[0106] The historical operating condition data includes key information about the battery's past charge and discharge cycles, charge and discharge depth, temperature, and charge and discharge rate. Based on this data, we can predict the battery's cumulative throughput after the first fixed time interval, generating the predicted cumulative throughput. By inputting this predicted cumulative throughput into the cycle decay prediction model, we can reverse-calculate the predicted cycle decay corresponding to this decay.

[0107] The cycle decay rate is then calculated based on the current decay, current cumulative throughput, predicted cumulative throughput, and predicted cycle decay. The cycle decay rate reflects the ratio between battery capacity loss and change in charge and discharge throughput within a first fixed time interval. Based on historical operating data, the number of charge and discharge cycles the battery will complete within the first fixed time interval (e.g., one day, one week, etc.) is predicted.

[0108] Finally, combined with the calculated cycle decay rate And the number of cycles of the battery in the first fixed time interval, the first cycle attenuation can be calculated using the following formula:

[0109]

[0110] By calculating the cyclic attenuation rate at a first fixed time interval and then calculating the first cyclic attenuation amount based on the cyclic attenuation rate, the battery aging rate can be evaluated more accurately, thereby improving the accuracy of life prediction.

[0111] Optionally, in the life prediction method of a single lithium battery provided in an embodiment of the present application, the current cumulative throughput corresponding to the current attenuation is obtained based on the current attenuation and the cycle attenuation prediction model, including: determining the current state of charge cycle interval of the single lithium battery based on historical operating condition data; calculating the current state of charge cycle interval through a discharge depth prediction model to obtain the current discharge depth; calculating the current discharge depth and the current attenuation through the cycle attenuation prediction model to obtain the current cumulative throughput.

[0112] In an optional embodiment, by analyzing historical operating condition data, the range of state of charge (SOC) variation of a single lithium battery during recent operation, i.e., the SOC cycle interval, can be determined. For example, if a battery was discharged from 80% SOC to 20% SOC and then recharged to 100% in the previous cycle, then its SOC cycle interval can be considered to be from 20% to 100%. Based on the SOC cycle interval, a discharge depth prediction model (i.e., the above formula (4)) is used to calculate the current discharge depth m. Then, combined with the current attenuation, these parameters are input into the cycle attenuation prediction model. Through model calculation, the cumulative throughput of the single lithium battery in the current state can be obtained.

[0113] The current cumulative throughput is obtained through the battery's SOC cycle range and current discharge depth, which can more accurately estimate the aging degree of the single lithium battery under the current usage conditions.

[0114] Optionally, in the life prediction method of a single lithium battery provided in an embodiment of the present application, calculation is performed based on the first cycle attenuation, historical operating condition data, and a calendar attenuation prediction model in the target attenuation prediction model to obtain the first calendar attenuation corresponding to the first fixed time interval, including: calculating based on the first cycle attenuation and the calendar attenuation prediction model to obtain the calendar attenuation rate of the first fixed time interval; predicting the storage time of the single lithium battery in the first fixed time interval based on the historical operating condition data; and calculating based on the calendar attenuation rate and storage time to obtain the first calendar attenuation corresponding to the first fixed time interval.

[0115] In an optional embodiment, the calendar decay rate is calculated based on the first cycle decay of the single lithium battery within the first fixed time interval in combination with the calendar decay prediction model. The first decay value is obtained by summing the first cycle decay and the current decay, and then the first decay value is brought into the calendar decay prediction model to reversely obtain the corresponding first storage time. Then, based on the analysis of historical operating data, the actual static or storage time of the battery within the first fixed time interval can be estimated. For example, if the first fixed time interval is set to one day, and the battery is only used for 6 hours in this day, the remaining 18 hours of storage time will be used for the calculation of subsequent calendar decay. The estimated storage time is input into the calendar decay prediction model to obtain the corresponding predicted calendar decay, and then the calendar decay rate can be calculated based on the first decay value, the first storage time, the estimated storage time and the predicted calendar decay. Finally, the calculated calendar decay rate is combined with the predicted storage time to calculate the first calendar decay corresponding to the first fixed time interval, for example, is the calendar decay rate, t2 is the first fixed time interval, and t1 is the charge and discharge duration.

[0116] By calculating the calendar decay rate and the first calendar decay amount at the first fixed time interval and combining them with the cycle decay of the battery, a more comprehensive and refined battery aging assessment can be provided.

[0117] In an optional embodiment, the following steps can be used to calculate the capacity decay Q within the target time: loss,all First, calculate the past cycle attenuation and past storage attenuation of the single lithium battery in each past time period before the target time; secondly, the sum of all past cycle attenuation and all past storage attenuation is taken as the total past attenuation Q loss,0 ; Determine the cycle SOC interval within the target time period, and calculate the current discharge depth m in combination with the discharge depth prediction model; use Q loss,0 Combined with the cyclic attenuation prediction model, the current cyclic attenuation rate is calculated and the decay rate of the current cyclic attenuation is determined. The cycle attenuation within the target time period is calculated by multiplying the current cycle attenuation rate by the number of cycles within the target cycle time period. Determine; determine all capacity losses within the target time period before the start of the storage time period Combined with the calendar decay prediction model, the calendar decay rate in the subsequent storage period is calculated The calendar decay amount in the target time period is the product of the current calendar decay rate and the storage time in the target time period. Confirm; the sum of all past total attenuation, cyclic attenuation within the current target time period, and calendar attenuation within the target time period Q loss,all As the total attenuation. For example, the total attenuation

[0118] Compared with directly substituting various parameters into the total attenuation of the cyclic attenuation function, the above calculation method can fully consider the changes in parameters such as temperature, charge and discharge rate, SOC cycle range, etc. during the target time period, making the calculation result closer to the actual attenuation value.

[0119] In an optional embodiment, the target battery has a certain service life, that is, a life span. The life span can be divided into multiple time periods according to predetermined time units. The divided time periods include the target time period and the past time periods before it, and the length of the past time periods may be inconsistent with the length of the target time period. The total cycle attenuation is added to the total storage attenuation to obtain the total attenuation (expressed as a percentage), and the total attenuation is subtracted from the target constant (for example, 1) to determine the operating life. It should be noted that the past cycle attenuation in the past time period is calculated in the same way as the target cycle attenuation, and the past storage attenuation is calculated in the same way as the target storage attenuation, and the specific method will not be repeated.

[0120] In an optional embodiment, the operating life decay trajectory within different SOC cycle ranges can be calculated through the above steps as follows: Figure 8 As shown, Figure 8 The operating life decay trajectories within the SOC cycle ranges of 0-100%, 10%-90% and 0-80% are shown.

[0121] This application considers the impact of storage aging on batteries during static storage and proposes a cycle life prediction method for different DOD and SOC cycle intervals based on m-value correction. This method has been verified by experimental data and is highly applicable to lithium-ion batteries of different systems. Taking into account the complexity of actual battery applications, the SOC cycle range is considered to compensate for the differences between laboratory test conditions and actual application conditions. This application significantly improves the accuracy of predicting the actual battery life.

[0122] The life prediction method of a single lithium battery provided in an embodiment of the present application obtains historical operating condition data of the single lithium battery to be predicted, wherein the historical operating condition data includes at least: historical charge and discharge parameter information of the single lithium battery; obtains the current attenuation of the power of the single lithium battery; calculates the attenuation of the single lithium battery within a fixed time interval based on the current attenuation, historical operating condition data and a target attenuation prediction model to obtain a cumulative attenuation; when the cumulative attenuation is greater than or equal to a preset threshold, determines the remaining life information of the single lithium battery based on the number of fixed time intervals, thereby solving the technical problem in related technologies of predicting the service life of lithium battery cells by constructing an electrochemical model, resulting in relatively low efficiency in life prediction.

[0123] In this solution, historical operating data of the lithium-ion battery to be predicted is collected, along with the battery's current charge decay. Then, based on the collected historical operating data and the measured current decay, a target decay prediction model is used to calculate the expected decay of the battery within a fixed time interval. The predicted decay over multiple future time intervals is then accumulated until the cumulative decay reaches a preset decay threshold. When the cumulative decay equals or exceeds this threshold, the battery's remaining life is determined based on the number of fixed time intervals that have elapsed. Compared to existing electrochemical models that require modeling and solving complex internal battery reactions, this solution's prediction method based on historical data and current state reduces computation time, improves prediction efficiency, and meets the battery management system's demand for real-time prediction. By analyzing historical operating data, this solution can more accurately capture the battery's aging patterns under specific charge and discharge conditions. Combined with the decay of the current battery state, the prediction model can dynamically adapt to individual battery differences and environmental changes, providing more accurate battery life predictions and ultimately improving the accuracy of battery life predictions.

[0124] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0125] Example 2

[0126] The present application also provides a device for predicting the lifespan of a single lithium battery. It should be noted that the device for predicting the lifespan of a single lithium battery provided in the present application can be used to execute the method for predicting the lifespan of a single lithium battery provided in the present application. The following describes the device for predicting the lifespan of a single lithium battery provided in the present application.

[0127] According to an embodiment of the present application, a device for implementing the above-mentioned method for predicting the life of a single lithium battery is also provided, such as Figure 9 As shown, the device includes: a first acquiring unit 901 , a second acquiring unit 902 , a calculating unit 903 and a first determining unit 904 .

[0128] The first acquisition unit 901 is configured to acquire historical operating condition data of a single lithium battery to be predicted, wherein the historical operating condition data at least includes historical charge and discharge parameter information of the single lithium battery;

[0129] The second acquiring unit 902 is configured to acquire a current attenuation of the power of the single lithium battery;

[0130] The calculation unit 903 is used to calculate the attenuation of the single lithium battery within a fixed time interval based on the current attenuation, historical operating condition data and the target attenuation prediction model to obtain the cumulative attenuation;

[0131] The first determining unit 904 is configured to determine the remaining life information of the single lithium battery according to the number of fixed time intervals when the accumulated attenuation amount is greater than or equal to a preset threshold.

[0132] The life prediction device for a single lithium battery provided in an embodiment of the present application obtains historical operating condition data of the single lithium battery to be predicted through a first acquisition unit 901, wherein the historical operating condition data includes at least: historical charge and discharge parameter information of the single lithium battery; a second acquisition unit 902 is used to obtain the current attenuation of the power of the single lithium battery; a calculation unit 903 calculates the attenuation of the single lithium battery within a fixed time interval based on the current attenuation, historical operating condition data and a target attenuation prediction model to obtain a cumulative attenuation; a first determination unit 904 determines the remaining life information of the single lithium battery based on the number of fixed time intervals when the cumulative attenuation is greater than or equal to a preset threshold, thereby solving the technical problem in the related art of predicting the service life of lithium battery cells by constructing an electrochemical model, resulting in relatively low efficiency in life prediction.

[0133] In this solution, historical operating data of the lithium-ion battery to be predicted is collected, along with the battery's current charge decay. Then, based on the collected historical operating data and the measured current decay, a target decay prediction model is used to calculate the expected decay of the battery within a fixed time interval. The predicted decay over multiple future time intervals is then accumulated until the cumulative decay reaches a preset decay threshold. When the cumulative decay equals or exceeds this threshold, the battery's remaining life is determined based on the number of fixed time intervals that have elapsed. Compared to existing electrochemical models that require modeling and solving complex internal battery reactions, this solution's prediction method based on historical data and current state reduces computation time, improves prediction efficiency, and meets the battery management system's demand for real-time prediction. By analyzing historical operating data, this solution can more accurately capture the battery's aging patterns under specific charge and discharge conditions. Combined with the decay of the current battery state, the prediction model can dynamically adapt to individual battery differences and environmental changes, providing more accurate battery life predictions and ultimately improving the accuracy of battery life predictions.

[0134] Optionally, in the life prediction device for a single lithium battery provided in an embodiment of the present application, the device further includes: an acquisition unit, for collecting first test data of the cycle aging of the historical single lithium battery and second test data of the storage aging of the single lithium battery before obtaining the historical operating condition data of the single lithium battery to be predicted; a first construction unit, for constructing a cycle attenuation prediction model for the historical single lithium battery regarding the relationship between the cycle attenuation and the cumulative throughput of the battery based on the first test data; a second construction unit, for constructing a calendar attenuation prediction model for the historical single lithium battery regarding the relationship between the calendar attenuation and the storage time based on the second test data; and a second determination unit, for determining a target attenuation prediction model based on the cycle attenuation prediction model and the calendar attenuation prediction model.

[0135] Optionally, in the life prediction device for a single lithium battery provided in an embodiment of the present application, the first construction unit includes: a first construction sub-unit, used to construct a discharge depth prediction model for a historical single lithium battery regarding the relationship between discharge depth and state of charge cycle interval based on the state of charge data in the first test data; and a second construction sub-unit, used to construct a cycle attenuation prediction model based on the charge and discharge rate data and the discharge depth prediction model in the first test data.

[0136] Optionally, in the life prediction device for a single lithium battery provided in an embodiment of the present application, the calculation unit includes: a first calculation subunit, used to calculate, for a first fixed time interval, a first cyclic attenuation for the first fixed time interval based on the current attenuation, historical operating condition data, and a cyclic attenuation prediction model in a target attenuation prediction model; a second calculation subunit, used to calculate, based on the first cyclic attenuation, historical operating condition data, and a calendar attenuation prediction model in the target attenuation prediction model, to obtain a first calendar attenuation corresponding to the first fixed time interval; and a determination subunit, used to obtain a cumulative attenuation based on the first cyclic attenuation and the first calendar attenuation.

[0137] Optionally, in the life prediction device for a single lithium battery provided in an embodiment of the present application, the first calculation subunit includes: a first calculation module, used to calculate based on the current attenuation, historical operating condition data and a cycle attenuation prediction model to obtain a cycle attenuation rate for a first fixed time interval; a determination module, used to determine the number of cycles of the single lithium battery within the first fixed time interval based on the historical operating condition data; and a second calculation module, used to calculate based on the cycle attenuation rate and the number of cycles to obtain a first cycle attenuation corresponding to the first fixed time interval.

[0138] Optionally, in the life prediction device for a single lithium battery provided in an embodiment of the present application, the first calculation module includes: a first determination submodule, used to obtain a current cumulative throughput corresponding to the current attenuation based on the current attenuation and a cycle attenuation prediction model; a second determination submodule, used to predict the predicted throughput of the single lithium battery after a first fixed time interval based on historical operating condition data, and obtain a predicted cumulative throughput based on the predicted throughput and the current cumulative throughput; a third determination submodule, used to obtain a predicted cycle attenuation based on the predicted cumulative throughput and the cycle attenuation prediction model; and the first calculation submodule, used to calculate based on the current attenuation, the current cumulative throughput, the predicted cumulative throughput, and the predicted cycle attenuation to obtain a cycle attenuation rate.

[0139] Optionally, in the life prediction device for a single lithium battery provided in an embodiment of the present application, the first determination submodule includes: a determination submodule, used to determine the current state of charge cycle interval of the single lithium battery based on historical operating condition data; a first calculation submodule, used to calculate the current state of charge cycle interval through a discharge depth prediction model to obtain the current discharge depth; and a second calculation submodule, used to calculate the current discharge depth and the current attenuation through a cycle attenuation prediction model to obtain the current cumulative throughput.

[0140] Optionally, in the life prediction device for a single lithium battery provided in an embodiment of the present application, the second calculation subunit includes: a second calculation submodule, used to perform calculations based on the first cycle attenuation and calendar attenuation prediction model to obtain the calendar attenuation rate of a first fixed time interval; a prediction submodule, used to predict the storage time of the single lithium battery within the first fixed time interval based on historical operating condition data; and a calculation submodule, used to perform calculations based on the calendar attenuation rate and storage time to obtain the first calendar attenuation corresponding to the first fixed time interval.

[0141] It should be noted that the first acquisition unit 901, the second acquisition unit 902, the calculation unit 903, and the first determination unit 904 described above correspond to steps S201 to S204 in the first embodiment. The examples and application scenarios implemented by the four units and the corresponding steps are the same, but are not limited to the contents disclosed in the first embodiment. It should be noted that the above modules or units can be hardware components or software components stored in a memory (e.g., memory 104) and processed by one or more processors (e.g., processors 102a, 102b, ..., 102n). The above modules can also be run as part of a device in the computer terminal 10 provided in the first embodiment.

[0142] Example 3

[0143] An embodiment of the present application may provide an electronic device, Figure 10 This is a structural block diagram of an electronic device according to an embodiment of the present application. Figure 10 As shown, the electronic device may include: one or more ( Figure 10 Only one is shown) processor 1002, memory 1004, storage controller, and peripheral interface, wherein the peripheral interface is connected to the radio frequency module, audio module and display.

[0144] Among them, the memory can be used to store software programs and modules, such as program instructions / modules corresponding to the methods and devices in the embodiments of the present application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, implementing the above-mentioned method. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the terminal via a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network and a combination thereof.

[0145] The processor can call the information and application stored in the memory through the transmission device to perform the following steps: obtain the historical operating condition data of the single lithium battery to be predicted, wherein the historical operating condition data at least includes: historical charge and discharge parameter information of the single lithium battery; obtain the current attenuation of the power of the single lithium battery; calculate the attenuation of the single lithium battery within a fixed time interval based on the current attenuation, historical operating condition data and target attenuation prediction model to obtain the cumulative attenuation; when the cumulative attenuation is greater than or equal to a preset threshold, determine the remaining life information of the single lithium battery based on the number of fixed time intervals.

[0146] The processor can call the information and application stored in the memory through the transmission device to perform the following steps: before obtaining the historical operating condition data of the single lithium battery to be predicted, the method also includes: collecting first test data of the cycle aging of the historical single lithium battery and second test data of the storage aging of the single lithium battery; based on the first test data, constructing a cycle attenuation prediction model for the historical single lithium battery regarding the relationship between the cycle attenuation and the cumulative throughput of the battery; based on the second test data, constructing a calendar attenuation prediction model for the historical single lithium battery regarding the relationship between the calendar attenuation and the storage time; and determining a target attenuation prediction model based on the cycle attenuation prediction model and the calendar attenuation prediction model.

[0147] The processor can call the information and application stored in the memory through the transmission device to perform the following steps: based on the first test data, constructing a cycle attenuation prediction model for the historical single lithium battery regarding the relationship between the cycle attenuation and the battery cumulative throughput, including: based on the state of charge data in the first test data, constructing a discharge depth prediction model for the historical single lithium battery regarding the relationship between the discharge depth and the state of charge cycle interval; based on the charge and discharge rate data and the discharge depth prediction model in the first test data, constructing a cycle attenuation prediction model.

[0148] The processor can call the information and application stored in the memory through the transmission device to perform the following steps: based on the current attenuation, historical operating condition data and the target attenuation prediction model, the attenuation of the single lithium battery within a fixed time interval is calculated to obtain the cumulative attenuation, including: for a first fixed time interval, based on the current attenuation, historical operating condition data and the cyclic attenuation prediction model in the target attenuation prediction model, calculating to obtain the first cyclic attenuation of the first fixed time interval; based on the first cyclic attenuation, historical operating condition data and the calendar attenuation prediction model in the target attenuation prediction model, calculating to obtain the first calendar attenuation corresponding to the first fixed time interval; and based on the first cyclic attenuation and the first calendar attenuation, obtaining the cumulative attenuation.

[0149] The processor can call the information and application stored in the memory through the transmission device to perform the following steps: calculating based on the current attenuation, historical operating condition data and the cyclic attenuation prediction model in the target attenuation prediction model to obtain the first cyclic attenuation of the first fixed time interval, including: calculating based on the current attenuation, historical operating condition data and the cyclic attenuation prediction model to obtain the cyclic attenuation rate of the first fixed time interval; determining the number of cycles of the single lithium battery in the first fixed time interval based on the historical operating condition data; and calculating based on the cyclic attenuation rate and the number of cycles to obtain the first cyclic attenuation corresponding to the first fixed time interval.

[0150] The processor can call the information and application stored in the memory through the transmission device to perform the following steps: calculating based on the current attenuation, historical operating condition data and the cycle attenuation prediction model to obtain the cycle attenuation rate for the first fixed time interval, including: obtaining the current cumulative throughput corresponding to the current attenuation based on the current attenuation and the cycle attenuation prediction model; predicting the predicted throughput of the single lithium battery after the first fixed time interval based on the historical operating condition data, and obtaining the predicted cumulative throughput based on the predicted throughput and the current cumulative throughput; obtaining the predicted cycle attenuation based on the predicted cumulative throughput and the cycle attenuation prediction model; and calculating based on the current attenuation, the current cumulative throughput, the predicted cumulative throughput, and the predicted cycle attenuation to obtain the cycle attenuation rate.

[0151] The processor can call the information and application stored in the memory through the transmission device to perform the following steps: based on the current attenuation and the cycle attenuation prediction model, obtain the current cumulative throughput corresponding to the current attenuation, including: determining the current state of charge cycle interval of the single lithium battery based on historical operating condition data; calculating the current state of charge cycle interval through the discharge depth prediction model to obtain the current discharge depth; calculating the current discharge depth and the current attenuation through the cycle attenuation prediction model to obtain the current cumulative throughput.

[0152] The processor can call the information and application stored in the memory through the transmission device to perform the following steps: calculating based on the first cycle attenuation, historical operating condition data and the calendar attenuation prediction model in the target attenuation prediction model to obtain the first calendar attenuation corresponding to the first fixed time interval, including: calculating based on the first cycle attenuation and the calendar attenuation prediction model to obtain the calendar attenuation rate of the first fixed time interval; predicting the storage time of the single lithium battery in the first fixed time interval based on the historical operating condition data; and calculating based on the calendar attenuation rate and the storage time to obtain the first calendar attenuation corresponding to the first fixed time interval.

[0153] It can be understood by those skilled in the art that Figure 10The structure shown is for illustration only, and the electronic device may also be a terminal device such as a smart phone, a tablet computer, a PDA, a mobile Internet device (MID), or a PAD. Figure 10 It does not limit the structure of the above electronic device. For example, the electronic device may also include Figure 10 More or fewer components (such as network interfaces, display devices, etc.) shown in, or with Figure 10 Different configurations shown.

[0154] A person skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a computer-readable storage medium, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0155] Example 4

[0156] The embodiment of the present application further provides a computer-readable storage medium. Optionally, in this embodiment, the storage medium can be used to store the program code executed by the single-cell lithium battery life prediction method provided in the first embodiment.

[0157] Optionally, in this embodiment, the storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0158] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing the steps of the method for predicting the life of a single lithium battery.

[0159] The serial numbers of the above embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0160] In the above embodiments of the present application, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0161] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units 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 units or modules, which can be electrical or other forms.

[0162] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0163] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0164] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0165] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for predicting the life of a single lithium battery, characterized in that: include: Acquire historical operating condition data of a single lithium battery to be predicted, wherein the historical operating condition data at least includes: historical charge and discharge parameter information of the single lithium battery; Obtaining a current attenuation of the power of the single lithium battery; Calculating the attenuation of the single lithium battery within a fixed time interval based on the current attenuation, the historical operating condition data, and a target attenuation prediction model to obtain a cumulative attenuation; When the accumulated attenuation is greater than or equal to a preset threshold, the remaining life information of the single lithium battery is determined according to the number of the fixed time intervals.

2. The method according to claim 1, characterized in that Before obtaining historical operating condition data of the single lithium battery to be predicted, the method further includes: Collecting first test data of cycle aging of historical single lithium batteries and second test data of storage aging of single lithium batteries; Constructing a cycle attenuation prediction model for the historical single lithium battery based on the relationship between cycle attenuation and battery cumulative throughput according to the first test data; Constructing a calendar decay prediction model for the historical single lithium battery regarding the relationship between calendar decay and storage duration based on the second test data; The target attenuation prediction model is determined according to the cyclic attenuation prediction model and the calendar attenuation prediction model.

3. The method according to claim 2, characterized in that Constructing a cycle attenuation prediction model for the historical single lithium battery regarding the relationship between cycle attenuation and battery cumulative throughput based on the first test data includes: Constructing a discharge depth prediction model for the historical single lithium battery regarding the relationship between discharge depth and state of charge cycle interval based on the state of charge data in the first test data; The cycle attenuation prediction model is constructed based on the charge and discharge rate data in the first test data and the discharge depth prediction model.

4. The method according to claim 1, wherein The attenuation of the single lithium battery within a fixed time interval is calculated based on the current attenuation, the historical operating condition data, and the target attenuation prediction model to obtain the cumulative attenuation, including: For a first fixed time interval, calculating according to the current attenuation, the historical operating condition data, and a cyclic attenuation prediction model in the target attenuation prediction model to obtain a first cyclic attenuation for the first fixed time interval; performing calculations based on the first cyclic attenuation, the historical operating condition data, and a calendar attenuation prediction model in the target attenuation prediction model to obtain a first calendar attenuation corresponding to the first fixed time interval; The cumulative attenuation is obtained according to the first cyclic attenuation and the first calendar attenuation.

5. The method according to claim 4, characterized in that Calculating according to the current attenuation, the historical operating condition data, and a cyclic attenuation prediction model in the target attenuation prediction model to obtain the first cyclic attenuation for the first fixed time interval includes: Calculating according to the current attenuation, the historical operating condition data, and the cyclic attenuation prediction model to obtain a cyclic attenuation rate at the first fixed time interval; Determining the number of cycles of the single lithium battery within the first fixed time interval based on the historical operating condition data; A first cyclic attenuation corresponding to the first fixed time interval is obtained by calculation based on the cyclic attenuation rate and the number of cycles.

6. The method according to claim 5, characterized in that Calculating according to the current attenuation, the historical operating condition data, and the cyclic attenuation prediction model to obtain the cyclic attenuation rate at the first fixed time interval includes: Obtaining a current cumulative throughput corresponding to the current attenuation according to the current attenuation and the cyclic attenuation prediction model; Predicting a predicted throughput of the single lithium battery after the first fixed time interval based on the historical operating condition data, and obtaining a predicted cumulative throughput based on the predicted throughput and the current cumulative throughput; Obtaining a predicted cyclic loss according to the predicted cumulative throughput and the cyclic loss prediction model; The cyclic attenuation rate is obtained by performing calculation according to the current attenuation, the current cumulative throughput, the predicted cumulative throughput, and the predicted cyclic attenuation.

7. The method according to claim 6, characterized in that Obtaining, according to the current attenuation and the cyclic attenuation prediction model, a current cumulative throughput corresponding to the current attenuation includes: Determining a current state of charge cycle interval of the single lithium battery based on the historical operating condition data; Calculating the current state of charge cycle interval using a discharge depth prediction model to obtain a current discharge depth; The current discharge depth and the current attenuation are calculated using the cyclic attenuation prediction model to obtain the current cumulative throughput.

8. The method according to claim 4, characterized in that Calculating according to the first cyclic attenuation, the historical operating condition data, and a calendar attenuation prediction model in the target attenuation prediction model to obtain the first calendar attenuation corresponding to the first fixed time interval includes: Calculating according to the first cyclic attenuation and the calendar attenuation prediction model to obtain a calendar attenuation rate for the first fixed time interval; Predicting the storage time of the single lithium battery within the first fixed time interval based on the historical operating condition data; A calculation is performed based on the calendar decay rate and the storage duration to obtain a first calendar decay amount corresponding to the first fixed time interval.

9. A device for predicting the life of a single lithium battery, characterized in that: include: A first acquiring unit is configured to acquire historical operating condition data of a single lithium battery to be predicted, wherein the historical operating condition data at least includes historical charge and discharge parameter information of the single lithium battery; A second acquiring unit is used to acquire a current attenuation of the power of the single lithium battery; a calculation unit, configured to calculate the attenuation of the single lithium battery within a fixed time interval based on the current attenuation, the historical operating condition data, and a target attenuation prediction model to obtain a cumulative attenuation; The first determining unit is configured to determine the remaining life information of the single lithium battery according to the number of the fixed time intervals when the accumulated attenuation amount is greater than or equal to a preset threshold.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the life prediction method for a single lithium battery according to any one of claims 1 to 8.

11. An electronic device, characterized in that: include: a memory storing an executable program; A processor is used to run the program, wherein when the program is run, the method described in any one of claims 1 to single lithium battery life prediction is executed.

12. A computer program product comprising computer instructions, characterized in that When the computer instructions are executed by a processor, the steps of the method for predicting the life of a single lithium battery according to any one of claims 1 to 8 are implemented.

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