Battery life prediction method and device, storage medium and electronic equipment

Abstract

The invention discloses a battery life prediction method and device, a storage medium and electronic equipment. The method comprises the steps that battery data of a target battery in the latest charge-discharge cycle are obtained, current battery data are obtained, and the battery data comprise at least one of coulombic efficiency, energy efficiency and voltage characteristic parameters; determining a target correction factor based on the current battery data and reference battery data of the target battery; and correcting the initial mathematical model based on the target correction factor to obtain a target mathematical model, and predicting the service life of the target battery based on the target mathematical model to obtain a service life prediction result. According to the invention, the problem of low accuracy of the predicted battery life caused by predicting the battery life based on a fixed model in the prior art is solved.

Description

Technical Field

[0001] This application relates to the field of batteries, and more specifically, to a method, apparatus, storage medium, and electronic device for predicting battery life. Background Technology

[0002] In recent years, with the rapid development of new energy vehicles and large-scale energy storage industries, lithium-ion batteries, especially lithium iron phosphate batteries, have been widely used due to their long lifespan and high safety. However, battery performance inevitably degrades during long-term cycling, and accurately predicting the remaining battery life has become a key technical challenge that the industry urgently needs to solve. Currently, industry and academia mainly rely on empirical models based on the number of cycles for life prediction, such as linear models, exponential models, or square root models. These models extrapolate the entire battery life curve by fitting parameters to initial cycle data. However, such open-loop static prediction methods have inherent defects: their core degradation parameters remain fixed after fitting, failing to perceive and respond to the constantly changing degradation dynamics within the battery as cycling progresses; the models heavily rely on historical capacity data, adjusting predictions only after significant capacity degradation has occurred, resulting in severe lag; most importantly, these models are completely unable to capture and warn of sudden "capacity plunges" caused by abnormal operating conditions such as lithium plating and electrolyte drying. Therefore, related technologies suffer from the problem of predicting battery life based on fixed models, leading to low accuracy in predicted battery life.

[0003] There is currently no effective solution to the aforementioned problems in the relevant technologies. Summary of the Invention

[0004] The main objective of this application is to provide a method, apparatus, storage medium, and electronic device for predicting battery life, in order to solve the problem that related technologies predict battery life based on fixed models, resulting in low accuracy of the predicted battery life.

[0005] To achieve the above objectives, according to one aspect of this application, a method for predicting battery life is provided. The method includes: acquiring battery data of a target battery in its latest charge-discharge cycle to obtain current battery data, wherein the battery data includes at least one of the following: coulombic efficiency, energy efficiency, and voltage characteristic parameters; determining a target correction factor based on the current battery data and reference battery data of the target battery; correcting an initial mathematical model based on the target correction factor to obtain a target mathematical model; and predicting the life of the target battery based on the target mathematical model to obtain a life prediction result.

[0006] Optionally, the method for predicting battery life may further include: determining coulombic efficiency based on the discharge capacity and charge capacity of the target battery in the latest charge-discharge cycle; determining energy efficiency based on the discharge energy and charge energy of the target battery in the latest charge-discharge cycle; determining voltage characteristic parameters based on discharge capacity, discharge energy, charge capacity, and charge energy; and determining current battery data based on at least one of coulombic efficiency, energy efficiency, and voltage characteristic parameters.

[0007] Optionally, the method for predicting battery life may further include: calculating the ratio between charging energy and charging capacity to obtain a first ratio; calculating the ratio between discharging energy and discharging capacity to obtain a second ratio; and determining voltage characteristic parameters based on the difference between the first ratio and the second ratio.

[0008] Optionally, the battery life prediction method further includes: before determining the target correction factor based on the current battery data and reference battery data of the target battery, determining the reference coulombic efficiency of the target battery based on the coulombic efficiency of the target battery in the first N charge-discharge cycles, where N is a positive integer; determining the reference energy efficiency of the target battery based on the energy efficiency of the target battery in the first N charge-discharge cycles; determining the reference voltage characteristic parameters of the target battery based on the voltage characteristic parameters of the target battery in the first N charge-discharge cycles; and determining the reference battery data based on at least one of the reference coulombic efficiency, reference energy efficiency, and reference voltage characteristic parameters.

[0009] Optionally, the battery life prediction method further includes: calculating the difference between the coulombic efficiency in the current battery data and the reference coulombic efficiency to obtain a first attenuation factor; calculating the difference between the energy efficiency in the current battery data and the reference energy efficiency to obtain a second attenuation factor; calculating the difference between the voltage characteristic parameter in the current battery data and the reference voltage characteristic parameter to obtain a target difference, and calculating the ratio between the target difference and the reference voltage characteristic parameter to obtain a parameter variation factor; and determining a target correction factor based on the first attenuation factor, the second attenuation factor, and the parameter variation factor.

[0010] Optionally, the battery life prediction method further includes: obtaining a set of weighting coefficients, wherein the set of weighting coefficients includes: a first weighting coefficient corresponding to coulombic efficiency, a second weighting coefficient corresponding to energy efficiency, and a third weighting coefficient corresponding to voltage characteristic parameters; and determining a target correction factor based on the set of weighting coefficients, a first attenuation factor, a second attenuation factor, and a parameter change factor.

[0011] Optionally, the method for predicting battery life also includes: determining the attenuation coefficient in the initial mathematical model; updating the attenuation coefficient based on the target correction factor to obtain the target attenuation coefficient; and replacing the attenuation coefficient in the initial mathematical model with the target attenuation coefficient to obtain the target mathematical model.

[0012] To achieve the above objectives, according to another aspect of this application, a battery life prediction apparatus is provided. The apparatus includes: an acquisition module for acquiring battery data of a target battery in its latest charge-discharge cycle to obtain current battery data, wherein the battery data includes at least one of the following: coulombic efficiency, energy efficiency, and voltage characteristic parameters; a first determination module for determining a target correction factor based on the current battery data and reference battery data of the target battery; and a processing module for correcting an initial mathematical model based on the target correction factor to obtain a target mathematical model, and predicting the lifespan of the target battery based on the target mathematical model to obtain a lifespan prediction result.

[0013] Optionally, the acquisition module further includes: a first determining submodule, used to determine the coulombic efficiency based on the discharge capacity and charge capacity of the target battery in the latest charge-discharge cycle; a second determining submodule, used to determine the energy efficiency based on the discharge energy and charge energy of the target battery in the latest charge-discharge cycle; a third determining submodule, used to determine the voltage characteristic parameters based on the discharge capacity, discharge energy, charge capacity, and charge energy; and a fourth determining submodule, used to determine the current battery data based on at least one of the coulombic efficiency, energy efficiency, and voltage characteristic parameters.

[0014] Optionally, the third determining submodule further includes: a first calculation unit for calculating the ratio between charging energy and charging capacity to obtain a first ratio; a second calculation unit for calculating the ratio between discharging energy and discharging capacity to obtain a second ratio; and a third calculation unit for determining voltage characteristic parameters based on the difference between the first ratio and the second ratio.

[0015] Optionally, the battery life prediction device further includes: a second determining module for determining a reference coulombic efficiency of the target battery based on the coulombic efficiency of the target battery in the first N charge-discharge cycles, where N is a positive integer; a third determining module for determining a reference energy efficiency of the target battery based on the energy efficiency of the target battery in the first N charge-discharge cycles; a fourth determining module for determining a reference voltage characteristic parameter of the target battery based on the voltage characteristic parameter of the target battery in the first N charge-discharge cycles; and a fifth determining module for determining reference battery data based on at least one of the reference coulombic efficiency, reference energy efficiency, and reference voltage characteristic parameter.

[0016] Optionally, the first determining module further includes: a first calculation submodule, used to calculate the difference between the coulombic efficiency in the current battery data and the reference coulombic efficiency to obtain a first attenuation factor; a second calculation submodule, used to calculate the difference between the energy efficiency in the current battery data and the reference energy efficiency to obtain a second attenuation factor; a third calculation submodule, used to calculate the difference between the voltage characteristic parameter in the current battery data and the reference voltage characteristic parameter to obtain a target difference, and calculate the ratio between the target difference and the reference voltage characteristic parameter to obtain a parameter change factor; and a fifth determining submodule, used to determine a target correction factor based on the first attenuation factor, the second attenuation factor, and the parameter change factor.

[0017] Optionally, the fifth determining submodule further includes: an acquisition unit, used to acquire a set of weighting coefficients, wherein the set of weighting coefficients includes: a first weighting coefficient corresponding to coulombic efficiency, a second weighting coefficient corresponding to energy efficiency, and a third weighting coefficient corresponding to voltage characteristic parameters; and a determining unit, used to determine a target correction factor based on the set of weighting coefficients, a first attenuation factor, a second attenuation factor, and a parameter change factor.

[0018] Optionally, the processing module further includes: a sixth determining submodule, used to determine the attenuation coefficient in the initial mathematical model; an updating submodule, used to update the attenuation coefficient based on the target correction factor to obtain the target attenuation coefficient; and a replacing submodule, used to replace the attenuation coefficient in the initial mathematical model based on the target attenuation coefficient to obtain the target mathematical model.

[0019] To achieve the above objectives, according to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is executed, the device on which the computer-readable storage medium is located executes the above-described battery life prediction method.

[0020] To achieve the above objectives, according to another aspect of this application, an electronic device is provided, the electronic device including a memory storing an executable program; and a processor for running the program, wherein the program executes the above-described battery life prediction method during runtime.

[0021] To achieve the above objectives, according to another aspect of this application, a computer program product is provided, including computer instructions that, when executed by a processor, implement the steps of the above-described battery life prediction method.

[0022] In this embodiment, a target correction factor is determined based on the current battery data of the target battery in the latest charge-discharge cycle and the reference battery data of the target battery. This enables real-time monitoring and analysis of changes in key electrical performance parameters of the target battery during the cycle. By correcting the initial mathematical model based on the target correction factor, a target mathematical model is obtained to predict battery life. This achieves a closed-loop dynamic prediction mechanism that combines the mathematical model with the current state of the battery, thereby effectively improving the accuracy of the predicted battery life.

[0023] Therefore, the method provided in this application achieves the goal of dynamically determining a mathematical model based on the current state of the target battery to predict its lifespan, thereby improving the accuracy of the predicted battery lifespan and solving the technical problem that related technologies predict battery lifespan based on a fixed model, resulting in low accuracy of the predicted battery lifespan. Attached Figure Description

[0024] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0025] Figure 1 This is a hardware structure block diagram of a computer terminal provided according to an embodiment of this application;

[0026] Figure 2 This is a flowchart of a battery life prediction method provided according to an embodiment of this application;

[0027] Figure 3 This is a schematic diagram of a battery life prediction device provided according to an embodiment of this application;

[0028] Figure 4 This is a structural block diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0029] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0030] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0031] It should be noted that the information collected in this application (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data used for analysis, etc.) are information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of this data all comply with relevant laws, regulations, and standards, necessary confidentiality measures have been taken, and they do not violate public order and good morals. Corresponding access points are provided for users to choose to authorize or refuse. For example, interfaces are set up between this system and relevant users or organizations, providing users with corresponding access points to choose to agree to or refuse automated decision-making results; if the user chooses to refuse, the process proceeds to the expert decision-making stage.

[0032] Example 1

[0033] According to an embodiment of this application, an embodiment of a method for predicting battery life is also provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Also, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0034] The method embodiment provided in Embodiment 1 of this application can be executed on a mobile terminal, computer terminal, or similar computing device. Figure 1 A hardware block diagram of a computer terminal (or mobile device) for implementing a method to predict battery life is shown. Figure 1As shown, the computer terminal 10 (or mobile device) may include one or more processors 102 (shown as 102a, 102b, ..., 102n in the figure) 102 (processor 102 may include, but is not limited to, a microprocessor (MCU) or a field-programmable gate array (FPGA), etc.), 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 (I / O) interface, a Universal Serial Bus (USB) port (which may be one of the ports on a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0035] It should be noted that the aforementioned one or more processors 102 and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10 (or mobile device). As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0036] 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 battery life prediction method in this embodiment. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the aforementioned battery life prediction method. The memory 104 may include high-speed random access memory, and may also include 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 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0037] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the computer terminal 10. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0038] The display can be, for example, a touchscreen liquid crystal display (LCD), which allows the user to interact with the user interface of the computer terminal 10 (or mobile device).

[0039] Under the aforementioned operating environment, this application provides the following: Figure 2 The method for predicting battery life is shown. Figure 2 This is a flowchart of a battery life prediction method according to Embodiment 1 of this application.

[0040] Step S201: Obtain battery data of the target battery in the latest charge-discharge cycle to obtain current battery data, wherein the battery data includes at least one of the following: coulombic efficiency, energy efficiency, and voltage characteristic parameters.

[0041] Optionally, electronic devices, application systems, servers, and other devices can be used as the execution subject of this application. In this embodiment, the target processing system is used as the execution subject to execute the above-mentioned battery life prediction method.

[0042] In an optional embodiment, while the battery is in actual use, the target processing system can execute the method provided in this application to predict battery life. For example, battery life prediction can be performed after each charge-discharge cycle of the target battery. Another example is that battery life prediction can be performed after every P charge-discharge cycles of the target battery, where P is a positive integer greater than 1.

[0043] For example, when a time point is reached where battery life prediction is needed (e.g., after one (or P) charge-discharge cycles as described above), battery data for the target battery in the latest charge-discharge cycle is obtained to obtain the current battery data. The target battery can be a lithium-ion battery, such as a lithium iron phosphate battery, or it can be other types of batteries.

[0044] Optionally, the coulombic efficiency (CE) can be determined based on the charge / discharge capacity of the target battery, and the energy efficiency can be determined based on the charge / discharge energy of the target battery. Voltage characteristic parameters can be quantified by characteristic values ​​such as the change in the peak value of the differential voltage analysis (dV / dQ) curve, the drift of the voltage plateau at the end of constant current charging, or the voltage relaxation rate after resting.

[0045] In an optional embodiment, the battery data includes coulombic efficiency, energy efficiency, and voltage characteristic parameters.

[0046] Step S202: Determine the target correction factor based on the current battery data and the reference battery data of the target battery.

[0047] Optionally, the reference battery data can be determined based on battery data in the initial stage or under ideal conditions. The target processing system can determine the target correction factor based on the deviation information between the current battery data and the reference battery data of the target battery. For example, the target correction factor can be determined by calculating the difference between the two. The target correction factor reflects the degree of deviation between the current state and the ideal state of the battery and can quantify the subtle changes occurring inside the battery.

[0048] Step S203: The initial mathematical model is modified based on the target correction factor to obtain the target mathematical model, and the lifespan of the target battery is predicted based on the target mathematical model to obtain the lifespan prediction result.

[0049] Initial mathematical models are predictive models built upon relevant technologies, such as square root models or exponential models, used to predict battery life. For example, predictive models based on relevant technologies can be divided into two categories: models based on physical mechanisms and models based on empirical data. Empirical models are widely used due to their simplicity and readily available parameters. For example, the linear model: C_n = C_0 × (1-k·n), where C_n represents the current remaining capacity, C_0 represents the initial capacity, n represents the number of charge-discharge cycles, and k represents the decay coefficient. This model is suitable for the initial approximately linear decay stage but cannot describe the nonlinear decay in the middle and later stages. The exponential model: C_n = C_0 × e^(-k·n), is suitable for describing accelerated decay behavior, but its parameters are constant and cannot reflect the dynamic changes in the internal state during cycling. The square root model: ΔC∝√n, where ΔC represents the capacity loss, is based on SEI film growth kinetics and describes the long-term decay trend under certain chemical systems, but it also lacks a response to real-time operating conditions. Multivariate integrated model: such as η= A·e^(B / T)·DOD^C·n^D, where η represents the capacity retention rate. Although this formula introduces external stress factors such as temperature (T) and depth of charge and discharge (DOD), its core parameters (A, B, C, D) remain fixed once the fitting is completed.

[0050] Optionally, the initial mathematical model can be either the mathematical model mentioned above or a model in the form of capacity decay, such as Q_loss=M*√n, where Q_loss represents the capacity decay amount (Ah), M represents the decay rate coefficient, and n represents the number of charge-discharge cycles.

[0051] After determining the initial mathematical model, the attenuation coefficient in the initial mathematical model is updated based on the target correction factor to obtain the target mathematical model. After obtaining the target mathematical model, the battery capacity for the next cycle can be predicted using the target mathematical model. When the next time the battery life needs to be predicted, the current battery data of the target battery is reacquired to redetermine the target correction factor, update the initial mathematical model, and obtain a new target mathematical model, thus achieving closed-loop dynamic updating and rolling prediction of the model.

[0052] In an optional embodiment, the form of the lifetime prediction result varies depending on the type of the initial mathematical model used. For example, the lifetime prediction result may be the capacity retention rate of the target battery, or the remaining capacity of the target battery, or the capacity decay of the target battery.

[0053] In an optional embodiment, after obtaining the lifetime prediction results output by the target mathematical model, the lifetime prediction results can be uniformly converted into the form of the capacity retention rate of the target battery.

[0054] It should be noted that the core of battery life prediction in related technologies is "open-loop, static, and posterior" prediction. Open-loop refers to the model operating unidirectionally, taking inputs such as cycle number (n) and temperature (T) and outputting the predicted capacity. The model itself cannot be corrected based on feedback from the actual state of the battery. Static means that the model's key parameters (such as the degradation coefficient k) remain fixed after the initial fitting, unable to respond to changes in battery degradation dynamics. Posterior means that the model's correction heavily relies on the lagging indicator of "capacity." Only when the battery capacity has significantly decreased can the model adjust through refitting, failing to provide early warning. In contrast, the core of battery life prediction in this application is "closed-loop, dynamic, and prior." Closed-loop refers to the construction of a feedback control system. The model's prediction results are compared and fused with a new set of dynamic electrical parameters (at least one of coulombic efficiency, energy efficiency, and voltage characteristic parameters) to form a self-correcting closed loop. Dynamic means that the model's key parameters (such as the degradation coefficient) are no longer constants but variables corrected in real-time by the dynamic electrical parameters. This allows the model to respond in real-time to subtle changes in the battery's internal state. Prior parameters, such as coulombic efficiency, energy efficiency, and voltage characteristics, are leading indicators of the internal chemical and physical states of a battery, and their abnormal changes precede significant capacity decay. Therefore, the method provided in this application can detect abnormal trends and correct predictions before significant capacity changes occur, achieving early warning.

[0055] By incorporating and deeply integrating dynamic electrical parameters that reflect real-time changes in the battery's internal state, such as ΔV (voltage characteristic change), coulombic efficiency, and energy efficiency, into an empirical degradation model, an adaptive prediction system with closed-loop feedback regulation capability was constructed. Its core advantage lies in achieving a technological leap from "static extrapolation" to "dynamic sensing and correction." It can keenly capture precursor signals triggered by abnormal operating conditions such as intensified side reactions, loss of active lithium, or micro-short circuits, thus issuing early warnings before significant capacity degradation occurs. This not only significantly improves the accuracy of long-term cycle life prediction, especially demonstrating excellent early warning capabilities for sudden "capacity plunges," but also enhances the model's adaptability to individual battery differences and complex operating conditions, providing a more reliable and forward-looking decision-making basis for battery health management.

[0056] In this embodiment, a target correction factor is determined based on the current battery data of the target battery in the latest charge-discharge cycle and the reference battery data of the target battery. This enables real-time monitoring and analysis of changes in key electrical performance parameters of the target battery during the cycle. By correcting the initial mathematical model based on the target correction factor, a target mathematical model is obtained to predict battery life. This achieves a closed-loop dynamic prediction mechanism that combines the mathematical model with the current state of the battery, thereby effectively improving the accuracy of the predicted battery life.

[0057] Therefore, the method provided in this application achieves the goal of dynamically determining a mathematical model based on the current state of the target battery to predict its lifespan, thereby improving the accuracy of the predicted battery lifespan and solving the technical problem that related technologies predict battery lifespan based on a fixed model, resulting in low accuracy of the predicted battery lifespan.

[0058] Optionally, in the battery life prediction method provided in this application embodiment, obtaining battery data of the target battery in the latest charge-discharge cycle to obtain current battery data includes: determining coulombic efficiency based on the discharge capacity and charge capacity of the target battery in the latest charge-discharge cycle; determining energy efficiency based on the discharge energy and charge energy of the target battery in the latest charge-discharge cycle; determining voltage characteristic parameters based on discharge capacity, discharge energy, charge capacity, and charge energy; and determining current battery data based on at least one of coulombic efficiency, energy efficiency, and voltage characteristic parameters.

[0059] Optionally, the ratio of the discharge capacity to the charge capacity of the target battery in the latest charge-discharge cycle can be calculated to obtain the coulombic efficiency, i.e., coulombic efficiency CE = discharge capacity / charge capacity. Coulombic efficiency is an important indicator for evaluating the reversible lithium-ion loss of a battery during charge and discharge, and its changes directly reflect the degree of side reactions inside the battery. Obtaining this data allows for timely identification of the battery's health status.

[0060] Optionally, the energy efficiency can be calculated by comparing the discharge energy to the charging energy of the target battery in the latest charge-discharge cycle, i.e., energy efficiency Eta = discharge energy / charging energy. Energy efficiency is a key indicator for comprehensively evaluating the energy conversion efficiency of a battery. A decrease in energy efficiency means that the battery will consume more energy to compensate for losses during operation. Therefore, monitoring changes in energy efficiency helps to assess the battery's availability and lifespan expectations.

[0061] Optionally, voltage characteristic parameters can be determined based on the target battery's discharge capacity, discharge energy, charge capacity, and charge energy in the latest charge-discharge cycle.

[0062] After determining the coulombic efficiency, energy efficiency, and voltage characteristic parameters, the current battery data is determined based on at least one of the coulombic efficiency, energy efficiency, and voltage characteristic parameters.

[0063] It should be noted that the above method enables accurate determination of coulombic efficiency, energy efficiency, and voltage characteristic parameters, thereby improving the accuracy of determining the current state of the battery.

[0064] Optionally, in the battery life prediction method provided in the embodiments of this application, the voltage characteristic parameters are determined based on the discharge capacity, discharge energy, charging capacity, and charging energy, including: calculating the ratio between charging energy and charging capacity to obtain a first ratio; calculating the ratio between discharge energy and discharge capacity to obtain a second ratio; and determining the voltage characteristic parameters based on the difference between the first ratio and the second ratio.

[0065] In an optional embodiment, the target processing system can calculate the voltage characteristic parameters based on the following formula:

[0066]

[0067] in, Equivalent to the first ratio, Equivalent to the second ratio, This represents the voltage characteristic parameter. The voltage characteristic parameter is highly sensitive to deterioration in reaction kinetics, and an increase in this value indicates severe polarization and active internal side reactions.

[0068] It should be noted that, through the above methods, voltage characteristic parameters can sensitively capture the energy loss of the battery during the charging and discharging process, especially early signals such as increased internal resistance, intensified polarization effect, or decreased utilization of active materials, providing key information for subsequent battery health status assessment and lifespan prediction.

[0069] Optionally, in the battery life prediction method provided in this application embodiment, before determining the target correction factor based on the current battery data and the reference battery data of the target battery, the method further includes: determining a reference coulombic efficiency of the target battery based on the coulombic efficiency of the target battery in the first N charge-discharge cycles, where N is a positive integer; determining a reference energy efficiency of the target battery based on the energy efficiency of the target battery in the first N charge-discharge cycles; determining a reference voltage characteristic parameter of the target battery based on the voltage characteristic parameter of the target battery in the first N charge-discharge cycles; and determining reference battery data based on at least one of the reference coulombic efficiency, reference energy efficiency, and reference voltage characteristic parameter.

[0070] In one alternative embodiment, N can be 100. Alternatively, N can take other values.

[0071] For example, the target processing system can average the coulombic efficiency of the target battery over the first N charge-discharge cycles to obtain a reference coulombic efficiency. Alternatively, the system can perform a weighted summation of the coulombic efficiencies over the first N charge-discharge cycles to obtain a reference coulombic efficiency. Yet another example is that the system can calculate the median of the coulombic efficiencies over the first N charge-discharge cycles to obtain a reference coulombic efficiency.

[0072] For example, the target processing system can calculate the average energy efficiency of the target battery over the first N charge-discharge cycles to obtain a reference energy efficiency. Alternatively, the system can perform a weighted summation of the energy efficiency over the first N charge-discharge cycles to obtain a reference energy efficiency. Yet another example is that the system can calculate the median energy efficiency of the target battery over the first N charge-discharge cycles to obtain a reference energy efficiency.

[0073] For example, the target processing system can average the voltage characteristic parameters of the target battery over the first N charge-discharge cycles to obtain the reference voltage characteristic parameters of the target battery. Alternatively, the target processing system can perform a weighted summation of the voltage characteristic parameters of the target battery over the first N charge-discharge cycles to obtain the reference voltage characteristic parameters of the target battery. Yet another example is that the target processing system can calculate the median of the voltage characteristic parameters of the target battery over the first N charge-discharge cycles to obtain the reference voltage characteristic parameters of the target battery.

[0074] Optionally, after determining the reference coulombic efficiency, reference energy efficiency, and reference voltage characteristic parameters, reference battery data is determined based on at least one of the reference coulombic efficiency, reference energy efficiency, and reference voltage characteristic parameters.

[0075] In an optional embodiment, the reference battery data includes reference coulombic efficiency, reference energy efficiency, and reference voltage characteristic parameters.

[0076] It should be noted that by extracting reference coulombic efficiency, reference energy efficiency, and reference voltage characteristic parameters from the initial cycle data of the target battery, the battery data of the target battery in a relatively healthy state is determined as the reference battery data, thereby improving the accuracy of the reference battery data.

[0077] Optionally, in the battery life prediction method provided in this application embodiment, when the current battery data includes coulombic efficiency, energy efficiency, and voltage characteristic parameters, a target correction factor is determined based on the current battery data and reference battery data of the target battery. This includes: calculating the difference between the coulombic efficiency in the current battery data and the reference coulombic efficiency to obtain a first attenuation factor; calculating the difference between the energy efficiency in the current battery data and the reference energy efficiency to obtain a second attenuation factor; calculating the difference between the voltage characteristic parameters in the current battery data and the reference voltage characteristic parameters to obtain a target difference, and calculating the ratio between the target difference and the reference voltage characteristic parameters to obtain a parameter change factor; and determining the target correction factor based on the first attenuation factor, the second attenuation factor, and the parameter change factor.

[0078] In an optional embodiment, if the current battery data includes coulombic efficiency, energy efficiency, and voltage characteristic parameters, the reference battery data includes reference coulombic efficiency, reference energy efficiency, and reference voltage characteristic parameters. That is, the data types included in the reference battery data correspond to the data types included in the current battery data.

[0079] Optionally, the first attenuation factor can be obtained by subtracting the coulombic efficiency in the current battery data from the reference coulombic efficiency. The second attenuation factor can be obtained by subtracting the energy efficiency in the current battery data from the reference energy efficiency. Alternatively, the target difference can be obtained by subtracting the reference voltage characteristic parameter from the voltage characteristic parameter in the current battery data, and then dividing the target difference by the reference voltage characteristic parameter to obtain the parameter variation factor.

[0080] In an optional embodiment, after obtaining the first attenuation factor, the second attenuation factor, and the parameter change factor, the first attenuation factor, the second attenuation factor, and the parameter change factor can be averaged to obtain the target correction factor.

[0081] In an optional embodiment, the first attenuation factor, the second attenuation factor, and the parameter change factor can be weighted and summed to obtain the target correction factor.

[0082] In an optional embodiment, the first attenuation factor, the second attenuation factor, and the parameter change factor can also be processed by other calculation methods to obtain the target correction factor.

[0083] It should be noted that by calculating the first decay factor, the change in current coulombic efficiency can be quantified, thereby assessing the extent of internal side reactions in the battery and providing crucial information for model correction. Calculating the second decay factor quantifies the change in current energy efficiency, which directly reflects the performance degradation caused by increased internal resistance and intensified polarization effects, thus providing an important basis for assessing battery life. Small changes in voltage characteristic parameters may indicate significant problems in the battery's internal structure or electrochemical reactions; calculating parameter change factors can effectively reflect these internal changes. Therefore, determining the target correction factor through the aforementioned methods improves its reliability, quantifies the deviation of current battery data into the model's predictive ability, and enables the model to respond sensitively to changes in the battery's internal state, thereby improving the accuracy of life prediction results.

[0084] Optionally, in the battery life prediction method provided in this application embodiment, determining the target correction factor based on the first attenuation factor, the second attenuation factor, and the parameter change factor includes: obtaining a set of weighting coefficients, wherein the set of weighting coefficients includes: a first weighting coefficient corresponding to coulombic efficiency, a second weighting coefficient corresponding to energy efficiency, and a third weighting coefficient corresponding to voltage characteristic parameters; and determining the target correction factor based on the set of weighting coefficients, the first attenuation factor, the second attenuation factor, and the parameter change factor.

[0085] In one optional instance, the set of weighting coefficients is preset by the user. For example, the user determines the weighting coefficients for each degradation factor based on their understanding of the degradation mechanism of lithium iron phosphate batteries and analysis of experimental data.

[0086] After obtaining the set of weight coefficients, the target first attenuation factor can be determined based on the first weight coefficient and the first attenuation factor in the set of weight coefficients, the target second attenuation factor can be determined based on the second weight coefficient and the second attenuation factor, and the target parameter change factor can be determined based on the third weight coefficient and the parameter change factor.

[0087] For example, the target first attenuation factor can be expressed as follows:

[0088]

[0089] in, Indicates the first decay factor of the target. This represents the first weighting coefficient. Indicates reference coulomb efficiency. This indicates the coulombic efficiency in the current battery data. This is equivalent to the first attenuation factor. In an optional embodiment, K_ce can be set to 200, meaning that for every 0.1% decrease in CE, the target correction factor increases by 0.2.

[0090] For example, the target second attenuation factor can be expressed as follows:

[0091]

[0092] in, Indicates the target second decay factor. This represents the second weighting coefficient. Indicates reference energy efficiency. This indicates the energy efficiency in the current battery data. This is equivalent to a second attenuation factor. In an optional embodiment, It can be set to 200.

[0093] For example, the target parameter variation factor can be expressed as follows:

[0094]

[0095] in, This represents the factor that changes the target parameter. This represents the third weighting coefficient. This represents the voltage characteristic parameter in the current battery data. Indicates the reference voltage characteristic parameter. Equivalent to the target difference, Equivalent to the parameter variation factor.

[0096] After determining the target first attenuation factor, the target second attenuation factor, and the target parameter change factor, the target correction factor is determined as (1+ξ_ce+ξ_eta+ξ_dv).

[0097] It should be noted that by introducing a set of weighting coefficients, the reasonable weights of different electrical performance parameters in model correction are ensured. This allows for a more accurate reflection of the contributions of coulombic efficiency, energy efficiency, and voltage characteristics to the battery's health status, improving the reliability of the target mathematical model and enabling it to respond more sensitively to subtle changes in the battery's internal state, thereby enhancing the accuracy of battery life prediction.

[0098] Optionally, in the battery life prediction method provided in this application embodiment, the initial mathematical model is modified based on the target correction factor to obtain the target mathematical model, including: determining the attenuation coefficient in the initial mathematical model; updating the attenuation coefficient based on the target correction factor to obtain the target attenuation coefficient; and replacing the attenuation coefficient in the initial mathematical model with the target attenuation coefficient to obtain the target mathematical model.

[0099] For example, in the initial mathematical model Q_loss=M In the case of √n, its attenuation coefficient is M, thus determining its target attenuation coefficient as M. (1+ξ_ce+ξ_eta+ξ_dv) yields the target mathematical model Q_loss=M √n (1+ ξ_ce+ξ_eta+ξ_dv).

[0100] For example, in the initial mathematical model C_n=C_0 Given e^(-k·n), its attenuation coefficient is k, thus determining its target attenuation coefficient as k_effective=k. (1+ξ_ce+ξ_eta+ξ_dv) yields the target mathematical model C_n =C_0 e^(-k_effective n).

[0101] For example, given the initial mathematical model η = A·e^(B / T)·DOD^C·n^D, its attenuation coefficient is determined to be A, thereby determining its target attenuation coefficient as A·(1+F(ξ_ce, ξ_eta, ξ_dv)), where F() is a preset function, resulting in the mathematical model Q_loss = A. e^(B / T) DOD^C n^D (1+F(ξ_ce,ξ_eta,ξ_dv)).

[0102] In one alternative embodiment, two specific examples are used to illustrate the battery life prediction method provided in this embodiment.

[0103] Example 1: Dynamic correction based on the square root model (suitable for describing long-term decay caused by SEI growth);

[0104] 1. Application scenarios and basic model selection;

[0105] This study focuses on a 280Ah lithium iron phosphate (LFP) battery cell intended for energy storage systems. It is known that the long-term degradation of LFP batteries is often related to the growth of the SEI film, and its capacity degradation is approximately proportional to the square root of the cycle number. Therefore, a square root model is chosen as the baseline model. Baseline model: Q_loss = M √n, where Q_loss is the capacity decay (Ah), M is the decay rate coefficient (obtained by fitting the initial cycle data), and n is the number of charge-discharge cycles.

[0106] 2. Data acquisition and processing;

[0107] During each complete charge-discharge cycle of the battery, the following data are collected and calculated: coulombic efficiency, energy efficiency, and voltage characteristic parameters.

[0108] 3. Calculate the dynamic correction factor;

[0109] CE attenuation factor (target first attenuation factor): ξ_ce = K_ce (CE_initial-CE_current); CE_initial is the average of the first 100 iterations (assumed to be 99.95%). K_ce is the weight coefficient, set to 200 through training with historical data (meaning that for every 0.1% decrease in CE, the correction factor increases by 0.2).

[0110] Energy efficiency degradation factor (target second degradation factor): ξ_eta = K_eta (Eta_initial-Eta_current); Eta_initial is the average of the first 100 iterations (assumed to be 95.5%). K_eta is the weighting coefficient, set to 100.

[0111] ΔV variation factor (target parameter variation factor): ξ_dv=K_dv (ΔV_current-ΔV_initial) / ΔV_initial; ΔV_initial is the average value of the first 100 cycles.

[0112] The target correction factor is defined as (1+ξ_ce+ξ_eta+ξ_dv).

[0113] 4. Model correction and prediction;

[0114] Introducing the above correction factors into the baseline square root model forms a dynamic correction model (i.e., the target mathematical model):

[0115] Q_loss_corrected=M √n (1+ξ_ce+ξ_eta+ξ_dv).

[0116] 5. Simulation results of the example;

[0117] At the 1500th iteration, the baseline model predicted a capacity retention rate of 88.5%.

[0118] However, at this time, it was observed that CE_current dropped to 99.82%, Eta_current dropped to 94.0%, and ΔV_current increased by 8% compared to the initial value.

[0119] Calculate the correction factor: ξ_ce = 200 (0.9995-0.9982)=0.26;ξ_eta=100 (0.955-0.940)=1.50; ξ_dv=50 0.08 = 4.0; Total correction = 0.26 + 1.50 + 4.00 = 5.76.

[0120] Corrected prediction: Q_loss_corrected=M √1500 (1+5.76)=M √1500 6.76.

[0121] This means the corrected decay rate is 6.76 times that predicted by the original model. The corrected prediction shows a capacity retention of only 82.1%. Actual result: The measured capacity retention at the 1500th cycle was 81.5%. Conclusion: The traditional model has a prediction error as high as 7%, while the dynamic correction model of this invention reduces the error to 0.6% and successfully warns of a severe decay trend.

[0122] Example 2: Dynamic correction based on multivariate exponential model (applicable to complex working conditions);

[0123] 1. Application scenarios and basic model selection;

[0124] This embodiment focuses on a 50Ah high-power LFP cell for electric vehicles, whose operating ambient temperature (T) and depth of charge / discharge (DOD) vary frequently. Therefore, a multivariate exponential model incorporating stress is selected as the benchmark. Benchmark model: C_n = C_0 e^(-k n), where e is the natural constant, and the attenuation coefficient k is set to be related to temperature (T) and depth of charge / discharge (DOD): k_base=A e^(B / T) DOD^C. Where A, B, and C are parameters fitted through experiments.

[0125] 2. Data acquisition and processing;

[0126] In addition to cycle number (n), temperature (T), and DOD, the following data are collected and calculated during each complete charge-discharge cycle of the battery: coulombic efficiency, energy efficiency, and voltage characteristic parameters.

[0127] 3. Calculate the dynamic correction factor and correct the core parameters;

[0128] The target correction factor is defined as (1+ξ_ce+ξ_eta+ξ_dv), and the target attenuation coefficient is k_effective=k (1+ξ_ce+ξ_eta+ξ_dv).

[0129] 4. Model correction and prediction;

[0130] Substituting the dynamic attenuation coefficient into the original model, we obtain the target mathematical model: C_n = C_0 e^(-k_effective n).

[0131] 5. Simulation results of the example:

[0132] At the 800th cycle, the cycle conditions were: T=303K, DOD=80%. The baseline model, after calculating k_base, predicted a capacity retention rate of 91.0%. However, after this cycle, the BMS detected that CE_current suddenly dropped to 99.0% (initially 99.9%), and the resting voltage relaxation value ΔV_current increased by 20mV. After correction and re-prediction, the model could immediately determine that the cell had entered a period of rapid degradation, predicting its remaining lifespan to be less than 50 cycles. Actual result: In the subsequent 40 cycles, the cell's capacity dropped sharply to 60% of its initial capacity, experiencing a "capacity plunge." Conclusion: Traditional multivariate models completely failed to predict this sudden failure, while this invention, by capturing the dramatic changes in CE and ΔV, achieved a successful early warning, providing a valuable time window for the battery system to take safety measures.

[0133] Therefore, the method provided in this application achieves the goal of dynamically determining a mathematical model based on the current state of the target battery to predict its lifespan, thereby improving the accuracy of the predicted battery lifespan and solving the technical problem that related technologies predict battery lifespan based on a fixed model, resulting in low accuracy of the predicted battery lifespan.

[0134] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0135] Example 2

[0136] This application also provides a battery life prediction device. It should be noted that the battery life prediction device of this application can be used to execute the battery life prediction method provided in this application. The following describes the battery life prediction device provided in this application.

[0137] According to embodiments of this application, an apparatus for implementing the above-described battery life prediction method is also provided, such as... Figure 3 As shown, the device includes:

[0138] The acquisition module 301 is used to acquire battery data of the target battery in the latest charge-discharge cycle to obtain the current battery data, wherein the battery data includes at least one of the following: coulombic efficiency, energy efficiency, and voltage characteristic parameters;

[0139] The first determining module 302 is used to determine the target correction factor based on the current battery data and the reference battery data of the target battery;

[0140] The processing module 303 is used to modify the initial mathematical model based on the target correction factor to obtain the target mathematical model, and to predict the lifespan of the target battery based on the target mathematical model to obtain the lifespan prediction result.

[0141] In this embodiment, a target correction factor is determined based on the current battery data of the target battery in the latest charge-discharge cycle and the reference battery data of the target battery. This enables real-time monitoring and analysis of changes in key electrical performance parameters of the target battery during the cycle. By correcting the initial mathematical model based on the target correction factor, a target mathematical model is obtained to predict battery life. This achieves a closed-loop dynamic prediction mechanism that combines the mathematical model with the current state of the battery, thereby effectively improving the accuracy of the predicted battery life.

[0142] Therefore, the method provided in this application achieves the goal of dynamically determining a mathematical model based on the current state of the target battery to predict its lifespan, thereby improving the accuracy of the predicted battery lifespan and solving the technical problem that related technologies predict battery lifespan based on a fixed model, resulting in low accuracy of the predicted battery lifespan.

[0143] Optionally, in the battery life prediction device provided in this application embodiment, the acquisition module further includes: a first determining submodule, used to determine the coulombic efficiency based on the discharge capacity and charging capacity of the target battery in the latest charge-discharge cycle; a second determining submodule, used to determine the energy efficiency based on the discharge energy and charging energy of the target battery in the latest charge-discharge cycle; a third determining submodule, used to determine voltage characteristic parameters based on discharge capacity, discharge energy, charging capacity, and charging energy; and a fourth determining submodule, used to determine current battery data based on at least one of coulombic efficiency, energy efficiency, and voltage characteristic parameters.

[0144] Optionally, in the battery life prediction device provided in the embodiments of this application, the third determining submodule further includes: a first calculation unit, used to calculate the ratio between charging energy and charging capacity to obtain a first ratio; a second calculation unit, used to calculate the ratio between discharging energy and discharging capacity to obtain a second ratio; and a third calculation unit, used to determine voltage characteristic parameters based on the difference between the first ratio and the second ratio.

[0145] Optionally, in the battery life prediction device provided in the embodiments of this application, the battery life prediction device further includes: a second determining module, used to determine a reference coulombic efficiency of the target battery based on the coulombic efficiency of the target battery in the first N charge-discharge cycles, where N is a positive integer; a third determining module, used to determine a reference energy efficiency of the target battery based on the energy efficiency of the target battery in the first N charge-discharge cycles; a fourth determining module, used to determine a reference voltage characteristic parameter of the target battery based on the voltage characteristic parameter of the target battery in the first N charge-discharge cycles; and a fifth determining module, used to determine reference battery data based on at least one of the reference coulombic efficiency, reference energy efficiency, and reference voltage characteristic parameter.

[0146] Optionally, in the battery life prediction device provided in this application embodiment, the first determining module further includes: a first calculation submodule, used to calculate the difference between the coulombic efficiency in the current battery data and the reference coulombic efficiency to obtain a first attenuation factor; a second calculation submodule, used to calculate the difference between the energy efficiency in the current battery data and the reference energy efficiency to obtain a second attenuation factor; a third calculation submodule, used to calculate the difference between the voltage characteristic parameter in the current battery data and the reference voltage characteristic parameter to obtain a target difference, and calculate the ratio between the target difference and the reference voltage characteristic parameter to obtain a parameter change factor; and a fifth determining submodule, used to determine a target correction factor based on the first attenuation factor, the second attenuation factor, and the parameter change factor.

[0147] Optionally, in the battery life prediction device provided in this application embodiment, the fifth determining submodule further includes: an acquisition unit, used to acquire a set of weighting coefficients, wherein the set of weighting coefficients includes: a first weighting coefficient corresponding to coulombic efficiency, a second weighting coefficient corresponding to energy efficiency, and a third weighting coefficient corresponding to voltage characteristic parameters; and a determining unit, used to determine a target correction factor based on the set of weighting coefficients, a first attenuation factor, a second attenuation factor, and a parameter change factor.

[0148] Optionally, in the battery life prediction device provided in the embodiments of this application, the processing module further includes: a sixth determining submodule, used to determine the attenuation coefficient in the initial mathematical model; an updating submodule, used to update the attenuation coefficient based on the target correction factor to obtain the target attenuation coefficient; and a replacing submodule, used to replace the attenuation coefficient in the initial mathematical model based on the target attenuation coefficient to obtain the target mathematical model.

[0149] It should be noted that the acquisition module 301, the first determination module 302, and the processing module 303 mentioned above correspond to steps S201 to S203 in Embodiment 1. The three modules and their corresponding steps implement the same instances and application scenarios, but are not limited to the content disclosed in Embodiment 1. It should be noted that the above modules or units can be hardware or software components stored in memory (e.g., memory 104) and processed by one or more processors (e.g., processors 102a, 102b, ..., 102n). The above modules can also be part of a device and run in the computer terminal 10 provided in Embodiment 1.

[0150] Example 3

[0151] Embodiments of this application may provide an electronic device. Figure 4 This is a structural block diagram of an electronic device according to an embodiment of this application. Figure 4 As shown, the electronic device may include: one or more ( Figure 4 (Only one is shown) Processor 1002, memory 1004, memory controller, and peripheral interface, wherein the peripheral interface is connected to the radio frequency module, audio module and display.

[0152] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the methods and apparatus in the embodiments of this application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby implementing the above-described methods. The memory may include high-speed random access memory, and may also include 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 memory remotely located relative to the processor, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0153] The processor can access information and applications stored in the memory via a transmission device to perform the following steps: acquire battery data of the target battery in the latest charge-discharge cycle to obtain current battery data, wherein the battery data includes at least one of the following: coulombic efficiency, energy efficiency, and voltage characteristic parameters; determine a target correction factor based on the current battery data and reference battery data of the target battery; correct the initial mathematical model based on the target correction factor to obtain a target mathematical model, and predict the lifetime of the target battery based on the target mathematical model to obtain a lifetime prediction result.

[0154] The processor can also invoke information and applications stored in the memory via a transmission device to perform the following steps: determining the coulombic efficiency based on the discharge capacity and charge capacity of the target battery in the latest charge-discharge cycle; determining the energy efficiency based on the discharge energy and charge energy of the target battery in the latest charge-discharge cycle; determining the voltage characteristic parameters based on the discharge capacity, discharge energy, charge capacity, and charge energy; and determining the current battery data based on at least one of the coulombic efficiency, energy efficiency, and voltage characteristic parameters.

[0155] The processor can also call the information and application programs stored in the memory through the transmission device to perform the following steps: calculate the ratio between charging energy and charging capacity to obtain a first ratio; calculate the ratio between discharging energy and discharging capacity to obtain a second ratio; and determine the voltage characteristic parameters based on the difference between the first ratio and the second ratio.

[0156] The processor can also invoke information and applications stored in the memory via a transmission device to perform the following steps: before determining the target correction factor based on the current battery data and the reference battery data of the target battery, determine the reference coulombic efficiency of the target battery based on the coulombic efficiency of the target battery in the first N charge-discharge cycles, where N is a positive integer; determine the reference energy efficiency of the target battery based on the energy efficiency of the target battery in the first N charge-discharge cycles; determine the reference voltage characteristic parameters of the target battery based on the voltage characteristic parameters of the target battery in the first N charge-discharge cycles; and determine the reference battery data based on at least one of the reference coulombic efficiency, reference energy efficiency, and reference voltage characteristic parameters.

[0157] The processor can also call the information and application programs stored in the memory through the transmission device to perform the following steps: calculate the difference between the coulombic efficiency in the current battery data and the reference coulombic efficiency to obtain a first attenuation factor; calculate the difference between the energy efficiency in the current battery data and the reference energy efficiency to obtain a second attenuation factor; calculate the difference between the voltage characteristic parameter in the current battery data and the reference voltage characteristic parameter to obtain a target difference, and calculate the ratio between the target difference and the reference voltage characteristic parameter to obtain a parameter change factor; determine a target correction factor based on the first attenuation factor, the second attenuation factor, and the parameter change factor.

[0158] The processor can also call the information and application program stored in the memory through the transmission device to perform the following steps: obtain a set of weighting coefficients, wherein the set of weighting coefficients includes: a first weighting coefficient corresponding to coulombic efficiency, a second weighting coefficient corresponding to energy efficiency, and a third weighting coefficient corresponding to voltage characteristic parameters; determine the target correction factor based on the set of weighting coefficients, the first attenuation factor, the second attenuation factor, and the parameter change factor.

[0159] The processor can also call the information and application programs stored in the memory through the transmission device to perform the following steps: determine the attenuation coefficient in the initial mathematical model; update the attenuation coefficient based on the target correction factor to obtain the target attenuation coefficient; replace the attenuation coefficient in the initial mathematical model with the target attenuation coefficient to obtain the target mathematical model.

[0160] In this embodiment, a target correction factor is determined based on the current battery data of the target battery in the latest charge-discharge cycle and the reference battery data of the target battery. This enables real-time monitoring and analysis of changes in key electrical performance parameters of the target battery during the cycle. By correcting the initial mathematical model based on the target correction factor, a target mathematical model is obtained to predict battery life. This achieves a closed-loop dynamic prediction mechanism that combines the mathematical model with the current state of the battery, thereby effectively improving the accuracy of the predicted battery life.

[0161] Therefore, the method provided in this application achieves the goal of dynamically determining a mathematical model based on the current state of the target battery to predict its lifespan, thereby improving the accuracy of the predicted battery lifespan and solving the technical problem that related technologies predict battery lifespan based on a fixed model, resulting in low accuracy of the predicted battery lifespan.

[0162] Those skilled in the art will understand that Figure 4 The structure shown is for illustrative purposes only. Electronic devices can also be smartphones, tablets, handheld computers, mobile internet devices (MIDs), PADs, and other terminal devices. Figure 4 This does not limit the structure of the aforementioned electronic device. For example, electronic devices may also include components that are more... Figure 4 The more or fewer components shown (such as network interfaces, display devices, etc.), or having the same Figure 4 The different configurations shown.

[0163] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0164] Example 4

[0165] Embodiments of this application also provide a storage medium. Optionally, in this embodiment, the storage medium can be used to store the program code executed by the battery life prediction method provided in Embodiment 1.

[0166] Optionally, in this embodiment, the storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0167] This application also provides a computer program product, which, when executed on a data processing device, is adapted to perform steps of a method for predicting battery life.

[0168] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0169] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0170] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0171] The units described as separate components may or may not be physically separate. 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 the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0172] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0173] If the integrated unit is implemented as 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 this application, in essence, 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. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0174] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method of predicting battery life, characterized by, The method comprises: obtaining battery data of a target battery in a latest charge-discharge cycle to obtain current battery data, wherein the battery data comprises at least one of coulomb efficiency, energy efficiency, and voltage characteristic parameter; determining a target correction factor based on the current battery data and reference battery data of the target battery; correcting an initial mathematical model based on the target correction factor to obtain a target mathematical model, and predicting the life of the target battery based on the target mathematical model to obtain a life prediction result.

2. The method of claim 1, wherein, The method comprises: determining the coulomb efficiency based on the discharge capacity and the charge capacity of the target battery in the latest charge-discharge cycle; determining the energy efficiency based on the discharge energy and the charge energy of the target battery in the latest charge-discharge cycle; determining the voltage characteristic parameter based on the discharge capacity, the discharge energy, the charge capacity, and the charge energy; determining the current battery data based on at least one of the coulomb efficiency, the energy efficiency, and the voltage characteristic parameter.

3. The method of claim 2, wherein, The method comprises: calculating a ratio between the charge energy and the charge capacity to obtain a first ratio; calculating a ratio between the discharge energy and the discharge capacity to obtain a second ratio; determining the voltage characteristic parameter based on a difference between the first ratio and the second ratio.

4. The method according to claim 1 or 2, characterized in that, Before the step of determining the target correction factor based on the current battery data and the reference battery data of the target battery, the method further comprises: determining a reference coulomb efficiency of the target battery based on the coulomb efficiency of the target battery in the previous N charge-discharge cycles, wherein N is a positive integer; determining a reference energy efficiency of the target battery based on the energy efficiency of the target battery in the previous N charge-discharge cycles; determining a reference voltage characteristic parameter of the target battery based on the voltage characteristic parameter of the target battery in the previous N charge-discharge cycles; determining the reference battery data based on at least one of the reference coulomb efficiency, the reference energy efficiency, and the reference voltage characteristic parameter.

5. The method of claim 4, wherein, In the case where the current battery data comprises the coulomb efficiency, the energy efficiency, and the voltage characteristic parameter, the step of determining the target correction factor based on the current battery data and the reference battery data of the target battery comprises: calculating a difference between the coulomb efficiency in the current battery data and the reference coulomb efficiency to obtain a first attenuation factor; calculating a difference between the energy efficiency in the current battery data and the reference energy efficiency to obtain a second attenuation factor; calculating a difference between the voltage characteristic parameter in the current battery data and the reference voltage characteristic parameter to obtain a target difference, and calculating a ratio between the target difference and the reference voltage characteristic parameter to obtain a parameter change factor; determining the target correction factor based on the first attenuation factor, the second attenuation factor and the parameter change factor.

6. The method of claim 5, wherein, The determining the target correction factor based on the first attenuation factor, the second attenuation factor and the parameter change factor comprises: obtaining a weight coefficient set, wherein the weight coefficient set comprises a first weight coefficient corresponding to the Coulomb efficiency, a second weight coefficient corresponding to the energy efficiency and a third weight coefficient corresponding to the voltage characteristic parameter; determining the target correction factor based on the weight coefficient set, the first attenuation factor, the second attenuation factor and the parameter change factor.

7. The method of claim 1, wherein, The correcting the initial mathematical model based on the target correction factor to obtain a target mathematical model comprises: determining an attenuation coefficient in the initial mathematical model; updating the attenuation coefficient based on the target correction factor to obtain a target attenuation coefficient; replacing the attenuation coefficient in the initial mathematical model with the target attenuation coefficient to obtain the target mathematical model.

8. A battery life prediction device, characterized by, comprises: an obtaining module configured to obtain battery data of a target battery in a latest charging and discharging cycle to obtain current battery data, wherein the battery data comprises at least one of the following: Coulomb efficiency, energy efficiency and voltage characteristic parameter; a first determining module configured to determine a target correction factor based on the current battery data and reference battery data of the target battery; a processing module configured to correct an initial mathematical model based on the target correction factor to obtain a target mathematical model, and predict the life of the target battery based on the target mathematical model to obtain a life prediction result.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium comprises a stored executable program, wherein the executable program controls the device where the computer readable storage medium is located to execute the battery life prediction method in any one of claims 1 to 7 when the executable program is running.

10. An electronic device, comprising: comprises: a memory storing an executable program; a processor configured to run the program, wherein the program executes the battery life prediction method in any one of claims 1 to 7 when the program is running.

Images