Battery dynamic capacity evaluation method and system oriented to on-line operation condition

Abstract

The invention provides an online operation condition-oriented battery dynamic capacity evaluation method and system. The method comprises the following steps of: completing a deduction model of internal resistance in a battery charging end period on the basis of battery aging experiment data; calculating the direct-current internal resistance of the battery charged to the upper limit voltage under the current charging cycle based on the actual working condition data; a dynamic capacity is calculated based on the thermodynamic capacity. According to the evaluation method and system, the internal resistance of the battery under different SOCs is collected through design experiments, trend extrapolation is achieved through a small sample learning technology, and the internal resistance of the battery after the battery is charged to the upper limit cut-off voltage is obtained. And then obtaining the difference between the dynamic capacity and the thermodynamic capacity by combining actual working condition data, and finally updating the dynamic capacity under the current working condition based on the thermodynamic capacity, thereby realizing the dynamic available residual capacity evaluation of the battery based on the operation working condition. According to the method, the applicability and the accuracy of battery capacity evaluation are remarkably improved, and the limitation that the performance change of the battery cannot be fully captured in a dynamic environment in a traditional method is overcome.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy storage batteries, and specifically, relates to a battery dynamic capacity evaluation method and system for online operating conditions, and more specifically, relates to a battery dynamic capacity evaluation method, system, electronic device and computer-readable storage medium for online operating conditions. Background Art

[0002] As the global demand for clean energy increases, batteries have been widely used as an important energy storage device. However, during long-term use, batteries will experience capacity decay and increased internal resistance, which directly affects their service life and safety. Therefore, accurate assessment of the available remaining capacity of the battery has become an important means to ensure the safety and reliability of the energy storage system. The available remaining capacity of a battery refers to the remaining power that the battery can provide in its current state, usually in milliampere hours (mAh) or watt-hours (Wh). It represents the electrical energy that the battery can output from the current charging state to full discharge.

[0003] The available remaining capacity can usually be further divided into thermodynamic capacity and kinetic capacity. Thermodynamic capacity refers to the total energy that the battery can release under ideal conditions. The kinetic capacity takes into account the energy loss in the actual working environment, such as the internal resistance of the battery during discharge, temperature effects, and chemical reaction rates. Therefore, the kinetic capacity is usually lower than the thermodynamic capacity, which describes the effective amount of electricity that the battery can provide under actual conditions of use. Compared with thermodynamic capacity, kinetic capacity can more accurately reflect the performance of the battery under specific working conditions and is more meaningful for reference in actual engineering.

[0004] At present, the estimation methods for the available capacity of batteries mainly revolve around thermodynamic capacity. The evaluation methods mainly include laboratory tests, physical models, and data-driven statistical models. However, there are relatively few studies on kinetic capacity, and there is a problem of insufficient in-depth exploration of the estimation methods and models for kinetic capacity. The existing estimation methods for the available capacity of batteries have the defect of not being able to fully capture the changes in battery performance in a dynamic environment. Summary of the invention

[0005] The purpose of the present invention is to provide a battery dynamic capacity evaluation method and system for online operating conditions, aiming to solve the technical problem that existing estimation methods cannot fully capture battery performance changes in a dynamic environment.

[0006] To achieve the above object, the technical solution adopted by the present invention is to provide a battery dynamic capacity evaluation method for online operation conditions, comprising:

[0007] Complete the deduction model of the battery internal resistance at the end of charging based on the battery aging experimental data;

[0008] Based on the actual working condition data, the DC internal resistance of the battery when it is charged to the upper limit voltage in the current charging cycle is estimated;

[0009] The kinetic capacity is calculated based on the thermodynamic capacity; wherein the calculation formula includes:

[0010]

[0011] Where U max is the upper cut-off voltage of the battery, I is the charging current at the moment before charging is completed, The DC internal resistance of the battery when it is charged to the upper voltage limit in the current cycle; C T is the thermodynamic capacity of the battery under the current cycle, is the kinetic capacity corresponding to the current I in the current cycle, It is the SOC corresponding to the upper limit cut-off voltage when the current I is charged.

[0012] Preferably, the deduction model of the internal resistance of the battery at the end of charging is completed based on the battery aging experimental data, comprising the following steps:

[0013] Perform battery aging experiments; measure the battery DC internal resistance corresponding to different battery charge states at different stages of battery aging;

[0014] Using small sample learning technology, a data relationship model between the battery's DC internal resistance and the battery's health status and battery's state of charge is constructed; based on the DC internal resistance sequence measured before the battery is charged to the upper limit cutoff point, the DC internal resistance of the battery when it is charged to the upper limit voltage is predicted.

[0015] Preferably, the battery aging experiment is performed; at different stages of battery aging, the battery DC internal resistance corresponding to the charge state of different batteries is measured, including:

[0016] Discharge the battery to the lower cut-off voltage. Then perform a charging test on the battery;

[0017] During the battery charging process, a constant current pulse discharge is applied every 4-9% state of charge point for 5-15 seconds;

[0018] After the charging pulse is finished, the battery is left to stand until the voltage reaches a steady state;

[0019] Record the changes in battery voltage and current; calculate the DC internal resistance at each state of charge point. The calculation formula for the DC internal resistance is:

[0020]

[0021] In the formula, ΔU i,kis the change in battery voltage after applying a pulse current at the kth SOC in the charging cycle corresponding to the ith SOH value (i.e., the difference between the voltage before the end of the pulse current and the steady-state voltage after the end of the pulse current), and I is the magnitude of the applied pulse current.

[0022] Preferably, the input of the small sample learning technology is the historical DC internal resistance sequence, the current battery health state value, and the battery state of charge range, and the output is the predicted DC internal resistance value under the given battery state of charge and the current battery health state value.

[0023] Preferably, the method of estimating the DC internal resistance of the battery when it is charged to the upper limit voltage in the current charging cycle based on the actual operating condition data comprises the following steps:

[0024] During the actual operation of the battery, the thermodynamic capacity corresponding to the current charging cycle and the health status of the battery are calculated;

[0025] Calculate the time series of the battery DC internal resistance changing with the battery state of charge during the current charging cycle;

[0026] The DC internal resistance of the battery when it is charged to the upper limit voltage in the current charging cycle is calculated based on the obtained DC internal resistance sequence and the DC internal resistance extrapolation model.

[0027] Preferably, the calculation of the kinetic capacity based on the thermodynamic capacity comprises the following steps:

[0028] Calculate the open circuit voltage when charging to the upper cut-off voltage at the current before the end of charging in the current cycle; the calculation formula is:

[0029]

[0030] Where U max is the upper cut-off voltage of the battery, I is the charging current at the moment before charging is completed, The DC internal resistance of the battery when it is charged to the upper limit voltage in the current cycle;

[0031] Based on the open circuit voltage and the state of charge-open circuit voltage curve, the state of charge of the battery corresponding to the upper cut-off voltage when the current I is charged in the current cycle is calculated and recorded as

[0032] Calculate the kinetic capacity corresponding to the current I in the current cycle using the following formula:

[0033]

[0034] In the formula, C T is the thermodynamic capacity of the battery under the current cycle, is the kinetic capacity corresponding to the current I in the current cycle, It is the SOC corresponding to charging to the upper cut-off voltage with current I.

[0035] A battery dynamic capacity evaluation system for online operating conditions, used to execute the steps in a battery dynamic capacity evaluation method for online operating conditions as described in any one of the above, comprising:

[0036] A deduction model acquisition unit, used to complete a deduction model of the internal resistance of the battery at the end of charging based on battery aging experimental data;

[0037] An internal resistance estimation unit, used to estimate the DC internal resistance of the battery when it is charged to the upper limit voltage in the current charging cycle based on actual operating condition data;

[0038] Capacity calculation unit, used to calculate kinetic capacity based on thermodynamic capacity.

[0039] An electronic device comprises a bus, a transceiver, a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory and the processor are connected via the bus, and wherein when the computer program is executed by the processor, the steps in a battery dynamic capacity evaluation method for online operating conditions as described in any one of the above items are implemented.

[0040] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps in a battery dynamic capacity evaluation method for online operating conditions as described in any one of the above are implemented.

[0041] The beneficial effect of a battery dynamic capacity evaluation method and assembly method for online operating conditions provided by the present invention is that: compared with the prior art, the battery dynamic capacity evaluation method for online operating conditions of the present invention collects the internal resistance of the battery at different SOCs by designing experiments, and then realizes trend extrapolation through small sample learning technology to obtain the internal resistance of the battery after charging to the upper limit cut-off voltage. Then, the difference between the dynamic capacity and the thermodynamic capacity is obtained in combination with the actual operating condition data, and finally the dynamic capacity under the current operating condition is updated based on the thermodynamic capacity to realize the evaluation of the available remaining capacity of the battery dynamics based on the operating condition. Highlighting the importance of its dynamic characteristics in actual application scenarios, it can more accurately reflect the performance of the battery under instantaneous load changes and environmental fluctuations. Unlike the traditional evaluation method that relies on thermodynamic capacity, the present invention can effectively consider the actual performance of the battery under complex conditions, especially the dynamic response under high load, rapid charging and discharging, and extreme environments. This method significantly improves the applicability and accuracy of battery capacity evaluation, and overcomes the limitation that traditional methods cannot fully capture battery performance changes in dynamic environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 A flowchart of a method for evaluating the available remaining capacity of a battery under online operating conditions according to the present invention;

[0044] Figure 2 The present invention is a structural block diagram of a battery dynamics available remaining capacity evaluation system for online operating conditions.

[0045] In the figure: 1. deduction model acquisition unit; 2. internal resistance calculation unit; 3. capacity calculation unit. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0047] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0048] In the following, in conjunction with the accompanying drawings, a battery dynamic capacity evaluation method for online operating conditions provided by an embodiment of the present application is described in detail through specific embodiments and application scenarios.

[0049] Reference Figure 1 and Figure 2 Now, a battery dynamic capacity evaluation method for online operation conditions provided by the present invention is described. The battery dynamic capacity evaluation method for online operation conditions comprises:

[0050] S1. Complete the deduction model of the battery internal resistance at the end of charging based on the battery aging experimental data;

[0051] In an optional embodiment of the present invention, step S1 may include the following sub-steps:

[0052] Sub-step S1.1, select a typical battery model commonly used in energy storage power stations for aging experiments. At different stages of battery aging (for example, the battery's state of health (SOH) is 100%, 95%, 90%, 85%, ...), measure the battery DC internal resistance corresponding to the state of charge (SOC) of different batteries.

[0053] In an optional embodiment of the present invention, typical battery models include but are not limited to: lithium-ion batteries, lead-acid batteries, flow batteries, and sodium-sulfur batteries.

[0054] Specific types of aging tests include, but are not limited to: cycle life tests, calendar life tests, accelerated aging tests, and aging tests under different environments.

[0055] SOH is calculated based on the thermodynamic capacity of the battery, which is the maximum amount of electricity that the battery can store and release.

[0056] In this embodiment, the method for testing the DC internal resistance of the battery includes the following steps:

[0057] The battery is discharged to the lower cut-off voltage, and then the battery is subjected to a Hybrid Pulse Power Characterization (HPPC) charging test.

[0058] During the charging process of the battery, a constant current pulse discharge (usually 3C of the rated capacity of the battery) is applied every 4-9% (preferably 5%) SOC point for 5-15 (preferably 10) seconds.

[0059] After the charging pulse ends, the battery is left to stand for a period of time (usually 300 seconds or longer) until the voltage reaches a steady state (i.e. the relaxation process ends).

[0060] Record the changes in battery voltage and current. Calculate the DC internal resistance at each SOC point. The calculation formula for DC internal resistance is:

[0061]

[0062] In the formula, ΔU i,kis the change in battery voltage after applying a pulse current at the kth SOC in the charging cycle corresponding to the ith SOH value (i.e., the difference between the voltage before the end of the pulse current and the steady-state voltage after the end of the pulse current), and I is the magnitude of the applied pulse current. The accuracy and reliability of the data are ensured by multiple measurements. Among them, the data specifically records the changes in the voltage and current of the battery. The calculation process of the thermodynamic capacity of the battery can be: SOH = actual capacity / nominal capacity. The actual capacity can be measured by charge and discharge tests: The specific method is: fully charge the battery. Discharge the battery with a certain discharge current until the battery voltage drops to the termination voltage (usually the cut-off voltage of the battery, such as 3.0V or 2.5V, etc.). Measure the total amount of electricity released by the battery during the discharge process, which is the actual capacity of the battery.

[0063] Step S1.2, construct a data relationship model between the battery DC internal resistance and the battery health state and the battery state of charge. Specifically, a small sample learning technology (such as a prototype network) is used to construct a data relationship model between the battery DC internal resistance and SOH and SOC, and the trend of the DC internal resistance data is extrapolated, so that the DC internal resistance of the battery when it is charged to the upper limit voltage can be predicted based on the DC internal resistance sequence measured before the battery is charged to the upper limit cutoff point under a charging cycle corresponding to a certain SOH.

[0064] More specifically, the input of the small sample learning technology is the historical DC internal resistance sequence, the current SOH value, and the SOC range, and the output is the predicted DC internal resistance value under given SOC and SOH conditions, especially the DC internal resistance when charged to the upper limit voltage.

[0065] Prototypical Networks achieve small sample learning by mapping data into an embedding space and classifying them according to the distance between the category center (prototype) and the sample. They use a classification loss function based on Euclidean distance to optimize the similarity between the support set samples and their corresponding category prototypes.

[0066] The classification loss function of Euclidean distance is often used in prototype networks. Its goal is to minimize the distance between the sample point and its category center (prototype) while maximizing the distance to other category prototypes. The classification loss function formula of Euclidean distance is:

[0067]

[0068] Among them, x i is the embedding representation of the sample point; c yi For the category y i Prototype center; c j is the prototype center of the jth class; d(x,c) is the Euclidean distance between the sample point x and the prototype center c, and the calculation formula is:

[0069]

[0070] Where K is the total number of categories and N is the total number of samples.

[0071] The specific implementation process of the data relationship model between the battery DC internal resistance and SOH, SOC is:

[0072] Data standardization: Standardize features such as internal resistance, SOC, and SOH so that they have a unified dimension and scale, which helps with model training.

[0073] Constructing embedding space: Use the prototype network to map SOH and SOC into a low-dimensional embedding space. For each sample (i.e. a pair of SOH and SOC), we will generate a corresponding embedding vector:

[0074] e SOH =f SOH (SOH)

[0075] e SOC =f SOH (SOC)

[0076] Among them, f SOH and f SOC They are the mapping functions learned through neural networks, mapping SOH and SOC to the embedding space.

[0077] Prototype calculation: For each SOH-SOC combination, by calculating its prototype, the representative internal resistance pattern of the combination can be obtained. Specifically, the goal of the prototype network is to calculate the internal resistance pattern prototype under each SOH-SOC state. Assume that we have N samples, and the SOH, SOC and internal resistance values ​​of each sample are The prototype network will learn the prototype DC internal resistance value of each SOH-SOC combination, which is calculated as follows:

[0078]

[0079] Among them, δ(SOH i ,SOH) and δ(SOC i ,SOC) is an indicator function that indicates whether a specific SOH and SOC state match.

[0080] S2. Based on the actual working condition data, the DC internal resistance of the battery when it is charged to the upper limit voltage in the current charging cycle is estimated;

[0081] In an optional embodiment of the present invention, step S2 may include the following sub-steps:

[0082] Step S2.1: During the actual operation of the battery, calculate the thermodynamic capacity C corresponding to the current charging cycle. T and SOH.

[0083] In this step, the kinetic capacity can be measured by charge and discharge test: The specific method is: fully charge the battery. Discharge the battery with a certain discharge current until the battery voltage drops to the termination voltage (usually the battery cut-off voltage, such as 3.0V or 2.5V, etc.). Measure the total amount of electricity released by the battery during the discharge process, which is the actual capacity of the battery.

[0084] SOH=actual capacity / nominal capacity

[0085] In this embodiment, the test method of the battery DC internal resistance includes the following steps: discharge the battery to the lower cut-off voltage. Then perform a Hybrid Pulse Power Characterization (HPPC) charging test on the battery. During the charging process of the battery, a constant current pulse discharge (usually 3C of the rated capacity of the battery) is applied every 4-9% (preferably 5%) SOC point for 5-15 (preferably 10) seconds. After the charging pulse ends, the battery is left to stand for a period of time (usually 300 seconds or longer) until the voltage reaches a steady state (i.e., the relaxation process ends). Record the voltage and current changes of the battery. Calculate the DC internal resistance at each SOC point, and the calculation formula of the DC internal resistance is:

[0086]

[0087] In the formula, ΔU i,k is the change in battery voltage after applying a pulse current at the kth SOC in the charging cycle corresponding to the i-th SOH value (i.e., the difference between the voltage before the end of the pulse current and the steady-state voltage after the end of the pulse current), and I is the magnitude of the applied pulse current. Multiple measurements are performed to ensure the accuracy and reliability of the data.

[0088] Step S2.2: Use the forgotten least squares method to calculate the time series of the battery DC internal resistance changing with SOC during the current charging cycle.

[0089] In this step, a linear model is used to describe the relationship between the DC internal resistance of the battery and the change of SOC:

[0090] R DC (t) = w T x(t)

[0091] Where: R DC (t) is the DC internal resistance of the battery at time t; w is the weight vector of the model, which contains parameters related to SOC; x(t) is the input feature vector, which is usually a combination of information such as the current SOC of the battery and the time step.

[0092] The forgotten least squares method recursively calculates model parameters and predictions through the following two key formulas:

[0093] Weight update formula

[0094] Assume that we have the model weight w(t-1) at time t-1. When the new data x(t) and the corresponding actual DC internal resistance R DC When (t) arrives, the weight vector can be updated by the following formula:

[0095] Forgetting factor: Set a forgetting factor λ, usually 0<λ≤1. When λ approaches 1, the model will rely more on historical data; when λ is smaller (such as 0.9 or lower), the model will pay more attention to new data.

[0096] Kalman gain vector: Calculate the gain vector k(t):

[0097]

[0098] Among them, P(t-1) is the covariance matrix at time t-1.

[0099] Update the covariance matrix:

[0100]

[0101] Update the weight vector:

[0102] w(t)=w(t-1)+k(t)(R DC (txt) T w(t-1))

[0103] Here, R DC (txt) T w(t-1) is the prediction error.

[0104] DC internal resistance prediction formula

[0105] Through the above update process, the model parameters w(t) are continuously optimized with the new data at each moment. As the charging process continues, the future internal resistance can be predicted by the model weights and the current SOC:

[0106] R DC (t+1)=w(t) T x(t+1)

[0107] Here, x(t+1) is the input feature vector at time t+1 (such as SOC, time step, etc.)

[0108] Calculation steps:

[0109] Initialization: Assume that at the initial time t = 0, the weight vector w(0) and the covariance matrix P(0) are known or initialized to some constant values. For example, P(0) can be initialized to the identity matrix.

[0110] first step:

[0111] a) Obtain the SOC data x(1) at time t=1 and the measured DC internal resistance R DC (1).

[0112] b) Calculate the gain vector k(1), update the covariance matrix P(1), and then update the weight vector w(1) based on the error.

[0113] c) Use the updated weight vector w(1) to predict the DC internal resistance at time t=2.

[0114] Subsequent steps: At each new time t+1, the model is updated using the same steps until the entire charging process is completed.

[0115] Step S2.3: Based on the DC internal resistance sequence obtained in step S2.1, the DC internal resistance extrapolation model obtained in step S1 is used to calculate the DC internal resistance of the battery when it is charged to the upper limit voltage in the current charging cycle, which is recorded as

[0116] S3. Calculate the kinetic capacity based on the thermodynamic capacity.

[0117] In an optional embodiment of the present invention, step S3 may include the following sub-steps:

[0118] Step S3.1, calculate the open circuit voltage when charging to the upper limit cut-off voltage at the current I before the end of charging in the current cycle The calculation formula is:

[0119]

[0120] Where U max is the upper cut-off voltage of the battery, I is the charging current at the moment before charging is completed, The DC internal resistance of the battery when it is charged to the upper limit voltage in the current cycle;

[0121] Step S3.2: Based on open circuit voltage And the state of charge-open circuit voltage curve (SOC-OCV curve), calculate the SOC corresponding to charging to the upper limit cut-off voltage at the current I in the current cycle, recorded as It is worth noting that It is calculated based on the thermodynamic capacity of the current cycle.

[0122]

[0123] In the formula, SOC t0 Based on the open circuit voltage and SOC-OCV curve, find the SOC at the open circuit voltage t0, C T is the thermodynamic capacity of the battery under the current cycle.

[0124] In this step, the measurement steps of the state of charge-open circuit voltage curve are as follows:

[0125] Battery charging and discharging preparation

[0126] Fully discharge: In order to obtain the full range SOC-OCV curve from 0 to 100%, the battery must first be fully discharged to ensure that the battery voltage drops to the lowest safe voltage (usually the battery cut-off voltage, such as 3.0V, 2.5V, etc., the specific value depends on the battery type and manufacturer specifications).

[0127] The battery voltage and current are recorded during the charge and discharge process.

[0128] Control the discharge current to be constant and ensure that the battery voltage remains within a safe range during the discharge process.

[0129] Fully charge: When charging, you also need to follow the maximum safe voltage of the battery (for example, for Li-ion batteries, it is usually 4.2V). Record the voltage and current changes during the charging process.

[0130] Open Circuit Voltage Measurement (OCV)

[0131] Static process: In order to accurately measure the open circuit voltage (OCV), the battery needs to be static for a period of time (usually 30 minutes to 1 hour) after each charge and discharge. This is done to ensure that the electrochemical reaction inside the battery reaches a stable state, so as to obtain an accurate open circuit voltage.

[0132] Measure OCV: After the battery is at rest, use a high-precision voltmeter to measure the open circuit voltage of the battery. Record the battery's OCV value and the corresponding SOC value.

[0133] Repeated measurement: During the charging and discharging process, record the OCV value at a certain SOC interval (for example, every 5% or every 10% SOC). Make sure that the distribution of sampling points is dense enough to fit an accurate SOC-OCV curve later.

[0134] SOC-OCV data recording

[0135] During the charge and discharge process, record the SOC and the corresponding open circuit voltage (OCV) value. During each charge or discharge process, the voltage needs to be measured after the battery is at rest.

[0136] o During discharge, OCV generally decreases linearly as SOC decreases.

[0137] oDuring the charging process, as the SOC increases, the OCV value also increases.

[0138] The recorded SOC value is usually calculated by integrating the charging or discharging current.

[0139] Step S3.3, calculate the kinetic capacity corresponding to the current I in the current cycle, and the calculation formula is:

[0140]

[0141] In the formula, C T is the thermodynamic capacity of the battery under the current cycle, is the kinetic capacity corresponding to the current I in the current cycle, It is the SOC corresponding to charging to the upper cut-off voltage with current I.

[0142] The present invention also provides a battery kinetic capacity evaluation method for online operating conditions. Compared with the prior art, the internal resistance of the battery at different SOCs is collected by designing experiments, and then the trend is extrapolated by a small sample learning technique to obtain the internal resistance of the battery after charging to the upper cut-off voltage. Then, the difference between the kinetic capacity and the thermodynamic capacity is obtained in combination with the actual operating condition data, and finally the kinetic capacity under the current operating condition is updated based on the thermodynamic capacity to realize the evaluation of the available remaining capacity of the battery kinetics based on the operating condition. The importance of its dynamic characteristics in actual application scenarios can be highlighted, and the performance of the battery under instantaneous load changes and environmental fluctuations can be more accurately reflected. Unlike traditional evaluation methods that rely on thermodynamic capacity, the present invention can effectively consider the actual performance of the battery under complex conditions, especially the dynamic response under high load, rapid charging and discharging, and extreme environments. This method significantly improves the applicability and accuracy of battery capacity evaluation, and overcomes the limitation that traditional methods cannot fully capture changes in battery performance in dynamic environments.

[0143] The present invention also provides a battery dynamic capacity evaluation system for online operation conditions, referring to Figure 1 and Figure 2 , used to execute the steps in a battery dynamic capacity evaluation method for online operating conditions as described in any one of the above, including: a deduction model acquisition unit 1, an internal resistance calculation unit 2 and a capacity calculation unit 3. The deduction model acquisition unit 1 is used to complete the deduction model of the internal resistance of the battery at the end of charging based on the battery aging test data; the internal resistance calculation unit 2 is used to calculate the DC internal resistance of the battery at the upper limit voltage under the current charging cycle based on the actual operating condition data; the capacity calculation unit 3 is used to calculate the dynamic capacity based on the thermodynamic capacity.

[0144] As for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0145] An embodiment of the present invention further provides an electronic device, comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, each process of the above-mentioned embodiment of a battery dynamic capacity evaluation method for online operating conditions is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0146] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, each process of the above-mentioned embodiment of a battery dynamic capacity evaluation method for online operating conditions is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0147] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0148] It will be appreciated by those skilled in the art that the embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the embodiments of the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the embodiments of the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0149] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0150] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0151] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0152] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0153] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0154] The above is a detailed introduction to a battery dynamic capacity evaluation method, system, electronic device and computer-readable storage medium for online operating conditions provided by the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A battery dynamic capacity evaluation method for online operating conditions, characterized in that: The following steps are involved: Complete the deduction model of the battery internal resistance at the end of charging based on the battery aging experimental data; Based on the actual working condition data, the DC internal resistance of the battery when it is charged to the upper limit voltage in the current charging cycle is estimated; The kinetic capacity is calculated based on the thermodynamic capacity; wherein the calculation formula includes: Where U max is the upper cut-off voltage of the battery, I is the charging current at the moment before charging is completed, The DC internal resistance of the battery when it is charged to the upper voltage limit in the current cycle; C T is the thermodynamic capacity of the battery under the current cycle, is the kinetic capacity corresponding to the current I in the current cycle, It is the SOC corresponding to the upper limit cut-off voltage when the current I is charged.

2. A battery dynamic capacity evaluation method for online operating conditions according to claim 1, characterized in that: The method of completing the deduction model of the internal resistance of the battery at the end of charging based on the battery aging experimental data includes the following steps: Perform battery aging experiments; measure the battery DC internal resistance corresponding to different battery charge states at different stages of battery aging; Using small sample learning technology, a data relationship model between the battery's DC internal resistance and the battery's health status and battery's state of charge is constructed; based on the DC internal resistance sequence measured before the battery is charged to the upper limit cut-off point, the DC internal resistance of the battery when it is charged to the upper limit voltage is predicted.

3. A battery dynamic capacity evaluation method for online operating conditions as claimed in claim 2, characterized in that: The battery aging experiment is performed on the battery; at different stages of battery aging, the battery DC internal resistance corresponding to different battery charge states is measured, including: Discharge the battery to the lower cut-off voltage; then perform a charging test on the battery; During the battery charging process, a constant current pulse discharge is applied every 4-9% state of charge point for 5-15 seconds; After the charging pulse is finished, the battery is left to stand until the voltage reaches a steady state; Record the changes in battery voltage and current; calculate the DC internal resistance at each state of charge point. The calculation formula for the DC internal resistance is: In the formula, ΔU i,k is the change in battery voltage after applying a pulse current at the kth SOC in the charging cycle corresponding to the ith SOH value (i.e., the difference between the voltage before the end of the pulse current and the steady-state voltage after the end of the pulse current), and I is the magnitude of the applied pulse current.

4. A battery dynamic capacity evaluation method for online operating conditions as claimed in claim 3, characterized in that: The input of the small sample learning technology is the historical DC internal resistance sequence, the current battery health state value, and the battery state of charge range. The output is the predicted DC internal resistance value under the given battery state of charge and the current battery health state value.

5. A battery dynamic capacity evaluation method for online operation conditions as claimed in claim 4, characterized in that: The method of estimating the DC internal resistance of the battery when it is charged to the upper limit voltage in the current charging cycle based on the actual working condition data includes the following steps: During the actual operation of the battery, the thermodynamic capacity corresponding to the current charging cycle and the health status of the battery are calculated; Calculate the time series of the battery DC internal resistance changing with the battery state of charge during the current charging cycle; The DC internal resistance of the battery when it is charged to the upper limit voltage in the current charging cycle is calculated based on the obtained DC internal resistance sequence and the DC internal resistance extrapolation model.

6. A battery dynamic capacity evaluation method for online operation conditions as claimed in claim 5, characterized in that: The method of calculating the kinetic capacity based on the thermodynamic capacity comprises the following steps: Calculate the open circuit voltage when charging to the upper cut-off voltage at the current before the end of charging in the current cycle; the calculation formula is: Where U max is the upper cut-off voltage of the battery, I is the charging current at the moment before charging is completed, The DC internal resistance of the battery when it is charged to the upper limit voltage in the current cycle; Based on the open circuit voltage and the state of charge-open circuit voltage curve, the state of charge of the battery corresponding to the upper cut-off voltage when the current I is charged in the current cycle is calculated and recorded as Calculate the kinetic capacity corresponding to the current I in the current cycle using the following formula: In the formula, C T is the thermodynamic capacity of the battery under the current cycle, is the kinetic capacity corresponding to the current I in the current cycle, It is the SOC corresponding to charging to the upper cut-off voltage with current I.

7. A battery dynamic capacity evaluation system for online operation conditions, used to execute the steps in a battery dynamic capacity evaluation method for online operation conditions as claimed in any one of claims 1 to 6, characterized in that: include: A deduction model acquisition unit, used to complete a deduction model of the internal resistance of the battery at the end of charging based on battery aging experimental data; An internal resistance estimation unit, used to estimate the DC internal resistance of the battery when it is charged to the upper limit voltage in the current charging cycle based on actual operating condition data; Capacity calculation unit, used to calculate kinetic capacity based on thermodynamic capacity.

8. An electronic device, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, wherein: When the computer program is executed by the processor, the steps in the battery dynamic capacity evaluation method for online operating conditions according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps in a battery dynamic capacity evaluation method for online operating conditions according to any one of claims 1 to 6 are implemented.

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