Methods, apparatus, electronic devices and storage media for detecting the internal resistance of lithium batteries

Abstract

This application relates to the field of lithium battery testing technology, and discloses a method, apparatus, electronic device, and storage medium for detecting the internal resistance of lithium batteries. The method for detecting the internal resistance of lithium batteries includes: performing pulse charge-discharge tests on the battery under test to obtain voltage response data of the battery under test under different states of charge; substituting the voltage response data into a pre-established equivalent circuit model of the lithium battery, and obtaining the internal resistance parameters in the equivalent circuit model of the lithium battery through parameter identification processing; and establishing a predictive model of the internal resistance with respect to the state of charge based on the internal resistance parameters obtained under different states of charge. This application uses software to process test data to replace the direct measurement of signals by physical hardware, thereby greatly simplifying the complexity of system wiring, integration, and maintenance at the system architecture level.

Description

Technical Field

[0001] This application relates to the field of lithium battery testing technology, specifically to lithium battery internal resistance testing methods, devices, electronic equipment, and storage media. Background Technology

[0002] With the application of large-scale energy storage systems based on lithium batteries, these systems have higher capacities and voltage levels. Consequently, the number and total number of batteries connected in series increase exponentially, making the safe charging and discharging management of each lithium battery more complex. The internal resistance of lithium batteries plays a crucial role in energy storage system capacity estimation, charging and discharging efficiency, operational safety, battery life, and thermal runaway.

[0003] In related technologies, real-time acquisition of the internal resistance of large-scale lithium batteries typically involves using dedicated hardware circuits to acquire the internal resistance of each lithium battery. However, this not only increases the complexity and maintenance difficulty of the hardware circuits but also leads to a significant increase in the cost of the hardware bill of materials (BOM). Summary of the Invention

[0004] This application provides a method, apparatus, electronic device, and storage medium for detecting the internal resistance of lithium batteries, in order to solve the problem of complex architecture in lithium battery internal resistance detection systems in related technologies.

[0005] In a first aspect, this application provides a method for detecting the internal resistance of a lithium battery, comprising: performing a pulse charge-discharge test on the battery under test to obtain voltage response data of the battery under test under different states of charge; substituting the voltage response data into a pre-established equivalent circuit model of a lithium battery, and obtaining internal resistance parameters in the equivalent circuit model of the lithium battery through parameter identification processing; and establishing a predictive model of internal resistance with respect to the state of charge based on the internal resistance parameters obtained under different states of charge.

[0006] Beneficial effects: This application replaces direct signal measurement by physical hardware with software processing of test data. First, it eliminates the need for configuring independent hardware circuits for each battery cell, thereby greatly simplifying the complexity of system wiring, integration, and maintenance at the system architecture level. Second, it removes potential failure points caused by a large number of discrete components, significantly improving the overall reliability of the large-scale battery pack internal resistance monitoring system. Finally, by processing all battery data through a unified mathematical model and algorithm, it avoids measurement inconsistencies caused by performance deviations in multi-channel hardware circuits. Thus, while ensuring high measurement accuracy, it achieves low-cost, high-reliability, and easily scalable online internal resistance detection, effectively solving the problems of system complexity, low reliability, and poor consistency of hardware solutions in large-scale applications.

[0007] In one optional implementation, the pulse charge-discharge test is a hybrid pulse power test, which includes the following process: after the battery is fully charged and left to stand for 12 hours, test cycles are sequentially executed at multiple preset state of charge points; after completing any one of the test cycles, the battery is discharged at a 1C rate for 6 minutes to reduce the battery's state of charge by 0.1, and then left to stand for 1 hour in preparation for entering the test cycle corresponding to the next state of charge point.

[0008] Beneficial effects: By "allowing the battery to stand still for a long time after full charge", all tests are ensured to begin when the battery voltage reaches the reference state of stable open circuit voltage, eliminating the interference of inconsistent initial states on the test results and providing a prerequisite for obtaining highly consistent voltage response data. By standardizing the procedure of "discharging for 6 minutes and standing still for 1 hour" after each test cycle, the state of charge (SOC) is accurately and controllably reduced by 0.1, and a series of discrete and stable test points covering the main operating range of the battery are constructed, laying a solid and systematic data foundation for the subsequent establishment of an accurate internal resistance prediction model strongly correlated with SOC.

[0009] In one alternative implementation, the test cycle performed at each state of charge point includes: discharging the battery with a 1C current for 10 seconds, followed by resting for 40 seconds; and charging the battery with a 0.75C current for 10 seconds.

[0010] Beneficial Effects: By employing a standardized hybrid pulse power test sequence of "1C discharge for 10 seconds – rest for 40 seconds – 0.75C charge for 10 seconds," the ohmic and electrochemical polarization responses of the battery can be simultaneously excited within a short time. This allows for the acquisition of a complete voltage response curve that simultaneously includes information on instantaneous voltage jumps and exponential gradual changes in a single test. The design of this specific timing and current parameters optimizes the balance between test efficiency and data information, ensuring that the key characteristic data required for subsequent parameter identification can be clearly and completely acquired. This is a crucial operational guarantee for achieving rapid and accurate extraction of internal resistance parameters.

[0011] In one optional implementation, the equivalent circuit model of the lithium battery adopts the Thevenin model, which includes a series ohmic internal resistance and a resistor-capacitor network consisting of a polarization resistor and a polarization capacitor connected in parallel.

[0012] Beneficial Effects: The Thevenin model uses a first-order RC network to equivalently describe the dynamic characteristics of the battery, which helps to achieve an optimal balance between model accuracy and computational complexity. The model has a simple structure and clear physical meaning, accurately characterizing the main dynamic response features of the battery under pulse excitation. Furthermore, the mathematical identification process of the model parameters is relatively simple, stable, and computationally inexpensive, making it highly suitable for online, real-time parameter identification and state estimation of large-scale battery packs. It provides an optimal mathematical model framework for the efficient engineering implementation of software algorithms.

[0013] In one optional implementation, obtaining the internal resistance parameter in the equivalent circuit model of the lithium battery through parameter identification processing includes: identifying the instantaneous voltage change segment caused by the step change of the charging and discharging current from the voltage response data, and calculating the ohmic internal resistance based on the voltage change amount of the instantaneous voltage change segment and the corresponding test current value.

[0014] Beneficial effects: By identifying the "instantaneous voltage change segment" directly caused by current step jumps in the voltage response data, and calculating the ohmic internal resistance based on the proportional relationship between the voltage change and the corresponding current value, this method achieves direct, rapid, and accurate decoupling of the battery's intrinsic ohmic internal resistance (determined by the properties of materials such as electrodes, electrolyte, and separator). This method avoids errors caused by hardware sampling delays and filtering circuits, directly extracting key features from the raw data using software algorithms. The calculation principle is clear and robust, which helps ensure the accuracy and real-time performance of the internal resistance measurement results.

[0015] In one optional implementation, obtaining the internal resistance parameters in the equivalent circuit model of the lithium battery through parameter identification processing further includes: identifying the exponentially gradual response segment of the voltage after a current step from the voltage response data, and determining the time constants of the polarization resistance and polarization capacitance based on the voltage change curve of the exponentially gradual response segment by curve fitting.

[0016] Beneficial Effects: By identifying the "gradual voltage exponential response segment" after a current step and performing curve fitting to determine the time constant of polarization parameters, effective characterization of the slow dynamic characteristics related to electrochemical reactions and ion diffusion kinetics, represented by the battery's polarization internal resistance and polarization capacitance, is achieved. Parameterizing the complex dynamic process as a time constant greatly simplifies the input dimensions of subsequent prediction models. Furthermore, replacing hardware circuitry with software curve fitting to track the dynamic process significantly improves the noise resistance of parameter extraction and the consistency across different battery batches, enabling the established prediction model to more accurately reflect battery aging and internal state changes.

[0017] In one optional implementation, establishing a predictive model of internal resistance with respect to the state of charge includes: calculating the internal resistance parameters at ten points with states of charge of 1.0, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, and 0.1, and then using the least squares method to perform polynomial fitting on the relationship between the state of charge and the internal resistance parameters to establish the predictive model.

[0018] Beneficial Effects: By uniformly and accurately selecting ten feature points across the entire main operating range of the battery (SOC 1.0 to 0.1) to obtain internal resistance parameters, and employing the least squares method for polynomial fitting, a continuous and smooth quantitative functional relationship between internal resistance parameters and state of charge is constructed. This predictive model can quickly and accurately interpolate or calculate the corresponding estimated internal resistance value based on the real-time estimated SOC value, thereby achieving online and continuous monitoring of the ohmic internal resistance of all battery cells without requiring any additional hardware or interrupting normal system operation.

[0019] Secondly, this application provides a lithium battery internal resistance detection device, comprising: a test control module for performing pulse charge-discharge tests on the battery under test to obtain voltage response data under different states of charge; a parameter identification module for substituting the voltage response data into a pre-established equivalent circuit model of a lithium battery and obtaining internal resistance parameters in the model through parameter identification processing; and a prediction model module for establishing a prediction model of internal resistance with respect to the state of charge based on the internal resistance parameters obtained under different states of charge.

[0020] Thirdly, this application provides an electronic device, including: a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to perform the lithium battery internal resistance detection method of the first aspect or any corresponding embodiment described above.

[0021] Fourthly, this application provides a computer-readable storage medium storing computer instructions for causing a computer to execute the lithium battery internal resistance detection method of the first aspect or any corresponding embodiment described above. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0023] Figure 1This is a schematic diagram illustrating an application scenario according to an embodiment of this application;

[0024] Figure 2 This is a schematic flowchart of the first method for detecting the internal resistance of a lithium battery according to an embodiment of this application; Figure 3 This is a test flowchart of a hybrid pulse power charge-discharge test experiment according to an embodiment of this application; Figure 4 This is a schematic diagram of the equivalent circuit model of a lithium battery according to an embodiment of this application; Figure 5 This is a current curve diagram of simulated hybrid pulse power charging and discharging according to an embodiment of this application; Figure 6 This is a voltage response curve of simulated hybrid pulse power charging and discharging according to an embodiment of this application; Figure 7 According to the embodiments of this application, Figure 5 A graph of a curve after it has been segmented. Figure 8 This is a structural block diagram of a lithium battery internal resistance detection device according to an embodiment of this application; Figure 9 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] It is understood that before using the technical solutions disclosed in the various embodiments of this application, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this application in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0027] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0028] As an optional application scenario of this application, the lithium battery internal resistance detection method provided in this application can be applied to various electronic devices and systems that require online, non-invasive monitoring and evaluation of the health and safety status of batteries (especially lithium batteries). (Refer to...) Figure 1 As shown, the system may include at least one Battery Management Unit (BMU) 101, at least one data aggregation or processing terminal 102, and optionally at least one server or cloud platform 103. Figure 1 The example shows that the system includes an in-cell controller for an electric vehicle battery pack, which serves as a battery management unit 101; a central monitoring computer for an energy storage power station, which serves as a data aggregation terminal 102; and a cloud platform server 103. The battery management unit 101 and the data aggregation terminal 102 are connected to the cloud platform server 103 via a network.

[0029] The battery management unit 101 is the hardware carrier that directly executes the internal resistance detection method of this application. Specifically, it can be an embedded microcontroller (MCU), digital signal processor (DSP), or application-specific integrated circuit (ASIC), integrated within the battery pack of an electric vehicle, the battery cluster of an energy storage system, or the battery module of a portable electronic device. The data aggregation or processing terminal 102 can be an in-vehicle central control computer, the host of an energy storage power station monitoring system, a laptop computer, or an industrial tablet computer, used to receive, display, analyze, or further process internal resistance data from one or more battery management units 101. The server or cloud platform 103 can be an independent physical server, a server cluster, or a distributed cloud server, used for historical analysis of large-scale battery data, model optimization, in-depth assessment of state of health (SOH), and early warning. The network can be a wired network (such as CAN bus, Ethernet) or a wireless network (such as 4G / 5G, Bluetooth, Wi-Fi), examples of which include, but are not limited to, in-vehicle networks, industrial local area networks, mobile communication networks, and combinations thereof.

[0030] In some embodiments, battery status monitoring application software is deployed in the data aggregation terminal 102 or the cloud platform server 103. Users or maintenance personnel can view real-time data, historical trends, health status reports, and early warning information of the battery internal resistance through the interactive interface of the application software.

[0031] For example, the interface of the application software may include, but is not limited to: a real-time data display page for displaying the real-time internal resistance value and its variation curve of each battery cell; a historical data analysis page for querying the evolution relationship of internal resistance over time and cycle count; and an early warning and reporting page for receiving and alerting on internal resistance anomalies and generating a battery health status assessment report. Figure 1In the application scenario shown, maintenance personnel can monitor the internal resistance consistency of all batteries in the station in real time through the application software interface on the data aggregation terminal 102 in the energy storage power station monitoring center, and promptly identify potential faulty batteries.

[0032] It should be noted that, Figure 1 This is merely an example of an application scenario and does not limit the scope of protection of this application. The lithium battery internal resistance detection method, apparatus, and equipment provided in this application are also applicable to, but not limited to: battery health management of consumer electronics products (such as mobile phones and laptops), life assessment of power tool battery packs, safety monitoring of drone batteries, and any scenario that uses lithium batteries and requires monitoring of their internal resistance.

[0033] The embodiments of this application will be described below with reference to the accompanying drawings. It should be understood that the system architecture, device connections, and software interfaces shown in the drawings are merely examples, and various designs may exist in actual deployments. The various components in the system may have different arrangements and connection methods, and one or more of the components may be omitted or replaced (for example, all processing is completed in the local battery management unit 101 without uploading to the cloud), and one or more other components may also exist (for example, additional data storage devices, gateway devices, etc.), which are not limited in any way in the embodiments of this application.

[0034] Currently, large-scale lithium battery internal resistance measurement uses dedicated hardware circuits to collect the internal resistance of each individual lithium battery. This not only increases the complexity and maintenance difficulty of the hardware circuits but also leads to a significant increase in hardware BOM material costs. This application proposes a lithium battery internal resistance detection method that enables online prediction of battery internal resistance in large-scale energy storage systems without increasing hardware costs. This method achieves relatively high-precision battery internal resistance detection at a low cost.

[0035] According to an embodiment of this application, a method for detecting the internal resistance of a lithium battery is 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. Furthermore, 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.

[0036] This embodiment provides a method for detecting the internal resistance of a lithium battery, which can be used in the aforementioned battery management unit, such as an embedded microcontroller, digital signal processor, or application-specific integrated circuit. Figure 2 This is a flowchart of a lithium battery internal resistance detection method according to an embodiment of this application, referred to... Figure 2 As shown, the process includes the following steps: Step S201: Perform pulse charge-discharge test on the battery under test to obtain voltage response data of the battery under test under different states of charge.

[0037] It should be noted that pulse charge-discharge testing refers to applying a series of short, controllable current excitation signals to the battery, which switch between charging and discharging states and include a rest (zero current) phase, in order to stimulate the battery's internal dynamic response. For example, a common test sequence may include a constant current discharge pulse, a rest period, followed by a constant current charging pulse.

[0038] State of Charge (SOC) is a key state parameter characterizing the percentage of a battery's remaining usable energy relative to its nominal total capacity, typically ranging from 0% to 100%. Different SOC points reflect different energy reserve stages of a battery as it progresses from fully charged to nearly fully discharged.

[0039] Voltage response data refers to the trajectory data of the battery's terminal voltage changing over time when subjected to the aforementioned pulsed current excitation. This data fully records the voltage jump caused by the instantaneous current step and the subsequent relaxation process.

[0040] In step S201, the battery undergoes a test process from high SOC to low SOC, pausing at several preset, representative SOC points and executing the same pulse test sequence. During each test, a high-precision sampling circuit synchronously captures the current excitation and the battery's voltage response, thereby obtaining a series of voltage-time curves corresponding one-to-one with each SOC point and containing rich dynamic information. These curves essentially reflect the comprehensive performance of various internal impedances of the battery (such as ohmic impedance and polarization impedance) under current excitation, providing raw data for subsequent parameter decoupling and extraction.

[0041] Step S202: Substitute the voltage response data into the pre-established lithium battery equivalent circuit model, and obtain the internal resistance parameters in the lithium battery equivalent circuit model through parameter identification processing.

[0042] It should be noted that the equivalent circuit model of a lithium battery is a circuit network used to simulate the external characteristics of a lithium battery. It approximates the complex electrochemical processes inside the battery by using ideal electrical components (such as resistors, capacitors, and voltage sources) and their combinations.

[0043] The goal of parameter identification processing is to find parameter values ​​(such as resistance and capacitance values) of components in a set of equivalent circuit models, such that the error between the calculated output voltage and the measured voltage response data in step S201 is minimized under the same input current excitation.

[0044] Furthermore, internal resistance parameters refer to resistive parameters identified from the equivalent circuit model that characterize the degree to which current flow is impeded within the battery. These typically include at least ohmic internal resistance, which characterizes the impedance of materials such as electrodes and electrolytes, and polarization internal resistance, which characterizes dynamic processes such as electrochemical polarization and concentration polarization.

[0045] In this step, for the voltage response data obtained at each SOC point, the algorithm compares it with the expected output of the equivalent circuit model. By repeatedly adjusting the values ​​of parameters such as resistance and capacitance in the model and calculating the difference between the model's predicted voltage and the measured voltage, it eventually converges to a set of optimal parameters. This set of parameters, especially the ohmic internal resistance and the parameter reflecting the time constant of the polarization process, is the internal resistance characteristic extracted from this test, which quantitatively describes the internal state of the battery at that SOC point.

[0046] Step S203: Based on the internal resistance parameters obtained under different charging states, establish a predictive model of internal resistance with respect to the charging state.

[0047] It should be noted that the prediction model is a function or mapping relationship used to describe the mathematical relationship between internal resistance parameters and the state of charge. Using this model, any given SOC value can be input, and the corresponding estimated internal resistance value will be predicted and output.

[0048] In step S203, a series of internal resistance parameter values ​​calculated at multiple discrete SOC points in step S202 are used as data samples. Using SOC as the independent variable and internal resistance parameters as the dependent variable, a series of scattered points are formed in a coordinate system. Then, an appropriate mathematical fitting method is used to find a continuous curve or function expression that best represents the distribution trend of these data points. This final functional relationship is the "prediction model of internal resistance with respect to state of charge". Subsequently, during normal battery use, the battery management unit only needs to estimate the current SOC of the battery in real time using conventional algorithms (such as the ampere-hour integration method combined with the open-circuit voltage method). It can then use this prediction model to instantly query or calculate the corresponding estimated internal resistance value under that state, thereby achieving online, continuous, and non-invasive monitoring of the battery's internal resistance. This provides crucial real-time input parameters for advanced functions such as battery health status assessment and thermal runaway early warning.

[0049] The lithium battery internal resistance detection method provided in this embodiment replaces the direct measurement of signals by physical hardware by processing test data through software. First, it eliminates the need to configure independent hardware circuits for each battery cell, thereby greatly simplifying the complexity of system wiring, integration, and maintenance at the system architecture level. Second, it removes potential failure points caused by a large number of discrete components, significantly improving the overall reliability of the large-scale battery pack internal resistance monitoring system. Finally, by processing all battery data through a unified mathematical model and algorithm, it avoids measurement inconsistencies caused by performance deviations in multi-channel hardware circuits. Thus, while ensuring high measurement accuracy, it achieves low-cost, high-reliability, and easily scalable online internal resistance detection, effectively solving the problems of system complexity, low reliability, and poor consistency of hardware solutions in large-scale applications.

[0050] This embodiment provides a method for detecting the internal resistance of a lithium battery, which can be used in the aforementioned battery management unit, such as an embedded microcontroller, digital signal processor, or application-specific integrated circuit. The method for detecting the internal resistance of a lithium battery includes the following steps: Step S301: Perform pulse charge-discharge test on the battery under test to obtain voltage response data of the battery under test under different states of charge.

[0051] Specifically, in step S301 above, the pulse charge-discharge test is a hybrid pulse power test, and the test includes the following process: Step S3011: After the battery is fully charged and left to stand for 12 hours, test cycles are executed sequentially at multiple preset state of charge points.

[0052] The test cycle performed at each state of charge includes: Step a1: Discharge the battery with a 1C current for 10 seconds, then let it stand for 40 seconds. Step a2: Charge the battery for 10 seconds using a 0.75C current rate.

[0053] By employing a standardized hybrid pulse power test sequence of "1C discharge for 10 seconds – rest for 40 seconds – 0.75C charge for 10 seconds" in steps a1 and a2 above, the ohmic polarization and electrochemical polarization responses of the battery can be simultaneously excited within a short time. This allows for the acquisition of a complete voltage response curve that simultaneously includes information on instantaneous voltage jumps and exponential gradual changes in a single test. The design of this specific timing and current parameters optimizes the balance between test efficiency and data volume, ensuring that the key characteristic data required for subsequent parameter identification can be clearly and completely acquired. This is a crucial operational guarantee for achieving rapid and accurate extraction of internal resistance parameters.

[0054] Step S3012: After completing any test cycle, discharge the battery at a 1C rate for 6 minutes to reduce the battery's state of charge by 0.1%, and let it rest for 1 hour in preparation for entering the next test cycle corresponding to the state of charge point.

[0055] In steps S3011 and S3012, the "long-term resting after full charge" ensures that all tests begin at the reference state where the battery voltage reaches a stable open-circuit voltage, eliminating the interference of inconsistent initial states on the test results and providing a prerequisite for obtaining highly consistent voltage response data. Through the standardized procedure of "discharging for 6 minutes and resting for 1 hour" after each test cycle, the state of charge (SOC) is accurately and controllably reduced by 0.1, and a series of discrete and stable test points covering the main operating range of the battery are constructed, laying a solid and systematic data foundation for the subsequent establishment of an accurate internal resistance prediction model strongly correlated with SOC.

[0056] In conjunction with the above steps, in some specific examples of this application, this application conducts hybrid pulse power charge-discharge tests on the PCS energy storage converter using an EMS energy manager. The test flowchart is shown below. Figure 3 As shown in the figure, after the battery is fully charged by constant current and constant voltage, it is left to stand still for 12 hours. The chemical reactions and thermal effects inside the battery gradually reach equilibrium. At this time, the battery voltage is equal to its open circuit voltage, and the battery voltage consistency of the entire battery pack is well maintained.

[0057] In some specific examples, the 1C discharge for 10 seconds, followed by a 40-second static rest and a 10-second 0.75% charge, primarily simulates mixed pulse power charge and discharge to obtain the battery voltage response curve. The 6-minute 1C discharge aims to reduce the State of Charge (SOC) by 0.1%, resulting in 10 SOC levels: 1, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, and 0.1. The 1-hour rest period following the 6-minute discharge also aims to allow the internal chemical reactions and thermal effects of the battery to gradually reach equilibrium, referring to... Figure 5 This represents the current curve for simulating hybrid pulse power charging and discharging. Figure 6 This represents the voltage response curve for simulated hybrid pulse power charging and discharging.

[0058] Step S302: Substitute the voltage response data into the pre-established lithium battery equivalent circuit model, and obtain the internal resistance parameters in the lithium battery equivalent circuit model through parameter identification processing.

[0059] Specifically, in step S302 above, the equivalent circuit model of the lithium battery adopts the Thevenin model. The Thevenin model includes a series ohmic internal resistance and a resistor-capacitor network consisting of a polarization resistor and a polarization capacitor connected in parallel.

[0060] The Thevenin model accurately represents the dynamic response of lithium batteries. The battery's ohmic internal resistance parameter represents the instantaneous change in voltage response during charging and discharging. The battery's excitation resistance and polarization capacitance reflect the gradual change in battery voltage during and after charging and discharging. Furthermore, the Thevenin model is simple in structure, has few parameters, low modeling equation complexity, and facilitates battery model parameter identification. It also serves as a reference for equivalent models. Figure 4 As shown. Among them, U oc Indicates the open-circuit voltage of the lithium battery. R o Indicates the ohmic resistance of a lithium battery. R p Indicates the polarization resistance of a lithium battery. C p Indicates the polarization capacitor of a lithium battery. U L This indicates the terminal voltage of the lithium battery.

[0061] The Thevenin model uses a first-order RC network to equivalently describe the dynamic characteristics of a battery, which helps to achieve an optimal balance between model accuracy and computational complexity. This model has a simple structure and clear physical meaning. It accurately characterizes the main dynamic response features of a battery under pulse excitation, while making the mathematical identification process of model parameters relatively simple, stable, and computationally inexpensive. It is highly suitable for online, real-time parameter identification and state estimation of large-scale battery packs, providing an optimal mathematical model framework for the efficient engineering implementation of software algorithms.

[0062] Optionally, in some embodiments of this application, the internal resistance parameters in the equivalent circuit model of the lithium battery are obtained through parameter identification processing, including: Step b1: Identify the instantaneous voltage change segment caused by the step change in charging and discharging current from the voltage response data, and calculate the ohmic internal resistance based on the voltage change amount of the instantaneous voltage change segment and the corresponding test current value.

[0063] In step b1, by identifying the "instantaneous voltage change segment" directly caused by current step jumps in the voltage response data, and calculating the ohmic internal resistance based on the proportional relationship between the voltage change and the corresponding current value, a direct, rapid, and accurate decoupling of the battery's intrinsic ohmic internal resistance (determined by the properties of materials such as electrodes, electrolyte, and separator) is achieved. This method avoids errors caused by hardware sampling delays and filtering circuits, directly extracting key features from the raw data using software algorithms. The calculation principle is clear and robust, which helps ensure the accuracy and real-time performance of the internal resistance measurement results.

[0064] Optionally, in some embodiments of this application, obtaining the internal resistance parameters in the equivalent circuit model of the lithium battery through parameter identification processing further includes: Step c1: Identify the exponentially gradual response segment of voltage after a current step from the voltage response data, and determine the time constants of polarization resistance and polarization capacitance based on the voltage change curve of the exponentially gradual response segment by curve fitting.

[0065] By identifying the "gradual voltage exponential response segment" after a current step and performing curve fitting to determine the time constant of polarization parameters, an effective characterization of the slow dynamic characteristics related to electrochemical reactions and ion diffusion kinetics, represented by the battery's polarization internal resistance and polarization capacitance, is achieved. Parameterizing the complex dynamic process as a time constant greatly simplifies the input dimensions of subsequent prediction models. Furthermore, replacing hardware circuitry with software curve fitting to track the dynamic process significantly improves the noise resistance of parameter extraction and its consistency across different battery batches, enabling the established prediction model to more accurately reflect battery aging and internal state changes.

[0066] Step S303: Based on the internal resistance parameters obtained under different charging states, establish a predictive model of internal resistance with respect to the charging state.

[0067] In step S303 above, a prediction model for the internal resistance with respect to the state of charge is established, including: calculating the internal resistance parameters at ten points with states of charge of 1.0, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, and 0.1 respectively, and then using the least squares method to perform polynomial fitting on the relationship between the state of charge and the internal resistance parameters to establish a prediction model.

[0068] In step S303, ten feature points are uniformly and accurately selected across the battery's main operating range (SOC 1.0 to 0.1) to obtain internal resistance parameters. A continuous and smooth quantitative functional relationship between the internal resistance parameters and the state of charge is constructed using the least squares method for polynomial fitting. This predictive model can quickly and accurately interpolate or calculate the corresponding estimated internal resistance value based on the real-time estimated SOC value, thereby achieving online and continuous monitoring of the ohmic internal resistance of all battery cells without requiring any additional hardware or interrupting normal system operation.

[0069] In conjunction with the steps above, in some more specific examples, refer to Figure 7 As shown, the voltage response curves of the simulated hybrid pulse power charge and discharge are analyzed after being segmented. The sudden voltage drop at point AB is due to the presence of discharge current, resulting in a voltage drop across the ohmic internal resistance of the lithium battery; points BC and DE are the zero-state response curves due to the polarization capacitance and polarization resistance of the lithium battery.

[0070] Define the pressure difference between A and B as Δ U ABThe voltage difference between CD is given, and the discharge current I during this stage is known. Therefore, according to Ohm's law, the internal resistance of the lithium battery can be calculated. R o :

[0071] The information obtained here R o The results were obtained with SOC=1. The same method can be used to calculate the remaining 9 points with SOC=0.1~0.9. R o The value of SOC is thus used as the independent variable. R o The relationship between the dependent and dependent variables is obtained through least squares polynomial fitting. R o Polynomials with SOC.

[0072] Since the voltage curve of BC is a zero-input response curve, according to Kirchhoff's voltage law, the relationship of the zero-state response curve of the battery terminal voltage can be obtained as follows:

[0073] The following expression is used for fitting:

[0074] Here a= U oc - IR 0, b= IR p c= τ , I Current and R o Given that this allows us to obtain the value corresponding to SOC=1. R p and C p The same method is used to calculate the remaining 9 points with SOC values ​​between 0.1 and 0.9. R p and C p The value is then obtained by least squares fitting. R p and C p The polynomials of SOC, respectively.

[0075] This embodiment also provides a lithium battery internal resistance detection device, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0076] This embodiment provides a lithium battery internal resistance detection device, referring to... Figure 8 As shown, it includes: The test control module 501 is used to perform pulse charge and discharge tests on the battery under test in order to obtain its voltage response data under different states of charge. The parameter identification module 502 is used to substitute the voltage response data into the pre-established equivalent circuit model of the lithium battery and obtain the internal resistance parameters in the model through parameter identification processing. The prediction model module 503 is used to establish a prediction model of the internal resistance with respect to the state of charge based on the internal resistance parameters obtained under different states of charge.

[0077] The lithium battery internal resistance detection device provided in this application can execute the lithium battery internal resistance detection method provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0078] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0079] The following is a detailed reference. Figure 9 This diagram illustrates a suitable structural schematic for implementing the electronic device described in the embodiments of this application. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 601, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 602 or a program loaded from memory 608 into random access memory (RAM) 603. The RAM 603 also stores various programs and data required for the operation of the electronic device. The processor 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0080] Typically, the following devices can be connected to I / O interface 605: input devices 606 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 607 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 608 including, for example, magnetic tapes, hard disks, etc.; and communication devices 609. Communication device 609 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 9 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0081] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 609, or installed from a memory 608, or installed from a ROM 602. When the computer program is executed by the processor 601, it performs the functions defined in the lithium battery internal resistance detection method of embodiments of this application.

[0082] Figure 9 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0083] This application also provides a computer-readable storage medium. The methods described in this application can be implemented in hardware or firmware, or implemented as recordable on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the lithium battery internal resistance detection method shown in the above embodiments is implemented.

[0084] A portion of this application can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to this application through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0085] Although embodiments of this application have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of this application, and all such modifications and variations fall within the scope defined by the appended claims.

Claims

1. A method for detecting the internal resistance of a lithium battery, characterized in that, include: A pulse charge-discharge test is performed on the battery under test to obtain voltage response data of the battery under test under different states of charge. The voltage response data is substituted into the pre-established equivalent circuit model of the lithium battery, and the internal resistance parameters in the equivalent circuit model of the lithium battery are obtained through parameter identification processing. Based on the internal resistance parameters obtained under different charging states, a predictive model for internal resistance with respect to the charging state is established.

2. The lithium battery internal resistance detection method according to claim 1, characterized in that, The pulse charge-discharge test is a hybrid pulse power test, and the test includes the following process: After the battery is fully charged and left to stand for 12 hours, test cycles are executed sequentially at multiple preset state of charge points. After completing any of the aforementioned test cycles, the battery is discharged at a 1C rate for 6 minutes to reduce its state of charge by 0.1%, and then left to rest for 1 hour in preparation for the next test cycle corresponding to the next state of charge point.

3. The lithium battery internal resistance detection method according to claim 2, characterized in that, The test cycle performed at each state of charge includes: Discharge the battery with a 1C rate current for 10 seconds, then let it stand for 40 seconds; Charge the battery for 10 seconds using a 0.75C current rate.

4. The lithium battery internal resistance detection method according to claim 1, characterized in that, The equivalent circuit model of the lithium battery adopts the Thevenin model, which includes a series ohmic internal resistance and a resistor-capacitor network consisting of a polarization resistor and a polarization capacitor connected in parallel.

5. The lithium battery internal resistance detection method according to claim 1, characterized in that, The process of obtaining the internal resistance parameters in the equivalent circuit model of the lithium battery through parameter identification includes: From the voltage response data, identify the instantaneous voltage change segment caused by the step change in charging and discharging current, and calculate the ohmic internal resistance based on the voltage change amount of the instantaneous voltage change segment and the corresponding test current value.

6. The lithium battery internal resistance detection method according to claim 6, characterized in that, The step of obtaining the internal resistance parameters in the equivalent circuit model of the lithium battery through parameter identification processing also includes: From the voltage response data, the exponentially gradual response segment of the voltage after the current step is identified, and based on the voltage change curve of the exponentially gradual response segment, the time constants of the polarization resistor and polarization capacitor are determined by curve fitting.

7. The lithium battery internal resistance detection method according to claim 1, characterized in that, The establishment of the predictive model for internal resistance with respect to the state of charge includes: After calculating the internal resistance parameters at ten points with states of charge of 1.0, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, and 0.1, the relationship between the state of charge and the internal resistance parameters is fitted using the least squares method to establish the prediction model.

8. A lithium battery internal resistance detection device, characterized in that, include: The test control module is used to perform pulse charge and discharge tests on the battery under test in order to obtain its voltage response data under different states of charge. The parameter identification module is used to substitute the voltage response data into a pre-established equivalent circuit model of a lithium battery, and obtain the internal resistance parameter in the model through parameter identification processing. The prediction model module is used to establish a prediction model of the internal resistance with respect to the state of charge based on the internal resistance parameters obtained under different states of charge.

9. An electronic device, characterized in that, include: A memory and a processor are interconnected, the memory storing computer instructions, and the processor executing the computer instructions to perform the lithium battery internal resistance detection method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the lithium battery internal resistance detection method according to any one of claims 1 to 7.

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