A method for online measurement of the internal resistance of a single battery cell

Abstract

This invention discloses an online measurement method for the internal resistance of a single-cell battery, relating to the field of battery testing technology. The method includes the following steps: S1, establishing a dynamic load disturbance model of the battery; S2, extracting effective voltage drop component data from the noise signal using an adaptive filtering method; S3, calculating the equivalent internal resistance using a complex impedance calculation method; S4, performing multi-scale internal resistance evolution analysis based on the calculated equivalent internal resistance; and S5, providing fault warnings for internal resistance anomalies based on the multi-scale internal resistance evolution analysis. This online measurement method for the internal resistance of a single-cell battery achieves high-precision online measurement of the internal resistance of a single-cell battery by constructing a dynamic load disturbance model, performing multi-scale internal resistance evolution analysis, and providing fault warnings based on internal resistance. Compared with existing technologies, this method can accurately capture the impact of load changes on internal resistance, provide early warnings of internal resistance anomalies using multi-scale internal resistance evolution analysis, and enhance health management capabilities by combining multi-scale analysis.

Description

Technical Field

[0001] This invention relates to the field of battery testing technology, specifically to an online method for measuring the internal resistance of a single-cell battery. Background Technology

[0002] The internal resistance of a single battery cell is a crucial parameter for assessing its health, affecting its charge-discharge performance, cycle life, and safety. Existing methods for measuring internal resistance mainly include the AC impedance method, the DC pulse method, and the open-circuit voltage method. Among these, the AC impedance method is widely used in laboratory environments due to its ability to provide multi-frequency impedance information, but its measurement equipment is complex and difficult to implement online. While the DC pulse method can be used for online monitoring, it is significantly affected by load fluctuations, resulting in insufficient measurement accuracy. The open-circuit voltage method relies on the battery's static state and is unsuitable for dynamic operating conditions.

[0003] In addition, existing measurement methods generally have the following shortcomings:

[0004] Traditional measurement methods are mostly based on steady-state or short-time dynamic analysis, ignoring the nonlinear impedance changes caused by load disturbances, making it difficult to accurately assess the true operating state of the battery.

[0005] Lack of modeling for the evolution trend of internal resistance: Existing methods usually focus on instantaneous impedance values ​​and lack the ability to predict long-term impedance changes, making it difficult to manage health status. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides an online method for measuring the internal resistance of a single battery cell, thereby solving the problems mentioned in the background section.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] In a first aspect, embodiments of the present invention provide a method for online measurement of the internal resistance of a single battery cell, comprising the following steps:

[0009] S1. Establish a dynamic load disturbance model for the battery to generate measurable voltage response data under load disturbance.

[0010] S2. Using the voltage response data generated by load disturbance, an adaptive filtering method is used to extract effective voltage drop component data from the noise signal.

[0011] S3. Based on the voltage drop component data, the equivalent internal resistance is calculated using the complex impedance calculation method.

[0012] S4. Based on the calculated equivalent internal resistance, perform multi-scale internal resistance evolution analysis;

[0013] S5. Based on multi-scale internal resistance evolution analysis, fault early warning is provided for internal resistance anomalies.

[0014] To further optimize this technical solution, in step S1, the dynamic load disturbance model includes a load disturbance current model and a load response voltage model;

[0015] The load disturbance current model sets load disturbance signals of different frequencies and amplitudes to simulate the current fluctuation of the battery under real operating conditions.

[0016] The load response voltage model is used to describe the change of battery voltage with load disturbance current.

[0017] To further optimize this technical solution, the load disturbance current model defines the disturbance current. as follows:

[0018] ;

[0019] in,

[0020] It is a time variable;

[0021] The reference operating current is the normal load current of the battery.

[0022] For the first The amplitude of each disturbance component;

[0023] For the first The frequency of each disturbance component;

[0024] For the first The phase of each disturbance component;

[0025] The number of disturbance components;

[0026] The load response voltage model is constructed based on the equivalent circuit of the disturbance current, and its voltage response data... Represented as:

[0027] ;

[0028] in,

[0029] This is the open-circuit voltage;

[0030] The reference internal resistance is ohms;

[0031] Polarization impedance;

[0032] It is an equivalent double-layer capacitor;

[0033] This represents the number of polarization elements.

[0034] To further optimize this technical solution, in step S2, the voltage response data obtained in step S1 is... Voltage response data for different time windows are obtained by windowing extraction based on the moment the disturbance current is applied. Furthermore, an adaptive filtering method was used to remove interference signals and extract voltage drop component data.

[0035] To further optimize this technical solution, in step S3, the method for calculating the complex impedance includes:

[0036] Voltage drop component data is acquired and Fourier transform is performed to convert the time-domain signal into a frequency-domain signal. Obtain the corresponding frequency voltage components ;

[0037] based on Obtain the corresponding frequency Current frequency components ;

[0038] Calculate the corresponding frequency Complex impedance under :

[0039] ;

[0040] The complex impedance at a fixed frequency is selected as the equivalent internal resistance.

[0041] To further optimize this technical solution, step S4, the multi-scale internal resistance evolution analysis, includes:

[0042] Construct a time-scale model of internal resistance variation;

[0043] Trend prediction is based on the evolution equation of internal resistance over time;

[0044] Identify abnormal changes in internal resistance and conduct a health assessment.

[0045] To further optimize this technical solution, the internal resistance variation model is based on the law of battery internal resistance variation over time, and applies it to different time points. Measured Analysis:

[0046] ;

[0047] in, For a moment of ; For a moment of ; For a moment of ;

[0048] Calculate different time intervals Internal resistance trend:

[0049] ;

[0050] in, This represents the change in internal resistance over adjacent time intervals. For phase difference The internal resistance value of the battery at that time.

[0051] To further optimize this technical solution, the evolution equation based on internal resistance over time is designed based on an exponential fitting or autoregressive model, and the evolution equation is as follows:

[0052] ;

[0053] in, For at any time The predicted internal resistance value; The initial internal resistance value at the reference time; This represents the rate of increase in internal resistance. This is the time decay factor.

[0054] To further optimize this technical solution, the health assessment is combined with... Based on the changing trend of the health status index (SOH), the health assessment model is as follows:

[0055] ;

[0056] in, It is the initial internal resistance. It sets a threshold.

[0057] To further optimize this technical solution, in step S5, during fault warning, the dynamic disturbance coefficient and internal impedance deviation are used to determine the internal resistance anomaly.

[0058] The dynamic disturbance coefficient measures the deviation between the internal resistance and the predicted internal resistance and takes the average value. It is used to measure the disturbance of the internal resistance over time. An abnormal increase in the dynamic disturbance coefficient indicates that the internal resistance disturbance is outside the normal range and there may be an abnormal situation.

[0059] Internal impedance deviation is calculated by measuring the relative rate of change of the current internal resistance with respect to the reference internal resistance. If the internal impedance deviation exceeds a set threshold, it indicates an abnormal impedance change, which may indicate a battery malfunction.

[0060] In a second aspect, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, and the computer program instructions, when executed by the processor, implement the steps of an online measurement method for the internal resistance of a single-cell battery as described in the first aspect of the present invention.

[0061] Thirdly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, they implement the steps of an online measurement method for the internal resistance of a single-cell battery as described in the first aspect of the present invention.

[0062] Compared with existing technologies, this invention provides an online method for measuring the internal resistance of a single battery cell, involving fault prediction and health management technology, and has the following beneficial effects:

[0063] This method for online measurement of the internal resistance of a single battery cell achieves high-precision online measurement of the battery's internal resistance by constructing a dynamic load disturbance model, performing multi-scale internal resistance evolution analysis, and implementing fault early warning based on internal resistance. Compared with existing technologies, this method can accurately capture the impact of load changes on internal resistance, provide early warning of internal resistance anomalies using multi-scale internal resistance evolution analysis, and enhance health management capabilities by combining multi-scale analysis. Furthermore, this method has low computational complexity, is suitable for long-term online monitoring, and can effectively improve the safety and reliability of the battery system. Attached Figure Description

[0064] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0065] Figure 1 This is a flowchart illustrating an online method for measuring the internal resistance of a single-cell battery proposed in this invention.

[0066] Figure 2 This is a schematic diagram of the multi-scale internal resistance evolution analysis in the online measurement method of single-cell battery internal resistance proposed in this invention. Detailed Implementation

[0067] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0068] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0069] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0070] Example 1:

[0071] Reference Figures 1-2 This is the first embodiment of the present invention, which provides a method for online measurement of the internal resistance of a single battery cell, including the following steps:

[0072] S1. Establish a dynamic load disturbance model for the battery to generate measurable voltage response data under load disturbance.

[0073] Traditional steady-state measurement methods for single-cell battery internal resistance measurement suffer from significant errors. Therefore, a dynamic load disturbance model is needed to measure the battery's internal resistance under operating conditions. The core idea of ​​this step is to utilize the actual operating load characteristics of the battery to design a dynamic disturbance signal. This signal induces measurable voltage fluctuations in the battery under varying external load conditions, simulating current fluctuations in real-world applications. Different frequencies and amplitudes of load disturbance signals are set to simulate these fluctuations. By controlling load changes, the current signal is made to exhibit step, pulse, or random variation patterns, and the corresponding voltage responses are recorded.

[0074] The dynamic load disturbance model includes a load disturbance current model and a load response voltage model:

[0075] The load disturbance current model sets load disturbance signals of different frequencies and amplitudes to simulate the current fluctuation of the battery under real operating conditions.

[0076] The load response voltage model is used to describe the change of battery voltage with load disturbance current.

[0077] In this embodiment, the dynamic load disturbance signal needs to have multi-frequency characteristics to simulate the complex current input of the battery under real operating conditions. Multi-frequency characteristics refer to the response capability of a signal or system at different frequency components, and are typically used to describe the behavioral changes of a system at different frequencies. For example, in battery internal resistance measurement, the load disturbance signal uses multiple frequency components to simulate the dynamic response of the battery under different operating conditions, thereby obtaining its comprehensive impedance characteristics. In the load disturbance current model, the disturbance current is defined. as follows:

[0078] ;

[0079] in,

[0080] It is a time variable;

[0081] The reference operating current is the normal load current of the battery.

[0082] For the first The amplitude of each disturbance component is used to adjust the intensity of components at different frequencies;

[0083] For the first The frequency of each disturbance component is selected within the range that the battery can respond to;

[0084] For the first The phase of each disturbance component is used to avoid the simultaneous superposition of all disturbance signals, which would lead to unreal current transients.

[0085] The number of disturbance components is usually chosen to ensure signal complexity without excessively interfering with the system.

[0086] The load response voltage model is constructed based on the equivalent circuit of the disturbance current, and its voltage response data... Represented as:

[0087] ;

[0088] in,

[0089] This is the open-circuit voltage;

[0090] The reference internal resistance is ohms;

[0091] It is a polarization impedance, mainly affected by chemical reactions;

[0092] It is an equivalent double-layer capacitor used to describe the dynamic changes in polarization phenomena;

[0093] This represents the number of polarization elements.

[0094] This model describes how the battery terminal voltage (i.e., voltage response data) changes with the load disturbance current, and includes the dynamic response characteristics of the battery.

[0095] In this step, by constructing the above mathematical model, the load disturbance signal of the battery is made to meet the actual measurement requirements.

[0096] First, set the reference operating current. This ensures that it conforms to the actual operating conditions of the battery.

[0097] Secondly, select an appropriate perturbation frequency range. to This ensures that the applied signal can effectively stimulate the battery's internal resistance characteristics.

[0098] Finally, calculate the disturbance current. And apply it to the battery load.

[0099] S2. Using the voltage response data generated by load disturbance, an adaptive filtering method is used to extract the effective voltage drop component data from the noise signal.

[0100] The voltage response data obtained in step S1 Voltage response data for different time windows are obtained by windowing extraction based on the moment the disturbance current is applied. In addition, an adaptive filtering method is used to remove interference signals and extract voltage drop component data.

[0101] The adaptive filtering method combines wavelet transform and adaptive Wiener filtering. It uses a multi-scale decomposition approach to divide the acquired voltage signal into different frequency bands and applies different filtering strategies to each band. Wavelet transform separates the low-frequency trend term and high-frequency interference term, while adaptive Wiener filtering optimizes signal processing by estimating the noise power spectrum, thereby extracting the voltage drop component data caused by load disturbances. Compared to traditional low-pass filtering and moving average filtering, this method has stronger anti-interference capabilities and adaptability, dynamically adjusting filtering parameters under different operating conditions to improve the accuracy of voltage drop measurement.

[0102] S3. Based on the voltage drop component data, the equivalent internal resistance is calculated using the complex impedance calculation method.

[0103] Methods for calculating complex impedance include:

[0104] Voltage drop component data is acquired and Fourier transform is performed to convert the time-domain signal into a frequency-domain signal. Obtain the corresponding frequency voltage components ;

[0105] based on Obtain the corresponding frequency Current frequency components The corresponding current frequency component can also be separated from the voltage component.

[0106] Calculate the corresponding frequency Complex impedance under :

[0107] ;

[0108] The complex impedance at a fixed frequency is selected as the equivalent internal resistance. The fixed frequency refers to the frequency used to extract key impedance parameters in battery internal resistance measurement, such as a frequency point in the range of several hundred hertz. Usually, a frequency that can effectively reduce the influence of polarization effect and improve measurement stability is selected as the benchmark for calculating the equivalent internal resistance.

[0109] S4. Based on the calculated equivalent internal resistance, perform multi-scale internal resistance evolution analysis.

[0110] Multi-scale internal resistance evolution analysis includes:

[0111] Construct a time-scale model of internal resistance variation;

[0112] The internal resistance variation model is based on the law of battery internal resistance variation over time, and applies it to different time points. Measured Analysis:

[0113] ;

[0114] in, For a moment of ; For a moment of ; For a moment of ;

[0115] Calculate different time intervals Internal resistance trend:

[0116] ;

[0117] in, This represents the change in internal resistance over adjacent time intervals. For phase difference The internal resistance value of the battery at that time.

[0118] like And continued growth may indicate a decline in battery performance.

[0119] like Oscillations may indicate temperature changes or abnormal operating conditions.

[0120] Trend prediction is based on the evolution equation of internal resistance over time;

[0121] The evolution equation based on internal resistance over time is designed based on an exponential fitting or autoregressive model, and the evolution equation is as follows:

[0122] ;

[0123] in, For at any time The predicted internal resistance value is obtained by fitting historical internal resistance data to predict the internal resistance value at future moments in order to assess the health status of the battery. The initial internal resistance value at the reference time; The rate of increase of internal resistance determines the trend of internal resistance change, which can be observed by... The value is obtained by performing exponential fitting or autoregressive analysis. It is determined by the impedance change trend and is used to predict the future impedance evolution. The time decay factor characterizes the stability or nonlinear effect of internal resistance growth, and is also expressed through... Obtained by nonlinear regression fitting.

[0124] The values ​​of both, once predicted, can be used as indicators to measure the battery's health status.

[0125] like This indicates that the internal resistance is continuously increasing, and the battery may need maintenance or replacement.

[0126] like An excessively high value indicates rapid degradation, suggesting the battery may be nearing failure.

[0127] It analyzes the internal resistance characteristics at different frequencies and considers the evolution of internal resistance over time, thus more accurately describing the battery aging process.

[0128] Identify abnormal changes in internal resistance and conduct health assessments;

[0129] During the aforementioned health assessment, in conjunction with The trend of impedance change is analyzed to determine the rate of impedance growth and the degree of impedance decay over time. Based on the current impedance data, possible future impedance changes are predicted and compared with historical trends to determine whether the impedance is growing normally or fluctuating abnormally. Based on the predicted impedance changes, if... If the SOH continues to grow rapidly, it needs to be corrected based on the predicted impedance to reflect the degree of degradation in advance. If If the change slows down, the SOH assessment results can be calculated using the same method, but future trend changes need to be monitored. If abnormal mutations occur, anomaly detection needs to be incorporated into the SOH calculation to avoid misleading health assessments by relying solely on SOH calculations.

[0130] The health status index (SOH) is calculated using the following health assessment model:

[0131] ;

[0132] in, It is the initial internal resistance. It is about setting a threshold;

[0133] SOH≥0.8: Healthy condition, battery performance is good, no replacement required.

[0134] 0.5≤SOH<0.8: Sub-healthy state, battery performance declines, but can still be used, but requires regular monitoring.

[0135] 0.3≤SOH<0.5: Critical state, battery performance is severely degraded, replacement or maintenance measures are recommended.

[0136] SOH < 0.3: Fault condition. The battery should be replaced immediately to avoid affecting system safety.

[0137] By comparing the current internal resistance with the initial internal resistance, the health status of the battery is determined and maintenance suggestions are provided.

[0138] S5. Based on multi-scale internal resistance evolution analysis, fault early warning is provided for internal resistance anomalies.

[0139] During fault warning, the dynamic disturbance coefficient and internal impedance deviation are used to determine the internal resistance anomaly.

[0140] The dynamic disturbance coefficient measures the deviation between the internal resistance and the predicted internal resistance and takes the average value. It is used to measure the disturbance of the internal resistance over time. An abnormal increase in the dynamic disturbance coefficient indicates that the internal resistance disturbance is outside the normal range and there may be an abnormal situation.

[0141] Internal impedance deviation is calculated by measuring the relative rate of change of the current internal resistance with respect to the reference internal resistance. If the internal impedance deviation exceeds a set threshold, it indicates an abnormal change in internal resistance, which may indicate a battery malfunction.

[0142] Example 2:

[0143] Based on the method described in Embodiment 1, this embodiment provides a calculation model for the dynamic disturbance coefficient and the internal impedance deviation when performing fault early warning for abnormal internal resistance.

[0144] Using the parameters obtained in steps S1-S4, the dynamic disturbance coefficient is calculated. The calculation model is shown below:

[0145] ;

[0146] in, The dynamic disturbance coefficient represents the current time. The degree of abnormality in internal resistance under load disturbance; This represents the number of frequency sampling points. The deviation between the measured impedance and the predicted internal resistance is averaged to measure the fluctuation of the internal resistance over time. If... An abnormally large increase indicates that the internal resistance disturbance exceeds the normal range (the normal range can be defined as "the average deviation over a period of time plus the standard deviation of normal fluctuations"; exceeding this range is considered an abnormal increase), suggesting a possible abnormal situation. In other words:

[0147] Short-term abnormal deviation: The deviation between the measured impedance value and the predicted value increases sharply in a short period of time, indicating that the measured value deviates from the predicted trend. This may indicate that the battery has been subjected to sudden impact, such as overload or accelerated short-term aging.

[0148] Long-term abnormal increase: The cumulative trend of the deviation between the measured impedance value and the predicted value continues to rise and exceeds the set long-term deviation threshold, indicating that the actual impedance of the battery is increasing much faster than the predicted trend, and may be in an accelerated degradation stage.

[0149] Utilize Combined with the reference internal resistance value Calculate the deviation of internal impedance:

[0150] ;

[0151] in, The internal resistance deviation indicates the degree of change of the current internal resistance value relative to the reference internal resistance.

[0152] Set a threshold for internal impedance deviation, when If the threshold is exceeded, an anomaly is considered to exist at that moment, triggering a fault warning.

[0153] If the deviation of internal resistance shows a significant upward trend but does not exceed the set threshold, it is marked as "warning" to indicate that there may be an early abnormality.

[0154] If the internal resistance deviation shows a significant increasing trend and exceeds the set threshold, it is marked as "abnormal," indicating that the battery may have entered a fault or degradation stage and requires further diagnosis or replacement.

[0155] If the internal resistance deviation exceeds the threshold but there is no obvious long-term growth trend, it may be a short-term fluctuation and does not necessarily constitute an anomaly.

[0156] Compared to traditional statistical methods, the above model considers the impact of dynamic load on internal resistance, improving the sensitivity of anomaly detection. Combined with impedance changes across the entire frequency band ( ) and time-domain perturbations ( This reduces the possibility of misjudgment based on a single feature, requires less computation in the formula, can monitor impedance anomalies in real time, does not require training with a large amount of historical data, and improves deployment efficiency.

[0157] Example 3:

[0158] This embodiment also provides a computer device applicable to an online measurement method for the internal resistance of a single-cell battery, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the online measurement method for the internal resistance of a single-cell battery as proposed in the above embodiment.

[0159] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements an online measurement method for the internal resistance of a single-cell battery as proposed in the above embodiments.

[0160] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0161] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0162] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0163] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0164] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0165] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for on-line measurement of internal resistance of a single battery, characterized by, The method comprises the following steps: S1, a dynamic load disturbance model of the storage battery is established, which is used to generate measurable voltage response data of the storage battery under load disturbance; S2, the voltage response data generated by the load disturbance is used to extract effective voltage drop component data from the noise signal by using an adaptive filtering method; S3, based on the voltage drop component data, the equivalent internal resistance is calculated by using a complex impedance calculation method; S4, based on the calculated equivalent internal resistance, multi-scale internal resistance evolution analysis is performed; S5, based on the multi-scale internal resistance evolution analysis, fault warning is performed on the internal resistance anomaly; The dynamic load disturbance model comprises a load disturbance current model and a load response voltage model; The load disturbance current model sets load disturbance signals with different frequencies and amplitudes, which are used to simulate the current fluctuation of the storage battery under real working conditions; The load response voltage model is used to describe the change of the storage battery voltage with the change of the load disturbance current; In the load disturbance current model, the disturbance current is defined as as follows: ； Wherein, t is a time variable; Iref is the reference operating current, i.e. the normal load current of the battery; the amplitude of the first disturbance component; the frequency of the first disturbance component; the phase of the first disturbance component; is the number of perturbation components; The load response voltage model is constructed according to an equivalent circuit of the disturbance current, and voltage response data is represented as: ； Wherein, OCV is open circuit voltage; R is the internal resistance of the reference ohm; is the polarization impedance; Equivalent double layer capacitance; is the number of polarized elements; The multi-scale internal resistance evolution analysis comprises: Constructing an internal resistance change model in the time scale; Based on the evolution equation of the internal resistance with time, the trend is predicted; Identify abnormal internal resistance changes and perform health assessment.

2. The method of claim 1, wherein the method comprises: The step S2 is to extract the voltage response data of different time windows based on the time of applying the disturbance current The step S2 is to extract the voltage response data of different time windows based on the time of applying the disturbance current And the adaptive filtering method is combined to remove the interference signal and extract the voltage drop component data.

3. The method of claim 1, wherein the method comprises: In the step S3, the complex impedance calculation method comprises: The voltage drop component data is collected, and Fourier transform is performed to convert the time domain signal into a frequency domain signal, and the voltage component at the corresponding frequency is obtained . ​ based on obtaining the current frequency component at the corresponding frequency ; Computing the complex impedance under the corresponding frequency :​ ； Selecting the complex impedance at a fixed frequency as the equivalent internal resistance.

4. The method of claim 1, wherein the method comprises: The internal resistance change model is based on the law of the change of the internal resistance of the battery over time, and the internal resistance of the battery at different times measured Analysis: ； wherein is the time of ; is the time of ; is the time of ; Computing the trend of the internal resistance at different time intervals of the internal resistance ； wherein, is the amount of change in internal resistance in adjacent time intervals; is the difference is the battery internal resistance value at the time.

5. The method for online measurement of the internal resistance of a single-cell storage battery according to claim 4, characterized in that, The evolution equation based on the internal resistance with time is designed based on an exponential fitting or an autoregressive model, and the evolution equation is as follows: ； wherein, is the predicted internal resistance value at time ; is the initial internal resistance value at the reference time; is the internal resistance growth rate; is the time decay factor.

6. The method for online measurement of the internal resistance of a single-cell storage battery according to claim 5, characterized in that, In the health assessment, in combination with the change trend, a health status indicator SOH is calculated, and a health assessment model is as follows: ； wherein, is an initial internal resistance, is a set threshold value.

7. The method of claim 1, wherein the method comprises: In the step S5, when the fault warning is performed, the dynamic disturbance coefficient and the internal resistance deviation degree are used to judge the internal resistance anomaly; The dynamic disturbance coefficient is obtained by measuring the deviation between the internal resistance and the predicted internal resistance and taking the average value, which is used to measure the disturbance of the internal resistance with time. Abnormal increase of the dynamic disturbance coefficient indicates that the internal resistance disturbance exceeds the normal range, and there may be abnormal situation; The internal resistance deviation degree is obtained by calculating the relative change rate of the current internal resistance relative to the reference internal resistance. When the internal resistance deviation degree exceeds the set threshold, it indicates that the impedance change is abnormal, which may indicate the fault of the storage battery.

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