A battery pack full life cycle resistance evolution tracking method

Abstract

The application discloses a battery pack full-life internal resistance evolution tracking method, and belongs to the technical field of battery state monitoring, which comprises the following steps: identifying the operation gap window in the operation process of the battery pack; applying a micro-excitation signal to the battery pack in the operation gap window to generate a voltage response signal; extracting the orthogonal component of the voltage response signal and calculating the amplitude to obtain a current internal resistance measurement value; storing the current internal resistance measurement value and corresponding battery state parameters together to obtain an internal resistance data sequence; extracting the trend of the internal resistance data sequence and modeling the deviation to obtain an updated internal resistance evolution prediction model; and performing health state mapping analysis on the updated internal resistance evolution prediction model to generate a battery health state evaluation parameter. The application adopts a dynamic excitation adjustment technology and combines a multi-source data time alignment strategy, so that the accurate tracking of the full-life internal resistance evolution of the battery pack and the accurate evaluation of the battery health state can be realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery state monitoring, in particular to a battery pack full life cycle resistance evolution tracking method. BACKGROUND

[0002] As the core component of modern electric vehicles, energy storage systems and other key fields, the performance and health state of the battery pack are directly related to the safety, reliability and economy of the entire system. Among the many parameters that characterize the health state of the battery, the internal resistance is a crucial indicator that will irreversibly increase with the cyclic use and calendar aging of the battery. Therefore, accurate and continuous tracking of the internal resistance of the battery pack throughout its life cycle is the basis for achieving fine battery management and accurate life prediction.

[0003] In the prior art, the measurement methods of battery internal resistance mainly include offline alternating current impedance method and online direct current internal resistance method. Although the alternating current impedance method has high accuracy, it usually requires a dedicated electrochemical workstation and the testing process is complex, making it difficult to integrate into a vehicle-mounted or energy storage battery management system for real-time online measurement. The online direct current internal resistance method calculates the internal resistance by applying a current pulse and measuring the voltage change it causes. This method is easy to implement, but it is very susceptible to disturbances such as large current charging and discharging, temperature changes and electrochemical polarization effects in the actual operation of the battery pack, resulting in large fluctuations in the measurement results and poor consistency, making it difficult to extract the weak change trend that reflects the true aging.

[0004] The main defect of the existing online measurement technology is its insufficient anti-interference ability and difficulty in distinguishing different influencing factors in internal resistance changes. In complex actual working conditions, the reversible fluctuations in internal resistance caused by real-time changes such as temperature and state of charge often overwhelm the irreversible growth caused by long-term aging, making the aging model based on these unstable data not very accurate. In addition, traditional measurement methods usually use fixed excitation signals, which cannot adapt to the changes in internal resistance characteristics of the battery at different life stages and under different working conditions, further reducing the quality and usability of the data. SUMMARY

[0005] To solve the above problems, the present application provides a battery pack full life cycle resistance evolution tracking method, which adopts dynamic excitation adjustment technology and combines multi-source data time alignment strategy to realize accurate tracking of the full life cycle evolution of the battery pack internal resistance and accurate assessment of the battery health state.

[0006] The above objectives can be achieved by the following solutions:

[0007] A battery pack full life resistance evolution tracking method, comprising identifying an operation gap window in the operation process of a battery pack; applying a micro-excitation signal to the battery pack in the operation gap window to generate a voltage response signal; performing orthogonal component extraction and amplitude calculation on the voltage response signal to calculate a current internal resistance measurement value; storing the current internal resistance measurement value and corresponding battery state parameters together to obtain an internal resistance data sequence; performing trend extraction and deviation modeling on the internal resistance data sequence to obtain an updated internal resistance evolution prediction model; and performing health state mapping analysis on the updated internal resistance evolution prediction model to generate battery health state evaluation parameters.

[0008] Optionally, the applying a micro-excitation signal to the battery pack in the operation gap window to generate a voltage response signal comprises: obtaining a current state of charge value and a temperature value of the battery in the operation gap window to generate real-time state data; adjusting an amplitude of the micro-excitation signal according to the real-time state data to obtain an adaptive micro-current pulse; and performing synchronous voltage sampling on the adaptive micro-current pulse to generate the voltage response signal.

[0009] Optionally, the performing orthogonal component extraction and amplitude calculation on the voltage response signal to calculate a current internal resistance measurement value comprises: extracting in-phase components and quadrature components of the voltage response signal; calculating amplitude information based on the in-phase components and the quadrature components; and calculating the current internal resistance measurement value according to the amplitude information.

[0010] Optionally, the performing trend extraction and deviation modeling on the internal resistance data sequence to obtain an updated internal resistance evolution prediction model comprises: obtaining a basic evolution trend based on the internal resistance data sequence; calculating a deviation between the internal resistance data sequence and the basic evolution trend to obtain a deviation sequence; and modeling the deviation sequence to generate the updated internal resistance evolution prediction model.

[0011] Optionally, the modeling the deviation sequence to generate the updated internal resistance evolution prediction model comprises: training the deviation sequence and corresponding battery operation parameters to obtain a deviation probability distribution model base function; establishing a probability mapping relationship based on the deviation probability distribution model base function; predicting future internal resistance deviation values and their confidence intervals based on the probability mapping relationship to generate the updated internal resistance evolution prediction model.

[0012] Optionally, the performing health state mapping analysis on the updated internal resistance evolution prediction model to generate battery health state evaluation parameters comprises: obtaining a battery internal resistance prediction value based on the updated internal resistance evolution prediction model; and performing health state evaluation calculation on the battery internal resistance prediction value to generate the battery health state evaluation parameters.

[0013] Optionally, the obtaining the battery internal resistance prediction value based on the updated internal resistance evolution prediction model comprises: performing bias prediction calculation on the updated internal resistance evolution prediction model to obtain an internal resistance bias prediction value; and performing superposition calculation on the basic evolution trend and the internal resistance bias prediction value to obtain the battery internal resistance prediction value.

[0014] Optionally, the jointly storing the current internal resistance measurement value and the corresponding battery state parameter to obtain the internal resistance data sequence comprises: performing time stamp association binding on the current internal resistance measurement value and the corresponding battery state parameter to generate a synchronization data unit; and performing serialization encapsulation storage on the synchronization data unit to obtain the internal resistance data sequence.

[0015] Optionally, the identifying the running gap window in the battery pack running process comprises: dynamically monitoring a current signal in the battery pack running process to generate real-time current waveform data; and performing silent interval analysis on the real-time current waveform data to obtain the running gap window.

[0016] Based on the same inventive concept, the application further provides a battery pack full-life internal resistance evolution tracking system, which comprises: a running gap detection module, configured to identify a running gap window in a battery pack running process; an excitation response acquisition module, configured to apply a micro-excitation signal to the battery pack in the running gap window to generate a voltage response signal; a signal processing module, configured to perform orthogonal component extraction and amplitude calculation on the voltage response signal to calculate a current internal resistance measurement value; a data management module, configured to jointly store the current internal resistance measurement value and a corresponding battery state parameter to obtain an internal resistance data sequence; an evolution modeling module, configured to perform trend extraction and bias modeling on the internal resistance data sequence to obtain an updated internal resistance evolution prediction model; and a health assessment module, configured to perform health state mapping analysis on the updated internal resistance evolution prediction model to generate a battery health state assessment parameter.

[0017] Compared with the prior art, the application has the following advantages:

[0018] The application can effectively avoid the serious interference of large current charging and discharging on measurement by measuring in the identified battery pack running gap window, and greatly suppresses the influence of wideband noise and non-excitation frequency signals by combining the micro-excitation signal and the orthogonal component extraction technology, thereby significantly improving the signal-to-noise ratio and anti-interference ability of online internal resistance measurement and ensuring the accuracy and stability of the measurement data.

[0019] This invention proposes an adaptive excitation measurement scheme that dynamically adjusts the amplitude of the micro-excitation signal based on real-time state data such as the battery's current state of charge and temperature. This adaptation mechanism ensures that the generated voltage response signal has appropriate strength regardless of the battery's operating conditions, allowing for accurate capture without impacting the battery. This ensures the continuous and stable acquisition of high-quality, highly consistent raw data throughout the entire battery lifespan.

[0020] This invention employs a composite analysis method combining trend extraction and deviation modeling to decompose the complex internal resistance evolution process into a fundamental evolution trend reflecting long-term irreversible aging and a dynamic deviation reflecting the impact of short-term operating conditions. This decoupling process allows the model to more deeply understand the intrinsic mechanism of internal resistance changes, accurately depicting not only the fundamental degradation path of the battery but also precisely capturing reversible fluctuations caused by operating conditions, thereby constructing a more comprehensive and accurate internal resistance evolution prediction model.

[0021] This invention combines a high-precision internal resistance prediction model with health status mapping analysis to generate battery health status assessment parameters with clear physical meaning and application-oriented characteristics. This method not only assesses the current health status of the battery but also provides forward-looking health status assessments and remaining life predictions based on reliable predictions of future internal resistance evolution trends. This offers crucial decision support for preventative maintenance, secondary utilization, and safety management of battery systems.

[0022] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This invention provides a method for tracking the evolution of internal resistance throughout the entire lifecycle of a battery pack.

[0025] A flowchart.

[0026] Figure 2 This is an adaptive excitation current amplitude mapping diagram according to an embodiment of the present invention.

[0027] Figure 3 This is a graph showing the evolution trend of internal resistance in an embodiment of the present invention.

[0028] Figure 4 This is an internal resistance deviation prediction diagram according to an embodiment of the present invention.

[0029] Figure 5 This is a diagram of the runtime gap window recognition in an embodiment of the present invention.

[0030] Figure 6 This invention provides a battery pack internal resistance evolution tracking system throughout its entire lifecycle.

[0031] A structural diagram. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] Reference Figure 1 One embodiment of the present invention proposes a method for tracking the evolution of internal resistance throughout the entire life cycle of a battery pack. By employing dynamic excitation adjustment technology and combining it with a multi-source data time alignment strategy, it is possible to achieve accurate tracking of the evolution of internal resistance throughout the entire life cycle of the battery pack and accurate assessment of battery health status.

[0034] The method described in this embodiment specifically includes:

[0035] Identify the operational gap window during battery pack operation;

[0036] A micro-excitation signal is applied to the battery pack within the operating gap window to generate a voltage response signal;

[0037] The voltage response signal is subjected to quadrature component extraction and amplitude calculation to obtain the current internal resistance measurement value;

[0038] The current internal resistance measurement value and the corresponding battery state parameters are stored together to obtain an internal resistance data sequence;

[0039] Trend extraction and deviation modeling are performed on the internal resistance data sequence to obtain an updated internal resistance evolution prediction model;

[0040] A health status mapping analysis is performed on the updated internal resistance evolution prediction model to generate battery health status assessment parameters.

[0041] Specifically, the method first employs an opportunistic measurement strategy to intelligently identify relatively stable operating gaps in the electrochemical state during normal battery operation. Within these windows, a weak electrical excitation probe signal is actively applied, and high-precision synchronous detection technology, namely orthogonal component extraction, is used to accurately extract the voltage response signal caused by this excitation from complex background noise, thereby obtaining a high signal-to-noise ratio current internal resistance measurement value. Then, the method binds and stores this internal resistance value with the real-time operating state parameters of the battery at the time of measurement, forming an internal resistance data sequence rich in contextual information. Based on this sequence, the method adopts a decomposition modeling approach, decoupling the complex internal resistance evolution process into two parts: a fundamental evolution trend representing long-term irreversible aging and a dynamic deviation representing short-term operating condition influences, and mathematically models each part. Finally, by mapping the internal resistance value predicted by this dual model to a health measurement space, intuitive health status assessment parameters are generated, and the model is continuously updated by collecting new data to achieve dynamic tracking and prediction throughout the entire battery lifecycle.

[0042] Optionally, applying a micro-excitation signal to the battery pack within the operating gap window to generate a voltage response signal includes:

[0043] Obtain the current state of charge and temperature of the battery within the operating interval window, and generate real-time status data;

[0044] Based on the real-time status data, the amplitude of the micro-excitation signal is adjusted to obtain an adaptive micro-current pulse;

[0045] The adaptive microcurrent pulse is synchronously voltage sampled to generate a voltage response signal.

[0046] Specifically, preparations for internal resistance measurement are initiated first during the identified battery pack operating gap window. The first step is to acquire the battery's current state of charge (SOC) and temperature. (SOC value) Temperature value, usually expressed as a percentage, is a key indicator of remaining battery capacity and can be obtained through estimation algorithms such as the coulomb integral method or the open-circuit voltage method. The temperature is measured in real time by temperature sensors deployed inside or on the surface of the battery pack. These two parameters together constitute the real-time state data characterizing the battery's immediate operating condition. The battery's internal resistance characteristics are strongly dependent on the state of charge (SOC) and temperature values; therefore, accurately acquiring real-time state data is fundamental to the accuracy of subsequent steps. Next, based on the acquired real-time state data, the micro-excitation signal to be applied is dynamically adjusted. This micro-excitation signal is a brief current pulse, the amplitude of which needs to be carefully designed to ensure that it elicits a clearly measurable voltage response without significantly disturbing the battery's electrochemical steady state. The adjustment process is crucial for generating the appropriate micro-current pulse. An internally preset adjustment function or a multi-dimensional lookup table establishes a mapping relationship between the battery's SOC and temperature T values ​​and the optimal excitation current amplitude. The adaptive excitation current amplitude mapping is as follows: Figure 2 As shown, this process can be expressed by the following functional relationship:

[0047] ,

[0048] in, This represents the amplitude of the microcurrent pulse after adaptation and adjustment. () indicates the preset adjustment function or lookup table logic. This is the currently acquired state of charge value. This refers to the currently acquired temperature value. For example, at low temperatures or under low charge conditions, the battery's internal resistance is high. To obtain a voltage response with a sufficient signal-to-noise ratio, the function... It will output a relatively large value. Value; however, at room temperature or under high charge conditions, the internal resistance is small, resulting in a smaller output value. The energy of the adapted microcurrent pulse generated in this way is precisely controlled to the level most suitable for the current battery state. The final step involves applying this adapted microcurrent pulse to both ends of the battery pack and simultaneously sampling the voltage throughout the entire pulse's duration. Synchronous voltage sampling means that the current application module and the voltage acquisition module are strictly aligned in time, simultaneously recording changes in current and the battery terminal voltage response with extremely high time resolution. This synchronization mechanism is crucial, ensuring that each voltage data point acquired precisely corresponds to the current state at the same moment, thus eliminating measurement errors introduced by time delay or phase mismatch. This series of voltage data points acquired sequentially over time constitutes the raw data required for subsequent internal resistance analysis, namely the voltage response signal. This signal completely records the dynamic potential changes generated by the battery under precisely adapted microcurrent excitation.

[0049] Optionally, the step of extracting orthogonal components and calculating the amplitude of the voltage response signal to obtain the current internal resistance measurement value includes:

[0050] Extract the in-phase and quadrature components of the voltage response signal;

[0051] Amplitude information is calculated based on the in-phase and quadrature components.

[0052] Based on the amplitude information, the current internal resistance measurement value is calculated.

[0053] Specifically, the first step is to extract the orthogonal components from the acquired voltage response signal. The core of this step is to decompose the voltage response signal into projections onto two mutually orthogonal reference vectors. Using the applied adaptive micro-current pulse signal as a reference, two reference signals with the same frequency but precisely 90-degree phase differences are generated; one is called the in-phase reference signal, and the other the quadrature reference signal. Then, the voltage response signal is mixed with these two reference signals (i.e., multiplied), and the product is low-pass filtered. After low-pass filtering, high-frequency components and most noise are removed, leaving the DC or slowly varying components, which are the desired in-phase and quadrature components. The in-phase component represents the part of the voltage response signal that is in phase with the excitation current, mainly reflecting the battery's resistance characteristics; the quadrature component represents the part of the voltage response signal that is 90 degrees out of phase with the excitation current, mainly reflecting the battery's capacitive or inductive reactance characteristics. Next, based on the extracted in-phase and quadrature components, amplitude information is calculated. The in-phase and quadrature components can be viewed as two orthogonal coordinates of a vector in vector space. Therefore, the total amplitude of the voltage response signal at the excitation frequency, i.e., the amplitude information, can be obtained by taking the square root of the sum of the squares of these two components, which conforms to the rule of vector composition. This calculation process can be expressed by the following formula:

[0054] ,

[0055] in, This represents the calculated amplitude information, i.e., the total amplitude of the voltage response signal; It is the in-phase component extracted from the voltage response signal; It is the orthogonal component extracted from the voltage response signal. and All values ​​are measured values ​​obtained through the aforementioned synchronous detection and low-pass filtering process. Finally, based on the calculated amplitude information, the current internal resistance measurement value is calculated. According to Ohm's law for AC circuits, the magnitude of the impedance is equal to the amplitude of the voltage divided by the amplitude of the current. In this method, the amplitude of the applied adaptive micro-current pulse is known and precisely controlled. Therefore, the current battery internal resistance measurement value, specifically referring to the magnitude of the AC impedance, can be calculated using the following formula:

[0056] ,

[0057] in, This is the final measured value of the current internal resistance; This is the voltage response amplitude information calculated in the previous step; It is the amplitude of the applied adaptive microcurrent pulse, which is dynamically generated based on real-time status data and is a known control variable.

[0058] Optionally, the step of performing trend extraction and deviation modeling on the internal resistance data sequence to obtain the updated internal resistance evolution prediction model includes:

[0059] Based on the internal resistance data sequence, the basic evolution trend is obtained;

[0060] Calculate the deviation between the internal resistance data sequence and the basic evolution trend to obtain the deviation sequence;

[0061] The deviation sequence is modeled to generate an updated internal resistance evolution prediction model.

[0062] Specifically, firstly, based on the internal resistance data sequence, the basic evolution trend is obtained, as shown in the figure. Figure 3 As shown. The internal resistance data sequence consists of a series of current internal resistance measurements ordered by time or cycle count, along with their corresponding battery state parameters. The fundamental evolution trend aims to capture the macroscopic law of the irreversible, slow increase in battery internal resistance as its lifespan progresses. To obtain this trend, data fitting techniques are employed. For example, polynomial functions, exponential functions, or other empirical models that conform to the battery aging mechanism can be used to perform regression analysis on the internal resistance values ​​in the internal resistance data sequence with aging indicators such as cycle count or usage time. Taking polynomial regression as an example, the fundamental evolution trend can be expressed as:

[0063] ,

[0064] in, This represents the state of aging. The predicted value of the internal resistance baseline is an indicator that quantifies the degree of battery aging, such as the equivalent full cycle count or calendar life days, and is obtained from the associated state parameters of the internal resistance data sequence. These are the coefficients of the polynomial model, obtained by fitting the entire internal resistance data sequence using optimization algorithms such as least squares. This fitted curve represents the basic evolution trend. Next, the deviation between the internal resistance data sequence and the basic evolution trend is calculated, resulting in a deviation sequence. After obtaining the basic evolution trend, each measured internal resistance value in the internal resistance data sequence is compared with its predicted trend value under the same aging state. The difference between the two is the internal resistance deviation at that moment. This process is performed on all data points in the sequence one by one, thus forming a new time series, i.e., the deviation sequence. The calculation formula is as follows:

[0065] ,

[0066] in, It is the first in the deviation sequence The element, i.e., the th element The internal resistance deviation value at each measurement point. It is the first in the internal resistance data sequence The current internal resistance measurement value. It was calculated based on the basic evolutionary trend model, in the... Aging status corresponding to each measurement point The internal resistance trend prediction value is obtained. This deviation sequence reflects the short-term, reversible fluctuations in internal resistance caused by real-time operating conditions such as temperature, state of charge (SOC), and charge / discharge rate, in addition to long-term aging. Finally, the deviation sequence is modeled to generate an updated internal resistance evolution prediction model. The deviation sequence is analyzed and modeled as a signal containing instantaneous battery state information. This deviation modeling process aims to establish the mapping relationship between internal resistance deviation and the real-time battery state parameters that cause these deviations. After completing the deviation modeling, the obtained deviation model is combined with the previously obtained basic evolution trend model to form the final updated internal resistance evolution prediction model. The structure of this composite model can be represented as:

[0067] ,

[0068] in, It is the predicted output of the updated internal resistance evolution prediction model for future internal resistance. It is a basic evolutionary trend model. It is a deviation model established based on the deviation sequence. Its input is the real-time battery state parameters, and its output is the prediction of internal resistance deviation.

[0069] Optionally, modeling the deviation sequence to generate an updated internal resistance evolution prediction model includes:

[0070] The deviation sequence and the corresponding battery operating parameters are trained to obtain the basis function of the deviation probability distribution model.

[0071] Based on the basis functions of the aforementioned deviation probability distribution model, a probability mapping relationship is established;

[0072] Based on the probability mapping relationship, the future internal resistance deviation value and its confidence interval are predicted, and an updated internal resistance evolution prediction model is generated.

[0073] Specifically, the deviation sequence and corresponding battery operating parameters are first trained to obtain the basis functions of the deviation probability distribution model. Here, the deviation sequence is a previously calculated numerical sequence reflecting short-term fluctuations in internal resistance, while the corresponding battery operating parameters are real-time state data such as State of Charge (SOC), temperature (T), and charge / discharge rate, recorded synchronously with each deviation value. A machine learning model capable of learning the probabilistic relationship between input and output, such as Gaussian process regression or a hybrid density network, is employed. During training, the model uses the battery operating parameters as input features and the deviation values ​​in the deviation sequence as output targets. Through optimization algorithms, it continuously adjusts its internal parameters, learning the complex nonlinear mapping relationship between the two. After training, a set of deviation probability distribution model basis functions is formed within the model. These basis functions are the core mathematical components describing this mapping relationship, and they can combine to generate a complete probability distribution based on the input operating parameters. Next, based on the deviation probability distribution model basis functions, a probability mapping relationship is established. The trained model itself solidifies this probability mapping relationship. This relationship is not a simple function from input to a single output value, but a mapping from the input state space to the output probability distribution space. This means that for any given set of battery operating parameters, the model output is not a fixed internal resistance deviation value, but a probability density function describing the possible distribution of that deviation value, typically a Gaussian distribution. This probability mapping relationship can be expressed as:

[0074] ,

[0075] in, This indicates that, given a set of battery operating parameters Under these conditions, internal resistance deviation The conditional probability distribution. It is a parameter vector that contains real-time operating conditions such as the current state of charge and temperature. It represents a normal distribution. It is the mean of the normal distribution, representing the value of the distribution under the operating parameters. When the internal resistance deviation is at that point, the most likely center value is the expected deviation. It is the variance of the distribution, which quantifies the uncertainty or range of fluctuation in the prediction. and The values ​​are all obtained through the basis functions of a trained bias probability distribution model, based on the input. The calculations are as follows. Finally, based on the aforementioned probability mapping relationship, the future internal resistance deviation value and its confidence interval are predicted, thereby generating an updated internal resistance evolution prediction model. When it is necessary to predict the future internal resistance, preset or real-time battery operating parameters are used. As input, substitute it into the established probability mapping relationship. The model will then calculate the corresponding mean in real time. and variance This mean This is used as a prediction of future internal resistance deviation. Simultaneously, variance is utilized. A confidence interval can be calculated, for example, a 95% confidence interval is... ±1.96 This range gives the range within which the actual internal resistance deviation value has a 95% probability of falling. Ultimately, this probabilistic deviation model, which can output the deviation prediction value and its confidence interval, is combined with the previously established basic evolutionary trend model to form the final, more informative, and updated internal resistance evolution prediction model.

[0076] Optionally, the step of performing health state mapping analysis on the updated internal resistance evolution prediction model to generate battery health state assessment parameters includes:

[0077] Based on the updated internal resistance evolution prediction model, the predicted value of battery internal resistance is obtained.

[0078] The predicted internal resistance of the battery is used to calculate the battery health status and generate battery health status assessment parameters.

[0079] Specifically, the first step involves obtaining the predicted internal resistance value of the battery based on the updated internal resistance evolution prediction model. The updated internal resistance evolution prediction model is a composite model that comprehensively considers the long-term aging trend and short-term operating condition fluctuations of the battery. To obtain a specific predicted internal resistance value, a clear prediction target point needs to be provided for the model. This target point is typically defined by a future time point, the expected number of cycles, or specific operating condition parameters. These target parameters are input into the updated internal resistance evolution prediction model. Internally, the model first calls the basic evolution trend part to calculate the basic trend value of the internal resistance based on the target aging state; simultaneously, it calls the deviation model part to predict the deviation value of the internal resistance based on the target operating condition parameters. Finally, the results of these two parts are superimposed to obtain the predicted internal resistance value of the battery at that target point. This predicted value represents the model's best estimate of the battery's internal resistance under specific future conditions. The second step involves performing a health status assessment calculation on the predicted internal resistance value to generate battery health status assessment parameters. After obtaining the predicted internal resistance value, it needs to be compared with a benchmark to quantify the battery's health level. This benchmark is typically the battery's initial internal resistance at the time of manufacture, or the internal resistance threshold defined in industry standards at the end of the battery's lifespan. A commonly used parameter for assessing battery health is the state of health. It is usually expressed as a percentage. One way to calculate it is based on the change in internal resistance, as shown in the following formula:

[0080] ,

[0081] in, These are health status assessment parameters obtained through internal resistance assessment. It is the predicted value of the battery internal resistance obtained in the previous step. It is the internal resistance threshold at the end of the battery's life. This is a critical value set according to the battery datasheet or relevant standards, indicating a severe degradation in battery performance. These are the initial internal resistance values ​​of a brand-new battery. These two baseline values ​​are predetermined constants. Using this formula, the rate of increase in internal resistance can be linearly mapped to a health status assessment parameter ranging from 100% (brand new) to 0% (recovery). Besides... The evaluation parameter can also be the remaining useful life. Predicting this typically requires comparing the predicted internal resistance value with the end-of-life threshold and combining it with an aging model to estimate the time or number of cycles required to reach that threshold.

[0082] Optionally, obtaining the predicted battery internal resistance value based on the updated internal resistance evolution prediction model includes:

[0083] The updated internal resistance evolution prediction model is used to perform deviation prediction calculations to obtain the internal resistance deviation prediction value.

[0084] The predicted internal resistance of the battery is obtained by superimposing the basic evolution trend and the predicted internal resistance deviation.

[0085] Specifically, the first step is to perform deviation prediction calculations on the updated internal resistance evolution prediction model to obtain the predicted internal resistance deviation value. This step initiates the deviation model portion of the prediction model. To obtain a meaningful deviation prediction, a set of target operating condition parameters needs to be set. These parameters describe the specific operating state of the battery when we want to predict the internal resistance, for example, a future state of charge. Value and an expected ambient temperature The target operating condition parameters are used as input to the pre-trained deviation model. The deviation model, especially those based on probabilistic methods, calculates the most likely deviation of the internal resistance under that specific operating condition based on these inputs; this is the predicted internal resistance deviation value. The predicted internal resistance deviation is as follows: Figure 4 As shown. This value represents the short-term fluctuation relative to the long-term aging trend caused by real-time operating conditions. Then, the basic evolution trend and the predicted internal resistance deviation are superimposed to calculate the final predicted battery internal resistance value. In this step, another core part of the prediction model, the basic evolution trend model, needs to be activated simultaneously. Similar to the deviation model requiring operating condition parameters, the basic evolution trend model requires an input parameter characterizing the degree of battery aging, such as the aging state corresponding to the target prediction time, like the equivalent full cycle count or calendar days. This target aging state is input into the basic evolution trend model to calculate the baseline or long-term trend value of the battery internal resistance at that aging stage. Subsequently, this basic trend value is arithmetically summed with the predicted internal resistance deviation value obtained in the first step. This superposition calculation process can be represented by the following formula:

[0086] ,

[0087] in, This is the final predicted value of the battery's internal resistance. It is a basic evolutionary trend model based on future aging states. The calculated internal resistance trend value. The deviation model is based on a set of future operating condition parameters. The calculated predicted value of internal resistance deviation. and These are all input parameters set to achieve specific prediction goals.

[0088] Optionally, storing the current internal resistance measurement value and the corresponding battery state parameters together to obtain an internal resistance data sequence includes:

[0089] The current internal resistance measurement value and the corresponding battery state parameters are timestamped and bound together to generate a synchronization data unit;

[0090] The synchronization data unit is serialized, encapsulated, and stored to obtain the internal resistance data sequence.

[0091] Specifically, firstly, the current internal resistance measurement value and the corresponding battery state parameters are timestamped and bound together to generate a synchronization data unit. After each internal resistance measurement, a current internal resistance measurement value is obtained. Simultaneously, battery state parameters completely synchronized with the measurement moment are collected and recorded. These parameters include at least the state of charge (SOC), temperature (T), cumulative cycle count, and current current. To ensure data consistency and traceability, a unique timestamp is assigned to each measurement event. This timestamp serves as a common key, firmly binding the current internal resistance measurement value with the entire set of synchronously collected battery state parameters, forming an indivisible data packet, i.e., a synchronization data unit. Structurally, this unit can be viewed as a data record, with fields including a timestamp, internal resistance value, and multiple state parameter values, ensuring a strong correlation between the internal resistance data and the physical environment background information from which it was generated. Next, the synchronization data unit is serialized, encapsulated, and stored to obtain an internal resistance data sequence. This first step is continuously repeated within each identified operating gap window, generating a series of synchronization data units. To facilitate subsequent analysis and model training, these independent synchronous data units need to be arranged according to their generated timestamps, forming an ordered set. This process is called serialization. Subsequently, this ordered set of synchronous data units is written to a non-volatile storage medium in a specific format (such as a CSV file, database table, or proprietary binary format), completing the encapsulation and storage. The final stored set of records, ordered by time and containing all internal resistance measurements and their corresponding operating conditions, is the required internal resistance data sequence. Each row or record in this sequence is a synchronous data unit, fully describing the battery's internal resistance characteristics at a specific moment and its electrochemical and physical environment.

[0092] Optionally, the window for identifying the operational gap during battery pack operation includes:

[0093] Dynamically monitor the current signal during battery pack operation and generate real-time current waveform data;

[0094] The real-time current waveform data is analyzed in a silent interval to obtain the operating gap window.

[0095] Specifically, the first step is to dynamically monitor the current signal during battery pack operation to generate real-time current waveform data. This process is accomplished using a high-precision current sensor, such as a Hall sensor or shunt, installed in the main circuit of the battery pack. This sensor is connected to a data acquisition unit, continuously measuring the total current flowing through the battery pack at a fixed high sampling frequency. Each sampling yields an instantaneous current value and its corresponding time point. This continuously acquired, time-ordered set of current data points constitutes the real-time current waveform data reflecting the dynamic behavior of battery charging and discharging. This waveform data accurately records the magnitude and direction of the current, providing the initial basis for subsequent analysis. Next, the real-time current waveform data undergoes silent interval analysis to ultimately obtain the operating gap window. The operating gap window is identified as follows: Figure 5 As shown. Quiet interval analysis is an algorithmic process designed to automatically identify the resting or low-current float charging phases of a battery from continuous current waveforms, where the battery is neither charging nor discharging at high current. A current quiescent threshold is preset; this threshold's absolute value is very small, slightly higher than the sensor's measurement noise floor. The algorithm compares the absolute values ​​of the current waveform data with this quiescent threshold in real time. When the absolute current values ​​at all sampling points remain below this threshold for a continuous period, the battery is considered to have entered a quiescent interval. To avoid misjudgments caused by brief current fluctuations or signal noise, a minimum duration is set. Only when the current remains below the quiescent threshold for longer than this minimum time is this period officially recognized as a valid operating gap window, and its start and end times are output. The operating gap window is essentially a short, relatively stable plateau period of the battery's electrochemical state that naturally occurs in actual usage scenarios.

[0096] Based on the same inventive concept, such as Figure 6 As shown, the present invention also provides a battery pack internal resistance evolution tracking system throughout its entire life cycle, the system comprising:

[0097] The operation gap detection module is used to identify the operation gap window during the operation of the battery pack;

[0098] The excitation response acquisition module is used to apply a micro-excitation signal to the battery pack within the operating gap window and generate a voltage response signal.

[0099] The signal processing module is used to extract orthogonal components and calculate amplitude of the voltage response signal to obtain the current internal resistance measurement value;

[0100] The data management module is used to store the current internal resistance measurement value and the corresponding battery state parameters together to obtain an internal resistance data sequence;

[0101] The evolution modeling module is used to extract trends and model deviations in the internal resistance data sequence to obtain an updated internal resistance evolution prediction model.

[0102] The health assessment module is used to perform health status mapping analysis on the updated internal resistance evolution prediction model and generate battery health status assessment parameters.

[0103] To verify the feasibility of this invention in practice, it was applied to a large-scale grid-side energy storage power station. This power station is responsible for mitigating the volatility of photovoltaic power generation and providing stable support for the power grid. Its battery packs operate under complex charging and discharging conditions. Traditional battery management systems (BMS) typically use simplified aging models, which struggle to accurately track the dynamic evolution of battery internal resistance under actual operating conditions. This leads to inaccurate assessments of the battery's state of health (SOH), potentially causing safety risks or economic losses. This invention aims to provide a high-precision, full-lifecycle internal resistance tracking method to optimize the operation and maintenance strategies of energy storage power stations.

[0104] In this embodiment, the central control system of the energy storage power station integrates the method described in this invention. The system continuously and dynamically monitors the current sensor data of the battery pack's main circuit. At 14:30 on August 15, 2024, due to cloud cover causing a brief drop in photovoltaic output, the power station suspended discharging to the grid. The system detected that the current remained below the preset quiescent threshold for 60 seconds, thus successfully identifying an operational gap window.

[0105] Within this window, the system immediately initiates the internal resistance measurement process. First, it acquires the real-time status data of the battery pack: the current state of charge (SOC) is 75%, and the battery module surface temperature is 32°C. Then, it uses a preset lookup table... The system generates an adaptive microcurrent pulse that matches the current state. This pulse is applied to the battery pack, while the high-frequency synchronous voltage sampling module records the complete voltage response signal.

[0106] Next, the system processes the acquired voltage response signal. By mixing and low-pass filtering with in-phase and quadrature reference signals, the in-phase and quadrature components of the voltage response are successfully extracted. Based on these two components, the amplitude of the voltage response is calculated, and then, according to the known amplitude of the adaptive micro-current pulse, the current internal resistance measurement value at that moment is calculated to be 1.25mΩ.

[0107] The internal resistance measurement is associated with and bound to corresponding status parameters such as timestamp, SOC (75%), temperature (32°C), and cumulative cycle count (850 times) to form a synchronous data unit. This unit is appended to non-volatile memory and becomes part of the internal resistance data sequence. As of September 2024, the system had accumulated tens of thousands of such data records.

[0108] The system is modeled and analyzed based on a complete internal resistance data sequence. First, multinomial regression is used to fit the relationship between internal resistance value and cycle number, resulting in a basic evolution trend curve reflecting long-term irreversible aging. Then, the deviation between each measured internal resistance data point and its predicted trend value at the corresponding cycle number is calculated, forming a deviation sequence. Analysis reveals a strong correlation between this deviation sequence and real-time operating parameters such as SOC and temperature.

[0109] To model the deviation, the system employs a Gaussian process regression model, using SOC and temperature as inputs and the deviation value as output for training, to obtain the basis function of the deviation probability distribution model. This model can not only predict the expected value of the internal resistance deviation under specific operating conditions but also provide the confidence interval for the prediction. Finally, the basic evolution trend model is combined with this probabilistic deviation model to form an updated internal resistance evolution prediction model.

[0110] When conducting a health status assessment, such as predicting the battery pack's health status after 6 months (estimated cycle life of 1200 cycles) under typical summer conditions (60% SOC, 35°C), the system inputs the target parameters into the updated model. The baseline trend model predicts an internal resistance trend value of 1.38 mΩ after 1200 cycles. The deviation model predicts a deviation internal resistance of +0.12 mΩ under 60% SOC and 35°C conditions. The two models are combined to obtain a predicted battery internal resistance value of 1.50 mΩ.

[0111] Finally, the system performs a health status mapping analysis based on the predicted internal resistance value. Assume the initial internal resistance of the battery... The internal resistance at the scrap threshold is 1.0 mΩ. It is 2.0mΩ. (Based on the formula:)

[0112] ,

[0113] The calculated predicted health status assessment parameter SOH is 50%. Simultaneously, the confidence interval given by the bias model is also converted into the fluctuation range of SOH (e.g., 48.5%). (51.5%), providing an uncertainty reference for operation and maintenance decisions. Based on this prediction, the system generates work orders for battery maintenance and capacity calibration in advance, avoiding insufficient grid support capacity due to sudden performance degradation.

[0114] Compared with traditional BMS methods for estimating State of Health (SOH), this invention demonstrates significant advantages in accuracy and reliability. The traditional method predicts an SOH of 58% at 18 months, while this invention predicts 50%, which is closer to the subsequent offline capacity test results (actual SOH of 51%). Because this invention accurately captures the impact of operating conditions on internal resistance, it avoids overly optimistic estimates of battery health, providing solid data support for the safe and efficient operation of energy storage power stations.

[0115] Table 1. Example of online measurement data for battery pack internal resistance.

[0116]

[0117] Table 2 Comparison of Predicted and Measured SOH of Battery Pack

[0118]

[0119] Tables 1 and 2 above record the actual application data of this invention in the battery pack of an energy storage power station, detailing the system's performance in internal resistance measurement, decomposition modeling, and health status prediction. In Table 1, the system successfully obtained high-precision current internal resistance measurements under different operating conditions (changing SOC and temperature), effectively decomposing them into a basic trend value reflecting long-term aging and a deviation value reflecting the impact of short-term operating conditions. For example, at low temperature and low SOC (such as data from 01:20 on August 17th), the internal resistance deviation increased significantly, proving that this method can accurately capture the instantaneous impact of operating conditions on internal resistance. In Table 2, the prediction accuracy of the method of this invention is significantly better than that of traditional methods. At the end of the 6th month, the error between the predicted value (50.0%) and the measured value (51.0%) of this invention was only 1%, while the error of the traditional method was as high as 7%. Furthermore, the prediction confidence interval provided by this invention provides an important basis for risk assessment and maintenance decisions. For example, the system can formulate a more conservative and safer scheduling strategy based on the lower limit of the confidence interval, thereby effectively improving the overall reliability of the energy storage power station.

[0120] It should be noted that the electrical connections between the various units described above do not necessarily represent direct or indirect connections. Any indirect connection method can be applied to the embodiments of the present invention as long as it achieves the purpose of the present invention. The above descriptions are merely exemplary embodiments of the present invention and should not be construed as limiting the scope of the present invention.

[0121] All equivalent changes and modifications made in accordance with the teachings of this invention are still within the scope of this invention. Those skilled in the art will readily conceive of other embodiments of this invention upon considering the specification and the disclosure of practical truth. This application is intended to cover any variations, uses, or adaptations of this invention that follow the general principles of this invention and include common knowledge or conventional techniques in the art not described herein.

Claims

1. A method for tracking the evolution of internal resistance throughout the entire life cycle of a battery pack, characterized in that, The method includes: Identify the operational gap window during battery pack operation; The operation gap window includes: first, a current quiescent threshold is preset, the absolute value of which is very small and slightly higher than the sensor's measurement noise limit; the absolute value of the current waveform data is compared with the quiescent threshold in real time; and a minimum duration is set. When the current is below the quiescent threshold for more than this minimum duration, this period is confirmed as a valid operation gap window. A micro-excitation signal is applied to the battery pack within the operating gap window to generate a voltage response signal; The voltage response signal is subjected to quadrature component extraction and amplitude calculation to obtain the current internal resistance measurement value; The current internal resistance measurement value and the corresponding battery state parameters are stored together to obtain an internal resistance data sequence; The internal resistance data sequence is subjected to trend extraction and deviation modeling to obtain an updated internal resistance evolution prediction model. This includes: obtaining a basic evolution trend based on the internal resistance data sequence; calculating the deviation between the internal resistance data sequence and the basic evolution trend to obtain a deviation sequence; and modeling the deviation sequence to generate an updated internal resistance evolution prediction model. This includes: training the deviation sequence and corresponding battery operating parameters to obtain a deviation probability distribution model basis function; establishing a probability mapping relationship based on the deviation probability distribution model basis function; and predicting future internal resistance deviation values ​​and their confidence intervals based on the probability mapping relationship to generate the updated internal resistance evolution prediction model. A health status mapping analysis is performed on the updated internal resistance evolution prediction model to generate battery health status assessment parameters.

2. The method for tracking the evolution of internal resistance throughout the entire life cycle of a battery pack according to claim 1, characterized in that, The step of applying a micro-excitation signal to the battery pack within the operating gap window to generate a voltage response signal includes: Obtain the current state of charge and temperature of the battery within the operating interval window, and generate real-time status data; Based on the real-time status data, the amplitude of the micro-excitation signal is adjusted to obtain an adaptive micro-current pulse; The adaptive microcurrent pulse is synchronously voltage sampled to generate a voltage response signal.

3. The method for tracking the evolution of internal resistance throughout the entire life cycle of a battery pack according to claim 1, characterized in that, The process of extracting orthogonal components and calculating the amplitude of the voltage response signal to obtain the current internal resistance measurement value includes: Extract the in-phase and quadrature components of the voltage response signal; Amplitude information is calculated based on the in-phase and quadrature components. Based on the amplitude information, the current internal resistance measurement value is calculated.

4. The method for tracking the evolution of internal resistance throughout the entire life cycle of a battery pack according to claim 1, characterized in that, The step of performing health state mapping analysis on the updated internal resistance evolution prediction model to generate battery health state assessment parameters includes: Based on the updated internal resistance evolution prediction model, the predicted value of battery internal resistance is obtained. The predicted internal resistance of the battery is used to calculate the battery health status and generate battery health status assessment parameters.

5. The method for tracking the evolution of internal resistance throughout the entire life cycle of a battery pack according to claim 4, characterized in that, The battery internal resistance prediction value obtained based on the updated internal resistance evolution prediction model includes: The updated internal resistance evolution prediction model is used to perform deviation prediction calculations to obtain the internal resistance deviation prediction value. The predicted internal resistance of the battery is obtained by superimposing the basic evolution trend and the predicted internal resistance deviation.

6. The method for tracking the evolution of internal resistance throughout the entire life cycle of a battery pack according to claim 1, characterized in that, The step of jointly storing the current internal resistance measurement value and the corresponding battery state parameters to obtain the internal resistance data sequence includes: The current internal resistance measurement value and the corresponding battery state parameters are timestamped and bound together to generate a synchronization data unit; The synchronization data unit is serialized, encapsulated, and stored to obtain the internal resistance data sequence.

7. The method for tracking the evolution of internal resistance throughout the entire life cycle of a battery pack according to claim 1, characterized in that, The identified operating gap window during battery pack operation includes: Dynamically monitor the current signal during battery pack operation and generate real-time current waveform data; The real-time current waveform data is analyzed in a silent interval to obtain the operating gap window.

8. A battery pack full life cycle internal resistance evolution tracking system, applied to the battery pack full life cycle internal resistance evolution tracking method as described in any one of claims 1-7, characterized in that, The system includes: The operation gap detection module is used to identify the operation gap window during the operation of the battery pack; The operation gap window includes: first, a current quiescent threshold is preset, the absolute value of which is very small and slightly higher than the sensor's measurement noise limit; the absolute value of the current waveform data is compared with the quiescent threshold in real time; and a minimum duration is set. When the current is below the quiescent threshold for more than this minimum duration, this period is confirmed as a valid operation gap window. The excitation response acquisition module is used to apply a micro-excitation signal to the battery pack within the operating gap window and generate a voltage response signal. The signal processing module is used to extract orthogonal components and calculate amplitude of the voltage response signal to obtain the current internal resistance measurement value; The data management module is used to store the current internal resistance measurement value and the corresponding battery state parameters together to obtain an internal resistance data sequence; An evolutionary modeling module is used to extract trends and model deviations in the internal resistance data sequence to obtain an updated internal resistance evolution prediction model. This includes: obtaining a basic evolution trend based on the internal resistance data sequence; calculating the deviation between the internal resistance data sequence and the basic evolution trend to obtain a deviation sequence; and modeling the deviation sequence to generate an updated internal resistance evolution prediction model. This includes: training the deviation sequence and corresponding battery operating parameters to obtain a deviation probability distribution model basis function; establishing a probability mapping relationship based on the deviation probability distribution model basis function; and predicting future internal resistance deviation values ​​and their confidence intervals based on the probability mapping relationship to generate an updated internal resistance evolution prediction model. The health assessment module is used to perform health status mapping analysis on the updated internal resistance evolution prediction model and generate battery health status assessment parameters.

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