A method and system for monitoring the state of health of parallel lithium batteries

Abstract

The present application relates to the technical field of battery management system, and particularly relates to a kind of parallel lithium battery health state monitoring method and system, by fusing the observation information under two different working conditions of pulse event and ripple event, the decoupling estimation of the ohmic resistance of each parallel branch is realized using joint observation model.The present application solves the technical problems that the ohmic resistance of each single battery in parallel lithium battery pack cannot be estimated, and the health state of each single battery is monitored without increasing hardware cost and interrupting normal operation of the system in the prior art.

Description

Technical Field

[0001] This invention relates to the field of battery management system technology, and more specifically, to a method and system for monitoring the health status of parallel lithium batteries. Background Technology

[0002] The ohmic internal resistance of a battery is a crucial indicator of its health. Monitoring its changing trends can effectively identify battery cells with performance degradation, providing a basis for equalization management and safety early warning. However, in parallel battery packs, the terminal voltages of each battery cell are equal, and the branch currents are automatically distributed according to differences in internal resistance. Externally, only the total current and total terminal voltage can be measured. This structural characteristic makes traditional impedance identification methods based on single-cell models unsuitable for direct application in parallel scenarios.

[0003] To address the aforementioned issues, existing technologies primarily employ the following solutions: First, global parameter fitting based on an equivalent circuit model estimates the overall model parameters using total voltage and total current. However, this approach cannot independently provide the internal resistance values ​​of each individual battery cell, resulting in ambiguous physical meanings for the parameters. Second, installing current sensors in each branch to directly measure the branch current. However, this significantly increases hardware costs and system complexity. Third, offline measurement methods based on AC impedance spectroscopy, while providing relatively accurate impedance information, require the battery pack to be in a stopped state, failing to meet the real-time requirements of online monitoring.

[0004] In summary, the problem with existing technologies is that, without increasing hardware costs or interrupting normal system operation, it is impossible to estimate the ohmic internal resistance of each individual cell in a parallel lithium battery pack, thereby enabling the monitoring of the health status of each individual cell. Summary of the Invention

[0005] The purpose of this application is to provide a health status monitoring method and system for parallel lithium batteries, which solves the technical problems existing in the prior art, such as the inability to estimate the ohmic internal resistance of each individual cell in a parallel lithium battery pack without increasing hardware costs or interrupting the normal operation of the system, thereby realizing the monitoring of the health status of each individual cell.

[0006] To solve the above-mentioned technical problems, the solution adopted in this application is as follows:

[0007] A method for monitoring the health status of a parallel lithium battery, comprising:

[0008] Identify pulse events and ripple events during the operation of parallel lithium battery packs, and obtain the time-domain data windows corresponding to pulse events and ripple events;

[0009] Extract time-domain features from the time-domain data window corresponding to the impulse event, and extract frequency-domain observation vectors from the time-domain data window corresponding to the ripple event;

[0010] A joint observation model for parallel lithium battery packs is established. In the joint observation model, the parallel branch where each lithium battery cell is located is equivalent to the series connection of the ohmic internal resistance of the lithium battery cell and the shared polarization impedance. The shared polarization impedance is the parallel combination of the polarization internal resistance and polarization capacitance shared by each parallel branch.

[0011] Using frequency domain observation vectors and time domain feature quantities, based on a joint observation model, the ohmic internal resistance and shared polarization impedance of each parallel branch containing a lithium battery cell are jointly estimated to obtain the estimated value of the ohmic internal resistance of each parallel branch.

[0012] Based on the estimated ohmic resistance of each parallel branch, the health status of each lithium battery cell in the parallel lithium battery pack is calculated.

[0013] Preferably, the method for identifying pulse events and ripple events during the operation of the parallel lithium battery pack and obtaining the time-domain data windows corresponding to the pulse events and the ripple events includes the following steps:

[0014] Real-time acquisition of the total voltage and total current of the parallel lithium battery pack;

[0015] Pulse events and ripple events are identified and labeled based on the characteristics of total current changes, specifically:

[0016] When the absolute value of the total current change rate is greater than the first preset threshold and the duration of the absolute value of the total current change rate being greater than the first preset threshold is less than the second preset threshold, it is determined to be a pulse event.

[0017] The total voltage and total current are extracted for the first duration before the pulse event occurs and the second duration after the pulse event occurs, and used as the time-domain data window corresponding to the pulse event.

[0018] When the absolute value of the total current change rate is less than the third preset threshold, the effective value of the ripple component in the total current is greater than the fourth preset threshold, and the duration of this state is greater than the fifth preset threshold, it is determined to be a ripple event.

[0019] The total voltage and total current of the parallel lithium battery pack during the duration of this state are extracted and used as the time-domain data window corresponding to the ripple event.

[0020] Preferably, after obtaining the time-domain data window corresponding to the ripple event, the following processing steps are also included:

[0021] Windowed Fast Fourier Transform is performed on the total voltage and total current within the time-domain data window corresponding to the ripple event to obtain the frequency-domain data window, namely the total voltage spectrum and the total current spectrum.

[0022] Preferably, time-domain features are extracted from the time-domain data window corresponding to the impulse event, and frequency-domain observation vectors are extracted from the time-domain data window corresponding to the ripple event. The specific implementation method includes the following steps:

[0023] For each pulse event's time-domain data window, extract the total voltage change and total current change within the first preset time window after the pulse event occurs;

[0024] The transient ohmic conductance measurement is calculated based on the total voltage change and the total current change, and the transient ohmic conductance measurement constitutes a time-domain characteristic quantity;

[0025] The online impedance measurements of the parallel battery pack at each effective frequency point are calculated based on the total voltage spectrum and the total current spectrum. The modulus of each online impedance measurement is then used to construct a frequency domain observation vector.

[0026] Preferably, a joint observation model for parallel lithium battery packs is established, and its specific implementation method includes the following steps:

[0027] Each parallel branch is equivalent to a first-order RC equivalent circuit model. Specifically, the first-order RC equivalent circuit model is composed of the ohmic internal resistance of the parallel branch and the shared polarization impedance connected in series. The shared polarization impedance is composed of the polarization internal resistance and polarization capacitance of the parallel branch connected in parallel.

[0028] calculate The predicted total impedance of all parallel branches is the reciprocal of the sum of the reciprocals of the impedances of each parallel branch. This represents the number of individual lithium-ion cells in a parallel lithium-ion battery pack; the impedance of each parallel branch includes its internal ohmic resistance and shared polarization impedance.

[0029] Calculate the predicted transient ohmic conductance of the parallel battery pack, that is: within the first preset time window after the pulse event occurs, calculate the sum of the transient ohmic conductance of each parallel branch;

[0030] By integrating frequency domain observation relationships and time domain observation relationships, a joint observation model is constructed.

[0031] Preferably, using frequency domain observation vectors and time domain feature quantities, based on a joint observation model, the ohmic internal resistance and shared polarization impedance of each lithium battery cell in the parallel branch are jointly estimated to obtain the estimated value of the ohmic internal resistance of each branch. The specific implementation method includes the following steps:

[0032] Using the ohmic internal resistance, polarization internal resistance, and polarization capacitance of each parallel branch as parameters to be determined, a joint observation equation set is constructed, which includes a frequency domain observation equation set and a time domain observation equation set.

[0033] The joint observation equations are transformed into a nonlinear least squares optimization problem.

[0034] Solve the nonlinear least squares optimization problem to obtain the estimated values ​​of the parameters to be determined.

[0035] Preferably, based on the obtained estimated ohmic resistance values ​​of each parallel branch, the health status of each lithium battery cell in the parallel lithium battery pack is evaluated and monitored online. Specific implementation methods include:

[0036] Based on the estimated ohmic internal resistance of the parallel branch, combined with the factory nominal ohmic internal resistance and the end-of-life ohmic internal resistance threshold of the lithium battery cell in the parallel branch, the health status of the lithium battery cell in the parallel branch is calculated.

[0037] The specific method for calculating the health status is as follows: the difference between the end-of-life ohmic internal resistance threshold of the lithium battery cell and the estimated value of the ohmic internal resistance of the lithium battery cell, divided by the difference between the end-of-life ohmic internal resistance threshold of the lithium battery cell and the nominal ohmic internal resistance of the lithium battery cell at the time of manufacture.

[0038] When the health status is below a preset threshold, the health status of the lithium battery cell is determined to be unqualified.

[0039] Preferably, a joint observation equation set is constructed, which includes frequency domain observation equations and time domain observation equations. The specific implementation methods include:

[0040] At each effective frequency point, the magnitude of the predicted total impedance of the parallel lithium battery pack at that effective frequency point is compared with the magnitude of the online impedance measurement in the frequency domain observation vector, and the difference between the two is defined as the frequency domain observation error at that effective frequency point.

[0041] The frequency domain observation equation is: at the effective frequency point, the magnitude of the total impedance prediction value is equal to the sum of the magnitude of the online impedance measurement value and the frequency domain observation error;

[0042] By combining the frequency domain observation equations corresponding to each of the effective frequency points, we obtain a set of frequency domain observation equations.

[0043] Preferably, a joint observation equation set is constructed, which includes frequency domain observation equations and time domain observation equations. The specific implementation method further includes:

[0044] Within the first preset time window after the pulse event occurs, the predicted value of the transient ohmic conductance of the parallel lithium battery pack is compared with the measured value of the transient ohmic conductance, and the difference between the two is defined as the time domain observation error.

[0045] The time-domain observation equation is as follows: within the first preset time window, the predicted value of transient ohmic conductance is equal to the sum of the measured value of transient ohmic conductance and the time-domain observation error;

[0046] The time-domain observation equations and the frequency-domain observation equations are combined to form a joint observation equation set.

[0047] A health status monitoring system for parallel lithium batteries, applicable to the aforementioned health status monitoring method for parallel lithium batteries, comprising:

[0048] The data acquisition module is used to collect the total voltage and total current of the parallel lithium battery pack in real time.

[0049] The event recognition module is connected to the data acquisition module and is used to identify and mark pulse events and ripple events based on the change characteristics of the total current, and to obtain the time domain data window corresponding to the pulse event and the time domain data window corresponding to the ripple event.

[0050] The feature extraction module, connected to the event recognition module, is used to extract time-domain features from the time-domain data window corresponding to the pulse event and to extract frequency-domain observation vectors from the time-domain data window corresponding to the ripple event.

[0051] The model building module is connected to the feature extraction module to establish a joint observation model for parallel lithium battery packs;

[0052] The parameter estimation module, connected to the model building module, is used to jointly estimate the ohmic internal resistance and shared polarization impedance of each parallel branch containing a lithium battery cell based on the joint observation model using frequency domain observation vectors and time domain feature quantities, thereby obtaining the estimated value of the ohmic internal resistance of each parallel branch.

[0053] The health status assessment module is used to calculate the health status of each lithium battery cell based on the estimated ohmic internal resistance of each parallel branch, as well as the nominal ohmic internal resistance at the factory and the end-of-life ohmic internal resistance threshold of each lithium battery cell.

[0054] The technical solution of this application has at least the following advantages and beneficial effects:

[0055] The parallel lithium battery health status monitoring method and system proposed in this invention achieves decoupled estimation of the ohmic internal resistance of each parallel branch by integrating observation information under two different operating conditions: pulse events and ripple events, and using a joint observation model. This overcomes the technical difficulty that traditional methods cannot identify the internal resistance of each individual cell in a parallel battery pack online.

[0056] Specifically, in the frequency domain, this invention extracts the voltage and current at each effective frequency point under ripple events, constructs a frequency domain impedance prediction model associated with the ohmic resistance and shared polarization parameters of each branch, and compares it with the online impedance measurement value to establish a frequency domain observation equation set, thereby transforming the frequency response information into a reverse constraint on the ohmic resistance. In the time domain, it utilizes the transient response within the first preset time window after the pulse event occurs to construct a time domain observation equation between the predicted and measured transient ohmic conductance values, thereby introducing additional constraints in the time domain. By combining the frequency domain observation equation set and the time domain observation equation set to form a joint observation equation set, the underdetermined problem caused by the insufficient number of equations under a single observation dimension is solved, realizing the joint identification of the ohmic resistance and shared polarization parameters of each branch.

[0057] Therefore, this invention does not require additional excitation signals or current sensors. It can complete online monitoring by utilizing the ripple and pulse events that are naturally generated during the normal operation of the battery system. This ensures the non-invasiveness and continuity of the monitoring process and reduces the system deployment cost. At the same time, the estimated ohmic internal resistance values ​​of each branch are used to determine the health status of each parallel cell. Attached Figure Description

[0058] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0059] 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, and 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.

[0060] See Figure 1 This invention discloses a method for monitoring the health status of parallel lithium batteries, wherein the parallel lithium battery pack consists of... It is composed of individual lithium battery cells connected in parallel. For integers greater than or equal to 2, the following steps are included:

[0061] Identify pulse events and ripple events during the operation of parallel lithium battery packs, and obtain the time-domain data windows corresponding to pulse events and ripple events;

[0062] Extract time-domain features from the time-domain data window corresponding to the impulse event, and extract frequency-domain observation vectors from the time-domain data window corresponding to the ripple event;

[0063] A joint observation model for parallel lithium battery packs is established. In the joint observation model, the parallel branch where each lithium battery cell is located is equivalent to the series connection of the ohmic internal resistance of the lithium battery cell and the shared polarization impedance.

[0064] Among them, the shared polarization impedance is formed by the parallel connection of the polarization internal resistance and polarization capacitance of the parallel branch where the lithium battery cell is located; the polarization internal resistance and polarization capacitance of each parallel branch have the same value.

[0065] The joint observation model uses the ohmic internal resistance, polarization internal resistance, and polarization capacitance of each parallel branch as parameters to be determined, and calculates the impedance and ohmic conductance of each parallel branch from these parameters.

[0066] The total impedance and transient ohmic conductance of the parallel lithium battery pack are calculated based on the impedance and ohmic conductance of each parallel branch.

[0067] By utilizing frequency domain observation vectors and time domain characteristic quantities, and based on a joint observation model, the ohmic internal resistance and shared polarization impedance of each lithium battery cell in the parallel branch are jointly estimated to obtain the estimated value of the ohmic internal resistance of each branch.

[0068] In this embodiment, pulse events and ripple events are identified during the operation of the parallel lithium battery pack, and time-domain data windows corresponding to pulse events and ripple events are obtained. The specific implementation method includes the following steps:

[0069] Real-time acquisition of the total voltage and total current of the parallel lithium battery pack;

[0070] Pulse events and ripple events are identified and labeled based on the characteristics of total current changes, specifically:

[0071] When the absolute value of the total current change rate is greater than the first preset threshold and the duration is less than the second preset threshold, it is determined to be a pulse event;

[0072] The total voltage and total current are extracted for the first duration before the pulse event occurs and the second duration after the pulse event occurs, and used as the time-domain data window corresponding to the pulse event.

[0073] When the absolute value of the total current change rate is less than the third preset threshold, the effective value of the ripple component in the total current is greater than the fourth preset threshold, and the duration of this state is greater than the fifth preset threshold, it is determined to be a ripple event.

[0074] The total voltage and total current during the duration of this state are extracted and used as the time-domain data window corresponding to the ripple event.

[0075] The first preset threshold is greater than the third preset threshold to ensure that pulse events and ripple events can be distinguished by the rate of change of current.

[0076] This embodiment is based on... Taking a parallel lithium battery pack composed of individual lithium battery cells connected in parallel as an example, the specific implementation process of this invention is described in detail. The nominal capacity of each lithium battery cell is 50 Ah, the nominal internal resistance at the factory is 1.5 milliohms, the internal resistance threshold at the end of its lifespan is 3 milliohms, and the sampling rate of the battery management system is set to 2kHz, that is, the total voltage and total current are collected every 0.5 milliseconds.

[0077] As an example, in a pulse event, the first preset threshold is set to 50A / s, and the second preset threshold is set to 100ms; in a ripple event, the third preset threshold is set to 5A / s, the fourth preset threshold is set to 2A, the fifth preset threshold is set to 0.5s, the first duration before the occurrence time is set to 5ms, and the second duration after the occurrence time is set to 50ms; the present invention does not limit the values ​​of each parameter, and can be set according to the actual situation of the parallel lithium battery pack.

[0078] For example, when identifying a pulse event, the battery management system calculates the rate of change of the total current in real time. If the total current rises from 10A to 80A within 20ms, the rate of change of the total current is 3500A / s. The duration of the rate of change of the total current being greater than the first preset threshold is 20ms and less than the second preset threshold is 100ms. This meets the pulse event determination condition and is marked as a pulse event.

[0079] It should be noted that the identification criteria for pulse events are as follows: when the total current undergoes a rapid step change, at the instant of the step change, the polarization capacitors of the individual lithium battery cells in each parallel branch have not yet been charged. Each parallel branch can be equivalent to a purely resistive network, and the total current is distributed among the parallel branches according to the ohmic conductance ratio. The overall port characteristics of the parallel lithium battery pack are entirely determined by the ohmic internal resistance of each parallel branch. Therefore, by detecting the changes in total voltage and total current when the total current undergoes a rapid step change, the transient ohmic conductance reflecting the sum of the ohmic conductances of each parallel branch can be obtained.

[0080] The identification criteria for ripple events are as follows: When the parallel lithium battery pack is in steady-state operation, the power electronic devices operating in the system operate at a fixed switching frequency, generating ripple components of that switching frequency and its harmonics in the total current. These ripple components can be regarded as excitation signals of the parallel lithium battery pack at multiple frequency points. Since the influence of battery polarization impedance varies at different frequencies, the polarization capacitor is approximately short-circuited and the polarization impedance approaches the ohmic internal resistance at high frequencies, while the polarization impedance is more pronounced at mid-to-low frequencies. Therefore, by detecting a data window with sufficient ripple energy and duration under steady-state conditions and performing frequency domain analysis, frequency domain observation vectors reflecting the internal resistance distribution and polarization parameters of each parallel branch can be obtained at multiple frequency points.

[0081] Furthermore, after obtaining the time-domain data window corresponding to the ripple event, the following processing steps are also included:

[0082] Windowed Fast Fourier Transform is performed on the total voltage and total current within the time-domain data window corresponding to the ripple event to obtain the total voltage spectrum and total current spectrum. The total voltage spectrum and total current spectrum are then used as the frequency-domain data window corresponding to the ripple event.

[0083] As an example, this embodiment uses a Hanning window with a window length of 4096 points; the window length of the Hanning window can be modified according to the actual application scenario, and the present invention does not limit its specific parameters.

[0084] The Fast Fourier Transform (FFT) is a commonly used signal transformation method in the field of signal processing and is common knowledge in this field, so it will not be elaborated here.

[0085] In this embodiment, time-domain features are extracted from the time-domain data window corresponding to the impulse event, and frequency-domain observation vectors are extracted from the time-domain data window corresponding to the ripple event. The specific implementation method includes the following steps:

[0086] For each pulse event's time-domain data window, the total voltage change and total current change are extracted within the first preset time window after the pulse event occurs. The transient ohmic conductance measurement value is calculated based on the total voltage change and total current change, and the transient ohmic conductance measurement value constitutes the time-domain characteristic quantity.

[0087] Calculate the online impedance measurement values ​​of the parallel battery pack based on the total voltage spectrum and the total current spectrum, and construct the frequency domain observation vector by taking the modulus of each online impedance measurement value.

[0088] Specifically, the extraction process of the frequency domain observation vector is as follows:

[0089] The total voltage within the time-domain data window for each ripple event is denoted as... The total current is denoted as The total voltage spectrum obtained by performing a fast Fourier transform on it is denoted as The obtained total current spectrum is denoted as ;in, For frequency, For time.

[0090] When the parallel lithium battery pack is in steady-state operation, the switching frequency of the power electronic devices operating in the system is denoted as: This generates harmonic components at the switching frequency and its harmonic frequencies in the total current. The switching frequency and its preceding harmonic frequencies are then considered. The harmonic frequencies are taken as the effective frequency points, that is... , It is an integer greater than or equal to 2. , The total number of effective frequency points. .

[0091] The formula for calculating the online impedance measurement value of a parallel lithium battery pack is:

[0092] ;

[0093] in, For the first The measured line impedance values ​​corresponding to each frequency point;

[0094] Take the modulus of each online impedance measurement value , constitute frequency domain observation vector :

[0095] .

[0096] Specifically, the extraction process of time-domain features is as follows:

[0097] For each pulse event, the time-domain data window is the first preset time window after the pulse event occurs. Calculate the transient ohmic conductance measurement value. ;

[0098] Among them, the first preset time window The time constant for establishing battery polarization is less than that of a single lithium battery cell, at which point the polarization capacitance of the single lithium battery cell is not charged.

[0099] Set the first preset time window The total voltage change of the internal parallel lithium battery pack is The total change in current is The transient ohmic conductance measurement value for:

[0100] ;

[0101] A single transient ohmic conductance measurement As a time-domain feature quantity;

[0102] It should be noted that the battery polarization establishment time constant refers to the rate at which the polarization voltage of a single lithium battery cell establishes from zero to its steady-state value after a current step, which is common knowledge in the field of technical expertise. Within the first preset time window... Inside, the polarized capacitor of a lithium battery cell is not charged, and the voltage across its terminals is approximately zero. Each parallel branch is equivalent to an internal resistance of one ohm.

[0103] As an example, the first preset time window Set to 0.1ms to 0.5ms, this value is less than the typical polarization settling time constant of lithium batteries, the first preset time window. The value of can be modified according to the actual situation such as the type of lithium battery. This is only an example and the present invention does not limit its value.

[0104] In this embodiment, a joint observation model for parallel lithium battery packs is established. In the joint observation model, the parallel branch containing each lithium battery cell is equivalent to the series connection of the ohmic internal resistance of that lithium battery cell and the shared polarization impedance. The specific implementation method includes the following steps:

[0105] Each parallel branch is equivalent to a first-order RC equivalent circuit model. Specifically, the first-order RC equivalent circuit model is composed of the ohmic internal resistance of the parallel branch and the shared polarization impedance connected in series. The shared polarization impedance is composed of the polarization internal resistance and polarization capacitance of the parallel branch connected in parallel.

[0106] Specifically, the ohmic internal resistance of each parallel branch is an independent parameter to be determined, that is, the ohmic internal resistance of different parallel branches can take different values; the polarization internal resistance of each parallel branch takes the same value, and the polarization capacitance of each parallel branch also takes the same value.

[0107] It should be noted that the reason for the above constraints is that, for parallel battery cells of the same batch and model, their ohmic internal resistance is mainly determined by process factors such as the welding quality of the electrode tabs and the contact resistance between the current collector and the electrolyte, resulting in a large dispersion among individual lithium battery cells; while the polarization internal resistance and polarization capacitance are mainly determined by the electrochemical reaction kinetics of the electrode materials, exhibiting high consistency among lithium battery cells of the same batch. Therefore, in this invention, the polarization parameters of each parallel branch are constrained to the same value, thereby reducing the total number of parameters to be determined from... The number was reduced to This transforms an underdetermined problem into an overdetermined problem.

[0108] In this embodiment, The number of parameters to be determined has been reduced from 9 to 5.

[0109] More specifically, in frequency Next, the The impedance of the branch is:

[0110] ;

[0111] in, The imaginary unit, The frequency is consistent with the frequency variable of the Fast Fourier Transform mentioned above; For the first The internal resistance of a parallel branch is expressed in ohms. The polarization internal resistance of each parallel branch; For each parallel branch, the polarization capacitor; Indicates the first A parallel branch road, , This represents the number of individual lithium battery cells.

[0112] calculate The predicted total impedance of all parallel branches is the reciprocal of the sum of the reciprocals of the impedances of each parallel branch.

[0113] Specifically, The formula for calculating the predicted total impedance of a parallel branch is as follows:

[0114] ;

[0115] in, This is the predicted total impedance of the parallel battery pack, in ohms.

[0116] Based on this, parallel lithium battery packs at the effective frequency point The predicted magnitude of the total impedance at point is:

[0117] ;

[0118] Therefore, the frequency domain observation vector The modulus of the online impedance measurement value corresponding to each effective frequency point The predicted total impedance of the equivalent circuit model described above is modulo By associating them, a frequency domain observation relationship is formed;

[0119] Calculate the predicted transient ohmic conductance of the parallel battery pack, that is: within the first preset time window after the pulse event occurs, calculate the sum of the transient ohmic conductance of each parallel branch;

[0120] Specifically, the formula for calculating the predicted transient ohmic conductance of a parallel battery pack is as follows:

[0121] ;

[0122] Thus, the time-domain observation relationship is obtained;

[0123] By fusing frequency domain observation relationships and time domain observation relationships, a joint observation model is constructed. The joint observation model is as follows:

[0124] ;

[0125] The model establishes the parameters to be determined. With frequency domain observation vector and time-domain features The mapping relationship between them.

[0126] It should be noted that in the above joint observation model, the frequency domain observation vector is related to the polarization parameters. , The frequency domain observation vector is more sensitive to changes in the total ohmic resistance, while the time domain characteristic is more sensitive to changes in the total ohmic resistance. Therefore, the frequency domain observation vector and the time domain characteristic are complementary, thus enabling effective identification of the parameters to be determined.

[0127] In this embodiment, using frequency domain observation vectors and time domain feature quantities, based on a joint observation model, the ohmic internal resistance and shared polarization impedance of each parallel branch containing a lithium battery cell are jointly estimated to obtain the estimated value of the ohmic internal resistance of each branch. The specific implementation method includes the following steps:

[0128] Using the ohmic internal resistance, polarization internal resistance, and polarization capacitance of each parallel branch as parameters to be determined, a joint observation equation set is constructed, which includes frequency domain observation equation set and time domain observation equation set.

[0129] Furthermore, a set of frequency domain observation equations is constructed, and its specific implementation methods include:

[0130] At the effective frequency point Predicted total impedance of parallel lithium batteries at [location] The following relationship exists between the online impedance measurements in the frequency domain observation vector and the measurements themselves:

[0131] ;

[0132] in, For the first Frequency domain observation error at each effective frequency point;

[0133] Will By simultaneously solving the frequency domain observation equations corresponding to the effective frequency points, we obtain the frequency domain observation equation set:

[0134] ;

[0135] Furthermore, the time-domain observation equation is constructed, and its specific implementation methods include:

[0136] Predicted transient ohmic conductance of the parallel lithium battery pack within the first preset time window after the pulse event occurs. With transient ohmic conductance measurement The relationship between them is:

[0137] ;

[0138] in, This represents the time-domain observation error; the above relationship is the time-domain observation equation.

[0139] By combining the time-domain observation equations and the frequency-domain observation equations, a joint observation equation set is formed:

[0140] ;

[0141] Wherein, the parameter vector to be determined is The total number of parameters to be determined is ;

[0142] In an embodiment, , , The value is set to 5, meaning the total number of effective frequency points is 5, and the total number of joint observation equations is 5. The total number of parameters to be determined is 5. At this time, the number of joint observation equations is greater than the number of parameters to be determined, forming an overdetermined system of equations, which can be solved by the nonlinear least squares method.

[0143] The joint observation equations are transformed into a nonlinear least squares optimization problem with the objective function being:

[0144] ;

[0145] in, For the first Weighting coefficients of each frequency domain observation equation These are the weighting coefficients for the time-domain observation equation. Each weighting coefficient is set according to the signal-to-noise ratio or measurement accuracy of its respective observation.

[0146] It should be noted that the principle for setting the weighting coefficients is as follows: for observations with high measurement accuracy and high signal-to-noise ratio, a larger weight is assigned; for observations with high measurement noise and low signal-to-noise ratio, a smaller weight is assigned.

[0147] As an example, in this embodiment, all weight coefficients are set to 1, meaning that each observation equation participates in the optimization with equal weight.

[0148] Solve the nonlinear least squares optimization problem to obtain the estimated values ​​of the parameters to be found;

[0149] Specifically, the estimated value of the parameter to be determined is denoted as... ;in For the first Estimates of the ohmic resistance of the parallel branches. This is an estimate of the polarization internal resistance. This is an estimated value for the polarization capacitance.

[0150] In this embodiment, the nonlinear least squares optimization problem is solved iteratively using the Levenberg-Marquardt algorithm. As a preferred implementation, the initial values ​​for the Levenberg-Marquardt algorithm are set as follows: the initial ohmic resistance of each parallel branch is set to the factory nominal ohmic resistance of 1.5 milliohms, the initial polarization resistance is set to 0.5 milliohms, and the initial polarization capacitance is set to 2000 farads. The iteration termination condition is set as follows: the objective function... The change between two adjacent iterations is less than Or the number of iterations reaches the maximum number of iterations, 100.

[0151] By using the above joint estimation method, the estimated ohmic internal resistance of each parallel branch can be obtained, thereby assessing and monitoring the battery health status of each lithium battery cell in the parallel lithium battery pack.

[0152] In this embodiment, based on the obtained estimated ohmic resistance values ​​of each parallel branch, the health status of each lithium battery cell in the parallel lithium battery pack is evaluated and monitored online. The specific implementation method includes:

[0153] Based on the estimated ohmic internal resistance of the parallel branch, combined with the factory nominal ohmic internal resistance and the end-of-life ohmic internal resistance threshold of the lithium battery cell in the parallel branch, the health status of the lithium battery cell in the parallel branch is calculated.

[0154] Specifically, the formula for calculating the health status of a single lithium battery cell is:

[0155] ;

[0156] in, For the first The nominal internal resistance of each lithium battery cell at the time of manufacture. For the first The ohmic internal resistance threshold at the end of life of a single lithium battery cell. The value range is 0%-100%; when When the percentage drops to 0% or below a preset threshold, the lifespan of the lithium battery cell is determined to have ended.

[0157] As an example, in this embodiment, the nominal internal resistance of each lithium battery cell is 1.5 milliohms, and the end-of-life internal resistance threshold is 3.0 milliohms. If the internal resistance of a certain parallel branch is 2 milliohms, then its health status is:

[0158] ;

[0159] It should be noted that the parallel lithium battery health status monitoring method proposed in this invention achieves decoupled estimation of the ohmic internal resistance of each parallel branch by fusing observation information from two different operating conditions: pulse events and ripple events, and using a joint observation model.

[0160] Another aspect of the present invention discloses a health status monitoring system for parallel lithium batteries, applicable to the aforementioned health status monitoring method for parallel lithium batteries, comprising:

[0161] The data acquisition module is used to collect the total voltage and total current of the parallel lithium battery pack in real time.

[0162] The event recognition module is connected to the data acquisition module and is used to identify and mark pulse events and ripple events based on the change characteristics of the total current, and to obtain the time domain data window corresponding to the pulse event and the time domain data window corresponding to the ripple event.

[0163] The feature extraction module, connected to the event recognition module, is used to extract time-domain features from the time-domain data window corresponding to the pulse event and to extract frequency-domain observation vectors from the time-domain data window corresponding to the ripple event.

[0164] The model building module is connected to the feature extraction module to establish a joint observation model for parallel lithium battery packs;

[0165] The parameter estimation module, connected to the model building module, is used to jointly estimate the ohmic internal resistance and shared polarization impedance of each parallel branch containing a lithium battery cell based on the joint observation model using frequency domain observation vectors and time domain feature quantities, thereby obtaining the estimated value of the ohmic internal resistance of each parallel branch.

[0166] The health status assessment module is used to calculate the health status of each lithium battery cell based on the estimated ohmic internal resistance of each parallel branch, as well as the nominal ohmic internal resistance at the factory and the end-of-life ohmic internal resistance threshold of each lithium battery cell.

[0167] The various embodiments of the present invention have now been described in detail. To avoid obscuring the concept of the invention, some details known in the art have not been described. Those skilled in the art will fully understand how to implement the technical solutions of this invention based on the above description, and the scope of the invention is defined by the appended claims.

Claims

1. A method for monitoring the health status of a parallel lithium battery, characterized in that, include: Identify pulse and ripple events during the operation of parallel lithium battery packs, and obtain the time-domain data windows corresponding to the pulse events and the ripple events, i.e.: Real-time acquisition of the total voltage and total current of the parallel lithium battery pack; Pulse events and ripple events are identified and labeled based on the characteristics of total current changes, specifically: When the absolute value of the total current change rate is greater than the first preset threshold and the duration of the absolute value of the total current change rate being greater than the first preset threshold is less than the second preset threshold, it is determined to be a pulse event. The total voltage and total current are extracted for the first duration before the pulse event occurs and the second duration after the pulse event occurs, and used as the time-domain data window corresponding to the pulse event. When the absolute value of the total current change rate is less than the third preset threshold, the effective value of the ripple component in the total current is greater than the fourth preset threshold, and the duration of this state is greater than the fifth preset threshold, it is determined to be a ripple event. The total voltage and total current of the parallel lithium battery pack during the duration of this state are extracted and used as the time-domain data window corresponding to the ripple event. Extract time-domain features from the time-domain data window corresponding to the impulse event, and extract frequency-domain observation vectors from the time-domain data window corresponding to the ripple event; A joint observation model for parallel lithium battery packs is established. In the joint observation model, the parallel branch where each lithium battery cell is located is equivalent to the series connection of the ohmic internal resistance of the lithium battery cell and the shared polarization impedance. The shared polarization impedance is the parallel combination of the polarization internal resistance and polarization capacitance shared by each parallel branch. Using frequency domain observation vectors and time domain feature quantities, based on a joint observation model, the ohmic internal resistance and shared polarization impedance of each parallel branch containing a lithium battery cell are jointly estimated to obtain the estimated value of the ohmic internal resistance of each parallel branch. Based on the estimated ohmic resistance of each parallel branch, the health status of each lithium battery cell in the parallel lithium battery pack is calculated, i.e.: Based on the estimated ohmic internal resistance of the parallel branch, combined with the factory nominal ohmic internal resistance and the end-of-life ohmic internal resistance threshold of the lithium battery cell in the parallel branch, the health status of the lithium battery cell in the parallel branch is calculated. The specific method for calculating the health status is as follows: the difference between the end-of-life ohmic internal resistance threshold of the lithium battery cell and the estimated value of the ohmic internal resistance of the lithium battery cell, divided by the difference between the end-of-life ohmic internal resistance threshold of the lithium battery cell and the nominal ohmic internal resistance of the lithium battery cell at the time of manufacture. When the health status is below a preset threshold, the health status of the lithium battery cell is determined to be unqualified.

2. The method for monitoring the health status of a parallel lithium battery according to claim 1, characterized in that, After obtaining the time-domain data window corresponding to the ripple event, the following processing steps are also included: Windowed Fast Fourier Transform is performed on the total voltage and total current within the time-domain data window corresponding to the ripple event to obtain the frequency-domain data window, namely the total voltage spectrum and the total current spectrum.

3. The method for monitoring the health status of a parallel lithium battery according to claim 2, characterized in that, Extracting time-domain features from the time-domain data window corresponding to impulse events and extracting frequency-domain observation vectors from the time-domain data window corresponding to ripple events, the specific implementation method includes the following steps: For each pulse event's time-domain data window, extract the total voltage change and total current change within the first preset time window after the pulse event occurs; The transient ohmic conductance measurement is calculated based on the total voltage change and the total current change, and the transient ohmic conductance measurement constitutes a time-domain characteristic quantity; The online impedance measurements of the parallel battery pack at each effective frequency point are calculated based on the total voltage spectrum and the total current spectrum. The modulus of each online impedance measurement is then used to construct a frequency domain observation vector.

4. The method for monitoring the health status of a parallel lithium battery according to claim 3, characterized in that, The joint observation model for parallel lithium battery packs is established, and its implementation method includes the following steps: Each parallel branch is equivalent to a first-order RC equivalent circuit model. Specifically, the first-order RC equivalent circuit model is composed of the ohmic internal resistance of the parallel branch and the shared polarization impedance connected in series. The shared polarization impedance is composed of the polarization internal resistance and polarization capacitance of the parallel branch connected in parallel. count The predicted total impedance of all parallel branches is the reciprocal of the sum of the reciprocals of the impedances of each parallel branch. This represents the number of individual lithium-ion cells in a parallel lithium-ion battery pack; the impedance of each parallel branch includes its internal ohmic resistance and shared polarization impedance. Calculate the predicted transient ohmic conductance of the parallel battery pack, that is: within the first preset time window after the pulse event occurs, calculate the sum of the transient ohmic conductance of each parallel branch; By integrating frequency domain observation relationships and time domain observation relationships, a joint observation model is constructed.

5. The method for monitoring the health status of a parallel lithium battery according to claim 4, characterized in that, Using frequency domain observation vectors and time domain feature quantities, and based on a joint observation model, the ohmic internal resistance and shared polarization impedance of each lithium battery cell in the parallel branch are jointly estimated to obtain the estimated value of the ohmic internal resistance of each branch. The specific implementation method includes the following steps: Using the ohmic internal resistance, polarization internal resistance, and polarization capacitance of each parallel branch as parameters to be determined, a joint observation equation set is constructed, which includes a frequency domain observation equation set and a time domain observation equation set. The joint observation equations are transformed into a nonlinear least squares optimization problem. Solve the nonlinear least squares optimization problem to obtain the estimated values ​​of the parameters to be determined.

6. The method for monitoring the health status of a parallel lithium battery according to claim 5, characterized in that, The joint observation equation set is constructed, which includes frequency domain observation equations and time domain observation equations. Specific implementation methods include: At each effective frequency point, the magnitude of the predicted total impedance of the parallel lithium battery pack at that effective frequency point is compared with the magnitude of the online impedance measurement in the frequency domain observation vector, and the difference between the two is defined as the frequency domain observation error at that effective frequency point. The frequency domain observation equation is: at the effective frequency point, the magnitude of the total impedance prediction value is equal to the sum of the magnitude of the online impedance measurement value and the frequency domain observation error; By combining the frequency domain observation equations corresponding to each of the effective frequency points, we obtain a set of frequency domain observation equations.

7. The method for monitoring the health status of a parallel lithium battery according to claim 6, characterized in that, The joint observation equation set is constructed, which includes frequency domain observation equations and time domain observation equations. Specific implementation methods also include: Within the first preset time window after the pulse event occurs, the predicted value of the transient ohmic conductance of the parallel lithium battery pack is compared with the measured value of the transient ohmic conductance, and the difference between the two is defined as the time domain observation error. The time-domain observation equation is as follows: within the first preset time window, the predicted value of transient ohmic conductance is equal to the sum of the measured value of transient ohmic conductance and the time-domain observation error; The time-domain observation equations and the frequency-domain observation equations are combined to form a joint observation equation set.

8. A health status monitoring system for a parallel lithium battery, applicable to the health status monitoring method for a parallel lithium battery as described in any one of claims 1-7, comprising: The data acquisition module is used to collect the total voltage and total current of the parallel lithium battery pack in real time. The event recognition module, connected to the data acquisition module, is used to identify and mark pulse events and ripple events based on the characteristics of total current changes, and to obtain the time-domain data windows corresponding to pulse events and ripple events. Specifically, it acquires the total voltage and total current of the parallel lithium battery pack in real time. The identification and marking of pulse events and ripple events based on the characteristics of total current changes are as follows: when the absolute value of the total current change rate is greater than a first preset threshold and the duration of the absolute value of the total current change rate being greater than the first preset threshold is less than a second preset threshold, it is determined to be a pulse event; the total voltage and total current for a first duration before the pulse event and a second duration after the pulse event are extracted as the time-domain data window corresponding to the pulse event. When the absolute value of the total current change rate is less than a third preset threshold, and the effective value of the ripple component in the total current is greater than a fourth preset threshold, and the duration of this state is greater than a fifth preset threshold, it is determined to be a ripple event; the total voltage and total current of the parallel lithium battery pack during the duration of this state are extracted as the time-domain data window corresponding to the ripple event. The feature extraction module, connected to the event recognition module, is used to extract time-domain features from the time-domain data window corresponding to the pulse event and to extract frequency-domain observation vectors from the time-domain data window corresponding to the ripple event. The model building module is connected to the feature extraction module to establish a joint observation model for parallel lithium battery packs; The parameter estimation module, connected to the model building module, is used to jointly estimate the ohmic internal resistance and shared polarization impedance of each parallel branch containing a lithium battery cell based on the joint observation model using frequency domain observation vectors and time domain feature quantities, thereby obtaining the estimated value of the ohmic internal resistance of each parallel branch. The health status assessment module is used to calculate the health status of each lithium battery cell based on the estimated ohmic resistance of each parallel branch, the nominal ohmic resistance at the factory gate, and the ohmic resistance threshold at the end of the battery's life. The specific calculation method is as follows: the difference between the ohmic resistance threshold at the end of the battery's life and the estimated ohmic resistance of the lithium battery cell is divided by the difference between the ohmic resistance threshold at the end of the battery's life and the nominal ohmic resistance at the factory gate. When the health status is lower than the preset threshold, the lithium battery cell is determined to be unqualified in terms of health status.

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