A battery cycle life prediction method and system under a wide temperature range condition

Abstract

The application relates to the technical field of battery testing and evaluation, and specifically discloses a battery cycle life prediction method and system under wide-temperature-range conditions, dynamic time sequence data sets are obtained by acquiring the end voltage, end current, shell temperature and corresponding time stamp of battery cycle charging and discharging; time-frequency domain decoupling transformation is performed on the data sets, and an extreme-phase deposition impedance factor representing lithium deposition dynamics and a lattice recession impedance factor representing positive electrode material structure recession characteristics are respectively extracted; according to the competition balance degree between the two factors, the bias direction and bias amplitude of the capacity attenuation rate under the current working condition are calculated by combining the current cumulative discharge energy; the reference attenuation track is corrected in the reverse direction according to the bias direction and bias amplitude, the residual cycle life prediction value under the current aging state is output, and the target operating temperature offset amount that makes the competition balance degree tend to a stable threshold value is synchronously calculated; the application realizes dynamic prediction of the battery cycle life and optimization of the operating temperature under wide-temperature-range conditions.

Description

Technical Field

[0001] This invention relates to the field of battery testing and evaluation technology, specifically to a method and system for predicting battery cycle life under a wide temperature range. Background Technology

[0002] In existing wide-temperature-range battery lifetime prediction methods, in view of the competitive relationship between the two degradation modes of low-temperature lithium plating and high-temperature material aging, accelerated cycle lifetime tests are usually carried out at different temperature points. The degradation rate measured at each temperature point is fitted into a quadratic polynomial with respect to temperature. The extreme point of the parabolic curve is used to characterize the temperature of the lowest degradation rate. Based on this, a functional relationship between capacity retention rate and cycle temperature and cumulative discharge energy is established to achieve lifetime prediction in a wide temperature range from low temperature to high temperature.

[0003] In existing wide-temperature-range battery life prediction methods, the competitive relationship between the two degradation modes of low-temperature lithium plating and high-temperature aging can only be described offline by an empirical model with fixed coefficients. It is impossible to dynamically quantify and determine the relative dominance of the two degradation modes based on the current aging state and operating conditions of the battery. As a result, the prediction model cannot be adaptively updated with the battery aging process, and a systematic deviation occurs between the life prediction results under wide temperature range and the actual degradation path of the battery. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for predicting battery cycle life under a wide temperature range, so as to solve the problems mentioned above.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] A method for predicting battery cycle life under wide temperature range conditions includes the following steps:

[0007] S1: Obtain the battery's terminal voltage, terminal current, casing temperature, and corresponding timestamps during the cyclic charging and discharging process to form a dynamic time-series dataset;

[0008] S2: Perform time-frequency domain decoupling transformation on the dynamic time-series dataset to extract the polar phase deposition impedance factor characterizing lithium deposition kinetics and the lattice decay impedance factor characterizing the structural decay characteristics of the cathode material.

[0009] S3: Based on the competitive balance between the polar phase deposition impedance factor and the lattice decay impedance factor, and combined with the current accumulated discharge energy, the bias direction and bias amplitude of the capacity decay rate under the current operating conditions are calculated.

[0010] S4: Based on the bias direction and bias magnitude, reverse the reference decay trajectory, output the predicted value of the remaining cycle life under the current aging state, and simultaneously calculate the target operating temperature offset that makes the competitive balance tend to the stable threshold.

[0011] As a further aspect of the present invention: the process for obtaining the polar phase deposition impedance factor is as follows:

[0012] A frequency sweep cross-correlation operation is performed on the terminal current and terminal voltage in the dynamic time series dataset to analyze the real and imaginary parts of the complex impedance at each frequency.

[0013] The slope of the change of the imaginary part relative to the real part in the low-frequency band is extracted, and the fluctuation amplitude of the slope within a preset temperature window is used as the polar phase deposition impedance factor.

[0014] As a further aspect of the present invention: the frequency sweep cross-correlation operation specifically includes:

[0015] Extract the terminal current and terminal voltage of the charging and discharging transition segment in the dynamic time series data, and discretize the terminal current into multiple current sub-segments according to equal time windows;

[0016] By performing cross-correlation integration between each current sub-segment and the terminal voltage, the peak value of the cross-correlation function corresponding to each window is obtained.

[0017] Using the lag time corresponding to the peak value as the horizontal axis and the peak value as the vertical axis, we construct the real part sequence and the imaginary part sequence of the complex impedance spectrum.

[0018] As a further aspect of the present invention: the process for obtaining the lattice decay impedance factor is as follows:

[0019] Time delay correlation analysis was performed on the shell temperature and terminal voltage in the dynamic time series dataset to obtain the response correlation coefficient sequence under different lag times;

[0020] The lag time corresponding to the peak value in the correlation coefficient sequence is taken, and the drift of the lag time with the number of cycles is used as the lattice decay impedance factor.

[0021] As a further aspect of the present invention: the process of obtaining the response correlation coefficient sequence is as follows:

[0022] Align the shell temperature sequence and terminal voltage sequence in the dynamic time series dataset with the same time axis, and extract the continuous change segment of the shell temperature sequence and terminal voltage sequence during the constant current discharge stage;

[0023] Based on the continuously changing segments, the shell temperature sequence is shifted backward step by step according to a preset lag step. The covariance between the shell temperature sequence and the terminal voltage sequence is calculated once for each shift.

[0024] The covariance values ​​calculated at each lag step are sequentially aggregated to form a response correlation coefficient sequence.

[0025] As a further aspect of the present invention: the calculation of the bias direction and bias magnitude of the capacity decay rate under the current operating condition specifically includes:

[0026] The deviations of the polar phase deposition impedance factor and the lattice decay impedance factor from their initial values ​​are taken, and the difference between the two deviations is taken as the competitive balance after nonlinear scaling according to the preset weights.

[0027] The competition balance degree and the current accumulated discharge energy are input into a preset decay deviation mapping table, and the offset direction of the capacity decay rate relative to the reference decay rate under the current operating condition is obtained by interpolation and table lookup.

[0028] The bias amplitude is determined based on the upper and lower limits of the attenuation deviation corresponding to the bias direction, combined with the absolute value of the competition balance.

[0029] As a further aspect of the present invention: the process of obtaining the competitive balance is as follows:

[0030] The polar phase deposition impedance factor and the lattice decay impedance factor are respectively calculated by subtracting their corresponding initial values ​​to obtain the deposition deviation amplitude and the lattice deviation amplitude.

[0031] Determine the deviation level range of both the deposition deviation range and the lattice deviation range, and each deviation level range corresponds to a different scaling factor;

[0032] The two deviations are scaled by various scaling factors, and the difference is taken as the competitive balance.

[0033] As a further aspect of the present invention: the output of the predicted remaining cycle life under the current aging state specifically includes:

[0034] The positive and negative directions of the correction for the reference attenuation trajectory are determined by the offset direction, and the magnitude of the correction at each node of the reference attenuation trajectory is determined by the offset magnitude.

[0035] The correction magnitude is applied point by point in the bias direction to each remaining lifetime node of the baseline attenuation trajectory to obtain the corrected attenuation trajectory.

[0036] The number of cycles at the intersection of the corrected decay trajectory and the preset failure threshold is used as the predicted value of the remaining cycle life.

[0037] As a further aspect of the present invention: the calculation process for the target operating temperature offset is as follows:

[0038] The actual drift of the current decay rate is calculated by back-calculating the remaining cycle life prediction and the accumulated number of cycles.

[0039] Substitute the actual drift amount into the mapping sequence between the competitive balance degree and the temperature deviation amount, and use the stability threshold as the target for back interpolation to obtain the temperature difference to be compensated.

[0040] The temperature difference to be compensated is superimposed on the current shell temperature measurement value to obtain the target operating temperature offset.

[0041] A battery cycle life prediction system under wide temperature range conditions includes:

[0042] The dynamic time-series acquisition module acquires the battery's terminal voltage, terminal current, casing temperature, and corresponding timestamps during the cyclic charging and discharging process, forming a dynamic time-series dataset.

[0043] The competitive feature decoupling extraction module performs time-frequency domain decoupling transformation on the dynamic time-series dataset to extract the polar phase deposition impedance factor characterizing lithium deposition kinetics and the lattice decay impedance factor characterizing the structural decay characteristics of the cathode material.

[0044] The attenuation bias calculation module, based on the competitive balance between the polar phase deposition impedance factor and the lattice decay impedance factor, combined with the current accumulated discharge energy, calculates the bias direction and bias amplitude of the capacity decay rate under the current operating conditions.

[0045] The lifetime prediction and temperature control calculation module corrects the baseline decay trajectory in reverse according to the bias direction and bias magnitude, outputs the remaining cycle lifetime prediction value under the current aging state, and simultaneously calculates the target operating temperature offset that makes the competitive balance tend to the stable threshold.

[0046] The beneficial effects of this invention are:

[0047] (1) This invention extracts the polar phase deposition impedance factor related to lithium deposition kinetics and the lattice decay impedance factor related to the decay characteristics of cathode material structure by performing time-frequency domain decoupling transformation on battery terminal voltage, terminal current and shell temperature. This allows the contributions of the two decay modes, low-temperature lithium plating and high-temperature aging, to be quantified independently. Thus, the lifetime prediction under wide temperature range is transformed from single temperature extrapolation to a two-factor competitive discrimination based on internal physical state, effectively expanding the prediction range of traditional test methods at low and high temperatures.

[0048] (2) This invention calculates the bias direction and bias amplitude of the capacity decay rate by combining the competitive balance degree with the cumulative discharge energy, and uses the bias direction and bias amplitude to correct the reference decay trajectory in reverse to obtain the remaining cycle life prediction value. At the same time, it calculates the target operating temperature offset that makes the competitive balance degree tend to be balanced, so that the degree of deviation of the current operating condition from the reference state can be dynamically included in the life prediction process, avoiding the systematic deviation of the prediction results of a single fixed model in different temperature ranges, and improving the consistency of the wide temperature range cycle life prediction results in different aging stages. Attached Figure Description

[0049] The invention will now be further described with reference to the accompanying drawings.

[0050] Figure 1 This is a flowchart of the method of the present invention;

[0051] Figure 2 This is a flowchart illustrating the process of obtaining the polar phase deposition impedance factor in this invention.

[0052] Figure 3 This is a flowchart of the process for obtaining the lattice decay impedance factor in this invention;

[0053] Figure 4 This is a system block diagram of the present invention. Detailed Implementation

[0054] 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.

[0055] Please see Figure 1 As shown, this invention provides a method for predicting battery cycle life under wide temperature range conditions, comprising the following steps:

[0056] S1: Obtain the battery's terminal voltage, terminal current, casing temperature, and corresponding timestamps during the cyclic charging and discharging process to form a dynamic time-series dataset;

[0057] S2: Perform time-frequency domain decoupling transformation on the dynamic time-series dataset to extract the polar phase deposition impedance factor characterizing lithium deposition kinetics and the lattice decay impedance factor characterizing the structural decay characteristics of the cathode material.

[0058] S3: Based on the competitive balance between the polar phase deposition impedance factor and the lattice decay impedance factor, and combined with the current accumulated discharge energy, the bias direction and bias amplitude of the capacity decay rate under the current operating conditions are calculated.

[0059] S4: Based on the bias direction and bias magnitude, reverse the reference decay trajectory, output the predicted value of the remaining cycle life under the current aging state, and simultaneously calculate the target operating temperature offset that makes the competitive balance tend to the stable threshold.

[0060] In S1, the battery's terminal voltage, terminal current, casing temperature, and corresponding timestamps during cyclic charging and discharging are acquired to form a dynamic time-series dataset, specifically including:

[0061] The battery under test is placed in a charge-discharge test device, which is a power battery test system with independent constant current-constant voltage charge-discharge control function (such as the BT2000 type or a battery test system with equivalent function).

[0062] Connect the positive and negative terminals of the battery to the corresponding positive and negative ports of the charge / discharge test equipment via copper wires. The copper wires have a diameter of 6 square millimeters and open copper lugs are crimped to both ends of the wires. The copper lugs are fastened to the terminals with bolts, and the contact resistance is less than 0.1 milliohms.

[0063] The charging and discharging test equipment integrates a voltage sampling circuit and a current sampling circuit. The positive and negative sampling lines of the voltage sampling circuit are independently connected to the surfaces of the positive and negative terminals of the battery, respectively. The cross-sectional area of ​​the core wire of the sampling line is 0.5 square millimeters, and the connection point with the terminal is located inside the crimping point of the current wire, 10 millimeters away from the root of the terminal, in order to eliminate the influence of the voltage drop caused by the large current on the resistance of the wire on the voltage measurement.

[0064] Current sampling employs the principle of a shunt or Hall effect and is performed by a standard current sensor inside the charge / discharge test equipment. The temperature coefficient of the sampling resistor is less than five parts per million per degree Celsius, ensuring the consistency and repeatability of current measurements over a wide temperature range.

[0065] The voltage sampling frequency and current sampling frequency of the charge / discharge test equipment are both set to 1 Hz.

[0066] The surface of the battery casing is divided into three temperature measurement zones: the positive terminal cover side, the middle of the casing, and the negative terminal cover side.

[0067] A thermocouple is attached to each temperature measurement zone. The temperature sensing end of the thermocouple is filled with thermally conductive silicone grease with a thermal conductivity of 3 W / m / Kelvin. The thermocouple wire is covered with a polytetrafluoroethylene insulation layer. The output end of each thermocouple is connected to the corresponding channel of the temperature acquisition instrument.

[0068] The sampling frequency of the temperature acquisition instrument is set to 1 Hz, synchronized with the sampling clock of the charge and discharge test equipment. The temperature measurement resolution is 0.1 degrees Celsius, and the measurement accuracy is ±0.5 degrees Celsius.

[0069] The charge and discharge test equipment has a built-in real-time clock circuit, whose time reference source is a temperature-compensated crystal oscillator with a frequency stability better than one part per million, which marks the corresponding timestamp for each voltage and current sampling data point.

[0070] The temperature acquisition instrument is also equipped with a synchronous clock circuit. The temperature acquisition instrument is connected to the charge and discharge test equipment through a pulse synchronization signal line. The temperature acquisition instrument triggers temperature data sampling and records the timestamp at the same time according to the rising edge of the synchronization pulse signal, so that the time reference of the temperature data and the voltage and current data are consistent.

[0071] The sampling data of the charge-discharge test equipment is uploaded to the data storage terminal in real time through its Ethernet interface. The sampling data of the temperature acquisition instrument is uploaded to the same data storage terminal in real time through its universal serial bus interface. The recording program running in the data storage terminal aligns the received terminal voltage, terminal current, shell temperature and corresponding timestamps according to a unified time axis and stores them as a comma-separated value format file. All data records in the comma-separated value format file constitute a dynamic time-series dataset.

[0072] Please see Figure 2 or Figure 3 As shown in S2, a time-frequency domain decoupling transformation is performed on the dynamic time-series dataset to extract the polar phase deposition impedance factor characterizing the lithium deposition kinetics and the lattice decay impedance factor characterizing the structural decay characteristics of the cathode material, specifically including:

[0073] The time-frequency domain decoupling transform involves performing two independent signal processing operations on the dynamic time-series dataset:

[0074] One is frequency sweep cross-correlation calculation, which is used to separate the impedance component related to the electrochemical polarization process from the response relationship between terminal current and terminal voltage;

[0075] The second is time-delay correlation analysis, which is used to separate the thermo-electric coupling response components related to the evolution of the lattice structure of the electrode material from the response relationship between shell temperature and terminal voltage.

[0076] The process for obtaining the polar phase deposition impedance factor is as follows:

[0077] A frequency sweep cross-correlation operation is performed on the terminal current and terminal voltage in the dynamic time series dataset to analyze the real and imaginary parts of the complex impedance at each frequency.

[0078] The slope of the change of the imaginary part to the real part in the low-frequency band is extracted, and the fluctuation amplitude of the slope within the preset temperature window is used as the polar phase deposition impedance factor.

[0079] The low-frequency band refers to the frequency range below 0.1 Hz, and the preset temperature window is a temperature range with 25 degrees Celsius as the midpoint and extending 5 degrees Celsius above and below it, that is, 20 degrees Celsius to 30 degrees Celsius.

[0080] The specific process of the frequency sweep cross-correlation operation is as follows:

[0081] The terminal current and terminal voltage of the charge-discharge switching transition segment are extracted from the dynamic time-series data. The charge-discharge switching transition segment refers to the time interval before the terminal voltage change rate first crosses zero after the battery switches from the charging state to the discharging state or from the discharging state to the charging state. The length of this time interval is fixed at 120 seconds.

[0082] The obtained terminal current is discretized into multiple current sub-segments according to equal time windows. The window width of the equal time window is 10 seconds, and the overlap rate between two adjacent windows is 50%, that is, the window slides once every 5 seconds, and a total of 23 current sub-segments are obtained.

[0083] Each current sub-segment is cross-correlated with the complete terminal voltage sequence, and the cross-correlation function value at each lag time is calculated. The peak value of the cross-correlation function and its corresponding lag time are recorded for each current sub-segment.

[0084] Using the lag time of the peak value of the cross-correlation function corresponding to each window as the horizontal axis and the amplitude of the peak value of the cross-correlation function corresponding to each window as the vertical axis, a sequence of the real part and the sequence of the imaginary part of the complex impedance spectrum are constructed, where the lag time is mapped to the real part of the impedance and the peak amplitude is mapped to the imaginary part of the impedance.

[0085] The above cross-correlation integral is calculated according to the following formula:

[0086] ;

[0087] in, Lag time The normalized cross-correlation function value under the following conditions The unit is seconds, and the value ranges from 0 seconds to 120 seconds; For current sub-segments at discrete time points The current value at that point, For the terminal voltage sequence at the delay time point Voltage value at; The arithmetic mean of the current sub-segments. This is the arithmetic mean of the portion of the terminal voltage sequence that is used in the calculation; This represents the total number of sampling points for the current sub-segment, with a value of 1200, corresponding to the product of a 120-second time length and a 1 Hz sampling frequency. This is a discrete-time index, with values ​​ranging from 1 to... Integers.

[0088] For each lag time Traverse all current sub-segments, calculate the corresponding cross-correlation function value for each, take the maximum value of the cross-correlation function value of each sub-segment as the peak value of the cross-correlation function under the lag time, and record the amplitude and lag time corresponding to the peak value.

[0089] The slope of the change in the polar phase deposition impedance factor is extracted as follows:

[0090] In the constructed complex impedance spectrum, the imaginary part is taken as the vertical axis and the real part as the horizontal axis. In the low-frequency band, a continuous interval in which the imaginary part changes with the real part is selected. The starting point of the continuous interval is the point where the real part is at its minimum value, and the ending point is the point where the imaginary part first appears a local minimum value. A straight line is fitted to all data points in the continuous interval, and the slope value of the fitted straight line is determined by the least squares method. This slope value is recorded as the instantaneous slope at the current moment.

[0091] Using the current moment as the endpoint, a time length of 120 seconds is traced back as the fluctuation analysis window. The fluctuation amplitude of the instantaneous slope within the fluctuation analysis window is calculated. The fluctuation amplitude is equal to the difference between the maximum instantaneous slope value and the minimum instantaneous slope value within the window.

[0092] The calculated fluctuation amplitude is output as the polar phase deposition impedance factor.

[0093] The process for obtaining the lattice decay impedance factor is as follows:

[0094] Time delay correlation analysis was performed on the shell temperature and terminal voltage in the dynamic time series dataset to obtain the response correlation coefficient sequence under different lag times;

[0095] The lag time corresponding to the peak value in the response correlation coefficient sequence is taken, and the drift of this lag time with the number of cycles is used as the lattice decay impedance factor.

[0096] The process of obtaining the response correlation coefficient sequence is as follows: align the shell temperature sequence and terminal voltage sequence in the dynamic time series dataset with the same time axis, and extract the continuous change segment of the shell temperature sequence and terminal voltage sequence during the constant current discharge stage. The constant current discharge stage refers to the period when the battery discharges with a constant current value and the terminal voltage is in the continuously decreasing range. The starting point of the extraction is the 10th second after the discharge current stabilizes, and the ending point of the extraction is the 10th second before the terminal voltage drops to the discharge cutoff voltage.

[0097] Using the obtained continuous temperature change segment of the shell as the base sequence, and the continuous voltage change segment as the response sequence, starting from a lag time of 0 seconds, with a lag step of 1 second, the terminal voltage sequence is shifted backward relative to the shell temperature sequence. Each shift calculates the covariance value between the shell temperature sequence and the terminal voltage sequence at the current alignment position. The calculated covariance value is stored in correspondence with the lag step, until the lag time reaches 120 seconds. The covariance values ​​corresponding to all lag steps are arranged in ascending order of lag time to form the response correlation coefficient sequence.

[0098] The covariance is calculated as follows:

[0099] Take the deviation of each data point in the shell temperature sequence at the current alignment position from its arithmetic mean, and take the deviation of each data point in the terminal voltage sequence from its arithmetic mean. Divide the sum of the products of the two sets of deviations by the total number of data points involved in the calculation. The resulting quotient is the covariance value at that hysteresis step size.

[0100] Find the peak value in the response correlation coefficient sequence. The peak value is the covariance value with the largest value in the sequence. Record the lag time value corresponding to the peak value. Record the lag time value as the thermal response lag time under the current cycle number.

[0101] Using the initial thermal response hysteresis time obtained during the first cycle of the battery as a reference value, the thermal response hysteresis time at the current cycle number is subtracted from the reference value, and the difference is the amount of hysteresis time drift with the cycle number. This amount of drift is output as the lattice decay impedance factor.

[0102] If the thermal response hysteresis time at the current cycle number is less than the reference value, the drift amount is negative, indicating that the thermal response is accelerated, and the corresponding crystal structure has microcracks, which leads to a shortening of the heat conduction path.

[0103] If the thermal response hysteresis time at the current cycle number is greater than the baseline value, the drift is positive, indicating that the thermal response hysteresis is aggravated, which corresponds to the expansion of the crystal structure leading to the extension of the heat conduction path.

[0104] The fluctuation amplitude of the aforementioned slope is calculated using the following formula:

[0105] ;

[0106] in, As a measure of fluctuation amplitude; The index of the instantaneous slope data point within the fluctuation analysis window, with a value ranging from 1 to... Integers; This represents the total number of instantaneous slope data points within the fluctuation analysis window, with a value of 120. For the first wave analysis window One instantaneous slope value; It is the minimum value among all instantaneous slope values ​​within the fluctuation analysis window; It is the maximum value among all instantaneous slope values ​​within the fluctuation analysis window; This is the arithmetic mean of all instantaneous slope values ​​within the fluctuation analysis window.

[0107] The calculation formula means that: for each instantaneous slope value within the fluctuation analysis window, the deviation from the minimum value is taken, the maximum deviation value is found, and this maximum deviation value is divided by the sum of the maximum, minimum, and average values. The resulting quotient is the fluctuation amplitude measure. The larger this measure value, the more drastic the fluctuation of the slope of the imaginary part of the impedance relative to the real part in the low-frequency band in the time dimension, and the more significant the corresponding lithium deposition kinetic characteristics.

[0108] In S3, based on the competitive balance between the polar phase deposition impedance factor and the lattice decay impedance factor, and combined with the current accumulated discharge energy, the bias direction and bias amplitude of the capacity decay rate under the current operating conditions are calculated, specifically including:

[0109] The polar phase deposition impedance factor and the lattice decay impedance factor each correspond to an initial value, which are obtained and stored respectively according to the method described in S2 during the first cycle of the battery.

[0110] Subtract the initial value from the polar phase deposition impedance factor obtained at the current cycle number to obtain the deposition deviation magnitude;

[0111] Subtract the initial value from the lattice decay impedance factor obtained at the current cycle number to obtain the lattice deviation magnitude.

[0112] A positive value for the deposition deviation indicates an enhanced lithium deposition trend, while a negative value indicates a weakened lithium deposition trend; a positive value for the lattice deviation indicates an intensified lattice degradation, while a negative value indicates a mitigated lattice degradation.

[0113] Establish a table in advance to correspond to the deviation level range and the scaling factor.

[0114] The physical upper limit of the deviation range in this correspondence table is determined as follows:

[0115] Samples of the same battery model were continuously cycled at an ambient temperature of 60 degrees Celsius using a constant current and constant voltage charging and discharging method at a 1C rate until the capacity retention rate dropped to 80% of the initial capacity. The difference between the polar phase deposition impedance factor measured at the end of the test and the initial value was taken as the physical upper limit of the polar phase deposition impedance factor, and the difference between the lattice decay impedance factor measured at the end of the test and the initial value was taken as the physical upper limit of the lattice decay impedance factor.

[0116] The above physical upper limits are recorded separately and used for the subsequent division of level ranges.

[0117] The deviation range is divided into five levels from zero to the above physical upper limit. The first level range is where the deviation range is greater than or equal to zero and less than 10% of the initial value, with a corresponding scaling factor of 0.3.

[0118] The second level range is defined as a deviation greater than or equal to 10% of the initial value and less than 30% of the initial value, with a corresponding scaling factor of 0.6.

[0119] The third level range is defined as a deviation greater than or equal to 30% of the initial value and less than 60% of the initial value, with a corresponding scaling factor of 1.0.

[0120] The fourth level range is defined as a deviation greater than or equal to 60% of the initial value but less than 100% of the initial value, with a corresponding scaling factor of 1.5.

[0121] The fifth level range is defined as a deviation greater than or equal to 100% of the initial value, with a corresponding scaling factor of 2.0.

[0122] The 10% of the initial value mentioned above refers to the value obtained by multiplying the initial value by 0.1. The boundary values ​​of other level intervals are calculated in the same way.

[0123] The scaling factor is determined as follows: Samples of the same battery model are subjected to accelerated cycle life tests at six temperature points: -10°C, 0°C, 15°C, 25°C, 45°C, and 60°C. Three samples are tested at each temperature point. The ratio of the actual decay rate measured at each temperature point to the reference decay rate constitutes a set of discrete data points. The mapping relationship between the deviation amplitude and the decay rate ratio is piecewise linearly fitted using this set of data points. The fitted slope of each segment interval is rounded down and used as the scaling factor corresponding to each level interval.

[0124] Determine which of the five grade intervals the currently calculated deposition deviation falls into, and extract the scaling factor corresponding to that grade interval as the deposition scaling factor.

[0125] Determine which of the five level intervals the currently calculated lattice deviation falls into, and extract the scaling factor corresponding to that level interval as the lattice scaling factor.

[0126] Multiply the deposition deviation magnitude by the deposition scaling factor to obtain the deposition weighted deviation value;

[0127] Multiply the lattice deviation magnitude by the lattice scaling factor to obtain the lattice weighted deviation value.

[0128] The difference between the deposition weighted deviation value and the lattice weighted deviation value is used as the competitive balance output.

[0129] When the competition balance is positive, it indicates that the lithium deposition kinetics are dominant, and the tendency for low-temperature lithium deposition is stronger than the tendency for high-temperature material aging.

[0130] When the competition balance is negative, it indicates that lattice decay is dominant and the aging tendency of high-temperature materials is stronger than that of low-temperature lithium plating; when the competition balance is zero, it indicates that the two decay modes are in equilibrium.

[0131] A pre-constructed attenuation deviation mapping table is constructed, which has two dimensions: the competition balance range and the cumulative discharge energy range, and uses the bias direction identifier and bias amplitude coefficient as table entries.

[0132] The competition balance range is divided into ten sub-intervals from -0.5 to +0.5, with each sub-interval having a width of 0.1. That is, the first sub-interval is -0.5 to -0.4, the second sub-interval is -0.4 to -0.3, and so on, up to the tenth sub-interval which is +0.4 to +0.5.

[0133] The cumulative discharge energy range is divided into ten sub-ranges, ranging from zero to ten times the battery's rated total energy. The width of each sub-range is one times the battery's rated total energy. That is, the first sub-range is zero to one times the rated total energy, the second sub-range is one to two times the rated total energy, and so on, up to the tenth sub-range which is nine to ten times the rated total energy.

[0134] Each intersection in the mapping table stores an offset direction identifier and an offset magnitude coefficient. The offset direction identifier can be positive or negative, and the offset magnitude coefficient can be a real number between 0.1 and 2.0.

[0135] The mapping table was constructed as follows: 18 sample batteries of the same model as the battery under test were taken and divided into 6 groups of 3. Accelerated life cycle tests were performed at six temperature points: -10 degrees Celsius, 0 degrees Celsius, 15 degrees Celsius, 25 degrees Celsius, 45 degrees Celsius, and 60 degrees Celsius. The charge and discharge mode at each temperature point was uniformly set as follows: constant current charging at 0.5C rate to 4.2 volts, followed by constant voltage charging. The cutoff current for the constant voltage stage was 0.05C. After resting for 10 minutes, constant current discharge at 0.5C rate to 2.8 volts was performed. After resting for 10 minutes, the next cycle began.

[0136] Each sample underwent a reference performance test after every 50 cycles. The reference performance test was conducted at an ambient temperature of 25 degrees Celsius, and the current actual capacity was calibrated by constant current discharge at a rate of 0.33C.

[0137] During each reference performance test, the current accumulated discharge energy, the currently measured polar phase deposition impedance factor, the currently measured lattice decay impedance factor, and the current actual decay rate are recorded simultaneously.

[0138] Data obtained from each sample at each temperature point and at each test time are processed according to the competition balance calculation method to obtain the competition balance value and cumulative discharge energy value corresponding to each test time. The actual decay rate at each test time is classified into the corresponding crosspoint of the mapping table according to the competition balance sub-interval and the cumulative discharge energy sub-interval. If there are multiple data in the same crosspoint, the arithmetic mean of the ratio of the actual decay rate to the reference decay rate of these data is taken as the bias amplitude coefficient of the crosspoint. The majority sign of the difference between the actual decay rate and the reference decay rate of these data is taken as the bias direction indicator of the crosspoint. That is, when the number of positive signs in the difference is greater than the number of negative signs, the bias direction indicator is recorded as negative; when the number of negative signs is greater than the number of positive signs, the bias direction indicator is recorded as positive; when the number of positive and negative signs is equal, the bias direction indicator is recorded as zero.

[0139] For intersections in the mapping table that are not filled with measured data, bilinear interpolation is used to fill them with the offset direction identifiers and offset magnitude coefficients that have been filled in the four adjacent intersections.

[0140] The calculated competition balance degree and the current accumulated discharge energy are input into the attenuation deviation mapping table. Specifically, the competition balance degree sub-interval number into which the competition balance degree falls is determined first, and the accumulated discharge energy sub-interval number into which the current accumulated discharge energy falls is determined. The two numbers are used as row index and column index to locate the unique intersection point in the mapping table. The bias direction identifier and bias amplitude coefficient stored at the intersection point are read, and the bias direction identifier is used as the bias direction of the capacity attenuation rate under the current operating condition.

[0141] The bias amplitude is obtained by multiplying the obtained bias amplitude coefficient by the product of the reference decay rate and the current accumulated discharge energy.

[0142] The reference decay rate refers to the capacity decay rate of the battery when it is cycled at a reference temperature of 25 degrees Celsius using a standard charge-discharge cycle. Specifically, the standard charge-discharge cycle involves constant current charging at a rate of 0.5C to 4.2 volts, followed by constant voltage charging. The cutoff current during the constant voltage stage is 0.05C. After resting for 10 minutes, the battery is discharged at a rate of 0.5C to 2.8 volts using a constant current. After resting for 10 minutes, the battery enters the next cycle.

[0143] The baseline decay rate was obtained by conducting accelerated life cycle tests on three samples of the same battery model at 25 degrees Celsius using the aforementioned standard charge-discharge method. Specifically, the number of cycles each sample experienced from initial capacity decay to 80% of initial capacity was recorded. The decay amount of 20% was divided by the product of the number of cycles and the discharge energy per cycle. The quotient was used as the baseline decay rate for a single sample. The arithmetic mean of the baseline decay rates of the three samples was taken as the final baseline decay rate.

[0144] When the bias direction is negative, it means that the current capacity decay rate is greater than the reference decay rate, that is, the lifetime decay is accelerated;

[0145] When the bias direction is positive, it means that the current capacity decay rate is less than the reference decay rate, that is, the lifetime decay is slowed down;

[0146] When the bias direction is zero, it means that the current capacity decay rate is equal to the reference decay rate.

[0147] In S4, the baseline decay trajectory is corrected in reverse based on the bias direction and bias magnitude, and the predicted value of the remaining cycle life under the current aging state is output. Simultaneously, the target operating temperature offset that makes the competitive balance tend towards the stable threshold is calculated, specifically including:

[0148] A baseline degradation trajectory is pre-established, which is the expected curve of the capacity retention rate as a function of the cumulative discharge energy when the battery is cycled at a reference temperature of 25 degrees Celsius using a standard charge-discharge method.

[0149] The specific construction method of the benchmark attenuation trajectory is as follows: Take three samples of the same battery model and perform a cycle accelerated life test at 25 degrees Celsius according to the standard charge and discharge mode described in S3. Perform a reference performance test every 50 cycles and record the cumulative discharge energy value and the corresponding capacity retention rate at each reference performance test time. Take the arithmetic mean of the capacity retention rates of the three samples at the same cumulative discharge energy value. Use the cumulative discharge energy value as the abscissa and the arithmetic mean of the capacity retention rate as the ordinate to generate a continuous curve using linear interpolation. Use this continuous curve as the benchmark attenuation trajectory.

[0150] Each node on the baseline decay trajectory corresponds to a cumulative discharge energy value and the expected capacity retention rate at that cumulative discharge energy value.

[0151] The positive and negative directions of the correction for the reference attenuation trajectory are determined by the bias direction of the S3 output. Specifically, when the bias direction is positive, the correction positive and negative directions are positive, indicating that the capacity retention rate of each node on the reference attenuation trajectory needs to be adjusted upward, which means that the predicted lifetime is extended.

[0152] When the bias direction is negative, the correction direction is negative, which means that the capacity retention rate of each node on the baseline decay trajectory needs to be adjusted downward, i.e., the predicted lifetime is shortened.

[0153] When the bias direction is zero, the correction direction is zero, indicating that no correction is made to the reference attenuation trajectory.

[0154] The correction magnitude at each node of the reference decay trajectory is determined by the offset magnitude output by S3. Specifically, the offset magnitude is divided by the total number of nodes in the reference decay trajectory, and the quotient is used as the reference correction amount for a single node. Starting from the first node of the reference decay trajectory, the correction magnitude of each node is equal to the product of the node number and the reference correction amount. That is, the closer the node is to the end of its lifespan, the greater the correction amount it bears.

[0155] The correction magnitude is applied point-by-point along the bias direction to each remaining lifetime node of the baseline decay trajectory, specifically as follows:

[0156] When the bias direction is positive, the correction magnitude of each node is superimposed on the original capacity retention rate value of that node.

[0157] When the bias direction is negative, the correction magnitude of each node is deducted from the original capacity retention value of that node.

[0158] When the bias direction is zero, the original capacity retention rate of each node remains unchanged. After the above point-by-point correction, the corrected decay trajectory is obtained.

[0159] The preset failure threshold is 80% of the initial capacity, which is a common lifespan determination standard in the battery industry.

[0160] The corrected degradation trajectory is compared with the preset failure threshold. The node in the corrected degradation trajectory where the capacity retention rate first falls below 80% is selected. The cumulative discharge energy value corresponding to this node is taken as the benchmark point. The cumulative discharge energy value at the benchmark point is divided by the average discharge energy per cycle of the battery. The quotient is rounded to the nearest integer and used as the predicted value of the remaining cycle life.

[0161] If the capacity retention rate of all nodes in the corrected degradation trajectory is higher than 80%, it is determined that the battery has not yet reached the end of its life under the current aging state. The end node of the corrected degradation trajectory is extrapolated to a node, and the capacity retention rate of the extrapolated node continues to extend according to the slope between the end node and the previous node. The cumulative discharge energy value corresponding to the first time the extrapolated node is lower than 80% is taken and converted into the remaining cycle life prediction value in the same way as above.

[0162] If the capacity retention rate of the first node in the corrected decay trajectory is below 80%, then the remaining cycle lifetime prediction value is determined to be zero.

[0163] The predicted total number of cycles is obtained by adding the remaining cycle life prediction value to the accumulated cycle number.

[0164] The actual capacity retention rate is obtained by subtracting the current actual capacity retention rate from the capacity retention rate value corresponding to the predicted total number of cycles in the baseline decay trajectory. This actual drift is used to calculate the subsequent temperature offset.

[0165] A mapping sequence between competitive balance and temperature deviation is pre-constructed. This mapping sequence is constructed as follows: Three samples of the same battery model are taken and subjected to accelerated life cycle tests at nine temperature deviation points (-15°C, -10°C, -5°C, +5°C, +10°C, +15°C, +20°C, +25°C, and +30°C) that deviate from the 25°C reference temperature. The actual ambient temperature at each temperature deviation point is 25°C plus the deviation value. Each sample is cycled at each temperature deviation point according to the standard charge-discharge mode described in S3. After every 50 cycles, the polar phase deposition impedance factor and lattice decay impedance factor at that moment are obtained according to the method described in S2. The competitive balance at that moment is calculated according to the method described in step S3.

[0166] The temperature deviation value obtained at each test time under each temperature deviation point is paired and recorded with the competition balance value. If there are multiple competition balance values ​​at the same temperature deviation point, the arithmetic mean of these values ​​is taken as the representative value of the competition balance corresponding to that temperature deviation point.

[0167] The nine temperature deviation points and their corresponding representative values ​​of competitive balance are arranged in ascending order of temperature deviation value to form a mapping sequence between competitive balance and temperature deviation.

[0168] The competition balance degree output by S3 is taken as the current competition balance degree. The two adjacent mapping points of the current competition balance degree are found from the mapping sequence. That is, the current competition balance degree value is between the representative value of the competition balance degree corresponding to a certain temperature deviation point and the representative value of the competition balance degree corresponding to the next adjacent temperature deviation point.

[0169] Back-interpolation is performed with a stability threshold as the target. The stability threshold is the state where the competition balance is zero as defined in S3, that is, the two decay modes of lithium deposition and lattice decay are in equilibrium.

[0170] The specific method of reverse interpolation is as follows: take the two adjacent mapping points where the current competitive balance is located, and record them as the first mapping point and the second mapping point respectively. The temperature deviation value of the first mapping point is less than the temperature deviation value of the second mapping point. The competitive balance representative value of the first mapping point is recorded as the first balance value, and the competitive balance representative value of the second mapping point is recorded as the second balance value.

[0171] Calculate the difference between the current competitive balance and the first balance value, divide it by the difference between the second balance value and the first balance value, and obtain the interpolation ratio.

[0172] Multiply the interpolation ratio by the difference between the temperature deviation of the second mapping point and the temperature deviation of the first mapping point to obtain the temperature difference to be compensated.

[0173] If the current competitive balance is positive, it indicates that lithium deposition is dominant and the operating temperature needs to be adjusted to a higher temperature to suppress lithium plating. At this time, the temperature difference to be compensated is positive and superimposed on the current shell temperature measurement value to increase the target operating temperature.

[0174] If the current competitive balance is negative, it indicates that lattice decay is dominant and the operating temperature needs to be adjusted to a lower temperature to suppress material aging. At this time, the temperature difference to be compensated is negative and superimposed on the current shell temperature measurement value to reduce the target operating temperature.

[0175] If the current competition balance is zero, it means that the two decay modes are in equilibrium, the temperature difference to be compensated is zero, and there is no need to adjust the operating temperature.

[0176] The temperature difference to be compensated is superimposed on the current shell temperature measurement value. Specifically, the temperature value at the temperature measurement zone in the middle of the shell measured by the temperature acquisition instrument in S1 at the current moment is taken as the current shell temperature measurement value. The current shell temperature measurement value is added to the temperature difference to be compensated, and the sum is output as the target operating temperature offset.

[0177] The target operating temperature offset represents the specific temperature value that the current operating temperature of the battery should be adjusted upward or downward relative to the current casing temperature measurement value in order to make the competitive balance tend to a stable threshold.

[0178] Please see Figure 4 As shown, a battery cycle life prediction system under a wide temperature range includes:

[0179] The dynamic time-series acquisition module acquires the battery's terminal voltage, terminal current, casing temperature, and corresponding timestamps during the cyclic charging and discharging process, forming a dynamic time-series dataset.

[0180] The competitive feature decoupling extraction module performs time-frequency domain decoupling transformation on the dynamic time-series dataset to extract the polar phase deposition impedance factor characterizing lithium deposition kinetics and the lattice decay impedance factor characterizing the structural decay characteristics of the cathode material.

[0181] The attenuation bias calculation module, based on the competitive balance between the polar phase deposition impedance factor and the lattice decay impedance factor, combined with the current accumulated discharge energy, calculates the bias direction and bias amplitude of the capacity decay rate under the current operating conditions.

[0182] The lifetime prediction and temperature control calculation module corrects the baseline decay trajectory in reverse according to the bias direction and bias magnitude, outputs the remaining cycle lifetime prediction value under the current aging state, and simultaneously calculates the target operating temperature offset that makes the competitive balance tend to the stable threshold.

[0183] The working principle of this invention is as follows: A dynamic time-series dataset is constructed by acquiring the terminal voltage, terminal current, casing temperature, and corresponding timestamps of the battery during cyclic charging and discharging. A time-frequency domain decoupling transformation is performed on this dataset to extract the polar phase deposition impedance factor characterizing lithium deposition kinetics and the lattice decay impedance factor characterizing the structural degradation characteristics of the cathode material. Based on the competitive balance between these two factors and combined with the current accumulated discharge energy, the bias direction and bias amplitude of the capacity decay rate under the current operating conditions are calculated. The baseline decay trajectory is then corrected in reverse according to this bias direction and bias amplitude, outputting the predicted remaining cycle life under the current aging state. Simultaneously, the target operating temperature offset that makes the competitive balance tend towards a stable threshold is calculated, thus achieving dynamic prediction of battery cycle life and optimization of operating temperature over a wide temperature range.

[0184] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A method for predicting battery cycle life under a wide temperature range, characterized in that, Includes the following steps: S1: Obtain the battery's terminal voltage, terminal current, casing temperature, and corresponding timestamps during the cyclic charging and discharging process to form a dynamic time-series dataset; S2: Perform time-frequency domain decoupling transformation on the dynamic time-series dataset to extract the polar phase deposition impedance factor characterizing lithium deposition kinetics and the lattice decay impedance factor characterizing the structural decay characteristics of the cathode material. S3: Based on the competitive balance between the polar phase deposition impedance factor and the lattice decay impedance factor, and combined with the current accumulated discharge energy, the bias direction and bias amplitude of the capacity decay rate under the current operating conditions are calculated. S4: Based on the bias direction and bias magnitude, reverse the reference decay trajectory, output the predicted value of the remaining cycle life under the current aging state, and simultaneously calculate the target operating temperature offset that makes the competitive balance tend to the stable threshold.

2. The method for predicting battery cycle life under a wide temperature range according to claim 1, characterized in that, The process for obtaining the polar phase deposition impedance factor is as follows: A frequency sweep cross-correlation operation is performed on the terminal current and terminal voltage in the dynamic time series dataset to analyze the real and imaginary parts of the complex impedance at each frequency. The slope of the change of the imaginary part relative to the real part in the low-frequency band is extracted, and the fluctuation amplitude of the slope within a preset temperature window is used as the polar phase deposition impedance factor.

3. The method for predicting battery cycle life under a wide temperature range according to claim 2, characterized in that, The frequency sweep cross-correlation operation specifically includes: Extract the terminal current and terminal voltage of the charging and discharging transition segment in the dynamic time series data, and discretize the terminal current into multiple current sub-segments according to equal time windows; By performing cross-correlation integration between each current sub-segment and the terminal voltage, the peak value of the cross-correlation function corresponding to each window is obtained. Using the lag time corresponding to the peak value as the horizontal axis and the peak value as the vertical axis, we construct the real part sequence and the imaginary part sequence of the complex impedance spectrum.

4. The method for predicting battery cycle life under a wide temperature range according to claim 1, characterized in that, The process for obtaining the lattice decay impedance factor is as follows: Time delay correlation analysis was performed on the shell temperature and terminal voltage in the dynamic time series dataset to obtain the response correlation coefficient sequence under different lag times; The lag time corresponding to the peak value in the correlation coefficient sequence is taken, and the drift of the lag time with the number of cycles is used as the lattice decay impedance factor.

5. The method for predicting battery cycle life under a wide temperature range according to claim 4, characterized in that, The process of obtaining the response correlation coefficient sequence is as follows: Align the shell temperature sequence and terminal voltage sequence in the dynamic time series dataset with the same time axis, and extract the continuous change segment of the shell temperature sequence and terminal voltage sequence during the constant current discharge stage; Based on the continuously changing segments, the shell temperature sequence is shifted backward step by step according to a preset lag step. The covariance between the shell temperature sequence and the terminal voltage sequence is calculated once for each shift. The covariance values ​​calculated at each lag step are sequentially aggregated to form a response correlation coefficient sequence.

6. The method for predicting battery cycle life under a wide temperature range according to claim 1, characterized in that, The calculation of the bias direction and bias magnitude for the capacity decay rate under the current operating conditions specifically includes: The deviations of the polar phase deposition impedance factor and the lattice decay impedance factor from their initial values ​​are taken, and the difference between the two deviations is taken as the competitive balance after nonlinear scaling according to the preset weights. The competition balance degree and the current accumulated discharge energy are input into a preset decay deviation mapping table, and the offset direction of the capacity decay rate relative to the reference decay rate under the current operating condition is obtained by interpolation and table lookup. The bias amplitude is determined based on the upper and lower limits of the attenuation deviation corresponding to the bias direction, combined with the absolute value of the competition balance.

7. The method for predicting battery cycle life under a wide temperature range according to claim 6, characterized in that, The process of obtaining the competitive balance is as follows: The polar phase deposition impedance factor and the lattice decay impedance factor are respectively calculated by subtracting their corresponding initial values ​​to obtain the deposition deviation amplitude and the lattice deviation amplitude. Determine the deviation level range of both the deposition deviation range and the lattice deviation range, and each deviation level range corresponds to a different scaling factor; The two deviations are scaled by various scaling factors, and the difference is taken as the competitive balance.

8. The method for predicting battery cycle life under a wide temperature range according to claim 1, characterized in that, The output of the predicted remaining cycle life under the current aging state specifically includes: The positive and negative directions of the correction for the reference attenuation trajectory are determined by the offset direction, and the magnitude of the correction at each node of the reference attenuation trajectory is determined by the offset magnitude. The correction magnitude is applied point by point in the bias direction to each remaining lifetime node of the baseline attenuation trajectory to obtain the corrected attenuation trajectory. The number of cycles at the intersection of the corrected decay trajectory and the preset failure threshold is used as the predicted value of the remaining cycle life.

9. The method for predicting battery cycle life under a wide temperature range according to claim 1, characterized in that, The calculation process for the target operating temperature offset is as follows: The actual drift of the current decay rate is calculated by back-calculating the remaining cycle life prediction and the accumulated number of cycles. Substitute the actual drift amount into the mapping sequence between the competitive balance degree and the temperature deviation amount, and use the stability threshold as the target for back interpolation to obtain the temperature difference to be compensated. The temperature difference to be compensated is superimposed on the current shell temperature measurement value to obtain the target operating temperature offset.

10. A battery cycle life prediction system under a wide temperature range, characterized in that, A method for predicting battery cycle life under a wide temperature range as described in any one of claims 1-9 includes: The dynamic time-series acquisition module acquires the battery's terminal voltage, terminal current, casing temperature, and corresponding timestamps during the cyclic charging and discharging process, forming a dynamic time-series dataset. The competitive feature decoupling extraction module performs time-frequency domain decoupling transformation on the dynamic time-series dataset to extract the polar phase deposition impedance factor characterizing lithium deposition kinetics and the lattice decay impedance factor characterizing the structural decay characteristics of the cathode material. The attenuation bias calculation module, based on the competitive balance between the polar phase deposition impedance factor and the lattice decay impedance factor, combined with the current accumulated discharge energy, calculates the bias direction and bias amplitude of the capacity decay rate under the current operating conditions. The lifetime prediction and temperature control calculation module corrects the baseline decay trajectory in reverse according to the bias direction and bias magnitude, outputs the remaining cycle lifetime prediction value under the current aging state, and simultaneously calculates the target operating temperature offset that makes the competitive balance tend to the stable threshold.

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