Battery pack current monitoring method and system

Abstract

The invention relates to the field of battery pack monitoring, in particular to a battery pack current monitoring method and system, and the method comprises the steps: obtaining the parameter data of a battery pack at each moment when the battery pack is charged; calculating a charging state evaluation index by using the obtained parameter data, wherein the charging state evaluation index is in negative correlation with the internal temperature change amplitude of the battery pack; and constructing an evaluation sequence by using the charging state evaluation indexes at a plurality of adjacent moments, calculating trend slope estimation of the evaluation sequence, taking the trend slope estimation of the evaluation sequence as a filter adjustment factor at a corresponding moment, and adjusting an FIR filter order by using the filter adjustment factor to obtain an adaptive filter order. And de-noising the current by using an FIR filter to obtain an optimal current so as to judge whether the optimal current is abnormal or not. By adjusting the order of the FIR filter, the working efficiency of the FIR filter is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of battery pack monitoring, in particular to a battery pack current monitoring method and system. BACKGROUND

[0002] With the rapid development of electric vehicles, portable electronic devices, and large-scale energy storage systems, battery packs as the core energy storage units, their performance, life, and safety are increasingly concerned. In the entire life cycle of the battery pack, the charging process is a crucial link, directly affecting the health status and service life of the battery. Improper charging behavior, especially abnormal charging current, can lead to serious consequences such as overcharging, overheating, internal short circuit, etc. of the battery, which may accelerate the aging of the battery, shorten the cycle life, or even cause thermal runaway, fire, and explosion, posing a great threat to personnel and property safety.

[0003] The Chinese patent application file with publication number CN119416597A discloses an intelligent method and system for integrated circuit package current monitoring and analysis. The method includes obtaining current time series data of the integrated circuit to be tested under different working conditions, and decomposing the calibrated sampling data into a feature coefficient matrix through adaptive wavelet transform. A deep feature analysis model is constructed based on the feature coefficient matrix, a dynamic feature map is constructed by combining the physical structure information of the integrated circuit with the current features using a graph attention network, and abnormal current features are identified and the abnormal area is located through multi-layer graph convolution operation. The abnormal current features and abnormal area information are input into a digital twin analysis system, the corresponding relationship between the abnormal current features and the circuit physical parameters is determined through iterative calculation, the comparison analysis of the measured data and the simulation results is combined, the fault positioning result and the fault development trend prediction are output, and a diagnostic analysis report is generated.

[0004] In the process of detecting the current of the battery pack, the switching power supply inside the charging device, the complex electromagnetic environment, and the high-frequency noise of the sensor itself will all be superimposed on the real charging current signal, so it is necessary to filter the current signal to eliminate the noise in the current signal. The current signal is usually filtered using an FIR filter. However, existing FIR filters usually use a fixed filter order, and a fixed-order filter is difficult to meet the monitoring needs of all working conditions. If the filter order is too low, it may not be able to effectively filter out noise, leading to false fluctuations in the system. If the filter order is too high, it will introduce too much delay, reducing the real-time response capability of the system. SUMMARY

[0005] To solve the problem that a fixed-order filter cannot meet the needs of battery pack current monitoring, the present application provides a battery pack current monitoring method and system.

[0006] In a first aspect, the present application provides a battery pack current monitoring method, which adopts the following technical scheme: Obtaining parameter data of the battery pack at each time during charging, the parameter data including the internal temperature and the current of the battery pack; calculating a charging state evaluation index using the obtained parameter data, the charging state evaluation index being negatively correlated with the internal temperature variation amplitude of the battery pack; Constructing an evaluation sequence using the charging state evaluation indexes of multiple adjacent times, calculating a trend slope estimate of the evaluation sequence, taking the trend slope estimate of the evaluation sequence as a filter adjustment factor of the corresponding time, adjusting the order of the FIR filter using the filter adjustment factor to obtain an adaptive filter order, and denoising the current using the FIR filter to obtain an optimal current, which is used to determine whether the optimal current is abnormal.

[0007] By calculating the charging state evaluation index, the order of the FIR filter is adjusted according to the charging state evaluation index to obtain an adaptive filter order. During the charging process of the battery pack, when the charging state is good, the adaptive filter order is reduced to improve the calculation efficiency; when the charging state is poor, the adaptive filter order is increased to improve the accuracy of the filtering result of the current, thereby avoiding the deterioration of the charging state of the battery pack and meeting the needs of monitoring the current of the battery pack.

[0008] Preferably, the parameter data further includes the voltage.

[0009] Preferably, the monitoring method further includes calculating a charging power variation factor, the expression being: ; In the formula, represents the charging power variation factor at the kth time, , respectively represent the voltage of the battery pack at the kth and (k-1)th times, , respectively represent the current of the battery pack at the kth and (k-1)th times, represents the time interval between the two adjacent times.

[0010] By calculating the charging power variation factor, the variation of the charging power can be reflected, and the charging state of the battery pack can be preliminarily reflected.

[0011] Preferably, the monitoring method further includes calculating a battery pack internal temperature variation factor, the expression being: ; In the formula, represents the battery pack internal temperature variation factor at the kth time, represents the internal temperature of the battery pack at the kth time, represents the rated operating temperature of the battery pack.

[0012] When the battery pack internal temperature is abnormal, it indicates that the charging state of the battery pack is abnormal, and therefore by calculating the battery pack internal temperature change factor, the change of the charging power can be further reflected.

[0013] Preferably, the expression of the charging state evaluation index is: ; In the formula, represents the charging state evaluation index at the kth moment, represents the charging power change factor at the kth moment, represents the battery pack internal temperature change factor at the kth moment, , respectively represent the first weight coefficient and the second weight coefficient.

[0014] The charging state evaluation index is obtained by fusing and calculating the charging power change factor and the internal temperature change factor, and the charging state of the battery pack is reflected through the charging state evaluation index, thereby improving the accuracy and robustness of the evaluation result.

[0015] Preferably, the calculation method of the first weight coefficient and the second weight coefficient is: obtaining the charging power change factors and the battery pack internal temperature change factors at the k-nth moment to the kth moment, respectively constructing the charging power change factor sequence and the battery pack internal temperature change factor sequence , fitting the sequences and by using the least square method to obtain the first curve and the second curve, respectively calculating the first slope and the second slope of the data points corresponding to the kth moment of the first curve and the second curve; the first weight coefficient is: ; and the second weight coefficient is: .

[0016] The first weight coefficient and the second weight coefficient are obtained by calculation, which reflects the weight of the charging state evaluation index and the charging power change factor, and further improves the accuracy of the charging state evaluation index.

[0017] Preferably, the expression of the adaptive filter order is: ; In the formula, N represents the adaptive filter order, , respectively represent the preset maximum filter order and the minimum filter order, represents the filter adjustment factor at the kth moment, a state-of-charge evaluation index at the kth moment, denotes a ceiling function.

[0018] An adaptive filter order is calculated by using the filter adjustment factor and the state-of-charge evaluation index, the flexibility of the FIR filter is improved, the filtering efficiency is improved, and the needs of the battery pack current monitoring can be met.

[0019] Preferably, the expression of the FIR filter for current denoising is:

[0020] In the formula, denotes the optimal current at the kth moment after denoising, N denotes the adaptive filter order, k-m denotes the index of the current sequence number, denotes the current at the k-mth moment, denotes the state-of-charge evaluation index at the kth moment.

[0021] The weight of the corresponding current is calculated by the state-of-charge evaluation index, and the requirements of the safety redundancy mechanism in the battery pack charging process are met.

[0022] Preferably, the method for judging whether the optimal current is abnormal or not is: calculating the change rate of the optimal current, and when the change rate of the optimal current at a plurality of adjacent moments is greater than a preset threshold, a warning prompt is sent.

[0023] In the second aspect, the present application provides a battery pack current monitoring system, which adopts the following technical scheme: A battery pack current monitoring system comprises a processor and a memory, and the memory stores computer program instructions, which are executed by the processor to realize the battery pack current monitoring method.

[0024] The battery pack current monitoring method is generated into a computer program and stored in the memory to be loaded and executed by the processor, so that the memory and the processor system are convenient to use.

[0025] The present application has the following technical effects: 1. By calculating the state-of-charge evaluation index, the adaptive filter order is obtained by adjusting the FIR filter order according to the state-of-charge evaluation index, in the battery pack charging process, when the charging pack state is good, the adaptive filter order is reduced to improve the calculation efficiency, when the charging pack state is poor, the adaptive filter order is increased to improve the accuracy of the filtering result of the current, the deterioration of the state of the battery pack is avoided, and the needs of the battery pack current monitoring are met.

[0026] 2. The weight of the corresponding current is calculated by the state of charge evaluation index, which meets the requirement of the safety redundancy mechanism in the battery pack charging process and is suitable for monitoring the current in the battery pack charging process. BRIEF DESCRIPTION OF DRAWINGS

[0027] Figure 1 is a flowchart of a battery pack current monitoring method. DETAILED DESCRIPTION

[0028] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0029] The embodiments of the present application disclose a battery pack current monitoring method, referring to Figure 1 , comprising the following steps: S1: obtaining parameter data of the battery pack during charging.

[0030] In the battery pack charging process, the multiple parameter data of the battery pack are obtained in real time, and the parameter data includes: current, voltage and internal temperature of the battery pack. The battery pack charging process is a dynamic physical process, and the current and voltage are core parameters reflecting the state of charge of the battery. The internal temperature of the battery pack can reflect the state of charge of the battery pack. If the internal temperature of the battery pack is high, it indicates that the possibility of the battery pack being in an abnormal state is greater.

[0031] S2: calculating a state of charge evaluation index by using the obtained parameter data.

[0032] S21: calculating a charging power change factor.

[0033] The expression is:

[0034] In the formula, represents the charging power change factor at the kth moment, , respectively represent the voltage of the battery pack at the kth and (k-1)th moments, , respectively represent the current of the battery pack at the kth and (k-1)th moments, represents the time interval between the adjacent two moments. UxI represents the charging power of the battery pack. The charging power change factor can directly reflect the stability of the charging process. When the charging power changes sharply, the value of the charging power change factor increases, indicating that the state of charge is unstable or the possibility of abnormality is greater.

[0035] S22: Calculate the battery pack internal temperature change factor.

[0036] The expression is:

[0037] In the formula, represents the battery pack internal temperature change factor at the kth moment, represents the battery pack internal temperature at the kth moment, represents the rated operating temperature of the battery pack. The battery pack internal temperature change factor reflects the change of the battery pack internal temperature relative to the rated operating temperature. The greater the value, the more intense the change of the battery pack internal temperature, and the greater the possibility of unstable charging state or abnormality. The charging power change factor and the battery pack internal temperature change factor are normalized by using a linear normalization algorithm.

[0038] S23: Calculate the charging state evaluation index.

[0039] The expression is:

[0040] In the formula, represents the charging state evaluation index at the kth moment, represents the charging power change factor at the kth moment, represents the battery pack internal temperature change factor at the kth moment, , respectively represent the first weight coefficient and the second weight coefficient. The charging state evaluation index is calculated by the charging power change factor and the battery pack internal temperature change factor, which can comprehensively reflect the charging state of the battery pack at the corresponding moment, and can comprehensively evaluate the health state degree of the current charging. It can be understood that the more intense the charging power change of the battery pack, the more intense the change of the battery pack internal temperature, and the smaller the value of the charging state evaluation index, indicating that the charging state of the battery pack is worse, and at this time, the battery pack faces greater risk, and at this time, the current of the battery pack needs to be monitored more strictly.

[0041] The calculation method of the first weight coefficient and the second weight coefficient is: obtaining the charging power change factor and the battery pack internal temperature change factor at the k-nth moment to the kth moment, respectively constructing the charging power change factor sequence and the battery pack internal temperature change factor sequence , fitting the sequence by using the least square method to obtain the first curve, and in the same way, fitting the sequence by using the least square method to obtain the second curve, calculating the first slope of the data point corresponding to the kth moment of the first curve , the second slope of the second curve corresponding to the data point at the kth moment is calculated .

[0042] The first weight coefficient: ; The second weight coefficient: .

[0043] The first slope represents the change of the charging power change factor, that is, the change trend of the charging power. The greater the value, the greater the change range of the charging power change factor, indicating that the power of the battery pack changes greatly in an irregular manner, and the charging current of the battery pack is more likely to be abnormal. Therefore, the charging power change factor should be focused on, and a greater weight coefficient should be given to the charging power change factor.

[0044] Similarly, the second slope represents the change of the battery pack internal temperature change factor, that is, the change trend of the battery pack internal temperature. The greater the value, the greater the change range of the charging power change factor, indicating that the battery pack internal temperature changes greatly in an irregular manner, and the battery pack internal temperature is more likely to be abnormal. Therefore, the battery pack internal temperature change factor should be focused on, and a greater weight coefficient should be given to the battery pack internal temperature change factor.

[0045] By dynamically adjusting the weight coefficient of the charging power change factor and the change coefficient of the battery pack internal temperature change factor, and detecting the charging state of the battery pack through two dimensions of charging power and battery pack internal temperature, the accuracy and robustness of the monitoring result are improved.

[0046] S3: Calculate the filter adjustment factor.

[0047] In the battery pack charging process, there is a charging state evaluation index corresponding to each moment. A plurality of consecutive charging state evaluation indexes are used to construct an evaluation sequence at each moment, and the Theil-Sen Median trend algorithm is used to calculate the trend line slope estimate of the evaluation sequence, and the trend line slope estimate of the sequence is used as the filter adjustment factor of the corresponding moment. The trend line slope estimate greater than zero indicates that the evaluation sequence shows an upward trend, and the trend line slope estimate less than zero indicates that the evaluation sequence shows a downward trend. The Theil-Sen Median trend algorithm is prior art, and the specific steps are not repeated here.

[0048] For example, the evaluation sequence at the kth moment is ( , , …, ), and the Theil-Sen Median trend algorithm is used to calculate the evaluation sequence the trend line slope estimation of the kth moment, taking the trend line slope estimation of the kth moment as the filter adjustment factor of the kth moment . reflects the rising and falling trend of the state of charge evaluation index at the kth moment. It can be understood that when is less than zero, it indicates that the state of charge evaluation index is in a downward trend, and when is greater than zero, it indicates that the state of charge evaluation index is in an upward trend.

[0049] S4: Adjust the FIR filter order using the filter adjustment factor to obtain the adaptive filter order, and use the FIR filter to denoise the current.

[0050] The expression of the adaptive filter order is:

[0051] In the formula, N represents the adaptive filter order, , respectively represent the preset maximum filter order and minimum filter order, the values of which are artificially set according to actual conditions, for example, the value of N is 100, the value of N is 50, represents the filter adjustment factor of the kth moment, represents the state of charge evaluation index of the kth moment, represents the upward rounding function, and in the formula, the denominator of the formula is 2, in order to make the overall result within (0, 1).

[0052] If the stability of the state of charge at the corresponding moment is poor (manifested as the filter adjustment factor and / or the state of charge evaluation index is small), it indicates that the battery pack state of charge at the current moment is abnormal, and the health state of the battery pack is poor. At this time, the accuracy of the current filtering result is more strict in order to obtain more accurate current, so the adaptive filter order should be increased to improve the denoising accuracy of the FIR filter.

[0053] If the stability of the state of charge at the corresponding moment is good (manifested as the filter adjustment factor and the state of charge evaluation index is large), it indicates that the battery pack state of charge at the current moment is relatively stable, and the health state of the battery pack is good. At this time, the adaptive filter order should be reduced to improve the operation speed of the FIR filter.

[0054] After obtaining the adaptive filter order, the current is denoised using the FIR filter to obtain the optimal current.

[0055] The expression of the FIR filter for denoising the current is:

[0056] In the formula, denotes the optimal current at the kth moment after denoising, N denotes the adaptive filter order, and k-m denotes the index of the current number, denotes the current at the k-mth moment, denotes the state of charge evaluation index at the kth moment.

[0057] In the process of denoising the current by the FIR filter, in the formula, denotes the weight of the current at the k-mth moment, based on the consideration of the safety redundancy mechanism, when the charging power of the battery pack at the corresponding moment changes more drastically, the change of the internal temperature of the battery pack is more drastic (the value of the state of charge evaluation index is smaller), at this time, the current at the corresponding moment should be given a larger weight (the smaller the value of the state of charge evaluation index, the larger the numerator term).

[0058] S5: Determine whether the optimal current is abnormal.

[0059] When the charging process of the battery pack is abnormal, the optimal current usually changes abruptly, therefore, the change rate of the optimal current is calculated, that is, , in the formula, denotes the change rate of the optimal current at the nth moment, , denote the optimal currents at the nth and (n-1)th moments respectively. When the change rates of the optimal currents at consecutive multiple adjacent moments are greater than a preset threshold value, a warning prompt is issued, and the threshold value is set artificially according to the actual situation.

[0060] For example, the threshold value is 0.4, when the change rates of the optimal currents at consecutive 5 moments are greater than 0.4, it indicates that the current state of the battery pack is abnormal, and a warning prompt is issued in time.

[0061] The embodiment of the application also discloses a battery pack current monitoring system, comprising a processor and a memory, and the memory stores computer program instructions, which realize the battery pack current monitoring method according to the application when executed by the processor.

[0062] The above system also comprises other components such as communication bus and communication interface which are well known to those skilled in the art, and the settings and functions thereof are known in the art, therefore, will not be described here.

[0063] The above are preferred embodiments of the application, which do not limit the protection scope of the application, therefore: any equivalent changes made on the structure, shape and principle of the application should be covered within the protection scope of the application.

Claims

1. A battery pack current monitoring method, characterized in that: The monitoring method comprises the steps of: Obtain parameter data of the battery pack at each moment during charging, including the internal temperature and current of the battery pack; The acquired parameter data is used to calculate the charging state evaluation index, which is negatively correlated with the temperature variation within the battery pack. An evaluation sequence is constructed using the charging state evaluation indicators of multiple adjacent moments. The trend slope estimate of the evaluation sequence is calculated, and the trend slope estimate of the evaluation sequence is used as the filter adjustment factor at the corresponding moment. The filter adjustment factor is used to adjust the FIR filter order to obtain the adaptive filter order. The FIR filter is used to denoise the current to obtain the optimal current, which is used to determine whether the optimal current is abnormal.

2. A battery pack current monitoring method according to claim 1, characterized in that: The parameter data also includes voltage.

3. The battery pack current monitoring method according to claim 2, characterized in that: The monitoring method further includes: calculating the charging power variation factor, which is expressed as: ； Where, represents the charging power change factor at the kth moment, 、 Represent the voltage of the battery pack at the kth and k-1th moments respectively, 、 Represent the current of the battery pack at the kth and k-1th moments respectively, Indicates the time interval between two adjacent moments.

4. The battery pack current monitoring method according to claim 3, characterized in that: The monitoring method also includes: calculating the temperature change factor inside the battery pack, which is expressed as: ； Where, Indicates the internal temperature change factor of the battery pack at the kth moment, represents the internal temperature of the battery pack at the kth moment, Indicates the rated operating temperature of the battery pack.

5. The battery pack current monitoring method according to claim 4, characterized in that: The expression of the charging state evaluation index is: ； Where, represents the charging state evaluation index at the kth moment, represents the charging power change factor at the kth moment, Indicates the internal temperature change factor of the battery pack at the kth moment, 、 Represent the first weight coefficient and the second weight coefficient respectively.

6. The battery pack current monitoring method according to claim 5, characterized in that: The calculation method of the first weight coefficient and the second weight coefficient is: obtain the charging power change factor and the battery pack internal temperature change factor from the knth to the kth moment, and construct the charging power change factor sequence respectively And the battery pack internal temperature change factor sequence , using the least squares method to respectively and Fitting is performed to obtain the first curve and the second curve, and the first slopes of the first curve and the second curve corresponding to the data point at the kth moment are calculated respectively. , second slope ; First weight coefficient: ; Second weight coefficient: .

7. The battery pack current monitoring method according to claim 1, characterized in that: The expression of the adaptive filter order is: ； Where N represents the adaptive filter order, 、 Respectively represent the preset maximum filter order and minimum filter order, represents the filter adjustment factor at the kth moment, represents the charging state evaluation index at the kth moment, Represents the ceiling function.

8. The battery pack current monitoring method according to claim 1, characterized in that: The expression for FIR filter to denoise current is: ； Where, represents the optimal current at the kth moment after denoising, N represents the order of the adaptive filter, km represents the index of the current sequence number, represents the current at the kmth moment, Represents the charging state evaluation index at the kth moment.

9. The battery pack current monitoring method according to claim 1, characterized in that: The method for determining whether the optimal current is abnormal is to calculate the rate of change of the optimal current and issue an early warning when the rate of change of the optimal current at multiple consecutive moments is greater than a preset threshold.

10. A battery pack current monitoring system, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a battery pack current monitoring method according to any one of claims 1 to 7 is implemented.

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