A battery pack current monitoring method and system

Abstract

The present application relates to the field of battery pack monitoring, and more particularly to a battery pack current monitoring method and system, the method comprising: obtaining parameter data of the battery pack at each time instant when charging; 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 time instants, 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 instant, adjusting the FIR filter order using the filter adjustment factor to obtain an adaptive filter order, and denoising the current using the FIR filter to obtain an optimal current for judging whether the optimal current is abnormal, The present application adjusts the FIR filter order, thereby improving the working efficiency of the FIR filter.

Description

Technical Field

[0001] This invention relates to the field of battery pack monitoring, and more particularly to a method and system for monitoring battery pack current. Background Technology

[0002] With the rapid development of electric vehicles, portable electronic devices, and large-scale energy storage systems, battery packs, as their core energy storage units, are receiving increasing attention for their performance, lifespan, and safety. The charging process is a crucial stage throughout the battery pack's lifespan, directly impacting the battery's health and lifespan. Improper charging practices, especially abnormal charging current, can lead to serious consequences such as overcharging, overheating, and internal short circuits. These can range from accelerating battery aging and shortening cycle life to causing thermal runaway or even fires and explosions, posing a significant threat to personal safety and property.

[0003] Chinese patent application CN119416597A discloses an intelligent method and system for monitoring and analyzing current in integrated circuit packages. The method includes acquiring current timing data of the integrated circuit under test under different operating states; decomposing the calibrated sampled data into a feature coefficient matrix through adaptive wavelet transform; constructing a deep feature analysis model based on the feature coefficient matrix; using a graph attention network to combine the physical structure information of the integrated circuit with the current characteristics to construct a dynamic feature map; identifying abnormal current characteristics and locating abnormal regions through multi-layer graph convolution operations; inputting the abnormal current characteristics and abnormal region information into a digital twin analysis system; determining the correspondence between the abnormal current characteristics and the circuit physical parameters through iterative calculation; combining the comparative analysis of measured data and simulation results; outputting fault location results and fault development trend predictions; and generating a diagnostic analysis report.

[0004] During battery pack current detection, the switching power supply inside the charging equipment, the complex electromagnetic environment, and the high-frequency noise of the sensor itself all superimpose onto the actual charging current signal. Therefore, the current signal needs to be filtered to eliminate noise. FIR filters are typically used for current signal filtering; however, existing FIR filters usually use a fixed filter order, which is insufficient to meet the monitoring requirements of all operating conditions. If the filter order is too low, it may not effectively filter out noise, leading to misjudgments of spurious fluctuations; if the filter order is too high, it will introduce excessive delay, reducing the system's real-time response capability. Summary of the Invention

[0005] To address the issue that fixed-order filters cannot meet the requirements for monitoring battery pack current, this invention provides a battery pack current monitoring method and system.

[0006] In a first aspect, the present invention provides a battery pack current monitoring method, which adopts the following technical solution:

[0007] The system acquires parameter data of the battery pack at each moment during charging, including the internal temperature and current of the battery pack; it then uses the acquired parameter data to calculate the state of charge evaluation index, which is negatively correlated with the change in the internal temperature of the battery pack.

[0008] An evaluation sequence is constructed using charging state evaluation indices at multiple adjacent time points. The trend slope estimate of the evaluation sequence is calculated and used as the filter adjustment factor at the corresponding time point. The order of the FIR filter is adjusted using the filter adjustment factor to obtain the adaptive filter order. The FIR filter is used to denoise the current to obtain the optimal current, which is then used to determine whether the optimal current is abnormal.

[0009] By calculating the state of charge evaluation index, the order of the FIR filter is adjusted according to the state of charge evaluation index to obtain the adaptive filter order. During the battery pack charging process, when the charging pack is in good condition, the adaptive filter order is reduced to improve the calculation efficiency. When the charging pack is in poor condition, the adaptive filter order is increased to improve the accuracy of the current filtering result, avoid the deterioration of the charging state of the battery pack, and meet the needs of battery pack current monitoring.

[0010] Preferably, the parameter data also includes voltage.

[0011] Preferably, the monitoring method further includes: calculating the charging power change factor, expressed as:

[0012] ;

[0013] In the formula, This represents the charging power change factor at time k. , These represent the voltages of the battery pack at the k-th and (k-1)-th time points, respectively. , These represent the current of the battery pack at time k and time (k-1), respectively. It represents the time interval between two adjacent moments.

[0014] By calculating the charging power variation factor, the changes in charging power can be reflected, which can provide a preliminary indication of the charging status of the battery pack.

[0015] Preferably, the monitoring method further includes: calculating the internal temperature change factor of the battery pack, expressed as:

[0016] ;

[0017] In the formula, This represents the internal temperature change factor of the battery pack at time k. This represents the internal temperature of the battery pack at time k. This indicates the rated operating temperature of the battery pack.

[0018] When the internal temperature of the battery pack becomes abnormal, it indicates that the charging state of the battery pack has become abnormal. Therefore, by calculating the internal temperature change factor of the battery pack, the change in charging power can be further reflected.

[0019] Preferably, the expression for the state of charge assessment index is:

[0020] ;

[0021] In the formula, This represents the charging state evaluation index at time k. This represents the charging power change factor at time k. This represents the internal temperature change factor of the battery pack at time k. , These represent the first weighting coefficient and the second weighting coefficient, respectively.

[0022] By fusing the charging power variation factor and the internal temperature variation factor, a charging state evaluation index is obtained. This index reflects the charging state of the battery pack, improving the accuracy and robustness of the evaluation results.

[0023] Preferably, the calculation method for the first weighting coefficient and the second weighting coefficient is as follows: obtain the charging power change factor and the battery pack internal temperature change factor from time kn to time k, and construct the charging power change factor sequence respectively. and battery pack internal temperature variation factor sequence Using the least squares method to process the sequences respectively and The first curve and the second curve are obtained by fitting the data. The first slope of the first curve and the second curve at the k-th time point are calculated respectively. Second slope First weighting coefficient: Second weighting coefficient: .

[0024] The first and second weighting coefficients are calculated to reflect the weights of the charging state assessment index and the charging power change factor, thereby further improving the accuracy of the charging state assessment index.

[0025] Preferably, the expression for the order of the adaptive filter is:

[0026] ;

[0027] In the formula, N represents the order of the adaptive filter. , These represent the preset maximum and minimum filter orders, respectively. This represents the filter adjustment factor at time k. This represents the charging state evaluation index at time k. This represents the function for rounding up.

[0028] The adaptive filter order is calculated using the filter adjustment factor and the state of charge evaluation index, which improves the flexibility and filtering efficiency of the FIR filter and meets the needs of battery pack current monitoring.

[0029] Preferably, the expression for current denoising by the FIR filter is:

[0030]

[0031] In the formula, Let represent the optimal current at the k-th time after denoising, where N represents the order of the adaptive filter, and km represents the index of the current ordinal number. This represents the current at time k. This represents the charging state evaluation index at time k.

[0032] The weight of the corresponding current is calculated by the state of charge assessment index, which meets the requirements of the safety redundancy mechanism during the battery pack charging process.

[0033] Preferably, the method for determining whether the optimal current is abnormal is as follows: calculate the rate of change of the optimal current, and issue an early warning when the rate of change of the optimal current is greater than a preset threshold at multiple consecutive adjacent time points.

[0034] Secondly, the present invention provides a battery pack current monitoring system, which adopts the following technical solution:

[0035] A battery pack current monitoring system includes 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 the above is implemented.

[0036] The aforementioned battery pack current monitoring method is used to generate a computer program, which is then stored in a memory for loading and execution by a processor. This allows for the creation of a system based on the memory and processor, making it convenient to use.

[0037] The present invention has the following technical effects:

[0038] 1. By calculating the state of charge evaluation index, the order of the FIR filter is adjusted according to the state of charge evaluation index to obtain the adaptive filter order. During the battery pack charging process, when the charging pack is in good condition, the adaptive filter order is reduced to improve the calculation efficiency. When the charging pack is in poor condition, the adaptive filter order is increased to improve the accuracy of the current filtering result, avoid the deterioration of the charging state of the battery pack, and meet the needs of battery pack current monitoring.

[0039] 2. The weight of the corresponding current is calculated through the charging state assessment index, which meets the requirements of the safety redundancy mechanism during the battery pack charging process and is suitable for monitoring the current during the battery pack charging process. Attached Figure Description

[0040] Figure 1 This is a flowchart of a battery pack current monitoring method according to the present invention. Detailed Implementation

[0041] 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, not all, of the embodiments of the present invention. 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.

[0042] This invention discloses a battery pack current monitoring method, referring to... Figure 1 This includes the following steps:

[0043] S1: Obtain parameter data of the battery pack during charging.

[0044] During the battery pack charging process, multiple parameter data of the battery pack are acquired in real time, including current, voltage, and internal temperature. The battery pack charging process is a dynamic physical process. Current and voltage are the core parameters reflecting the battery charging status. The internal temperature of the battery pack can reflect the charging status of the battery pack. If the internal temperature of the battery pack is high, it indicates that the battery pack is likely in an abnormal state.

[0045] S2: Calculate the charging status evaluation index using the acquired parameter data.

[0046] S21: Calculate the charging power variation factor.

[0047] The expression is:

[0048]

[0049] In the formula, This represents the charging power change factor at time k. , These represent the voltages of the battery pack at the k-th and (k-1)-th time points, respectively. , These represent the current of the battery pack at time k and time (k-1), respectively. This represents the time interval between two adjacent moments. U×I represents the charging power of the battery pack, and the charging power variation factor directly reflects the stability of the charging process. When the charging power changes drastically, the value of the charging power variation factor increases, indicating that the charging state is unstable or there is a greater possibility of an anomaly.

[0050] S22: Calculate the internal temperature variation factor of the battery pack.

[0051] The expression is:

[0052]

[0053] In the formula, This represents the internal temperature change factor of the battery pack at time k. This represents the internal temperature of the battery pack at time k. This indicates the rated operating temperature of the battery pack. The internal temperature variation factor of the battery pack reflects the change in internal temperature relative to the rated operating temperature. A larger value indicates more drastic temperature changes within the battery pack, suggesting a greater likelihood of unstable charging or abnormalities. The charging power variation factor and the internal temperature variation factor of the battery pack are normalized using a linear normalization algorithm.

[0054] S23: Calculate the charging status assessment index.

[0055] The expression is:

[0056]

[0057] In the formula, This represents the charging state evaluation index at time k. This represents the charging power change factor at time k. This represents the internal temperature change factor of the battery pack at time k. , These represent the first and second weighting coefficients, respectively. The charging status evaluation index is calculated using the charging power variation factor and the battery pack internal temperature variation factor. This index comprehensively reflects the charging status of the battery pack at a given moment and can fully assess the current charging health. It is understandable that the more drastic the changes in the charging power and internal temperature of the battery pack, the smaller the value of the charging status evaluation index, indicating a worse charging status. In this case, the battery pack faces greater risks, and more stringent monitoring of the battery pack current is required.

[0058] The calculation method for the first and second weighting coefficients is as follows: obtain the charging power change factor and the battery pack internal temperature change factor from time kn to time k, and construct the charging power change factor sequence respectively. and battery pack internal temperature variation factor sequence Using the least squares method to process the sequence The first curve is obtained by fitting the data. Similarly, the least squares method is used to fit the sequence. The second curve is obtained by fitting the data, and the first slope of the first curve at the data point corresponding to the k-th time is calculated. Calculate the second slope of the second curve at the data point corresponding to the k-th time. .

[0059] First weighting coefficient: ;

[0060] Second weighting coefficient: .

[0061] The first slope represents the change in the charging power variation factor, that is, the trend of charging power change. The larger the value, the greater the change in the charging power variation factor, indicating that the power of the battery pack is changing in an irregular way and the charging current of the battery pack is likely to be abnormal. Therefore, the charging power variation factor should be given more attention and a larger weight coefficient.

[0062] Similarly, the second slope represents the change of the internal temperature change factor of the battery pack, that is, the trend of the internal temperature of the battery pack. The larger the value, the greater the change of the charging power change factor, indicating that the internal temperature of the battery pack is changing in an irregular way and the internal temperature of the battery pack is more likely to be abnormal. Therefore, the internal temperature change factor of the battery pack should be given more attention and a larger weight coefficient.

[0063] By dynamically adjusting the weighting coefficients of the charging power variation factor and the variation coefficients of the battery pack internal temperature variation factor, and by detecting the charging status of the battery pack from two dimensions—charging power and battery pack internal temperature—the accuracy and robustness of the monitoring results are improved.

[0064] S3: Calculate the filter adjustment factor.

[0065] During battery pack charging, each moment corresponds to a state of charge (SOC) evaluation index. An evaluation sequence for each moment is constructed using multiple consecutive SOC evaluation indices. The Theil-Sen Median trend algorithm is then used to calculate the slope estimate of the trend line in the evaluation sequence, which is used as the filter adjustment factor for the corresponding moment. A trend line slope estimate greater than zero indicates an upward trend in the evaluation sequence, while a trend line slope estimate less than zero indicates a downward trend. The Theil-Sen Median trend algorithm is existing technology, and its specific steps will not be detailed here.

[0066] For example, the evaluation sequence at time k. for( , … The evaluation sequence was calculated using the Theil-Sen Median trend algorithm. The trend slope estimate is obtained, and the trend slope estimate at time k is used as the filter adjustment factor at time k. . The rising and falling trend of the charging state assessment index at time k can be understood as follows: A value less than zero indicates that the charging status assessment index is showing a downward trend. A value greater than zero indicates that the charging status assessment index is on an upward trend.

[0067] S4: The order of the FIR filter is adjusted by the filter adjustment factor to obtain the adaptive filter order, and the FIR filter is used to denoise the current.

[0068] The expression for the order of an adaptive filter is:

[0069]

[0070] In the formula, N represents the order of the adaptive filter. , These represent the preset maximum and minimum filter orders, respectively. Their values ​​are manually set based on actual conditions. For example... The value is 100. The value is 50. This represents the filter adjustment factor at time k. This represents the charging state evaluation index at time k. This represents the floor function, in the formula. The denominator is 2, in order to make The overall result is within (0,1).

[0071] If the charging state stability at a given moment is poor (manifested as a small filter adjustment factor and / or a small charging state evaluation index), it indicates that the battery pack charging state is abnormal at the current moment and the battery pack's health is poor. In this case, the accuracy of the current filtering result is more critical in order to obtain a more accurate current. Therefore, the order of the adaptive filter should be increased to improve the noise reduction accuracy of the FIR filter.

[0072] If the charging state stability at a given moment is good (manifested as a larger filter adjustment factor and charging state evaluation index), it indicates that the battery pack charging state is relatively stable at the current moment and the battery pack is in good health. In this case, the order of the adaptive filter should be reduced and the operation speed of the FIR filter should be increased.

[0073] After obtaining the order of the adaptive filter, the optimal current is obtained by using an FIR filter to denoise the current.

[0074] The expression for current denoising using an FIR filter is:

[0075]

[0076] In the formula, Let represent the optimal current at the k-th time after denoising, where N represents the order of the adaptive filter, and km represents the index of the current ordinal number. This represents the current at time k. This represents the charging state evaluation index at time k.

[0077] In the process of denoising the current using an FIR filter, in the formula, The weight of the current at time k is given. Based on the consideration of safety redundancy mechanism, when the charging power of the battery pack changes more drastically at the corresponding time, the internal temperature of the battery pack changes more drastically (the smaller the value of the state of charge evaluation index), the current at the corresponding time should be given a larger weight (the smaller the value of the state of charge evaluation index, the larger the numerator).

[0078] S5: Determine if the optimal current is abnormal.

[0079] When an anomaly occurs during the charging process of the battery pack, the optimal current often changes abruptly. Therefore, the rate of change of the optimal current is calculated. In the formula, This represents the rate of change of the optimal current at time n. , These represent the optimal current at time n and time n-1, respectively. An early warning is issued when the rate of change of the optimal current at multiple consecutive adjacent time points exceeds a preset threshold, which is manually set based on actual conditions.

[0080] For example, with a threshold of 0.4, if the rate of change of the optimal current is greater than 0.4 for five consecutive time periods, it indicates that the current battery pack is in an abnormal state, and a warning is issued in a timely manner.

[0081] This invention also discloses a battery pack current monitoring system, including a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement a battery pack current monitoring method according to the present invention.

[0082] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

[0083] The above are all preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Therefore, all equivalent changes made in accordance with the structure, shape and principle of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for monitoring battery pack current, characterized in that, The monitoring method includes the following steps: Acquire parameter data of the battery pack at each moment during charging, including the internal temperature and current of the battery pack; The state-of-charge (SOC) assessment index is calculated using the acquired parameter data. This index is negatively correlated with the temperature change within the battery pack, and its expression is: ； In the formula, This represents the charging state evaluation index at time k. This represents the charging power change factor at time k. This represents the internal temperature change factor of the battery pack at time k. , These represent the first weighting coefficient and the second weighting coefficient, respectively. An evaluation sequence is constructed using charging state evaluation indices at multiple adjacent time points. The trend slope estimate of the evaluation sequence is calculated and used as the filter adjustment factor for the corresponding time point. This filter adjustment factor is then used to adjust the order of the FIR filter to obtain the adaptive filter order. The expression for the adaptive filter order is as follows: ； In the formula, N represents the order of the adaptive filter. , These represent the preset maximum and minimum filter orders, respectively. This represents the filter adjustment factor at time k. This represents the charging state evaluation index at time k. This represents the floor function; The optimal current is obtained by using an FIR filter to denoise the current, which is then used to determine whether the optimal current is abnormal.

2. The 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 also includes: calculating the charging power variation factor, expressed as: ； In the formula, This represents the charging power change factor at time k. , These represent the voltages of the battery pack at the k-th and (k-1)-th time points, respectively. , These represent the current of the battery pack at time k and time (k-1), respectively. It represents 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 internal temperature change factor of the battery pack, expressed as: ； In the formula, This represents the internal temperature change factor of the battery pack at time k. This represents the internal temperature of the battery pack at time k. This indicates the rated operating temperature of the battery pack.

5. A battery pack current monitoring method according to claim 4, characterized in that, The calculation method for the first and second weighting coefficients is as follows: obtain the charging power change factor and the battery pack internal temperature change factor from time kn to time k, and construct the charging power change factor sequence respectively. and battery pack internal temperature variation factor sequence Using the least squares method to process the sequences respectively and The first curve and the second curve are obtained by fitting the data. The first slope of the first curve and the second curve at the k-th time point are calculated respectively. Second slope First weighting coefficient: Second weighting coefficient: .

6. The battery pack current monitoring method according to claim 1, characterized in that, The expression for current denoising using an FIR filter is: ； In the formula, Let represent the optimal current at the k-th time after denoising, where N represents the order of the adaptive filter, and km represents the index of the current ordinal number. This represents the current at time k. This represents the charging state evaluation index at time k.

7. The battery pack current monitoring method according to claim 1, characterized in that, The method to determine whether the optimal current is abnormal is to calculate the rate of change of the optimal current. When the rate of change of the optimal current is greater than a preset threshold for multiple consecutive adjacent time moments, an early warning is issued.

8. A battery pack current monitoring system, characterized in that, include: A processor and a memory, the memory storing computer program instructions that, when executed by the processor, implement a battery pack current monitoring method according to any one of claims 1-5.

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