Battery pack cell voltage monitoring method and system

Abstract

The invention relates to the technical field of battery management, in particular to a battery pack cell voltage monitoring method and system, and the monitoring method comprises the steps: collecting the voltage of each cell in a battery pack in a charging process; and calculating a rising abnormal score and a falling abnormal score of the battery pack at any moment in the charging process, and carrying out abnormal monitoring on the voltage of the battery core of the battery pack according to the rising abnormal score and the falling abnormal score. The abnormal rising score of the battery pack at any moment reflects the difference between the voltage rising value of each battery cell at the moment and the mean value of the voltage rising values of the other battery cells. By introducing two independent indexes of a voltage rise abnormal score and a voltage drop abnormal score, quantitative analysis is performed on the rise amplitude abnormity and the drop behavior abnormity of the battery cell voltage, so that the abnormal point of the voltage deviation trend in the charging process can be identified, and serious risk signals such as sudden drop of the battery cell and the like can be effectively captured; therefore, the accuracy of abnormity monitoring is improved.

Description

Technical Field

[0001] The present invention relates to the field of battery management technology. More specifically, the present invention relates to a method and system for monitoring the voltage of battery cells in a battery pack. Background Art

[0002] A battery pack is a power system composed of multiple cells connected in series, parallel, or hybrid configurations. It is commonly used as a power source in electric vehicles, energy storage power stations, portable electronic devices, and other applications. Connecting cells in series increases voltage, while connecting them in parallel increases capacity, thereby meeting varying power and energy requirements. The performance of a battery pack depends not only on the quality of the individual cells but also on the overall structural design and management strategy. To ensure the safety, reliability, and lifespan of a battery pack, a battery management system (BMS) is typically deployed. A BMS monitors each cell's voltage, temperature, current, and other parameters in real time, providing multiple protection functions such as overvoltage, undervoltage, overtemperature, and short circuit. Furthermore, the BMS supports state estimation (such as SOC and SOH), thermal management control, and balancing management, effectively preventing thermal runaway, extending the battery pack's lifespan, and enhancing the system's intelligent operation and fault response capabilities.

[0003] When monitoring battery pack voltage, because a battery pack contains multiple cells and the voltage of each cell can be monitored, current methods for detecting abnormal cell voltages during charging often use fixed thresholds to determine whether the voltage is abnormal. This approach struggles to account for differences in cell characteristics and the nonlinear trends in voltage changes during charging. Furthermore, brief drops in cell voltage during passive balancing are easily misidentified as abnormalities in existing systems, resulting in a high false alarm rate and impacting the reliability and accuracy of battery pack voltage monitoring. Summary of the Invention

[0004] The present invention provides a battery cell voltage monitoring method and system, aiming to address the current problem in related technologies where abnormal cell voltage monitoring during battery charging, which mostly relies on fixed thresholds, is difficult to account for differences in cell characteristics and the nonlinear trend of voltage changes under charging conditions. Furthermore, brief drops in cell voltage during passive balancing are easily misidentified as abnormalities in existing systems, resulting in a high false alarm rate.

[0005] In a first aspect, the application provides a battery cell voltage monitoring method, the monitoring method comprising: collecting voltages of each cell in a battery during charging; calculating an abnormality score of the battery at any time during charging and an abnormality score of the battery at any time during charging, and performing abnormality monitoring on the battery cell voltage according to the size of the abnormality score and the abnormality score, the abnormality score of the battery at any time reflecting the difference between the voltage rise value of each cell at that time and the average of the voltage rise values of the remaining cells; the abnormality score is related to the state of the cell at that time, if the cell is in a passive balancing state, the abnormality score of the battery is positively correlated with the number of times the voltage of the cell exceeds the target drop phase at the continuous voltage drop time before that time and the voltage drop value at each voltage drop time, otherwise, the abnormality score of the battery is positively correlated with the voltage drop value at that time. By constructing a set of cell voltage monitoring methods based on trend consistency analysis and abnormality score evaluation, accurate identification and hierarchical early warning of abnormal voltage fluctuation of the battery during charging are realized.

[0006] Further, the abnormality monitoring of the battery cell voltage comprises: hierarchical early warning according to the size of the abnormality score and the abnormality score, wherein the hierarchical early warning is divided into first-level early warning and second-level early warning. The hierarchical early warning threshold is set according to the distribution characteristics of the voltage abnormality score, and first-level or second-level early warning response of the cell voltage state is realized. This mechanism enables the system not only to respond quickly when the abnormality appears, but also to perform high-level alarm (second-level early warning level is greater than first-level early warning) when multiple abnormality indicators meet at the same time, thereby improving the sensitivity to potential faults.

[0007] Further, the hierarchical early warning comprises: in response to the abnormality score of the battery at the current time during charging being greater than the rise threshold, or the abnormality score of the battery at the current time during charging being greater than the drop threshold, it is determined that the current battery appears abnormal, and first-level early warning is performed.

[0008] Further, the hierarchical early warning comprises: in response to the abnormality score of the battery at the current time during charging being greater than the rise threshold, and the abnormality score of the battery at the current time during charging being greater than the drop threshold, it is determined that the current battery appears abnormal, and second-level early warning is performed, and the experience value of the rise threshold is 0.8.

[0009] Further, the calculation method of the decline abnormal score of the battery pack at any moment comprises: if the battery cell is not in a passive balancing state, calculating the sum of voltage drop values of all battery cells in the battery pack at the moment, and performing normalization processing on the sum of voltage drop values, and taking the normalized value as the decline abnormal score of the battery pack at the moment. In combination with the state of the battery cell (such as whether it is in a balancing stage) and historical behavior, the magnitude of the actual voltage drop of the battery cell is quantified, which can reflect the severity of the drop, and the larger the value is, the more obvious the voltage drop is, and the higher the potential risk is.

[0010] Further, the calculation method of the decline abnormal score of the battery pack at any moment comprises: if the battery cell is not in a passive balancing state, calculating the sum of voltage drop values of all battery cells in the battery pack at the moment, and performing normalization processing on the sum of voltage drop values, and taking the normalized value as the decline abnormal score of the battery pack at the moment. In combination with the state of the battery cell (such as whether it is in a balancing stage) and historical behavior, the magnitude of the actual voltage drop of the battery cell is quantified, which can reflect the severity of the drop, and the larger the value is, the more obvious the voltage drop is, and the higher the potential risk is.

[0011] Further, the calculation method of the rise abnormal score of the battery pack at any moment comprises: calculating the product of the voltage rise value of each battery cell at the moment and the mean value of the voltage rise values of the remaining battery cells, and performing normalization processing on the sum of all mean values, and taking the normalized value as the rise abnormal score of the battery pack. By comparing the voltage rise amplitude of a single battery cell with the average rise amplitude of other battery cells, it can be effectively identified which battery cell has a rise behavior obviously deviating from the overall trend. The greater the difference is, the more abnormal the voltage change of the battery cell is, and there may be internal failure or connection problem.

[0012] Further, the calculation method of the decline abnormal score of each battery cell voltage at the moment comprises: calculating the sum of the voltage drop values of all battery cells at the moment compared with the voltage at the last moment, as the decline abnormal score of each battery cell voltage at the moment. The magnitude of the actual voltage drop of the battery cell is quantified, which can reflect the severity of the drop, and the larger the value is, the more obvious the voltage drop is, and the higher the potential risk is.

[0013] Further, the voltage rise value at any moment is the difference between the voltage at the moment and the voltage at the last moment. The second aspect of the application also provides a battery cell voltage monitoring system, comprising a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to realize the battery cell voltage monitoring method of any one of the above aspects.

[0014] Advantages: (1) By introducing two independent indicators, "voltage rise anomaly score" and "voltage drop anomaly score", the abnormal increase amplitude and decrease behavior of the battery cell voltage are quantitatively analyzed. This can not only identify abnormal points where the voltage deviates from the trend during the charging process, but also effectively capture serious risk signals such as sudden drop of the battery cell, thereby improving the accuracy of abnormal monitoring.

[0015] (2) During the charging process, a short-term drop in cell voltage may be caused by passive balancing, which is a normal phenomenon. The present invention calculates the probability that the drop is abnormal by collecting historical data and establishing a target drop stage model, combining the difference between the cell and the minimum voltage value and the balancing threshold, thereby effectively distinguishing between abnormalities caused by balancing and abnormalities caused by non-balancing, significantly reducing the misjudgment rate, and improving the reliability and accuracy of the battery pack voltage monitoring process. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 FIG. 1 is a flowchart schematically illustrating a process for monitoring the voltage of battery cells according to an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0018] like Figure 1 As shown, S101: collecting the voltage of each cell in the battery pack during the charging process.

[0019] In one embodiment, a battery pack consists of multiple cells, each of which can monitor its voltage. This allows monitoring of the voltage of all cells. Specifically, each cell is equipped with an independent high-precision voltage sensor (such as a 12-bit or 16-bit ADC) to minimize voltage measurement errors. The sensor is connected to the BMS via a multiplexer or parallel acquisition circuit, reducing hardware complexity and monitoring the voltage of each cell.

[0020] It's important to note that during the charging process, we expect the voltages of each cell to change in a coordinated and stable manner, following the expected trend. If the voltage change trends of some cells do not align with the overall trend, these cell voltages may be outliers. Converting this "trend consistency" into a quantifiable metric allows for effective battery pack voltage monitoring. Specifically, during charging, we expect the voltages of all normal cells to rise. If the voltage of an individual cell drops, or if the increase is significantly lower than that of other cells, this may indicate an anomaly. Therefore, we need to determine the rise and fall anomaly scores for the cell voltages in the battery pack at any given moment. Based on the magnitude of these scores, we can monitor the battery pack's cell voltages for anomalies.

[0021] Step S102: Calculate the rising abnormality score of the battery pack at any moment.

[0022] In one embodiment, the battery pack's rising anomaly score at any moment reflects the difference between the voltage rise of each cell at that moment and the average of the voltage rises of the remaining cells, where the voltage rise at any moment is the difference between the voltage at that moment and the voltage at the previous moment. That is, for any cell, the difference between its voltage rise and the average of the voltage rises of the remaining cells is calculated (the difference is the absolute value of the difference between its voltage rise and the average of the voltage rises of the remaining cells). The larger the difference, the greater the deviation of the voltage rise of that cell from the average of the voltage rises of the remaining cells, indicating a greater likelihood of an anomaly in the voltage of that cell. The mean of the sum of the differences in all cell voltages is then normalized, and the normalized value is used as the battery pack's rising anomaly score at that moment, where the normalization process uses a sigmoid function.

[0023] For example, a battery pack contains three cells (cells A, B, and C). At a certain moment, the voltage rise values ​​are: cell A: 2 mV, cell B: 2 mV, and cell C: 10 mV. The difference of cell A is 4. The difference of cell B is , the difference of cell C is The battery pack's rising anomaly score at this moment is sigmoid .

[0024] S103: Calculate the battery pack's drop abnormality score at any moment.

[0025] In one embodiment, the decline abnormality score is related to the state of the battery cell at that moment. If the battery cell is in a passive balancing state, the decline abnormality score of the battery pack is positively correlated with the number of consecutive voltage decline moments of the battery cell exceeding the target decline stage before that moment and the voltage drop value at each voltage drop moment. Conversely, the decline abnormality score of the battery pack is positively correlated with the voltage drop value at that moment.

[0026] In one embodiment, if the battery cells at that moment are not in a balanced state, a calculation formula for calculating the voltage drop abnormality score of each battery cell at any moment is provided, and the calculation formula is: ; Indicates the The battery pack's abnormal score decreases at that moment. Indicates the Moment The voltage change of each cell, (the voltage change is Moment The voltage of each cell is The time of the difference value of the voltage of the cell, represents the number of cells in the battery pack, represents the maximum function.

[0027] It should be noted that by The drop amplitude of all the voltage drop cells in the battery is accumulated to capture serious and direct abnormalities. During the charging process, any drop in the voltage of a cell is a strong abnormal signal, which may mean that there is a problem inside the cell (such as internal short circuit) or a connection failure with the BMS. The larger the value, the more cells with voltage drop, and the larger the voltage drop value, the greater the drop abnormal score of the cell voltage at the time, The greater the drop abnormal score of the cell voltage at the time, the higher the possibility of abnormality of the battery pack.

[0028] In an embodiment, the BMS balancing is one of the key functions of the battery management system, which aims to solve the inherent inconsistency between each single cell in the battery pack. The purpose is to prevent overcharging and overdischarging: in a series battery pack, if there is a difference between the cells, during the charging process, the cell with smaller capacity will reach the full state first, and if it continues to charge, it will be overcharged. Conversely, during the discharging process, the cell with smaller capacity will be exhausted first, and if it continues to discharge, it will be overdischarged. Overcharging and overdischarging will seriously damage the cell, shorten its life, and even cause safety problems (such as thermal runaway). And currently, many battery packs on the market use passive balancing, because the circuit design of passive balancing method is simple, the cost is lower, and it is easy to implement. This method connects a resistor in parallel across the cell with higher voltage to dissipate the excess energy in the form of heat until its voltage is reduced to a level similar to other cells. During passive balancing, the voltage of the cell being balanced may drop temporarily, which is a normal phenomenon because energy is being dissipated. Therefore, during the charging process of the battery pack, the voltage drop of the high-voltage cell under passive balancing is a normal situation, so the probability of abnormality of the voltage drop of each cell needs to be considered.

[0029] In an embodiment, a calculation formula for the probability of abnormality of the voltage drop of each cell at any time is provided, and the calculation formula is: . In the formula, represents the probability of abnormality of the voltage drop of the cell voltage at the time , the voltage of the cell at the time , the minimum voltage value of all cells in the battery pack at the time , the balancing threshold, represents the​​​ The number of moments before the moment the voltage drops continuously, Indicates the target descent phase, Represents the maximum function.

[0030] Where, When it is greater than 0, it means Voltage of each cell The minimum value of all cell voltages Greater than the balance threshold , where the equilibrium threshold The empirical value is 30mv, which means that the passive equalization system needs to intervene. The voltage of each cell will drop within the target time period. It is normal for the voltage of each cell to drop within the target time period. However, during the balancing process, The voltage of each cell will drop in a short period of time. That is, the drop caused by balancing is usually short-term and will not last long. If the continuous drop time is greater than the target drop stage, it means that the voltage drop is abnormal. The cell voltage is That is, if the cell voltage continuously decreases within the target decrease phase, it indicates that the battery cell is in a balanced state and the short-term decrease in cell voltage is normal. The probability that the cell voltage decrease is abnormal is 0. If the cell voltage continuously decreases beyond the target decrease phase, there may be signs of abnormality. The probability that the cell voltage decrease is abnormal is positively correlated with the time when the target decrease phase is exceeded. The longer the time when the target decrease phase is exceeded, the higher the probability of abnormality.

[0031] In one embodiment, determining a target decreasing phase includes: obtaining historical voltage data of each cell of multiple battery packs during charging, organizing the voltage data of each cell into a sequence to obtain a historical voltage sequence for each cell, clustering all historical voltage sequences to obtain two clusters, and determining the cluster with the most sequences as the normal state cluster; counting the moments of continuous decreasing in the passive equilibrium state in all sequences in the cluster under normal state, using the moments of continuous decreasing in the passive equilibrium state as sample segments to obtain sample segments for all sequences in the cluster under normal state; then counting the proportions of sample segments of different lengths to obtain the proportions of all sample segments of different lengths; accumulating the proportions of each sample segment (accumulating in ascending order of length); comparing the accumulated value with a threshold, and determining the target decreasing phase for the time period corresponding to the sample segment whose accumulated value exceeds the threshold as the target decreasing phase, where the empirical value of the threshold is 80%. By clustering historical data to extract the normal state decreasing period and determining the target decreasing phase using the cumulative proportion method of sample segments, the subjectivity of manually setting a fixed time threshold is avoided, making abnormality determination more adaptive and universal. Setting the empirical value of 80% for the division threshold can cover most samples of normal descent behavior and reduce misjudgments caused by over-convergence.

[0032] For example, under normal conditions, there are sequences A, B, C, D, E, and F in the cluster, where the sample segment of sequence A is , the sample segment of sequence B is , the sample segment of sequence C is , the sample segment of sequence D is , the sample segment of sequence E is , the sample segment of sequence F is ,like and The length of the moments is equal, 、 and The length of the moments is equal, The length of the moment of is not equal to that of and The time length is less than 、 and The length of time, 、 and The time length is less than The length of the moment; at this time, the proportion of sample segments of different lengths is counted. If and The time length accounts for 2 / 6, 、 and The length of the moment accounts for 3 / 6, The length of the moment accounts for 1 / 6, at this time The length of the moment accounts for 1 / 6, at this time The length of the moment accounts for 1 / 6, at this time The length of the moment accounts for 1 / 6, at this time The length of the moment accounts for 1 / 6, at this time The length of the moment accounts for 1 / 6, at this time The length of the moment accounts for 1 / 6, at this time The length of the moment accounts for 1 / 6, at this time The length of the moment accounts for 1 / 6, at this time

[0033] In another embodiment, if the battery cell in the moment is in the state of equilibrium, according to the above description, another formula for calculating the abnormal score of the voltage drop in any moment is provided, and the specific formula is: . The abnormal score of the battery pack in the moment, The probability that the voltage drop of the voltage of the The probability that the voltage drop of the voltage of the The abnormal score of the voltage drop of the voltage of the The number of battery cells in the battery pack, The hyperbolic tangent function, wherein, The product of the voltage drop amplitude of the voltage of the The probability that the voltage drop of the voltage of the The product of the voltage drop amplitude of the voltage of the The probability that the voltage drop of the voltage of the The probability that the voltage drop of the voltage of the The probability that the voltage drop of the voltage of the The probability that the voltage drop of the voltage of the The probability that the voltage drop of the voltage of the The probability that the voltage drop of the voltage of the The probability that the voltage drop of the voltage of the The probability that the voltage drop of the voltage of the The probability that the voltage drop of the voltage of the

[0034] S104: Abnormal monitoring of the voltage of the battery cell of the battery pack.

[0035] In one embodiment, the rising anomaly score and falling anomaly score of the battery pack at any moment during the charging process are calculated, and the battery pack cell voltage is monitored for abnormalities based on the rising anomaly score and falling anomaly score at any moment. Specifically, a graded warning is issued based on the conditions that the rising anomaly score and falling anomaly score meet, where the graded warning is divided into a first-level warning and a second-level warning. For example, if the rising anomaly score of the battery pack at the current moment during the charging process is greater than the rising threshold, or the rising anomaly score at the current moment is greater than the falling threshold, and either of these conditions is met, then the current battery pack is determined to be abnormal and a first-level warning is issued, where the empirical value of the rising threshold is 0.8. If the rising anomaly score of the battery pack at the current moment during the charging process is greater than the rising threshold, and the rising anomaly score at the current moment is greater than the falling threshold, that is, if both conditions are met, then the current battery pack is determined to be abnormal and a second-level warning is issued, where the empirical value of the falling threshold is 0.8. This satisfies the method for monitoring the battery pack cell voltage. It should be noted that in other embodiments, both the rising threshold and the falling threshold can be adjusted according to implementation circumstances.

[0036] The present invention also provides a battery cell voltage monitoring system. The system includes a processor and a memory, wherein the memory stores computer program instructions. When the computer program instructions are executed by the processor, the battery cell voltage monitoring method according to the first aspect of the present invention is implemented.

[0037] The system further includes other components well known to those skilled in the art, such as a communication bus and a communication interface. The configuration and functions of these components are known in the art and thus will not be described in detail here.

[0038] In the present invention, the aforementioned memory can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, the computer-readable storage medium can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc., or any other medium that can be used to store the required information and can be accessed by an application, module, or both. Any such computer storage medium can be part of, accessible to, or connectable to a device. Any application or module described in the present invention can be implemented using computer-readable / executable instructions stored or otherwise retained on such a computer-readable medium.

[0039] The above-described embodiments merely illustrate several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, and all such variations and improvements fall within the scope of protection of the present invention.

Claims

1. A method for monitoring battery cell voltage, characterized in that: The monitoring method comprises: Collect the voltage of each cell in the battery pack during charging; Calculate the rising anomaly score and falling anomaly score of the battery pack at any time during the charging process, and monitor the abnormality of the battery cell voltage according to the magnitude of the rising anomaly score and the falling anomaly score. The rising anomaly score of the battery pack at any time reflects the difference between the rising value of each battery cell voltage at that time and the average of the rising values ​​of the remaining battery cell voltages; The decline abnormality score is related to the state of the battery cell at that moment. If the battery cell is in a passive balancing state, the decline abnormality score of the battery pack is positively correlated with the number of consecutive voltage decline moments of the battery cell exceeding the target decline stage before that moment and the voltage drop value at each voltage drop moment. Conversely, the decline abnormality score of the battery pack is positively correlated with the voltage drop value at that moment.

2. The battery cell voltage monitoring method according to claim 1, wherein: Monitor battery cell voltage abnormalities, including: A graded warning is performed according to the magnitude of the rising anomaly score and the falling anomaly score, wherein the graded warning is divided into a first-level warning and a second-level warning.

3. The battery cell voltage monitoring method according to claim 2, wherein: The graded warning includes: In response to the rising abnormality score of the battery pack at the current moment being greater than the rising threshold, or the falling abnormality score at the current moment being greater than the falling threshold during the charging process, it is determined that the current battery pack is abnormal, and a level one warning is issued.

4. The battery cell voltage monitoring method according to claim 2, wherein: The graded warning includes: In response to the rising abnormality score of the battery pack at the current moment being greater than the rising threshold during the charging process, and the falling abnormality score at the current moment being greater than the falling threshold, it is determined that the current battery pack is abnormal, and a second-level warning is issued.

5. The battery cell voltage monitoring method according to claim 1, wherein: The calculation method of the battery pack's decline anomaly score at any moment includes: If the battery cell is not in a passive balancing state, the sum of the voltage drop values ​​of all the battery cells in the battery pack at that moment is calculated, and the sum of the voltage drop values ​​is normalized, and the normalized value is used as the drop abnormality score of the battery pack at that moment.

6. The battery cell voltage monitoring method according to claim 1, wherein: The calculation method of the battery pack's decline anomaly score at any moment includes: If the battery cell is in a passive balancing state, calculate the product of the number of consecutive voltage drop moments of the battery cell that exceed the target drop stage before this moment and the voltage drop value at each voltage drop moment, and normalize the sum of the products of all battery cell voltages, and use the normalized value as the drop abnormality score of the battery pack at this moment.

7. The battery cell voltage monitoring method according to claim 1, wherein: The calculation method of the rising anomaly score of the battery pack at any time includes: Calculate the average of the voltage rise value of each battery cell at that moment and the voltage rise values ​​of the remaining battery cells, normalize the sum of all the averages, and use the normalized value as the rise abnormality score of the battery pack.

8. The battery cell voltage monitoring method according to claim 1, wherein: The voltage rise at any moment is the difference between the voltage at that moment and the voltage at the previous moment.

9. The battery cell voltage monitoring method according to claim 3, wherein: The empirical value of the rising threshold is 0.

8.

10. A battery cell voltage monitoring system, comprising a processor and a memory, characterized in that: The memory stores a computer program, and the processor executes the computer program to implement the battery cell voltage monitoring method according to any one of claims 1 to 9.

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