Method and System for Tracing the Source of Power Battery Risks Based on the Consistency of Equivalent Capacity

Abstract

The present invention relates to the technical field of power batteries, and discloses a method and system for tracing the source of power battery risks based on the consistency of equivalent capacity, including the following steps: Step 1: Collect the historical operation data of the power battery; Step 2: Preprocess the historical operation data to obtain standard operation data; Step 3: Calculate the equivalent capacity Q C ; Step 4: Quantify the equivalent capacity Q using variance entropy C , and obtain the quantization feature p; Step 5: Determine the risk probability value according to the risk assessment strategy based on the quantization feature; Step 6: Determine the risk category based on the risk probability value and the equivalent capacity. This solution can equivalently obtain a true capacity value with high accuracy, can achieve a high risk assessment accuracy and risk tracing accuracy, and the method has a high operation efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of power batteries, and particularly to a method and system for tracing the risks of power batteries based on the consistency of equivalent capacity. Background Art

[0002] With the adjustment of the energy structure and the rapid development of technologies such as battery, motor, and electronic control, the production and sales volume of new energy vehicles have been increasing in recent years, and people's attention to the operation safety issues of new energy vehicles has also been increasing.

[0003] As the main energy system of new energy vehicles, the operation state data of the power battery system is closely related to the overall operation performance of new energy vehicles. Currently, when evaluating the safety state of new energy vehicles, it is often based on the historical operation data of the vehicle, more specifically, based on the historical operation data of the power battery, and the safety state analysis is carried out by comparing thresholds or analyzing the numerical fluctuation conditions, etc.

[0004] However, the actual operation process environment of new energy vehicles is changeable and the scenarios are complex. The data characterizing their safety state has characteristics such as multi-dimensionality, redundancy, heterogeneity, and strong coupling. Considering the physical characteristics of the battery system itself and the influence of uncertain factors such as different designs and different acquisition precisions of different sensors, there must be information coupling, redundancy, and errors between different signal data, which brings great challenges to exploring and mining the vehicle safety characteristics contained in the operation big data. The existing evaluation methods cannot be separated from the influence of the above information coupling, redundancy, errors, etc., cannot accurately mine the vehicle safety characteristics, the risk assessment accuracy is not high, and the risk tracing cannot be carried out. Summary of the Invention

[0005] The present invention aims to provide a method and system for tracing the risks of power batteries based on the consistency of equivalent capacity, which can equivalently obtain a high-precision capacity value, can achieve a high risk assessment accuracy, and the method has a high operation efficiency.

[0006] To achieve the above object, the present invention provides the following basic solution:

[0007] A method for tracing the risks of power batteries based on the consistency of equivalent capacity, comprising the following steps:

[0008] Step 1: Collect the historical operation data of the power battery;

[0009] Step 2: Preprocess the historical operation data and obtain the standard operation data;

[0010] Step 3: Calculate the equivalent capacity Q C ; when calculating the equivalent capacity Q C based on the current battery charge Q tAccording to the preset equivalent conversion strategy and the single-cell voltage V, the instantaneous capacity of the power battery is equivalently converted into a voltage change speed, and the voltage change speed value is denoted as Q C ; and when calculating the voltage change speed, based on the standard operation data, a single-cell voltage matrix for the time series is obtained; and based on the single-cell voltage matrix, the voltage change speed on the local time window is sequentially obtained as Q C ;

[0011] Step 4: Quantify the equivalent capacity Q using variance entropy C and obtain the quantization feature p;

[0012] Step 5: Determine the risk probability value according to the risk assessment strategy based on the quantization feature;

[0013] Step 6: Determine the risk category based on the risk probability value and the equivalent capacity.

[0014] A power battery risk traceability system based on equivalent capacity consistency is used to execute the power battery risk traceability method based on equivalent capacity consistency as described above; it includes an acquisition module, a preprocessing module, an equivalent module, a quantization module, and an analysis module; the acquisition module is used to acquire the historical operation data of the power battery; the preprocessing module is used to preprocess the historical operation data and obtain the standard operation data; the equivalent module is used to calculate the equivalent capacity Q C ; the quantization module is used to calculate the quantization feature p; the analysis module is used to determine the risk probability value and the risk category.

[0015] The working principle and advantages of the present invention are as follows: First, based on the historical operation data of the power battery, the capacity parameter item is specifically selected as the risk determination parameter item. For many risk problems that occur in the power battery system (such as abnormal self-discharge, abnormal sampling, abnormal capacity decay, etc.), the capacity parameter will fluctuate accordingly. Based on the capacity parameter, risks can be better perceived.

[0016] Secondly, in this solution, the equivalent capacity is used to equivalently represent the actual instantaneous capacity value, and the equivalent capacity is used as a risk assessment factor. Further, the equivalent capacity is quantified by variance entropy, and the obtained quantization feature can show the fluctuation of the equivalent capacity consistency. Under normal conditions, the capacities of different battery cells of the power battery should be consistent. When they are inconsistent, it means that the power battery has a certain degree of abnormality and affects the cell capacity, and is characterized by poor capacity consistency. Correspondingly, the risk probability value of the power battery can be obtained through the quantization feature that quantifies and characterizes the capacity consistency, realizing the accurate determination of the power battery risk.

[0017] Specifically, the capacity used in the analysis in this solution is the equivalent capacity obtained through equivalent conversion, which is different from the instantaneous capacity collected conventionally. The instantaneous capacity collected conventionally often requires a long measurement time and strict measurement requirements. It is necessary to discharge the battery with a constant current, record the time from when the battery is fully charged to when it is discharged, and then take the product value of the constant current and time as the capacity value. This process takes a long time, and considering the actual usage situations of different vehicles, there are many uncertain influencing factors that will affect this measurement process. Therefore, the accuracy of the measured value is relatively poor. Moreover, the SOC value data parsed from the self-message log cannot accurately represent the capacity either. The minimum measurement unit of such data is specified as 1% SOC, and many SOC fluctuations less than 1% SOC are completely ignored.

[0018] In this solution, however, through a certain strategy, the capacity value that is difficult to accurately measure is equivalently converted into a voltage change speed value and recorded as the equivalent capacity, enabling the quantity required for capacity analysis to be equivalently migrated to the voltage quantity that can be collected with high precision and is more convenient to collect. Correspondingly, the accuracy of the equivalent capacity is no longer limited by 1% SOC. Even for more subtle jumps in the micro-capacity change less than 1% SOC, the equivalent capacity can accurately represent it, and it is no longer restricted by the measurement environment. The true capacity values of different battery systems of different vehicles can all be obtained through equivalent capacity analysis, with higher accuracy. This further helps to obtain a more accurate and detailed capacity consistency situation, and thus achieve a higher risk assessment accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic flowchart of the method for the risk traceability method and system of power battery based on equivalent capacity consistency according to an embodiment of the present invention;

[0020] Figure 2 It is a schematic structural diagram of the system for the risk traceability method and system of power battery based on equivalent capacity consistency according to an embodiment of the present invention;

[0021] Figure 3 It is the first basic determination diagram of the risk traceability method and system of power battery based on equivalent capacity consistency according to an embodiment of the present invention;

[0022] Figure 4 It is the second basic determination diagram of the risk traceability method and system of power battery based on equivalent capacity consistency according to an embodiment of the present invention;

[0023] Figure 5 It is the third basic determination diagram of the risk traceability method and system of power battery based on equivalent capacity consistency according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] The following is a more detailed description through specific embodiments:

[0025] The embodiment is basically as shown in the appendix Figure 1 : A method for tracing the source of power battery risks based on the consistency of equivalent capacity includes the following steps:

[0026] Step 1: Collect historical operation data of the power battery.

[0027] Specifically, in this embodiment, the corresponding battery signal data is parsed from the message log of the power battery system that complies with the GB32960 protocol as the historical operation data of the power battery; the data obtained in this way is fully complete and meets the standards.

[0028] Step 2: Preprocess the historical operation data and obtain standard operation data;

[0029] The preprocessing includes:

[0030] (1) Limit the signal boundary values, that is: based on the first boundary threshold, remove the data in the voltage signal data and current signal data of the historical operation data that exceed the first boundary threshold. This setting can eliminate the interference of abnormal and excessive data on subsequent risk source tracing and help improve the accuracy of risk source tracing.

[0031] (2) Identify and mark interference pulses, that is: identify and determine the voltage data in the historical operation data. If the difference between the current frame voltage data and the previous frame exceeds the second boundary threshold, then mark this frame of data.

[0032] (3) Identify and mark time discontinuity points, that is: identify and determine the timestamp data in the historical operation data. If the difference between the current frame timestamp data and the previous frame exceeds the specified threshold, then mark this frame of data.

[0033] (4) Mean filtering, that is, perform mean filtering on the historical operation data to weaken the noise in the basic data.

[0034] Step 3: Calculate the equivalent capacity Q C ; When calculating the equivalent capacity Q C , based on the current power Q t of the power battery and the single-cell voltage V, the instantaneous capacity of the power battery is equivalently converted into a voltage change speed according to a preset equivalent conversion strategy, and the voltage change speed value is denoted as Q C value; moreover, when calculating the voltage change speed, based on the standard operation data, obtain the single-cell voltage matrix for the time series; and based on the single-cell voltage matrix, sequentially obtain the voltage change speed on the local time window as Q C .

[0035] The preset equivalent conversion strategy includes:

[0036] S1: Obtain the first equivalent formula based on the conversion of the state of charge mechanism formula and the current mechanism formula; the state of charge mechanism formula is: SOC = Q t / Q C ; the current mechanism formula is I = dQ t / dt; the first equivalent formula is

[0037] Specifically, the conversion process includes:

[0038]

[0039] S2: Equivalent SOC in the first equivalent formula to the single - cell voltage;

[0040] S3: Obtain the second equivalent formula; the second equivalent formula is

[0041] And based on the analysis of the current mechanism formula, it can be obtained that dQ t = I·dt. In the power battery system, when all the battery cells in the battery pack are in series, the equivalent capacity Q calculated in the second equivalent formula C can be equivalent to the consistency of the voltage change rate.

[0042] Through the analysis and transformation of the state of charge calculation mechanism and the current calculation mechanism, this solution can equivalently convert the instantaneous capacity value of the power battery (i.e., the current capacity value of the power battery), which is difficult to accurately measure in the conventional capacity analysis, into a voltage change rate value that can be accurately measured and calculated. Furthermore, a more real and accurate instantaneous capacity value can be analyzed. And because the equivalent simulation in this solution is completely obtained based on the mechanism characteristics of each physical quantity of the battery, the equivalent capacity and the actual instantaneous capacity can maintain a high degree of consistency, and the reliability of the equivalent capacity value is relatively high. Furthermore, when conducting risk traceability analysis subsequently, since the accuracy of the data used as the analysis basis has been improved, the accuracy of the risk traceability analysis can be further improved.

[0043] Step 4: Quantify the equivalent capacity Q C , and obtain the quantization feature p.

[0044] The quantization feature p = 1 - λ; where λ is the basic quantization value of the variance entropy, and λ = E 2 (Q C ) / ( C 2 ).

[0045] Among them, 0 ≤ λ ≤ 1, and the closer λ is to 1, it indicates that on the time scale, the fluctuation degree of the equivalent capacity consistency feature is smaller, and the state of the power battery is safer. The quantization feature value after quantization processing is convenient for realizing the feature description of de - thresholding.

[0046] Step 5: Based on the quantization features, determine the risk probability value according to the risk assessment strategy.

[0047] The risk assessment strategy includes: preparing the quantization features into a risk function on the time scale, and taking the amplitude z of the slope k of the risk function as the risk probability value. And when the risk probability value is greater than the preset threshold, the moment corresponding to the risk probability value is defined as the high-risk point. Such a setting can further visually confirm the risk situation. In this embodiment, the preset threshold is set to 0.5.

[0048] Specifically, perform discrete integration on the quantization features on the time scale, and we can get Sp = ∑p; Sp is the risk function on the time scale, and this risk function shows a monotonically increasing curve, and the slope value of this curve is the slope value k of the risk function. The amplitude z of the slope k can quantitatively represent the state risk situation of the capacity consistency of the battery cells in the battery power system. The larger the z value, the higher the state risk degree.

[0049] Step 6: Based on the risk probability value and the equivalent capacity, determine the risk category.

[0050] When determining the risk category, taking the high-risk point as the benchmark, determine the distribution of the equivalent capacity Q of each battery cell within the preset time period before and after the benchmark and prepare it into a basic judgment diagram, and determine the risk category from the fluctuation of the numerical curves corresponding to each battery cell in the basic judgment diagram. C As shown in the figure, in the basic judgment diagram, the fluctuation of the numerical curve is as follows: if the numerical curve of a certain battery cell has an abnormal drop during a certain discharge process and remains normal during other charge and discharge processes, then the risk category is determined to be sampling abnormality under specific working conditions.

[0051] As attached Figure 3 shown, in the basic judgment diagram, the fluctuation of the numerical curve is as follows: if the numerical curve of a certain battery cell shows a gradual voltage drop and stratification during the discharge process (that is, the discharge speed of the battery cell is faster than that of other battery cells), and during the charging process, the charging rate and the cut-off voltage value corresponding to this battery cell are higher than those of other battery cells, then the risk category is determined to be capacity decay abnormality.

[0052] As attached Figure 4 shown, in the basic judgment diagram, the fluctuation of the numerical curve is as follows: if the numerical curve of a certain battery cell has an abnormal voltage drop at low SOC during the discharge process, then the risk category is determined to be poor battery consistency. Further, if during the charging process of this battery cell, the corresponding charging rate is lower than that of other battery cells and it cannot be fully charged, and the corresponding voltage difference value increases, then the risk category is determined to be self-discharge abnormality.

[0053] As attached Figure 5 shown, in the basic judgment diagram, the fluctuation of the numerical curve is as follows: if the numerical curve of a certain battery cell has an abnormal voltage drop at low SOC during the discharge process, then the risk category is determined to be poor battery consistency. Further, if during the charging process of this battery cell, the corresponding charging rate is lower than that of other battery cells and it cannot be fully charged, and the corresponding voltage difference value increases, then the risk category is determined to be self-discharge abnormality.

[0054] As shown in the appendix Figure 2 This embodiment also provides a power battery risk tracing system based on equivalent capacity consistency, which is used to execute the power battery risk tracing method based on equivalent capacity consistency as described above; it includes a collection module, a preprocessing module, an equivalent module, a quantization module, and an analysis module; the collection module is used to collect the historical operation data of the power battery; the preprocessing module is used to preprocess the historical operation data and obtain the standard operation data; the equivalent module is used to calculate the equivalent capacity Q C ; the quantization module is used to calculate the quantization feature p; the analysis module is used to determine the risk probability value and the risk category.

[0055] A power battery risk tracing method and system based on equivalent capacity consistency provided by this embodiment take the capacity parameter of the power battery as the basis for abnormal risk assessment, and convert it into a quantization feature for risk identification, and then complete risk tracing. In this process, this solution particularly uses the equivalent capacity to equivalently represent the actual instantaneous capacity value. Different from the conventionally collected instantaneous capacity, the equivalent capacity is closer to the true value than the collected value and has higher accuracy. Different from the collected value whose collection accuracy can only reach 1% SOC, the accuracy of the equivalent capacity is not limited by 1% SOC. For more subtle jumps in trace capacity changes less than 1% SOC, the equivalent capacity can also accurately represent them, and the subtle changes in the battery state can be carefully analyzed, so that the risk can be determined more accurately and comprehensively. Moreover, this solution uses the accurate equivalent capacity as a safety factor and quantizes it into a quantization feature. The quantization feature can quantitatively represent the fluctuations in the equivalent capacity consistency on the time scale. Based on this fluctuation situation, the battery risk state is further analyzed, and a high risk assessment accuracy can be achieved.

[0056] Moreover, this solution conducts risk identification based on capacity consistency, and can comprehensively identify risk anomalies. Some special risk anomalies that cannot be simply identified from numerical appearances such as voltage value fluctuations can also be accurately captured from the dimension of the capacity consistency feature of this solution. This solution has a more detailed and comprehensive perception of risks and provides a new and effective risk assessment route. In addition, this solution can also form a basic judgment diagram based on the analyzed risk probability value and combine it with the cell operation data to conduct a traceability analysis of the risk causes of the power battery, accurately deduce the risk causes, help guide the elimination of risk hazards, and provide reliable data guarantee for the safe operation of new energy vehicles.

[0057] Moreover, the risk categories that can be identified and traced in this solution are diverse. In addition to sampling anomalies, self-discharge anomalies, etc., it can also identify capacity fade anomalies. In practical applications, if the capacity shows abnormal attenuation, it can be determined that there may be certain abnormalities in the battery. The anode and cathode materials of the battery and the loss of lithium ions may all cause abnormal attenuation of the capacity. Since the anode and cathode materials of the battery are relatively stable, the most likely cause of battery capacity fade is the loss of lithium ions, and the most likely cause of lithium ion loss is lithium plating in the battery. Although lithium plating in the battery does not directly cause an internal short circuit, lithium plating in the battery is likely to trigger thermal runaway in two aspects. After lithium plating, the thermal runaway temperature drops significantly, making it easier to trigger thermal runaway; long-term lithium plating in the battery is likely to induce the formation of lithium dendrites, which ultimately pierce the separator, causing an internal short circuit and ultimately leading to thermal runaway. Therefore, there are also great potential risks in capacity fade anomalies, and the accurate traceability determination of such anomalies in this solution helps to further improve the safety of the operation of the power system and new energy vehicles.

[0058] The above are only embodiments of the present invention. Specific structures and common knowledge such as characteristics that are well-known in the art are not described in detail here. Those of ordinary skill in the art know all the common technical knowledge in the technical field to which the invention belongs before the application date or the priority date, can know all the existing technologies in this field, and have the ability to apply the conventional experimental means before this date. Those of ordinary skill in the art can, under the inspiration given in this application, combine their own abilities to improve and implement this solution. Some typical well-known structures or well-known methods should not be an obstacle for those of ordinary skill in the art to implement this application. It should be noted that for those skilled in the art, without departing from the structure of the present invention, several deformations and improvements can still be made, and these should also be regarded as the protection scope of the present invention, and these will not affect the implementation effect of the present invention and the practicality of the patent.

Claims

1. A method for tracing the source of power battery risks based on the consistency of equivalent capacity, characterized in that Including the following steps: Step 1: Collect historical operation data of the power battery; Step 2: Preprocess the historical operation data and obtain standard operation data; Step 3: Calculate the equivalent capacity ; When calculating the equivalent capacity , based on the current battery level and the single cell voltage V of the power battery, the instantaneous capacity of the power battery is equivalent to the voltage change speed according to a preset equivalent conversion strategy, and the voltage change speed value is denoted as value; moreover, when calculating the voltage change speed, based on the standard operation data, obtain the single cell voltage matrix for the time series; and based on the single cell voltage matrix, sequentially obtain the voltage change speed on the local time window as ; The preset equivalent conversion strategy includes: S1: Obtain a first equivalent formula based on the conversion of the state-of-charge mechanism formula and the current mechanism formula; the state-of-charge mechanism formula is; ; the current mechanism formula is ; the first equivalent formula is ; S2: Equivalent the SOC in the first equivalent formula to the single-cell voltage; S3: Obtain a second equivalent formula; the second equivalent formula is ; Step 4: Quantify the equivalent capacity using variance entropy , and obtain the quantization feature p; The quantization feature ; wherein is the basic quantization value of variance entropy ; Step 5: Based on the quantization features, determine the risk probability value according to the risk assessment strategy; Step 6: Determine the risk category based on the risk probability value and the equivalent capacity.

2. The method for tracing the source of power battery risks based on the consistency of equivalent capacity according to claim 1, characterized in that The risk assessment strategy includes: preparing the quantitative feature as a risk function on a time scale , ; the risk function is represented by a monotonically increasing curve, and the slope value of the curve is the slope value k of the risk function; the magnitude z of the slope k of the risk function is used as the risk probability value.

3. The method for tracing the source of power battery risks based on the consistency of equivalent capacity according to claim 2, wherein The risk assessment strategy further includes: When the risk probability value is greater than the preset threshold, define the moment corresponding to the risk probability value as a high-risk point.

4. The method for tracing the source of power battery risks based on the consistency of equivalent capacity according to claim 3, wherein, In step 6, when determining the risk category, using the high-risk points as the benchmark, determine the equivalent capacity of each battery cell within the preset time periods before and after the benchmark, and prepare it as a basic judgment graph. Determine the risk category based on the fluctuation of the corresponding numerical curves of each battery cell in the basic judgment graph. The distribution is prepared as a basic judgment graph, and the risk category is determined by the fluctuation of the corresponding numerical curves of each battery cell in the basic judgment graph.

5. The power battery risk traceability system based on the consistency of equivalent capacity is characterized in that For performing the power battery risk tracing method based on equivalent capacity consistency as described in any one of claims 1-4; including a collection module, a preprocessing module, an equivalent module, a quantization module, and an analysis module; the collection module is used to collect historical operation data of the power battery; the preprocessing module is used to preprocess the historical operation data and obtain standard operation data; the equivalent module is used to calculate the equivalent capacity ; the quantization module is used to calculate the quantization feature p; The analysis module is used to determine the risk probability value and the risk category.

Images