Risk tracing method for power battery based on equivalent internal resistance consistency

Abstract

The application relates to a new energy automobile power battery fault analysis technology, and discloses a power battery risk tracing method based on equivalent internal resistance consistency, which comprises the following steps: collecting power battery related data to form a data set, and preprocessing the data set; extracting a safety element in the preprocessed data set, and quantifying the safety feature of the equivalent internal resistance in the safety element to obtain a safety quantization feature; obtaining a risk quantization feature from the safety quantization feature, identifying a safety state according to the risk quantization feature, and then combining a safety element image to trace a power battery safety risk. The application has the beneficial effects of improving the accuracy of power battery internal resistance calculation results, improving the accuracy of power battery fault analysis and risk tracing results, and guaranteeing the safe operation of electric vehicles.

Description

Technical Field

[0001] This invention relates to the field of fault analysis technology for power batteries in new energy vehicles, specifically to a method for tracing the source of power battery risks based on the consistency of equivalent internal resistance. Background Technology

[0002] With the depletion of oil resources, the development and application of new energy technologies have achieved unprecedented progress, among which electric vehicles have developed the most rapidly. As a result, the number of electric vehicles on the market has increased dramatically in recent years. However, with the increase in the use of electric vehicles, the problems that have been exposed have also gradually increased, especially their safety issues, which have attracted widespread attention.

[0003] In actual operation, electric vehicles face diverse driving environments and complex scenarios. As a result, the data characterizing their safety status is multidimensional, redundant, heterogeneous, and strongly coupled, which greatly complicates the analysis of the causes of electric vehicle safety risks. In particular, the safety of the power battery, which is the power source of the electric vehicle, is related to the overall driving safety of the electric vehicle. Therefore, to ensure the safe operation of electric vehicles, it is necessary to clearly analyze the causes of various safety risks of the power battery in order to more accurately prevent and control the risks, thereby ensuring the driving safety of electric vehicles.

[0004] When investigating the causes of risks in power batteries, the internal resistance of the battery is the most direct data related to its safety status. The internal resistance of a power battery refers to the resistance encountered when current flows through the battery's interior during operation. It includes ohmic internal resistance and polarization internal resistance, with polarization internal resistance further divided into electrochemical polarization internal resistance and concentration polarization internal resistance. Because polarization internal resistance involves complex electrochemical reaction processes and varies significantly under different operating conditions and environmental factors, it is difficult to measure and estimate accurately. Therefore, the current accuracy rate for identifying safety risks in power batteries is low, and a new, reliable, and effective method for tracing the source of power battery safety risks is urgently needed. Summary of the Invention

[0005] The present invention aims to provide a method for tracing the source of power battery risks based on the consistency of equivalent internal resistance, so as to improve the accuracy of the analysis results of the causes of power battery safety risks.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a risk tracing method for power batteries based on equivalent internal resistance consistency, comprising the following steps:

[0007] Step S1: Collect relevant data of the power battery to form a data set, and preprocess the data set;

[0008] Step S2: Extract the security elements from the preprocessed dataset, and quantify the equivalent internal resistance of the security elements to obtain the security quantification features.

[0009] Step S3: Obtain risk quantification features from safety quantification features, identify safety status based on risk quantification features, and then trace the source of power battery safety risks by combining safety element images.

[0010] The principle and advantages of this scheme are as follows: In practical applications, the basic data of the collected power battery is first cleaned and preprocessed. Then, the equivalent internal resistance of the power battery is calculated based on the collected data. The equivalent internal resistance is then transformed nonlinearly and amplified to obtain a safety element vector. Based on the safety element vector, the safety characteristics of the equivalent internal resistance are quantified. Then, the risk accumulation method is combined to complete the identification of the electric vehicle's safety status and output high-risk point image information. Finally, the risk source analysis of the electric vehicle's power battery is completed. Compared to existing technologies, the advantage of this solution lies in its accurate calculation of the equivalent internal resistance of the power battery based on an equivalent circuit model. This is because the internal resistance of a power battery includes ohmic internal resistance and polarization internal resistance, which in turn includes electrochemical polarization internal resistance and concentration polarization internal resistance. Since polarization internal resistance involves complex electrochemical reaction processes and varies significantly under different operating conditions and environmental factors, it is difficult to accurately measure and estimate. Therefore, this solution can indirectly and equivalently calculate the internal resistance of the power battery, ensuring the accuracy of the calculation results. Furthermore, based on the consistent safety characteristics of the equivalent internal resistance, the safety status of the power battery is analyzed and judged, and its safety risks are traced back to their source. This allows for accurate identification of the true causes of different power battery failures, enabling targeted improvements and significantly enhancing the operational safety of electric vehicles.

[0011] Preferably, as an improvement, preprocessing the dataset includes the following steps:

[0012] Limit the boundary values ​​of data signals and remove abnormal data of voltage and current signals that exceed the specified threshold;

[0013] Interference pulse identification and marking are performed. If the difference between the voltage data of the current frame and the previous frame exceeds the interference pulse judgment threshold, the data of this frame is marked.

[0014] Perform time discontinuity identification and marking. If the difference between the timestamp data of the current frame and the previous frame exceeds the time jump judgment threshold, then the data of this frame is marked.

[0015] Finally, mean filtering is applied to the dataset.

[0016] Preferably, as an improvement, extracting security elements in step S2 includes the following steps:

[0017] Speed ​​filtering is applied to voltage and current. The speed filtering operation involves sequentially calculating the ratio of the change in the signal over a specified time window to the length of the time window.

[0018] Derive and calculate the equivalent internal resistance of the power battery;

[0019] The safety element vector is obtained by performing nonlinear transformation and signal amplification on the equivalent internal resistance.

[0020] Calculate the product of voltage velocity and current, and mark the positions where the product value is greater than 0.

[0021] Preferably, as an improvement, in step S2, the security feature quantification includes the following steps:

[0022] The obtained safety element vector is quantified using variance entropy, and the safety element vector is substituted into the variance entropy calculation formula to obtain the safety quantification features.

[0023] Set the variance entropy at the marked location to 1.

[0024] Preferably, as an improvement, the equivalent internal resistance of the power battery is derived and calculated based on two sampling points in the time series, combined with the direction of the current under charging and discharging conditions during vehicle operation and historical vehicle operation data, to obtain the equivalent internal resistance of the power battery:

[0025] , where V v To specify the rate of voltage change over a given time window, I v The rate of change of current over a specified time window.

[0026] Preferably, as an improvement, the safety element vector is Sf = e αR Where α is the amplification factor constant.

[0027] Preferably, as an improvement, the formula for calculating variance entropy is λ = E. 2 (Sf) / E(Sf 2 ); where 0≤λ≤1.

[0028] Preferably, as an improvement, step S3, security status identification includes the following steps:

[0029] First, let p = 1 - λ be the risk quantification feature, and then perform discrete integration on the risk quantification feature over the time scale to obtain the risk curve Sp = ∑p;

[0030] The slope value of the risk curve is used to identify the safety status, and the amplitude of the identified slope value is taken as the absolute risk probability corresponding to the consistent safety feature.

[0031] Preferably, as an improvement, the source tracing of power battery safety risks involves defining the moment when the absolute risk probability exceeds the probability threshold as a high-risk point, drawing safety element images for a local time period before and after the high-risk point, and determining the safety status of the power battery and tracing the source of safety risks based on the local characteristics of the changes in safety elements in the images.

[0032] Preferably, as an improvement, the specified thresholds include a voltage threshold and a current threshold, wherein the voltage threshold ranges from 1000 to 6000 mV and the current threshold ranges from -1000 to 1000 A. Attached Figure Description

[0033] Figure 1 This is a flowchart illustrating an embodiment of the power battery risk tracing method based on equivalent internal resistance consistency according to the present invention.

[0034] Figure 2 This is a flowchart illustrating the traceability process of the first embodiment of the power battery risk traceability method based on equivalent internal resistance consistency of the present invention.

[0035] Figure 3 This is a schematic diagram of the power battery risk mode 1, which is an embodiment of the power battery risk tracing method based on equivalent internal resistance consistency of the present invention.

[0036] Figure 4 This is a schematic diagram of the power battery risk mode 2, which is an embodiment of the power battery risk tracing method based on equivalent internal resistance consistency of the present invention.

[0037] Figure 5 This is a schematic diagram of the power battery risk mode 3 in Embodiment 1 of the power battery risk tracing method based on equivalent internal resistance consistency of the present invention.

[0038] Figure 6 This is a schematic diagram of the power battery risk mode 4 in Embodiment 1 of the power battery risk tracing method based on equivalent internal resistance consistency of the present invention. Detailed Implementation

[0039] The following detailed description illustrates the specific implementation method:

[0040] Example 1:

[0041] This embodiment is basically as shown in the appendix. Figure 1 As shown: A risk tracing method for power batteries based on equivalent internal resistance consistency includes the following steps:

[0042] Step S1: Collect relevant data of the power battery to form a data set, and preprocess the data set;

[0043] Step S2: Extract the security elements from the preprocessed dataset, and quantify the equivalent internal resistance of the security elements to obtain the security quantification features.

[0044] Step S3: Obtain risk quantification features from safety quantification features, identify safety status based on risk quantification features, and then trace the source of power battery safety risks by combining safety element images.

[0045] As attached Figure 2 As shown, based on the collected basic data of the power battery, the equivalent internal resistance of the power battery is calculated. The consistency of the equivalent internal resistance allows for the analysis, determination, and risk tracing of the power battery's safety status. The specific process is as follows:

[0046] First, acquire and preprocess the data. Specifically, first acquire the data packet of the electric vehicle's power battery, parse the data packet to obtain the basic data set of the power battery, and then preprocess the data. First, limit the boundary values ​​of the data signals to remove abnormal data such as voltage and current signals that exceed specified thresholds; then, identify and mark interference pulses. If the difference between the voltage data of the current frame and the previous frame exceeds the interference pulse judgment threshold, then the data of this frame is marked; next, identify and mark time discontinuities. If the difference between the timestamp data of the current frame and the previous frame exceeds the time jump judgment threshold, then the data of this frame is marked; finally, perform mean filtering on the data.

[0047] Specifically, the threshold range for voltage signal data is 1000–6000 mV; the threshold range for current signal data is -1000–1000 A; the threshold for interference pulse detection is 3 times the standard deviation; and the threshold for time jump detection is 150 seconds. By limiting these thresholds, data preprocessing can be completed more accurately, improving data precision. This provides a precise basis for calculating the equivalent internal resistance of the power battery, ensuring the accuracy of the equivalent internal resistance and consequently guaranteeing the accuracy of subsequent system analysis of the power battery.

[0048] Second, extract the safety elements from the preprocessed dataset. Specifically, perform velocity filtering on voltage and current. The velocity filtering operation involves sequentially calculating the ratio of the change in signal over a specified time window to the length of the time window.

[0049] Then, the equivalent internal resistance of the power battery is derived and calculated. The equivalent circuit equation is obtained as follows: V = E - IR, where V is the voltage, E is the electromotive force, I is the total current, and R is the equivalent internal resistance.

[0050] Then, taking two sampling points t1 and t2 on the time series, we have:

[0051] V2-V1=(E-I1R)-(E-I2R)=(I2-I1)R

[0052] The equivalent internal resistance R is: Where Δt is the length of the time window, V v To specify the rate of voltage change over a given time window, I v The rate of change of current over a specified time window.

[0053] Considering the current direction during vehicle charging and discharging, the above equation is modified to obtain:

[0054]

[0055] Combining this with historical vehicle operating data, the equivalent internal resistance of the power battery can be calculated as follows:

[0056]

[0057] Through the above derivation process, the internal resistance of the power battery can be represented by the rate of change of voltage and current, instead of voltage and current. This avoids the situation where inaccurate voltage or current measurements lead to inaccurate internal resistance calculations. The rate of change can be measured stably, so the equivalent internal resistance calculated in this way is more reliable. This effectively improves the accuracy of the assessment of the safety status of the power battery and the analysis of the causes of risks, thereby ensuring the safe operation of electric vehicles.

[0058] Then, a nonlinear transformation and signal amplification are performed on the equivalent internal resistance to obtain the safety factor Sf = e αR Where α is the amplification factor constant, the product of voltage velocity and current is calculated, and the positions where the product value is greater than 0 are marked.

[0059] Third, perform safety feature quantification. Specifically, use variance entropy to quantify the obtained safety element vector, substituting the safety element vector Sf into the variance entropy calculation formula λ = E. 2 (Sf) / E(Sf 2 The safety quantification feature p is obtained from the formula; where 0≤λ≤1, and the closer λ is to 1, the smaller the fluctuation of the consistency feature on the time scale, and the safer the battery state; finally, the value of the variance entropy λ at the marked position is set to 1.

[0060] Fourth, conduct safety status identification. Specifically, first, let p = 1 - λ be the risk quantification feature, and then perform discrete integration on the risk quantification feature over the time scale to obtain the risk curve Sp = ∑p. The risk curve is a monotonically increasing curve.

[0061] Then, the slope value z of the risk curve is used for safety status identification, and the amplitude of the identified slope value z is taken as the absolute risk probability corresponding to the consistent safety feature.

[0062] Fifth, conduct safety risk tracing. Specifically, the moment when the absolute risk probability exceeds the probability threshold is defined as a high-risk point, and safety element images are drawn for local time periods before and after the high-risk point. Based on the local characteristics of the changes in safety elements in the images, the safety status of the power battery is determined and safety risks are traced.

[0063] Specifically, the probability threshold for the absolute risk probability mentioned above is 0.5. By limiting this probability threshold, high-risk points can be identified more accurately, thereby improving the accuracy of the safety element image and, consequently, the accuracy of the results of power battery safety status and risk tracing.

[0064] As attached Figure 3 As shown in the figure, the voltage of the batch of cells containing the abnormal cell exhibits obvious stratification, but the overall consistency is good. Therefore, it can be determined that this phenomenon is due to the performance difference between the module containing the abnormal cell and the other cells. This phenomenon is usually caused by replacing the battery module.

[0065] As attached Figure 4 As shown in the figure, the voltage of the abnormal cell gradually drops and delaminates during several consecutive charge-discharge cycles, and the voltage difference gradually increases significantly. This phenomenon indicates that the power battery has a relatively serious self-discharge, and may even have an internal short circuit, which can easily lead to thermal runaway. This is a relatively dangerous fault, and the safety of the power battery can be determined from this.

[0066] As attached Figure 5 As shown in the figure, the abnormal cell exhibits a significant "high charge, low discharge" characteristic compared to other cells during the discharge process, which can be identified as a power battery connection abnormality fault. The reason for this fault is that during the acceleration and braking transition of an electric vehicle, the power battery switches between discharging and regenerative current charging states. Due to the higher internal resistance of cells with abnormal connections, the voltage fluctuation amplitude is greater.

[0067] As attached Figure 6 As shown in the figure, the abnormal cell suddenly exhibits abnormal voltage fluctuations during the operation of the electric vehicle. This phenomenon is mostly caused by sampling abnormalities due to interference with the sampling chip. When the interference is eliminated, the abnormal situation also disappears.

[0068] The specific implementation process of this embodiment is as follows:

[0069] The first step is to acquire the data packet of the electric vehicle's power battery and parse it to obtain the basic data of the power battery. Then, the obtained data is cleaned and preprocessed, including limiting the boundary values ​​of the data signals, identifying and marking interference pulses, identifying and marking time discontinuities, and finally performing mean filtering on the data.

[0070] The second step is to extract the safety elements from the preprocessed data, perform velocity filtering on the voltage and current, calculate the ratio of the signal change over a specified time window to the length of the time window, derive and calculate the equivalent internal resistance of the power battery, perform nonlinear transformation and signal amplification on the equivalent internal resistance to obtain the safety element vector, and finally calculate the product of voltage velocity and current, and mark the positions where the product value is greater than 0.

[0071] The third step is to quantify the safety features of the safety element vector to obtain safety quantification features. Variance entropy is used to quantify the safety element vector to obtain safety quantification features, and the value of variance entropy at the marked position is set to 1.

[0072] The fourth step is to identify the safety status of the power battery. Based on the safety quantification characteristics, the risk quantification characteristics are obtained. Then, the risk quantification characteristics on the time scale are discretely integrated to obtain the risk curve. The slope value of the risk curve is used to identify the safety status. The amplitude of the identified slope value is used as the absolute risk probability corresponding to the consistent safety characteristics.

[0073] The fifth step is to determine the safety status and trace the source of risks of the power battery. The moment when the absolute risk probability exceeds the probability threshold is defined as a high-risk point, and safety element images are drawn before and after the high-risk point in a local time period. The safety status of the power battery and the source of safety risks are determined based on the local characteristics of the changes in safety elements in the images.

[0074] With the widespread promotion and popularization of new energy vehicles, people's travel methods and means of transportation have undergone tremendous changes, making transportation increasingly environmentally friendly. Among the many types of new energy vehicles, electric vehicles have the largest market share, and their range has now officially broken through the 1,000-kilometer mark, truly surpassing traditional fuel vehicles. However, with the widespread application of electric vehicles and the increased range, the safety requirements for electric vehicle power batteries are becoming increasingly stringent. If a power battery malfunctions, at best the electric vehicle will lose its power source, affecting driving safety; at worst, the power battery will overheat and catch fire, seriously threatening the personal safety of passengers. Therefore, the safety of the power battery is a prerequisite for ensuring the safe use of electric vehicles.

[0075] Currently, the safety testing of power batteries generally relies on analyzing their operational data to determine their safety. However, electric vehicles operate in diverse and complex environments, resulting in data characterizing their safety status that is multidimensional, redundant, heterogeneous, and strongly coupled. This poses a significant challenge to understanding the causes of safety risks in electric vehicle power batteries. The internal resistance of a power battery is the most direct indicator of its safety status. However, conventional methods for measuring internal resistance are generally inaccurate and unsuitable for analyzing battery safety. This is because internal resistance includes ohmic internal resistance and polarization internal resistance. Polarization internal resistance further includes electrochemical polarization internal resistance and concentration polarization internal resistance. Polarization internal resistance involves complex electrochemical reaction processes, and its variation under different operating conditions and environmental factors makes accurate measurement and estimation difficult. Therefore, the accuracy of current methods for analyzing the safety of power batteries based on their internal resistance is not reliably guaranteed.

[0076] This solution specifically addresses the aforementioned issues through in-depth research, proposing an equivalent circuit model-based method for calculating the equivalent internal resistance. This method accurately calculates the equivalent internal resistance of the power battery based on two sampling points in a time series, combined with the direction of the current during the charging and discharging process of the vehicle and historical vehicle data. Then, it performs nonlinear transformation and signal amplification on the equivalent internal resistance to obtain a safety element vector. Based on the safety element vector, it quantifies the safety characteristics of the equivalent internal resistance consistency. Finally, it combines the risk accumulation method to identify the safety status of electric vehicles and output high-risk point image information, ultimately completing the risk tracing analysis of electric vehicle power batteries.

[0077] This solution not only enables the rapid and convenient calculation of the equivalent internal resistance of the power battery, allowing for the analysis and assessment of power battery safety risks based on the consistency of the equivalent internal resistance, thus ensuring the operational safety of electric vehicles, but also allows for the creation of safety element images within local timeframes before and after high-risk points based on the absolute risk probability corresponding to the consistent safety characteristics. Finally, it facilitates the source analysis of power battery safety risks, accurately identifying the fault type and cause of the fault, which in turn facilitates subsequent improvements to the relevant operating rules and circuits of the power battery, effectively enhancing its safety and stability, and ultimately ensuring the operational safety of electric vehicles. In particular, this solution is especially effective when power battery voltage and current data acquisition is inaccurate or difficult. It transforms the approach to variable extraction, using the rate of change to equivalently analyze the safety status of the power battery, thereby enabling accurate assessment of the power battery's safety status under any circumstances, thus improving the safety of electric vehicles.

[0078] The above descriptions are merely embodiments of the present invention. Commonly known structures and characteristics are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are aware of all existing technologies in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can, under the guidance of this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A method for tracing the risk of power batteries based on the consistency of equivalent internal resistance, characterized in that: Includes the following steps: Step S1: Collect relevant data of the power battery to form a data set, and preprocess the data set; Step S2: Extract the security elements from the preprocessed dataset, and quantify the equivalent internal resistance of the security elements to obtain the security quantification features. Step S3: Obtain risk quantification features from safety quantification features, identify safety status based on risk quantification features, and then trace the source of power battery safety risks by combining safety element images. In step S2, security feature quantification includes the following steps: The obtained safety element vector is quantified using variance entropy, and the safety element vector is substituted into the variance entropy calculation formula to obtain the safety quantification features. Set the variance entropy at the marked location to 1; Extracting security elements in step S2 includes the following steps: Speed ​​filtering is applied to voltage and current. The speed filtering operation involves sequentially calculating the ratio of the change in the signal over a specified time window to the length of the time window. Derive and calculate the equivalent internal resistance of the power battery; The safety element vector is obtained by performing nonlinear transformation and signal amplification on the equivalent internal resistance. Calculate the product of voltage velocity and current, and mark the positions where the product value is greater than 0; The derivation and calculation of the equivalent internal resistance of the power battery is based on two sampling points in the time series, combined with the direction of the current under charging and discharging conditions during vehicle operation and historical vehicle operation data, to obtain the equivalent internal resistance of the power battery: ,in, To specify the rate of voltage change over a given time window, The rate of change of current over a specified time window; The security element vector is: ;in, This is the amplification factor constant; The formula for calculating the variance entropy is as follows: ;in ; In step S3, the security status identification includes the following steps: First let The risk is quantified by defining risk characteristics, and the risk curve is obtained by discrete integration of these characteristics over a time scale. ; The slope value of the risk curve is used to identify the safety status, and the amplitude of the identified slope value is taken as the absolute risk probability corresponding to the consistent safety feature.

2. The method for tracing the risk of power batteries based on equivalent internal resistance consistency according to claim 1, characterized in that: The preprocessing of the data set includes the following steps: Limit the boundary values ​​of data signals and remove abnormal data of voltage and current signals that exceed the specified threshold; Interference pulse identification and marking are performed. If the difference between the voltage data of the current frame and the previous frame exceeds the interference pulse judgment threshold, the data of this frame is marked. Perform time discontinuity identification and marking. If the difference between the timestamp data of the current frame and the previous frame exceeds the time jump judgment threshold, then the data of this frame is marked. Finally, mean filtering is applied to the dataset.

3. The method for tracing the risk of power batteries based on equivalent internal resistance consistency according to claim 1, characterized in that: The process of tracing the source of safety risks in power batteries involves defining the moment when the absolute risk probability exceeds the probability threshold as a high-risk point, drawing safety element images within a local time before and after the high-risk point, and determining the safety status of the power battery and tracing the source of safety risks based on the local characteristics of the changes in safety elements in the images.

4. The method for tracing the risk of power batteries based on equivalent internal resistance consistency according to claim 2, characterized in that: The specified thresholds include a voltage threshold and a current threshold, wherein the voltage threshold ranges from 1000 to 6000 mV and the current threshold ranges from -1000 to 1000 A.

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