Lithium battery capacity attenuation analysis method based on multi-source data fusion analysis

Abstract

This invention discloses a lithium battery capacity decay analysis method based on multi-source data fusion analysis, belonging to the field of lithium battery analysis technology. The method is based on the constant discharge test, but specifically for new energy vehicles, it employs a variable discharge test. This involves four variables: battery temperature, ambient temperature, vibration frequency, and discharge current. The invention primarily uses multi-source data fusion analysis, with key aspects being the feature extraction process and the differential compensation based on the extracted features. Essentially, it analyzes the influence of battery temperature, ambient temperature, and vibration frequency on the battery capacity decay rate, using feature weighting coefficients as key coefficients. During the decay rate calculation across multiple time periods, the obtained decay rate is amplified or devalued based on the feature weighting coefficients to obtain a more accurate actual decay rate value.

Description

Technical Field

[0001] This invention relates to the field of lithium battery analysis technology, and more specifically to a lithium battery capacity decay analysis method based on multi-source data fusion analysis. Background Technology

[0002] Considering both internal and external factors, the causes of lithium battery capacity decay include changes in electrode / electrolyte materials, electrical connections, and environmental factors. Taking lithium batteries used in new energy vehicles or retired lithium batteries as an example, current detection methods can be used to obtain the capacity status of lithium batteries and thus determine their subsequent use status.

[0003] Referring to the technical content involved in relevant patent documents such as CN115308630A and CN110501652A, its essence is the constant current / power discharge method. This type of analysis method mainly focuses on the analysis of a single factor (electricity). However, in reality, cycle degradation is often caused by multiple factors. The capacity degradation of lithium-ion batteries can be divided into two categories: thermodynamic loss and kinetic loss. Thermodynamic loss includes the irreversible loss of active lithium and positive and negative electrode active materials; kinetic loss is mainly manifested as an increase in impedance and polarization. The two are fundamentally different but also influence each other. This is especially true for lithium batteries used in new energy vehicles. On the one hand, the aforementioned constant current / power discharge method requires offline operation to ensure that the battery is evaluated in a relatively ideal state. On the other hand, the environmental factors of lithium batteries in new energy vehicles are more complex. Due to limitations of vehicle bumps, ambient temperature, operating temperature, and other conditions, the actual degradation rate obtained has significant errors. If multiple data are summarized and analyzed, the calculation process is relatively complex. Therefore, this invention proposes a technical solution. Summary of the Invention

[0004] The purpose of this invention is to provide a lithium battery capacity degradation analysis method based on multi-source data fusion analysis. Based on the lithium battery capacity evaluation method, especially for the application of new energy vehicles, the degradation rate obtained by analyzing only a single factor has a large error, especially because the environmental factors of lithium batteries in new energy vehicles are more complex.

[0005] The objective of this invention can be achieved through the following technical solution: a lithium battery capacity decay analysis method based on multi-source data fusion analysis, applied to lithium batteries for new energy vehicles, employing a fusion matrix system composed of static modules and multi-source modules, using the static module as a reference standard for the multi-source module, performing constant discharge test and variable discharge test in the static module and multi-source module respectively, and obtaining the initial value and variable value of decay rate in the two modules, and setting the static module and multi-source module as the first node and second node respectively with different operating scenarios;

[0006] Three actions are set in the multi-source module: multi-source data input, feature extraction and differential compensation. The multi-source data input action obtains four parameters: battery temperature, ambient temperature, vibration frequency and discharge current value. The four parameters are then substituted into the variable discharge test action in the multi-source module.

[0007] The feature extraction action has complete control over the variable discharge test action, and performs interference test actions in the variable discharge test action according to four parameters to obtain feature weight coefficients;

[0008] The differential compensation action fills in the attenuation rate variation in the multi-source module with differential values ​​based on the feature weight coefficients to obtain the actual attenuation rate value.

[0009] Further settings include: the lithium batteries in the static module, the multi-source module constant discharge test, and the variable discharge test are from the same batch; the first node indicates that the battery has not been put into the test state of new energy vehicles; and the second node indicates that the battery has been put into the test state of new energy vehicles and is in operation.

[0010] Further defined as follows: battery temperature represents the temperature generated during the discharge of the lithium battery and is denoted as Ta; ambient temperature represents the external ambient temperature; Ta represents the total temperature generated during the operation of the internal components of the new energy vehicle, Tb; Ta and Tb are obtained through temperature sensors; vibration frequency is represented in Hz; discharge current value is represented in A; and battery degradation rate is represented by η.

[0011] Further settings include: Ta, Tb, Hz, η, and A are variable values, re-expressed in time unit t as Ta. t 、Tb t Hz t η t and A t， by The feature weight coefficients are represented.

[0012] Further settings: based on Ta t 、Tb t Hz t The interference factor is obtained by multi-time-period progressive scaling of the three parameters, and is expressed as: Ma = (Ta t -Ta t-1 ) / Ta t Mb = (Tb) t -Tb t-1 ) / Tb t Mz = (Hz) t -Hz t-1 ) / Hz t ,

[0013] Further configuration: During variable discharge testing, the battery is acquired for a specific time period based on the discharge current value and discharge time, and a battery degradation curve is established. Simultaneously, a curve about Ta is constructed within the battery degradation curve. t 、Tb t Hz t Interference factor curves for three parameters.

[0014] Further settings include: the calculation method for generating the associated feature weight coefficients using Ma, Mb, and Mz: =Ma 1 ×Mb 1 ×Mz 1 +Ma 2 ×Mb 2 ×Mz 2 +…+Ma t ×Mb t ×Mz t .

[0015] Further settings are as follows: the values ​​of the three interference factors Ma, Mb and Mz are in the interval [-1,1], and the absolute values ​​of Ma, Mb and Mz are taken. If there are two or more interference factors with an absolute value <0.25, the interference factors in that time period are not included in the calculation of the feature weight coefficient, and Ma, Mb and Mz are not taken as absolute values ​​in the calculation of the feature weight coefficient.

[0016] Further settings include: in the differential fill action, using η t Represents the real value of the attenuation rate, and η t =η t-1 ×(1+ The attenuation rate is reduced or increased by using the difference between battery temperature, ambient temperature, and vibration frequency in two time periods.

[0017] The present invention has the following beneficial effects:

[0018] 1. This invention is based on the constant discharge test method in the lithium battery capacity decay analysis process, and mainly adopts the variable discharge test action for the main application of new energy vehicles. Specifically, through multi-source data fusion analysis, it comprehensively considers the influence of four variable parameters on the lithium battery capacity decay rate: battery temperature, ambient temperature, vibration frequency, and discharge current value. This method not only improves the accuracy of the analysis, but also further optimizes the calculation results of the decay rate through feature extraction and differential compensation. In practical applications, this method can more realistically reflect the capacity decay of new energy vehicle lithium batteries in complex environments.

[0019] 2. In conjunction with the above, it should also be noted that the key content of this invention lies in the interference factors calculated for the three variable parameters of battery temperature, ambient temperature, and vibration frequency during the feature extraction process. Essentially, these three variable parameters are not included in the calculation of battery degradation rate, but rather serve as reference objects in the degradation rate curve. This requires multi-time-period progressive proportional conversion based on the three variable parameters. The purpose is to initially obtain the degree of influence of the three variable parameters on the degradation rate. Secondly, based on the interference factors obtained from the three variable parameters, the key parameter of feature weight coefficient is re-obtained. It should be noted that the three interference factors have positive and negative distinctions, and to reduce the complexity of the calculation process, the interference factors are selectively discarded. Finally, the degradation rate is further increased or decreased based on the positive or negative distinction of the three interference factors to obtain a more accurate real value of the degradation rate. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating the operation of the lithium battery capacity decay analysis method based on multi-source data fusion analysis proposed in this invention. Detailed Implementation

[0022] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Example 1: Based on lithium battery capacity evaluation methods, especially for new energy vehicles, the degradation rate obtained by analyzing only a single factor has a large error. This is particularly true because the environmental factors affecting lithium batteries in new energy vehicles are more complex. Therefore, the following technical solution is proposed:

[0024] Reference Figure 1A lithium battery capacity decay analysis method based on multi-source data fusion analysis is applied to lithium batteries for new energy vehicles. It adopts a fusion matrix system composed of static modules and multi-source modules, using the static module as the reference standard for the multi-source module. Constant discharge test and variable discharge test are performed in the static module and the multi-source module respectively, and the initial value and variable value of decay rate in the two modules are obtained. The static module and the multi-source module are set as the first node and the second node respectively, with different operating scenarios.

[0025] Three actions are set in the multi-source module: multi-source data input, feature extraction and differential compensation. The multi-source data input action obtains four parameters: battery temperature, ambient temperature, vibration frequency and discharge current value. The four parameters are then substituted into the variable discharge test action in the multi-source module.

[0026] The feature extraction action has complete control over the variable discharge test action, and performs interference test actions in the variable discharge test action according to four parameters to obtain feature weight coefficients;

[0027] The differential compensation action fills the differential value of the decay rate in the multi-source module according to the feature weight coefficient to obtain the actual value of the decay rate. The lithium batteries in the static module, the multi-source module and the constant discharge test action and the variable discharge test action are from the same batch. The first node indicates that it has not been put into the test state of new energy vehicles, and the second node indicates that it has been put into the test state of new energy vehicles and is in operation.

[0028] Basic Principle Explanation: First, a brief explanation of the capacity decay analysis process for lithium batteries: It mainly uses the constant discharge test method (referred to as the constant discharge test action in this invention). The principle is as follows: by controlling a constant current to discharge the battery, a standard test method is used to accurately measure its capacity and performance parameters. Discharge capacity (Ah) = discharge current (A) × discharge time (h). For example, if the battery is discharged at a current of 1A for 2 hours, the measured capacity is 2Ah. The battery capacity decay rate is calculated as: (1 - current discharge capacity / initial discharge capacity) × 100%. This is the basic calculation method in the constant discharge test method.

[0029] This invention primarily considers lithium batteries used in new energy vehicles. Its purpose is to obtain the battery degradation rate in real time and further divide them into two categories: static modules and multi-source modules. The lithium batteries to be tested in both static and multi-source modules must be from the same batch and of the same model. The first node of the static module indicates that it has not been installed in a new energy vehicle and the test is completed in a relatively static state. Therefore, the initial value of the degradation rate can be obtained by simply performing a constant discharge test. Without considering production quality factors such as electrolyte quality and electrode quality, the degradation rate is close to 0. The only common point with the multi-source module is the temperature change during the discharge process, which corresponds to the battery temperature in the multi-source module. The following estimation method can be used: ΔT = (heating power × time) / (battery mass × specific heat capacity).

[0030] However, it is important to note that when a module is used in a new energy vehicle and is in operation, it differs from a static module in the following ways:

[0031] 1. Because the actual discharge process of lithium batteries varies depending on the driving speed required during the operation of new energy vehicles, the discharge current is not a constant value.

[0032] 2. The bumps generated during vehicle operation may affect the electrolyte and electrodes inside the lithium battery. However, it is difficult to obtain the quality of the electrolyte and the state of the electrodes in the lithium battery installed in new energy vehicles. But the bumps may also affect the electrolyte and electrodes. Therefore, this embodiment uses the vibration frequency to provide feedback on the possible impact on the inside of the lithium battery.

[0033] 3. During vehicle operation, the external ambient temperature will directly affect the lithium battery temperature, and the heat generated by the lithium battery during discharge will also vary. Both internal and external temperature changes will affect the lithium battery discharge process.

[0034] It is understandable that in the second node, if the decay rate is calculated based on the discharge current in a certain period, there will be a large deviation. The key technology of this invention is to adopt the variable discharge test action for the multi-source module. In the variable discharge test action, it is also necessary to combine four parameters, namely battery temperature, ambient temperature, vibration frequency and discharge current value, for differential compensation action. The purpose is to correct the decay rate obtained in a certain period. The constant discharge test action performed by the static module is only used as a reference standard to determine the maximum capacity of the lithium battery under test.

[0035] Example 2: The following supplementary explanation is provided regarding the feature extraction action in Example 1:

[0036] When performing variable discharge, the battery degradation curve can be established by obtaining the battery in a certain period of time based on the discharge current value and discharge time. Specifically, it is expressed as follows: During the period from t1 to t2, the lithium battery continuously discharges with a stable discharge current value of A1, thereby obtaining the battery degradation rate during this period. Similarly, the battery degradation rate is obtained again during the period from t2 to t3, and η represents the battery degradation rate.

[0037] The four parameters in the multi-source data entry process are explained:

[0038] Battery temperature refers to the temperature generated during the discharge process of a lithium battery, and is denoted as Ta;

[0039] Ambient temperature refers to the external ambient temperature Ta and the total temperature Tb generated by the internal components of the new energy vehicle during operation. However, the two are not calculated by simple addition and subtraction. Both Ta and Tb can be obtained through temperature sensors.

[0040] Vibration frequency represents the vibration caused by bumps during the operation of new energy vehicles. It can also be obtained by a vibration frequency sensor and expressed as Hz. Its essence is to represent the vibration felt by the lithium battery.

[0041] The discharge current value is a key parameter in this embodiment, represented by A;

[0042] The values ​​Ta, Tb, Hz, η, and A mentioned above are all variable values, which are then re-expressed in time unit t as Ta. t 、Tb t Hz t η t and A t， Furthermore, the aforementioned Ta, Tb, Hz, and A are not used as parameters in the battery degradation rate calculation process. They are mainly used to represent the interference factors in the battery degradation rate calculation process over a certain period of time, thereby completing the interference test. The feature weight coefficients are represented by Ta. t 、Tb t Hz t The interference factor is obtained by multi-time-period progressive scaling of the three parameters, and is expressed as: Ma = (Ta t -Ta t-1 ) / Ta t Mb = (Tb) t -Tb t-1 ) / Tb t Mz = (Hz) t -Hz t-1 ) / Hz t Simultaneously construct information about Ta in the battery degradation curve. t 、Tb t Hzt Interference factor curves for three parameters, and Ma... t Mb t Mz t The three representations based on the time unit t are used to calculate the correlation feature weight coefficients for Ma, Mb, and Mz: =Ma 1 ×Mb 1 ×Mz 1 +Ma 2 ×Mb 2 ×Mz 2 +…+Ma t ×Mb t ×Mz t .

[0043] Example 3: The calculation method of the feature weight coefficients in Example 2 is applied to the differential compensation action and explained as follows:

[0044] Ma, Mb, and Mz have a positive or negative relationship and their values ​​are in the interval [-1, 1]. The absolute values ​​of Ma, Mb, and Mz are taken. If there are two or more interference factors whose absolute values ​​are <0.25, the interference factors in that time period are not included in the calculation of the feature weight coefficient. In the process of calculating the feature weight coefficient, Ma, Mb, and Mz are not taken as absolute values.

[0045] Therefore, in the differential filling action, if the decay rate obtained through the variable discharge test action during a certain period is η... t The real value of the attenuation rate is equal to η. t-1 ×(1+ Specifically, this means that during the variable discharge test, the decision is made based on each interference factor. The positive and negative values ​​can be understood as follows: if the battery temperature, ambient temperature, and vibration frequency in the later time period are lower than those in the previous time period, the overall calculated attenuation rate needs to be reduced; otherwise, it needs to be increased to improve the accuracy of the overall attenuation rate data.

[0046] In summary, this invention analyzes the capacity decay of lithium batteries based on the constant discharge test method, but specifically for new energy vehicles, it employs a variable discharge test. This involves four variables: battery temperature, ambient temperature, vibration frequency, and discharge current. The invention primarily utilizes multi-source data fusion analysis, focusing on feature extraction and differential compensation based on these features. Essentially, it analyzes the influence of battery temperature, ambient temperature, and vibration frequency on the battery capacity decay rate, using feature weighting coefficients as key factors. During the calculation of the decay rate across multiple time periods, the obtained decay rate is amplified or devalued based on the feature weighting coefficients to obtain a more accurate actual decay rate value.

[0047] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

[0048] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0049] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to specific implementations. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A lithium battery capacity decay analysis method based on multi-source data fusion analysis, characterized in that, In lithium batteries for new energy vehicles, a fusion matrix system consisting of static modules and multi-source modules is adopted. The static module is used as a reference standard for the multi-source module. The static module is subjected to constant discharge test to obtain the initial value of the decay rate, and the multi-source module is subjected to multi-source module test to obtain the decay rate variation value. The static module and the multi-source module are set as the first node and the second node respectively, with different operating scenarios. The first node represents the test state before being put into new energy vehicles, and the second node represents the test state when put into new energy vehicles and in operation. Three actions are set in the multi-source module: multi-source data input, feature extraction and differential compensation. The multi-source data input action obtains four parameters: battery temperature, ambient temperature, vibration frequency and discharge current value. The four parameters are then substituted into the variable discharge test action in the multi-source module. The battery temperature represents the temperature generated during the discharge of the lithium battery and is denoted as Ta. The ambient temperature represents the total temperature generated by the external environment and internal components of the new energy vehicle during operation, Tb. The vibration frequency is represented by Hz, the discharge current value is represented by A, and the battery degradation rate is represented by η. The feature extraction action has complete control over the variable discharge test action, and performs interference test actions based on four parameters to obtain feature weight coefficients. Ta, Tb, Hz, η, and A are variable values, and are re-expressed in time unit t as Ta. t 、Tb t Hz t η t and A t ,by The feature weight coefficients are represented based on Ta. t 、Tb t Hz t The interference factor is obtained by multi-time-period progressive scaling of the three parameters, and is expressed as: Ma = (Ta t -Ta t-1 ) / Ta t Mb = (Tb) t -Tb t-1 ) / Tb t Mz = (Hz) t -Hz t-1 ) / Hz t , =Ma 1 ×Mb 1 ×Mz 1 +Ma 2 ×Mb 2 ×Mz 2 +…+Ma t ×Mb t ×Mz t ; The differential compensation action fills in the attenuation rate variation in the multi-source module based on the feature weight coefficient to obtain the real value of the attenuation rate. In the differential filling action, η... t Represents the real value of the attenuation rate, and η t =η t-1 ×(1+ The attenuation rate is reduced or increased by using the difference between battery temperature, ambient temperature, and vibration frequency in two time periods.

2. The lithium battery capacity decay analysis method based on multi-source data fusion analysis according to claim 1, characterized in that, The lithium batteries used in the static module, multi-source module constant discharge test, and variable discharge test are from the same batch and model.

3. The lithium battery capacity decay analysis method based on multi-source data fusion analysis according to claim 1, characterized in that, Ta and Tb are obtained through a temperature sensor.

4. The lithium battery capacity decay analysis method based on multi-source data fusion analysis according to claim 1, characterized in that, In the variable discharge test, the battery is acquired for a certain period based on the discharge current value and discharge time, and a battery degradation curve is established. Simultaneously, a parameter related to Ta is constructed within the battery degradation curve. t 、Tb t Hz t Interference factor curves for three parameters.

5. The lithium battery capacity decay analysis method based on multi-source data fusion analysis according to claim 1, characterized in that, The values ​​of the three interference factors Ma, Mb, and Mz are in the interval [-1,1]. The absolute values ​​of Ma, Mb, and Mz are taken. If the absolute values ​​of two or more interference factors are <0.25, the interference factors in that time period are not included in the calculation of the feature weight coefficient. In the process of calculating the feature weight coefficient, Ma, Mb, and Mz are not taken as absolute values.

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