Battery monitoring system and method based on automobile battery parameters

Abstract

The invention relates to the technical field of battery monitoring technologies, and particularly discloses a battery monitoring system and method based on automobile battery parameters, and the system comprises a parameter collection module, a data preprocessing module, an edge calculation module, a state evaluation module and an early warning interaction module. Multi-level parameters of a battery are collected through a distributed sensor array, and after data cleaning and standardization processing, comprehensive state coefficients of a battery cell, a module and a battery pack are sequentially calculated by an edge calculation module based on working and power-off state parameters. And quantitatively evaluating key indexes such as internal resistance health, polarization recovery, capacity attenuation and temperature influence of the battery by adopting a multi-sub-coefficient weighted fusion model. The system achieves hierarchical state evaluation according to a preset threshold value and pushes early warning information and maintenance suggestions to the user terminal, and real-time, accurate and hierarchical intelligent monitoring and active early warning of the health state of the automobile battery are achieved.

Description

Technical Field

[0001] This invention relates to the field of battery monitoring technology, specifically to a battery monitoring system and method based on automotive battery parameters. Background Technology

[0002] With the rapid development of the new energy vehicle industry, the performance of power batteries, as its core component, directly affects the safety, driving range, and service life of the entire vehicle.

[0003] Currently, traditional battery monitoring systems have the following problems: 1. Most rely on threshold alarms for single or a few parameters such as voltage, current, and temperature, which can usually only reflect the real-time operating status of the battery and are difficult to quantify and assess the internal health status of the battery and provide early warnings; 2. For battery modules and battery packs composed of multiple cells, existing methods lack a hierarchical status assessment model from individual cells to the system, which cannot accurately identify the impact of performance degradation of individual cells or modules on the entire battery system, resulting in crude maintenance strategies, waste of resources, or safety hazards.

[0004] Therefore, this invention proposes a battery monitoring system and method based on automotive battery parameters. Summary of the Invention

[0005] The purpose of this invention is to provide a battery monitoring system and method based on automotive battery parameters, thereby solving the above-mentioned technical problems: The objective of this invention can be achieved through the following technical solutions: A battery monitoring system based on automotive battery parameters includes a parameter acquisition module, a data preprocessing module, an edge computing module, a status assessment module, and an early warning interaction module, with each module connected in a sequential manner. The parameter acquisition module includes a distributed sensor array, which is deployed at the cell, module and battery pack levels of the automotive battery to collect multi-dimensional parameters of the battery and add timestamps to each collected parameter. The data processing module is used to receive parameter data transmitted by the parameter acquisition module, and to perform outlier removal, missing value completion, and standardization processing on the data. The outlier removal uses a 3D model. The criteria for missing value completion are: adjacent time series data interpolation method; the standardization process maps the parameter data to the [0, 1] interval to obtain standardized time series data. The edge computing module is used to receive standardized data output by the data preprocessing module, and based on the battery operating parameters and power-off parameters, it obtains the real-time state coefficients of the cell, the module, and the battery pack layer by layer through weighted calculation of state sub-coefficients. The status assessment module is used to receive real-time status coefficients and assess the status of battery cells, modules and battery packs according to preset thresholds. The early warning interaction module is used to push abnormal early warning information, battery health reports and maintenance suggestions to the user's mobile terminal based on the status assessment results.

[0006] As a further description of the technical solution of the present invention, the parameter acquisition module has a built-in clock unit for adding timestamps to the acquired parameter data; The parameter acquisition module is equipped with an adaptive acquisition frequency control unit, which can automatically adjust the acquisition frequency according to the battery's working state: when the battery is powered on, a high-frequency acquisition of 10Hz is used; when the battery is powered off and idle, a low-frequency acquisition of once every 30 minutes is used.

[0007] As a further description of the technical solution of the present invention, the working process of the edge computing module includes: S1. Obtain battery operating parameters and parameters when power is off. The operating parameters include operating voltage. Operating current and operating temperature The power outage parameters include the open-circuit voltage at the instant of power outage. Recovery voltage after a predetermined power outage time and settling temperature ; S2. Based on the aforementioned operating parameters and power-off parameters, calculate the four state sub-coefficients of the battery cell, including: internal resistance health coefficient. Polarization recovery coefficient Capacity attenuation coefficient and temperature influence coefficient ; S3. Assign weights to the four state sub-coefficients of the battery cell, and then calculate the overall state coefficient of the battery cell: , among which, if >1, then =1, , , and These are the weighting coefficients corresponding to the internal resistance health coefficient, polarization recovery coefficient, capacity decay coefficient, and temperature influence coefficient, respectively. S4. Based on the numerical range of the comprehensive state coefficient of the battery cell, output the battery cell state evaluation result.

[0008] As a further description of the technical solution of the present invention, the working process of S2 includes: The internal resistance health coefficient is calculated using the following formula: ; In the formula, The internal resistance of the battery during operation is given by, where, , Open circuit voltage, This refers to the battery's internal resistance at the time of manufacture. The internal resistance at the end of the battery's lifespan. This refers to the internal resistance of the battery when it is at rest. This refers to the battery's internal resistance when it is stationary at the factory. The polarization recovery coefficient is calculated using the following formula: In the formula, Rated voltage; The capacity attenuation coefficient is calculated using the following formula: ; The temperature influence coefficient is assigned a value based on the temperature difference. ,when At <10℃, When 10℃≤ At <20℃, 0.95; when 20℃≤ At <30℃, 0.9; when When the temperature is >30℃, 0.8.

[0009] As a further description of the technical solution of the present invention, the working process of the edge computing module also includes: M1, Obtain the comprehensive state coefficient of each of the n cells in the module. , where i = 1, 2, ..., n; M2, based on the comprehensive state coefficients of the n cells respectively Calculate the three state sub-coefficients of the module, including: module balance coefficient. Module average health coefficient And the weak influence coefficient of the module ; M3. Assign weights to the three state sub-coefficients of the module, and then calculate the module's overall state coefficients: ,in, , and These are the weighting coefficients corresponding to the module balance coefficient, the module average health coefficient, and the module weakness impact coefficient, respectively. M4. Based on the numerical range of the module's comprehensive state coefficient, output the battery module state evaluation result.

[0010] As a further description of the technical solution of the present invention, the working process of M2 includes: The module balance coefficient is calculated using the following formula: In the formula, and These are the maximum and minimum terms of the overall state coefficient among n cells, respectively; The average health coefficient of the module is calculated using the following formula: ; The module's weak impact coefficient is calculated using the following formula: ,in, The first adjustment factor is the first adjustment factor. The value is determined based on the number of weak cells, m: when m=1... =0.8; when 2≤m≤n×0.2, =0.9; when m>n×0.2, =1.0, the weak cell is defined as < ×0.8 battery cells.

[0011] As a further description of the technical solution of the present invention, the working process of the edge computing module also includes: P1. Obtain the comprehensive state coefficients of each of the x modules in the battery pack. , where y = 1, 2, ..., x; P2, Based on the comprehensive state coefficients of each of the x modules The three state sub-coefficients of the battery pack are calculated, including: battery pack balance coefficient, battery pack average health coefficient, and battery pack weakness impact coefficient. P3. Assign weights to the three state sub-coefficients of the battery pack, and then calculate the overall state coefficient of the battery pack: ,in, , and These are the weighting coefficients corresponding to the battery pack balance coefficient, the battery pack average health coefficient, and the battery pack key module influence coefficient, respectively. P4. Based on the numerical range of the module's comprehensive state coefficient, output the battery module state evaluation result.

[0012] As a further description of the technical solution of the present invention, the working process of P2 includes: The average health coefficient of the battery pack is calculated using the following formula: ; The battery pack balance coefficient is calculated using the following formula: ; The battery pack weakness impact factor is calculated using the following formula: ,in, This is the second adjustment factor. The number of weak modules, g, is determined as follows: When g=1... =0.8; when 2≤g≤x×0.2, =0.9; when g>x×0.2, =1.0, the weak cell is defined as < ×0.8 battery cells.

[0013] As a further description of the technical solution of the present invention, the working process of S4 includes: Compare the cell's overall state coefficient C with the corresponding threshold range set by the system. If ≥ If the cell condition is good, then the cell condition is assessed as excellent; if ≤ < If the cell condition is generally good, attention is needed; if ≤ If the cell is found to be in substandard condition, it needs to be replaced. The working process of M4 includes: Compare the module's overall state coefficient M with the corresponding threshold range set by the system. If ≥ If the module is rated as excellent; ≤ < If the module status is generally good, attention is needed; if ≤ If the module status is deemed unqualified, it needs to be replaced. The working process of P4 includes: Compare the battery pack's overall state coefficient P with the corresponding threshold range set by the system. If... ≥ If the battery pack is in excellent condition, then the battery pack is assessed as being in good condition; if ≤ < If the battery pack condition is generally good, attention is needed; if ≤ If the battery pack fails the assessment, it is deemed unqualified and needs to be replaced.

[0014] A battery monitoring method based on automotive battery parameters, the method being implemented using a battery monitoring system based on automotive battery parameters.

[0015] The beneficial effects of this invention are as follows: This invention collects multi-level battery parameters through a distributed sensor array. After data cleaning and standardization, the edge computing module calculates the comprehensive state coefficients of the cells, modules, and battery pack sequentially based on operating and power-off state parameters. A multi-sub-coefficient weighted fusion model is employed to quantitatively evaluate key indicators such as battery internal resistance health, polarization recovery, capacity decay, and temperature effects. The system performs graded state assessments based on preset thresholds and pushes early warning information and maintenance suggestions to user terminals, achieving real-time, accurate, and hierarchical intelligent monitoring and proactive early warning of the vehicle battery's health status. Attached Figure Description

[0016] The invention will now be further described with reference to the accompanying drawings.

[0017] Figure 1 This is a partial structural diagram of the battery monitoring system based on automotive battery parameters according to the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some 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.

[0019] Please see Figure 1 As shown, the present invention provides a battery monitoring system based on automotive battery parameters, including a parameter acquisition module, a data preprocessing module, an edge computing module, a status assessment module, and an early warning interaction module, with each module being connected in communication in sequence; The parameter acquisition module includes a distributed sensor array, which is deployed at the cell, module and battery pack levels of the automotive battery to collect multi-dimensional parameters of the battery and add timestamps to each collected parameter. The data processing module is used to receive parameter data transmitted by the parameter acquisition module, and to perform outlier removal, missing value completion, and standardization processing on the data. The outlier removal uses a 3D model. The criteria for missing value completion are: adjacent time series data interpolation method; the standardization process maps the parameter data to the [0, 1] interval to obtain standardized time series data. The edge computing module is used to receive standardized data output by the data preprocessing module, and based on the battery operating parameters and power-off parameters, it obtains the real-time state coefficients of the cell, the module, and the battery pack layer by layer through weighted calculation of state sub-coefficients. The status assessment module is used to receive real-time status coefficients and assess the status of battery cells, modules and battery packs according to preset thresholds. The early warning interaction module is used to push abnormal early warning information, battery health reports and maintenance suggestions to the user's mobile terminal based on the status assessment results.

[0020] Through the above technical solution, the present invention provides a battery monitoring system and method based on automotive battery parameters. Its working principle is to achieve comprehensive health status assessment and early warning of automotive batteries from cells to modules to battery packs through multi-level real-time data acquisition and intelligent analysis.

[0021] At the cell level, the module assesses cell health status based on operating and power-off parameters using a weighted sum of four physical sub-coefficients: the internal resistance health coefficient reflects the degree of internal resistance degradation, calculated by comparing the deviations of the internal resistance during operation, the internal resistance at rest, and the factory reference value; the polarization recovery coefficient characterizes voltage recovery capability, calculated based on the ratio of operating voltage to recovery voltage; the capacity decay coefficient assesses capacity loss by comparing the voltage drop with the theoretical value; and the temperature influence coefficient quantifies and corrects the performance impact based on the temperature difference between operating and resting conditions. The four sub-coefficients are integrated according to preset weights to obtain the cell's overall state coefficient, where the capacity decay coefficient uses (…). The coefficients are used in the calculation in a consistent manner to ensure that the contribution of each coefficient to the health status is in the same direction.

[0022] At the module level, the system integrates the state coefficients of all cells within the module and evaluates the overall state of the module through calculations in three dimensions: the module balance coefficient uses the range method (1 - the difference between the maximum and minimum values) to assess the consistency between cells; the module average health coefficient reflects the overall health level through an arithmetic mean; and the module weakness impact coefficient identifies and quantifies the impact of underperforming cells, dynamically weighting them based on the number of weak cells using an adjustment coefficient. These three coefficients are combined according to their weights to form the module's comprehensive state coefficient, with the weakness impact coefficient serving as a subtraction term to highlight the restrictive effect of performance shortcomings on the overall module.

[0023] At the battery pack level, the system further integrates the comprehensive state coefficients of each module, using a similar calculation framework: the average health coefficient of the battery pack reflects the overall health level; the battery pack balance coefficient uses the standard deviation method to evaluate the consistency between modules; the battery pack weakness impact coefficient identifies the performance bottlenecks of key modules, and uses an adjustment coefficient to dynamically adjust according to the number of weak modules, and finally synthesizes the comprehensive state coefficient of the battery pack through weights.

[0024] The status assessment module compares the calculated status coefficients at each level with preset threshold ranges to generate graded assessment results: excellent, average, unqualified, etc., and provides replacement suggestions for unqualified statuses. The early warning interaction module pushes multi-level early warning information, customized health reports, and maintenance suggestions to users through mobile terminals based on the assessment results, realizing a closed loop from status perception to decision support.

[0025] The entire system achieves real-time analysis through edge computing, reducing reliance on the cloud and improving response speed while ensuring assessment accuracy. It provides a systematic, quantitative, and operable intelligent monitoring solution for the safe operation, life prediction, and preventive maintenance of automotive batteries.

[0026] As a further description of the technical solution of the present invention, the parameter acquisition module has a built-in clock unit for adding timestamps to the acquired parameter data; The parameter acquisition module is equipped with an adaptive acquisition frequency control unit, which can automatically adjust the acquisition frequency according to the battery's working state: when the battery is powered on, a high-frequency acquisition of 10Hz is used; when the battery is powered off and idle, a low-frequency acquisition of once every 30 minutes is used.

[0027] As a further description of the technical solution of the present invention, the working process of the edge computing module includes: S1. Obtain battery operating parameters and parameters when power is off. The operating parameters include operating voltage. Operating current and operating temperature The power outage parameters include the open-circuit voltage at the instant of power outage. Recovery voltage after a predetermined power outage time and settling temperature ; S2. Based on the aforementioned operating parameters and power-off parameters, calculate the four state sub-coefficients of the battery cell, including: internal resistance health coefficient. Polarization recovery coefficient Capacity attenuation coefficient and temperature influence coefficient ; S3. Assign weights to the four state sub-coefficients of the battery cell, and then calculate the overall state coefficient of the battery cell: , among which, if >1, then =1, , , and These are the weighting coefficients corresponding to the internal resistance health coefficient, polarization recovery coefficient, capacity decay coefficient, and temperature influence coefficient, respectively. S4. Based on the numerical range of the comprehensive state coefficient of the battery cell, output the battery cell state evaluation result.

[0028] As a further description of the technical solution of the present invention, the working process of S2 includes: The internal resistance health coefficient is calculated using the following formula: In the formula, The internal resistance of the battery during operation is given by, where, , Open circuit voltage, This refers to the battery's internal resistance at the time of manufacture. The internal resistance at the end of the battery's lifespan. This refers to the internal resistance of the battery when it is at rest. This refers to the battery's internal resistance when it is stationary at the factory. The polarization recovery coefficient is calculated using the following formula: In the formula, Rated voltage; The capacity attenuation coefficient is calculated using the following formula: ; The temperature influence coefficient is assigned a value based on the temperature difference. ,when At <10℃, When 10℃≤ At <20℃, 0.95; when 20℃≤ At <30℃, 0.9; when When the temperature is >30℃, 0.8.

[0029] Through the above technical solution, this embodiment compares the differences in key electrical parameters of the battery in its working state and its power-off and static state, quantifies the calculation of four physical sub-coefficients, and weights them to synthesize a comprehensive state coefficient. Specifically, the system first synchronously collects real-time parameters of the battery during operation and static parameters after power failure, using the time-dimensional change characteristics of these two types of parameters to reveal the internal state of the battery. The internal resistance health coefficient is calculated by averaging through dual calculations: on the one hand, it is based on the degradation ratio of the internal resistance during operation relative to the factory internal resistance and the internal resistance at the end of its life; on the other hand, it combines the ratio of the static internal resistance to the internal resistance during operation and its benchmark relationship with the factory static internal resistance to comprehensively characterize the degree of internal resistance aging. The polarization recovery coefficient reflects the battery's ability to recover from polarization by calculating the ratio of the difference between the recovery voltage and the operating voltage to the difference between the rated voltage and the operating voltage. The capacity decay coefficient directly assesses the loss of battery capacity by using the ratio of the voltage drop to the theoretical voltage drop. The temperature influence coefficient is assigned values ​​in segments according to the temperature difference between operation and static conditions to quantify the impact of temperature changes on battery performance. Finally, the four sub-coefficients are weighted and synthesized according to preset weights, with the capacity decay coefficient being weighted by... The form is used to participate in the calculation to ensure consistency, obtain the comprehensive state coefficient of the cell, and realize the graded assessment and early warning of the cell's health status by comparing it with the preset threshold.

[0030] As a further description of the technical solution of the present invention, the working process of the edge computing module also includes: M1, Obtain the comprehensive state coefficient of each of the n cells in the module. , where i = 1, 2, ..., n; M2, based on the comprehensive state coefficients of the n cells respectively Calculate the three state sub-coefficients of the module, including: module balance coefficient. Module average health coefficient And the weak influence coefficient of the module ; M3. Assign weights to the three state sub-coefficients of the module, and then calculate the module's overall state coefficients: ,in, , and These are the weighting coefficients corresponding to the module balance coefficient, the module average health coefficient, and the module weakness impact coefficient, respectively. M4. Based on the numerical range of the module's comprehensive state coefficient, output the battery module state evaluation result.

[0031] As a further description of the technical solution of the present invention, the working process of M2 includes: The module balance coefficient is calculated using the following formula: In the formula, and These are the maximum and minimum terms of the overall state coefficient among n cells, respectively; The average health coefficient of the module is calculated using the following formula: ; The module's weak impact coefficient is calculated using the following formula: ,in, The first adjustment factor is the first adjustment factor. The value is determined based on the number of weak cells, m: when m=1... =0.8; when 2≤m≤n×0.2, =0.9; when m>n×0.2, =1.0, the weak cell is defined as < ×0.8 battery cells.

[0032] Through the above technical solution, this embodiment achieves aggregated health diagnosis from individual cells to the entire module by coordinating the analysis and quantification of the imbalance of the states of each cell within the module. The system first obtains the comprehensive state coefficients of each of the n cells in the module, and then constructs a module state evaluation model based on this dataset from three dimensions: the module balance coefficient is calculated using the range method, i.e., through... The consistency of performance among battery cells is quantified; a value closer to 1 indicates better cell balance, effectively identifying the "weakest link" effect caused by the aging of individual cells. The module average health coefficient reflects the overall health level of the module through an arithmetic mean, representing the average state of the cell group. The module weakness impact coefficient focuses on assessing the drag effect of the worst-performing cell on the module, through... Calculate, where the adjustment factor is... The evaluation mechanism dynamically adjusts based on the number of weak cells. Three sub-coefficients are linearly synthesized using weights to obtain the module's overall state coefficient, with the weakness impact coefficient serving as a subtraction term to highlight the constraining effect of performance shortcomings on the entire module. This evaluation mechanism not only considers the overall module level but also achieves early warning of internal consistency degradation and critical cell failure risks through balance analysis and weakness identification. This provides a multi-level judgment basis for module-level maintenance decisions, from group statistics to anomaly localization.

[0033] As a further description of the technical solution of the present invention, the working process of the edge computing module also includes: P1. Obtain the comprehensive state coefficients of each of the x modules in the battery pack. , where y = 1, 2, ..., x; P2, Based on the comprehensive state coefficients of each of the x modules The three state sub-coefficients of the battery pack are calculated, including: battery pack balance coefficient, battery pack average health coefficient, and battery pack weakness impact coefficient. P3. Assign weights to the three state sub-coefficients of the battery pack, and then calculate the overall state coefficient of the battery pack: ,in, , and These are the weighting coefficients corresponding to the battery pack balance coefficient, the battery pack average health coefficient, and the battery pack key module influence coefficient, respectively. P4. Based on the numerical range of the module's comprehensive state coefficient, output the battery module state evaluation result.

[0034] As a further description of the technical solution of the present invention, the working process of P2 includes: The average health coefficient of the battery pack is calculated using the following formula: ; The battery pack balance coefficient is calculated using the following formula: ; The battery pack weakness impact factor is calculated using the following formula: ,in, This is the second adjustment factor. The number of weak modules, g, is determined as follows: When g=1... =0.8; when 2≤g≤x×0.2, =0.9; when g>x×0.2, =1.0, the weak cell is defined as < ×0.8 battery cells.

[0035] Through the above technical solution, this embodiment achieves macroscopic health diagnosis from module to battery pack by integrating the analysis of the states of multiple modules and evaluating systemic imbalances. The system first obtains the comprehensive state coefficients of x modules in the battery pack and constructs a triple evaluation system: the battery pack average health coefficient reflects the overall performance level of the battery pack through an arithmetic mean, reflecting the concentration trend of each module's state. The battery pack balance coefficient is calculated using the standard deviation normalization method, evaluating the consistency between modules by quantifying the dispersion of the comprehensive state coefficients of each module. This indicator can sensitively detect systemic imbalances caused by uneven thermal management, connection aging, or individual module abnormalities. The battery pack weakness impact coefficient focuses on evaluating the constraint of the worst-performing module on the overall system, through... Calculate, where the second adjustment factor The system dynamically adjusts based on the number of weak modules. Three sub-coefficients are linearly synthesized into a comprehensive battery pack state coefficient through weighted analysis. The weak module impact coefficient serves as a negative correction term, reinforcing the engineering principle that the weakest module determines the battery pack's safety boundary. This assessment mechanism not only focuses on the overall health of the battery pack but also achieves early identification of systemic risks such as connection system aging, thermal management failure, and module collaborative degradation through inter-module balance analysis and quantification of the impact of key modules. This provides multi-dimensional decision support, from macro-statistics to key bottleneck location, for battery pack-level safety warnings, lifespan predictions, and maintenance strategy formulation.

[0036] As a further description of the technical solution of the present invention, the working process of S4 includes: Compare the cell's overall state coefficient C with the corresponding threshold range set by the system. If ≥ If the cell condition is good, then the cell condition is assessed as excellent; if ≤ < If the cell condition is generally good, attention is needed; if ≤ If the cell is found to be in substandard condition, it needs to be replaced. The working process of M4 includes: Compare the module's overall state coefficient M with the corresponding threshold range set by the system. If ≥ If the module is rated as excellent; ≤ < If the module status is generally good, attention is needed; if ≤ If the module status is deemed unqualified, it needs to be replaced. The working process of P4 includes: Compare the battery pack's overall state coefficient P with the corresponding threshold range set by the system. If... ≥ If the battery pack is in excellent condition, then the battery pack is assessed as being in good condition; if ≤ < If the battery pack condition is generally good, attention is needed; if ≤ If the battery pack fails the assessment, it is deemed unqualified and needs to be replaced.

[0037] A battery monitoring method based on automotive battery parameters, the method being implemented using a battery monitoring system based on automotive battery parameters.

[0038] For ease of understanding, the following are calculation examples from embodiments of the present invention: A certain new energy vehicle battery pack includes the following configuration: Battery pack: Composed of 3 modules connected in series (x=3) Each module consists of 4 cells connected in series (n=4). Battery type: Lithium-ion battery Factory reference parameters: Rated voltage 3.7, factory working internal resistance 0.08Ω, factory static internal resistance 0.05Ω, end-of-life internal resistance 0.16Ω; Measurement data of cell 1 Operating parameters: operating voltage 3.5V, operating current 2A, operating temperature 35℃; Parameters after power failure: Open circuit voltage 3.8V, recovery voltage (after 300 seconds) 3.7V, static temperature 25℃; The cell state factor was calculated separately, with a static internal resistance of 0.1Ω and an internal resistance health factor. =0.596, polarization recovery coefficient =1. Capacity attenuation coefficient =1 and temperature influence coefficient =0.95; Weight settings: , , and The values ​​are 0.35, 0.25, 0.2, and 0.2 respectively, therefore, C = 0.65; Similarly, the comprehensive state coefficients of the remaining 11 cells are calculated as follows: 0.72, 0.68, 0.81, 0.75, 0.69, 0.83, 0.77, 0.71, 0.65, 0.78, and 0.73. Therefore, the state coefficient of module 1, including cells 1-4, is 0.68, where there are no weak cells and the weighting is... , and The values ​​were set to 0.3, 0.6, and 0.1 respectively, and the overall state coefficients of other modules were calculated to be 0.75 and 0.71. Therefore, the overall state coefficient of the battery pack is 0.55, with no weak modules. , and Set them to 0.2, 0.5, and 0.3 respectively; Finally, the calculated comprehensive state coefficients of the cells, modules, and battery packs are compared with the corresponding threshold ranges set by the system to evaluate their status.

[0039] It should be noted that the formulas in this application are all dimensionless and calculated numerically. The thresholds, threshold ranges and coefficients involved in this application are all empirical values, and the selection should be made by those skilled in the art according to the actual situation.

[0040] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A battery monitoring system based on automotive battery parameters, characterized in that, It includes a parameter acquisition module, a data preprocessing module, an edge computing module, a status assessment module, and an early warning interaction module, with each module communicating with each other in sequence; The parameter acquisition module includes a distributed sensor array, which is deployed at the cell, module and battery pack levels of the automotive battery to collect multi-dimensional parameters of the battery and add timestamps to each collected parameter. The data processing module is used to receive parameter data transmitted by the parameter acquisition module, and to perform outlier removal, missing value completion, and standardization processing on the data. The outlier removal uses a 3D model. The criteria for missing value completion are: adjacent time series data interpolation method; the standardization process maps the parameter data to the [0, 1] interval to obtain standardized time series data. The edge computing module is used to receive standardized data output by the data preprocessing module, and based on the battery operating parameters and power-off parameters, it obtains the real-time state coefficients of the cell, the module, and the battery pack layer by layer through weighted calculation of state sub-coefficients. The status assessment module is used to receive real-time status coefficients and assess the status of battery cells, modules and battery packs according to preset thresholds. The early warning interaction module is used to push abnormal early warning information, battery health reports and maintenance suggestions to the user's mobile terminal based on the status assessment results.

2. The battery monitoring system based on automotive battery parameters according to claim 1, characterized in that, The parameter acquisition module has a built-in clock unit, which is used to add timestamps to the acquired parameter data. The parameter acquisition module is equipped with an adaptive acquisition frequency control unit, which can automatically adjust the acquisition frequency according to the battery's working state: when the battery is powered on, a high-frequency acquisition of 10Hz is used; when the battery is powered off and idle, a low-frequency acquisition of once every 30 minutes is used.

3. The battery monitoring system based on automotive battery parameters according to claim 1, characterized in that, The edge computing module operates as follows: S1. Obtain battery operating parameters and parameters when power is off. The operating parameters include operating voltage. Operating current and operating temperature The power outage parameters include the open-circuit voltage at the instant of power outage. Recovery voltage after a predetermined power outage time and settling temperature ; S2. Based on the aforementioned operating parameters and power-off parameters, calculate the four state sub-coefficients of the battery cell, including: internal resistance health coefficient. Polarization recovery coefficient Capacity attenuation coefficient and temperature influence coefficient ; S3. Assign weights to the four state sub-coefficients of the battery cell, and then calculate the overall state coefficient of the battery cell: , among which, if >1, then =1, , , and These are the weighting coefficients corresponding to the internal resistance health coefficient, polarization recovery coefficient, capacity decay coefficient, and temperature influence coefficient, respectively. S4. Based on the numerical range of the comprehensive state coefficient of the battery cell, output the battery cell state evaluation result.

4. The battery monitoring system based on automotive battery parameters according to claim 3, characterized in that, The working process of S2 includes: The internal resistance health coefficient is calculated using the following formula: In the formula, The internal resistance of the battery during operation is given by, where, , Open circuit voltage, This refers to the battery's internal resistance at the time of manufacture. The internal resistance at the end of the battery's lifespan. This refers to the internal resistance of the battery when it is at rest. This refers to the battery's internal resistance when it is stationary at the factory. The polarization recovery coefficient is calculated using the following formula: In the formula, Rated voltage; The capacity attenuation coefficient is calculated using the following formula: ; The temperature influence coefficient is assigned a value based on the temperature difference. ,when At <10℃, When 10℃≤ At <20℃, 0.95; when 20℃≤ At <30℃, 0.9; when When the temperature is >30℃, 0.

8.

5. The battery monitoring system based on automotive battery parameters according to claim 3, characterized in that, The edge computing module's operation also includes: M1, Obtain the comprehensive state coefficient of each of the n cells in the module. , where i = 1, 2, ..., n; M2, based on the comprehensive state coefficients of the n cells respectively Calculate the three state sub-coefficients of the module, including: module balance coefficient. Module average health coefficient And the weak influence coefficient of the module ; M3. Assign weights to the three state sub-coefficients of the module, and then calculate the module's overall state coefficients: ,in, , and These are the weighting coefficients corresponding to the module balance coefficient, the module average health coefficient, and the module weakness impact coefficient, respectively. M4. Based on the numerical range of the module's comprehensive state coefficient, output the battery module state evaluation result.

6. The battery monitoring system based on automotive battery parameters according to claim 5, characterized in that, The working process of M2 includes: The module balance coefficient is calculated using the following formula: ; In the formula, and These are the maximum and minimum terms of the overall state coefficient among n cells, respectively; The average health coefficient of the module is calculated using the following formula: ; The module's weak impact coefficient is calculated using the following formula: ,in, The first adjustment factor is the first adjustment factor. The value is determined based on the number of weak cells, m: when m=1... =0.8; when 2≤m≤n×0.2, =0.9; when m>n×0.2, =1.0, the weak cell is defined as < ×0.8 battery cells.

7. The battery monitoring system based on automotive battery parameters according to claim 5, characterized in that, The edge computing module's operation also includes: P1. Obtain the comprehensive state coefficients of each of the x modules in the battery pack. , where y = 1, 2, ..., x; P2, Based on the comprehensive state coefficients of each of the x modules The three state sub-coefficients of the battery pack are calculated, including: battery pack balance coefficient, battery pack average health coefficient, and battery pack weakness impact coefficient. P3. Assign weights to the three state sub-coefficients of the battery pack, and then calculate the overall state coefficient of the battery pack: ,in, , and These are the weighting coefficients corresponding to the battery pack balance coefficient, the battery pack average health coefficient, and the battery pack key module influence coefficient, respectively. P4. Based on the numerical range of the module's comprehensive state coefficient, output the battery module state evaluation result.

8. The battery monitoring system based on automotive battery parameters according to claim 7, characterized in that, The P2 working process includes: The average health coefficient of the battery pack is calculated using the following formula: The battery pack balance coefficient is calculated using the following formula: ; The battery pack weakness impact factor is calculated using the following formula: ,in, This is the second adjustment factor. The number of weak modules, g, is determined as follows: When g=1... =0.8; when 2≤g≤x×0.2, =0.9; when g>x×0.2, =1.0, the weak cell is defined as < ×0.8 battery cells.

9. The battery monitoring system based on automotive battery parameters according to claim 7, characterized in that, The working process of S4 includes: Compare the cell's overall state coefficient C with the corresponding threshold range set by the system. If ≥ If the cell condition is good, then the cell condition is assessed as excellent; if ≤ < If the cell condition is generally good, attention is needed; if ≤ If the cell is found to be in substandard condition, it needs to be replaced. The working process of M4 includes: Compare the module's overall state coefficient M with the corresponding threshold range set by the system. If ≥ If the module is rated as excellent; ≤ < If the module status is generally good, attention is needed; if ≤ If the module status is deemed unqualified, it needs to be replaced. The working process of P4 includes: Compare the battery pack's overall state coefficient P with the corresponding threshold range set by the system. If... ≥ If the battery pack is in excellent condition, then the battery pack is assessed as being in good condition; if ≤ < If the battery pack condition is generally good, attention is needed; if ≤ If the battery pack fails the assessment, it is deemed unqualified and needs to be replaced.

10. A battery monitoring method based on automotive battery parameters, characterized in that, The method is implemented based on the battery monitoring system based on automotive battery parameters as described in any one of claims 1-9.

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