Battery state monitoring method and system based on sensor

Abstract

The invention relates to the technical field of battery state monitoring, in particular to a battery state monitoring method and system based on a sensor. The method comprises the following steps: acquiring operation parameter information of a vehicle electromagnetic interference source and operation data of all battery sensors; according to the operation parameter information and the operation data of all the battery sensors, each noise sensitivity index of a sensor wire to electromagnetic interference is determined; comparing each noise sensitivity index with a corresponding sensitivity index threshold value to obtain a noise sensitivity difference value; state judgment is conducted on the noise sensitivity difference value, and the abrasion state of each battery sensor is determined; and monitoring the battery state by using the wear state of each battery sensor and a preset battery state model to obtain a battery state monitoring result. The objective of the invention is to solve the problem of inaccurate monitoring of the battery state caused by deviation in estimation of the battery state by the existing method.

Description

Technical Field

[0001] This invention relates to the field of battery state monitoring technology, and specifically to a sensor-based battery state monitoring method and system. Background Technology

[0002] Existing electric vehicle battery management systems acquire real-time battery operating data by integrating various sensors, including voltage probes, Hall effect sensors, and temperature sensors. To ensure the stability and accuracy of data transmission, the connecting wires between the sensors and the battery management unit are carefully planned and securely fixed in wiring harness clips and protective slots inside the battery pack. This aims to reduce mechanical stress, vibration, and electromagnetic interference, thereby ensuring the reliability of battery status monitoring.

[0003] However, in actual vehicle use and maintenance, vehicles inevitably experience continuous bumps and vibrations. Simultaneously, during frequent charge-discharge cycles, the internal temperature of the power battery periodically rises and falls, causing slight thermal expansion and contraction of the battery module and its surrounding metal structure. Without some fixed support, the previously constrained sensor wiring harness, at a specific point inside the battery pack, will begin to experience slight and repeated friction with the metal edges of the battery module or other structural components within the battery pack. This leads to gradual wear of the insulation sheath of some sensor wires, resulting in a thinner insulation layer. The reduction in insulation thickness significantly decreases the wires' shielding ability against external electromagnetic interference, thus creating signal noise closely related to vehicle driving behavior (especially high-power output or regenerative braking conditions such as rapid acceleration or deceleration), which existing fixed-parameter filtering mechanisms often struggle to effectively filter out. Furthermore, maintenance personnel may inadvertently damage the plastic clips used to secure the sensor wiring harness. Because of the compact internal space of the battery pack and the fact that some clips are located in difficult-to-observe positions, and because the wiring harness is usually supported by multiple clips, even if one clip fails, the wiring harness can still maintain a roughly fixed state for a short period of time, making it difficult to detect or repair immediately after maintenance. This leads to short-term and drastic jumps in the estimation results of the battery's available capacity and aging level using existing methods, resulting in biases in the estimation of battery status and inaccurate monitoring of battery status using current methods. Summary of the Invention

[0004] The purpose of this invention is to provide a sensor-based battery status monitoring method and system to solve the problem that existing methods have short-term and drastic jumps in the estimation results of battery available power and aging degree, resulting in deviations in battery status estimation and inaccurate monitoring of battery status.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a sensor-based battery state monitoring method, comprising the following steps: Acquire the operating parameter information of the vehicle's electromagnetic interference sources and the operating data of all battery sensors; Based on the operating parameter information and the operating data of all battery sensors, determine each noise sensitivity index of the sensor wires to electromagnetic interference; Each noise sensitivity index is compared with its corresponding sensitivity index threshold to obtain the noise sensitivity difference. The noise sensitivity difference is used to determine the wear state of each battery sensor. By utilizing the wear status of each battery sensor and a preset battery status model, the battery status is monitored, and the battery status monitoring results are obtained.

[0006] Further, the step of determining each noise sensitivity index of the sensor wires to electromagnetic interference based on the operating parameter information and the operating data of all battery sensors includes: Determine the associated fluctuation characteristics in the operating data of the battery sensor that are related to the operating parameter information; Using the associated fluctuation characteristics, feature extraction is performed on the operating parameter information to obtain noise feature parameters; The noise characteristic parameters and noise characteristic thresholds are compared to determine the noise sensitivity index of the sensor wire to electromagnetic interference.

[0007] Furthermore, the step of extracting noise feature parameters from the operating parameter information using the associated fluctuation characteristics includes: The associated fluctuation characteristics are decomposed into frequency bands to obtain noise component information for each frequency band; Analyze the noise component information of each frequency band and the operating parameter information for each noise correlation degree; Based on the noise correlation degree and noise component information of each frequency band, noise characteristic parameters are obtained.

[0008] Further, the step of determining the wear state of each battery sensor by judging the noise sensitivity difference includes: The noise sensitivity difference is used to determine the grading coefficient of each battery sensor and the initial wear state of each battery sensor. By using the grading coefficient of each battery sensor, the stability of each battery sensor is judged, and the stability parameters of each battery sensor are obtained. By using the stable parameters of each battery sensor, the initial wear state of each battery sensor is adjusted to determine the wear state of each battery sensor.

[0009] Furthermore, the step of adjusting the initial wear state of each battery sensor using the stable parameters of each battery sensor to determine the wear state of each battery sensor includes: Based on the stability parameters of each battery sensor, determine the stability coefficient of each sensor's data; By using historical wear coefficients, the stability coefficient of each sensor data is corrected to obtain the corrected stability coefficient; Using the corrected stability coefficient, the initial wear state of each battery sensor is adjusted to determine the wear state of each battery sensor.

[0010] Furthermore, the step of adjusting the initial wear state of each battery sensor using the corrected stability coefficient to determine the wear state of each battery sensor includes: The wear adjustment range is determined using the corrected stability coefficient. The wear adjustment range is verified using a wear adjustment model, and the verification results are obtained. Based on the test results and the corrected stability coefficient, the initial wear state of each battery sensor is adjusted to determine the wear state of each battery sensor.

[0011] Furthermore, the step of monitoring the battery status using the wear state of each battery sensor and a preset battery status model to obtain the battery status monitoring results includes: By utilizing the wear condition of each battery sensor, the damage type and damage index of each battery sensor are determined; Based on the damage type and damage index of each battery sensor and the preset battery state model, the battery state is monitored to obtain the battery state monitoring results.

[0012] Furthermore, the step of acquiring the operating parameter information of the vehicle's electromagnetic interference source and the operating data of all battery sensors includes: Obtain raw information on the operating parameters of the vehicle's electromagnetic interference sources and raw operating data from all battery sensors; The original operating parameters of the vehicle electromagnetic interference source and the original operating data of all battery sensors are preprocessed to obtain the operating parameters of the vehicle electromagnetic interference source and the operating data of all battery sensors.

[0013] Furthermore, after the step of acquiring the raw operating parameters of the vehicle's electromagnetic interference source and the raw operating data of all battery sensors, the method further includes: Based on the original operating parameter information of the vehicle electromagnetic interference source, an interference suppression scheme for electromagnetic interference generated by the battery sensor is determined. Using the aforementioned interference suppression scheme, the raw operating data of all battery sensors are adjusted to obtain the adjusted raw operating data.

[0014] The present invention also provides a sensor-based battery state monitoring system, the system comprising: The data acquisition module is used to acquire the operating parameter information of the vehicle's electromagnetic interference source and the operating data of all battery sensors; The index determination module is used to determine the noise sensitivity index of the sensor wires to electromagnetic interference based on the operating parameter information and the operating data of all battery sensors. The difference comparison module is used to compare each noise sensitivity index with the corresponding sensitivity index threshold to obtain the noise sensitivity difference. The status determination module is used to determine the status of the noise sensitivity difference and determine the wear status of each battery sensor. The battery monitoring module is used to monitor the battery status using the wear status of each battery sensor and a preset battery status model, and obtain the battery status monitoring results.

[0015] Compared with the prior art, the sensor-based battery state monitoring method and system of the present invention have the following advantages: This invention acquires operating parameter information of vehicle electromagnetic interference sources and operating data of all battery sensors, and determines the noise sensitivity index of sensor wires to electromagnetic interference. The noise sensitivity index is then compared with a threshold to obtain the noise sensitivity difference, and the wear state of each battery sensor is determined. Finally, using the sensor wear state and a preset battery state model, the battery state is monitored to obtain accurate monitoring results. Furthermore, by introducing the noise sensitivity index and wear state judgment, sensor wires sensitive to electromagnetic interference due to insulation wear can be accurately identified, and their wear degree quantified. This allows for a direct correlation between dynamic and driving-related electromagnetic noise and the physical wear of sensor wires, enabling precise fault location. Simultaneously, by accurately assessing the wear state and incorporating it into the battery state monitoring model, the battery state estimation deviation caused by electromagnetic interference can be effectively corrected, significantly improving the accuracy and reliability of battery state monitoring. Attached Figure Description

[0016] To more clearly illustrate the specific embodiments of the present invention, the accompanying drawings used in the specific embodiments will be briefly described below. In all the drawings, the elements or parts are not necessarily drawn to scale.

[0017] Figure 1 This is a flowchart of a sensor-based battery state monitoring method according to the present invention.

[0018] Figure 2 This is a structural block diagram of a sensor-based battery state monitoring system according to the present invention.

[0019] In the diagram: 210, Data Acquisition Module; 220, Index Determination Module; 230, Difference Comparison Module; 240, Status Determination Module; 250, Battery Monitoring Module.

[0020] The implementation and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] The following drawings disclose several embodiments of the present invention. For clarity, many practical details will be described in the following description. However, it should be understood that these practical details are not intended to limit the invention. That is, in some embodiments of the invention, these practical details are not essential. Furthermore, for the sake of simplicity, some conventional structures and components will be shown in the drawings in a simple schematic manner.

[0022] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0023] Furthermore, in this invention, the use of terms such as "first" and "second" is for descriptive purposes only and does not specifically refer to any order or sequence, nor is it intended to limit the invention. They are merely used to distinguish components or operations described using the same technical terms, and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but only if they are feasible for those skilled in the art. If a combination of technical solutions is contradictory or impossible to implement, such a combination should be considered nonexistent and not within the scope of protection claimed by this invention.

[0024] The battery management system (BMS) of electric vehicles acquires real-time battery operating data by integrating various sensors, including voltage probes, Hall effect sensors, and temperature sensors. To ensure the stability and accuracy of data transmission, the connecting wires between the sensors and the BMS are carefully planned and securely fixed in wiring harness clips and protective slots inside the battery pack. This aims to reduce mechanical stress, vibration, and electromagnetic interference, thereby ensuring the reliability of battery status monitoring. However, during actual vehicle use and maintenance, repair personnel may inadvertently damage the plastic clips used to secure the sensor wiring harnesses. Due to the compact internal space of the battery pack and the fact that some clips are located in difficult-to-observe positions, and because wiring harnesses are typically supported by multiple clips, even if one clip is damaged, the wiring harness can still maintain a roughly fixed state in the short term. Therefore, such damage is often difficult to detect or repair immediately after maintenance. Simultaneously, as electric vehicles operate for extended periods under various complex road conditions, they inevitably experience continuous bumps and vibrations. During frequent charge-discharge cycles, the internal temperature of the power battery periodically rises and falls, causing minute thermal expansion and contraction of the battery module and its surrounding metal structure. Without some fixed support, the previously constrained sensor wiring harness will begin to experience slight, repetitive friction at a specific point within the battery pack against the metal edges of the battery module or other structural components. This thinning of the insulation layer significantly reduces the wires' shielding ability against external electromagnetic interference. Existing fixed-parameter filtering mechanisms often struggle to effectively filter out specific electromagnetic noise generated by the vehicle's powertrain, whose intensity and frequency characteristics dynamically change with driving operations. When sensor data is mixed with noise closely related to the vehicle's high-power output, existing methods become interfered with, causing brief and drastic jumps in the estimation of battery capacity and aging level. This leads to biases in the current methods' estimation of battery status, resulting in inaccurate monitoring of battery condition.

[0025] To further understand the content, features, and effects of this invention, the following embodiments are provided, and detailed descriptions are given below in conjunction with the accompanying drawings: Please see Figure 1 This invention provides a sensor-based battery state monitoring method, comprising the following steps: S100: Acquire operating parameter information of vehicle electromagnetic interference sources and operating data of all battery sensors. The operating parameter information of vehicle electromagnetic interference sources refers to various parameters of components in the electric vehicle's powertrain that generate electromagnetic interference (such as inverters and motors) during operation, such as the inverter's operating frequency, current, voltage, and switching status, as well as the motor's speed and torque. This directly reflects the activity level and characteristics of the electromagnetic interference source. The operating data of the battery sensors refers to the real-time data collected by the battery management system from various battery sensors (such as voltage sensors, temperature sensors, and current sensors); it is the basis for assessing battery health and performance. Specifically, the operating parameter information of vehicle electromagnetic interference sources can be read in real time through the on-board diagnostic (OBD) interface or the vehicle's internal communication network (such as the CAN bus), for example, acquiring the inverter's operating frequency, output current, and switching status. The operating data of all battery sensors is directly provided by the battery management system (BMS), including but not limited to battery cell voltage, module temperature, and charging / discharging current; this can be obtained through the data acquisition unit, which is responsible for receiving data from various subsystems of the vehicle and transmitting it to the processing unit.

[0026] S200. Based on the operating parameter information and the operating data of all battery sensors, determine the noise sensitivity index of each sensor wire to electromagnetic interference. The noise sensitivity index is an indicator that quantifies the degree of interference to data transmission of the sensor wire under a specific electromagnetic interference environment. The higher the index, the more sensitive the wire is to electromagnetic interference, and the greater the possibility of data contamination. Specifically, this is achieved by analyzing abnormal fluctuations or noise components in the battery sensor data under the operating conditions of a specific electromagnetic interference source. Alternatively, spectral analysis can be performed on the sensor data to identify noise peaks related to the operating frequency of the electromagnetic interference source, and the noise sensitivity index can be calculated based on the amplitude and frequency characteristics of these peaks. Alternatively, a machine learning model can be used, inputting the operating parameters of the electromagnetic interference source and the sensor data, to train the model and output the noise sensitivity index.

[0027] S300. Compare each noise sensitivity index with its corresponding sensitivity index threshold to obtain the noise sensitivity difference. The sensitivity index threshold is a preset reference value used to determine whether the noise sensitivity of the sensor wire is abnormal. When the noise sensitivity index exceeds this threshold, it means that the wire is worn or has other abnormalities. The noise sensitivity difference is the difference between the actual noise sensitivity index and the sensitivity index threshold, used to visually measure the degree of sensitivity abnormality. Specifically, the sensitivity index threshold is preset and can be determined based on a large amount of experimental data, wire material properties, and electromagnetic compatibility standards. For example, a baseline threshold can be set; when the noise sensitivity index exceeds this threshold, it indicates that the sensor wire may have an abnormality. The comparison process can be a simple numerical subtraction, obtaining a positive or negative value representing the degree to which the sensitivity index deviates from the threshold.

[0028] S400. The noise sensitivity difference is used to determine the wear state of each battery sensor. The wear state refers to the degree of damage to the sensor wire insulation layer caused by physical friction, including different levels such as slight wear, moderate wear, and severe wear. Specifically, based on the magnitude of the noise sensitivity difference, the wear state is divided into different levels, such as no wear, slight wear, moderate wear, or severe wear. When the difference is small, it may be determined as no wear or slight wear; when the difference is large, it may be determined as moderate or severe wear. This can be achieved through a preset rule table or decision tree model.

[0029] S500 monitors the battery status using the wear condition of each battery sensor and a preset battery status model, obtaining battery status monitoring results. The preset battery status model is a mathematical model or algorithm used within the battery management system to estimate key parameters such as battery health and state of charge. The model is typically built based on extensive experimental data and battery characteristics. The battery status monitoring results are assessments of battery health and performance, derived from sensor data, the preset battery status model, and the wear condition of the sensor leads. Specifically, the preset battery status model is typically used to estimate the battery's health and state of charge. In existing monitoring methods, the model directly uses raw sensor data for calculations; wear condition is introduced as a correction factor. For example, if the wear condition of a battery sensor is determined to be moderate, when using that sensor data for battery status estimation, the data can be weighted or a correction coefficient can be introduced to reduce the impact of wear on the estimation results, thereby obtaining more accurate battery status monitoring results.

[0030] Specifically, when a vehicle's electromagnetic interference source (such as an inverter) is operating, the electromagnetic field it generates affects the sensor wires through spatial coupling or conductive coupling. If the insulation layer of the sensor wires thins due to wear, its shielding ability against electromagnetic interference decreases, leading to more electromagnetic noise coupling into the sensor signal. This invention, by acquiring the operating parameter information of the vehicle's electromagnetic interference source and the operating data of all battery sensors, can capture this specific noise characteristic related to the operating state of the electromagnetic interference source caused by wear. By determining each noise sensitivity index of the sensor wires to electromagnetic interference based on the operating parameter information and the operating data of the battery sensors, this invention quantifies the degree to which the wires are affected by electromagnetic interference. This reflects the sensitivity of the sensor signal to noise contamination under a specific electromagnetic environment. Subsequently, each noise sensitivity index is compared with its corresponding sensitivity index threshold to obtain a noise sensitivity difference, which can determine whether the noise sensitivity of the current sensor wires exceeds the normal range, thereby initially identifying potential wear problems. Further, a state judgment is made on the noise sensitivity difference to determine the wear state of each battery sensor. The quantified sensitivity difference is converted into an understandable physical state, namely the degree of wear of the sensor wires. For example, a larger difference in noise sensitivity may correspond to more severe wire wear. Ultimately, the battery status is monitored using the wear state of each battery sensor and a preset battery status model to obtain battery status monitoring results. The wear state of the sensor wires is considered when estimating key parameters such as battery health or charging status. If a sensor wire is worn, its data may be considered less reliable, thus requiring corresponding corrections or weighting in the battery status model to reduce the impact of wear-introduced noise on the final monitoring results. For example, additional filtering can be applied to wear-affected sensor data, or its weight can be reduced when fusing multiple sensor data. This invention effectively avoids the misleading influence of electromagnetic interference caused by sensor wire wear on battery status monitoring results, thereby providing more accurate and reliable battery status information.

[0031] In this embodiment, by acquiring the operating parameter information of the vehicle's electromagnetic interference source and the operating data of all battery sensors, a correlation between the activity of the electromagnetic interference source and the noise in the sensor data can be established. By determining the noise sensitivity index of each sensor wire to electromagnetic interference, an objective indicator for quantifying the degree of interference to the wire is provided. Furthermore, by comparing the noise sensitivity index with a sensitivity index threshold and obtaining the noise sensitivity difference, abnormal electromagnetic sensitivity of the sensor wire can be effectively identified. This invention can determine the wear state of each battery sensor based on the noise sensitivity difference. It no longer passively processes contaminated data but actively diagnoses the physical wear of the sensor wires, making fault location more accurate and avoiding blind replacement of parts or ineffective repairs. Finally, this invention uses the wear state of each battery sensor and a preset battery state model to monitor the battery state and obtain battery state monitoring results. When estimating key parameters such as battery health and charging status, the data is corrected or weighted according to the wear degree of the sensor wires. For example, for severely worn sensors, the weight of their data in the estimation may be reduced, or their data may undergo more stringent filtering. The accuracy and reliability of battery state estimation are significantly improved by an adaptive monitoring mechanism based on wear condition.

[0032] In some embodiments of this application described above, the step of determining each noise sensitivity index of the sensor wire to electromagnetic interference based on the operating parameter information and the operating data of all battery sensors includes: The process involves identifying correlated fluctuation characteristics in the battery sensor's operating data that are associated with the operating parameter information. This step can involve performing time-domain or frequency-domain analysis on the battery sensor's operating data to identify fluctuation patterns that are synchronous with or correlated with the operating parameter information of the vehicle's electromagnetic interference sources (e.g., motor speed, current, and voltage changes). These fluctuation characteristics may manifest as noise peaks at specific frequencies, abnormal changes in signal amplitude, or signal distortion within a specific time period.

[0033] By utilizing the associated fluctuation characteristics, feature extraction is performed on the operating parameter information to obtain noise characteristic parameters. This step refers to using these characteristics as guidance after identifying the associated fluctuation characteristics to extract specific parameters related to electromagnetic interference from the operating parameter information of the vehicle's electromagnetic interference source. For example, the electromagnetic field strength, frequency distribution, or transient pulse characteristics generated by the electromagnetic interference source under specific operating conditions can be analyzed; these parameters are considered as noise characteristic parameters.

[0034] The noise characteristic parameters and noise characteristic thresholds are compared to determine the noise sensitivity index of the sensor wire to electromagnetic interference. The purpose of this step is to quantify the degree to which the sensor wire is affected by electromagnetic interference. The noise characteristic threshold can be set based on empirical data, industry standards, or pre-conducted experimental calibration. When the extracted noise characteristic parameters exceed this threshold, it indicates that the sensor wire is subject to significant electromagnetic interference, and its sensitivity index is high; conversely, the sensitivity index is low. This sensitivity index can be a continuous value or a graded value, used for subsequent state determination.

[0035] This embodiment identifies fluctuation characteristics in the battery sensor's operating data that are correlated with the operating parameters of an electromagnetic interference (EMI) source, thereby accurately pinpointing the impact of EMI on the sensor data. Subsequently, based on these correlated fluctuation characteristics, specific noise characteristic parameters are extracted from the EMI source's operating parameter information, making the quantification of EMI more accurate. Finally, by comparing these noise characteristic parameters with preset noise characteristic thresholds, the noise sensitivity index of the sensor's wiring to EMI can be objectively determined. This makes the assessment of the EMI impact on the sensor wiring more refined and reliable, providing more accurate input data for subsequent battery status monitoring.

[0036] In some embodiments of this application described above, the step of extracting noise feature parameters from the operating parameter information using the associated fluctuation characteristics includes: The correlated fluctuation characteristics are decomposed into frequency bands to obtain noise component information for each frequency band. Frequency band decomposition refers to dividing the original correlated fluctuation characteristic signal into multiple independent frequency bands in the frequency domain and extracting the signal components within each band. This process can be implemented using various signal processing techniques. For example, a Fast Fourier Transform (FFT) can be used to convert the time-domain signal to the frequency domain, and then a bandpass filter bank can be used to separate signals in different frequency ranges. Alternatively, time-frequency analysis methods such as wavelet transform can be used to decompose the signal at different scales to obtain noise component information for different frequency bands. The aim is to decompose complex noise signals into independent frequency bands that are easier to analyze and process, thereby enabling more accurate identification and quantification of noise characteristics.

[0037] This analysis examines the correlation between noise component information and operating parameter information for each frequency band. Noise correlation can be understood as an indicator measuring the degree of correlation between noise components in a specific frequency band and the operating parameters of the vehicle's electromagnetic interference source. Specifically, this correlation can be quantified using statistical or signal processing methods such as Pearson correlation coefficient, cross-correlation function, and coherence. For example, the correlation between the power spectral density of noise components in a specific frequency band and the changing trends of operating parameters (such as motor speed, current, and voltage) can be calculated. The aim is to identify which frequency band noise components have a significant causal or correlational relationship with the operating state of the electromagnetic interference source, thereby focusing on noise truly caused by electromagnetic interference.

[0038] Based on the noise correlation degree and noise component information of each frequency band, noise characteristic parameters are obtained. Specifically, the noise component intensity of each frequency band and its correlation with operating parameters can be comprehensively considered to construct one or more parameters that can comprehensively characterize the electromagnetic interference noise characteristics. For example, the noise energy, peak frequency, and bandwidth of high-correlation frequency bands can be used as noise characteristic parameters, or the noise component information and correlation degree of multiple frequency bands can be integrated into a comprehensive noise characteristic parameter through methods such as weighted averaging or principal component analysis. The purpose is to transform the scattered frequency band noise information and correlation degree into a unified and quantifiable feature, providing accurate input for subsequently determining the noise sensitivity index of sensor wires to electromagnetic interference.

[0039] Specifically, the main source of electromagnetic interference in a vehicle is the drive motor, whose operating parameters include motor speed, current, and voltage. Battery sensor operating data includes output signals such as voltage, current, or temperature. First, after identifying the associated fluctuation characteristics in the battery sensor operating data that are related to the operating parameters, frequency band decomposition can be performed to more accurately extract noise characteristic parameters. For example, a Fast Fourier Transform can be used to convert the time-domain signal of the associated fluctuation characteristics to the frequency domain, and then a digital filter can be used to decompose it into multiple frequency bands, such as low-frequency (e.g., 0-100Hz), mid-frequency (e.g., 100-500Hz), and high-frequency (e.g., 500-2000Hz), to obtain the noise component information for each band. Next, for each frequency band's noise component information, the noise correlation between it and the drive motor's operating parameters can be analyzed. For example, the Pearson correlation coefficient between the power spectral density of the mid-frequency noise component and the change in drive motor speed can be determined. If a high positive correlation is found between the mid-frequency noise component and the motor speed, while the correlation is lower in the low-frequency and high-frequency bands, it indicates that the mid-frequency noise component is more likely caused by electromagnetic interference from the drive motor. Finally, based on the noise correlation and noise component information for each frequency band obtained from the analysis, noise characteristic parameters are derived. For example, the energy, peak frequency, or bandwidth of the mid-frequency noise component can be used as the main noise characteristic parameters, as these parameters are significantly correlated with the operating state of the electromagnetic interference source. This ensures that the extracted noise characteristic parameters accurately and comprehensively reflect the electromagnetic interference affecting the sensor wires.

[0040] This embodiment decomposes the associated fluctuation characteristics into frequency bands, enabling detailed segmentation of complex electromagnetic interference noise signals in the frequency domain, thus avoiding the omission of key noise components. By analyzing the noise correlation between the noise component information and operating parameter information of each frequency band, noise frequency bands closely related to the vehicle's electromagnetic interference source operating status can be accurately identified, effectively filtering out irrelevant noise or background interference, ensuring that the extracted feature parameters have higher specificity and accuracy. Based on the noise component information of frequency bands with high correlation, more refined and comprehensive noise feature parameters can be obtained, laying the foundation for subsequent accurate assessment of the sensor wires' sensitivity to electromagnetic interference.

[0041] In some embodiments of this application described above, the step of determining the wear state of each battery sensor by judging the noise sensitivity difference includes: The noise sensitivity difference is used to determine the status of each battery sensor, identifying its grading coefficient and initial wear state. Specifically, the grading coefficient for each battery sensor can be a level identifier obtained by dividing the noise sensitivity difference into intervals; for example, the difference range can be divided into multiple levels, each corresponding to a grading coefficient, to initially quantify the degree of sensor wear. Simultaneously, based on this grading coefficient or directly on the noise sensitivity difference, the initial wear state of each battery sensor can be initially determined. This initial state is uncorrected and based on the wear assessment results from real-time data.

[0042] By utilizing the grading coefficient of each battery sensor, a stability assessment is performed on each battery sensor to obtain its stability parameters. The purpose of this step is to evaluate the reliability or sustainability of the current initial wear state. Stability assessment can be based on historical data, trend analysis, or statistical methods. For example, the stability of the sensor's current state can be determined by analyzing the changing trend, fluctuation range, or frequency of the grading coefficient over a period of time. Thus, the stability parameters of each battery sensor can be obtained. These stability parameters are quantitative indicators of the sensor's current state stability; for example, they can be a stability score, a confidence index, or a fluctuation factor.

[0043] By utilizing the stable parameters of each battery sensor, the initial wear state of each battery sensor is adjusted to determine its wear state. The purpose of this step is to improve the accuracy and robustness of wear state determination. The stable parameters reflect the reliability of the initial wear state and can therefore serve as a basis for correcting it. For example, when the stable parameters indicate a relatively stable initial wear state, minor adjustments or no adjustment may be made; conversely, when the stable parameters indicate significant fluctuations or low reliability in the initial wear state, a more substantial correction can be made based on the stable parameters to eliminate the influence of instantaneous fluctuations or abnormal data, thereby determining a more accurate and reliable wear state for each battery sensor.

[0044] Specifically, within the monitoring period, firstly, the noise sensitivity difference is classified according to a preset difference range rule. For example, if the noise sensitivity difference is in the range [0, 0.1), the classification coefficient is 1; if it is in the range [0.1, 0.3), the classification coefficient is 2; and if it is in the range [0.3, 0.5), the classification coefficient is 3. Simultaneously, based on this classification coefficient or the noise sensitivity difference, the initial wear state of the battery sensor is preliminarily determined. For example, a classification coefficient of 1 corresponds to slight wear, a classification coefficient of 2 corresponds to moderate wear, and a classification coefficient of 3 corresponds to severe wear. Secondly, the stability of the battery sensor is assessed using historical data of its classification coefficient over a past period (e.g., the last 10 monitoring periods). For example, if the classification coefficient of the battery sensor remains consistently at 2 or fluctuates slightly between 2 and 3 over the past 10 periods, its state can be considered relatively stable, resulting in a high stability parameter, such as 0.9. Conversely, if the grading coefficient frequently jumps between 1, 2, or 3, the state is considered unstable, resulting in a lower stability parameter, such as 0.4. Finally, this stability parameter is used to adjust the initial wear state. For example, if the initial wear state is moderate wear and the stability parameter is 0.9, the moderate wear state can be considered reliable, and the final wear state will still be determined as moderate wear. However, if the initial wear state is also moderate wear, but the stability parameter is only 0.4, it indicates that the initial judgment may be unstable. In this case, the moderate wear can be corrected by combining historical data over a longer period or using weighted averaging, for example, adjusting it to moderate to light wear or an unobserved state, to avoid misjudgments caused by instantaneous fluctuations, thereby determining the final wear state of the battery sensor.

[0045] This embodiment effectively avoids potential instantaneous fluctuations and inaccuracies in wear state assessment by introducing a grading coefficient, stability judgment, and a stability parameter, and adjusting the initial wear state based on the stability parameter. Specifically, a preliminary judgment is made based on the noise sensitivity difference to obtain the grading coefficient and the initial wear state, providing a foundation for subsequent fine-tuning. Secondly, by judging the stability of the grading coefficient, the reliability of the initial wear state can be identified, avoiding the one-sidedness of judging solely based on instantaneous data. Because a key indicator of stability parameter is introduced, intelligent adjustments can be made based on the stability of the initial wear state, thereby filtering out noise caused by electromagnetic interference or other instantaneous factors, ensuring that the final determined wear state more closely reflects the actual wear condition of the sensor.

[0046] In some embodiments of this application described above, the step of adjusting the initial wear state of each battery sensor using the stable parameters of each battery sensor to determine the wear state of each battery sensor includes: Based on the stability parameters of each battery sensor, a stability coefficient is determined for each sensor's data. The stability coefficient can be understood as a quantitative indicator reflecting the degree of fluctuation or reliability of sensor data under specific operating conditions. Specifically, it can be characterized by statistical analysis of the sensor data, such as determining the standard deviation, variance, or fluctuation range of the data. Its purpose is to provide a quantitative basis for subsequent wear condition adjustments based on the current data stability.

[0047] By utilizing historical wear coefficients, the stability coefficient of each sensor's data is corrected, resulting in a revised stability coefficient. The historical wear coefficient refers to an empirical or model-based coefficient established based on wear data accumulated by the sensor over past operating cycles and the corresponding trends in stability parameters. Its purpose is to incorporate wear patterns over time, making the assessment of the current stability coefficient more comprehensive and accurate, avoiding the limitations of judging solely based on instantaneous data. The revised stability coefficient, after considering historical wear trends, provides a more precise reflection of the stability of the current sensor data.

[0048] Using the corrected stability coefficient, the initial wear state of each battery sensor is adjusted to determine its wear state. This step refines the initially determined initial wear state based on the stability coefficient corrected using historical data. Specifically, the corrected stability coefficient can be used as an adjustment factor; based on its magnitude and trend, the initial wear state is incrementally or subtractively adjusted to obtain a wear state that better reflects reality. The aim is to improve the accuracy and reliability of wear state assessment by combining historical experience with the stability of current data.

[0049] Specifically, during long-term operation, the stability parameters of a battery sensor exhibit small fluctuations in the early stages of wear, while showing a significant increase in fluctuations in the later stages. Upon obtaining the current stability parameters of the sensor, a preliminary stability coefficient is first determined based on these parameters. Then, a pre-established historical wear coefficient database is consulted. This database may store models or empirical values ​​of the stability coefficient changes for this type of sensor at different wear stages. For example, if historical data shows that the stability coefficient drops from 0.8 to 0.6 when the sensor wear reaches a certain level, these historical wear coefficients are used to correct the currently calculated stability coefficient. If the current stability coefficient is 0.7, but historical wear coefficients indicate that under current operating conditions, the stability coefficient should be closer to 0.65, then the corrected stability coefficient will be adjusted to 0.65. Finally, using this corrected stability coefficient of 0.65, combined with a preset adjustment model, the initial wear state of the battery sensor is finely adjusted. For example, if the initial wear state is mild wear, the corrected stability coefficient may adjust it to moderate wear, thus more accurately reflecting the actual wear condition of the sensor.

[0050] This embodiment introduces a historical wear coefficient, enabling the evaluation of sensor data stability coefficients to go beyond current instantaneous data and incorporate wear patterns and performance trends accumulated during long-term sensor operation. Specifically, the historical wear coefficient reflects the typical performance of the sensor's stability parameters at different wear stages, allowing for the correction of the stability coefficient for each sensor data point using historical experience. The resulting corrected stability coefficient more accurately characterizes the sensor's current stability, considering not only current data fluctuations but also incorporating historical information about sensor wear evolution. Ultimately, adjusting the initial wear state based on the more accurate corrected stability coefficient effectively avoids judgment biases caused by relying solely on short-term data, making the determination of the wear state more scientific and reliable.

[0051] In some embodiments of this application described above, the step of adjusting the initial wear state of each battery sensor using the modified stability coefficient and determining the wear state of each battery sensor includes: The corrected stability coefficient is used to determine the wear adjustment range. Specifically, determining the wear adjustment range means quantifying the specific value or range for adjusting the initial wear state of the battery sensor based on the corrected stability coefficient. For example, a higher corrected stability coefficient may mean a more stable sensor and lower wear, so the wear adjustment range may be negative or a small positive value; conversely, a lower corrected stability coefficient may mean poor sensor stability and high wear, so the wear adjustment range may be a large positive value. The purpose is to provide a quantitative basis for subsequent wear state adjustments.

[0052] The wear adjustment range is validated using a wear adjustment model to obtain the verification results. The wear adjustment model is a pre-established mathematical model or algorithm used to evaluate and verify the rationality of the determined wear adjustment range. This model can be constructed based on historical data, expert experience, or physical laws; for example, it can be a regression model, a classification model, or a rule-based expert system. Validating the wear adjustment range involves inputting the initially determined wear adjustment range into the wear adjustment model. Through the model's calculations or judgments, the model assesses whether the adjustment range conforms to the expected wear change pattern or is within a reasonable range, thus obtaining the verification results. The verification results can be Boolean values ​​(pass or fail), confidence scores, or correction suggestions. The purpose is to ensure the accuracy and reliability of the wear adjustment range and avoid bias caused by a single coefficient judgment.

[0053] Based on the test results and the corrected stability coefficients, the initial wear state of each battery sensor is adjusted to determine the wear state of each sensor. This step involves comprehensively considering the verification results of the wear adjustment model and the corrected stability coefficients to make a final correction to the previously determined initial wear state. For example, if the test results show that the wear adjustment range is reasonable and passes the verification, the initial wear state is adjusted according to that range; if the test results show that the wear adjustment range is unreasonable or fails the verification, it may be necessary to correct the wear adjustment range according to the model's recommendations, or to use a backup strategy for adjustment. The purpose is to improve the accuracy and reliability of determining the wear state of the battery sensors through a multi-verification mechanism.

[0054] Specifically, after correction, the battery sensor's stability coefficient is 0.85. First, based on this corrected stability coefficient of 0.85, and in conjunction with preset rules or lookup tables, a wear adjustment range is determined. For example, if the stability coefficient is higher than 0.8, the wear adjustment range might be -0.02 (indicating a slight improvement or maintenance of the wear condition). Next, this wear adjustment range of -0.02 is input into a preset wear adjustment model for verification. This wear adjustment model might be a neural network model trained based on historical wear data, or an expert system containing multiple physical parameters (such as temperature, vibration frequency, and usage time). The model will evaluate whether the adjustment range of -0.02 is reasonable under the current operating conditions. For example, if the model determines that under the current operating parameters of the vehicle's electromagnetic interference sources, the sensor is unlikely to show wear improvement, or the improvement range should not exceed -0.01, the verification result may indicate that the adjustment range is unreasonable. If the verification results indicate that the wear adjustment range of -0.02 is unreasonable (for example, if the model suggests an adjustment range of -0.01), then the initial wear state of the battery sensor will be adjusted based on the verification results and the corrected stability coefficient of 0.85. Assuming the initial wear state of the sensor is slight wear, adjusting it with the original -0.02 range might result in no wear; however, based on the verification results, it might ultimately be adjusted to slight wear (close to no wear), or the model-suggested adjustment of -0.01 might be adopted directly, thus obtaining a more accurate wear state. This verification mechanism effectively avoids over-adjustment or under-adjustment that might result from a single stability coefficient judgment, ensuring the accuracy of the wear state determination.

[0055] This embodiment effectively avoids the potential inaccuracies and lack of verification that may arise from directly adjusting the wear state based solely on the corrected stability coefficient by introducing a wear adjustment range, a wear adjustment model, and a verification process. Specifically, the wear adjustment range is quantified using the corrected stability coefficient, providing a concrete quantitative basis for adjusting the wear state. Secondly, the adjustment range is verified using the wear adjustment model, introducing additional intelligent judgment and verification mechanisms to assess the rationality of the adjustment range, thereby avoiding errors that may arise from judging a single coefficient. Finally, the verification results and the corrected stability coefficient are combined to perform the final initial wear state adjustment, ensuring the rigor and accuracy of the adjustment process. Through multi-stage, multi-dimensional data processing and verification mechanisms, the determination of the wear state becomes more scientific and reliable.

[0056] In some embodiments of this application described above, the step of monitoring the battery status using the wear state of each battery sensor and a preset battery status model to obtain battery status monitoring results includes: By analyzing the wear state of each battery sensor, the damage type and damage index of each sensor are determined. This step identifies potential damage modes and quantifies their severity by analyzing the specific manifestations, trends, and correlations of the battery sensor wear state with other operational data. The damage type can be understood as a specific fault or degradation mode occurring within the battery, such as capacity decay, increased internal resistance, increased self-discharge rate, and localized overheating risk. Damage types can be identified using a pre-established fault mode library or a machine learning-based model. The damage index is a quantitative indicator of the severity of a specific damage type. For example, for capacity decay, the damage index can be the percentage decrease in current capacity relative to the initial capacity; for increased internal resistance, it can be the percentage increase in current internal resistance relative to the initial internal resistance. The index is typically normalized for subsequent model calculations.

[0057] Based on the damage type and damage index of each battery sensor and the preset battery state model, the battery state is monitored to obtain the battery state monitoring results.

[0058] Specifically, after a period of operation, a battery sensor exhibits a continuously increasing noise sensitivity difference, which is identified as a moderate wear state by the status judgment module. To more accurately monitor the battery status, this embodiment further analyzes the specific characteristics of this moderate wear state. For example, by analyzing specific frequency domain components or time domain waveform characteristics of the sensor data under this wear state, the battery damage type can be identified as capacity decay, and its damage index can be calculated as 0.2 (indicating a 20% capacity decrease). Subsequently, this capacity decay damage type and the damage index of 0.2, along with other relevant data, are input into a preset battery status model. This model no longer relies solely on the vague information of moderate wear, but can more accurately predict the remaining cycle life and driving range reduction based on the specific and quantifiable damage information of 20% capacity decay. It can even combine other damage types (such as increased internal resistance) for a comprehensive evaluation, thereby outputting a more comprehensive and accurate battery status monitoring result.

[0059] This embodiment enhances the depth and breadth of battery condition monitoring by further refining the wear state of each battery sensor into specific damage types and damage indices. When monitoring only wear state, a preset battery condition model may only be able to make relatively macroscopic judgments. However, by introducing damage types and damage indices, the rich information contained in the wear state is fully explored. For example, the underlying cause of a moderately worn sensor may be a combination of slight capacity decay and significant increase in internal resistance, or simply a moderate increase in self-discharge rate. By clarifying these specific damage types and quantifying the damage indices, the preset battery condition model can receive more accurate and targeted input parameters. Therefore, the model can invoke more refined algorithms or parameter sets based on different damage types and their severity, thereby providing a more accurate assessment and prediction of the battery's overall health, remaining lifespan, and potential risks. Through detailed damage analysis, the monitoring results are no longer simply about the degree of wear, but rather a comprehensive insight into the battery's internal health condition.

[0060] In some embodiments of this application described above, the step of obtaining the operating parameter information of the vehicle electromagnetic interference source and the operating data of all battery sensors includes: Obtain raw operating parameters of the vehicle's electromagnetic interference sources and raw operating data from all battery sensors. This step refers to the unprocessed initial data collected directly from the vehicle's electromagnetic interference sources (e.g., motors, inverters, and high-voltage wiring harnesses) and battery sensors (e.g., voltage sensors, current sensors, and temperature sensors). The raw data may contain various interferences and errors caused by environmental factors, sensor malfunctions, or transmission processes.

[0061] The raw operating parameters of the vehicle's electromagnetic interference source and the raw operating data of all battery sensors are preprocessed to obtain the operating parameters of the vehicle's electromagnetic interference source and the operating data of all battery sensors. This step involves a series of data cleaning, transformation, and normalization operations on the raw data to eliminate noise, correct errors, handle missing values, and standardize the data format, thereby obtaining high-quality, reliable operating parameter information and operating data suitable for subsequent analysis. Preprocessing may include, but is not limited to, filtering, noise reduction, outlier detection and handling, data smoothing, and data normalization or standardization, with the aim of improving the purity and consistency of the data, laying the foundation for subsequent accurate calculations and judgments.

[0062] Specifically, the vehicle's battery sensors are affected by electromagnetic interference generated during motor startup. First, raw operating parameters such as current and voltage during motor startup are acquired, along with raw operating data such as voltage, current, and temperature collected by the battery sensors within the same time period. This raw data may contain high-frequency noise or transient spikes. Next, the raw data is preprocessed. For example, a low-pass filter can be applied to the sensor operating data to remove high-frequency noise components; simultaneously, outlier detection is performed on the motor operating parameter data, and transient spikes exceeding a preset threshold are smoothed or replaced with the average of adjacent data. Furthermore, data of different dimensions can be normalized to ensure they fall within a uniform numerical range. After these preprocessing steps, clean, consistent, and reliable operating parameter information for the vehicle's electromagnetic interference sources and operating data from all battery sensors are obtained, which are then used to accurately calculate the noise sensitivity index.

[0063] This embodiment first acquires the raw operating parameters of the vehicle's electromagnetic interference source and the raw operating data of all battery sensors. Then, it preprocesses this raw data, effectively avoiding noise, outliers, and inconsistencies. Because the raw data is cleaned and optimized, the operating parameters and data used in subsequent steps have higher accuracy and reliability. This ensures the quality of the input data, providing a solid data foundation for accurately determining the noise sensitivity index of the sensor wires to electromagnetic interference, thereby accurately judging the wear state of each battery sensor and ultimately reliably monitoring the battery status.

[0064] In some embodiments of this application described above, after the step of obtaining the raw operating parameters of the vehicle electromagnetic interference source and the raw operating data of all battery sensors, the method further includes: Based on the raw operating parameters of the vehicle's electromagnetic interference sources, an interference suppression scheme for electromagnetic interference to the battery sensor is determined. This step involves analyzing the raw operating parameters of the vehicle's electromagnetic interference sources, such as motor speed and current, inverter operating frequency, and switching frequency of switching devices, to identify specific frequencies, amplitudes, or patterns that may cause electromagnetic interference to the battery sensor. Based on the identified interference characteristics, one or more targeted interference suppression strategies can be constructed or selected. For example, when periodic interference at a specific frequency is identified, a narrowband notch filter scheme can be used; when broadband random interference is identified, an adaptive noise cancellation scheme can be used. This provides accurate and effective guidance for subsequent data adjustments.

[0065] Using the aforementioned interference suppression scheme, the raw operating data of all battery sensors is adjusted to obtain adjusted raw operating data. This step applies the determined interference suppression scheme to the raw operating data of all battery sensors. Specifically, data adjustment may include applying digital filters, such as notch filters or low-pass filters, to filter out noise at specific frequencies; employing adaptive filtering algorithms to dynamically adjust filtering parameters based on the real-time operating parameters of the interference source to achieve real-time noise elimination; or using signal reconstruction techniques to repair damaged data using undisturbed signal components. By minimizing the impact of electromagnetic interference on the raw sensor operating data without losing original valid information, a purer and more accurate adjusted raw operating data is obtained.

[0066] This embodiment effectively avoids the impact of electromagnetic interference (EMI) on the quality of raw data from battery sensors by introducing an interference suppression step before data preprocessing. Specifically, by conducting in-depth analysis of the raw operating parameters of vehicle EMI sources, the source, type, and characteristics of EMI can be accurately identified. This targeted identification allows for the determination of a highly suitable interference suppression scheme, avoiding blind or excessive data processing. Secondly, the determined interference suppression scheme is used to adjust the raw operating data of all battery sensors, significantly weakening or eliminating EMI components before the data enters subsequent preprocessing steps. This pre-emptive interference suppression mechanism ensures that subsequent preprocessing steps can be performed on a higher-quality data basis, thereby improving the overall efficiency and accuracy of data processing.

[0067] For a sensor-based battery state monitoring method based on any of the above, please refer to [link / reference]. Figure 2 The present invention also provides a sensor-based battery status monitoring system, which includes a data acquisition module 210, an index determination module 220, a difference comparison module 230, a status determination module 240, and a battery monitoring module 250.

[0068] The data acquisition module 210 is used to acquire the operating parameter information of the vehicle's electromagnetic interference source and the operating data of all battery sensors.

[0069] The index determination module 220 is used to determine each noise sensitivity index of the sensor wire to electromagnetic interference based on the operating parameter information and the operating data of all battery sensors.

[0070] The difference comparison module 230 is used to compare each noise sensitivity index with the corresponding sensitivity index threshold to obtain the noise sensitivity difference.

[0071] The state determination module 240 is used to determine the state of the noise sensitivity difference and determine the wear state of each battery sensor.

[0072] The battery monitoring module 250 is used to monitor the battery status by utilizing the wear status of each battery sensor and a preset battery status model, and obtain the battery status monitoring results.

[0073] In this embodiment, through the configuration of a data acquisition module 210, an index determination module 220, a difference comparison module 230, a state determination module 240, and a battery monitoring module 250, the data acquisition module 210 and the index determination module 220 can capture and quantify specific noise characteristics caused by the combined activity of electromagnetic interference sources and wire wear. The difference comparison module 230 and the state determination module 240 further transform the quantified results into an understandable wear state, achieving accurate diagnosis of the root cause of the fault, rather than merely a passive response to abnormal data. Finally, the battery monitoring module 250 incorporates the wear state as a key correction factor into the battery state estimation model, enabling the system to adaptively adjust the monitoring strategy and effectively reduce the negative impact of noise introduced by wear on the battery state estimation results. This improves the accuracy and reliability of battery state monitoring, thereby providing a more solid guarantee for the operational safety of electric vehicles and the user experience.

[0074] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the present invention specification.

Claims

1. A sensor-based battery state monitoring method, characterized in that, Includes the following steps: Acquire the operating parameter information of the vehicle's electromagnetic interference sources and the operating data of all battery sensors; Based on the operating parameter information and the operating data of all battery sensors, determine each noise sensitivity index of the sensor wires to electromagnetic interference; Each noise sensitivity index is compared with its corresponding sensitivity index threshold to obtain the noise sensitivity difference. The noise sensitivity difference is used to determine the wear state of each battery sensor. By utilizing the wear status of each battery sensor and a preset battery status model, the battery status is monitored, and the battery status monitoring results are obtained.

2. The sensor-based battery state monitoring method according to claim 1, characterized in that, The step of determining each noise sensitivity index of the sensor wires to electromagnetic interference based on the operating parameter information and the operating data of all battery sensors includes: Determine the associated fluctuation characteristics in the operating data of the battery sensor that are related to the operating parameter information; Using the associated fluctuation characteristics, feature extraction is performed on the operating parameter information to obtain noise feature parameters; The noise characteristic parameters and noise characteristic thresholds are compared to determine the noise sensitivity index of the sensor wire to electromagnetic interference.

3. The sensor-based battery state monitoring method according to claim 2, characterized in that, The steps of extracting noise feature parameters from the operating parameter information using the associated fluctuation characteristics include: The associated fluctuation characteristics are decomposed into frequency bands to obtain noise component information for each frequency band; Analyze the noise component information of each frequency band and the operating parameter information for each noise correlation degree; Based on the noise correlation degree and noise component information of each frequency band, noise characteristic parameters are obtained.

4. The sensor-based battery state monitoring method according to claim 1, characterized in that, The steps for determining the wear state of each battery sensor by analyzing the noise sensitivity difference include: The noise sensitivity difference is used to determine the grading coefficient of each battery sensor and the initial wear state of each battery sensor. By using the grading coefficient of each battery sensor, the stability of each battery sensor is judged, and the stability parameters of each battery sensor are obtained. By using the stable parameters of each battery sensor, the initial wear state of each battery sensor is adjusted to determine the wear state of each battery sensor.

5. The sensor-based battery state monitoring method according to claim 4, characterized in that, The steps for adjusting the initial wear state of each battery sensor using the stable parameters of each battery sensor and determining the wear state of each battery sensor include: Based on the stability parameters of each battery sensor, determine the stability coefficient of each sensor's data; By using historical wear coefficients, the stability coefficient of each sensor data is corrected to obtain the corrected stability coefficient; Using the corrected stability coefficient, the initial wear state of each battery sensor is adjusted to determine the wear state of each battery sensor.

6. The sensor-based battery state monitoring method according to claim 5, characterized in that, The steps for adjusting the initial wear state of each battery sensor using the corrected stability coefficient and determining the wear state of each battery sensor include: The wear adjustment range is determined using the corrected stability coefficient. The wear adjustment range is verified using a wear adjustment model, and the verification results are obtained. Based on the test results and the corrected stability coefficient, the initial wear state of each battery sensor is adjusted to determine the wear state of each battery sensor.

7. The sensor-based battery state monitoring method according to claim 1, characterized in that, The steps for monitoring battery status and obtaining battery status monitoring results by utilizing the wear state of each battery sensor and a preset battery status model include: By utilizing the wear condition of each battery sensor, the damage type and damage index of each battery sensor are determined; Based on the damage type and damage index of each battery sensor and the preset battery state model, the battery state is monitored to obtain the battery state monitoring results.

8. The sensor-based battery state monitoring method according to claim 1, characterized in that, The steps for obtaining the operating parameter information of the vehicle's electromagnetic interference source and the operating data of all battery sensors include: Obtain raw information on the operating parameters of the vehicle's electromagnetic interference sources and raw operating data from all battery sensors; The original operating parameters of the vehicle electromagnetic interference source and the original operating data of all battery sensors are preprocessed to obtain the operating parameters of the vehicle electromagnetic interference source and the operating data of all battery sensors.

9. A sensor-based battery state monitoring method according to claim 8, characterized in that, Following the steps of acquiring the raw operating parameters of the vehicle's electromagnetic interference source and the raw operating data of all battery sensors, the method further includes: Based on the original operating parameter information of the vehicle electromagnetic interference source, an interference suppression scheme for electromagnetic interference generated by the battery sensor is determined. Using the aforementioned interference suppression scheme, the raw operating data of all battery sensors are adjusted to obtain the adjusted raw operating data.

10. A sensor-based battery state monitoring system, characterized in that, The system includes: The data acquisition module is used to acquire the operating parameter information of the vehicle's electromagnetic interference source and the operating data of all battery sensors; The index determination module is used to determine the noise sensitivity index of the sensor wires to electromagnetic interference based on the operating parameter information and the operating data of all battery sensors. The difference comparison module is used to compare each noise sensitivity index with the corresponding sensitivity index threshold to obtain the noise sensitivity difference. The status determination module is used to determine the status of the noise sensitivity difference and determine the wear status of each battery sensor. The battery monitoring module is used to monitor the battery status using the wear status of each battery sensor and a preset battery status model, and obtain the battery status monitoring results.

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