New energy automobile condenser operation data processing method and system

Abstract

The invention relates to the field of new energy automobile thermal management, and provides a new energy automobile condenser operation data processing method and system, and the method comprises the steps: obtaining the original data of an outlet pressure sensor of a condenser of a vehicle within a preset duration before the current moment; determining whether the outlet pressure sensor has a data drift trend according to the original data; under the condition that the outlet pressure sensor has the data drift trend, determining a target data drift mode in a plurality of data drift modes of the outlet pressure sensor according to the original data; determining a target data correction value of the outlet pressure sensor at a subsequent detection moment after the current moment according to the target data drift mode; and controlling the condenser based on the target data correction value at each subsequent detection moment and the data of the outlet pressure sensor at each subsequent detection moment. The accuracy of vehicle condenser operation data processing can be improved.

Description

Technical Field

[0001] This application relates to the field of thermal management of new energy vehicles, and in particular to a method and system for processing operating data of a new energy vehicle condenser. Background Technology

[0002] In the field of thermal management for new energy vehicles, the condenser, as a core component, is crucial for accurate monitoring of its operating status. However, existing data processing methods often struggle to effectively identify and compensate for subtle signal drift caused by long-term sensor aging. This problem is particularly pronounced when data acquisition frequencies are dynamically adjusted for energy saving, leading to data sparsity, and when conventional noise smoothing logic misjudges and masks the true drift trend. This not only causes the control system to make decisions based on inaccurate data, resulting in long-term overload and accelerated wear of related components, but also poses a hidden threat to the overall vehicle energy efficiency and the lifespan of critical components, while failing to trigger traditional fault warnings.

[0003] During long-term vehicle operation, critical sensors installed on the condenser, such as the outlet pressure sensor, inevitably undergo frequent thermal cycling and the natural aging of their internal electronic components. This degradation is not a sudden failure, but a slow, gradual process, manifested as a continuous, minute positive drift in the pressure signal output by the sensor. This drift value is typically very small, far below the normal tolerance range of the sensor design, and does not reach any system-preset fault threshold. Therefore, from the perspective of a single data point, it is still considered "normal," but in reality, it no longer accurately reflects the true physical pressure inside the condenser.

[0004] The extended data acquisition intervals lead to sparse data points, and there is also slight signal drift from the outlet pressure sensor. The system's internal moving average calculation logic, used for noise filtering, misinterprets this sparse data with slight drift as normal background fluctuations and smooths it out. While the moving average algorithm eliminates random noise by calculating the average of data over a period, it actually absorbs this slow, continuous baseline drift into the average, masking the slow upward trend of the pressure baseline. This results in the system failing to identify true anomalies in the condenser's internal pressure, deeply obscuring potential operational problems and leading to low accuracy in processing vehicle condenser operating data. Summary of the Invention

[0005] This application provides a method and system for processing condenser operation data in new energy vehicles, which can improve the accuracy of condenser operation data processing.

[0006] To achieve the above objectives, this application adopts the following technical solution: In a first aspect, this application discloses a method for processing condenser operation data in a new energy vehicle, comprising: acquiring raw data from the outlet pressure sensor of the vehicle's condenser within a preset time period prior to the current moment; determining whether the outlet pressure sensor exhibits a data drift trend based on the raw data; if the outlet pressure sensor exhibits a data drift trend, determining a target data drift mode for multiple data drift modes of the outlet pressure sensor based on the raw data; determining a target data correction value for the outlet pressure sensor at subsequent detection times after the current moment based on the target data drift mode; and controlling the condenser based on the target data correction value at each subsequent detection time and the data from the outlet pressure sensor at each subsequent detection time.

[0007] Furthermore, the target data drift mode of multiple data drift modes of the outlet pressure sensor is determined based on the original data, including: inputting the original data into a target Kalman filter model to obtain enhanced data output by the target Kalman filter model; the target Kalman filter model is used to enhance the data drift trend of the original data; and the target data drift mode of multiple data drift modes of the outlet pressure sensor is determined based on the enhanced data.

[0008] Furthermore, based on the enhanced data, the target data drift pattern of multiple data drift patterns of the outlet pressure sensor is determined, including: determining the target data features of the enhanced data; for each data drift pattern of multiple data drift patterns, determining the feature similarity between the preset data features of the data drift pattern and the target data features; and taking the data drift pattern with the highest corresponding feature similarity as the target data drift pattern.

[0009] Based on the above, this application further proposes that the raw data includes sub-raw data for each time period before the current time, and that determining whether the outlet pressure sensor has a data drift trend based on the raw data includes: for each sub-raw data, determining the data difference between the sub-raw data and the sub-raw data of the previous time; and determining that the outlet pressure sensor has a data drift trend when the standard deviation of the multiple data differences is less than a preset standard deviation threshold and the minimum value of the multiple data differences is greater than a preset difference.

[0010] Preferably, determining the target data correction value of the outlet pressure sensor for each subsequent detection time after the current time based on the target data drift pattern includes: determining the target data drift change rate of the original data based on multiple data differences; and determining the target data correction value of the outlet pressure sensor for each subsequent detection time after the current time based on the target data drift pattern and the target data drift change rate.

[0011] In one implementation, when the target data drift pattern is linear drift, the target data correction value of the outlet pressure sensor at each subsequent detection time after the current time is determined based on the target data drift pattern and the target data drift change rate, including: determining a target duration; the target duration is the duration between the subsequent detection time and the target time, and the target time is the time corresponding to the first data of the original data; processing the target data drift change rate and the target duration based on the target data drift pattern to obtain the target data correction value of the outlet pressure sensor at the subsequent detection time.

[0012] To enhance functionality, the condenser is controlled based on the target data correction value and the data from the outlet pressure sensor at each subsequent detection time. This includes: using the sum of the target data correction value and the data from the outlet pressure sensor at each subsequent detection time as the correction data for the outlet pressure sensor at each subsequent detection time; and inputting the correction data into a preset PID control model to obtain the condenser control strategy output by the preset PID control model.

[0013] As a technical improvement, before acquiring the raw data of the vehicle's condenser outlet pressure sensor within a preset time period prior to the current moment, the method further includes: acquiring an initial standard deviation threshold, a second correspondence, and the vehicle's target operating environment information within a preset time period prior to the current moment; the second correspondence includes a one-to-one correspondence between multiple operating environment information and multiple adjustment coefficients; the adjustment coefficient corresponding to the target operating environment information in the second correspondence is used as the target adjustment coefficient; and the product of the initial standard deviation threshold and the target adjustment coefficient is used as the preset standard deviation threshold.

[0014] As a further improvement, the target operating environment information of the vehicle within a preset time period before the current moment is obtained, including: obtaining a third correspondence relationship and the average altitude and average speed of the vehicle within a preset time period before the current moment; the third correspondence relationship includes a one-to-one correspondence relationship between multiple first information and multiple operating environments, the first information including altitude range and speed range; the operating environment corresponding to the first information that matches the third correspondence relationship with the average altitude and average speed is taken as the target operating environment information.

[0015] Secondly, this application also discloses a new energy vehicle condenser operation data processing system, the system comprising: an acquisition device and a processing device; the acquisition device is used to acquire raw data of the outlet pressure sensor of the vehicle's condenser within a preset time period before the current moment; the processing device is used to determine whether there is a data drift trend of the outlet pressure sensor based on the raw data; if there is a data drift trend of the outlet pressure sensor, determine a target data drift mode of multiple data drift modes of the outlet pressure sensor based on the raw data; determine a target data correction value of the outlet pressure sensor at each detection time after the current moment based on the target data drift mode; and control the condenser based on the target data correction value at each detection time and the data of the outlet pressure sensor at each detection time.

[0016] Beneficial Effects: This application discloses a method for processing condenser operation data in new energy vehicles. It acquires raw data from the vehicle's condenser outlet pressure sensor over a preset time period prior to the current moment and determines whether the outlet pressure sensor exhibits a data drift trend based on this raw data. After confirming the existence of a data drift trend, it further determines a target data drift pattern among multiple data drift patterns based on the raw data. Subsequently, based on this target data drift pattern, it predicts and determines the target data correction value for the outlet pressure sensor at subsequent detection times after the current moment. Finally, based on the target data correction value for each subsequent detection time and the actual data from the outlet pressure sensor at each subsequent detection time, it precisely controls the condenser.

[0017] This method effectively solves the problem of difficulty in identifying and compensating for minute signal drift caused by long-term sensor aging in existing technologies, especially addressing the challenge of data sparsity and the misjudgment of conventional noise smoothing logic that masks the true drift trend. By introducing data drift trend judgment and pattern recognition, this application can accurately capture minute, gradual drifts that are difficult to detect by traditional methods, avoiding confusion between normal fluctuations and true drifts, thereby improving the accuracy of condenser operation data processing. Attached Figure Description

[0018] Figure 1 A flowchart illustrating a method for processing operating data of a new energy vehicle condenser provided in this application; Figure 2 A flowchart illustrating another method for processing operating data of a new energy vehicle condenser provided in this application; Figure 3 A flowchart illustrating another method for processing operating data of a new energy vehicle condenser provided in this application; Figure 4 This application provides a schematic diagram of the architecture of a new energy vehicle condenser operation data processing system. Detailed Implementation

[0019] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0020] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0021] In the field of thermal management for new energy vehicles, the condenser, as a core component, is crucial for accurate monitoring of its operating status. However, existing data processing methods often struggle to effectively identify and compensate for subtle signal drift caused by long-term sensor aging. This problem is particularly pronounced when data acquisition frequencies are dynamically adjusted for energy saving, leading to data sparsity, and when conventional noise smoothing logic misjudges and masks the true drift trend. This not only causes the control system to make decisions based on inaccurate data, resulting in long-term overload and accelerated wear of related components, but also poses a hidden threat to the overall vehicle energy efficiency and the lifespan of critical components, while failing to trigger traditional fault warnings.

[0022] During long-term vehicle operation, critical sensors installed on the condenser, such as the outlet pressure sensor, inevitably undergo frequent thermal cycling and the natural aging of their internal electronic components. This degradation is not a sudden failure, but a slow, gradual process, manifested as a continuous, minute positive drift in the pressure signal output by the sensor. This drift value is typically very small, far below the normal tolerance range of the sensor design, and does not reach any system-preset fault threshold. Therefore, from the perspective of a single data point, it is still considered "normal," but in reality, it no longer accurately reflects the true physical pressure inside the condenser.

[0023] The extended data acquisition intervals lead to sparse data points, and there is also slight signal drift from the outlet pressure sensor. The system's internal moving average calculation logic, used for noise filtering, misinterprets this sparse data with slight drift as normal background fluctuations and smooths it out. While the moving average algorithm eliminates random noise by calculating the average of data over a period, it actually absorbs this slow, continuous baseline drift into the average, masking the slow upward trend of the pressure baseline. This results in the system failing to identify true anomalies in the condenser's internal pressure, deeply obscuring potential operational problems and leading to low accuracy in processing vehicle condenser operating data.

[0024] In this regard, such as Figure 1 As shown, this application proposes a method for processing condenser operation data in new energy vehicles, including: S101. Obtain the raw data of the vehicle's condenser outlet pressure sensor within a preset time period before the current moment.

[0025] S102. Determine whether there is a data drift trend in the outlet pressure sensor based on the raw data.

[0026] S103. When there is a data drift trend in the outlet pressure sensor, determine the target data drift mode among the multiple data drift modes of the outlet pressure sensor based on the original data.

[0027] S104. Determine the target data correction value of the outlet pressure sensor at subsequent detection times after the current time based on the target data drift mode.

[0028] S105. The condenser is controlled based on the target data correction value at each subsequent detection time and the data from the outlet pressure sensor at each subsequent detection time.

[0029] This application determines the drift trend, pattern, and correction value of the raw data from the condenser outlet pressure sensor, and controls the condenser based on the corrected data. This effectively solves the shortcomings of traditional methods in identifying and compensating for small signal drift of the sensor, thereby avoiding misjudgment of the control system and overload of components due to inaccurate data, and improving the energy efficiency of the whole vehicle and the life of key components.

[0030] To better understand the technical solution proposed in this application, some key terms involved will be explained first.

[0031] The "condenser" is an important component in the thermal management system of new energy vehicles. It is mainly responsible for condensing refrigerant vapor into liquid and releasing heat in the process to maintain the operation of key components such as batteries and motors within a suitable temperature range, while also providing cooling for the passenger compartment.

[0032] The "outlet pressure sensor" is a device installed at the condenser outlet to monitor the refrigerant pressure inside the condenser in real time. Its output data is an important basis for assessing and controlling the condenser's operating status.

[0033] "Raw data" refers to the data directly collected by the outlet pressure sensor without any processing or correction.

[0034] "Data drift trend" refers to the slow, continuous shift in the baseline or measured value of sensor output data over time, without any change in external physical quantities. This drift is usually caused by factors such as sensor aging and environmental changes.

[0035] "Data drift pattern" refers to a specific regularity or type of data drift trend, such as linear drift, exponential drift, periodic drift, etc. Identifying different drift patterns helps to make more accurate data corrections.

[0036] "Target data correction value" refers to the value predicted and calculated based on the identified data drift pattern to compensate for sensor drift.

[0037] "Subsequent detection time" refers to a future point in time after the current moment when the system needs to detect and control the condenser's operating data.

[0038] This application provides a method for processing the operating data of a condenser in a new energy vehicle. Its core lies in intelligently identifying drift, determining the mode, and correcting the operating data of the condenser outlet pressure sensor, thereby achieving precise control of the condenser.

[0039] Specifically, this method first acquires raw data from the vehicle's condenser outlet pressure sensor over a preset time period prior to the current moment. For example, the preset time period can be set to 10 minutes, with the system collecting the outlet pressure sensor reading every second to obtain 600 raw data points over 10 minutes. These data points can be voltage signals read directly from the sensor or pressure values ​​that have undergone preliminary conversion.

[0040] Next, the presence of data drift trend in the outlet pressure sensor is determined based on the acquired raw data. For example, the statistical characteristics of the raw data, such as mean, variance, and trend line slope, can be analyzed to determine if there is a persistent, non-random offset. If the raw data shows a slow upward or downward trend within a preset time period, and this trend exceeds the normal fluctuation range, a preliminary judgment can be made that a data drift trend exists.

[0041] When the outlet pressure sensor exhibits a data drift trend, the system determines the target data drift mode from among multiple data drift patterns of the outlet pressure sensor based on the raw data. For example, the system can preset multiple data drift modes, such as linear drift, quadratic drift, and exponential drift. By performing curve fitting on the raw data or using machine learning algorithms to analyze the data features, the raw data is matched with the preset drift modes to determine the target data drift mode that best matches the current data characteristics.

[0042] Subsequently, the target data correction value for subsequent detection times after the current time is determined based on the target data drift pattern. For example, if the target data drift pattern is determined to be linear drift with a drift slope of 0.01 MPa per minute, then the target data correction value for the subsequent detection time 1 minute after the current time can be calculated as 0.01 MPa; for the subsequent detection time 2 minutes later, the correction value is 0.02 MPa, and so on.

[0043] Finally, the condenser is controlled based on the target data correction value and the data from the outlet pressure sensor at each subsequent detection time. For example, at a subsequent detection time, the system acquires new data from the outlet pressure sensor and superimposes it with the corresponding target data correction value to obtain the corrected pressure value. This corrected pressure value is then input into the condenser's control logic, such as a PID controller, to adjust the cooling fan speed, compressor power, etc., thereby ensuring that the condenser operates under true and accurate pressure data and avoiding misjudgments and over-control caused by sensor drift.

[0044] The proposed data processing method for condenser operation in new energy vehicles aims to address the limitations of traditional methods in handling minute signal drift from sensors. Existing data processing methods often struggle to effectively identify and compensate for minute signal drift caused by long-term sensor aging, particularly in cases of data sparsity and misjudgments in conventional noise smoothing logic. This not only leads to control systems making decisions based on inaccurate data, causing long-term overload and accelerated wear of related components, but also poses a hidden threat to overall vehicle energy efficiency and the lifespan of critical components.

[0045] This application achieves intelligent processing of condenser outlet pressure sensor data by introducing data drift trend judgment, data drift pattern recognition, and target data correction value determination. First, by acquiring raw data within a preset time period and determining whether a data drift trend exists, this application can promptly detect potential, minute drift problems in the sensor, avoiding the drawback of traditional methods where small drift values ​​fail to trigger fault warnings. Second, after confirming the existence of a drift trend, the target data drift pattern is further determined based on the raw data. This makes data correction no longer a simple fixed compensation, but a dynamic adjustment based on the actual drift pattern, significantly improving the accuracy of the correction. For example, if the drift is linear, linear correction is used; if it is exponential, exponential correction is used. This refined pattern recognition effectively avoids the over-correction or under-correction problems that may result from the "one-size-fits-all" correction method in traditional methods. Finally, the target data correction value for subsequent detection times is determined based on the target data drift pattern and combined with the actual detection data to control the condenser, ensuring that the control system always makes decisions based on pressure data closest to the true value.

[0046] Compared to existing technologies, the advantages of this application lie in its sensitivity to and accuracy in correcting minute sensor drifts. Traditional methods may misjudge minute drifts as normal fluctuations and smooth them out, thus masking the real problem. This application, however, effectively identifies such hidden drifts through a specialized drift trend judgment mechanism. Furthermore, by identifying specific drift patterns, this application can provide more targeted correction schemes, avoiding new control problems caused by improper correction. Therefore, this application can effectively prevent problems such as over-driving of cooling fans and prolonged high-load operation of compressors caused by inaccurate sensor data, thereby extending the service life of related components, reducing energy consumption, and improving the overall operational reliability and economy of new energy vehicles. This method not only solves the pain point of difficulty in identifying and compensating for sensor drift in existing technologies, but also represents a significant step forward in improving the intelligent and refined control level of the thermal management system of new energy vehicles.

[0047] like Figure 2 As shown, this application further proposes the following steps for determining the target data drift mode among multiple data drift modes of the outlet pressure sensor based on the raw data: S201. Input the original data into the target Kalman filter model to obtain the enhanced data output by the target Kalman filter model.

[0048] The target Kalman filter model is used to enhance the data drift trend of the original data.

[0049] S202. Determine the target data drift mode among multiple data drift modes of the outlet pressure sensor based on the enhanced data.

[0050] Specifically, raw data refers to unprocessed pressure data acquired from the condenser outlet pressure sensor within a preset time period prior to the current moment. The objective Kalman filter (AKF) model is an optimization algorithm for estimating system state, providing a more accurate state estimate by fusing sensor measurements and a system dynamics model. Here, the AKF model is configured to effectively filter out random noise in the raw data while highlighting and enhancing potential drift trends present in the data.

[0051] In this way, the target Kalman filter model can transform the raw data into enhanced data, making the characteristics of data drift more obvious and easier to identify. Enhanced data refers to data processed by the target Kalman filter model, where the noise level is significantly reduced, while the trend of data drift is effectively highlighted. Determining the target data drift pattern among multiple data drift patterns of the outlet pressure sensor based on the enhanced data means using this optimized data to analyze and identify the specific drift pattern that the current sensor data conforms to. For example, these drift patterns can include various preset patterns such as linear drift, exponential drift, and periodic drift. By performing pattern matching or feature extraction on the enhanced data, it is possible to more accurately determine which target data drift pattern the current outlet pressure sensor data drift belongs to.

[0052] This application's solution effectively addresses the noise interference problem encountered when directly determining data drift patterns using raw data by introducing a target Kalman filter model. The target Kalman filter model can perform real-time or near-real-time filtering on raw data. Its core lies in using system state equations and observation equations, combined with prediction and update mechanisms, to optimally estimate sensor data. Specifically, when raw data is input into the target Kalman filter model, the model predicts the state at the next moment based on its internal dynamic model and updates it by incorporating actual sensor measurements. This process effectively suppresses random noise and measurement errors in the raw data, allowing the true trend in the data, especially the data drift trend, to be clearly presented. Therefore, the enhanced data obtained after filtering has more significant drift characteristics, providing high-quality input for subsequent accurate identification of target data drift patterns.

[0053] Through the above technical solution, this application can significantly improve the accuracy and robustness of drift pattern recognition for outlet pressure sensor data. Because the target Kalman filter model effectively suppresses noise and enhances trends in the raw data, the subsequent data drift pattern recognition process is less susceptible to the effects of occasional noise or minor fluctuations. This ensures that even in complex operating environments, the sensor's true drift behavior can be accurately captured, thus providing a more reliable basis for determining subsequent target data correction values. Compared to directly using raw data for pattern recognition, this solution avoids misjudgments or omissions caused by noise interference, thereby improving the reliability and control accuracy of the entire condenser operation data processing method.

[0054] In some preferred embodiments, it is assumed that during operation, the raw data from the condenser outlet pressure sensor of a new energy vehicle is affected by factors such as engine vibration and electromagnetic interference, exhibiting significant random noise, making it difficult to determine whether a clear linear drift trend exists by direct observation of the data. In this case, this noisy raw data is input into a pre-trained target Kalman filter model. The target Kalman filter model smooths the raw data according to its internal prediction and update algorithms, filtering out most of the random noise while retaining and highlighting potential slow upward or downward trends in the data. For example, if the raw data shows a fluctuating but slightly upward trend over a period of time, after processing by the target Kalman filter model, the output enhanced data will present a smoother and clearer upward curve, making its linear drift characteristics immediately apparent. Subsequently, the system can accurately identify the current outlet pressure sensor data drift mode as a linear drift mode based on this clear enhanced data and a pattern matching algorithm.

[0055] like Figure 3 As shown, this application further proposes a step for determining a target data drift mode of multiple data drift modes of the aforementioned outlet pressure sensor based on the aforementioned enhanced data, including: S301. Determine the target data characteristics for augmented data.

[0056] S302. For each of the multiple data drift patterns, determine the feature similarity between the preset data features of the data drift pattern and the target data features.

[0057] S303. Select the data drift pattern with the highest corresponding feature similarity as the target data drift pattern.

[0058] Specifically, target data features can be understood as key attributes or patterns extracted from augmented data that characterize its drift trend. For example, these features may include the slope, curvature, fluctuation frequency, peak position, and trend line parameters of the augmented data. The purpose is to transform complex time-series data into quantifiable and easily comparable feature vectors for subsequent pattern matching. In practical applications, target data features can be extracted using various signal processing or machine learning methods, such as Fourier transform, wavelet analysis, principal component analysis (PCA), or deep learning models. Multiple data drift patterns refer to predefined or learned patterns representing different types of data drift (e.g., linear drift, exponential drift, periodic drift, step drift, etc.). Each data drift pattern has its corresponding preset data features, which are obtained during the training phase through analysis and extraction of a large amount of known drift data. Feature similarity is an indicator that measures the degree of matching between the target data features and each preset data feature. Its purpose is to quantify the similarity between the current drift trend of the augmented data and known drift patterns. In practical applications, feature similarity can be calculated using various distance or similarity metrics, such as Euclidean distance, cosine similarity, Pearson correlation coefficient, and dynamic time warping (DTW). Therefore, by comparing the similarity between the target data features of the augmented data and the preset data features of all preset data drift patterns, the pattern with the highest similarity is selected as the current data drift pattern of the outlet pressure sensor. The purpose is to ensure that the identified drift pattern best represents the actual drift behavior of the sensor, thus providing an accurate basis for subsequent data correction.

[0059] The proposed solution first extracts target data features that characterize the drift trend from augmented data, transforming complex time-series data into quantifiable feature representations. Then, these target data features are compared with pre-stored preset data features representing different data drift patterns, quantifying the degree of matching by calculating the feature similarity between them. It is precisely this feature-matching mechanism that enables the system to overcome the challenges of noise or mixed drift patterns in augmented data, accurately identifying the target data drift pattern that best matches the current data drift trend. This method avoids the inaccuracies of direct subjective judgment or simple threshold determination, significantly improving the robustness and accuracy of drift pattern recognition.

[0060] The above technical solution enables accurate identification of drift patterns in outlet pressure sensor data. Compared to relying solely on augmented data for fuzzy judgment, this application significantly improves the accuracy and reliability of drift pattern identification by extracting target data features and performing feature similarity matching. This ensures that subsequent data correction values ​​more accurately reflect the actual drift of the sensor, providing a more reliable data foundation for precise condenser control, effectively avoiding control deviations caused by inaccurate drift pattern identification, and improving the stability and efficiency of condenser operation in new energy vehicles.

[0061] In some preferred embodiments, a specific example is given below. Assume that enhanced outlet pressure sensor data for a certain time period is obtained after processing by a target Kalman filter model. First, feature extraction can be performed on this enhanced data, such as calculating its average slope, second derivative (representing curvature), and dominant frequency component after Fourier transform within a preset time window, combining these values ​​into a target data feature vector. Simultaneously, the system pre-stores preset data feature vectors for various data drift modes. For example, the feature vector for a "linear drift mode" might primarily exhibit a stable slope, the feature vector for an "exponential drift mode" might exhibit gradually increasing slope and curvature, and the feature vector for a "periodic drift mode" might exhibit a specific dominant frequency component. Next, the extracted target data feature vector is compared with the feature vector of each preset data drift mode using cosine similarity calculation. For example, if the cosine similarity between the target data feature vector and the preset data feature vector of the "linear drift pattern" is 0.95, the similarity with the "exponential drift pattern" is 0.70, and the similarity with the "periodic drift pattern" is 0.30, then the system will determine that the "linear drift pattern" is the target data drift pattern of the current outlet pressure sensor. In this way, the drift pattern that best matches the current data trend can be quantitatively and accurately identified.

[0062] This application further proposes a method for determining whether an outlet pressure sensor exhibits a data drift trend based on raw data, which specifically includes: The raw data includes sub-raw data for each time point within a preset time period prior to the current time. Based on the raw data, it is determined whether the outlet pressure sensor exhibits a data drift trend, including: For each of the multiple sub-raw data, determine the data difference between the sub-raw data and the sub-raw data at the previous time step; if the standard deviation of multiple data differences is less than a preset standard deviation threshold, and the minimum value among the multiple data differences is greater than a preset difference, it is determined that the outlet pressure sensor has a data drift trend.

[0063] Specifically, sub-raw data refers to the pressure data points collected by the condenser outlet pressure sensor at each sampling moment within a preset time period. These sub-raw data constitute the sensor's operating trajectory over a period of time. Determining the data difference between the sub-raw data and the sub-raw data at the previous moment aims to quantify the magnitude of change between data points at adjacent moments. These data differences can reflect the instantaneous fluctuations in sensor readings. The standard deviation is a statistical measure used to measure the dispersion of multiple data differences. When the standard deviation of multiple data differences is less than a preset standard deviation threshold, it indicates that the data changes are relatively stable with low volatility. Simultaneously, if the minimum value among multiple data differences is greater than the preset difference, it means that the data as a whole exhibits continuous, unidirectional, small changes, rather than random fluctuations. The preset standard deviation threshold and preset differences can be set according to the actual application scenario, sensor characteristics, and desired drift detection sensitivity; for example, they can be determined through historical data analysis or expert experience.

[0064] This application's solution determines data drift trends by combining the standard deviation and minimum value of data differences, effectively distinguishing between normal fluctuations in sensor data and actual drift phenomena. Specifically, when sensor data drifts, its readings exhibit a slow and continuous unidirectional change. At this time, the data differences between adjacent time points are relatively stable and in the same direction, resulting in a small standard deviation for these differences. Simultaneously, due to the continuous unidirectional change, the minimum value among these differences will be greater than a preset small difference, thus eliminating random noise or short-term fluctuations. This dual-judgment mechanism avoids misinterpreting normal sensor fluctuations as data drift, improving the accuracy and robustness of drift detection.

[0065] Through the above technical solution, this application provides a more accurate and reliable method for judging the drift trend of outlet pressure sensor data. This method, by comprehensively considering the dispersion and direction of change of data differences, effectively avoids the misjudgment problems that may exist in traditional methods, such as incorrectly identifying normal system fluctuations or measurement noise as data drift. Therefore, it ensures that subsequent data correction and condenser control strategies are based on the actual data drift situation, thereby improving the stability and control accuracy of the entire new energy vehicle condenser operation data processing system, extending the sensor's service life, and ensuring the optimized operation of the condenser.

[0066] In some preferred embodiments, it is assumed that the condenser outlet pressure sensor continuously collected 100 sub-raw data points within a preset time period prior to the current moment. First, the data difference between each of these 100 sub-raw data points and its sub-raw data point at the previous moment is calculated, resulting in 99 data difference values. For example, if the sensor reading starts at 100 kPa and slowly increases at a rate of 0.01 kPa / s, then these data differences will be approximately 0.01 kPa. Next, the standard deviation of these 99 data differences is calculated. If this standard deviation is less than a preset standard deviation threshold (e.g., 0.005 kPa), it indicates that the data change is very stable. Simultaneously, the minimum value among these 99 data differences is checked. If this minimum value is greater than a preset difference (e.g., 0.008 kPa), it can be determined that the outlet pressure sensor exhibits a data drift trend. Conversely, if the standard deviation is large, or the minimum value is close to zero or even negative (indicating fluctuation or decline), then a drift trend is not considered to exist. In this way, the system can intelligently identify slow and continuous sensor drift, providing an accurate basis for subsequent data correction and condenser control.

[0067] This application further proposes a more refined method for determining target data correction values, which improves the accuracy of corrections by introducing the data drift rate.

[0068] In the aforementioned method for processing operating data of condensers in new energy vehicles, the target data correction value of the outlet pressure sensor is determined for each subsequent detection time after the current time based on the aforementioned target data drift pattern. Specifically, this includes: The target data drift rate of change of the original data is determined based on multiple data differences; the target data correction value of the outlet pressure sensor for each subsequent detection time after the current time is determined based on the target data drift pattern and the target data drift rate of change.

[0069] Specifically, when a data drift trend is determined in the outlet pressure sensor, the data difference between each of the multiple sub-raw data sets and the sub-raw data set from the previous time step is determined. These data differences reflect the amount of change in sensor data over consecutive time steps. This application uses these data differences to calculate the target data drift rate of change of the raw data. The target data drift rate of change can be understood as the average rate or trend strength of sensor data drift within a preset time period, which can quantify the dynamic characteristics of data drift. For example, this rate of change can be determined by statistical analysis of these data differences, such as calculating their average value, weighted average value, or through regression analysis.

[0070] After obtaining the target data drift change rate, it is combined with the determined target data drift pattern to jointly determine the target data correction value for each subsequent detection time after the current time of the outlet pressure sensor. The target data drift pattern describes the overall shape of the drift (e.g., linear drift, exponential drift, etc.), while the target data drift change rate provides information on the dynamic intensity of the drift. By comprehensively considering both, a more accurate and dynamically adaptive correction value can be generated, thereby more accurately predicting and compensating for future sensor drift.

[0071] This application's solution effectively addresses the problem of discrepancies between correction values ​​and actual drift conditions that may arise from relying solely on preset target data drift patterns by introducing a target data drift change rate. Specifically, the method first analyzes the raw data from the vehicle condenser's outlet pressure sensor over a preset time period prior to the current moment and calculates the data differences between multiple sub-raw data sets, thereby dynamically capturing the actual rate of change of sensor data drift. This quantification of the drift change rate allows for the subsequent determination of the target data correction value, considering not only the overall drift pattern but also the real-time dynamic intensity of the drift. Consequently, the target data correction value more accurately reflects the actual drift of the sensor in future moments, providing a more reliable data foundation for condenser control.

[0072] Through the above technical solution, this application can more accurately predict and compensate for future data drift of the outlet pressure sensor. Compared with correction based solely on a preset drift pattern, this application, by combining the target data drift change rate, allows the correction value to dynamically adapt to the actual rate and intensity of sensor drift, significantly improving the accuracy and real-time performance of data correction. This improvement ensures that the condenser obtains accurately corrected sensor data under various operating conditions, thereby optimizing the condenser's control strategy, improving its operating efficiency and stability, and effectively avoiding performance degradation or increased energy consumption due to inaccurate data correction.

[0073] This application further proposes a method for determining the target data correction value of the outlet pressure sensor at each subsequent detection time after the current time when the target data drift mode is linear drift.

[0074] When the target data drift pattern is linear, the target data correction value for the outlet pressure sensor at each subsequent detection time after the current time is determined based on the target data drift pattern and the target data drift change rate, including: Determine the target duration; the target duration is the time between the subsequent detection time and the target time, and the target time is the time corresponding to the first data in the original data; based on the target data drift pattern, process the target data drift change rate and the target duration to obtain the target data correction value of the outlet pressure sensor at the subsequent detection time.

[0075] Specifically, the target duration refers to the time span from the moment corresponding to the first data point in the original data (i.e., the target moment) to subsequent detection moments. For example, if the target moment is T0 and the subsequent detection moment is T_k, then the target duration can be expressed as T_k - T0. Here, the moment corresponding to the first data point in the original data can be understood as the starting point of the original data sequence used to analyze the drift trend. Further, based on the target data drift pattern, the target data drift change rate and target duration are processed to obtain the target data correction value for the outlet pressure sensor at subsequent detection moments. When the target data drift pattern is determined to be linear drift, this means that the sensor's drift trend can be approximated as a linearly changing pattern over time. In this case, the target data correction value can be calculated by multiplying the target data drift change rate by the target duration, i.e.: Target data correction value = Target data drift change rate × Target duration. This processing method aims to predict the drift amount at future moments based on the identified linear drift trend, thereby providing corresponding corrections for the data at subsequent detection moments.

[0076] This application's solution addresses the issue of potentially inaccurate or inefficient correction value calculations under general drift patterns by explicitly calculating the correction value using the target data drift change rate and target duration when the target data drift pattern is identified as linear drift. Because linear drift patterns are predictable, directly linking the drift change rate to the time span enables rapid and relatively accurate prediction of future drift amounts. This approach allows the system to provide more customized correction strategies for specific and common drift types, avoiding the computational complexity and potential errors associated with using a general model.

[0077] Through the above technical solution, when the outlet pressure sensor exhibits a linear data drift trend, the target data correction value for subsequent detection moments can be accurately predicted and calculated based on a defined linear drift pattern and drift change rate, combined with the target duration. This significantly improves the accuracy and real-time performance of data correction, especially under conditions where the sensor exhibits stable linear drift. Compared to using general or more complex drift models for correction, this solution simplifies the calculation process, reduces system resource consumption, and enhances the reliability of condenser control, thereby effectively avoiding control deviations caused by sensor linear drift.

[0078] In some preferred embodiments, it is assumed that within a preset time period prior to the current moment, analysis of the raw data determines that the outlet pressure sensor exhibits a linear data drift trend, and the target data drift rate is calculated to be 0.01 MPa / s. If the target time (the time corresponding to the first data point in the raw data) is T0, and a subsequent detection time is T0+10 seconds, then the target duration is 10 seconds. According to this scheme, the target data correction value for the outlet pressure sensor at that subsequent detection time will be calculated as: 0.01 MPa / s × 10s = 0.1 MPa. This means that at T0+10 seconds, the sensor reading is expected to be 0.1 MPa higher than its true value, thus requiring a corresponding downward correction. In this way, the system can provide precisely corrected pressure data for condenser control, ensuring optimized control of the condenser under various operating conditions.

[0079] This application further proposes that the control steps for the above-mentioned condenser include: The sum of the target data correction value and the data from the outlet pressure sensor at each subsequent detection time is used as the correction data for the outlet pressure sensor at each subsequent detection time. The correction data is then input into the preset PID control model to obtain the control strategy for the condenser output by the preset PID control model.

[0080] Specifically, the correction data refers to the data obtained by superimposing or summing the original detection data of the outlet pressure sensor at each subsequent detection moment with the target data correction value determined for that moment. Its purpose is to compensate for the error caused by drift in the original sensor data, thereby obtaining pressure data that more closely approximates the true physical quantity, providing accurate input for subsequent control decisions. The preset PID control model can be understood as a feedback controller widely used in industrial control, which adjusts the control output by calculating the weighted sum of three parameters: proportional (P), integral (I), and derivative (D). Specifically, this model receives the correction data as input and compares it with the preset target operating pressure of the condenser. Based on the magnitude, accumulation, and rate of change of the deviation, it generates a control strategy in real time to adjust the condenser's operating state. Its purpose is to ensure that the condenser can operate stably at its optimal operating point under various operating conditions, achieving precise pressure control and efficient energy utilization. In practical applications, the control strategy of the condenser is specifically a set of instructions that guide the condenser actuators (such as fan speed, compressor power, electronic expansion valve opening, etc.) to perform actions. For example, it may include adjusting the speed of the condenser fan to change the heat dissipation, or adjusting the operating frequency of the compressor to change the refrigerant flow. Its purpose is to maintain the condenser outlet pressure within the desired range, thereby optimizing the performance of the entire refrigeration or air conditioning system.

[0081] The proposed solution first sums the raw data from the outlet pressure sensor at subsequent detection times with the corresponding target data correction value to generate corrected data. This effectively eliminates errors caused by sensor data drift, ensuring the accuracy of the data input to the control system. Subsequently, this high-precision corrected data is input into a preset PID control model. Because the PID control model, based on precise input data and combined with proportional, integral, and derivative control algorithms, can perform real-time, dynamic, and precise adjustments to the condenser's operating state, the condenser can respond quickly and operate stably according to actual operating conditions and target setpoints. This overcomes the control hysteresis or over-adjustment problems that may occur in traditional control schemes when sensor data drifts.

[0082] Through the above technical solution, this application provides a more accurate and robust condenser control method. By combining sensor drift correction values ​​with the original data to generate high-confidence correction data, the accuracy of the control system's perception of the actual operating state of the condenser is greatly improved. Based on this, processing the correction data using a preset PID control model enables refined and adaptive control of the condenser operation, effectively avoiding misjudgments and control deviations caused by sensor drift. Compared to control based solely on uncorrected sensor data, the solution of this application significantly improves the operational stability, energy efficiency ratio, and response speed of the condenser system, while extending the service life of related components, providing more reliable operational assurance for the air conditioning or thermal management systems of new energy vehicles.

[0083] This application further proposes a method that, before acquiring raw data from the vehicle's condenser outlet pressure sensor over a preset time period prior to the current moment, includes: Obtain the initial standard deviation threshold, the second correspondence, and the target operating environment information of the vehicle within a preset time period before the current time; the second correspondence includes a one-to-one correspondence between multiple operating environment information and multiple adjustment coefficients; the adjustment coefficient corresponding to the target operating environment information in the second correspondence is used as the target adjustment coefficient; the product of the initial standard deviation threshold and the target adjustment coefficient is used as the preset standard deviation threshold.

[0084] Specifically, the initial standard deviation threshold can be understood as a benchmark threshold used to judge the drift trend of sensor data under standard or baseline operating conditions. This initial standard deviation threshold can be set through extensive historical data analysis and expert experience to reflect the normal fluctuation range of the sensor under ideal conditions. The second correspondence refers to a pre-established mapping relationship that associates different operating environment information with corresponding adjustment coefficients. Multiple operating environment information can include various environmental parameters or combinations thereof, such as the vehicle's geographical altitude, average vehicle speed, and ambient temperature. Multiple adjustment coefficients are correction factors determined based on the degree of influence of different operating environments on sensor data fluctuations. For example, under certain extreme operating conditions, the normal fluctuation range of sensor data may be larger, and the corresponding adjustment coefficient will be greater than 1; while under certain stable operating conditions, the fluctuation range is smaller, and the adjustment coefficient may be close to or less than 1.

[0085] The second correspondence can be obtained through statistical analysis and machine learning training of historical data of the vehicle under different operating environments to ensure its accuracy and applicability. Target operating environment information refers to the characteristics of the vehicle's actual operating environment within a preset time period prior to the current moment. This information can be acquired in real time through onboard sensors (such as GPS modules, speed sensors, temperature sensors, etc.) or read through the onboard diagnostic system (OBD). The target adjustment coefficient refers to the adjustment coefficient found in the second correspondence that matches the target operating environment information, based on the current target operating environment information. This adjustment coefficient is used to dynamically correct the initial standard deviation threshold. Therefore, the preset standard deviation threshold is the final threshold obtained by multiplying the initial standard deviation threshold by the target adjustment coefficient. This preset standard deviation threshold is a threshold dynamically adjusted based on the current actual operating environment of the vehicle and is used to subsequently determine whether the outlet pressure sensor exhibits a data drift trend.

[0086] This application's solution effectively solves the problem of poor adaptability of traditional fixed thresholds in complex and variable operating environments by introducing a mechanism for dynamically adjusting a preset standard deviation threshold. Specifically, before determining whether there is a data drift trend in the outlet pressure sensor, the actual operating environment information of the current vehicle is first obtained. Given the influence of different operating environments on the fluctuation characteristics of sensor data, a pre-established second correspondence can associate specific operating environment information with corresponding adjustment coefficients. By querying this second correspondence, a target adjustment coefficient matching the current target operating environment information can be obtained. Subsequently, the initial standard deviation threshold is multiplied by this target adjustment coefficient to obtain a preset standard deviation threshold that is dynamically adjusted according to the current operating environment. It is precisely because this threshold can adaptively adjust according to the actual operating environment that subsequent data drift trend judgments are more accurate, avoiding misjudgments or omissions caused by environmental changes.

[0087] Through the above technical solution, this application can dynamically adjust the preset standard deviation threshold used to judge data drift trends according to the actual operating environment of the vehicle. Compared with the traditional method of using a fixed threshold, the solution of this application significantly improves the accuracy and robustness of data drift trend judgment. Specifically, under different operating environments such as altitude, vehicle speed, or temperature, the normal fluctuation range of sensor data may vary significantly. The dynamically adjusted threshold can better adapt to these changes, thereby effectively avoiding false alarms or missed alarms caused by threshold mismatch under complex operating conditions, and ensuring the reliability and efficiency of condenser control.

[0088] Specifically, when obtaining the target operating environment information of a vehicle within a preset time period prior to the current moment, the following methods can be used.

[0089] In the aforementioned method for processing operating data of new energy vehicle condensers, obtaining the target operating environment information of the vehicle within a preset time period prior to the current moment includes: Obtain the third correspondence relationship and the average altitude and average speed of vehicles within a preset time period before the current time; the third correspondence relationship includes a one-to-one correspondence between multiple first information and multiple operating environments, and the first information includes the altitude range and the speed range; the operating environment corresponding to the first information in the third correspondence relationship that matches the average altitude and average speed is taken as the target operating environment information.

[0090] The third mapping relationship can be understood as a pre-established mapping table or database used to associate the vehicle's operating status within a specific altitude and speed range with a specific operating environment. For example, this third mapping relationship can be stored in the vehicle's controller or a cloud server for querying when needed. Specifically, the first information refers to the combined information describing the vehicle's operating status, including the altitude range and speed range. The altitude range can be defined as a series of continuous altitude intervals, such as 0-500 meters, 500-1000 meters, etc. The speed range can be defined as a series of continuous speed intervals, such as 0-60 km / h, 60-120 km / h, etc.

[0091] By combining altitude and speed ranges, multiple unique primary information sets can be generated, each corresponding to a specific operating environment. For example, an altitude of 0-500 meters and a speed of 0-60 km / h might correspond to a "low-speed urban" operating environment; an altitude of 1000-2000 meters and a speed of 80-120 km / h might correspond to a "high-speed mountain" operating environment. Average altitude and average speed refer to the average altitude and speed of the vehicle's location over a preset time period prior to the current moment. This data can be collected in real-time using the vehicle's built-in Global Positioning System (GPS) module and speed sensor, and then averaged.

[0092] Using the operating environment corresponding to the first piece of information in the third correspondence that matches the average altitude and average vehicle speed as the target operating environment information means that the system searches for the first piece of information that best matches the currently calculated average altitude and average vehicle speed in the third correspondence. Once a matching first piece of information is found, the operating environment corresponding to that first piece of information is determined as the current target operating environment information. For example, if the calculated average altitude is 300 meters and the average vehicle speed is 50 kilometers per hour, the system will search for the first piece of information in the third correspondence that contains "altitude range 0-500 meters" and "vehicle speed range 0-60 kilometers per hour", and use its corresponding operating environment (such as "urban low speed") as the target operating environment information.

[0093] This application's solution obtains the vehicle's average altitude and average speed over a preset time period prior to the current moment, and uses a preset third correspondence to map these dynamically changing operating parameters to specific operating environment information. Because the vehicle's operating environment (such as urban, mountainous, or highway environments) significantly affects the data characteristics of the condenser outlet pressure sensor—for example, the sensor's response or drift pattern may differ at different altitudes and speeds—accurately identifying the target operating environment information provides a more targeted basis for subsequently determining the preset standard deviation threshold. This dynamic adjustment mechanism based on the actual operating environment makes the judgment of data drift trends more accurate, avoiding misjudgments or omissions that may occur when using a single fixed threshold.

[0094] The above technical solution enables the dynamic determination of target operating environment information based on the vehicle's actual operating conditions (average altitude and average speed). Compared to solutions that do not consider the operating environment or only use static environmental parameters, this method can more accurately reflect the vehicle's true operating conditions, allowing the preset standard deviation threshold used to judge sensor data drift trends to better adapt to the current environment. This improves the accuracy and robustness of data drift trend judgment, avoids misjudgments caused by environmental changes, and ultimately enhances the precision and reliability of condenser control.

[0095] In the field of thermal management for new energy vehicles, the condenser, as a core component, is crucial for accurate monitoring of its operating status. However, existing data processing methods often struggle to effectively identify and compensate for subtle signal drift caused by long-term sensor aging. This problem is particularly pronounced when data acquisition frequencies are dynamically adjusted for energy saving, leading to data sparsity, and when conventional noise smoothing logic misjudges and masks the true drift trend. This not only causes the control system to make decisions based on inaccurate data, resulting in long-term overload and accelerated wear of related components, but also poses a hidden threat to the overall vehicle energy efficiency and the lifespan of critical components, while failing to trigger traditional fault warnings.

[0096] In this regard, a specific embodiment of this application also discloses a new energy vehicle condenser operation data processing system, the system comprising: an acquisition device and a processing device; the acquisition device is used to acquire raw data of the outlet pressure sensor of the vehicle's condenser within a preset time period before the current moment; the processing device is used to determine whether there is a data drift trend of the outlet pressure sensor based on the raw data; if there is a data drift trend of the outlet pressure sensor, determine a target data drift mode among multiple data drift modes of the outlet pressure sensor based on the raw data; determine a target data correction value of the outlet pressure sensor at each detection time after the current moment based on the target data drift mode; and control the condenser based on the target data correction value at each detection time and the data of the outlet pressure sensor at each detection time.

[0097] The system described in this application, through the configuration of acquisition and processing devices, achieves intelligent drift identification, pattern determination, and data correction of the operating data from the condenser outlet pressure sensor, thereby enabling precise control of the condenser. This systematic solution effectively addresses the shortcomings of traditional methods in identifying and compensating for minute sensor signal drift, avoiding misjudgments in the control system and component overload caused by inaccurate data, thus improving overall vehicle energy efficiency and the lifespan of key components.

[0098] The above embodiments have described a specific method for acquiring raw data from the outlet pressure sensor of the vehicle's condenser within a preset time period prior to the current moment, determining whether the outlet pressure sensor exhibits a data drift trend based on the raw data, determining a target data drift mode among multiple data drift modes of the outlet pressure sensor if a data drift trend exists, determining a target data correction value for the outlet pressure sensor at each detection time after the current moment based on the target data drift mode, and controlling the condenser based on the target data correction value at each detection time and the data from the outlet pressure sensor at each detection time. It should be emphasized that the system implementation of this application achieves this by assigning these functions to specific devices.

[0099] Specifically, the acquisition device can be configured to be directly electrically connected to the outlet pressure sensor to acquire the analog or digital signals output by the sensor. For example, the acquisition device can be a data acquisition module integrated into the vehicle's electronic control unit (ECU), or a standalone sensor interface unit containing an analog-to-digital converter (ADC) and a data buffer to periodically read and store sensor data.

[0100] The processing device can be a microcontroller, digital signal processor (DSP), or a vehicle's central processing unit (CPU), programmed to execute the aforementioned data processing logic. For example, the processing device can receive raw data streams transmitted by the acquisition device and run a pre-defined algorithm to analyze data trends, identify drift patterns, and calculate corresponding correction values. Subsequently, the processing device combines these correction values ​​with real-time sensor data to generate instructions for controlling the condenser (e.g., adjusting cooling fan speed or compressor operating status). The processing device may also include memory for storing historical data, a drift model library, and control strategies.

[0101] This application proposes a data processing system for the operation of a condenser in a new energy vehicle, aiming to solve the problem of effectively identifying and compensating for minute signal drift caused by sensors in existing technologies. Traditional systems often struggle to effectively identify and compensate for minute signal drift caused by long-term sensor aging, especially under conditions of data sparsity and misjudgment by conventional noise smoothing logic. This not only leads to the control system making decisions based on inaccurate data, causing long-term overload and accelerated wear of related components, but also poses a hidden threat to the vehicle's energy efficiency and the lifespan of critical components.

[0102] Compared with existing technologies, the system of this application achieves intelligent and automated processing of condenser outlet pressure sensor data by clearly configuring the acquisition and processing devices. The acquisition device can stably and accurately collect raw data, providing a reliable foundation for subsequent analysis. The processing device can efficiently perform data drift trend judgment, data drift pattern recognition, and determination of target data correction values, thereby ensuring that the control system always makes decisions based on pressure data closest to the true value. This systematic solution effectively avoids problems such as over-driving of cooling fans and long-term high-load operation of compressors caused by inaccurate sensor data in traditional systems, thereby extending the service life of related components, reducing energy consumption, and improving the overall operational reliability and economy of new energy vehicles. Therefore, the system of this application has significant progress in improving the intelligent and refined control level of the thermal management system of new energy vehicles.

[0103] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for processing condenser operation data in new energy vehicles, characterized in that, include: Obtain the raw data from the vehicle's condenser outlet pressure sensor within a preset time period prior to the current moment; Determine whether the outlet pressure sensor exhibits a data drift trend based on the raw data; In the case where the outlet pressure sensor exhibits a data drift trend, a target data drift mode is determined from among multiple data drift modes of the outlet pressure sensor based on the original data. The target data correction value of the outlet pressure sensor at subsequent detection times after the current time is determined based on the target data drift pattern. The condenser is controlled based on the target data correction value at each subsequent detection time and the data from the outlet pressure sensor at each subsequent detection time.

2. The method for processing condenser operation data in a new energy vehicle according to claim 1, characterized in that, Based on the raw data, determine the target data drift mode among multiple data drift modes of the outlet pressure sensor, including: The original data is input into the target Kalman filter model to obtain the enhanced data output by the target Kalman filter model; the target Kalman filter model is used to enhance the data drift trend of the original data. The target data drift mode among multiple data drift modes of the outlet pressure sensor is determined based on the enhanced data.

3. The method for processing condenser operation data in a new energy vehicle according to claim 2, characterized in that, Based on the enhanced data, a target data drift mode is determined among multiple data drift modes of the outlet pressure sensor, including: Determine the target data features of the enhanced data; For each of the multiple data drift patterns, determine the feature similarity between the preset data features of the data drift pattern and the target data features; The data drift pattern with the highest corresponding feature similarity is taken as the target data drift pattern.

4. The method for processing condenser operation data in a new energy vehicle according to claim 1, characterized in that, The raw data includes sub-raw data for each time period prior to the current time. Determining whether the outlet pressure sensor exhibits a data drift trend based on the raw data includes: For each of the multiple sub-raw data, determine the data difference between the sub-raw data and the sub-raw data at the previous time step; If the standard deviation of multiple data differences is less than a preset standard deviation threshold, and the minimum value among the multiple data differences is greater than a preset difference, it is determined that the outlet pressure sensor has a data drift trend.

5. The method for processing condenser operation data in a new energy vehicle according to claim 4, characterized in that, Based on the target data drift pattern, the target data correction value of the outlet pressure sensor is determined for each subsequent detection time after the current time, including: Determine the target data drift rate of change of the original data based on multiple data differences; The target data correction value of the outlet pressure sensor for each subsequent detection time after the current time is determined based on the target data drift pattern and the target data drift change rate.

6. The method for processing operating data of a condenser in a new energy vehicle according to claim 5, characterized in that, When the target data drift pattern is linear drift, the target data correction value of the outlet pressure sensor for each subsequent detection time after the current time is determined based on the target data drift pattern and the target data drift change rate, including: Determine the target duration; the target duration is the time between the subsequent detection time and the target time, and the target time is the time corresponding to the first data in the original data; Based on the target data drift pattern, the target data drift change rate and the target duration are processed to obtain the target data correction value of the outlet pressure sensor at subsequent detection times.

7. The method for processing condenser operation data in a new energy vehicle according to claim 1, characterized in that, The condenser is controlled based on the target data correction value at each subsequent detection time and the data from the outlet pressure sensor at each subsequent detection time, including: The sum of the target data correction value at each subsequent detection time and the data of the outlet pressure sensor at each subsequent detection time is used as the correction data of the outlet pressure sensor at each subsequent detection time; The corrected data is input into a preset PID control model to obtain the control strategy of the condenser output by the preset PID control model.

8. The method for processing condenser operation data in a new energy vehicle according to claim 4, characterized in that, Before acquiring the raw data from the vehicle's condenser outlet pressure sensor for a preset time period prior to the current moment, the method further includes: The initial standard deviation threshold, the second correspondence, and the target operating environment information of the vehicle within a preset time period before the current moment are obtained; the second correspondence includes a one-to-one correspondence between multiple operating environment information and multiple adjustment coefficients. The adjustment coefficient corresponding to the target operating environment information in the second correspondence is used as the target adjustment coefficient; The product of the initial standard deviation threshold and the target adjustment coefficient is used as the preset standard deviation threshold.

9. A method for processing operating data of a condenser in a new energy vehicle according to claim 8, characterized in that, Obtain the target operating environment information of the vehicle within a preset time period prior to the current moment, including: Obtain the third correspondence relationship and the average altitude and average speed of the vehicle within a preset time period before the current moment; the third correspondence relationship includes a one-to-one correspondence between multiple pieces of first information and multiple operating environments, and the first information includes an altitude range and a speed range. The operating environment corresponding to the first information in the third correspondence that matches the average altitude and average vehicle speed is taken as the target operating environment information.

10. A data processing system for the operation of a condenser in a new energy vehicle, characterized in that, The system includes: Acquisition device and processing device; The acquisition device is used to acquire the raw data of the vehicle's condenser outlet pressure sensor within a preset time period before the current moment; The processing device is used to determine whether the outlet pressure sensor has a data drift trend based on the raw data; In the case where the outlet pressure sensor exhibits a data drift trend, a target data drift mode is determined from among multiple data drift modes of the outlet pressure sensor based on the original data. The target data correction value of the outlet pressure sensor is determined for each detection time after the current time based on the target data drift pattern. The condenser is controlled based on the target data correction value at each detection time and the data from the outlet pressure sensor at each detection time.

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