Battery aging prediction method and system based on data analysis

Abstract

The present invention provides a data-analysis-based battery aging prediction method and system, relating to the field of battery aging prediction. The method comprises: clustering usage features to obtain the proportions of multiple usage features, and constructing a battery aging predictor based on the proportions of multiple usage features; performing extreme driving compensation analysis based on real-time driving data to output a driving compensation coefficient; inputting battery monitoring data, real-time driving data, and environmental data into the battery aging predictor to output a predicted battery capacity decay ratio; and correcting the predicted battery capacity decay ratio based on the driving compensation coefficient to obtain a corrected battery capacity decay ratio as a prediction result. This method aims to address the technical problem that traditional battery aging prediction models are typically trained based on data under normal driving conditions, ignoring extreme driving behaviors that may occur in actual driving, resulting in inaccurate prediction results. This method can effectively improve the accuracy and reliability of battery aging predictions under extreme driving conditions.

Description

Technical Field

[0001] The present invention relates to the field of battery aging prediction, and in particular to a battery aging prediction method and system based on data analysis. Background Art

[0002] The battery aging process is the result of the combined action of multiple factors, mainly including the number of battery charge and discharge times, temperature fluctuations, charging speed, discharge depth, usage environment, and driving behavior. The battery's capacity decay and internal resistance increase are the main indicators for measuring the degree of battery aging, and these degradation processes are significantly affected by driving behavior. For example, frequent sudden acceleration and braking will increase the battery's instantaneous load, causing the internal battery temperature to rise rapidly, accelerating the degradation of the battery's chemical reaction; and long-term high-speed driving will keep the battery in a high-load state, causing the electrochemical reaction inside the battery to intensify, which also accelerates battery aging.

[0003] While many battery aging prediction methods based on standard driving behavior exist, these methods typically rely on prediction models trained on data from conventional driving scenarios. However, they fail to fully account for the individual driving behaviors of different drivers and the actual impact of different environmental factors on battery degradation. Consequently, traditional battery aging prediction models have significant limitations and are unable to accurately predict the degradation rate and health of batteries under extreme driving conditions. Summary of the Invention

[0004] The purpose of this invention is to provide a battery aging prediction method and system based on data analysis to address the technical problem that traditional battery aging prediction models are typically trained based on data under normal driving conditions, ignoring extreme driving behaviors that may occur in actual driving, resulting in inaccurate prediction results. The method and system include:

[0005] In a first aspect, the present invention provides a battery aging prediction method based on data analysis, comprising: clustering usage features based on historical usage data of a target vehicle to obtain a proportion of multiple usage features, and constructing a battery aging predictor based on the proportion of multiple usage features; when the vehicle is driven aggressively, collecting battery monitoring data, real-time driving data, and environmental data within a preset time zone, performing extreme driving compensation analysis based on the real-time driving data, and outputting a driving compensation coefficient; inputting the battery monitoring data, real-time driving data, and environmental data into the battery aging predictor, and outputting a predicted battery capacity attenuation ratio; and correcting the predicted battery capacity attenuation ratio based on the driving compensation coefficient to obtain a corrected battery capacity attenuation ratio as a battery aging prediction result for the preset time zone.

[0006] Preferably, the battery aging prediction method based on data analysis also includes: configuring usage characteristic indicators, wherein the usage characteristic indicators include driving surface characteristics, driving characteristics, acceleration characteristics, braking characteristics and charging characteristics; based on the usage characteristic indicators, performing usage characteristic clustering according to the historical usage data of the target vehicle to obtain the proportion of multiple usage characteristics, wherein the proportion of multiple usage characteristics includes the proportion of road surface characteristics, the proportion of driving characteristics, the proportion of acceleration characteristics, the proportion of braking characteristics and the proportion of charging characteristics.

[0007] Preferably, the battery aging prediction method based on data analysis also includes: randomly selecting a first usage characteristic indicator and obtaining multiple first characteristic intervals of the first usage characteristic indicator; performing feature extraction on historical usage data according to the first usage characteristic indicator to obtain a first usage data set; based on the first usage data set, counting the feature proportions within the multiple first characteristic intervals, constructing a first usage feature ratio, and adding it to the multivariate usage feature ratio.

[0008] Preferably, the battery aging prediction method based on data analysis also includes: configuring a feature tolerance interval, and expanding the multivariate usage feature proportion according to the feature tolerance interval to obtain an expanded multivariate usage feature proportion; using the expanded multivariate usage feature proportion as a conditional constraint and the target vehicle model as a vehicle body constraint, performing information retrieval based on big data to obtain a sample battery monitoring data set, a sample driving data set, and a sample environment data set, as well as obtaining the battery capacity attenuation ratio of different sample battery monitoring data, sample driving data, and sample environment data in a historical time zone, and constructing a sample battery capacity attenuation ratio set; using the sample battery monitoring data set, sample driving data set, sample environment data set, and sample battery capacity attenuation ratio set to perform supervised training on a feedforward neural network until the model converges to obtain the battery aging predictor.

[0009] Preferably, the battery aging prediction method based on data analysis further includes: using the sample battery monitoring data set, the sample driving data set, the sample environment data set and the sample battery capacity attenuation ratio set as training data, and dividing them into K equal parts to obtain K training sets, wherein K is an integer greater than 10; using the K training sets to perform supervised training and verification on the feedforward neural network respectively until the model converges, thereby obtaining K battery aging prediction branches, which are integrated to construct the battery aging predictor.

[0010] Preferably, the battery aging prediction method based on data analysis also includes: performing driving characteristics, acceleration characteristics and braking characteristics analysis according to the real-time driving data, and calculating the average driving speed, the average acceleration and the average braking speed; calculating the historical average driving speed, the historical average acceleration and the historical average braking speed according to the historical usage data of the target vehicle; and calculating the driving compensation coefficient according to the average driving speed, the average acceleration, the average braking speed, the historical average driving speed, the historical average acceleration and the historical average braking speed.

[0011] Preferably, the battery aging prediction method based on data analysis also includes: setting the ratio of the average driving speed to the average historical driving speed as a driving compensation coefficient, setting the ratio of the average acceleration to the average historical acceleration as an acceleration compensation coefficient, and setting the ratio of the average braking speed to the average historical braking speed as a braking compensation coefficient; and obtaining the driving compensation coefficient by weighted calculation based on the driving compensation coefficient, the acceleration compensation coefficient and the braking compensation coefficient.

[0012] Preferably, the battery aging prediction method based on data analysis further includes: setting the ratio of the driving compensation coefficient to the historical maximum driving compensation coefficient as a compensation scale coefficient, and multiplying the compensation scale coefficient by K and rounding it to obtain Q, where Q is an integer greater than or equal to 1 and less than or equal to K; randomly selecting Q battery aging prediction branches from the K battery aging prediction branches of the battery aging predictor, performing prediction based on the battery monitoring data, real-time driving data, and environmental data, outputting Q predicted attenuation ratios, and calculating the average of the Q predicted attenuation ratios to obtain the predicted battery capacity attenuation ratio.

[0013] Preferably, the battery aging prediction method based on data analysis further includes: taking the product of the driving compensation coefficient and the predicted battery capacity attenuation ratio as the corrected battery capacity attenuation ratio.

[0014] In a second aspect, the present invention further provides a battery aging prediction system based on data analysis, which is used to execute a battery aging prediction method based on data analysis as described in the first aspect, including: a battery aging predictor construction module, which is used to cluster usage features based on historical usage data of the target vehicle, obtain the proportion of multivariate usage features, and construct a battery aging predictor based on the proportion of multivariate usage features; an extreme driving compensation analysis module, which is used to collect battery monitoring data, real-time driving data, and environmental data within a preset time zone when the vehicle is driving vigorously, perform extreme driving compensation analysis based on the real-time driving data, and output a driving compensation coefficient; a battery capacity decay prediction module, which is used to input the battery monitoring data, real-time driving data, and environmental data into the battery aging predictor and output a predicted battery capacity decay ratio; a battery capacity decay ratio correction module, which is used to correct the predicted battery capacity decay ratio based on the driving compensation coefficient to obtain a corrected battery capacity decay ratio as the battery aging prediction result for the preset time zone.

[0015] The embodiments of the present invention include the following advantages:

[0016] By clustering usage features based on the target vehicle's historical usage data, the proportion of multiple usage features is obtained, and a battery aging predictor is constructed based on the proportion of multiple usage features. Next, during intense driving, battery monitoring data, real-time driving data, and environmental data are collected within a preset time zone. Extreme driving compensation analysis is performed based on the real-time driving data, and a driving compensation coefficient is output. The battery monitoring data, real-time driving data, and environmental data are then input into the battery aging predictor, which outputs a predicted battery capacity decay ratio. Finally, the predicted battery capacity decay ratio is corrected based on the driving compensation coefficient to obtain a corrected battery capacity decay ratio as the battery aging prediction result for the preset time zone. In other words, by introducing a driving behavior compensation coefficient to compensate for the battery aging prediction results under intense driving conditions, the accuracy and reliability of battery aging prediction under extreme driving conditions can be effectively improved, thereby improving the overall performance and reliability of electric vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flowchart of the steps of a battery aging prediction method based on data analysis of the present invention;

[0018] Figure 2 The figure is a structural diagram of a battery aging prediction system based on data analysis of the present invention.

[0019] Description of reference numerals:

[0020] A battery aging predictor construction module 11, an extreme driving compensation analysis module 12, a battery capacity attenuation prediction module 13, and a battery capacity attenuation ratio correction module 14. DETAILED DESCRIPTION

[0021] This invention provides a battery aging prediction method and system based on data analysis. This method addresses the technical problem that traditional battery aging prediction models are typically trained based on data from conventional driving conditions, ignoring the extreme driving behaviors that may occur in real-world driving, resulting in inaccurate prediction results. By introducing a driving behavior compensation factor to compensate for battery aging predictions under extreme driving conditions, the accuracy and reliability of battery aging predictions under extreme driving conditions can be effectively improved, thereby enhancing the overall performance and reliability of electric vehicles.

[0022] Below, the technical solutions of the present invention will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments described herein. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. It should also be noted that, for the convenience of description, only the parts related to the present invention, rather than all, are shown in the accompanying drawings.

[0023] For example, see the attached Figure 1 The present invention provides a battery aging prediction method based on data analysis, which is applied to a battery aging prediction system based on data analysis, and specifically includes the following steps:

[0024] S1: Clustering usage features based on historical usage data of the target vehicle to obtain multivariate usage feature proportions, and constructing a battery aging predictor based on the multivariate usage feature proportions.

[0025] Furthermore, step S1 of the present invention further includes:

[0026] S11: Configuring usage characteristic indicators, wherein the usage characteristic indicators include driving road characteristics, driving characteristics, acceleration characteristics, braking characteristics, and charging characteristics.

[0027] Specifically, configuring usage characteristic indicators is crucial for battery aging prediction. These characteristics provide a comprehensive understanding of vehicle usage, particularly the impact of driver behavior and environmental conditions on battery aging. Properly configuring usage characteristic indicators not only improves prediction accuracy but also enables personalized adjustments and optimizes battery management. First, configure usage characteristic indicators. These indicators include road surface characteristics, driving characteristics, acceleration characteristics, braking characteristics, and charging characteristics. Road surface characteristics include road surface type, driving characteristics include driving speed, acceleration characteristics include acceleration force and frequency, braking characteristics include braking frequency and intensity, and charging characteristics include charging frequency and depth. By configuring road surface characteristics, driving characteristics, acceleration characteristics, braking characteristics, and charging characteristics, a comprehensive understanding of the key factors influencing battery aging can be achieved. These characteristics not only help predict battery degradation trends but also enable dynamic adjustments based on real-time data for more accurate battery management and aging prediction.

[0028] S12: Based on the usage characteristic indicators, the usage characteristics are clustered according to the historical usage data of the target vehicle to obtain the multi-dimensional usage characteristic proportion, wherein the multi-dimensional usage characteristic proportion includes the road surface characteristic proportion, the driving characteristic proportion, the acceleration characteristic proportion, the braking characteristic proportion and the charging characteristic proportion.

[0029] Furthermore, step S12 of the present invention further includes:

[0030] S121: Randomly select a first usage feature indicator and obtain multiple first feature intervals of the first usage feature indicator; S122: Perform feature extraction on historical usage data according to the first usage feature indicator to obtain a first usage data set; S123: Based on the first usage data set, count the feature proportions within the multiple first feature intervals, construct a first usage feature proportion, and add it to the multivariate usage feature proportion.

[0031] Specifically, any one of the multiple usage characteristic indicators is randomly selected as the first usage characteristic indicator. This indicator can be one of the driving surface characteristics, driving characteristics, acceleration characteristics, braking characteristics or charging characteristics. Taking driving speed as an example, this is the selected first usage characteristic indicator; in order to better analyze the first usage characteristic indicator (such as driving speed), the indicator can be divided into multiple characteristic intervals. These characteristic intervals will help understand the load and degradation effects of the battery at different driving speeds, for example, a low-speed interval (0 to 30 km / h), a medium-speed interval (31 to 60 km / h), a high-speed interval (61 to 120 km / h), and an ultra-high-speed interval (greater than 120 km / h).

[0032] Then, the first usage data set is extracted based on the historical usage data. This means that the system will screen and count the driving speed characteristic indicator from the vehicle's historical records, extract relevant values, and the historical usage data may include the vehicle's real-time driving speed, driving duration, driving route and other information to obtain the first usage data set. Further based on the first usage data set, the feature ratios within the multiple first feature intervals are counted, that is, the proportion of each feature interval in the overall data is counted; based on the statistical feature ratios, a first usage feature ratio is constructed. This ratio reflects the usage of the vehicle in different usage scenarios, where the driving feature ratio table is shown in Table 1:

[0033] Table 1: Driving characteristics ratio

[0034]

[0035] This table intuitively lists the proportion of each speed range, which is convenient for subsequent analysis and application.

[0036] Using the same method as that used to obtain the first usage feature proportion (driving feature proportion), feature cluster analysis is performed on other usage feature indicators (driving road surface characteristics, acceleration characteristics, braking characteristics, and charging characteristics) in turn to obtain the multivariate usage feature proportion. The multivariate usage feature proportion includes the road surface characteristic proportion, driving feature proportion, acceleration characteristic proportion, braking characteristic proportion, and charging characteristic proportion. For example, for driving road surface characteristics, features are extracted according to different road surface types (such as urban roads, highways, mountain roads, etc.), and the proportion of each road surface type is counted; for acceleration characteristics, intervals are divided according to different acceleration intensities or frequencies, and can usually be classified into sudden acceleration, smooth acceleration, and slow acceleration; for braking characteristics, features are distinguished according to the intensity of braking (sudden braking, smooth braking, and light braking); for charging characteristics, intervals can be divided according to charging methods (such as fast charging, slow charging, and charging frequency). The multivariate usage feature proportion table is shown in Table 2:

[0037] Table 2: Percentage of multiple usage characteristics

[0038]

[0039] Furthermore, step S1 of the present invention further includes:

[0040] S13: Configure a feature tolerance interval, and expand the proportion of the multi-use features according to the feature tolerance interval to obtain an expanded multi-use feature proportion; S14: Use the expanded multi-use feature proportion as a conditional constraint and the target vehicle model as a vehicle body constraint, perform information retrieval based on big data, obtain a sample battery monitoring data set, a sample driving data set, and a sample environment data set, as well as obtain the battery capacity attenuation ratio of different sample battery monitoring data, sample driving data, and sample environment data in a historical time zone, and construct a sample battery capacity attenuation ratio set.

[0041] Specifically, a feature tolerance interval is configured. The feature tolerance interval refers to a certain range of variation allowed within the interval of each usage feature to reflect fluctuations or abnormal situations that may occur during actual driving. Then, the proportion of the multiple usage features is expanded according to the feature tolerance interval. That is, based on the results of the tolerance interval, the proportion of each feature interval is recalculated to ensure the accuracy and breadth of the data. Through this method, the characteristics of the vehicle under different usage conditions can be more comprehensively captured. For example, the driving speed range is expanded. Assuming that the original range is 0 to 30 km / h and the tolerance range is ±5 km / h, the expanded range becomes 25 to 35 km / h. Similarly, for other acceleration, braking and other features, similar tolerance extensions are performed to obtain new feature interval proportions.

[0042] Next, with the proportion of the extended multivariate usage features as a conditional constraint and the target vehicle model as a vehicle body constraint, information retrieval is performed based on big data to obtain sample data similar to the target vehicle, and a sample battery monitoring data set, a sample driving data set, and a sample environment data set are obtained. Among them, the sample battery monitoring data includes battery voltage, current, temperature, SOC (state of charge) and other data; the sample driving data includes driver speed, acceleration, braking, driving route and other data; the sample environment data includes external environment data such as temperature, humidity, and terrain; further obtain the battery capacity attenuation ratio of different sample battery monitoring data, sample driving data and sample environment data in the historical time zone, and construct a sample battery capacity attenuation ratio set.

[0043] By configuring and expanding feature tolerances, a vehicle's diverse usage characteristics can be more accurately described. By combining the target vehicle model as a body constraint and performing information retrieval based on big data, we ultimately obtain sample battery monitoring, driving, and environmental datasets. We then calculate the capacity decay ratios for different sample batteries, forming a complete set of sample decay ratios. This process significantly improves the accuracy and reliability of battery aging predictions.

[0044] S15: Using the sample battery monitoring data set, the sample driving data set, the sample environment data set, and the sample battery capacity attenuation ratio set, supervised training is performed on a feedforward neural network until the model converges, thereby obtaining the battery aging predictor.

[0045] Furthermore, step S15 of the present invention further includes:

[0046] S151: Use the sample battery monitoring dataset, sample driving dataset, sample environment dataset and sample battery capacity attenuation ratio set as training data, and divide them into K equal parts to obtain K training sets, where K is an integer greater than 10; S152: Use the K training sets to perform supervised training and verification on the feedforward neural network respectively until the model converges, obtain K battery aging prediction branches, and integrate them to construct the battery aging predictor.

[0047] Specifically, the sample battery monitoring data set, the sample driving data set, the sample environment data set and the sample battery capacity attenuation ratio set are used as training data, and the training data are divided into K equal parts, where K is an integer greater than 10, and the specific value of K can be set according to actual needs, for example, K is set to 20; and K training sets are obtained.

[0048] A feedforward neural network is a common artificial neural network architecture, typically consisting of an input layer, one or more hidden layers, and an output layer. In this model, the input layer receives sample data (such as battery monitoring data, driving data, and environmental data), performs calculations in the hidden layers, and finally predicts the battery capacity decay rate through the output layer. Next, the feedforward neural network undergoes supervised training and validation using the K training sets. During each training step, the training set is used to train the model and optimize the neural network's weight parameters, while the validation set is used to verify the model's performance and ensure good predictive ability on unseen data. During training, gradient descent or other optimization algorithms (such as the Adam optimizer) are used to update the neural network's weights. After each training step, the validation set is used to evaluate the model's performance and calculate the error. The training process continues until the model's performance on the validation set stops improving, indicating convergence. K-fold cross-validation training is then performed to obtain K different model branches. Each model branch is trained on different training data and validated on a different validation set. Finally, a battery aging predictor is constructed by combining these K battery aging prediction branches.

[0049] By integrating K training branches to construct a battery aging predictor, more robust and accurate battery aging prediction results can be obtained, providing strong support for battery management of electric vehicles.

[0050] S2: When the vehicle is driven intensely, battery monitoring data, real-time driving data, and environmental data within a preset time zone are collected, extreme driving compensation analysis is performed based on the real-time driving data, and a driving compensation coefficient is output.

[0051] Furthermore, step S2 of the present invention further includes:

[0052] S21: Analyze the driving characteristics, acceleration characteristics and braking characteristics based on the real-time driving data, and calculate the average driving speed, average acceleration and average braking speed; S22: Calculate the historical average driving speed, historical average acceleration and historical average braking speed based on the historical usage data of the target vehicle.

[0053] Specifically, during intense vehicle driving, battery monitoring data, real-time driving data, and environmental data are collected within a preset time zone (e.g., the last hour). Battery monitoring data includes battery voltage, battery current, battery temperature, and SOC (State of Charge). Real-time driving data includes vehicle speed, driving characteristics, braking characteristics, and environmental data includes temperature and humidity. By collecting battery monitoring data, real-time driving data, and environmental data within a preset time zone, we can fully understand the performance changes of batteries under extreme driving conditions. This data not only provides accurate input for battery aging prediction, but also helps predict the battery's degradation rate and remaining service life under different environments and driving behaviors.

[0054] Next, based on the real-time driving data, the driving characteristics, acceleration characteristics, and braking characteristics are analyzed to calculate the average driving speed, average acceleration, and average braking speed. The average driving speed represents the average driving speed of the vehicle over a period of time; the average acceleration represents the average acceleration of the vehicle over a period of time, reflecting the acceleration intensity of the vehicle; and the average braking speed represents the average braking force of the vehicle over a period of time. This indicator reflects the frequency and intensity of braking. Furthermore, based on the historical usage data of the target vehicle, the historical average driving speed, historical average acceleration, and historical average braking speed are calculated. The historical average driving speed represents the average driving speed of the vehicle over a period of time; the historical average acceleration represents the average acceleration of the vehicle over a period of time; and the historical average braking speed represents the average braking of the vehicle over a period of time.

[0055] S23: Calculate the driving compensation coefficient based on the average driving speed, the average acceleration, the average braking speed, the historical average driving speed, the historical average acceleration, and the historical average braking speed.

[0056] Furthermore, step S23 of the present invention further includes:

[0057] S231: The ratio of the average driving speed to the average historical driving speed is set as the driving compensation coefficient, the ratio of the average acceleration to the average historical acceleration is set as the acceleration compensation coefficient, and the ratio of the average braking speed to the average historical braking speed is set as the braking compensation coefficient; S232: The driving compensation coefficient is obtained by weighted calculation based on the driving compensation coefficient, the acceleration compensation coefficient and the braking compensation coefficient.

[0058] Specifically, first, the ratio of the average driving speed to the average historical driving speed is set as the driving compensation coefficient, the ratio of the average acceleration to the average historical acceleration is set as the acceleration compensation coefficient, and the ratio of the average braking speed to the average historical braking speed is set as the braking compensation coefficient. The driving compensation coefficient reflects the deviation between the current driving speed of the vehicle and its historical driving state; the acceleration compensation coefficient reflects the difference between the current acceleration intensity and the historical acceleration state; and the braking compensation coefficient reflects the difference between the current braking force and the historical braking conditions.

[0059] Next, configure the driving compensation weight, acceleration compensation weight, and braking compensation weight. The sum of the driving compensation weight, acceleration compensation weight, and braking compensation weight is 1, reflecting the impact of each characteristic on battery aging. These weights can be adjusted based on actual experience or experimental data to ensure that each characteristic contributes reasonably to the compensation coefficient. Then, a weighted calculation is performed on the driving compensation coefficient, acceleration compensation coefficient, and braking compensation coefficient based on the driving compensation weight, acceleration compensation weight, and braking compensation weight. The weighted calculation result is used as the driving compensation coefficient.

[0060] S3: Inputting the battery monitoring data, real-time driving data and environmental data into the battery aging predictor, and outputting a predicted battery capacity attenuation ratio.

[0061] Furthermore, step S3 of the present invention further includes:

[0062] S31: The ratio of the driving compensation coefficient to the historical maximum driving compensation coefficient is set as the compensation scale coefficient, and the compensation scale coefficient is multiplied by K and rounded to obtain Q, where Q is an integer greater than or equal to 1 and less than or equal to K; S32: Q battery aging prediction branches are randomly selected from the K battery aging prediction branches of the battery aging predictor, and predictions are performed based on the battery monitoring data, real-time driving data, and environmental data. Q predicted attenuation ratios are output, and the predicted battery capacity attenuation ratio is obtained after average calculation.

[0063] Specifically, a driving compensation coefficient and a historical maximum driving compensation coefficient are obtained, wherein the driving compensation coefficient is a coefficient calculated based on driving, acceleration, and braking behaviors, and the historical maximum driving compensation coefficient is the value of the maximum driving compensation coefficient in all historical data; then, the ratio of the driving compensation coefficient to the historical maximum driving compensation coefficient is set as the compensation scale coefficient, and the compensation scale coefficient is multiplied by K and rounded to obtain Q, where Q is an integer greater than or equal to 1 and less than or equal to K. For example, assuming that the driving compensation coefficient is 1.5, the historical maximum driving compensation coefficient is 3, and K is 20, then Q is 1.5 / 3*20, which is equal to 10.

[0064] Then, Q battery aging prediction branches are randomly selected from the K battery aging prediction branches of the battery aging predictor, and the battery monitoring data, real-time driving data, and environmental data are respectively input into the Q battery aging prediction branches for prediction, and Q predicted attenuation ratios are output. The output Q predicted attenuation ratios are averaged to obtain the predicted battery capacity attenuation ratio.

[0065] By calculating the compensation scale coefficient based on the current driving state and selecting an appropriate number of battery aging prediction branches based on the compensation scale coefficient for predictive analysis, when the compensation scale coefficient is large, it means that the current driving conditions are significantly different from the historical maximum driving conditions. In this case, more prediction branches can be selected to capture more battery aging information and ensure the accuracy of the prediction results. When the compensation scale coefficient is small, it means that the current driving conditions are close to the historical conditions. The number of required prediction branches can be reduced, thereby saving computing resources. By dynamically adjusting the number of selected prediction branches through the compensation scale coefficient, it is possible to reduce unnecessary calculations and save computing resources while ensuring the accuracy of battery aging prediction.

[0066] S4: Correcting the predicted battery capacity attenuation ratio according to the driving compensation coefficient to obtain a corrected battery capacity attenuation ratio as the battery aging prediction result for the preset time zone.

[0067] Furthermore, step S4 of the present invention further includes:

[0068] S41: taking the product of the driving compensation coefficient and the predicted battery capacity attenuation ratio as the corrected battery capacity attenuation ratio.

[0069] Specifically, the predicted battery capacity decay ratio is corrected based on the driving compensation coefficient. This correction is achieved by multiplying the predicted battery capacity decay ratio by the driving compensation coefficient to obtain a corrected battery capacity decay ratio. This correction process makes the prediction more consistent with the impact of current driving behavior, particularly under extreme driving conditions (such as aggressive acceleration, high-speed driving, and frequent braking), and more accurately reflects the actual battery aging. Finally, the corrected battery capacity decay ratio is used as the battery aging prediction result for the preset time zone. By multiplying the driving compensation coefficient by the predicted battery capacity decay ratio, a more accurate battery aging prediction result is obtained. This process optimizes the battery degradation prediction by dynamically compensating for driving behavior, making it more suitable for aggressive driving conditions and improving the reliability and accuracy of the battery management system.

[0070] In summary, the battery aging prediction method based on data analysis provided by the present invention has the following technical effects:

[0071] By clustering usage features based on the target vehicle's historical usage data, the proportion of multiple usage features is obtained, and a battery aging predictor is constructed based on the proportion of multiple usage features. Next, during intense driving, battery monitoring data, real-time driving data, and environmental data are collected within a preset time zone. Extreme driving compensation analysis is performed based on the real-time driving data, and a driving compensation coefficient is output. The battery monitoring data, real-time driving data, and environmental data are then input into the battery aging predictor, which outputs a predicted battery capacity decay ratio. Finally, the predicted battery capacity decay ratio is corrected based on the driving compensation coefficient to obtain a corrected battery capacity decay ratio as the battery aging prediction result for the preset time zone. In other words, by introducing a driving behavior compensation coefficient to compensate for the battery aging prediction results under intense driving conditions, the accuracy and reliability of battery aging prediction under extreme driving conditions can be effectively improved, thereby improving the overall performance and reliability of electric vehicles.

[0072] In the second embodiment, based on the same inventive concept as the battery aging prediction method based on data analysis in the above embodiment, the present invention also provides a battery aging prediction system based on data analysis, please refer to the attached Figure 2, including: a battery aging predictor construction module 11, used to cluster usage features according to the historical usage data of the target vehicle, obtain the proportion of multivariate usage features, and construct a battery aging predictor based on the proportion of multivariate usage features; an extreme driving compensation analysis module 12, used to collect battery monitoring data, real-time driving data and environmental data in a preset time zone when the vehicle is driving vigorously, perform extreme driving compensation analysis based on the real-time driving data, and output a driving compensation coefficient; a battery capacity decay prediction module 13, used to input the battery monitoring data, real-time driving data and environmental data into the battery aging predictor, and output a predicted battery capacity decay ratio; a battery capacity decay ratio correction module 14, used to correct the predicted battery capacity decay ratio according to the driving compensation coefficient, and obtain a corrected battery capacity decay ratio as the battery aging prediction result for the preset time zone.

[0073] Furthermore, the battery aging prediction system based on data analysis is also used to: configure usage characteristic indicators, wherein the usage characteristic indicators include driving surface characteristics, driving characteristics, acceleration characteristics, braking characteristics and charging characteristics; based on the usage characteristic indicators, perform usage characteristic clustering according to the historical usage data of the target vehicle to obtain the proportion of multiple usage characteristics, wherein the proportion of multiple usage characteristics includes the proportion of road surface characteristics, the proportion of driving characteristics, the proportion of acceleration characteristics, the proportion of braking characteristics and the proportion of charging characteristics.

[0074] Furthermore, the battery aging prediction system based on data analysis is also used to: randomly select a first usage characteristic indicator and obtain multiple first characteristic intervals of the first usage characteristic indicator; perform feature extraction on historical usage data according to the first usage characteristic indicator to obtain a first usage data set; based on the first usage data set, count the feature proportions within the multiple first characteristic intervals, construct a first usage feature ratio, and add it to the multivariate usage feature ratio.

[0075] Furthermore, the battery aging prediction system based on data analysis is also used to: configure a feature tolerance interval, and expand the proportion of the multivariate usage features according to the feature tolerance interval to obtain an expanded multivariate usage feature proportion; use the expanded multivariate usage feature proportion as a conditional constraint and the target vehicle model as a vehicle body constraint, perform information retrieval based on big data, obtain a sample battery monitoring data set, a sample driving data set, and a sample environmental data set, and obtain the battery capacity attenuation ratio of different sample battery monitoring data, sample driving data, and sample environmental data in a historical time zone, and construct a sample battery capacity attenuation ratio set; use the sample battery monitoring data set, sample driving data set, sample environmental data set, and sample battery capacity attenuation ratio set to perform supervised training on a feedforward neural network until the model converges, thereby obtaining the battery aging predictor.

[0076] Furthermore, the data analysis-based battery aging prediction system is also used to: use the sample battery monitoring data set, the sample driving data set, the sample environmental data set, and the sample battery capacity attenuation ratio set as training data, and divide them into K equal parts to obtain K training sets, where K is an integer greater than 10; use the K training sets to perform supervised training and verification on the feedforward neural network respectively until the model converges, thereby obtaining K battery aging prediction branches, which are integrated to construct the battery aging predictor.

[0077] Furthermore, the battery aging prediction system based on data analysis is also used to: analyze the driving characteristics, acceleration characteristics and braking characteristics according to the real-time driving data, and calculate the average driving speed, the average acceleration and the average braking speed; calculate the historical average driving speed, the historical average acceleration and the historical average braking speed according to the historical usage data of the target vehicle; and calculate the driving compensation coefficient according to the average driving speed, the average acceleration, the average braking speed, the historical average driving speed, the historical average acceleration and the historical average braking speed.

[0078] Furthermore, the battery aging prediction system based on data analysis is also used to: set the ratio of the average driving speed to the average historical driving speed as a driving compensation coefficient, set the ratio of the average acceleration to the average historical acceleration as an acceleration compensation coefficient, and set the ratio of the average braking speed to the average historical braking speed as a braking compensation coefficient; and obtain the driving compensation coefficient by weighted calculation based on the driving compensation coefficient, the acceleration compensation coefficient, and the braking compensation coefficient.

[0079] Furthermore, the data analysis-based battery aging prediction system is further configured to: set a ratio of the driving compensation coefficient to a historical maximum driving compensation coefficient as a compensation scale coefficient, and multiply the compensation scale coefficient by K and round it to obtain Q, where Q is an integer greater than or equal to 1 and less than or equal to K; randomly select Q battery aging prediction branches from the K battery aging prediction branches of the battery aging predictor, perform predictions based on the battery monitoring data, real-time driving data, and environmental data, output Q predicted attenuation ratios, and calculate the average of the Q predicted attenuation ratios to obtain the predicted battery capacity attenuation ratio.

[0080] Furthermore, the battery aging prediction system based on data analysis is further configured to: use the product of the driving compensation coefficient and the predicted battery capacity attenuation ratio as the corrected battery capacity attenuation ratio.

[0081] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The battery aging prediction method based on data analysis and the specific examples in the aforementioned embodiment 1 are also applicable to the battery aging prediction system based on data analysis in this embodiment. Through the aforementioned detailed description of the battery aging prediction method based on data analysis, those skilled in the art can clearly understand the battery aging prediction system based on data analysis in this embodiment. Therefore, for the sake of brevity of the specification, it will not be described in detail here. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple. For relevant details, please refer to the method description.

[0082] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

[0083] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the present invention and its equivalents, the present invention is intended to include these modifications and variations.

Claims

1. A battery aging prediction method based on data analysis, characterized in that: include: Clustering usage features based on historical usage data of the target vehicle to obtain a proportion of multiple usage features, and building a battery aging predictor based on the proportion of the multiple usage features; When the vehicle is driven intensely, the battery monitoring data, real-time driving data, and environmental data within a preset time zone are collected, and an extreme driving compensation analysis is performed based on the real-time driving data to output a driving compensation coefficient; Inputting the battery monitoring data, real-time driving data and environmental data into the battery aging predictor and outputting a predicted battery capacity attenuation ratio; The predicted battery capacity attenuation ratio is corrected according to the driving compensation coefficient to obtain a corrected battery capacity attenuation ratio as the battery aging prediction result for the preset time zone.

2. The battery aging prediction method based on data analysis according to claim 1, characterized in that: Use the historical usage data of the target vehicle to cluster usage characteristics and obtain the proportion of multiple usage characteristics, including: Configuring usage characteristic indicators, wherein the usage characteristic indicators include road surface characteristics, driving characteristics, acceleration characteristics, braking characteristics, and charging characteristics; Based on the usage characteristic indicators, usage characteristic clustering is performed according to the historical usage data of the target vehicle to obtain the multivariate usage characteristic proportion, wherein the multivariate usage characteristic proportion includes the road surface characteristic proportion, the driving characteristic proportion, the acceleration characteristic proportion, the braking characteristic proportion and the charging characteristic proportion.

3. The battery aging prediction method based on data analysis according to claim 2, characterized in that: Obtain the proportion of multiple usage features, including: Randomly selecting a first usage characteristic indicator and obtaining a plurality of first characteristic intervals of the first usage characteristic indicator; Extracting features from the historical usage data according to the first usage feature indicator to obtain a first usage data set; Based on the first usage data set, feature proportions within the plurality of first feature intervals are counted to construct a first usage feature proportion, which is added to the multivariate usage feature proportion.

4. The battery aging prediction method based on data analysis according to claim 1, characterized in that: A battery aging predictor is constructed based on the proportion of the multivariate usage features, including: configuring a feature tolerance interval, and expanding the proportion of the multiple usage features according to the feature tolerance interval to obtain an expanded proportion of the multiple usage features; Using the proportion of the extended multivariate usage features as a conditional constraint and the target vehicle model as a vehicle body constraint, information retrieval is performed based on big data to obtain sample battery monitoring data sets, sample driving data sets, and sample environment data sets, as well as battery capacity attenuation ratios of different sample battery monitoring data, sample driving data, and sample environment data within historical time zones, to construct a sample battery capacity attenuation ratio set; The sample battery monitoring data set, the sample driving data set, the sample environment data set and the sample battery capacity attenuation ratio set are used to perform supervised training on a feedforward neural network until the model converges, thereby obtaining the battery aging predictor.

5. The battery aging prediction method based on data analysis according to claim 4, characterized in that: Obtaining the battery aging predictor, including: The sample battery monitoring data set, the sample driving data set, the sample environment data set, and the sample battery capacity attenuation ratio set are used as training data and divided into K equal parts to obtain K training sets, where K is an integer greater than 10; The K training sets are used to perform supervised training and verification on the feedforward neural network until the model converges, thereby obtaining K battery aging prediction branches, which are integrated to construct the battery aging predictor.

6. The battery aging prediction method based on data analysis according to claim 5, characterized in that: Performing extreme driving compensation analysis based on the real-time driving data and outputting a driving compensation coefficient includes: Analyzing driving characteristics, acceleration characteristics, and braking characteristics based on the real-time driving data to calculate average driving speed, average acceleration, and average braking speed; Based on the historical usage data of the target vehicle, the historical average driving speed, historical average acceleration and historical average braking speed are calculated; The driving compensation coefficient is calculated based on the average driving speed, the average acceleration, the average braking speed, the historical average driving speed, the historical average acceleration, and the historical average braking speed.

7. The battery aging prediction method based on data analysis according to claim 6, characterized in that: The driving compensation coefficient is calculated, including: The ratio of the average driving speed to the average historical driving speed is set as the driving compensation coefficient, the ratio of the average acceleration to the average historical acceleration is set as the acceleration compensation coefficient, and the ratio of the average braking speed to the average historical braking speed is set as the braking compensation coefficient; The driving compensation coefficient is obtained by weighted calculation based on the travel compensation coefficient, the acceleration compensation coefficient and the braking compensation coefficient.

8. The battery aging prediction method based on data analysis according to claim 7, characterized in that: The battery monitoring data, real-time driving data, and environmental data are input into the battery aging predictor, and a predicted battery capacity attenuation ratio is output, including: The ratio of the driving compensation coefficient to the historical maximum driving compensation coefficient is set as the compensation scale coefficient, and the compensation scale coefficient is multiplied by K and rounded to obtain Q, where Q is an integer greater than or equal to 1 and less than or equal to K; Q battery aging prediction branches are randomly selected from the K battery aging prediction branches of the battery aging predictor, predictions are made based on the battery monitoring data, real-time driving data, and environmental data, and Q predicted attenuation ratios are output. The predicted battery capacity attenuation ratio is obtained after mean calculation.

9. The battery aging prediction method based on data analysis according to claim 8, characterized in that: The product of the driving compensation coefficient and the predicted battery capacity attenuation ratio is used as the corrected battery capacity attenuation ratio.

10. The battery aging prediction system based on data analysis is characterized by: The steps for implementing the battery aging prediction method based on data analysis according to any one of claims 1 to 9 include: A battery aging predictor construction module is used to cluster usage features based on historical usage data of the target vehicle, obtain multivariate usage feature proportions, and construct a battery aging predictor based on the multivariate usage feature proportions; An extreme driving compensation analysis module is used to collect battery monitoring data, real-time driving data, and environmental data within a preset time zone when the vehicle is driving vigorously, perform extreme driving compensation analysis based on the real-time driving data, and output a driving compensation coefficient; a battery capacity attenuation prediction module, configured to input the battery monitoring data, real-time driving data, and environmental data into the battery aging predictor and output a predicted battery capacity attenuation ratio; A battery capacity attenuation ratio correction module is used to correct the predicted battery capacity attenuation ratio according to the driving compensation coefficient to obtain a corrected battery capacity attenuation ratio as the battery aging prediction result for the preset time zone.

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