Lithium battery dynamic charging method based on user behaviors and environmental conditions

Abstract

The invention discloses a lithium battery dynamic charging method based on user behaviors and environmental conditions, and relates to the technical field of lithium battery dynamic charging, and the method comprises the following steps: for a high-frequency user, in the dynamic charging process of a lithium battery, firstly collecting real-time charging data from all battery cells in the charging process of the current user, and establishing a first data set; through real-time data analysis and deep learning prediction, the performance state of the battery cell is accurately identified, the battery cell is divided into a normal charging unit and a low-performance charging unit, a targeted charging strategy is adopted, the low-performance charging unit adopts fixed low-power charging, the heating risk is reduced, aging is delayed, and the service life of the battery cell is prolonged. And the normal unit realizes dynamic power distribution through fuzzy logic control, preferentially distributes higher charging power to the high-performance battery cell, improves the charging efficiency, shortens the charging time, effectively solves the problems of energy waste and potential safety hazards in traditional synchronous charging, and remarkably improves the charging speed, the service life and the overall reliability.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium battery dynamic charging, and in particular to a lithium battery dynamic charging method based on user behavior and environmental conditions. Background Art

[0002] Dynamic charging of lithium batteries based on user behavior and environmental conditions is an intelligent charging technology that dynamically adjusts charging strategies by collecting real-time user usage habits (such as daily usage frequency, charging time period) and environmental conditions (such as temperature, humidity and air pressure). The system analyzes user device usage patterns and environmental changes to intelligently allocate charging power and charging time to extend battery life, optimize charging efficiency, and reduce the risk of loss caused by overcharging or fast charging. In high or low temperature environments, the system will automatically reduce the charging power, and before the peak period of user demand, it will give priority to replenishing power to ensure that the device's battery life is maximized.

[0003] In the dynamic charging process of lithium batteries, for users with high frequency of use, the existing technology usually adopts fast charging to quickly replenish electric energy, and synchronously charges all cells with fixed charging power (ensuring synchronous management). Over time, even if the design parameters of each cell in the lithium battery are exactly the same, due to aging, production errors and environmental differences, the performance of the cells will gradually become inconsistent in actual use. During synchronous charging, the performance of some cells may become poor, while the system still charges all cells at the same charging power, which will cause the charging efficiency of cells with poor performance to drop significantly, and more energy is converted into heat during the charging process, and electric energy cannot be stored efficiently. At the same time, cells with better performance may not be charged to the maximum because the charging efficiency of the overall system is dragged down by cells with poor performance. In this case, the charging time is extended and the user waiting time is increased, which seriously affects the user experience. In addition, fixed power synchronous charging will lead to more energy waste, aggravate battery aging, and affect the overall life and safety of the battery. Therefore, in the case of obvious performance differences, continuing to use fixed power synchronous charging will not only reduce charging efficiency and user experience, but also increase unnecessary power loss and potential safety hazards of the battery.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0005] The purpose of the present invention is to provide a dynamic charging method for lithium batteries based on user behavior and environmental conditions. Through real-time data analysis and deep learning prediction, the performance status of battery cells can be accurately identified, and they can be divided into normal charging units and low-performance charging units. Targeted charging strategies are adopted. Low-performance units are charged with fixed low power to reduce the risk of heat generation and delay aging. Normal units achieve dynamic power allocation through fuzzy logic control, and higher charging power is preferentially allocated to high-performance battery cells, thereby improving charging efficiency and shortening charging time. It effectively solves the problems of energy waste and safety hazards in traditional synchronous charging, and significantly improves the charging speed, life and overall reliability, so as to solve the problems in the above-mentioned background technology.

[0006] In order to achieve the above object, the present invention provides the following technical solution: a lithium battery dynamic charging method based on user behavior and environmental conditions, comprising the following steps: For high-frequency users, during the dynamic charging process of lithium batteries, real-time charging data is first collected from all cells in the current user's charging process to establish a first data set. At the same time, the factory charging data of each cell is called to establish a second data set as a benchmark. Compare and analyze the first data set with the second data set, extract key features reflecting the dynamic changes of the charging performance of the battery cell from the compared data, analyze the extracted features under the monitoring window, and quantify the extracted dynamic change features; Input the quantified feature data into a pre-trained deep learning model to predict the current state and future performance of the battery cell; Based on the prediction results of the deep learning model, all cells are divided into normal charging units and low-performance charging units; For low-performance charging units, a fixed low charging power is set for real-time charging to reduce the risk of heating and delay further aging of the battery cells. During the charging process, the battery cells corresponding to the low-performance charging units are marked and stored in the database for subsequent maintenance or replacement plans; For the battery cells corresponding to the normal charging units, fuzzy logic control is used to dynamically allocate the charging power in real time based on the changing characteristics of the battery cells' charging performance. During the dynamic power allocation process, higher charging power is preferentially allocated to high-performance battery cells to maximize the overall charging efficiency of the battery pack.

[0007] Preferably, the identification criteria for high-frequency users are based on the usage frequency, charge and discharge cycles of the device, and the consumption characteristics and behavior patterns of the battery.

[0008] Preferably, real-time charging data is collected from all cells in the current user's charging process to establish a first data set, and the specific steps are as follows: During the dynamic charging process of lithium batteries, charging data is first collected from each battery cell in real time; After completing data collection, pre-process the raw data to ensure the accuracy, continuity and consistency of the data; After data cleaning and preprocessing, the real-time charging data of all battery cells will be classified and structured to form the first data set. The data set is constructed in a hierarchical storage manner, that is, multi-dimensional classification by time, equipment and battery cell number to facilitate subsequent dynamic analysis and feature extraction. At the same time, the real-time data is compared and associated with the historical data to establish a time series model of the charging data to better capture the changing trend of battery cell performance.

[0009] Preferably, key features reflecting the dynamic changes in the charging performance of the battery cell are extracted, wherein the extracted key features include the growth ratio of the internal resistance of the battery cell after multiple charge and discharge cycles compared to the initial internal resistance and the offset of the charging time in the same power range compared to the time when it leaves the factory. Under the monitoring window, the growth ratio of the internal resistance of the battery cell after multiple charge and discharge cycles compared to the initial internal resistance and the offset of the charging time in the same power range compared to the time when it leaves the factory are analyzed, and an internal resistance growth quantification value and a charging time drift quantification value are generated respectively. The internal resistance growth quantification value is used to quantify the degree of increase in the internal resistance of the battery cell after multiple charge and discharge cycles, reflecting the decrease in the electrochemical activity and the energy conversion efficiency of the battery cell. The charging time drift quantification value is used to quantify the offset of the charging time in the same power range, reflecting the impact on the charging speed during the charging process.

[0010] Preferably, after obtaining the quantized value of internal resistance growth and the quantized value of charging time drift, the quantized value of internal resistance growth and the quantized value of charging time drift are input into a pre-trained deep learning model to generate a performance degradation index, and the current state and future performance of the battery cell are predicted through the performance degradation index.

[0011] Preferably, the performance degradation index generated when predicting the current state and future performance of the battery cell under the monitoring window is compared and analyzed with a preset performance degradation index reference threshold, and the battery cells in the lithium battery are divided. The specific division steps are as follows: If the performance degradation index is less than the performance degradation index reference threshold, the battery cell is classified as a normal charging unit. The battery cells in the normal unit are in a stable state and can store energy efficiently.

[0012] If the performance degradation index is greater than or equal to the performance degradation index reference threshold, the battery cell is classified as a low-performance charging unit, and the battery cells in the low-performance unit have obvious degradation or abnormality.

[0013] Preferably, under the monitoring window, the specific steps of analyzing the growth ratio of the internal resistance of the battery cell after multiple charge and discharge cycles compared to the initial internal resistance to generate the internal resistance growth quantification value are as follows: Collect real-time internal resistance data of each battery cell during multiple charge and discharge cycles within the monitoring window , and call its initial internal resistance at the factory as the reference value, where represents the internal resistance of the battery cell measured at the tth charging cycle, is the initial internal resistance of the battery cell; In order to quantify the increase in internal resistance after multiple charge and discharge cycles, the internal resistance growth rate of each cycle is calculated as follows: , where is the internal resistance growth rate of the tth cycle, represents the internal resistance of the battery cell measured at the t-1th charging cycle, is the correction term for the internal resistance growth due to ambient temperature, where is the ambient temperature at the tth cycle, is the ambient reference temperature; The accumulated internal resistance growth quantitative value in the monitoring window is calculated according to the internal resistance growth ratio of each cycle. The calculation expression of the internal resistance growth quantitative value is: , where is the quantitative value of internal resistance growth, which is used to quantify the overall internal resistance change trend of the battery cell within the monitoring window. n is the total number of charge and discharge cycles within the monitoring window. is the weight coefficient, which is used to reflect the importance difference between cycles. is a periodic correction term that captures the fluctuation characteristics of internal resistance over time.

[0014] Preferably, under the monitoring window, the specific steps of analyzing the offset of the charging time in the same power interval compared with the factory time to generate the charging time drift quantization value are as follows: In the dynamic charging process of lithium batteries, the charging time offset of the same power interval is defined and extracted. Assume that the charging process of each battery cell can be divided into multiple fixed power intervals. For each interval, record the actual charging time in the current charging process and compare it with the charging time of the battery cell in the same power interval when it leaves the factory. Calculate the charging time offset. The calculation expression is: , where and Respectively represent the starting point and end point of the power range, Indicates that the current charging cycle starts from the starting point Charging to the end The actual time required, The battery cell is from the starting point when it leaves the factory. Charging to the end The time required, is the charging time offset; After obtaining the charging time offset of each power interval, the next step is to calculate the charging time drift rate. The drift rate is defined as the ratio of the charging time offset of each interval to the charging time at the factory, which indicates the degree of change in charging performance. The calculation expression is: , where From the starting point Charging to the end Charging time drift rate, used to measure the degree of charging time deviation in each power interval; In order to comprehensively quantify the charging time drift value in the entire charging process, a nonlinear weighting function is used to weight the charging time drift rate of all power intervals, so that the contribution of the charging time drift rate of the power interval to the charging time drift quantification value is exponentially related to the drift amplitude. The calculation expression is: , where It is the quantitative value of charging time drift, which comprehensively quantifies the severity of time drift during the entire charging process. For the i-th power interval from the starting point Charging to the end The charge time drift rate, and Non-linear weighting coefficients, Controls the amplification degree of the charge time drift quantization value, Control the weighted influence of the large charging time drift rate on the quantized value of the overall charging time drift, and m represents the total number of power intervals in the monitoring window.

[0015] Preferably, for the battery cells corresponding to the normal charging units, fuzzy logic control is used to dynamically allocate the charging power in real time, and the specific steps are as follows: The charging priority is calculated based on the relationship between the performance degradation index of each battery cell and the performance degradation index reference threshold. The calculation expression is: , where is the charging priority of the jth battery cell. The higher the value, the higher the priority. is the performance degradation index of the jth battery cell, reflecting the current state of the battery cell. is the reference threshold of the performance degradation index; After evaluating the priority, fuzzy logic is used to determine the charging power allocation weight for each battery cell. The fuzzy logic rule combines the performance priority of the battery cell with the actual demand. The formula is as follows: , is the charging weight of the jth battery cell, which determines the proportion of power allocated. is a fuzzy logic control parameter used to amplify the charging weight of high-priority cells. N is the total number of normal charging unit cells. Indicates the charging priority of the kth battery cell; The actual charging power is dynamically allocated according to the charging weight of each battery cell. The total power is the maximum charging power of the battery pack. The dynamic allocation formula is as follows: , where is the charging power allocated to the jth battery cell, is the total available charging power of the battery pack.

[0016] In the above technical solution, the technical effects and advantages provided by the present invention are: The present invention uses real-time data analysis and deep learning prediction to accurately identify the performance status of the battery cells, divide the battery cells into normal charging units and low-performance charging units, and adopts targeted charging strategies to significantly improve the charging efficiency. Fixed low-power charging is adopted for low-performance units, which reduces the risk of heating and avoids high power aggravating the degradation of the battery cells, thereby effectively delaying the aging of the battery cells. For normal charging units, dynamic allocation of charging power is achieved through fuzzy logic control, and higher charging power is preferentially allocated to high-performance batteries, maximizing charging efficiency, shortening charging time, and improving user experience. Secondly, by specially marking low-performance batteries and storing them in a database, an accurate basis is provided for subsequent maintenance and replacement, ensuring the long-term stability and safety of the battery pack, effectively solving the energy waste and safety hazards in traditional synchronous charging, significantly improving the charging speed, life and overall reliability of the battery pack, and providing a more efficient and safe charging experience for high-frequency users. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0018] Figure 1 This is a flow chart of the method for dynamically charging a lithium battery based on user behavior and environmental conditions of the present invention. DETAILED DESCRIPTION

[0019] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0020] The present invention provides Figure 1 The method for dynamic charging of lithium batteries based on user behavior and environmental conditions shown includes the following steps: For high-frequency users, during the dynamic charging process of lithium batteries, real-time charging data is first collected from all cells in the current user's charging process to establish a first data set. At the same time, the factory charging data of each cell is called to establish a second data set as a benchmark. The identification criteria for high-frequency users are usually based on the frequency of use of the device, the charge and discharge cycle, and the battery consumption characteristics and behavior patterns. Specifically, high-frequency users refer to users who frequently use the device every day or multiple times and consume a large amount of power. Their performance includes: 1. The average daily usage time exceeds the set threshold (e.g. 8 hours); 2. The charging frequency is high, such as charging once a day or every 1-2 days; 3. The average number of charge and discharge cycles of the equipment is much higher than that of other user groups within a certain period of time (e.g. more than 30-50 cycles per month); 4. The deep discharge ratio is high, and the battery is often consumed to less than 20% before charging; 5. The equipment’s operating scenarios are complex, such as mobile phones used for frequent calls and video conferencing, or electric vehicles used for long-distance, high-frequency driving.

[0021] Through these indicators, the system can identify high-frequency users and adjust the charging strategy accordingly to ensure efficient battery management and optimized user experience.

[0022] Collect real-time charging data from all cells in the current user's charging process to establish a first data set. The specific steps are as follows: During the dynamic charging process of lithium batteries, the system first needs to collect charging data from each battery cell in real time. This process is achieved through multiple sensors installed in the battery management system (BMS), which can monitor and record the key parameters of each battery cell, including voltage, current, temperature, internal resistance, etc. These data are usually collected at a high frequency to ensure that the changes in status during the charging process can be reflected in real time. In order to ensure the accuracy of the data, the system needs to correct possible errors in real time through the self-calibration mechanism of the sensor to ensure that the data reflects the true status of the battery cell.

[0023] After completing the data collection, the system will preprocess the raw data. This process includes noise filtering, outlier processing, and time alignment of sensor data. Since the battery cell may be disturbed by external factors during operation, such as electromagnetic interference or accidental failure of the sensor, the collected raw data may contain abnormal fluctuations or deviations. The system uses a preset data cleaning algorithm to remove illogical abnormal data and ensure the accuracy, continuity, and consistency of the data. In addition, since the data collection frequency of different sensors may be different, the system needs to synchronize the data in time to ensure that the data of each battery cell can be compared within the same time window. This step is crucial because the relationship between parameters such as voltage, current, and temperature during the charging process needs to be captured at the exact time point.

[0024] After data cleaning and preprocessing, the system will classify and structure the real-time charging data of all cells to form the first data set. The construction of this data set takes into account the individual characteristics and real-time status of the cells, including the capacity, internal resistance, voltage stability, etc. of different cells, and groups and records them by cell number. The data set is constructed in a hierarchical storage manner, that is, multi-dimensional classification by time, equipment and cell number for subsequent dynamic analysis and feature extraction. In addition, the system will compare and associate real-time data with historical data to establish a time series model of charging data to better capture the changing trend of cell performance. Once established, the data will be securely stored in a local database or cloud, and a regular backup and update mechanism will be set up to ensure the long-term availability and security of the data.

[0025] The factory data of each battery cell is usually recorded during the production stage and reflects the initial performance of the battery cell. Through real-time monitoring and recording, the system can build a historical archive of the battery cell status to achieve data continuity and accuracy. The battery management system (BMS) in the existing technology already supports a variety of sensors to collect key parameters of the battery cell and store the factory data in the cloud or local database.

[0026] Compare and analyze the first data set with the second data set, extract key features reflecting the dynamic changes of the charging performance of the battery cell from the compared data, analyze the extracted features under the monitoring window, and quantify the extracted dynamic change features; Input the quantified feature data into a pre-trained deep learning model to predict the current state and future performance of the battery cell; The key features reflecting the dynamic changes of the charging performance of the battery cell are extracted, wherein the extracted key features include the growth ratio of the internal resistance of the battery cell compared to the initial internal resistance after multiple charge and discharge cycles and the offset of the charging time in the same power range compared to the time when the battery cell leaves the factory. Under the monitoring window, the growth ratio of the internal resistance of the battery cell compared to the initial internal resistance after multiple charge and discharge cycles and the offset of the charging time in the same power range compared to the time when the battery cell leaves the factory are analyzed, and the internal resistance growth quantification value and the charging time drift quantification value are generated respectively. The internal resistance growth quantification value is used to quantify the degree of increase in the internal resistance of the battery cell after multiple charge and discharge cycles, reflecting the decrease in the electrochemical activity and the energy conversion efficiency of the battery cell. The charging time drift quantification value is used to quantify the offset of the charging time in the same power range, reflecting the impact on the charging speed during the charging process.

[0027] After obtaining the quantized internal resistance growth quantization value and the charging time drift quantization value, the quantized internal resistance growth quantization value and the charging time drift quantization value are input into a pre-trained deep learning model to generate a performance degradation index, and the current state and future performance of the battery cell are predicted through the performance degradation index.

[0028] A pre-trained deep learning model means that before it is officially applied, the model has been fully trained with a large amount of historical data and feature data, so that it has the ability to recognize complex patterns and make accurate predictions. In the dynamic charging management of lithium batteries, the deep learning model has undergone multiple rounds of training and is able to capture the dynamic changes in the charging performance of the battery cell and predict the current state and future performance. The training process usually involves the use of supervised learning methods, that is, the deep learning model adjusts its internal parameters through a large amount of labeled data (such as historical charging performance data and battery cell degradation patterns) to minimize prediction errors. With the continuous iteration of the deep learning model, it can identify potential relationships and trends in multi-dimensional data (such as the quantification value of internal resistance growth and the quantification value of charging time drift), and learn how to accurately evaluate the state of the battery cell.

[0029] The deep learning model may adopt structures such as long short-term memory network (LSTM), Transformer or convolutional neural network (CNN), which are good at processing time series data and multivariate inputs. In the application of lithium batteries, the model not only analyzes the current quantitative value of internal resistance growth and charging time drift, but also builds complex time dynamic relationships in combination with historical charging data. This ability enables the deep learning model to identify early performance degradation signals and predict future charging performance changes. For example, it can predict which batteries may have performance bottlenecks in the next charging cycle through exponential change trends. This prediction function is especially important for high-frequency users, and can promptly remind the system to take preventive maintenance measures to prevent unexpected failures. Through the introduction of pre-trained deep learning models, the charging management system realizes data-driven intelligent management, which not only improves charging efficiency, but also ensures the safety and long life of the battery.

[0030] The internal resistance of the battery cell has a higher growth rate compared to the initial internal resistance after multiple charge and discharge cycles, indicating that the charging efficiency of the battery cell is getting worse. The reason is that the increase in the internal resistance of the battery cell will cause more electrical energy to be lost in the form of heat energy during the charging process, reducing the effective stored energy. As the internal resistance increases, the electrochemical reaction activity of the battery cell decreases, and the resistance to energy transmission between the electrodes increases, resulting in a decrease in the charging efficiency of the battery cell. In addition, the increase in internal resistance makes the battery cell more likely to heat up during charging, which not only reduces the charging speed, but also aggravates the aging of the battery cell, further deteriorating its performance. Therefore, a high internal resistance growth rate is a clear sign of a deterioration in the charging efficiency of the battery cell.

[0031] Under the monitoring window, the specific steps for analyzing the growth ratio of the cell internal resistance after multiple charge and discharge cycles compared to the initial internal resistance to generate the internal resistance growth quantitative value are as follows: Collect real-time internal resistance data of each battery cell during multiple charge and discharge cycles within the monitoring window , and call its initial internal resistance at the factory as the reference value, where represents the internal resistance of the battery cell measured at the tth charging cycle, It is the initial internal resistance of the battery cell, usually recorded during factory testing; At this stage, the data structure is a time series, which records the change in the internal resistance of the battery cell in each charge and discharge cycle.

[0032] In order to quantify the increase in internal resistance after multiple charge and discharge cycles, the internal resistance growth rate of each cycle is calculated as follows: , where is the internal resistance growth rate of the tth cycle, represents the internal resistance of the battery cell measured at the t-1th charging cycle, is the correction term for the internal resistance growth due to ambient temperature, where is the ambient temperature at the tth cycle, is the ambient reference temperature; This step reflects the nonlinear effect of ambient temperature on the internal resistance growth through an exponential term, avoiding the error that may be caused by a simple linear relationship. During long-term use, the performance degradation of the battery cell is not only related to the change in internal resistance, but also significantly affected by temperature. Therefore, introducing a temperature correction term can improve the accuracy of the model.

[0033] The accumulated internal resistance growth quantitative value in the monitoring window is calculated according to the internal resistance growth ratio of each cycle. The calculation expression of the internal resistance growth quantitative value is: , where is the quantitative value of internal resistance growth, which is used to quantify the overall internal resistance change trend of the battery cell within the monitoring window. n is the total number of charge and discharge cycles within the monitoring window. is a weight coefficient, which is used to reflect the difference in importance between cycles. It is not specifically limited here and can be changed dynamically according to actual conditions to adapt to the performance changes of the battery cells in different time periods or under different environmental conditions. Its main purpose is to reflect the difference in importance of different cycles in the calculation of the quantified value of internal resistance growth, and to give greater weight to the internal resistance changes in certain time periods. For example, when the temperature fluctuation of the battery cell is large or the charging and discharging is frequent in certain cycles, the weight of the cycle can be appropriately increased to more accurately reflect the impact of these cycles on the overall internal resistance change. In actual applications, the weight coefficient may be set according to a variety of factors, such as the battery's historical charging data, usage environment, temperature conditions, etc., to ensure that the model can flexibly adapt to various charging scenarios and improve the accuracy and practicality of the quantified value of internal resistance growth. is a periodic correction term that captures the fluctuation characteristics of internal resistance over time; This step generates a quantitative value of internal resistance growth by accumulating the internal resistance growth ratio per cycle and weighting the periodic change. The periodic correction term enables the model to capture the nonlinear changes of internal resistance at different time points, further improving the accuracy of the quantitative results.

[0034] It can be seen from the quantified value of internal resistance growth that, under the monitoring window, the growth ratio of the internal resistance of the battery cell after multiple charge and discharge cycles compared to the initial internal resistance is analyzed. The larger the performance value of the quantified value of internal resistance growth, the greater the increase in the internal resistance of the battery cell, which means that the electrochemical reaction efficiency of the battery cell is reduced, and the energy conversion efficiency during the charging process becomes worse, resulting in more energy loss in the form of heat, which seriously affects the charging efficiency and battery cell life. On the contrary, if the performance value of the quantified value of internal resistance growth is small, it means that the increase in the internal resistance of the battery cell is limited, the performance and energy conversion efficiency of the battery cell remain relatively stable, the loss during the charging process is small, and the charging efficiency does not drop significantly. The size of the quantified value of internal resistance growth provides a reliable reference for the battery management system, enabling the system to identify severely degraded batteries and take timely measures to ensure the overall performance and safety of the battery.

[0035] The higher offset of charging time in the same power range compared to the factory time indicates that the charging efficiency of the battery cell is getting worse. This is because within the same power range, the battery cell takes longer to fully charge, which means that the energy conversion process is no longer as efficient as it was when it left the factory. The main reason may be that the active materials inside the battery cell have degraded, causing the movement of lithium ions between the electrodes to slow down, or side reactions have occurred on the electrode surface, reducing the effective area of ​​the electrode and hindering normal electrochemical reactions. These phenomena will lead to a decrease in the reaction efficiency during the charging process, which ultimately manifests as a longer charging time.

[0036] In the monitoring window, the specific steps for analyzing the offset of the charging time in the same power range compared with the factory time to generate the charging time drift quantification value are as follows: In the dynamic charging process of lithium batteries, the charging time offset of the same power interval is defined and extracted. Assume that the charging process of each battery cell can be divided into multiple fixed power intervals. For each interval, record the actual charging time in the current charging process and compare it with the charging time of the battery cell in the same power interval when it leaves the factory. Calculate the charging time offset. The calculation expression is: , where and Respectively represent the starting point and end point of the power range, the unit is mAh or percentage (% power), Indicates that the current charging cycle starts from the starting point Charging to the end The actual time required, The battery cell is from the starting point when it leaves the factory. Charging to the end The time required, is the charging time offset; This step quantifies the offset of charging time in each power interval in preparation for subsequent analysis.

[0037] After obtaining the charging time offset of each power interval, the next step is to calculate the charging time drift rate. The drift rate is defined as the ratio of the charging time offset of each interval to the charging time at the factory, which indicates the degree of change in charging performance. The calculation expression is: , where From the starting point Charging to the end Charging time drift rate, used to measure the degree of charging time deviation in each power interval; The charge time drift rate quantifies the degree of variation between the actual charge time and the ideal charge time. A higher drift rate means that the charge time is significantly extended, reflecting that the charging efficiency of the battery cell has dropped significantly within this power range.

[0038] In order to comprehensively quantify the charging time drift value in the entire charging process, a nonlinear weighting function is used to weight the charging time drift rate of all power intervals, so that the contribution of the charging time drift rate of the power interval to the charging time drift quantification value is exponentially related to the drift amplitude. The calculation expression is: , where It is the quantitative value of charging time drift, which comprehensively quantifies the severity of time drift during the entire charging process. For the i-th power interval from the starting point Charging to the end The charge time drift rate, and Non-linear weighting coefficients, Controls the amplification degree of the charge time drift quantization value, Control the weighted influence of the large charging time drift rate on the quantized value of the overall charging time drift, and m represents the total number of power intervals in the monitoring window.

[0039] It can be seen from the charge time drift quantification value that, under the monitoring window, the larger the performance value of the charge time drift quantification value generated after analyzing the offset of the charge time in the same power range compared with the factory, the more serious the efficiency decline of the battery cell during the charging process, that is, the battery cell needs significantly longer time to complete charging in the same power range. This may reflect the degradation of the internal materials of the battery cell, the reduction of activity, or the deterioration of the electrochemical reaction efficiency. Conversely, the smaller the value of the charge time drift quantification value, the smaller the change in the charge time compared with the factory, the better the performance of the battery cell, and the less obvious the decline in charging efficiency. Therefore, the charge time drift quantification value is an important indicator for quantifying the dynamic changes in the charging performance of the battery cell, and can intuitively judge the current state of the battery cell and the health level of its charging efficiency.

[0040] The deep learning model is not specifically limited here, and can quantify the internal resistance growth value. and charging time drift quantification value Perform comprehensive analysis to generate a performance degradation index In order to realize the technical solution of the present invention, the present invention provides a specific implementation method; the performance degradation index The resulting calculation formula is: , where , They are the quantitative values ​​of internal resistance growth and charging time drift quantification value The preset scaling factor of , Both are greater than 0.

[0041] It can be seen from the performance degradation index that, under the monitoring window, the larger the performance value of the internal resistance growth quantization value generated by analyzing the growth ratio of the internal resistance of the battery cell after multiple charge and discharge cycles compared to the initial internal resistance, the larger the performance value of the charging time drift quantization value generated after analyzing the offset of the charging time in the same power range compared with the time when leaving the factory, that is, the larger the performance value of the performance degradation index generated when predicting the current state and future performance of the battery cell under the monitoring window, the more serious the performance degradation of the battery cell, and vice versa, it indicates that the performance of the battery cell is well maintained.

[0042] Based on the prediction results of the deep learning model, all cells are divided into normal charging units and low-performance charging units; The performance degradation index generated when predicting the current state and future performance of the battery cell under the monitoring window is compared and analyzed with the pre-set performance degradation index reference threshold, and the battery cells in the lithium battery are divided. The specific division steps are as follows: If the performance degradation index is less than the performance degradation index reference threshold, the battery cell is classified as a normal charging unit. The battery cells in the normal unit are in a stable state and can store energy efficiently.

[0043] If the performance degradation index is greater than or equal to the performance degradation index reference threshold, the battery cell is classified as a low-performance charging unit, and the battery cells in the low-performance unit have obvious degradation or abnormality.

[0044] This unit division helps the system adopt differentiated charging strategies for different types of cells, ensuring that normal cells and low-performance cells do not interfere with each other. The accuracy of state recognition is further enhanced through deep learning prediction.

[0045] For low-performance charging units, a fixed low charging power is set for real-time charging to reduce the risk of heating and delay further aging of the battery cells. During the charging process, the battery cells corresponding to the low-performance charging units are specially marked and stored in the database for subsequent maintenance or replacement plans; For the cells corresponding to the identified low-performance charging units, specific charging strategies are adopted to ensure safety and delay cell degradation. During the charging process, the cells of these low-performance charging units will be set to a fixed low charging power to slow down the charging speed, thereby reducing the risk of heating. Since low-performance cells are more likely to heat up under high-power charging, this low-power charging method not only reduces the risk of temperature rise, but also effectively avoids accelerated aging and extends the remaining life of the cells. At the same time, the system will specially mark the cells of low-performance units, and record their status information, charging history, etc. in the database to form a complete data archive. These data can be used to determine the degree of aging of the cells, predict the remaining life, and provide a basis for maintenance plans in the future. The marking information can help maintenance personnel quickly identify the cells that need further inspection or replacement to avoid greater problems caused by the accumulation of faults. Through this measure, not only is charging safety effectively guaranteed, but also detailed records are provided for future replacement and maintenance, realizing the intelligence and maintainability of the charging process.

[0046] For the cells corresponding to the normal charging units, fuzzy logic control is used to dynamically allocate the charging power in real time based on the charging performance change characteristics of the cells. During the dynamic power allocation process, higher charging power is preferentially allocated to high-performance cells to maximize the overall charging efficiency of the battery pack. For the cells corresponding to the normal charging units, fuzzy logic control is used to dynamically allocate the charging power in real time. The specific steps are as follows: The charging priority is calculated based on the relationship between the performance degradation index of each battery cell and the performance degradation index reference threshold. The calculation expression is: , where is the charging priority of the jth battery cell. The higher the value, the higher the priority. is the performance degradation index of the jth battery cell, reflecting the current state of the battery cell. It is the reference threshold of the performance degradation index, which is used to determine whether the battery cell is in a high-efficiency state; The charging priority is determined by comparing the current performance with the ideal state. The higher the charging priority of the battery cell, the more charging power needs to be allocated to maximize efficiency.

[0047] After evaluating the priority, fuzzy logic is used to determine the charging power allocation weight for each battery cell. The fuzzy logic rule combines the performance priority of the battery cell with the actual demand. The formula is as follows: , is the charging weight of the jth battery cell, which determines the proportion of power allocated. is a fuzzy logic control parameter used to amplify the charging weight of high-priority cells. N is the total number of normal charging unit cells. Indicates the charging priority of the kth battery cell; This step applies exponential weighting to priorities, ensuring that high-performance cells receive higher weights while the weights of low-priority cells are suppressed, thereby prioritizing the allocation of charging resources.

[0048] The actual charging power is dynamically allocated according to the charging weight of each battery cell. The total power is the maximum charging power of the battery pack. The dynamic allocation formula is as follows: , where is the charging power allocated to the jth battery cell, is the total available charging power of the battery pack; Through this allocation formula, the actual charging power of each battery cell is dynamically adjusted based on its charging priority weight to ensure optimal allocation of resources, so that high-efficiency batteries can obtain higher power support and improve overall charging efficiency.

[0049] Fuzzy logic control is used to dynamically allocate charging power to the cells of normal charging units in real time, giving priority to allocating higher charging power to high-performance cells, which can effectively improve the overall charging efficiency of the battery pack. This strategy brings multiple benefits: First, charging resources can be flexibly allocated according to the status of the battery cells, avoiding wasting too much power on batteries with low charging efficiency, thereby reducing energy loss; second, by charging high-performance batteries first, the total charging time can be shortened and the user experience can be improved; finally, this intelligent power management helps to extend the life of the battery cells, because low-performance batteries are charged at low power, heat generation and aging are slowed down, and the stability and safety of the entire battery pack are also improved.

[0050] Therefore, this dynamic allocation strategy not only improves charging efficiency, but also takes into account safety and life extension.

[0051] The present invention uses real-time data analysis and deep learning prediction to accurately identify the performance status of the battery cells, divide the battery cells into normal charging units and low-performance charging units, and adopt targeted charging strategies to significantly improve the charging efficiency. Fixed low-power charging is adopted for low-performance units to reduce the risk of heating and avoid high power aggravating the degradation of the battery cells, thereby effectively delaying the aging of the battery cells. For normal charging units, dynamic allocation of charging power is achieved through fuzzy logic control, and higher charging power is preferentially allocated to high-performance batteries to maximize charging efficiency, shorten charging time, and improve user experience. At the same time, the system specially marks low-performance batteries and stores them in the database, providing accurate basis for subsequent maintenance and replacement, and ensuring the long-term stability and safety of the battery pack. This solution effectively solves the problems of energy waste and safety hazards in traditional synchronous charging, significantly improves the charging speed, life and overall reliability of the battery pack, and provides a more efficient and safe charging experience for high-frequency users.

[0052] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0053] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0054] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A dynamic charging method for lithium batteries based on user behavior and environmental conditions, characterized in that: The following steps are involved: For high-frequency users, during the dynamic charging process of lithium batteries, real-time charging data is first collected from all cells in the current user's charging process to establish a first data set. At the same time, the factory charging data of each cell is called to establish a second data set as a benchmark. Compare and analyze the first data set with the second data set, extract key features reflecting the dynamic changes of the charging performance of the battery cell from the compared data, analyze the extracted features under the monitoring window, and quantify the extracted dynamic change features; Extract key features that reflect the dynamic changes in the charging performance of the battery cell. The extracted key features include the growth rate of the battery cell internal resistance after multiple charge and discharge cycles compared to the initial internal resistance and the offset of the charging time in the same power range compared to the factory time. Input the quantified feature data into a pre-trained deep learning model to predict the current state and future performance of the battery cell; Based on the prediction results of the deep learning model, all cells are divided into normal charging units and low-performance charging units; For low-performance charging units, a fixed low charging power is set for real-time charging to reduce the risk of heating and delay further aging of the battery cells. During the charging process, the battery cells corresponding to the low-performance charging units are marked and stored in the database for subsequent maintenance or replacement plans; For the battery cells corresponding to the normal charging units, fuzzy logic control is used to dynamically allocate the charging power in real time based on the changing characteristics of the battery cells' charging performance. During the dynamic power allocation process, higher charging power is preferentially allocated to high-performance battery cells to maximize the overall charging efficiency of the battery pack.

2. The method for dynamic charging of lithium batteries based on user behavior and environmental conditions according to claim 1, characterized in that: The criteria for identifying high-frequency users are based on the frequency of device usage, charge and discharge cycles, and battery consumption characteristics and behavior patterns.

3. The method for dynamic charging of lithium batteries based on user behavior and environmental conditions according to claim 1, characterized in that: Collect real-time charging data from all cells in the current user's charging process to establish a first data set. The specific steps are as follows: During the dynamic charging process of lithium batteries, charging data is first collected from each battery cell in real time; After completing data collection, pre-process the raw data to ensure the accuracy, continuity and consistency of the data; After data cleaning and preprocessing, the real-time charging data of all battery cells will be classified and structured to form the first data set. The data set is constructed in a hierarchical storage manner, that is, multi-dimensional classification by time, equipment and battery cell number to facilitate subsequent dynamic analysis and feature extraction. At the same time, the real-time data is compared and associated with the historical data to establish a time series model of the charging data to better capture the changing trend of battery cell performance.

4. The method for dynamic charging of lithium batteries based on user behavior and environmental conditions according to claim 1, characterized in that: Under the monitoring window, the growth ratio of the internal resistance of the battery cell after multiple charge and discharge cycles compared to the initial internal resistance and the offset of the charging time in the same power range compared to the time when leaving the factory are analyzed, and the internal resistance growth quantification value and the charging time drift quantification value are generated respectively. The internal resistance growth quantification value is used to quantify the degree of increase in the internal resistance of the battery cell after multiple charge and discharge cycles, reflecting the decrease in the electrochemical activity and the energy conversion efficiency of the battery cell. The charging time drift quantification value is used to quantify the offset of the charging time in the same power range, reflecting the impact on the charging speed during the charging process.

5. The method for dynamic charging of lithium batteries based on user behavior and environmental conditions according to claim 4, characterized in that: After obtaining the quantized internal resistance growth quantization value and the charging time drift quantization value, the quantized internal resistance growth quantization value and the charging time drift quantization value are input into a pre-trained deep learning model to generate a performance degradation index, and the current state and future performance of the battery cell are predicted through the performance degradation index.

6. The method for dynamic charging of lithium batteries based on user behavior and environmental conditions according to claim 5, characterized in that: The performance degradation index generated when predicting the current state and future performance of the battery cell under the monitoring window is compared and analyzed with the pre-set performance degradation index reference threshold, and the battery cells in the lithium battery are divided. The specific division steps are as follows: If the performance degradation index is less than the performance degradation index reference threshold, the battery cell is classified as a normal charging unit, and the battery cell in the normal unit is in a stable state and can store energy efficiently; If the performance degradation index is greater than or equal to the performance degradation index reference threshold, the battery cell is classified as a low-performance charging unit, and the battery cells in the low-performance unit have obvious degradation or abnormality.

7. The method for dynamic charging of lithium batteries based on user behavior and environmental conditions according to claim 4, characterized in that: Under the monitoring window, the specific steps for analyzing the growth ratio of the cell internal resistance after multiple charge and discharge cycles compared to the initial internal resistance to generate the internal resistance growth quantitative value are as follows: Collect real-time internal resistance data of each battery cell during multiple charge and discharge cycles within the monitoring window , and call its initial internal resistance at the factory as the reference value, where represents the internal resistance of the battery cell measured at the tth charging cycle, is the initial internal resistance of the battery cell; In order to quantify the increase in internal resistance after multiple charge and discharge cycles, the internal resistance growth rate of each cycle is calculated as follows: , where is the internal resistance growth rate of the tth cycle, represents the internal resistance of the battery cell measured at the t-1th charging cycle, is the correction term for the internal resistance growth due to ambient temperature, where is the ambient temperature at the tth cycle, is the ambient reference temperature; The accumulated internal resistance growth quantitative value in the monitoring window is calculated according to the internal resistance growth ratio of each cycle. The calculation expression of the internal resistance growth quantitative value is: , where is the quantitative value of internal resistance growth, which is used to quantify the overall internal resistance change trend of the battery cell within the monitoring window. n is the total number of charge and discharge cycles within the monitoring window. is the weight coefficient, which is used to reflect the importance difference between cycles. is a periodic correction term that captures the fluctuation characteristics of internal resistance over time.

8. The method for dynamic charging of lithium batteries based on user behavior and environmental conditions according to claim 4, characterized in that: In the monitoring window, the specific steps for analyzing the offset of the charging time in the same power range compared with the factory time to generate the charging time drift quantification value are as follows: In the dynamic charging process of lithium batteries, the charging time offset of the same power interval is defined and extracted. Assume that the charging process of each battery cell can be divided into multiple fixed power intervals. For each interval, record the actual charging time in the current charging process and compare it with the charging time of the battery cell in the same power interval when it leaves the factory. Calculate the charging time offset. The calculation expression is: , where and They represent the starting point and end point of the power range respectively. Indicates that the current charging cycle starts from the starting point Charging to the end The actual time required, The battery cell is from the starting point when it leaves the factory. Charging to the end The time required, is the charging time offset; After obtaining the charging time offset of each power interval, the next step is to calculate the charging time drift rate. The drift rate is defined as the ratio of the charging time offset of each interval to the charging time at the factory, which indicates the degree of change in charging performance. The calculation expression is: , where From the starting point Charging to the end Charging time drift rate, used to measure the degree of charging time deviation in each power interval; In order to comprehensively quantify the charging time drift value in the entire charging process, a nonlinear weighting function is used to weight the charging time drift rate of all power intervals, so that the contribution of the charging time drift rate of the power interval to the charging time drift quantification value is exponentially related to the drift amplitude. The calculation expression is: , where It is the quantitative value of charging time drift, which comprehensively quantifies the severity of time drift during the entire charging process. For the i-th power interval from the starting point Charging to the end The charge time drift rate, and Nonlinear weighting coefficients, Controls the amplification degree of the charge time drift quantization value, Control the weighted influence of the large charging time drift rate on the quantized value of the overall charging time drift, and m represents the total number of power intervals in the monitoring window.

9. The method for dynamic charging of lithium batteries based on user behavior and environmental conditions according to claim 6, characterized in that: For the cells corresponding to the normal charging units, fuzzy logic control is used to dynamically allocate the charging power in real time. The specific steps are as follows: The charging priority is calculated based on the relationship between the performance degradation index of each battery cell and the performance degradation index reference threshold. The calculation expression is: , where is the charging priority of the jth battery cell. The higher the value, the higher the priority. is the performance degradation index of the jth battery cell, reflecting the current state of the battery cell. is the reference threshold of the performance degradation index; After evaluating the priority, fuzzy logic is used to determine the charging power allocation weight for each battery cell. The fuzzy logic rule combines the performance priority of the battery cell with the actual demand. The formula is as follows: , is the charging weight of the jth battery cell, which determines the proportion of power allocated. is a fuzzy logic control parameter used to amplify the charging weight of high-priority cells. N is the total number of normal charging unit cells. Indicates the charging priority of the kth battery cell; The actual charging power is dynamically allocated according to the charging weight of each battery cell. The total power is the maximum charging power of the battery pack. The dynamic allocation formula is as follows: , where is the charging power allocated to the jth battery cell, is the total available charging power of the battery pack.

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