CN118889593A - A lithium battery and a charging control method thereof - Google Patents

Abstract

The invention relates to the technical field of lithium battery charging, in particular to a lithium battery and a charging control method thereof. The method comprises the following steps: acquiring impedance spectrum data of a lithium battery; carrying out impedance spectrum analysis on lithium battery impedance spectrum data by using preset lithium battery basic data to obtain lithium battery discharge state data; acquiring historical electric quantity consumption data of a lithium battery; building a power consumption model of the historical electric quantity consumption data of the lithium battery by using a long-short-term memory network algorithm to generate a lithium battery power consumption prediction model; acquiring instant time data; and predicting the battery power consumption of the instant time data by using a lithium battery power consumption prediction model to obtain battery utilization rate period prediction data. The invention improves the charging efficiency, prolongs the service life of the battery and provides safer and more reliable charging experience by orienting to user habits, comprehensive data, real-time intelligent control and personalized strategies.

Description

A lithium battery and a charging control method thereof

Technical Field

The present invention relates to the technical field of lithium battery charging, and in particular to a lithium battery and a charging control method thereof.

Background Art

Lithium batteries were first introduced in the 1970s. After years of research and development, lithium-ion batteries have become the most widely used lithium battery technology and are widely used in portable electronic devices, electric vehicles, energy storage and other fields. Although lithium batteries have obtained mature battery technology in the continuous development, they still have some defects and limitations. For example, during the charging process, improper operation such as excessive charging current, overcharging or over-discharging may cause the battery to overheat, release gas, leak electrolyte and even catch fire and explode. With the development of science and technology, people have explored various methods to avoid lithium battery charging, such as voltage-controlled charging, timed charging and constant current constant voltage charging. However, these methods cannot guarantee to meet the user's demand for fast charging while ensuring charging safety. In recent years, intelligent charging control systems have begun to be applied to lithium battery charging, using advanced algorithms and sensors to monitor and control the charging process in real time, and dynamically adjust the charging current and voltage according to the battery status and characteristics to achieve higher charging efficiency and safety. However, they usually lack the ability to interact with users and cannot be changed according to user habits, so that user-oriented intelligent charging cannot be completed.

Summary of the invention

Based on this, it is necessary to provide a lithium battery and a charging control method thereof to solve at least one of the above technical problems.

To achieve the above object, a charging control method for a lithium battery is provided, the method comprising the following steps:

Step S1: obtaining lithium battery impedance spectrum data; performing impedance spectrum analysis on the lithium battery impedance spectrum data using preset lithium battery basic data to obtain lithium battery discharge state data;

Step S2: Obtaining historical power consumption data of the lithium battery; using the long short-term memory network algorithm to construct a power consumption model for the historical power consumption data of the lithium battery, and generating a lithium battery power consumption prediction model;

Step S3: acquiring real-time data; using a lithium battery power consumption prediction model to predict battery power consumption based on the real-time data to obtain battery usage time period prediction data; combining the lithium battery discharge state data and the battery usage time period prediction data to perform charging speed demand analysis to obtain charging speed demand data, wherein the charging speed demand data includes fast charging demand data and normal charging demand data;

Step S4: When it is determined that the charging speed requirement data is ordinary charging requirement data, the lithium battery is subjected to constant voltage charging and discharging processing to obtain ordinary charging calibration lithium battery data; when it is determined that the charging speed requirement data is fast charging requirement data, the charging power requirement calculation is performed on the lithium battery discharge state data based on the fast charging requirement data to obtain the lithium battery charging power requirement change data;

Step S5: dynamically changing power charging processing is performed on the lithium battery according to the charging power demand change data of the lithium battery, and real-time temperature monitoring processing is performed on the lithium battery to obtain real-time temperature data of the lithium battery; feedback voltage adjustment calculation is performed on the real-time temperature data of the lithium battery according to the preset lithium battery basic data to obtain a feedback voltage adjustment strategy;

Step S6: adjust the real-time voltage according to the feedback voltage adjustment strategy to generate fast charging calibration lithium battery data; merge the normal charging calibration lithium battery data and the fast charging calibration lithium battery data to generate lithium battery voltage control data; dynamically adjust the real-time voltage of the lithium battery according to the lithium battery voltage control data to implement intelligent lithium battery charging control.

The present invention performs dynamic power charging according to the real-time demand of the lithium battery to improve the charging efficiency and extend the battery life. By analyzing the lithium battery impedance spectrum data and the historical power consumption data, the discharge state data and the power consumption prediction model can be obtained. Using these data, the battery usage rate in different time periods can be predicted, and the charging speed demand can be determined accordingly. When the charging speed demand is ordinary charging, the constant voltage charging and discharging process can be used to charge the battery to achieve the effect of stable charging. When the charging speed demand is fast charging, it is necessary to calculate the charging power demand change data according to the fast charging demand data to meet the requirements of fast charging. During the charging process, it is also necessary to monitor the temperature change of the battery in real time, and perform feedback voltage adjustment calculation according to the real-time temperature data and the preset basic data to ensure that the battery works within a safe range. According to the feedback voltage adjustment strategy, the real-time voltage is adjusted to generate fast charging calibration lithium battery data, thereby realizing intelligent lithium battery charging control, improving the charging efficiency of the lithium battery, extending the battery life, and ensuring the safety during the charging process, so as to achieve the goal of intelligent lithium battery charging control. Therefore, the present invention improves the charging efficiency, extends the battery life, and provides a safer and more reliable charging experience by facing user habits, comprehensive data, real-time intelligent control and personalized strategies.

In this specification, a lithium battery is provided, which is used to execute the charging control method of the lithium battery as described above, including:

The state acquisition module is used to acquire the impedance spectrum data of the lithium battery; the impedance spectrum data of the lithium battery is analyzed by using the preset basic data of the lithium battery to obtain the discharge state data of the lithium battery;

The model building module is used to obtain the historical power consumption data of lithium batteries; the power consumption model is constructed based on the historical power consumption data of lithium batteries using the long short-term memory network algorithm to generate a lithium battery power consumption prediction model;

The demand acquisition module is used to obtain real-time data; use the lithium battery power consumption prediction model to predict the battery power consumption of the real-time data to obtain the battery usage period prediction data; combine the lithium battery discharge state data and the battery usage period prediction data to perform charging speed demand analysis to obtain charging speed demand data, wherein the charging speed demand data includes fast charging demand data and ordinary charging demand data;

The demand calculation module is used to perform constant voltage charging and discharging processing on the lithium battery to obtain normal charging calibration lithium battery data when the charging speed demand data is determined to be normal charging demand data; when the charging speed demand data is determined to be fast charging demand data, the charging power demand calculation is performed on the lithium battery discharge state data based on the fast charging demand data to obtain the lithium battery charging power demand change data;

The temperature monitoring module is used to dynamically change the power charging process of the lithium battery according to the change data of the lithium battery charging power demand and perform real-time temperature monitoring on the lithium battery to obtain the real-time temperature data of the lithium battery; perform feedback voltage adjustment calculation on the real-time temperature data of the lithium battery according to the preset lithium battery basic data to obtain the feedback voltage adjustment strategy;

The dynamic adjustment module is used to adjust the real-time voltage according to the feedback voltage adjustment strategy to generate fast charging calibration lithium battery data; merge the ordinary charging calibration lithium battery data and the fast charging calibration lithium battery data to generate lithium battery voltage control data; and dynamically adjust the real-time voltage of the lithium battery according to the lithium battery voltage control data to implement intelligent lithium battery charging control.

The beneficial effect of the present invention is that by integrating lithium battery impedance spectrum data, historical power consumption data and real-time time data, the method integrates information from different aspects, provides a more comprehensive grasp of battery status and usage mode, and the power consumption prediction model constructed by the long short-term memory network algorithm can more accurately predict the power consumption of the lithium battery, providing a reliable basis for subsequent charging control. By analyzing the battery usage period prediction data, data of different charging speed requirements are obtained, including ordinary charging and fast charging. This helps to adopt corresponding charging strategies according to different needs, dynamically change the power charging process of the lithium battery, and combine with real-time temperature monitoring to more flexibly adjust the charging power according to the demand and real-time state, improve the charging efficiency, and perform feedback voltage adjustment calculation according to the real-time temperature data to adjust the voltage during the charging process, which helps to optimize the charging process, reduce the heat generation of the battery, and improve safety. By merging the first calibration and fast charging calibration lithium battery data, a multi-stage calibration is realized, which can more accurately reflect the actual state of the battery and improve the accuracy of charging control. Through the above steps, the system can make intelligent decisions based on real-time data and prediction models to achieve optimized charging control of lithium batteries, improve charging efficiency, extend battery life, and meet the personalized needs of users. Therefore, the present invention improves charging efficiency, extends battery life, and provides a safer and more reliable charging experience by focusing on user habits, comprehensive data, real-time intelligent control, and personalized strategies.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic flow chart of the steps of a charging control method for a lithium battery;

FIG2 is a schematic diagram of a detailed implementation process of step S3 in FIG1 ;

FIG3 is a schematic flow chart of detailed implementation steps of step S4 in FIG1 ;

FIG. 4 is a schematic flow chart of detailed implementation steps of step S5 in FIG. 1 .

The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings.

DETAILED DESCRIPTION

The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and/or processor methods and/or microcontroller methods.

It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and/or" used herein includes any and all combinations of one or more of the listed associated items.

To achieve the above object, please refer to Figures 1 to 4, a charging control method for a lithium battery, the method comprising the following steps:

Step S1: obtaining lithium battery impedance spectrum data; performing impedance spectrum analysis on the lithium battery impedance spectrum data using preset lithium battery basic data to obtain lithium battery discharge state data;

Step S2: Obtaining historical power consumption data of the lithium battery; using the long short-term memory network algorithm to construct a power consumption model for the historical power consumption data of the lithium battery, and generating a lithium battery power consumption prediction model;

Step S3: acquiring real-time data; using a lithium battery power consumption prediction model to predict battery power consumption based on the real-time data to obtain battery usage time period prediction data; combining the lithium battery discharge state data and the battery usage time period prediction data to perform charging speed demand analysis to obtain charging speed demand data, wherein the charging speed demand data includes fast charging demand data and normal charging demand data;

Step S4: When it is determined that the charging speed requirement data is ordinary charging requirement data, the lithium battery is subjected to constant voltage charging and discharging processing to obtain ordinary charging calibration lithium battery data; when it is determined that the charging speed requirement data is fast charging requirement data, the charging power requirement calculation is performed on the lithium battery discharge state data based on the fast charging requirement data to obtain the lithium battery charging power requirement change data;

Step S5: dynamically changing power charging processing is performed on the lithium battery according to the charging power demand change data of the lithium battery, and real-time temperature monitoring processing is performed on the lithium battery to obtain real-time temperature data of the lithium battery; feedback voltage adjustment calculation is performed on the real-time temperature data of the lithium battery according to the preset lithium battery basic data to obtain a feedback voltage adjustment strategy;

Step S6: adjust the real-time voltage according to the feedback voltage adjustment strategy to generate fast charging calibration lithium battery data; merge the normal charging calibration lithium battery data and the fast charging calibration lithium battery data to generate lithium battery voltage control data; dynamically adjust the real-time voltage of the lithium battery according to the lithium battery voltage control data to implement intelligent lithium battery charging control.

The beneficial effect of the present invention is that by integrating lithium battery impedance spectrum data, historical power consumption data and real-time time data, the method integrates information from different aspects, provides a more comprehensive grasp of battery status and usage mode, and the power consumption prediction model constructed by the long short-term memory network algorithm can more accurately predict the power consumption of the lithium battery, providing a reliable basis for subsequent charging control. By analyzing the battery usage period prediction data, data of different charging speed requirements are obtained, including ordinary charging and fast charging. This helps to adopt corresponding charging strategies according to different needs, dynamically change the power charging process of the lithium battery, and combine with real-time temperature monitoring to more flexibly adjust the charging power according to the demand and real-time state, improve the charging efficiency, and perform feedback voltage adjustment calculation according to the real-time temperature data to adjust the voltage during the charging process, which helps to optimize the charging process, reduce the heat generation of the battery, and improve safety. By merging the first calibration and fast charging calibration lithium battery data, a multi-stage calibration is realized, which can more accurately reflect the actual state of the battery and improve the accuracy of charging control. Through the above steps, the system can make intelligent decisions based on real-time data and prediction models to achieve optimized charging control of lithium batteries, improve charging efficiency, extend battery life, and meet the personalized needs of users. Therefore, the present invention improves charging efficiency, extends battery life, and provides a safer and more reliable charging experience by focusing on user habits, comprehensive data, real-time intelligent control, and personalized strategies.

In the embodiment of the present invention, referring to FIG. 1 , a schematic flow chart of a lithium battery and a charging control method thereof is shown. In this example, the lithium battery and the charging control method thereof include the following steps:

Step S1: obtaining lithium battery impedance spectrum data; performing impedance spectrum analysis on the lithium battery impedance spectrum data using preset lithium battery basic data to obtain lithium battery discharge state data;

In an embodiment of the present invention, a suitable measuring device, such as an AC impedance spectrometer, is used to test the target lithium battery to obtain its impedance spectrum data. The measurement conditions include frequency range, current amplitude, etc. The preset lithium battery basic data includes basic parameters of the battery, such as capacity, voltage range, electrochemical characteristics, etc. The acquired impedance spectrum data is matched and analyzed with the preset lithium battery basic data. The impedance spectrum data is analyzed using a suitable model to extract information about the battery state, such as spectrum analysis, complex impedance model and other methods. Through impedance spectrum analysis, information about the discharge state of the lithium battery is extracted from the data. These state data may include the internal resistance of the battery, charge transfer resistance, electrolyte conductivity, etc. The obtained lithium battery discharge state data is recorded and stored.

Step S2: Obtaining historical power consumption data of the lithium battery; using the long short-term memory network algorithm to construct a power consumption model for the historical power consumption data of the lithium battery, and generating a lithium battery power consumption prediction model;

In an embodiment of the present invention, data is collected by means of log recording and the like to obtain historical power consumption data of lithium batteries, missing values and outliers are processed to ensure the integrity and accuracy of the data, time series processing is performed to ensure that the data is arranged in chronological order, the historical power consumption data is normalized to ensure that the data is within the same scale range, the time series data is organized into a sequence suitable for LSTM network input, an LSTM model architecture is created, including an input layer, an LSTM layer, an output layer, etc., the data set is divided into a training set and a test set to evaluate model performance, the LSTM model is trained using the training set, the training process is monitored to ensure that the model converges and is not overfitted, the performance of the model is evaluated using the test set, indicators such as root mean square error (RMSE) or mean absolute error (MAE) are considered, and new power consumption data is input into the trained model to perform real-time power consumption prediction.

Step S3: acquiring real-time data; using a lithium battery power consumption prediction model to predict battery power consumption based on the real-time data to obtain battery usage time period prediction data; combining the lithium battery discharge state data and the battery usage time period prediction data to perform charging speed demand analysis to obtain charging speed demand data, wherein the charging speed demand data includes fast charging demand data and normal charging demand data;

In an embodiment of the present invention, by accessing a specific network service or API, the time information provided by the server is obtained to obtain real-time time data. The real-time time data is cleaned and formatted to ensure that it is consistent with the model input requirements, and the lithium battery power consumption prediction model is used to predict the real-time data to obtain the time period prediction data of the battery usage rate. The lithium battery discharge state data is combined with the battery usage rate time period prediction data to form a complete charging demand data set. According to business needs and battery specifications, the standards for fast charging demand and ordinary charging demand are determined, and the charging speed demand data is integrated to ensure that the data format meets the requirements of subsequent analysis. The changes in charging speed demand in different time periods are analyzed, and the differences between peak and off-peak periods are considered. A charging speed demand report is generated, including the demand data for fast charging and ordinary charging, as well as the corresponding time period analysis results, and the accuracy of the battery power consumption prediction model is verified. The actual charging data is used for comparison to ensure the quality of the data and the accuracy of the analysis results.

Step S4: When it is determined that the charging speed requirement data is ordinary charging requirement data, the lithium battery is subjected to constant voltage charging and discharging processing to obtain ordinary charging calibration lithium battery data; when it is determined that the charging speed requirement data is fast charging requirement data, the charging power requirement calculation is performed on the lithium battery discharge state data based on the fast charging requirement data to obtain the lithium battery charging power requirement change data;

In an embodiment of the present invention, it is determined whether the charging demand belongs to normal charging or fast charging according to the charging speed demand data. When it is determined to be a normal charging demand, a constant voltage charging and discharging strategy is adopted to discharge to a specific voltage or current threshold to simulate the behavior of the battery under normal use conditions. According to the battery specifications and manufacturer's recommendations, a suitable constant voltage value is set, and the battery is charged at this fixed voltage until the current drops to a preset threshold. During the entire charging and discharging process, the battery's voltage, current, temperature and other parameters are recorded. By analyzing these data, the performance parameters of the battery under normal charging conditions are obtained. When it is determined to be a fast charging demand, the charging power demand needs to be calculated, and the key parameters of the battery in the discharge state (such as voltage, current, temperature, etc.) are collected. The required charging power is calculated using the discharge data combined with the characteristics of the fast charging demand, and the change in charging power is recorded during the charging process. The data is analyzed to optimize future charging strategies to ensure the health and safety of the battery. During the entire charging process, the battery temperature and voltage are continuously monitored to prevent overcharging and overheating.

Step S5: dynamically changing power charging processing is performed on the lithium battery according to the charging power demand change data of the lithium battery, and real-time temperature monitoring processing is performed on the lithium battery to obtain real-time temperature data of the lithium battery; feedback voltage adjustment calculation is performed on the real-time temperature data of the lithium battery according to the preset lithium battery basic data to obtain a feedback voltage adjustment strategy;

In the embodiment of the present invention, the charging power is adjusted in real time using the charging power demand change data, and the charging power is dynamically adjusted according to the change of the power demand using an appropriate algorithm. A monitoring system is set to detect the current, voltage, temperature and other parameters of the battery in real time. A safety control mechanism is implemented based on the monitoring data to prevent the battery from overheating or overcharging. A temperature sensor is installed on the surface of the battery to obtain real-time temperature data. The temperature data is collected in real time to ensure the accuracy of the data. The temperature sensor is integrated with the monitoring system to monitor the battery temperature in real time, and a temperature threshold is set. Once the temperature exceeds the safe range, an alarm is triggered and corresponding measures are taken. During the charging process, the real-time temperature data is continuously recorded, and key events in which the temperature changes significantly, such as power adjustment, charging stage change, etc., are recorded. The adjustment parameters required for feedback voltage adjustment are set. The real-time temperature data is used to analyze the influence of temperature on battery performance. According to the analysis results, the adjustment amount of the feedback voltage is calculated to optimize the battery charging performance. The voltage during the charging process is adjusted in real time to adapt to temperature changes to ensure that the adjustment strategy does not affect the stability and life of the battery. The feedback voltage adjustment data is recorded for subsequent analysis and improvement of the strategy.

Step S6: adjust the real-time voltage according to the feedback voltage adjustment strategy to generate fast charging calibration lithium battery data; merge the normal charging calibration lithium battery data and the fast charging calibration lithium battery data to generate lithium battery voltage control data; dynamically adjust the real-time voltage of the lithium battery according to the lithium battery voltage control data to implement intelligent lithium battery charging control.

In an embodiment of the present invention, according to a preset feedback voltage adjustment strategy, the real-time voltage is compared with the target voltage, and the voltage is adjusted according to the difference, which is achieved by controlling the output voltage or charging current of the charging device. According to the fast charging demand data and the real-time voltage adjustment result, the calibration lithium battery data for fast charging is generated. These data may include information such as the current power, voltage, temperature, etc. of the battery, as well as parameters such as power and charging time during the charging process. The calibration lithium battery data for normal charging and the calibration lithium battery data for fast charging are merged to generate lithium battery voltage control data. These data should include information such as charging mode (normal charging or fast charging), battery state (discharging state, charging state or standby state), current power, voltage, etc., and the output voltage or charging current of the charging device is controlled to achieve the voltage control data according to the lithium battery, monitor the battery voltage in real time, and dynamically adjust it as needed. The battery voltage is maintained within a set range to ensure the safety and efficiency of the charging process.

Preferably, step S1 comprises the following steps:

Step S11: Acquire lithium battery impedance spectrum data;

Step S12: performing spectrum analysis on the lithium battery impedance spectrum data to obtain battery electrochemical characteristic data;

Step S13: Establishing a charge-impedance relationship model for the battery electrochemical characteristic data according to the preset lithium battery basic data to obtain a charge-impedance relationship model;

Step S14: using the power-impedance relationship model to perform battery power analysis on the lithium battery impedance spectrum data to obtain lithium battery power data;

Step S15: performing a discharge status analysis on the lithium battery power data based on a preset discharge status threshold to generate lithium battery discharge status data.

The present invention can obtain the electrochemical characteristic data of the lithium battery by acquiring the lithium battery impedance spectrum data and performing analysis and processing. Further, by establishing a charge-impedance relationship model and performing battery charge analysis on the lithium battery impedance spectrum data, the charge data of the lithium battery can be obtained. These data can be used to accurately obtain the state of the lithium battery, including charge, discharge state, etc. By obtaining the electrochemical characteristic data of the lithium battery and the charge-impedance relationship model, the charging strategy can be optimized according to the charge and charging state of the lithium battery, thereby improving the charging efficiency. This can avoid unnecessary charging or overcharging, extend the life of lithium batteries, and improve charging efficiency. By accurately obtaining the state of lithium batteries and analyzing the discharge state, abnormal states of lithium batteries, such as over-discharge and over-charging, can be discovered in time, so that corresponding measures can be taken to ensure the safety of the system. The impedance spectrum data provides a high-resolution view of the internal characteristics of the battery, thereby increasing the accurate understanding of the battery state. Through spectrum analysis, the electrochemical characteristics of lithium batteries can be obtained, including parameters such as resistance and capacitance inside the battery. The charge-impedance relationship model established based on the basic data of lithium batteries can better adapt to the characteristics of specific batteries and improve the accuracy of the model. The charge-impedance relationship model provides a more accurate way to estimate the charge of lithium batteries, which is more accurate than traditional methods. By setting the discharge state threshold, the discharge state of lithium batteries can be more accurately determined, which helps to monitor the health status of batteries in real time. Through the analysis of impedance spectrum and electrochemical characteristics, the lithium battery management system can more accurately understand the internal state of the battery and improve the estimation accuracy of the charge and state. Accurate charge and state estimation helps prevent overcharging or over-discharging of the battery, thereby improving the safety of the lithium battery system. Through the charge-impedance relationship model and discharge state analysis, the system can more intelligently control the charging and discharging process and optimize the performance and life of the battery.

In the embodiment of the present invention, the test instrument is connected to the lithium battery to be tested, ensuring that the connection between the instrument and the battery is correct, and setting the test parameters of the test instrument, including the test frequency range, current size, etc. The test instrument is started and the test is started. The test time is determined according to the actual situation. The lithium battery impedance spectrum data obtained from the test instrument is extracted and saved in a digital format. The digital impedance spectrum data is processed using a spectrum analysis algorithm to obtain battery electrochemical characteristic data, such as charge transfer resistance, electrode interface capacitance, etc. The basic data of the lithium battery, including power, current, voltage, etc., are collected. According to the collected basic data and electrochemical characteristic data of the lithium battery, a power impedance relationship model is established using a mathematical modeling method, and parameter fitting and optimization are performed. The obtained lithium battery impedance spectrum data is analyzed using the power impedance relationship model, and the power data of the lithium battery is calculated. According to actual needs, the discharge state threshold of the lithium battery is set, for example, the power is set to be less than 20% as a low power state. The lithium battery power data is analyzed according to the set discharge state threshold, the discharge state of the lithium battery is judged, and recorded.

Preferably, step S2 comprises the following steps:

Step S21: Obtaining historical power consumption data of the lithium battery;

Step S22: Establish a time series index for the lithium battery historical power consumption data according to a preset timestamp to obtain the indexed historical power consumption data;

Step S23: dividing the indexed historical power consumption data into time series to obtain periodic power consumption data;

Step S24: Use the long short-term memory network algorithm to construct a power consumption model for the periodic power consumption data to generate a lithium battery power consumption prediction model.

The present invention can analyze and predict the trend of power consumption by acquiring the historical data of lithium batteries and establishing it as a time series index. It helps users to understand the remaining power of the battery in advance, so as to make corresponding decisions on charging or replacing the battery. The historical power consumption data can be effectively divided by using the time series division method, and periodic power consumption data can be obtained. By analyzing these data, the use rules and characteristics of the battery can be found, and then the battery use strategy can be optimized, the battery life and use effect can be extended. The long short-term memory network algorithm is used to model the periodic power consumption data, and a power consumption prediction model for lithium batteries can be generated. The model can predict the power consumption in the future based on past consumption data, provide users with more accurate battery use suggestions and decision-making basis, improve the use efficiency and convenience of lithium batteries, and help users better manage and use battery resources. At the same time, by predicting and optimizing power consumption, the impact on the environment can also be reduced, and the goal of sustainable development can be achieved.

In an embodiment of the present invention, actual data of historical power consumption of a lithium battery is obtained through appropriate sensors or monitoring equipment, ensuring that the obtained data includes information such as a timestamp and specific values of power consumption, and is recorded. According to a preset timestamp, the historical power consumption data is organized into a time series, and a time series analysis method, such as a sliding window or Fourier transform, is used to identify periodic patterns in the historical power consumption data. Power consumption data with periodicity is extracted from the result of the time series division, and the periodic power consumption data is organized into a format suitable for input into a long short-term memory network (LSTM). The number of layers, the number of neurons, and other hyperparameters of the LSTM network are selected, a training set is used to train the LSTM model, a test set is used to verify the performance of the model, and adjustments are made as needed to improve the accuracy of the model.

Preferably, step S3 comprises the following steps:

Step S31: Obtaining real-time time data;

Step S32: Perform time linear inference on the instant time data to obtain future time period data;

Step S33: using the lithium battery power consumption prediction model to predict the battery power consumption of the future time period data, and obtaining the battery usage rate time period prediction data;

Step S34: performing charging demand analysis on the lithium battery discharge state data to obtain lithium battery charging demand data;

Step S35: Based on the battery usage rate time period prediction data, a charging speed demand analysis algorithm is used to perform speed demand analysis on the lithium battery charging demand data to obtain charging speed demand data.

The present invention provides key information of the current battery status through real-time data, which provides a basis for subsequent prediction and analysis. This step provides prediction data for a period of time in the future through linear inference, which provides input for the prediction model. By predicting the power consumption of the battery, the use of the battery in the future period can be understood in advance, which is helpful to optimize the charging plan and resource management. By analyzing the discharge state of the battery, the current charging demand of the battery can be better understood, which is helpful to plan appropriate charging strategies. By analyzing the battery usage prediction data and charging speed requirements, the charging speed and timing can be optimized to adapt to the changes in battery demand in different time periods. By predicting future battery usage and charging needs, plans can be made in advance to avoid insufficient power or low charging efficiency. According to the predicted battery usage and charging demand data, the charging strategy and speed are optimized to maximize the use of the battery and effectively manage charging resources. By analyzing the battery status and prediction, the charging speed and timing are optimized, the charging efficiency is improved, the battery life is extended, and it is ensured that the energy needs of the device can be met.

As an example of the present invention, referring to FIG. 2 , in this example, step S3 includes:

Step S31: Obtaining real-time time data;

In an embodiment of the present invention, the time accuracy that needs to be obtained is determined, such as year, month, day, hour, minute, second, etc., and local time (based on the time zone where the system is located) is selected as needed, and the corresponding function or method is called to obtain time data, and the obtained time data is stored in a variable or data structure.

Step S32: Perform time linear inference on the instant time data to obtain future time period data;

In the embodiment of the present invention, the time period for which time linear extrapolation is required is determined, and the time difference between each time point and the current time is calculated based on the current time data and the required time period, and the time trend in the historical data is calculated using regression analysis or other suitable methods. This can be achieved by fitting a straight line, a curve or other mathematical model, and using the calculated time trend to perform linear extrapolation on the future time period. Linear extrapolation usually involves making linear changes based on the trend, that is, using the slope of the historical trend for prediction, and calculating the predicted value of each future time point based on the result of linear extrapolation, thereby obtaining data for the future time period.

Step S33: using the lithium battery power consumption prediction model to predict the battery power consumption of the future time period data, and obtaining the battery usage rate time period prediction data;

In the embodiment of the present invention, the lithium battery power consumption prediction model is used to predict the data of the future time period. The characteristics of the future time period are input to obtain the corresponding battery power consumption prediction results, and the prediction results of the model are explained to understand the factors affecting the battery power consumption of the model. The prediction results are presented through visualization tools such as charts or curves.

Step S34: performing charging demand analysis on the lithium battery discharge state data to obtain lithium battery charging demand data;

In an embodiment of the present invention, the collected lithium battery discharge status data is analyzed, the value of the charging demand index is calculated, and based on the changing trend of the index, it is determined whether the charging demand of the lithium battery increases or decreases. Considering factors such as charging efficiency and charging speed, combined with the actual situation of the battery, the current charging demand is comprehensively analyzed, and the future charging demand is predicted. The charging demand data is processed, such as removing outliers and smoothing, and the charging demand data is analyzed and visualized.

Step S35: Based on the battery usage rate time period prediction data, a charging speed demand analysis algorithm is used to perform speed demand analysis on the lithium battery charging demand data to obtain charging speed demand data.

In the embodiment of the present invention, an indicator for measuring the charging speed requirement is determined according to the demand and application scenario. For example, the charging speed requirement can be represented by indicators such as charging time and battery charging rate, and the battery usage period prediction data and charging demand data are analyzed using a lithium battery charging speed requirement analysis algorithm, and the collected lithium battery charging demand data is input into the charging speed requirement analysis algorithm to obtain the charging speed requirement data for each period, and the charging speed requirement data is analyzed and processed.

Preferably, the lithium battery charging speed demand analysis algorithm in step S35 is as follows:

In the formula, v(t) is the charging speed requirement result, T is the time interval value, t is the time variable value, R is the internal resistance of the battery during the charging process, and C is the storage energy capacity of the battery. is the time derivative, dt is the small time interval, _a1 is the weighted parameter for controlling the contribution of the first-order time derivative to the charging speed, _a2 is the weighted parameter for controlling the contribution of the second-order time derivative to the charging speed, and _a3 is the weighted parameter for controlling the contribution of the third-order time derivative to the charging speed.

The present invention constructs a lithium battery charging speed demand analysis algorithm, and the charging speed demand calculated in the algorithm is the result under a specific time variable value t. It represents the required charging speed within the time interval T, that is, at what speed can the charging meet the requirement. Represents the rate of change of internal resistance and storage energy capacity over time. It reflects the change of battery state and affects the charging speed. Its contribution to the charging speed requirement is adjusted by weighting parameter a _1. By using the battery usage period prediction data, the charging speed requirement is calculated according to the change rate of the battery's internal resistance and storage energy capacity, as well as the contribution of different order time derivatives. In this way, the charging speed can be intelligently adjusted according to the change of battery state and the demand at a specific time point to meet the charging requirements and achieve a more efficient, safe and reliable charging process. By adjusting the weighting parameters, the influence of different order time derivatives can be flexibly adjusted according to the actual application scenario to meet specific charging requirements. This algorithm provides an analysis method based on battery usage period prediction data, which can predict the charging speed requirement according to the change of the battery's internal resistance and storage energy capacity. This helps to optimize the charging strategy, improve charging efficiency, extend battery life, and provide a better user experience.

Preferably, step S4 comprises the following steps:

Step S41: When it is determined that the charging speed requirement data is normal charging requirement data, shallow discharge processing is performed on the lithium battery to obtain the minimum battery power data;

Step S42: performing constant voltage charging processing on the battery data with the lowest power to obtain normal charging calibration lithium battery data;

Step S43: When it is determined that the charging speed requirement data is fast charging requirement data, the charging power requirement calculation formula is used to calculate the charging power requirement of the lithium battery discharge state data to obtain the charging power requirement data;

Step S44: Real-time recording and processing of charging power demand data is performed to obtain lithium battery charging power demand change data.

The present invention can simulate the behavior of the battery in the lowest power state by performing shallow discharge treatment. This is important for understanding and modeling the performance of lithium batteries under low power conditions. The lowest power battery data provides information about the battery discharge process, which helps to determine the basic characteristics of the battery. The constant voltage charging process can simulate the battery behavior under normal charging conditions and provide data in the charging stage, which helps to establish the characteristics of the battery during normal charging and provides a benchmark for the subsequent charging speed demand data. By using the charging power demand calculation formula, the charging power demand data can be calculated from the battery discharge state data. The charging power demand data is the key to understanding the battery's fast charging requirements and provides important information for designing a fast charging system. Real-time recording of changes in charging power demand provides insights into the dynamic changes in battery demand, which helps to adjust the charging system in real time to meet the changing charging requirements of the battery in different time periods. The above steps help to fully understand the behavior of lithium batteries under different conditions and provide key data for designing and optimizing charging systems. By obtaining the lowest power battery data, normal charging calibration data and fast charging demand data, different charging demand scenarios can be better adapted to improve the efficiency, stability and performance of the charging system. This is of great significance for promoting the development of electric vehicles and renewable energy and other fields.

As an example of the present invention, referring to FIG3 , in this example, step S4 includes:

Step S41: When it is determined that the charging speed requirement data is normal charging requirement data, shallow discharge processing is performed on the lithium battery to obtain the minimum battery power data;

In the embodiment of the present invention, the minimum power of the battery during shallow discharge is determined according to actual needs and battery specifications, and a suitable discharge method is selected according to battery characteristics and equipment conditions. The discharge process can be performed by constant current discharge or constant power discharge. The battery is connected to the discharge device and the discharge process is performed according to the selected discharge method, so that the battery power is gradually reduced until the target power is reached. During the discharge process, the battery voltage, current and other parameters are monitored in real time to ensure that the battery discharge process is stable and safe. When the battery reaches the target power, the discharge operation is stopped in time to avoid damage to the battery caused by excessive discharge, and the relevant parameters of the battery with the lowest power are recorded, such as battery power, time, discharge current, etc. By analyzing these data, the characteristics and performance information of the battery with the lowest power can be obtained.

Step S42: performing constant voltage charging processing on the battery data with the lowest power to obtain normal charging calibration lithium battery data;

In an embodiment of the present invention, a charging device provided by the manufacturer, including a charger, a power cord, etc., is prepared, and the battery with the lowest power is connected to the charging device to ensure that the connection is correct and stable. The parameters of constant voltage charging are set according to the battery specifications and charging requirements. These parameters include charging current, charging time, and the voltage threshold for stopping charging. The charging device is started to allow the battery to be charged at a constant voltage. During the charging process, the charger will maintain a constant output charging voltage according to the set parameters until the battery reaches the voltage threshold for stopping charging. During the charging process, the charging status of the battery is monitored in real time, including parameters such as voltage, current, and charging time. Ensure that the charging process is stable and safe. When the battery voltage reaches the set stop charging voltage threshold, stop the charging operation in time to avoid damage to the battery caused by overcharging. Record the relevant parameters of the ordinary charging calibration lithium battery, such as charging current, charging time, battery voltage, etc.

Step S43: When it is determined that the charging speed requirement data is fast charging requirement data, a charging power requirement calculation formula for a lithium battery is used to calculate the charging power requirement of the lithium battery discharge state data to obtain charging power requirement data;

In the embodiment of the present invention, appropriate battery discharge state data is selected, including battery voltage, current, power and other parameters, and the battery discharge state data is substituted into the lithium battery charging power demand calculation formula, and the charging power demand is calculated according to the formula. According to the charging power demand and the performance parameters of the charging device, the output power of the charging device and the charging power demand are compared. If the output power of the device is less than the charging power demand, it means that the charging device cannot meet the fast charging demand, and it is necessary to replace the appropriate device, and formulate a corresponding charging strategy according to the charging power demand and the charging device capacity.

Step S44: Real-time recording and processing of charging power demand data is performed to obtain lithium battery charging power demand change data.

In the embodiment of the present invention, a battery tester or other device is used to record the charging power demand data. The tester is connected to the battery and the charging device to ensure that the connection is correct and stable. The tester is started to start recording the charging power demand data. During the recording process, it is necessary to monitor the changes in the charging power demand in real time, and record the data. The recorded charging power demand data can be processed, and computer software or other tools can be used for data analysis and processing. During the recording and processing process, it is necessary to monitor the charging status and the operation of the charging device in real time to ensure the safety and stability of the charging process, and save and back up the recorded results.

Preferably, the lithium battery charging power requirement calculation formula in step S43 is as follows:

Where P(t) is the charging power demand result at time t, Q(t) is the lithium battery discharge state data at time t, C(t) is the lithium battery capacity at time t, N(t) is the battery voltage at time t, t is the time value, and f is the control factor of the sine wave amplitude. is the speed at which the battery capacity gradually decreases with time t, It is a periodic oscillation component that depends on the discharge state data Q(t).

The present invention constructs a lithium battery charging power demand calculation formula, where Represents the lithium battery discharge state data Q(t) multiplied by the rate at which the battery capacity decreases over time Contribution to charging power demand, It represents the contribution of the sine wave amplitude control factor f multiplied by sin(t) and divided by cos(Q(t)) to the charging power demand. Through the parameters and functional relationship in the above formula, the charging power demand can be calculated according to the discharge state, capacity, voltage and time changes of the lithium battery. In this way, the charging power can be intelligently adjusted according to the real-time state of the battery and the demand at a specific time point to meet the charging requirements. By adjusting the value of the parameter, the impact of different factors on the charging power demand can be flexibly adjusted, thereby optimizing the charging strategy, improving charging efficiency, extending battery life, and providing a better user experience.

Preferably, step S5 comprises the following steps:

Step S51: performing dynamic power charging processing on the lithium battery according to the charging power demand change data of the lithium battery to generate charging state curve data;

Step S52: performing real-time temperature monitoring processing on the lithium battery based on the charging state curve data to obtain real-time temperature data of the lithium battery;

Step S53: performing fuzzy logic processing on the real-time temperature data of the lithium battery to obtain feedback voltage adjustment value data;

Step S54: performing data integration processing on the feedback voltage adjustment value data according to the preset lithium battery basic data to obtain a feedback voltage adjustment strategy.

The present invention realizes dynamic power charging processing according to the change data of the charging power demand of the lithium battery, and can adjust the charging power according to the change of the charging state to meet the charging demand of the lithium battery. This helps to improve the charging efficiency and charging speed, and at the same time reduce the energy loss in the charging process. By performing real-time temperature monitoring processing on the lithium battery, the temperature information of the lithium battery can be obtained in time. This helps to protect the safety of the lithium battery and prevent the battery from being damaged or causing a safety accident due to excessive temperature. Fuzzy logic processing is performed on the real-time temperature data of the lithium battery, and the temperature state of the lithium battery can be judged according to the change of the temperature data, and a corresponding feedback voltage adjustment value is generated. This helps to control the temperature of the lithium battery and avoid the influence of excessively high or low temperature on the battery performance and life. Data integration processing is performed according to the preset lithium battery basic data and feedback voltage adjustment value data to generate a feedback voltage adjustment strategy. This helps to optimize the charging process of the lithium battery, ensure the charging speed and charging effect, and achieve the best charging effect within the temperature control range. Through the implementation of these steps, dynamic power adjustment, real-time temperature monitoring and feedback voltage adjustment of the lithium battery can be realized, thereby improving the charging efficiency, extending the battery life, and ensuring safety and stability.

As an example of the present invention, referring to FIG. 4 , in this example, step S5 includes:

Step S51: performing dynamic power charging processing on the lithium battery according to the charging power demand change data of the lithium battery to generate charging state curve data;

In an embodiment of the present invention, the charging power demand data is analyzed to observe the changing trend and fluctuation of the charging power, and a corresponding dynamically changing power strategy is formulated according to the changing situation of the charging power demand. For example, low-power charging is adopted when the charging power demand is low, and high-power charging is adopted when the power demand is high. The dynamically changing power strategy is applied to the charging device to control the charging power output by the charging device. An automatic control system is used or the parameters of the charging device are adjusted to realize the dynamically changing power charging process. During the charging process, the charging status is monitored in real time, including the charging current, charging voltage and other parameters of the battery. The changes in the charging status are recorded and saved as charging status curve data.

Step S52: performing real-time temperature monitoring processing on the lithium battery based on the charging state curve data to obtain real-time temperature data of the lithium battery;

In an embodiment of the present invention, recorded charging state curve data is obtained from a charging device or a testing instrument, and data related to the temperature of the lithium battery, such as the surface temperature of the battery, the maximum temperature inside the battery during charging, etc., are extracted from the charging state curve data, and the extracted temperature data is used to monitor the temperature of the lithium battery in real time. A sensor or other temperature monitoring device can be used to obtain real-time temperature data, analyze the temperature change trend of the lithium battery, observe the temperature fluctuation and whether there are abnormal conditions. Statistical methods or other data analysis tools can be used to process temperature data, and a temperature report of the lithium battery can be generated based on the real-time temperature data and the temperature change trend. The report can include information such as the average temperature, maximum temperature, minimum temperature, and temperature change curve of the lithium battery.

Step S53: performing fuzzy logic processing on the real-time temperature data of the lithium battery to obtain feedback voltage adjustment value data;

In an embodiment of the present invention, a set of fuzzy logic rules is set based on the temperature characteristics and control requirements of the lithium battery. These rules include the relationship between the input (temperature) and the output (feedback voltage adjustment value), and the real-time temperature data is fuzzified and converted into a fuzzy set. Fuzzification methods, such as triangular membership functions, Gaussian membership functions, etc., can be used to perform a fuzzy reasoning process according to the set fuzzy logic rules. The fuzzified temperature data is matched with the fuzzy logic rules to determine the corresponding feedback voltage adjustment value, and the fuzzy set obtained by fuzzy reasoning is converted into a specific feedback voltage adjustment value. Defuzzification methods, such as the maximum value method, the average value method, etc., can be used to apply the feedback voltage adjustment value obtained by defuzzification to the battery management system or the charging device to achieve voltage adjustment control of the lithium battery.

Step S54: performing data integration processing on the feedback voltage adjustment value data according to the preset lithium battery basic data to obtain a feedback voltage adjustment strategy.

In an embodiment of the present invention, preset basic data related to the performance and characteristics of the lithium battery, including information such as battery capacity, internal resistance, and charge and discharge efficiency, are obtained, and a feedback voltage adjustment strategy is defined based on the basic data and control requirements of the lithium battery. These strategies can be a set of rules, algorithms, or models, which are used to formulate a specific control scheme based on the feedback voltage adjustment value data, and integrate the feedback voltage adjustment value data with the basic data. Data processing techniques, such as weighted averaging, normalization, and other methods, can be used to integrate different data to obtain a comprehensive adjustment strategy, and a specific feedback voltage adjustment strategy is generated based on the integrated data. This strategy may include adjustment rules and targets under different temperature ranges, SOC (State of Charge, battery state of charge) ranges, workloads, etc., and the generated feedback voltage adjustment strategy is optimized and verified using simulation or actual testing methods, and the performance and effect of the strategy under different working conditions are evaluated, and necessary adjustments and improvements are made.

Preferably, step S53 includes the following steps:

Step S531: using a triangular membership function to perform fuzzy data mapping on the real-time temperature data of the lithium battery to obtain fuzzy temperature data and membership value data;

Step S532: establishing voltage-temperature fuzzy rules for the fuzzy temperature data and the membership value data according to expert judgment, and obtaining voltage-temperature fuzzy rule set data;

Step S533: performing fuzzy reasoning processing on the fuzzy temperature data and the membership value data based on the voltage-temperature fuzzy rule set data to generate fuzzy voltage adjustment output data;

Step S534: defuzzify the fuzzy voltage adjustment output data to obtain feedback voltage adjustment value data.

The present invention uses a triangular membership function to perform fuzzy processing on the real-time temperature data of the lithium battery, and can map a specific temperature value into fuzzy temperature data, and at the same time obtain various membership values, indicating the membership degree of the temperature value corresponding to different fuzzy sets. According to the experience and knowledge of experts, a voltage-temperature fuzzy rule set can be established, and the relationship between the fuzzy temperature data and the membership value data and the voltage adjustment value can be defined as a set of rules. These rules can describe the degree of influence of temperature on voltage adjustment based on actual conditions and needs. Based on the established voltage-temperature fuzzy rule set, fuzzy reasoning processing can be performed on the fuzzy temperature data and the membership value data to determine the fuzzy voltage adjustment output data. Through fuzzy reasoning, the corresponding fuzzy voltage adjustment output data can be calculated according to the membership value of the fuzzy temperature data and the weight of the rule, and the fuzzy voltage adjustment output data can be defuzzified, so that the fuzzy data can be converted into specific feedback voltage adjustment value data. Defuzzification can use various methods, such as fuzzy weighted average method, fuzzy center method, etc., to convert fuzzy data into specific values and obtain the final feedback voltage adjustment value data. By using fuzzy logic to process and reason about temperature data, the feedback voltage adjustment value can be determined more accurately to adapt to the working state of lithium batteries at different temperatures. This can improve the operating efficiency and stability of lithium batteries, extend the service life of batteries, and ensure their safety performance. At the same time, by establishing expert rule sets and fuzzy reasoning, the voltage adjustment can be optimized according to actual conditions and needs, providing a more flexible and accurate control strategy.

Step S531: using a triangular membership function to perform fuzzy data mapping on the real-time temperature data of the lithium battery to obtain fuzzy temperature data and membership value data;

In an embodiment of the present invention, real-time temperature data of a lithium battery is obtained. These data may be temperature values in digital form, such as degrees Celsius or degrees Fahrenheit, and an appropriate triangular membership function is selected to map the real-time temperature data. A triangular membership function is usually defined by three parameters: a left boundary, a vertex, and a right boundary. According to the temperature range and requirements, appropriate parameter values are set, and for each real-time temperature data, it is input into the triangular membership function to calculate the corresponding fuzzy temperature data. At the same time, the membership value of each fuzzy temperature data is recorded, indicating the degree of membership of the temperature value corresponding to different fuzzy sets, and the fuzzy temperature data and the membership value are stored in a suitable data structure, such as an array, a matrix, or a database. Ensure the integrity and accessibility of the data.

Step S532: establishing voltage-temperature fuzzy rules for the fuzzy temperature data and the membership value data according to expert judgment, and obtaining voltage-temperature fuzzy rule set data;

In an embodiment of the present invention, experts related to batteries and temperature are sought. These experts may be engineers, scientists, technicians, etc., and the statements of each rule are determined based on the experts' knowledge and experience. The rule statements are based on the relationship between the fuzzy temperature data and the membership value data and the voltage adjustment value to determine the fuzzy set name and parameters used in each rule. These sets can be any shape such as a triangle, trapezoid or Gaussian shape, and a suitable weight is assigned to each rule to indicate the degree of influence of the rule on the result. The weights can be assigned based on the opinions and experience of the expert group, and all rule statements, fuzzy sets and weights are stored in the rule base. Ensure the integrity and accessibility of the rule base, and use some typical temperature data to test the correctness and validity of the rule base. Check whether the output of the rule base meets the expected results.

Step S533: performing fuzzy reasoning processing on the fuzzy temperature data and the membership value data based on the voltage-temperature fuzzy rule set data to generate fuzzy voltage adjustment output data;

In an embodiment of the present invention, fuzzy temperature data and membership value data are obtained, and the range of the voltage adjustment value is determined according to the characteristics and requirements of the battery. For example, the voltage adjustment value can be any value between -1V and +1V. According to the voltage-temperature fuzzy rule set data, the fuzzy temperature data and membership value data are established with the voltage adjustment value. For each input fuzzy set, the rule to which it belongs is parsed, and the weight of the rule is calculated. At the same time, the output fuzzy set and its membership value of each rule are recorded, and all output fuzzy sets are aggregated to obtain fuzzy voltage adjustment output data. This process can be completed using fuzzy logic methods, such as the minimum and maximum method or the weighted average method, and the fuzzy voltage adjustment output data is converted into a specific voltage adjustment value. The defuzzification method can use single peak conversion, centroid method or average method, etc., to output the defuzzified voltage adjustment value, which is used for the battery management module in the control system to adjust the voltage.

Step S534: defuzzify the fuzzy voltage adjustment output data to obtain feedback voltage adjustment value data.

In an embodiment of the present invention, fuzzy voltage adjustment output data is obtained, and a reverse mapping function is established according to the range of the voltage adjustment value and the application requirements to convert the fuzzy voltage adjustment output data into feedback voltage adjustment value data. This process is usually completed using a defuzzification method, and a suitable defuzzification method is selected according to specific application requirements and signal-to-noise ratio requirements. Common methods include single peak conversion, centroid method, average method, weighted average method, etc. The fuzzy inverse method is used to convert the fuzzy voltage adjustment output data into feedback voltage adjustment value data, and the defuzzified feedback voltage adjustment value data is output for voltage adjustment of the battery management module in the control system.

Preferably, step S6 comprises the following steps:

Step S61: performing voltage compensation on the real-time voltage according to the feedback voltage adjustment strategy to generate compensation voltage data;

Step S62: performing boundary check on the compensation voltage data based on the preset maximum voltage data to obtain a voltage compensation result;

Step S63: performing data mapping processing on the compensation voltage data based on the voltage compensation result to obtain fast charging calibration lithium battery data;

Step S64: merging the normal charging calibration lithium battery data and the fast charging calibration lithium battery data to generate lithium battery voltage control data;

Step S65: dynamically adjusting the voltage of the lithium battery in real time according to the lithium battery voltage control data to implement intelligent lithium battery charging control.

The present invention compensates the real-time voltage according to the feedback voltage adjustment strategy of the battery management system. The purpose of compensation is to correct the difference between the actual voltage and the target voltage to ensure the voltage stability and safety of the battery during the charging process. The compensated voltage data is compared with the preset maximum voltage data for boundary checking. If the compensated voltage exceeds the maximum voltage limit, corresponding processing is required, such as limiting the charging speed or stopping charging to protect the safety of the battery. According to the voltage compensation result, the compensated voltage data is subjected to data mapping processing. This process can be customized according to actual needs, for example, mapping is performed according to factors such as battery type and charging mode to obtain fast charging calibration lithium battery data, and ordinary charging calibration lithium battery data and fast charging calibration lithium battery data are merged. In this way, the voltage control requirements in both ordinary charging and fast charging can be comprehensively considered, and comprehensive lithium battery voltage control data can be generated. According to the lithium battery voltage control data, the real-time voltage of the lithium battery is dynamically adjusted. By monitoring the real-time voltage of the battery and making corresponding adjustments according to the voltage control data, intelligent lithium battery charging control can be implemented. This ensures the safety, stability and efficiency of lithium batteries during the charging process. Through voltage compensation and dynamic adjustment, the charging process of lithium batteries can be effectively controlled, charging efficiency can be improved, battery life can be extended, and safety during the charging process can be ensured. At the same time, through data merging and mapping processing, customized voltage control strategies can be provided according to different charging requirements. These control strategies can implement intelligent lithium battery charging control and provide better user experience and charging effect.

In the embodiment of the present invention, the real-time voltage data of the lithium battery management system is obtained, and the voltage compensation amount required is calculated according to the preset voltage adjustment strategy, and the voltage compensation amount is added to the real-time voltage data to obtain the compensated voltage data, and the preset maximum voltage data is obtained. The compensated voltage data is compared with the maximum voltage data to determine whether it exceeds the range. If it exceeds the range, corresponding processing is performed, such as limiting the charging speed or stopping charging to protect the safety of the battery. If it does not exceed the range, the compensated voltage data is output as a result. Different mapping functions are preset according to factors such as battery type and charging mode, and the voltage compensation result and the preset mapping function are used to calculate the compensation value. Function, calculates fast charging calibration lithium battery data, takes the fast charging calibration lithium battery data as output, merges the normal charging calibration lithium battery data and the fast charging calibration lithium battery data to obtain the lithium battery voltage control data, calculates the required voltage adjustment amount according to the lithium battery voltage control data, adds the voltage adjustment amount to the real-time voltage data to obtain the adjusted voltage data, performs boundary check on the adjusted voltage data to ensure that it does not exceed the range, if it exceeds the range, performs corresponding processing, such as reducing the charging voltage to limit the charging speed or stopping charging to protect the safety of the battery, if it does not exceed the range, outputs the adjusted voltage data as the result.

In this specification, a lithium battery is provided, which is used to execute the charging control method of the lithium battery as described above, including:

The state acquisition module is used to acquire the impedance spectrum data of the lithium battery; the impedance spectrum data of the lithium battery is analyzed by using the preset basic data of the lithium battery to obtain the discharge state data of the lithium battery;

The model building module is used to obtain the historical power consumption data of lithium batteries; the power consumption model is constructed based on the historical power consumption data of lithium batteries using the long short-term memory network algorithm to generate a lithium battery power consumption prediction model;

The demand acquisition module is used to obtain real-time data; use the lithium battery power consumption prediction model to predict the battery power consumption of the real-time data to obtain the battery usage period prediction data; combine the lithium battery discharge state data and the battery usage period prediction data to perform charging speed demand analysis to obtain charging speed demand data, wherein the charging speed demand data includes fast charging demand data and ordinary charging demand data;

The demand calculation module is used to perform constant voltage charging and discharging processing on the lithium battery to obtain normal charging calibration lithium battery data when the charging speed demand data is determined to be normal charging demand data; when the charging speed demand data is determined to be fast charging demand data, the charging power demand calculation is performed on the lithium battery discharge state data based on the fast charging demand data to obtain the lithium battery charging power demand change data;

The temperature monitoring module is used to dynamically change the power charging process of the lithium battery according to the change data of the lithium battery charging power demand and perform real-time temperature monitoring on the lithium battery to obtain the real-time temperature data of the lithium battery; perform feedback voltage adjustment calculation on the real-time temperature data of the lithium battery according to the preset lithium battery basic data to obtain the feedback voltage adjustment strategy;

The dynamic adjustment module is used to adjust the real-time voltage according to the feedback voltage adjustment strategy to generate fast charging calibration lithium battery data; merge the ordinary charging calibration lithium battery data and the fast charging calibration lithium battery data to generate lithium battery voltage control data; and dynamically adjust the real-time voltage of the lithium battery according to the lithium battery voltage control data to implement intelligent lithium battery charging control.

The beneficial effect of the present invention is that by integrating lithium battery impedance spectrum data, historical power consumption data and real-time time data, the method integrates information from different aspects, provides a more comprehensive grasp of battery status and usage mode, and the power consumption prediction model constructed by the long short-term memory network algorithm can more accurately predict the power consumption of the lithium battery, providing a reliable basis for subsequent charging control. By analyzing the battery usage period prediction data, data of different charging speed requirements are obtained, including ordinary charging and fast charging. This helps to adopt corresponding charging strategies according to different needs, dynamically change the power charging process of the lithium battery, and combine with real-time temperature monitoring to more flexibly adjust the charging power according to the demand and real-time state, improve the charging efficiency, and perform feedback voltage adjustment calculation according to the real-time temperature data to adjust the voltage during the charging process, which helps to optimize the charging process, reduce the heat generation of the battery, and improve safety. By merging the first calibration and fast charging calibration lithium battery data, a multi-stage calibration is realized, which can more accurately reflect the actual state of the battery and improve the accuracy of charging control. Through the above steps, the system can make intelligent decisions based on real-time data and prediction models to achieve optimized charging control of lithium batteries, improve charging efficiency, extend battery life, and meet the personalized needs of users. Therefore, the present invention improves charging efficiency, extends battery life, and provides a safer and more reliable charging experience by focusing on user habits, comprehensive data, real-time intelligent control, and personalized strategies.

Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those 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 will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims (10)

1. A method for controlling charge of a lithium battery, comprising the steps of:

step S1: acquiring impedance spectrum data of a lithium battery; carrying out impedance spectrum analysis on lithium battery impedance spectrum data by using preset lithium battery basic data to obtain lithium battery discharge state data;

step S2: acquiring historical electric quantity consumption data of a lithium battery; building a power consumption model of the historical electric quantity consumption data of the lithium battery by using a long-short-term memory network algorithm to generate a lithium battery power consumption prediction model;

Step S3: acquiring instant time data; predicting the battery power consumption of the instant time data by using a lithium battery power consumption prediction model to obtain battery utilization rate period prediction data; carrying out charging speed demand analysis by combining lithium battery discharging state data and battery utilization rate period prediction data to obtain charging speed demand data, wherein the charging speed demand data comprises quick charging demand data and common charging demand data;

Step S4: when the charging speed requirement data is determined to be common charging requirement data, performing constant-voltage charging and discharging treatment on the lithium battery to obtain common charging calibration lithium battery data; when the charging speed demand data is determined to be the quick charging demand data, charging power demand calculation is carried out on the lithium battery discharging state data based on the quick charging demand data, and charging power demand change data of the lithium battery is obtained;

Step S5: carrying out dynamic change power charging treatment on the lithium battery according to the lithium battery charging power demand change data and carrying out real-time temperature monitoring treatment on the lithium battery to obtain real-time temperature data of the lithium battery; according to preset lithium battery basic data, feedback voltage adjustment calculation is carried out on the real-time temperature data of the lithium battery, and a feedback voltage adjustment strategy is obtained;

Step S6: performing voltage adjustment on the real-time voltage according to a feedback voltage adjustment strategy to generate quick charge calibration lithium battery data; data combination is carried out on the common charging calibration lithium battery data and the quick charging calibration lithium battery data, and lithium battery voltage control data is generated; and carrying out real-time voltage dynamic adjustment on the lithium battery according to the lithium battery voltage control data so as to implement intelligent lithium battery charging control.

2. The charge control method of a lithium battery according to claim 1, wherein the step S1 includes the steps of:

step S11: acquiring impedance spectrum data of a lithium battery;

step S12: carrying out spectrum analysis processing on the impedance spectrum data of the lithium battery to obtain electrochemical characteristic data of the battery;

step S13: establishing an electric quantity impedance relation model for electrochemical characteristic data of the battery according to preset lithium battery basic data to obtain the electric quantity impedance relation model;

Step S14: carrying out battery electric quantity analysis on the lithium battery impedance spectrum data by using an electric quantity impedance relation model to obtain lithium battery electric quantity data;

Step S15: and carrying out discharge state analysis on the lithium battery electric quantity data based on a preset discharge state threshold value to generate lithium battery discharge state data.

3. The charge control method of a lithium battery according to claim 1, wherein step S2 includes the steps of:

step S21: acquiring historical electric quantity consumption data of a lithium battery;

step S22: establishing a time sequence index for the historical lithium battery electricity consumption data according to a preset time stamp to obtain index historical electricity consumption data;

Step S23: performing time sequence division on the index historical electric quantity consumption data to obtain periodic electric quantity consumption data;

Step S24: and constructing a power consumption model of the periodic electric quantity consumption data by using a long-short-term memory network algorithm to generate a lithium battery power consumption prediction model.

4. The charge control method of a lithium battery according to claim 1, wherein step S3 includes the steps of:

Step S31: acquiring instant time data;

step S32: performing time linear deduction on the instant time data to obtain future time period data;

Step S33: predicting battery power consumption of future time period data by using a lithium battery power consumption prediction model to obtain battery utilization rate time period prediction data;

Step S34: carrying out charging demand analysis on the lithium battery discharging state data to obtain lithium battery charging demand data;

step S35: based on the battery utilization rate period prediction data, carrying out speed demand analysis on the lithium battery charging demand data by utilizing a lithium battery charging speed demand analysis algorithm to obtain charging speed demand data,

The lithium battery charging speed demand analysis algorithm is as follows:

Wherein v (T) is the charging speed demand result, T is the time interval value, T is the time variable value, R is the internal resistance of the battery in the charging process, C is the energy storage capacity of the battery, For the time derivative, dt is a minute time interval, a ₁ is a weighting parameter for controlling the contribution of the first time derivative to the charge speed, a ₂ is a weighting parameter for controlling the contribution of the second time derivative to the charge speed, and a ₃ is a weighting parameter for controlling the contribution of the third time derivative to the charge speed.

5. The method of controlling charge of a lithium battery according to claim 1, wherein step S4 includes the steps of:

Step S41: when the charging speed requirement data is determined to be common charging requirement data, shallow discharging treatment is carried out on the lithium battery, so that battery data with the lowest electric quantity is obtained;

step S42: performing constant voltage charging treatment on the battery data with the lowest electric quantity to obtain common charging calibration lithium battery data;

Step S43: when the charging speed demand data is determined to be the quick charging demand data, charging power demand calculation is carried out on the lithium battery discharging state data by utilizing a lithium battery charging power demand calculation formula, so as to obtain charging power demand data;

Step S44: and carrying out real-time recording processing on the charging power demand data to obtain the charging power demand change data of the lithium battery.

6. The method according to claim 5, wherein the calculation formula of the charging power demand of the lithium battery in step S43 is as follows:

Wherein P (t) is the charging power demand result at time t, Q (t) is lithium battery discharge state data of the lithium battery at time t, C (t) is lithium battery capacity at time t, N (t) is battery voltage at time t, t is time value, f is control factor of sine wave amplitude, For a speed value at which the battery capacity gradually decreases with time t,Is a periodic oscillation component depending on the discharge state data Q (t).

7. The charge control method of a lithium battery according to claim 1, wherein step S5 includes the steps of:

Step S51: carrying out dynamic change power charging treatment on the lithium battery according to the lithium battery charging power demand change data to generate charging state curve data;

step S52: performing real-time temperature monitoring treatment on the lithium battery based on the charging state curve data to obtain real-time temperature data of the lithium battery;

step S53: performing fuzzy logic processing on the real-time temperature data of the lithium battery to obtain feedback voltage adjustment value data;

step S54: and carrying out data integration processing on the feedback voltage adjustment value data according to preset lithium battery basic data to obtain a feedback voltage adjustment strategy.

8. The charge control method of a lithium battery according to claim 8, wherein step S53 includes the steps of:

Step S531: performing fuzzy data mapping on the real-time temperature data of the lithium battery by using a triangular membership function to obtain fuzzy temperature data and membership value data;

Step S532: establishing a voltage temperature fuzzy rule according to expert judgment on fuzzy temperature data and membership value data to obtain voltage temperature fuzzy rule set data;

Step S533: fuzzy reasoning is carried out on fuzzy temperature data and membership value data based on the voltage temperature fuzzy rule set data, and fuzzy voltage adjustment output data is generated;

step S534: and de-blurring the fuzzy voltage adjustment output data to obtain feedback voltage adjustment value data.

9. The method of controlling charge of a lithium battery according to claim 1, wherein step S6 includes the steps of:

step S61: performing voltage compensation on the real-time voltage according to a feedback voltage adjustment strategy to generate compensation voltage data;

step S62: performing boundary inspection on the compensation voltage data based on preset maximum voltage data to obtain a voltage compensation result;

step S63: performing data mapping processing on the compensation voltage data based on the voltage compensation result to obtain quick charge calibration lithium battery data;

Step S64: data combination is carried out on the common charging calibration lithium battery data and the quick charging calibration lithium battery data, and lithium battery voltage control data is generated;

Step S65: and carrying out real-time voltage dynamic adjustment on the lithium battery according to the lithium battery voltage control data so as to implement intelligent lithium battery charging control.

10. A lithium battery for performing the charge control method of the lithium battery according to claim 1, the lithium battery comprising:

The state acquisition module is used for acquiring impedance spectrum data of the lithium battery; carrying out impedance spectrum analysis on lithium battery impedance spectrum data by using preset lithium battery basic data to obtain lithium battery discharge state data;

The model building module is used for obtaining historical electric quantity consumption data of the lithium battery; building a power consumption model of the historical electric quantity consumption data of the lithium battery by using a long-short-term memory network algorithm to generate a lithium battery power consumption prediction model;

The demand acquisition module is used for acquiring instant time data; predicting the battery power consumption of the instant time data by using a lithium battery power consumption prediction model to obtain battery utilization rate period prediction data; carrying out charging speed demand analysis by combining lithium battery discharging state data and battery utilization rate period prediction data to obtain charging speed demand data, wherein the charging speed demand data comprises quick charging demand data and common charging demand data;

The demand calculation module is used for carrying out constant-voltage charge and discharge treatment on the lithium battery when the charging speed demand data is determined to be common charging demand data, so as to obtain common charging calibration lithium battery data; when the charging speed demand data is determined to be the quick charging demand data, charging power demand calculation is carried out on the lithium battery discharging state data based on the quick charging demand data, and charging power demand change data of the lithium battery is obtained;

the temperature monitoring module is used for carrying out dynamic change power charging treatment on the lithium battery according to the lithium battery charging power demand change data and carrying out real-time temperature monitoring treatment on the lithium battery to obtain real-time temperature data of the lithium battery; according to preset lithium battery basic data, feedback voltage adjustment calculation is carried out on the real-time temperature data of the lithium battery, and a feedback voltage adjustment strategy is obtained;

The dynamic adjustment module is used for carrying out voltage adjustment on the real-time voltage according to a feedback voltage adjustment strategy to generate quick charge calibration lithium battery data; data combination is carried out on the common charging calibration lithium battery data and the quick charging calibration lithium battery data, and lithium battery voltage control data is generated; and carrying out real-time voltage dynamic adjustment on the lithium battery according to the lithium battery voltage control data so as to implement intelligent lithium battery charging control.