A method for improving charging efficiency of lead-acid batteries

Abstract

The present invention discloses a method for improving the charging efficiency of a lead-acid battery, and relates to the technical field of lead-acid battery charging. Specifically, the method comprises the following steps: obtaining the temperature dynamic information of the lead-acid battery in the charging process during the day-night alternation period in real time, and analyzing it after obtaining it, and predicting whether the temperature difference during the day-night alternation period will affect the charging efficiency of the lead-acid battery; constructing a charging parameter adjustment mechanism for dynamically adjusting the charging current and voltage of the lead-acid battery according to the prediction result, and applying it to the charging process of the lead-acid battery; obtaining the charging feedback information of the charging parameter adjustment mechanism in the application process in real time, and analyzing it after obtaining it, and evaluating whether the adjustment degree of the charging parameter adjustment mechanism can improve the charging efficiency of the lead-acid battery. The present invention solves the problem of reduced charging efficiency of lead-acid batteries under the day-night temperature difference in desert areas, and realizes dynamic optimization of charging efficiency and extension of battery life.

Description

Technical Field

[0001] The invention relates to the technical field of lead-acid battery charging, and in particular to a method for improving the charging efficiency of a lead-acid battery. Background Art

[0002] In desert areas with large temperature differences between day and night, the external temperature of lead-acid batteries changes rapidly while the internal temperature changes lag behind. Since the external temperature of the battery rises or falls rapidly with the drastic changes in the environment, while the internal temperature changes slowly due to the lag in heat conduction, the existing technology for improving the charging efficiency of lead-acid batteries mainly relies on external temperature sensors and cannot capture the temperature difference inside the battery in real time. The existing technology usually adjusts the charging current and voltage based on external sensor data, but ignores the existence of the temperature gradient inside the battery, resulting in inaccurate adjustment. When the external temperature rises rapidly during the day, the system will mistakenly believe that the battery as a whole is overheated, and reduce the charging current too early, resulting in the battery not being fully charged at high temperatures. When the external temperature drops rapidly at night, the system continues to maintain a high charging current because it cannot perceive the delayed change in the internal temperature of the battery, resulting in overcharging and overheating inside the battery. The existing technology cannot effectively handle this internal and external temperature difference, which will cause the battery charging efficiency to decrease, local overheating and plate sulfidation to increase, thereby accelerating the aging of the battery and shortening its service life. At the same time, the decline in battery performance will also increase the frequency and cost of equipment maintenance, especially in environments with drastic temperature differences between day and night, which may lead to problems such as insufficient power supply, overheating shutdown or unstable equipment operation, which will seriously affect the overall reliability and safety of the system.

[0003] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0004] In order to achieve the above object, the present invention provides the following technical solution: a method for improving the charging efficiency of a lead-acid battery, specifically comprising the following steps:

[0005] When the day and night alternate in desert areas, the time period of day and night alternation can be identified by real-time monitoring of ambient temperature changes, and the data collection frequency of lead-acid batteries can be adjusted before the temperature difference changes drastically;

[0006] Real-time acquisition of temperature dynamic information of the lead-acid battery during the charging process during the day-night period, and analysis after acquisition to predict whether the temperature difference during the day-night period will affect the charging efficiency of the lead-acid battery;

[0007] According to the prediction results, a charging parameter adjustment mechanism for dynamically adjusting the charging current and voltage of the lead-acid battery is constructed and applied to the charging process of the lead-acid battery;

[0008] Acquire charging feedback information of the charging parameter adjustment mechanism in real time during its application, analyze it after acquisition, evaluate whether the adjustment degree of the charging parameter adjustment mechanism can improve the charging efficiency of the lead-acid battery, and optimize the charging parameter adjustment mechanism according to the evaluation results;

[0009] Based on long-term collected data and evaluation results, the charging parameter adjustment mechanism is dynamically updated so that it can be adaptively adjusted according to long-term data under conditions of changing day and night temperature differences.

[0010] Preferably, the temperature dynamic information of the lead-acid battery during the charging process in the day-night alternation period is obtained in real time, and analyzed after acquisition to predict whether the temperature difference in the day-night alternation period will affect the charging efficiency of the lead-acid battery, which specifically includes the following steps:

[0011] Real-time acquisition of temperature dynamic information of lead-acid batteries during charging during the day and night period, and pre-processing after acquisition;

[0012] Extracting battery thermal conduction information and charging dynamic response information from the preprocessed temperature dynamic information, and analyzing them after extraction to generate a thermal conduction hysteresis coefficient and a charging dynamic response index, respectively;

[0013] The generated thermal conduction hysteresis coefficient and charging dynamic response index are used to construct a charging efficiency impact model to generate an efficiency impact coefficient. The generated efficiency impact coefficient is compared with the pre-set efficiency impact coefficient threshold. Based on the comparison results, it is predicted whether the temperature difference during the day and night alternation period will affect the charging efficiency of the lead-acid battery.

[0014] Preferably, the acquisition logic of the heat conduction hysteresis coefficient and the charging dynamic response index is as follows:

[0015] The battery heat conduction information is extracted from the preprocessed temperature dynamic information, including the average temperature of the external environment at different times during the charging process of the lead-acid battery during the day and night period, the battery shell temperature, the temperature at different depths inside the battery, and the distance from the corresponding depth to the battery shell, and calibrated as , , and , Indicates the period of time during which the lead-acid battery is charged during the day and night alternation period. The average external environment temperature at the time, Indicates the period of time during which the lead-acid battery is charged during the day and night alternation period. The battery case temperature at the time, Indicates the period of time during which the lead-acid battery is charged during the day and night alternation period. The internal depth of the battery at the moment is The temperature at The corresponding depth is The distance from the battery casing, , , and are all positive integers; the specific calculation formula for calculating the heat conduction hysteresis coefficient is as follows: In the formula, is the heat conduction hysteresis coefficient; extract the charging dynamic response information from the preprocessed temperature dynamic information, specifically including the charging current, charging voltage and average temperature inside the battery at different times during the charging process of the lead-acid battery during the day and night alternation period, and calibrate them as , and , Indicates the period of time during which the lead-acid battery is charged during the day and night alternation period. The charging current at the moment, Indicates the period of time during which the lead-acid battery is charged during the day and night alternation period. The charging voltage at the moment, Indicates that the acid battery is charged during the day and night period. The average temperature inside the battery at the moment; calculate the charging dynamic response index, the specific calculation formula is as follows: In the formula, is the charging dynamic response index. Preferably, the generated thermal conduction hysteresis coefficient and charging dynamic response index Construct a charging efficiency impact model and generate efficiency impact coefficients through weighted summation , and the resulting efficiency impact coefficient The efficiency impact coefficient threshold is set in advance Compare and predict whether the temperature difference during the day and night period will affect the charging efficiency of the lead-acid battery based on the comparison results. The specific comparison analysis is as follows: , the temperature difference during the day and night period will not affect the charging efficiency of the lead-acid battery; if , the temperature difference during the day and night period will affect the charging efficiency of the lead-acid battery. Preferably, according to the prediction results, a charging parameter adjustment mechanism for dynamically adjusting the charging current and voltage of the lead-acid battery is constructed, and applied to the charging process of the lead-acid battery, specifically: when the prediction result is that the temperature difference during the day and night period will affect the charging efficiency of the lead-acid battery, the charging current and voltage adjustment scheme suitable for the current temperature change conditions is determined, and the specific adjustment values ​​of the charging current and voltage are automatically generated through a preset control algorithm to form a charging parameter adjustment mechanism; the adjustment mechanism dynamically calculates the adjustment range of the current and voltage by real-time monitoring of temperature changes, and applies the calculated current and voltage adjustment ranges to the charging process of the lead-acid battery to dynamically adjust the charging current and voltage.

[0016] Preferably, the charging feedback information of the charging parameter adjustment mechanism during the application process is obtained in real time, and analyzed after acquisition to evaluate whether the adjustment degree of the charging parameter adjustment mechanism can improve the charging efficiency of the lead-acid battery, and optimize the charging parameter adjustment mechanism according to the evaluation result, which specifically includes the following steps:

[0017] Real-time acquisition of charging feedback information during the application of the charging parameter adjustment mechanism, and pre-processing after acquisition;

[0018] Extracting charging dynamic feedback information and charging efficiency feedback information from the preprocessed charging feedback information, and analyzing them after extraction to generate a charging feedback response coefficient and a charging efficiency improvement index respectively;

[0019] The generated charging feedback response coefficient and charging efficiency improvement index are used to construct an adjustment degree feedback model to generate an adjustment feedback coefficient. The generated adjustment feedback coefficient is compared with a preset adjustment feedback coefficient threshold. Based on the comparison results, it is evaluated whether the adjustment degree of the charging parameter adjustment mechanism can improve the charging efficiency of the lead-acid battery, and the charging parameter adjustment mechanism is optimized based on the evaluation results.

[0020] Preferably, the acquisition logic of the charging feedback response coefficient and the charging efficiency improvement index is as follows:

[0021] Extract the charging dynamic feedback information from the preprocessed charging feedback information, specifically including the charging current, charging voltage, average temperature of the battery external environment and the corresponding time points of the lead-acid battery at different times during a period of time during the application of the charging parameter adjustment mechanism, and use the function to calculate the charging dynamic feedback information according to the time series. , and To express, Indicates that during the application of the charging parameter adjustment mechanism, the lead-acid battery The charging current at the moment, Indicates that during the application of the charging parameter adjustment mechanism, the lead-acid battery The charging voltage at the moment, Indicates that during the application of the charging parameter adjustment mechanism, the lead-acid battery The average temperature of the battery's external environment at the moment, is a time point, and the time period is defined as ; Calculate the charging feedback response coefficient. The specific calculation formula is as follows: In the formula, is the charging feedback response coefficient, It indicates the rate of change of the charging current of the lead-acid battery at different times over a period of time during the application of the charging parameter adjustment mechanism. It indicates the rate of change of the charging voltage of the lead-acid battery at different times over a period of time during the application of the charging parameter adjustment mechanism. Indicates the rate of change of the average temperature of the battery external environment of the lead-acid battery at different times within a period of time during the application of the charging parameter adjustment mechanism; extracts the charging efficiency feedback information from the preprocessed charging feedback information, specifically including the actual charging capacity, charging power, average temperature inside the battery and corresponding time points of the lead-acid battery at different times within a period of time during the application of the charging parameter adjustment mechanism, and uses the function , and To express, For time point, Indicates that during the application of the charging parameter adjustment mechanism, the lead-acid battery The actual charge level at the moment, Indicates that during the application of the charging parameter adjustment mechanism, the lead-acid battery Charging power at the moment, Indicates that during the application of the charging parameter adjustment mechanism, the lead-acid battery The average temperature inside the battery at the time; calculate the charging efficiency improvement index, the specific calculation formula is as follows: In the formula, is the charging efficiency improvement index. Preferably, the generated charging feedback response coefficient and charging efficiency improvement index Construct an adjustment degree feedback model and generate adjustment feedback coefficients through weighted summation , and the resulting adjustment feedback coefficient Adjust the feedback factor with the pre-set threshold Compare and evaluate whether the adjustment degree of the charging parameter adjustment mechanism can improve the charging efficiency of the lead-acid battery according to the comparison results, and optimize the charging parameter adjustment mechanism according to the evaluation results. The specific comparison analysis is as follows: , the adjustment degree of the charging parameter adjustment mechanism can improve the charging efficiency of the lead-acid battery, and there is no need to optimize the charging parameter adjustment mechanism; if , the adjustment degree of the charging parameter adjustment mechanism cannot improve the charging efficiency of the lead-acid battery, and the charging parameter adjustment mechanism needs to be optimized, specifically including resetting the adjustment strategy of the charging current and voltage. In the above technical scheme, the technical effects and advantages provided by the present invention are:

[0022] 1. The present invention collects temperature change information of lead-acid batteries in real time under a large temperature difference between day and night, especially through the heat conduction hysteresis coefficient and the charging dynamic response index, to solve the problem that the existing technology cannot capture the hysteresis caused by the internal temperature change of lead-acid batteries. Due to the drastic fluctuation of the temperature difference, the rapid change of the external temperature of the battery poses a challenge to the charging system. The present invention accurately predicts the impact of temperature difference on battery charging efficiency through multi-dimensional data analysis and complex mathematical models, ensuring that the system makes correct adjustments under temperature difference conditions, thereby improving the charging efficiency of the battery.

[0023] 2. The core advantage of the present invention lies in its dynamic adaptability, that is, by analyzing temperature and charging data in real time and automatically building a charging parameter adjustment mechanism, the system can adjust the charging current and voltage parameters in real time according to the evaluation results of the temperature difference change. This not only solves the problem of insufficient or overcharging caused by traditional reliance on external temperature sensors, but also avoids the shortening of battery life due to temperature lag. The long-term data collection and evaluation steps also provide the system with long-term adaptability. The system can continuously optimize its own charging strategy as the temperature difference between day and night continues to change, further improving the charging efficiency and life of the battery.

[0024] 3. The present invention improves the overall reliability of the system. Through real-time monitoring and analysis of charging feedback information, the system can ensure that the battery will not be damaged due to overheating or overcooling in extreme temperature environments, which is particularly important for equipment that needs to operate in harsh environments. The present invention reduces the maintenance frequency of equipment and reduces the risk of equipment downtime caused by temperature differences. It is particularly suitable for desert environments with large temperature differences between day and night, thereby greatly enhancing the stability and safety of the system. The system uses software to achieve dynamic optimization of the charging parameter adjustment mechanism to ensure the accuracy and efficiency of the entire charging process. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0026] Figure 1 The present invention is a flow chart of a method for improving the charging efficiency of a lead-acid battery. DETAILED DESCRIPTION

[0027] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0028] The present invention provides Figure 1 A method for improving the charging efficiency of a lead-acid battery is shown, which specifically comprises the following steps:

[0029] When the day and night alternate in desert areas, the time period of day and night alternation can be identified by real-time monitoring of ambient temperature changes, and the data collection frequency of lead-acid batteries can be adjusted before the temperature difference changes drastically;

[0030] The software can be used to determine the time of day and night in desert areas. It can be based on a series of preset geographic data and the sun trajectory calculation model, combined with the longitude and latitude information of the area, to calculate the local sunrise and sunset times. For example, the system can obtain the precise sunrise and sunset time periods by querying the astronomical database of a specific desert area. In addition, the temperature change characteristics during the day and night transition in desert areas can be analyzed through historical meteorological data, and a certain time window can be set based on these data to identify the day and night transition period.

[0031] By combining software with temperature sensors, the changes in ambient temperature in desert areas can be monitored in real time, and the time periods of day and night alternation can be identified. The specific implementation method is to use sensors to obtain ambient temperature data in real time and analyze it through the temperature change rate algorithm built into the software. When the rate of change of ambient temperature reaches a certain preset threshold (such as a specific rate at which the temperature rises or falls rapidly), the system can determine that it is a time period of day and night alternation. At the same time, the historical data of sunrise and sunset can be combined as an auxiliary reference to further accurately locate the specific time of day and night alternation.

[0032] When the temperature change identifies that the time period of day and night is approaching, the software can automatically adjust the data collection frequency of the lead-acid battery according to the temperature change trend. The specific implementation method is to monitor the temperature change rate. When the ambient temperature changes rapidly and is about to exceed the preset threshold, the system will trigger an internal instruction to increase or decrease the frequency of the sensor collecting temperature data. For example, the system can adjust the temperature data collection from once every 10 minutes to once every 1 minute according to the speed of temperature difference change, so as to better capture the subtle differences in temperature changes, thereby accurately reflecting the impact of temperature changes on lead-acid batteries.

[0033] The temperature difference between day and night in desert areas is extremely large. During the alternation of day and night, the ambient temperature changes extremely dramatically, especially during sunrise and sunset, when the temperature may rise or fall rapidly. Such drastic temperature changes will affect the charging efficiency of lead-acid batteries, especially the difference between the speed of external temperature change and the hysteresis of the temperature change inside the battery. Therefore, by timely adjusting the data acquisition frequency during the alternation of day and night, it is possible to monitor the drastic changes in ambient temperature more frequently, ensuring that the system captures sufficiently detailed data and accurately adjusts the charging current and voltage, thereby preventing the battery from being overcharged or undercharged when the temperature difference changes dramatically, improving charging efficiency and extending battery life.

[0034] Real-time acquisition of temperature dynamic information of the lead-acid battery during the charging process during the day-night period, and analysis after acquisition to predict whether the temperature difference during the day-night period will affect the charging efficiency of the lead-acid battery;

[0035] In this embodiment, the temperature dynamic information of the lead-acid battery during the charging process in the day-night alternation period is obtained in real time, and analyzed after acquisition to predict whether the temperature difference in the day-night alternation period will affect the charging efficiency of the lead-acid battery, which specifically includes the following steps:

[0036] Real-time acquisition of temperature dynamic information of lead-acid batteries during charging during the day and night period, and pre-processing after acquisition;

[0037] During the day and night period, temperature dynamic information can be collected in real time through multiple temperature sensors installed on the outside and inside of the lead-acid battery. These sensors monitor the ambient temperature, the battery shell temperature, and the temperature at different depths inside the battery to ensure complete coverage of the battery's temperature change process. The collected temperature data is transmitted to the central processing system wirelessly or wired, and the system receives and records these temperature data in real time. Through software analysis and processing, the system can automatically identify the moment of drastic temperature changes in order to capture the potential impact of temperature differences on charging efficiency. The entire process is implemented through an automated sensor network and data acquisition module to ensure the continuity and real-time nature of the data.

[0038] Preprocessing is to clean and optimize the collected temperature data to ensure the quality and accuracy of the data. The necessity of preprocessing lies in the fact that the collected temperature dynamic information may contain noise, missing data points or abnormal fluctuations. Common preprocessing steps include: removing sensor noise, using filtering algorithms to smooth temperature change data, filling missing data points, and removing outliers (such as extreme temperature values ​​caused by sensor failure). These steps ensure that subsequent data analysis is based on accurate and consistent temperature information, thereby improving the accuracy of generating thermal conduction hysteresis coefficients and charging dynamic response indexes. These preprocessing are automatically performed through software algorithms to ensure that the data has been optimized and cleaned before entering the analysis phase.

[0039] Extracting battery thermal conduction information and charging dynamic response information from the preprocessed temperature dynamic information, and analyzing them after extraction to generate a thermal conduction hysteresis coefficient and a charging dynamic response index, respectively;

[0040] Extracting the battery thermal conduction information and charging dynamic response information from the preprocessed temperature dynamic information can be achieved through the data layering processing and feature extraction algorithm in the software. First, the system will classify the data according to the location of the sensor and the acquisition time of the temperature data, and layer the battery's external ambient temperature, shell temperature, and temperature data at different depths inside. For the extraction of battery thermal conduction information, the software will analyze the relationship between the internal temperature changes at different depths and the external temperature, and calculate the temperature gradient and conduction delay. For the charging dynamic response information, the software analyzes the linkage relationship between temperature data and the charging current and voltage, and uses data correlation analysis and mathematical models to extract the dynamic response characteristics of the battery to temperature changes during charging. The entire extraction process is automatically completed by the algorithm module in the software to ensure that key information affecting battery charging efficiency is accurately captured.

[0041] The generated thermal conduction hysteresis coefficient and charging dynamic response index are used to construct a charging efficiency impact model to generate an efficiency impact coefficient. The generated efficiency impact coefficient is compared with the pre-set efficiency impact coefficient threshold. Based on the comparison results, it is predicted whether the temperature difference during the day and night alternation period will affect the charging efficiency of the lead-acid battery.

[0042] The pre-set efficiency impact coefficient threshold can be determined by historical data analysis and model training. First, the system collects a large amount of historical data, including information such as the charging efficiency, temperature change rate, charging current and voltage of lead-acid batteries at different ambient temperatures. Through the software's machine learning algorithm, the system can find the correlation pattern between charging efficiency and temperature change in these historical data. Then, using regression analysis or classification algorithms, the software can train a mathematical model to predict the impact of temperature differences on charging efficiency and generate corresponding efficiency impact coefficient thresholds. The threshold is verified by software simulation of multiple experimental results to ensure its accuracy. Ultimately, the threshold will be dynamically adjusted according to the temperature difference characteristics and battery performance in different desert areas so that it can adapt to actual usage scenarios.

[0043] In this embodiment, the logic for obtaining the heat conduction hysteresis coefficient and the charging dynamic response index is as follows:

[0044] The battery heat conduction information is extracted from the preprocessed temperature dynamic information, including the average temperature of the external environment at different times during the charging process of the lead-acid battery during the day and night period, the battery shell temperature, the temperature at different depths inside the battery, and the distance from the corresponding depth to the battery shell, and calibrated as , , and , Indicates the period of time during which the lead-acid battery is charged during the day and night alternation period. The average external environment temperature at the time, Indicates the period of time during which the lead-acid battery is charged during the day and night alternation period. The battery case temperature at the time, Indicates the period of time during which the lead-acid battery is charged during the day and night alternation period. The internal depth of the battery at the moment is The temperature at The corresponding depth is The distance from the battery casing, , , and All are positive integers;

[0045] Extracting the battery heat conduction information from the preprocessed temperature dynamic information can usually be achieved through software combined with an external data acquisition system. During the charging process of the lead-acid battery, the external environment temperature, the battery shell temperature, and the temperature at different depths inside are all acquired in real time through high-precision temperature detection equipment. First, the average temperature of the external environment can be acquired by the environmental monitoring equipment installed outside the battery, and the multi-time temperature data acquired by the equipment is transmitted to the central data processing software through the data transmission module. The acquisition of the battery shell temperature can be acquired through a series of sensor modules installed on the surface of the battery, which can acquire the temperature changes of the battery shell at different time periods. For the temperature at different depths inside the battery, although there is no physical sensor directly entering the battery, it can be simulated and calculated and reversed through the shell temperature data and the existing heat conduction model based on the thermal conductivity characteristics of the battery and the known physical properties of the material. As for the distance from different depths inside the battery to the battery shell, it is based on the geometric structure parameters in the battery design. These parameters can be input into the software system through the battery design drawings or pre-calibrated data files. After data acquisition, the software will automatically preprocess the data collected at different times, remove outliers, and smooth and standardize the data within the time period to ensure the accuracy and consistency of the data. Through this series of software implementation steps, various temperature data and corresponding depth distances used to calculate the thermal conduction hysteresis coefficient can be obtained. The specific calculation formula for calculating the thermal conduction hysteresis coefficient is as follows: In the formula, is the heat conduction hysteresis coefficient; the calculation formula of the heat conduction hysteresis coefficient calculates the heat conduction hysteresis of the battery during the day and night alternation period by double summation. First, the summation of the outer layer For different moments, it is used to integrate the temperature changes at different time points, reflecting the dynamic heat conduction characteristics of the battery during the entire time period; the sum of the inner layer For different depths, it is used to capture the temperature conduction differences of the battery at different depth levels. Outer layer summation: represents a comprehensive evaluation of heat conduction at different times. During the alternation of day and night, temperature changes have dynamic characteristics, especially the response of external temperature and internal temperature of the battery at different time points. Therefore, by summing up at different moments, the temperature changes over the entire time period are captured. Inner layer summation: represents the analysis of different depth layers of lead-acid batteries. In practice, the temperature conduction inside the battery is affected by the depth. The greater the depth, the slower the heat transfer. Therefore, this layer of summation is used to comprehensively consider the temperature differences and conduction characteristics at different depths. Temperature difference part :The temperature difference here Indicates that the battery case is The temperature of the moment, Indicates the battery internal Layer The temperature at the moment. The temperature difference is the driving force of heat conduction. The greater the temperature difference, the stronger the conduction. Therefore, calculating the temperature difference can directly reflect the degree of influence of the external temperature on the internal temperature of the battery.

[0046] Depth distance :This part indicates the distance from the battery shell to different depths inside. According to the law of heat conduction, the longer the conduction path, the more obvious the temperature conduction lag. Therefore, As an important parameter of depth distance, it directly affects the length of conduction time. Using it as a multiplier in the formula can reflect the hysteresis effect of different depths. External ambient temperature : External ambient temperature It is a key factor affecting the heat transfer rate, indicating the driving effect of ambient temperature on the changes in the external and internal temperatures of the battery. The denominator affects the formula, reflecting the physical property that the higher the external environment temperature, the faster the conduction.

[0047] Denominator :This part combines the relationship between the internal temperature of the battery and the depth distance. Internal temperature Reflects the temperature of the battery at different depths. It represents the path length of heat conduction. This part is used to adjust the calculation results to avoid nonlinear effects on the results caused by excessive or small internal temperature changes, thereby ensuring that the calculation of the heat conduction hysteresis coefficient is more stable. Cube root operation: In the formula, the cube root operation is used to balance the influence of temperature difference. Temperature conduction is nonlinear, and the cube root can reduce the influence of excessive temperature difference on hysteresis effect, while ensuring that small temperature difference can also reflect the actual hysteresis effect. This operation reconciles the excessive temperature difference, making the calculation result of conduction hysteresis more realistic.

[0048] The size of the heat conduction hysteresis coefficient directly reflects the degree of delay of the internal temperature of the lead-acid battery relative to the external temperature change. The larger the hysteresis coefficient, the longer it takes for the change in external ambient temperature to be transmitted to the inside of the battery, and the more obvious the impact of the temperature difference on the battery charging process. During the day and night period, if the hysteresis coefficient is large, the rapid change in external temperature (such as a rapid rise in temperature during the day or a sharp drop in temperature at night) cannot be transmitted to the inside of the battery in time, which may cause the charging system to incorrectly adjust the current and voltage, thereby affecting the charging efficiency and causing overcharging or undercharging. On the contrary, if the hysteresis coefficient is small, it means that the temperature conduction is faster, the battery can respond more quickly to external temperature changes, and the charging system can also make timely adjustments to ensure charging efficiency. Therefore, the size of the hysteresis coefficient is positively correlated with the impact of temperature differences on charging efficiency. The larger the hysteresis coefficient, the higher the risk of charging efficiency being affected by temperature changes.

[0049] Extract the charging dynamic response information from the preprocessed temperature dynamic information, including the charging current, charging voltage and average temperature inside the battery at different times during the charging process of the lead-acid battery during the day and night alternation period, and calibrate them as , and , Indicates the period of time during which the lead-acid battery is charged during the day and night alternation period. The charging current at the moment, Indicates the period of time during which the lead-acid battery is charged during the day and night alternation period. The charging voltage at the moment, Indicates that the acid battery is charged during the day and night period. The average temperature inside the battery at the moment; when extracting the charging dynamic response information from the preprocessed temperature dynamic information, the software can be combined with the real-time data acquisition module to realize the extraction of the charging current, charging voltage and average temperature inside the battery at different moments during the charging process of the lead-acid battery. First, the charging current and charging voltage can be monitored in real time by the current sensor and voltage sensor built into the battery management system (BMS). Each sensor will regularly record the current and voltage data at different time points during the charging period of alternating day and night, and transmit these data to the central processing software through the data transmission interface. Secondly, the average temperature inside the battery is the temperature data measured by temperature sensors installed at multiple depths in the battery. The temperature information obtained by these sensors is summarized by the data acquisition module and weighted averaged by the software to obtain the average temperature inside the battery. After all these data are collected, the software will automatically preprocess them, including noise removal, filtering and standardization of data format to ensure the accuracy and consistency of the data. In these ways, the software can accurately extract the current, voltage and internal temperature data of the battery at each moment, providing reliable data support for subsequent dynamic response analysis.

[0050] Calculate the charging dynamic response index. The specific calculation formula is as follows: In the formula, The dynamic response index of charging is calculated by summing the charging current, voltage and temperature difference. and charging voltage changes, combined with the external ambient temperature and the average temperature inside the battery The change of current and voltage is used to evaluate the battery's response speed to temperature changes during the day and night period. Specifically, the difference between current and voltage It reflects the change of power at different times, and this power change directly affects the battery charging state. It is used to quantify the change in external and internal temperature, indicating the impact of temperature difference on the battery charging process. By combining these two parts, the formula can evaluate the charging response dynamics of the battery at different time points. The summation calculation is used to accumulate data at different times to comprehensively reflect the charging dynamic response of the battery during the entire day and night alternation period, and finally generate the charging dynamic response index. It can be used to predict the impact of temperature difference on charging efficiency. The size of the charging dynamic response index directly reflects the response speed of the lead-acid battery to temperature changes during the day and night period. The larger the index, the faster the battery's charging power responds to temperature changes, and the smaller the impact of temperature differences on charging efficiency; on the contrary, the smaller the index, the more obvious the lag of the battery's power change under different temperature conditions, and the greater the impact of temperature differences on charging efficiency. When the temperature difference between day and night is large, if the charging dynamic response index is low, the battery cannot adjust the charging power in time, which may cause overcharging at high temperatures and undercharging at low temperatures, thereby affecting charging efficiency and shortening battery life. Conversely, a higher index means that the battery can respond quickly to temperature changes, ensure that the charging power under different temperature conditions is properly adjusted, maintain an efficient charging process, and reduce the negative impact of temperature differences on battery charging performance. Therefore, the higher the charging dynamic response index, the more effective it is in reducing the adverse effects of temperature differences on charging efficiency.

[0051] In this embodiment, the generated heat conduction hysteresis coefficient and charging dynamic response index Construct a charging efficiency impact model and generate efficiency impact coefficients through weighted summation , and the resulting efficiency impact coefficient The efficiency impact coefficient threshold is set in advance A comparison is made, and based on the comparison results, it is predicted whether the temperature difference during the day and night period will affect the charging efficiency of the lead-acid battery. The specific comparison analysis is as follows:

[0052] like , the temperature difference during the day and night period will not affect the charging efficiency of the lead-acid battery, which means that the temperature difference during the day and night period has little effect on the charging efficiency of the lead-acid battery, the temperature change inside the battery is relatively stable, and the charging power and the dynamic response of the battery can adapt to the temperature fluctuation. In this case, the system can maintain the existing charging strategy without additional adjustments, the charging process is still efficient, and the battery performance will not be significantly affected, ensuring the continuity and stability of charging;

[0053] like ,The temperature difference during the day and night period will affect the charging efficiency of the lead-acid battery, which means that the temperature difference has a greater impact on the charging efficiency of the lead-acid battery. The rapid change of external temperature causes the internal temperature of the battery to lag, and the battery cannot respond to the temperature change in time, which in turn affects the charging efficiency. In this case, the system must take measures to optimize the charging strategy to prevent overcharging or undercharging, avoid negative impact on battery life, and ensure stable operation of the equipment.

[0054] The generated heat conduction hysteresis coefficient and charging dynamic response index Construct a charging efficiency impact model and generate efficiency impact coefficients through weighted summation The specific method is to firstly calculate the hysteresis coefficient and dynamic response index Different weight coefficients are assigned to each of their impacts on charging efficiency. Since the thermal conduction hysteresis coefficient and the impact of temperature difference on charging efficiency are positively correlated, the weight coefficient The larger the hysteresis coefficient is, the more significant the negative impact on charging efficiency is. The higher the charging dynamic response index is, the more effective it is in reducing the adverse effect of temperature difference on charging efficiency. Therefore, the weight coefficient should be relatively small, reflecting the regulation ability of the charging response. Finally, through the formula Perform weighted summation to generate That is, the overall efficiency impact coefficient. The weight setting ensures that both the hysteresis and response factors can accurately reflect the comprehensive impact of temperature changes on charging efficiency. Based on the prediction results, a charging parameter adjustment mechanism for dynamically adjusting the charging current and voltage of lead-acid batteries is constructed and applied to the charging process of lead-acid batteries;

[0055] In this embodiment, based on the prediction results, a charging parameter adjustment mechanism for dynamically adjusting the charging current and voltage of the lead-acid battery is constructed and applied to the charging process of the lead-acid battery. Specifically, when the prediction result is that the temperature difference during the day and night period will affect the charging efficiency of the lead-acid battery, a charging current and voltage adjustment scheme suitable for the current temperature change conditions is determined, and specific adjustment values ​​of the charging current and voltage are automatically generated through a preset control algorithm to form a charging parameter adjustment mechanism; the adjustment mechanism dynamically calculates the adjustment range of the current and voltage by real-time monitoring of temperature changes, and applies the calculated current and voltage adjustment ranges to the charging process of the lead-acid battery to dynamically adjust the charging current and voltage.

[0056] The prediction results can be obtained by analyzing the previously generated thermal conduction hysteresis coefficient and charging dynamic response index through the software. These parameters are obtained through real-time acquisition of battery temperature, current, voltage and other data through preset calculation models and formulas. After analyzing these data, the software will predict whether the temperature difference during the day and night will affect the charging efficiency. When the prediction results show that the temperature difference may affect the charging efficiency, the software will build a mechanism for dynamically adjusting the charging current and voltage based on the current temperature change. This mechanism relies on a preset control algorithm, which automatically adjusts the parameters of the charging current and voltage according to the charging response characteristics of the battery under different temperature conditions (such as hysteresis coefficient and dynamic response index). The software will dynamically generate the adjustment range based on these parameters to ensure that the adjustment mechanism adapts to real-time environmental changes. Through the built-in control algorithm, the software will automatically calculate the appropriate charging current and voltage adjustment values ​​based on the battery temperature and other relevant data. The algorithm can determine the adjustment range based on historical data or machine learning models, and dynamically adjust the current and voltage in combination with real-time monitored temperature data to ensure that the battery can be charged safely and efficiently when the temperature difference changes drastically.

[0057] The specific control algorithm settings in the charging parameter adjustment mechanism are achieved through multi-step analysis and tuning. First, based on the long-term collected temperature, current, voltage and charging efficiency data, the software uses data mining technology to identify the optimal charging current and voltage adjustment range of the battery under different environmental conditions (especially when the temperature difference between day and night changes dramatically). Next, using these historical data, the system presets a set of charging current and voltage adjustment rules through machine learning algorithms (such as decision trees or neural networks) or optimization algorithms based on physical models. The specific control algorithm settings include: 1) defining the temperature fluctuation threshold. When the ambient temperature changes to the set threshold, the algorithm triggers parameter adjustment; 2) setting the functional relationship between current and voltage adjustment to ensure that the battery can respond quickly when the temperature fluctuates; 3) optimizing the control strategy, optimizing the control parameters through repeated simulation verification and actual data feedback, and ensuring that the adjustment mechanism adapts to the changing temperature difference conditions. After the algorithm generates the rules, the software system automatically calls the algorithm to output the corresponding charging current and voltage adjustment values ​​according to these preset control logics by real-time monitoring of the external temperature and battery status when the temperature changes are detected, and dynamically applies them to the charging process to form an intelligent adaptive adjustment mechanism.

[0058] During the charging process, the temperature, current and voltage data are collected in real time by sensors and transmitted to the monitoring system. The software will continuously calculate and update the charging parameters based on this data. Whenever the temperature or other conditions change, the software will instantly adjust the charging current and voltage through the control system to ensure that the charging process remains stable under different environmental conditions. This dynamic adjustment is achieved through a continuous feedback mechanism, allowing the charging system to reasonably adjust the battery according to the actual ambient temperature changes.

[0059] When the temperature difference between day and night is large, the external temperature of the lead-acid battery changes faster than the internal temperature. The hysteresis effect will cause the internal temperature of the battery to fail to reflect the drastic changes in the external environment in a timely manner during the charging process. Therefore, the purpose of building a dynamic adjustment mechanism is to ensure that the battery avoids overcharging or undercharging under extreme temperature differences, thereby maintaining battery life and charging efficiency. By dynamically adjusting the current and voltage through software, the system can flexibly adapt to environmental changes and ensure the safety and efficiency of the charging process.

[0060] Acquire charging feedback information of the charging parameter adjustment mechanism in real time during its application, analyze it after acquisition, evaluate whether the adjustment degree of the charging parameter adjustment mechanism can improve the charging efficiency of the lead-acid battery, and optimize the charging parameter adjustment mechanism according to the evaluation results;

[0061] In this embodiment, charging feedback information of the charging parameter adjustment mechanism during the application process is obtained in real time, and analyzed after acquisition to evaluate whether the adjustment degree of the charging parameter adjustment mechanism can improve the charging efficiency of the lead-acid battery, and optimize the charging parameter adjustment mechanism according to the evaluation result, which specifically includes the following steps:

[0062] Real-time acquisition of charging feedback information during the application of the charging parameter adjustment mechanism, and pre-processing after acquisition;

[0063] Charging feedback information can be obtained in real time through sensors and monitoring equipment installed in lead-acid batteries and charging systems. These sensors monitor key data such as current, voltage, temperature, etc. during the charging process. Especially during the application of the charging parameter adjustment mechanism, this information will be uploaded to the central control software in real time through the sensor acquisition module. The data is transmitted to the software system through a wireless communication module or a wired interface. The software will collect charging dynamic feedback information and charging efficiency feedback information according to the acquisition frequency at different time points. This method ensures that the charging process of the lead-acid battery can be monitored in real time, and the data can be obtained accurately and continuously.

[0064] Preprocessing is to ensure the accuracy and consistency of charging feedback information and avoid data noise or outliers from misleading subsequent analysis. During the preprocessing process, the collected current, voltage, and temperature data are first filtered through a denoising algorithm to eliminate abnormal fluctuations caused by equipment errors or environmental interference. Then, the data is standardized to ensure that feedback information at different times is compared and analyzed under the same benchmark conditions. In addition, data smoothing is performed so that sudden outliers do not affect the overall evaluation results. After preprocessing, the quality of charging feedback information is guaranteed, laying the foundation for subsequent analysis and calculation to generate reliable charging feedback response coefficients and charging efficiency improvement indexes.

[0065] Extracting charging dynamic feedback information and charging efficiency feedback information from the preprocessed charging feedback information, and analyzing them after extraction to generate a charging feedback response coefficient and a charging efficiency improvement index respectively;

[0066] Extracting the charging dynamic feedback information and charging efficiency feedback information from the preprocessed charging feedback information can be achieved by classifying and extracting features from the preprocessed data through software. First, the software will classify the charging feedback information into two categories according to the preprocessed data type: one is the data related to the dynamic changes of current, voltage and temperature during the charging process, which is classified as charging dynamic feedback information; the other is the data related to charging power, charging time and actual charging amount, which is classified as charging efficiency feedback information. The extraction process is implemented through the feature extraction algorithm. The software will automatically identify the time series characteristics of these data according to the pre-set rules, such as the rate of change of current and voltage, temperature fluctuations, and the relationship between charging power and charging amount. Subsequently, the software summarizes and analyzes the two types of extracted data respectively, and generates the charging feedback response coefficient and charging efficiency improvement index for subsequent model construction and mechanism optimization. This process is efficiently completed through data classification and automatic feature extraction algorithms to ensure that the extracted information can accurately reflect the dynamic changes and efficiency performance of the charging process.

[0067] The generated charging feedback response coefficient and charging efficiency improvement index are used to construct an adjustment degree feedback model to generate an adjustment feedback coefficient. The generated adjustment feedback coefficient is compared with a preset adjustment feedback coefficient threshold. Based on the comparison results, it is evaluated whether the adjustment degree of the charging parameter adjustment mechanism can improve the charging efficiency of the lead-acid battery, and the charging parameter adjustment mechanism is optimized based on the evaluation results.

[0068] The preset threshold of the adjustment feedback coefficient can be determined by software in a variety of ways, including historical data analysis, experimental results, and system simulation. First, based on a large amount of historical charging data, the software analyzes the charging efficiency performance of lead-acid batteries under different temperatures, charging currents, and voltages, and summarizes the normal range of adjustment feedback coefficients under different charging states. Next, through experimental measurement and simulation, the software can further determine the change pattern of the adjustment feedback coefficient when charging lead-acid batteries under various extreme environments (such as drastic changes in temperature between day and night), and find out the critical points with less and greater impact on charging efficiency. Finally, through comparative analysis, the software uses these critical points as a reference to set a reasonable threshold for the adjustment feedback coefficient to evaluate the performance of the actual charging parameter adjustment mechanism. The setting of this threshold can be automatically optimized by the algorithm, and it is continuously updated and adjusted according to the feedback of the system in actual use to ensure that it can adapt to different charging conditions.

[0069] In this embodiment, the logic for obtaining the charging feedback response coefficient and the charging efficiency improvement index is as follows:

[0070] Extract the charging dynamic feedback information from the preprocessed charging feedback information, specifically including the charging current, charging voltage, average temperature of the battery external environment and the corresponding time points of the lead-acid battery at different times during a period of time during the application of the charging parameter adjustment mechanism, and use the function to calculate the charging dynamic feedback information according to the time series. , and To express, Indicates that during the application of the charging parameter adjustment mechanism, the lead-acid battery The charging current at the moment, Indicates that during the application of the charging parameter adjustment mechanism, the lead-acid battery The charging voltage at the moment, Indicates that during the application of the charging parameter adjustment mechanism, the lead-acid battery The average temperature of the battery's external environment at the moment, is a time point, and the time period is defined as ; To extract the charging dynamic feedback information from the pre-processed charging feedback information, it can be achieved through the real-time monitoring system and data acquisition module of the software. Specifically, the data of charging current, charging voltage and external ambient temperature are continuously collected by sensors during the charging process, and the sensors record the changes of these parameters in real time. The software system will associate the data collected by these sensors at different times through timestamps to ensure that the current, voltage and temperature data at each moment correspond one to one. The extraction process is carried out through a feature classification algorithm. The software will classify the collected data, extract the information related to the charging dynamics and store it in the corresponding data set. The software can also collect and process data according to the set time interval to ensure the continuity and accuracy of the data. Finally, through the data screening and extraction algorithm, the system combines the current, voltage and temperature data at these different time points into complete charging dynamic feedback information for subsequent analysis and calculation. Calculate the charging feedback response coefficient. The specific calculation formula is as follows: In the formula, is the charging feedback response coefficient, It indicates the rate of change of the charging current of the lead-acid battery at different times over a period of time during the application of the charging parameter adjustment mechanism. It indicates the rate of change of the charging voltage of the lead-acid battery at different times over a period of time during the application of the charging parameter adjustment mechanism. It indicates the rate of change of the average temperature of the battery external environment of the lead-acid battery at different times over a period of time during the application of the charging parameter adjustment mechanism;

[0071] This calculation formula is used to evaluate the charging feedback response coefficient , which is achieved by charging current at different times , Charging voltage and external ambient temperature The rate of change is integrated to obtain the system's ability to respond to temperature changes. and Indicates the time period for evaluation, and the integral sign indicates the calculation of the cumulative rate of change of these parameters over the entire time period. In the formula, the product of the rate of change of current and voltage is used as the numerator, which represents the dynamic electrical characteristics of the battery during the charging process and reflects the adjustment of the charging parameters by the system. The external temperature change rate is used as the denominator to reflect the fluctuation of the external ambient temperature and evaluate how the battery charging process is affected by the temperature difference. By integrating this ratio, it is possible to evaluate how the charging system dynamically responds to the external temperature difference changes during the entire charging cycle.

[0072] : This expression represents the rate of change of charging current over time, specifically reflecting how the current changes over time during the charging process. The current change rate is a key indicator for evaluating the speed at which the charging system adjusts the current, especially when the temperature changes drastically. The rate of change of current can reflect whether the system adjusts in time to cope with changes in the battery state.

[0073] : This expression represents the rate of change of charging voltage over time, indicating the dynamic change of voltage over time during the charging process. The rate of change of voltage reflects the reaction speed of the charging parameter adjustment mechanism to voltage control, which directly affects the charging efficiency and safety of the battery at different ambient temperatures.

[0074] : This expression represents the rate of change of the external ambient temperature over time. It reflects the external temperature fluctuation of the battery under different environmental conditions, especially when the temperature difference is large. This rate of change is critical for the dynamic adjustment of charging parameters. Large temperature differences will have a greater impact on the battery charging process, so monitoring this change can ensure safe and efficient charging of the battery.

[0075] There is a direct relationship between the size of the charging feedback response coefficient and whether the adjustment degree of the charging parameter adjustment mechanism can improve the charging efficiency of the lead-acid battery. Specifically, a larger charging feedback response coefficient This indicates that the rate of change of charging current and voltage can respond quickly to changes in external ambient temperature, indicating that the charging parameter adjustment mechanism can quickly adapt to temperature fluctuations, thereby better maintaining charging efficiency and reducing the risk of overcharging or undercharging. This means that the current and voltage are adjusted slowly and cannot effectively follow the external temperature changes, which may lead to a decrease in charging efficiency, resulting in overcharging, overheating or inefficiency of the battery. The size of can be used to determine whether the charging parameter adjustment mechanism is timely and effective, and based on this result, the adjustment mechanism can be further optimized to ensure that the charging process of the lead-acid battery remains efficient under various environmental conditions.

[0076] Extract the charging efficiency feedback information from the preprocessed charging feedback information, including the actual charging capacity, charging power, average temperature inside the battery and corresponding time points of the lead-acid battery at different times during a period of time during the application of the charging parameter adjustment mechanism, and use the function to calculate the charging efficiency feedback information according to the time series. , and To express, For time point, Indicates that during the application of the charging parameter adjustment mechanism, the lead-acid battery The actual charge level at the moment, Indicates that during the application of the charging parameter adjustment mechanism, the lead-acid battery Charging power at the moment, Indicates that during the application of the charging parameter adjustment mechanism, the lead-acid battery The average temperature inside the battery at the time;

[0077] Extracting the charging efficiency feedback information from the preprocessed charging feedback information can be achieved through the real-time data acquisition module installed in the software system. Specifically, the actual charge amount, charging power and average temperature inside the battery are obtained in real time during the charging process through sensors installed in the charging system and inside the battery. Each sensor continuously monitors this data and sends it to the software system through the data transmission module. The software associates these data points at different times through timestamps to ensure that the corresponding charge amount, power and temperature data can be obtained at the same time point. The data acquisition module collects this information regularly according to the set sampling frequency and stores it in the data cache for further analysis. The extraction process is carried out through the feature extraction algorithm in the software. The software can automatically classify and identify the data of the actual charge amount, charging power and internal temperature, and perform subsequent processing through the analysis module. This method ensures the accuracy, real-time and integrity of the data. Calculate the charging efficiency improvement index, the specific calculation formula is as follows: In the formula, To improve the charging efficiency index.

[0078] This calculation formula is used to evaluate the charging efficiency improvement index. , by measuring the actual charge at different times With charging power and the average temperature inside the battery The integral is calculated by the ratio of and Indicates the evaluation period, and the integral sign indicates the cumulative calculation of the above ratio within this period. Represents the actual charge amount obtained by the battery at each moment, the denominator Represents the product of charging power and internal battery temperature, which reflects the impact of power and temperature on efficiency during battery charging. By integrating this ratio, it is possible to evaluate how the battery charging efficiency improves over time under specific power and temperature conditions during the entire charging cycle.

[0079] Charging efficiency improvement index The size of directly reflects the impact of the charging parameter adjustment mechanism on the charging efficiency of lead-acid batteries. If the value is larger, it means that during the charging process, the actual charge capacity is higher than the charging power and the internal temperature of the battery, indicating that the charging parameter adjustment mechanism can effectively adapt to different power and temperature change conditions, thereby improving the charging efficiency. This means that the current and voltage adjustment mechanism of the system operates well, can quickly respond to environmental changes, and maintain an efficient charging state. On the contrary, if If the value is smaller, it means that under the same power and temperature conditions, the actual charging capacity is small, the adjustment degree of the charging parameter adjustment mechanism is insufficient, and the charging efficiency cannot be effectively improved. The size of can be used to determine whether the charging parameter adjustment mechanism has a positive impact on the battery charging efficiency, and the charging strategy can be optimized based on this result.

[0080] In this embodiment, the generated charging feedback response coefficient and charging efficiency improvement index Construct an adjustment degree feedback model and generate adjustment feedback coefficients through weighted summation , and the resulting adjustment feedback coefficient Adjust the feedback factor with the pre-set threshold A comparison is made, and based on the comparison results, it is evaluated whether the adjustment degree of the charging parameter adjustment mechanism can improve the charging efficiency of the lead-acid battery, and the charging parameter adjustment mechanism is optimized based on the evaluation results. The specific comparison analysis is as follows:

[0081] like , the adjustment degree of the charging parameter adjustment mechanism can improve the charging efficiency of the lead-acid battery, and there is no need to optimize the charging parameter adjustment mechanism;

[0082] This means that the adjustment degree of the charging parameter adjustment mechanism has successfully improved the charging efficiency of the lead-acid battery, and the system adjustment effect is good. In this case, there is no need to make major modifications or optimizations to the existing adjustment mechanism, because the system has achieved effective response to temperature fluctuations and reasonable adjustment of power. The system only needs to be monitored and fine-tuned regularly, and by continuously monitoring the battery status data, adjusting small changes in charging current and voltage, to ensure that the charging parameters can adapt to future environmental changes. This process can be achieved in software through adaptive control algorithms and real-time monitoring feedback modules, so as to keep the system running stably at a higher efficiency.

[0083] like , the adjustment degree of the charging parameter adjustment mechanism cannot improve the charging efficiency of the lead-acid battery, and the charging parameter adjustment mechanism needs to be optimized, specifically including resetting the adjustment strategy of the charging current and voltage.

[0084] This means that the current charging parameter adjustment mechanism is not sufficient to cope with changes in ambient temperature and cannot effectively improve the charging efficiency of lead-acid batteries. In this case, the charging parameter adjustment mechanism needs to be optimized. Specific optimization measures include resetting the adjustment strategy of charging current and voltage to better adapt to changes in temperature differences. Optimization can be achieved through the algorithm adjustment module in the software. First, according to the actual charging data, the reasonable current and voltage adjustment range is recalculated; then, the new charging strategy is dynamically calculated through machine learning or optimization algorithms (such as fuzzy control or adaptive control algorithms) and applied to the system in real time. This method ensures that the system can optimize the adjustment strategy according to different environmental conditions, thereby improving the charging efficiency of lead-acid batteries.

[0085] When constructing the adjustment degree feedback model, the first step is to generate the charging feedback response coefficient and charging efficiency improvement index As two key parameters, these two parameters reflect the system's ability to respond to temperature changes and the degree of improvement in charging efficiency. In order to build a feedback model, it is necessary to perform a weighted summation of these two parameters to generate an adjustment feedback coefficient that comprehensively reflects the system adjustment effect. The weight coefficient is used to balance and The relative importance of the feedback model, specifically, the weight and The determination can be based on historical data and optimization results in actual applications. If the system response speed is more critical in different environments, The weight will be larger; if the improvement of charging efficiency is more important, then The weight should be larger. The weighted sum formula is: ;in and The sum of the parameters is equal to 1 to ensure that the contribution of each parameter to the adjustment feedback coefficient is appropriate. In this way, the overall adjustment capability of the system can be evaluated by combining the effects of both aspects to ensure that the charging parameter adjustment mechanism can play the best effect under different environmental conditions.

[0086] Based on long-term collected data and evaluation results, the charging parameter adjustment mechanism is dynamically updated so that it can be adaptively adjusted according to long-term data under conditions of changing day and night temperature differences.

[0087] Long-term data collection can be achieved through sensors and data acquisition modules in the charging system. Specifically, in a desert environment with a large temperature difference between day and night, the system continuously monitors key data such as temperature changes, current, and voltage during battery charging, and sends these data to the server or central control system at regular intervals through wireless or wired transmission. These data are stored in the data storage module at fixed time intervals to form a long-term data record. Through these historical data, the system can capture the charging behavior and charging efficiency changes of lead-acid batteries under different environmental conditions. This long-term data collection ensures that the system can obtain sufficient samples for more accurate evaluation and adjustment.

[0088] The collected data will be processed regularly through the software analysis module. The specific method is to calculate the trends and anomalies in the long-term data through the data analysis algorithm, and evaluate the efficiency changes of the system during the long-term charging process. Through machine learning algorithms or statistical analysis models, the system can automatically identify the impact of different environmental conditions (such as day and night temperature differences) on charging efficiency and generate relevant evaluation results. These evaluation results can be quantified into corresponding evaluation coefficients, such as charging efficiency index, charging feedback response index, etc. The system uses these coefficients to determine whether the current charging parameter adjustment mechanism meets the long-term adaptability requirements. This ensures that the system is always optimized and adjusted according to actual conditions.

[0089] Based on the evaluation results, the software system will dynamically update the charging parameter adjustment mechanism. The specific implementation method is to automatically adjust the current and voltage setting range in the system through an adaptive control algorithm or an optimization algorithm. The system will generate a new charging parameter adjustment model based on the temperature difference changes in the long-term data, adjust the charging current and voltage feedback mechanism, and ensure that the battery can maintain the optimal charging efficiency under different temperature difference conditions. For example, when the evaluation results show that the system is undercharged at low temperatures at night, the system will automatically increase the charging current setting value at night. This process is implemented through control rules and real-time feedback in the software to ensure that the system can dynamically respond to environmental changes.

[0090] Adaptive adjustment is to ensure that the system can maintain stable and efficient charging efficiency for a long time in an environment with a temperature difference between day and night. The temperature difference between day and night in desert areas is large. If adaptive adjustment is not performed, the system may affect the battery life or efficiency due to temperature fluctuations during long-term operation. Through adaptive adjustment based on long-term data, the system can update the adjustment strategy in real time according to changes in environmental conditions to ensure that the battery always operates with the optimal charging parameters under different temperature conditions, extending battery life and improving charging efficiency. This dynamic update can prevent short-term adjustment deviations from causing long-term damage to the battery, while ensuring the adaptability of the system in a changing environment.

[0091] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0092] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired or wireless (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0093] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0094] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0095] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0096] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0097] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0098] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for improving the charging efficiency of a lead-acid battery, characterized in that: When day and night alternate in desert areas, the time period of day and night alternate is identified by real-time monitoring of environmental temperature changes, and the data collection frequency of lead-acid batteries is adjusted before the temperature difference changes drastically; the temperature dynamic information of the lead-acid battery during the charging process during the day and night alternate time period is obtained in real time, and analyzed after acquisition to predict whether the temperature difference during the day and night alternate time period will affect the charging efficiency of the lead-acid battery; the specific steps include: real-time acquisition of the temperature dynamic information of the lead-acid battery during the charging process during the day and night alternate time period, and preprocessing after acquisition; extracting the battery thermal conduction information and charging dynamic response information from the preprocessed temperature dynamic information, and analyzing after extraction to generate a thermal conduction hysteresis coefficient and a charging dynamic response index respectively; constructing a charging efficiency influence model with the generated thermal conduction hysteresis coefficient and charging dynamic response index, generating an efficiency influence coefficient by weighted summation, and comparing the generated efficiency influence coefficient with a preset efficiency influence coefficient threshold, and predicting whether the temperature difference during the day and night alternate time period will affect the charging efficiency of the lead-acid battery according to the comparison result; According to the prediction results, a charging parameter adjustment mechanism for dynamically adjusting the charging current and voltage of the lead-acid battery is constructed and applied to the charging process of the lead-acid battery; the charging feedback information of the charging parameter adjustment mechanism during the application process is obtained in real time, and analyzed after acquisition to evaluate whether the adjustment degree of the charging parameter adjustment mechanism can improve the charging efficiency of the lead-acid battery, and the charging parameter adjustment mechanism is optimized according to the evaluation results; based on the long-term collected data and evaluation results, the charging parameter adjustment mechanism is dynamically updated so that it can be adjusted according to the long-term data under the conditions of changing day and night temperature differences.

2. A method for improving the charging efficiency of a lead-acid battery according to claim 1, characterized in that: The acquisition logic of the heat conduction hysteresis coefficient and the charging dynamic response index is as follows: The battery heat conduction information is extracted from the preprocessed temperature dynamic information, including the average temperature of the external environment at different times during the charging process of the lead-acid battery during the day and night period, the battery shell temperature, the temperature at different depths inside the battery, and the distance from the corresponding depth to the battery shell, and calibrated as , , and , It represents the average temperature of the external environment at time i during the charging process of the lead-acid battery during the day and night period. It indicates the battery shell temperature at time i during the charging process of the lead-acid battery during the day and night alternation period. It represents the temperature at a depth m inside the battery at time i during the charging process of the lead-acid battery during the day and night alternation period. Indicates the distance from the battery shell to the corresponding depth m. , , g and h are both positive integers; calculate the heat conduction hysteresis coefficient, the specific calculation formula is as follows: In the formula, is the heat conduction hysteresis coefficient; extract the charging dynamic response information from the preprocessed temperature dynamic information, specifically including the charging current, charging voltage and average temperature inside the battery at different times during the charging process of the lead-acid battery during the day and night alternation period, and calibrate them as , and , It represents the charging current of the lead-acid battery at time i during the charging process of the day and night alternation period. Indicates the period of time during which the lead-acid battery is charged during the day and night alternation period. The charging voltage at the moment, Indicates that the acid battery is charged during the day and night period. The average temperature inside the battery at the moment; calculate the charging dynamic response index, the specific calculation formula is as follows: In the formula, It is the charging dynamic response index.

3. A method for improving the charging efficiency of a lead-acid battery according to claim 2, characterized in that: The generated heat conduction hysteresis coefficient and charging dynamic response index Construct a charging efficiency impact model and generate efficiency impact coefficients through weighted summation , and the resulting efficiency impact coefficient The efficiency impact coefficient threshold is set in advance Compare and predict whether the temperature difference during the day and night period will affect the charging efficiency of the lead-acid battery based on the comparison results. The specific comparison analysis is as follows: , the temperature difference during the day and night period will not affect the charging efficiency of the lead-acid battery; if , the temperature difference during the day and night period will affect the charging efficiency of lead-acid batteries.

4. A method for improving the charging efficiency of a lead-acid battery according to claim 3, characterized in that: According to the prediction results, a charging parameter adjustment mechanism for dynamically adjusting the charging current and voltage of the lead-acid battery is constructed and applied to the charging process of the lead-acid battery. Specifically, when the prediction result is that the temperature difference during the day and night period will affect the charging efficiency of the lead-acid battery, a charging current and voltage adjustment scheme suitable for the current temperature change conditions is determined, and specific adjustment values ​​of the charging current and voltage are automatically generated through a preset control algorithm to form a charging parameter adjustment mechanism; the adjustment mechanism dynamically calculates the adjustment range of the current and voltage by real-time monitoring of temperature changes, and applies the calculated current and voltage adjustment ranges to the charging process of the lead-acid battery to dynamically adjust the charging current and voltage.

5. A method for improving the charging efficiency of a lead-acid battery according to claim 4, characterized in that: The charging feedback information of the charging parameter adjustment mechanism during the application process is obtained in real time, and analyzed after acquisition to evaluate whether the adjustment degree of the charging parameter adjustment mechanism can improve the charging efficiency of the lead-acid battery, and optimize the charging parameter adjustment mechanism according to the evaluation results, which specifically includes the following steps: Real-time acquisition of charging feedback information during the application of the charging parameter adjustment mechanism, and pre-processing after acquisition; Extracting charging dynamic feedback information and charging efficiency feedback information from the preprocessed charging feedback information, and analyzing them after extraction to generate a charging feedback response coefficient and a charging efficiency improvement index respectively; The generated charging feedback response coefficient and charging efficiency improvement index are used to construct an adjustment degree feedback model to generate an adjustment feedback coefficient. The generated adjustment feedback coefficient is compared with a preset adjustment feedback coefficient threshold. Based on the comparison results, it is evaluated whether the adjustment degree of the charging parameter adjustment mechanism can improve the charging efficiency of the lead-acid battery, and the charging parameter adjustment mechanism is optimized based on the evaluation results.

6. A method for improving the charging efficiency of a lead-acid battery according to claim 5, characterized in that: The logic for obtaining the charging feedback response coefficient and the charging efficiency improvement index is as follows: Extract the charging dynamic feedback information from the preprocessed charging feedback information, specifically including the charging current, charging voltage, average temperature of the battery external environment and the corresponding time points of the lead-acid battery at different times during a period of time during the application of the charging parameter adjustment mechanism, and use the function to calculate the charging dynamic feedback information according to the time series. , and To express, It represents the charging current of the lead-acid battery at time t within a period of time during the application of the charging parameter adjustment mechanism. It represents the charging voltage of the lead-acid battery at time t within a period of time during the application of the charging parameter adjustment mechanism. It represents the average temperature of the external environment of the lead-acid battery at time t during the application of the charging parameter adjustment mechanism. t is the time point and the time period is defined as ; Calculate the charging feedback response coefficient. The specific calculation formula is as follows: In the formula, is the charging feedback response coefficient, It indicates the rate of change of the charging current of the lead-acid battery at different times over a period of time during the application of the charging parameter adjustment mechanism. It indicates the rate of change of the charging voltage of the lead-acid battery at different times over a period of time during the application of the charging parameter adjustment mechanism. It indicates the rate of change of the average temperature of the battery external environment of the lead-acid battery at different times over a period of time during the application of the charging parameter adjustment mechanism; Extract the charging efficiency feedback information from the preprocessed charging feedback information, including the actual charging capacity, charging power, average temperature inside the battery and corresponding time points of the lead-acid battery at different times during a period of time during the application of the charging parameter adjustment mechanism, and use the function to calculate the charging efficiency feedback information according to the time series. , and is represented by, t is the time point, It indicates the actual charge amount of the lead-acid battery at time t within a period of time during the application of the charging parameter adjustment mechanism. It represents the charging power of the lead-acid battery at time t within a period of time during the application of the charging parameter adjustment mechanism. It indicates the average temperature inside the lead-acid battery at time t within a period of time during the application of the charging parameter adjustment mechanism; the charging efficiency improvement index is calculated, and the specific calculation formula is as follows: In the formula, To improve the charging efficiency index.

7. A method for improving the charging efficiency of a lead-acid battery according to claim 6, characterized in that: The generated charging feedback response coefficient and charging efficiency improvement index Construct an adjustment degree feedback model and generate adjustment feedback coefficients through weighted summation , and the resulting adjustment feedback coefficient Adjust the feedback factor with the pre-set threshold Compare and evaluate whether the adjustment degree of the charging parameter adjustment mechanism can improve the charging efficiency of the lead-acid battery according to the comparison results, and optimize the charging parameter adjustment mechanism according to the evaluation results. The specific comparison analysis is as follows: , the adjustment degree of the charging parameter adjustment mechanism can improve the charging efficiency of the lead-acid battery, and there is no need to optimize the charging parameter adjustment mechanism; if , the adjustment degree of the charging parameter adjustment mechanism cannot improve the charging efficiency of the lead-acid battery, and the charging parameter adjustment mechanism needs to be optimized, specifically including resetting the adjustment strategy of the charging current and voltage.

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