Electric vehicle battery control system and method

Abstract

The present invention discloses an electric vehicle battery control system and method, which relates to the technical field of electric vehicles. The system includes the following components: a sensor module: through a high-precision sensor network, the key parameters of the battery pack, such as voltage, current, temperature and internal resistance, are monitored in real time. The high-precision and high-reliability characteristics of the sensor are utilized to respond to changes in the battery pack in real time to ensure the accuracy and real-time nature of the data. The present invention uses a high-precision sensor network to monitor the key parameters of voltage, current, temperature and internal resistance of each single battery in real time, thereby achieving precise monitoring of the single battery, and can conduct in-depth analysis of the data of the single battery, accurately determine the working status of the single battery, such as charging, discharging, and idleness, predict its remaining life and potential failures, issue early warnings, and provide timely information to maintenance personnel, thereby avoiding vehicle downtime or safety accidents caused by battery failure.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicles, and in particular to an electric vehicle battery control system and method. Background Art

[0002] In the field of electric vehicles, the battery control system, as a core component of electric vehicles, is of self-evident importance. With the popularization of electric vehicles and the rapid growth of the market, the requirements for battery control systems are also increasing. Traditional battery control systems often have many limitations and cannot meet the high requirements of modern electric vehicles for battery management. For example, the battery pack of an electric vehicle is composed of multiple single cells, and the performance and status of each single cell are different. The traditional battery control system can only monitor the battery pack as a whole, but cannot accurately monitor the single cell. This leads to inaccurate judgment of the status of the single cell, making it difficult to discover and solve potential problems in a timely manner.

[0003] Secondly, the traditional battery control system has a single control strategy. The usage scenarios of electric vehicles are complex and changeable, and different usage scenarios have different requirements for batteries. Traditional battery control systems often adopt fixed control strategies and cannot make intelligent adjustments based on the actual status and usage scenarios of the battery. This leads to low efficiency of the battery during use and may even pose safety hazards.

[0004] However, with the rapid development of electric vehicles, the performance requirements for battery energy density and power density are also constantly increasing. Due to technical limitations, traditional battery control systems are difficult to meet the management needs of these high-performance batteries. At the same time, with the continuous advancement of battery technology, new batteries such as solid-state batteries and lithium-sulfur batteries are constantly emerging. The performance characteristics and management requirements of these new batteries are also quite different from those of traditional batteries, resulting in incomplete monitoring, single control strategy and inability to meet the management needs of high-performance batteries in traditional battery control systems. Summary of the Invention

[0005] The purpose of the present invention is to make up for the shortcomings of the existing technology and provide an electric vehicle battery control system and method. It can automatically adjust the key parameters of charging current, discharging current and temperature through advanced sensor technology, data analysis technology and control algorithms to optimize the performance and life of the battery, realize comprehensive and intelligent monitoring and management of electric vehicle battery packs, and improve the battery's efficiency, safety and life.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: an electric vehicle battery control system, which includes the following components:

[0007] Sensor module: Through a high-precision sensor network, it monitors the key parameters of the battery pack, including voltage, current, temperature, and internal resistance, in real time. Utilizing the high precision and high reliability of the sensors, it responds to changes in the battery pack in real time, ensuring data accuracy and real-time performance.

[0008] Data acquisition module: collects data generated by the sensor module and transmits it to the data analysis module. It uses high-speed data transmission technology to ensure the real-time and integrity of the data. At the same time, it performs necessary cleaning, filtering and compression on the raw data to reduce the burden of data transmission and storage.

[0009] Data Analysis Module: Utilizes advanced data analysis technology to conduct in-depth analysis and processing of collected data, identifying the operating status of the battery pack, such as charging, discharging, and idle, and predicting key information such as battery life and remaining power. Based on the battery pack's historical data and current status, it predicts possible faults or abnormal conditions and issues early warnings.

[0010] Control module: Based on the results of the data analysis module, it formulates corresponding control strategies and uses actuators to achieve intelligent control of the battery pack and precise control of key parameters such as charging current, discharging current, and temperature, ensuring that the battery pack operates safely and efficiently. It also automatically adjusts the control strategy based on the actual situation of the battery pack to adapt to different working environments and user needs.

[0011] User interface and interaction module: Through a graphical interface and human-computer interaction technology, it provides users with key information such as the real-time status, historical data and forecast information of the battery pack. Through this module, users can understand the working conditions of the battery pack and perform corresponding operations and adjustments.

[0012] Furthermore, the sensor module uses a voltage divider circuit and a resistor divider to measure the voltage of the battery pack during voltage measurement, monitors the voltage data of the battery pack in real time, accurately evaluates the status of the battery pack, predicts the battery life, and formulates a more reasonable charging and discharging strategy, thereby improving the efficiency and safety of the battery. The algorithm formula is: The voltage measured by the sensor is V sensor , the actual voltage of the battery pack is V battery The resistance ratio of the voltage divider circuit is R1:R2. Adjust the resistance values ​​of R1 and R2 and change the ratio of the voltage divider circuit to adapt to the measurement requirements of different voltage ranges, and adjust the charging current and charging time according to the voltage changes to formulate a more reasonable charging and discharging strategy.

[0013] Furthermore, the data acquisition module specifically comprises the following steps:

[0014] Data reception: Receives data from the sensor module, including key parameters of the battery pack, such as voltage, current, temperature, and internal resistance;

[0015] Data verification: Verify the received data to ensure its integrity and accuracy;

[0016] Data preprocessing: Clean, filter, and compress the verified raw data to reduce the burden of data transmission and storage;

[0017] Data transmission: The pre-processed data is transmitted to the data analysis module for further analysis and processing. High-speed data transmission technology is used during the transmission process to ensure the real-time and integrity of the data.

[0018] Furthermore, the data acquisition module may be affected by various interferences and noises during the transmission of sensor data, resulting in errors and fluctuations in the data. The median filter cleans and filters the raw data to remove noise and outliers. Its operation process is: all data points in a window are sorted by size, and then the median value is taken as the output. The output y[n] of the median filter can be expressed as: Median filtering does not significantly change the overall distribution and edge information of the data during the noise reduction process. While maintaining the original characteristics of the data, it improves the signal-to-noise ratio of the data and ensures the accuracy and reliability of key parameters of the battery pack.

[0019] Furthermore, the data analysis module predicts the remaining capacity and life of the battery through the linear regression algorithm in the data analysis technology. Based on the historical data and the current battery status (such as voltage, current, temperature), the historical data of the number of charge and discharge cycles, temperature and discharge depth of the battery are analyzed to predict the capacity or remaining life of the battery at a certain point in the future. The algorithm formula is: y = β0 + β1x1 + β2x2 + ... + β n x n , y is the predicted value (battery life), (x1, x2, ..., x n ) are input characteristics (voltage, current, temperature), (β0, β1, ..., β n ) is the regression coefficient. Linear regression can provide the battery management system with a prediction of battery life, helping the system to formulate strategies for maintenance or replacement of batteries in advance. In addition, through linear regression, the system can also analyze the impact of different factors on battery performance, thereby optimizing battery use and management, so as to facilitate the subsequent prediction of faults or abnormal conditions and issue early warnings.

[0020] Furthermore, the data analysis module uses the Z-score method to detect abnormal changes in battery parameters, such as a sudden increase or decrease in voltage or current, to help the data analysis module quickly identify abnormal changes in battery parameters, thereby discovering possible problems or faults in the battery in advance, monitoring battery parameters in real time and providing early warnings, thereby improving the intelligent level of battery management. The algorithm formula is: x is the original data value, μ is the mean of the data, and σ is the standard deviation of the data.

[0021] Furthermore, the control module specifically comprises the following steps:

[0022] Receive data analysis results: Receive processed data and analysis results from the data analysis module, including key information such as the working status of the battery pack and predicted battery life;

[0023] Intelligent control strategy formulation: Based on the data analysis results, the control module formulates corresponding control strategies, including adjusting the charging current, limiting the discharge current, and activating the cooling system to ensure that the battery pack operates in a safe and efficient state;

[0024] Automatic adjustment control strategy: According to the actual situation of the battery pack and user needs, the control strategy is automatically adjusted to adapt to different working environments and user needs;

[0025] Send control instructions: The control module converts the formulated control strategy into specific control instructions and sends them to the actuator;

[0026] Execution and control: Actuators (such as electronic switches, relays, and fans) perform corresponding operations and adjustments based on the instructions issued by the control module to achieve intelligent control of the battery pack.

[0027] Furthermore, the user interface and interaction module uses a linear interpolation algorithm to smoothly display real-time status display and historical data query, intuitively displaying the key parameters of the battery pack voltage, current and temperature collected by the system, and uses a linear interpolation algorithm to estimate the continuous changes between these key data points and present them to the user in a graphical manner, making the real-time status and historical data display of the battery pack easy to understand, improving the user experience and enabling corresponding operations and adjustments as needed, namely: y0 and y1 represent battery status parameter values, such as voltage, current, and temperature.

[0028] Furthermore, the electric vehicle battery control method is characterized in that the method includes:

[0029] Real-time monitoring and data acquisition: The sensor module monitors the various parameters of the battery pack in real time, and the data acquisition module collects these data to provide raw data for subsequent analysis and control;

[0030] Data analysis and prediction: Conduct in-depth analysis of the collected data to determine the working status of the battery pack and predict key information about the battery life, which will serve as the basis for formulating subsequent control strategies;

[0031] Intelligent control strategy formulation: Based on the results of the data analysis module, corresponding control strategies are formulated, including adjusting charging current, limiting discharge current, and activating the cooling system. The control strategy can also be automatically adjusted according to the actual situation of the battery pack and user needs to adapt to different working environments and user needs;

[0032] Execution and control: Intelligent control of the battery pack is achieved through actuators (electronic switches, relays, fans), and corresponding operations and adjustments are performed according to the instructions issued by the control module;

[0033] User feedback and adjustments: Users can understand the working conditions of the battery pack through the user interface and interactive modules, and perform corresponding operations and adjustments as needed. At the same time, users can provide usage feedback and suggestions to the system so that the system can be continuously optimized and improved.

[0034] Furthermore, the control method also includes: after the control instruction is issued, diagnosing faults or potential problems in the battery pack through data analysis, such as abnormal single cell voltage and excessive temperature, attempting automatic repair, adjusting the charging and discharging strategy and starting the cooling system, and issuing an alarm to prompt the user to take further action.

[0035] Compared with the existing technology, the electric vehicle battery control system and method have the following beneficial effects:

[0036] 1. Through a high-precision sensor network, the present invention can monitor the key parameters of each single battery, including voltage, current, temperature, and internal resistance, in real time, thereby achieving precise monitoring of the single battery. It can also conduct in-depth analysis of the single battery data, accurately determine the working status of the single battery, such as charging, discharging, and idle, predict its remaining life and potential failures, issue early warnings, and provide timely information to maintenance personnel, thereby avoiding vehicle downtime or safety accidents caused by battery failure.

[0037] 2. The present invention adopts advanced data analysis technology, which can dynamically adjust the control strategy according to the battery's historical data, current status and usage scenarios, and intelligently adjust the charging current and charging time according to the battery's remaining power and temperature information to ensure that the battery is charged in a safe and efficient state, so that the system can adapt to different usage environments and user needs, improve the adaptability and flexibility of the battery, and provide more stable and reliable battery support for electric vehicles.

[0038] Other advantages, objects and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be learned from the practice of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0040] Figure 1 This is the operation flow chart of the electric vehicle battery control system.

[0041] Figure 2 Flowchart of the electric vehicle battery control method. DETAILED DESCRIPTION

[0042] The following is a clear and complete description of the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0043] Electric vehicle battery control system, which includes the following components:

[0044] Sensor module: Through a high-precision sensor network, it monitors the key parameters of the battery pack, including voltage, current, temperature, and internal resistance, in real time. Utilizing the high precision and high reliability of the sensors, it responds to changes in the battery pack in real time, ensuring data accuracy and real-time performance.

[0045] Data acquisition module: collects data generated by the sensor module and transmits it to the data analysis module. It uses high-speed data transmission technology to ensure the real-time and integrity of the data. At the same time, it performs necessary cleaning, filtering and compression on the raw data to reduce the burden of data transmission and storage.

[0046] Data Analysis Module: Utilizes advanced data analysis technology to conduct in-depth analysis and processing of collected data, identifying the operating status of the battery pack, such as charging, discharging, and idle, and predicting key information such as battery life and remaining power. Based on the battery pack's historical data and current status, it predicts possible faults or abnormal conditions and issues early warnings.

[0047] Control module: Based on the results of the data analysis module, it formulates corresponding control strategies and uses actuators to achieve intelligent control of the battery pack and precise control of key parameters such as charging current, discharging current, and temperature, ensuring that the battery pack operates safely and efficiently. It also automatically adjusts the control strategy based on the actual situation of the battery pack to adapt to different working environments and user needs.

[0048] User interface and interaction module: Through a graphical interface and human-computer interaction technology, it provides users with key information such as the real-time status, historical data and forecast information of the battery pack. Through this module, users can understand the working conditions of the battery pack and perform corresponding operations and adjustments.

[0049] Example 1

[0050] This embodiment describes an electric vehicle battery control system that monitors key parameters of a battery pack in real time through a high-precision sensor network. It employs high-speed data transmission and median filtering techniques to ensure real-time data accuracy. It also uses advanced linear regression algorithms to accurately predict battery life and remaining capacity, providing decision support for the battery management system and intelligently adjusting control strategies to ensure that the battery pack operates safely and efficiently.

[0051] In the specific implementation, first, a high-precision sensor network is used to monitor the voltage, current, temperature and internal resistance of the battery pack in real time. In the voltage measurement, the voltage divider circuit and resistor divider technology are used to accurately evaluate the status of the battery pack, predict the battery life, and formulate more reasonable charging and discharging strategies, thereby improving the efficiency and safety of the battery. For example, if the voltage measured by the sensor is V sensor , the actual voltage of the battery pack is V battery , the resistance ratio of the voltage divider circuit is R1:R2, and the algorithm formula is: V sensor = By adjusting the resistance values ​​of R1 and R2, accurate measurement of different voltage ranges can be achieved, and real-time monitoring data is transmitted to the data acquisition module through the sensor network.

[0052] Then, the data acquisition module receives the data from the sensor module and verifies the data to ensure the integrity and accuracy of the data. The verified data is preprocessed, including data cleaning, filtering and compression, to reduce the burden of data transmission and storage. The raw data is cleaned and filtered using median filtering technology to remove noise and outliers in the data. The operation process is: all data points in a window are sorted by size, and then the median value is taken as the output. The output y[n] of the median filter can be expressed as: Median filtering does not significantly change the overall distribution and edge information of the data during the noise reduction process. While maintaining the original characteristics of the data, it improves the signal-to-noise ratio of the data and transmits it to the data analysis module through high-speed data transmission technology.

[0053] Subsequently, the data analysis module uses the linear regression algorithm in data analysis technology to conduct in-depth analysis and processing of the collected data. Based on historical data and current battery status, it identifies the working status of the battery pack and predicts the battery life and remaining power. The algorithm formula y = β0 + β1x1 + β2x2 + ... + β n x n , the system can predict the capacity or remaining life of the battery at a certain point in the future, through the Z-score method: Detecting abnormal changes in battery parameters, such as sudden increases or decreases in voltage and current, helps the data analysis module quickly identify abnormal changes in battery parameters, thereby discovering possible problems or faults in the battery in advance, monitoring and warning battery parameters in real time, improving the intelligence level of battery management, and formulating corresponding control strategies. Through actuators, intelligent control of battery packs is achieved, and key parameters of charging current, discharging current and temperature are accurately controlled to ensure that the battery pack operates in a safe and efficient state. At the same time, the control module can automatically adjust the control strategy according to the actual situation of the battery pack to adapt to different working environments and user needs.

[0054] Finally, the user interface and interaction module uses a graphical interface and linear interpolation algorithm to smoothly display real-time status display and historical data query to the user. It intuitively displays the key parameters of the battery pack voltage, current, and temperature collected by the system, and uses a linear interpolation algorithm to estimate the continuous changes between these key data points and presents them to the user in a graphical form. This makes the real-time status and historical data of the battery pack easy to understand and improves the user experience. Specifically: y0 and y1 represent the battery status parameter values, such as voltage, current, and temperature. They also provide the real-time status, historical data, and forecast information of the battery pack. Users can use this module to understand the working conditions of the battery pack and perform corresponding operations and adjustments.

[0055] The electric vehicle battery control system described in this embodiment achieves safe and efficient management of battery packs through high-precision monitoring, efficient data acquisition, advanced data analysis and intelligent control strategies, which not only improves the efficiency and safety of battery use, but also provides users with a convenient battery management experience.

[0056] Example 2

[0057] This embodiment aims to describe in detail a method for controlling an electric vehicle battery, which includes the following steps: real-time monitoring and data acquisition, data analysis and prediction, intelligent control strategy formulation, execution and control, and user feedback and adjustment.

[0058] First, enter the real-time monitoring and data acquisition stage. The battery pack of an electric vehicle is equipped with multiple sensor modules, which can monitor various parameters of the battery pack in real time, such as voltage, current, and temperature. The data acquisition module will regularly collect these sensor data and transmit them to the data analysis module for processing. For example, during driving, the sensor module will monitor the temperature changes of the battery pack in real time. When the temperature of a single cell exceeds the preset threshold, the data acquisition module will immediately transmit the information to the data analysis module.

[0059] Next, the data analysis and prediction stage begins. The data analysis module conducts in-depth analysis of the collected data, determines the working status of the battery pack, and predicts key information about the battery life. The results of these data analyses will serve as the basis for the formulation of subsequent control strategies. Taking temperature data as an example, the data analysis module will compare historical data to determine whether the current temperature is abnormal. If it is abnormal, it will further analyze the cause of the abnormality, such as excessive charging current and cooling system failure. At the same time, the data analysis module will also predict the remaining life of the battery and provide users with more accurate maintenance recommendations.

[0060] Subsequently, it enters the intelligent control strategy formulation stage. According to the results of the data analysis module, the intelligent control strategy formulation module will formulate corresponding control strategies. These strategies include adjusting the charging current, limiting the discharge current, and starting the cooling system. In addition, the module can automatically adjust the control strategy according to the actual situation of the battery pack and user needs to adapt to different working environments and user needs. For example, when the data analysis module detects that the temperature of a single battery is too high, the intelligent control strategy formulation module will immediately reduce the charging current of the battery and start the cooling system to reduce the temperature. At the same time, it will also appropriately adjust the discharge current of other batteries according to the user's driving needs to ensure the normal driving of the electric vehicle.

[0061] Then, it enters the execution and control stage. The execution and control module realizes intelligent control of the battery pack through actuators (such as electronic switches, relays, fans). It performs corresponding operations and adjustments according to the instructions issued by the control module. During the execution process, the execution and control module will monitor the status of the actuators in real time to ensure that they work normally according to the instructions. If an actuator fails or is abnormal, it will immediately send an alarm message to the control module for timely processing.

[0062] Finally, it enters the user feedback and adjustment stage. Users can understand the working status of the battery pack through the user interface and interactive module, and perform corresponding operations and adjustments as needed. At the same time, users can also provide usage feedback and suggestions to the system so that the system can be continuously optimized and improved. For example, users can view the real-time data, historical records and maintenance recommendations of the battery pack through the mobile phone APP. If users have questions about a suggestion or need to adjust certain parameter settings, they can operate and provide feedback through the APP, and the system will make corresponding optimizations and improvements based on the user's feedback and suggestions.

[0063] This embodiment introduces a method for controlling electric vehicle batteries. This method achieves precise management and control of battery packs through the steps of real-time monitoring and data acquisition, data analysis and prediction, intelligent control strategy formulation, execution and control, and user feedback and adjustment. This method not only improves the performance and safety of electric vehicles, but also provides users with a more convenient and intelligent user experience.

[0064] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

Claims

1. Electric vehicle battery control system, characterized in that, The system includes the following components: Sensor module: Through a high-precision sensor network, it monitors the key parameters of the battery pack, including voltage, current, temperature, and internal resistance, in real time. Utilizing the high precision and high reliability of the sensors, it responds to changes in the battery pack in real time, ensuring data accuracy and real-time performance. Data acquisition module: collects data generated by the sensor module and transmits it to the data analysis module. It uses high-speed data transmission technology to ensure the real-time and integrity of the data. At the same time, it cleans, filters and compresses the raw data to reduce the burden of data transmission and storage. Data Analysis Module: Utilizes advanced data analysis technology to conduct in-depth analysis and processing of collected data, identifying the operating status of the battery pack, such as charging, discharging, and idle, and predicting key information such as battery life and remaining power. Based on the battery pack's historical data and current status, it predicts possible faults or abnormal conditions and issues early warnings. Control module: Based on the results of the data analysis module, it formulates corresponding control strategies and uses actuators to achieve intelligent control of the battery pack and precise control of key parameters such as charging current, discharging current, and temperature, ensuring that the battery pack operates safely and efficiently. It also automatically adjusts the control strategy based on the actual situation of the battery pack to adapt to different working environments and user needs. User interface and interaction module: Through a graphical interface and human-computer interaction technology, it provides users with key information such as the real-time status, historical data, and forecast information of the battery pack. Through this module, users can understand the working conditions of the battery pack and perform corresponding operations and adjustments. The data acquisition module has the following specific steps: Data reception: Receives data from the sensor module, including key parameters of the battery pack, such as voltage, current, temperature, and internal resistance; Data verification: Verify the received data to ensure its integrity and accuracy; Data preprocessing: Clean, filter, and compress the verified raw data to reduce the burden of data transmission and storage; Data transmission: The pre-processed data is transmitted to the data analysis module for further analysis and processing. High-speed data transmission technology is used during the transmission process to ensure the real-time and integrity of the data; The data acquisition module may be affected by various interferences and noises during the transmission of sensor data, resulting in errors and fluctuations in the data. The median filter cleans and filters the original data to remove noise and outliers in the data. The operation process is: all data points in a window are sorted by size, and then the median value is taken as the output. The output of the median filter is It can be expressed as: , median filtering does not significantly change the overall distribution and edge information of the data during the noise reduction process. While maintaining the original characteristics of the data, it improves the signal-to-noise ratio of the data and ensures the accuracy and reliability of the key parameters of the battery pack; The data analysis module predicts the remaining capacity and life of the battery using a linear regression algorithm in data analysis technology. Based on historical data and current battery status (such as voltage, current, and temperature), it analyzes the historical data of the battery's charge and discharge cycles, temperature, and depth of discharge to predict the battery's capacity or remaining life at a certain point in the future. The algorithm formula is: , y is the predicted value (battery life), ( , ,……, ) are input characteristics (voltage, current, temperature), ( , ) is the regression coefficient. Linear regression can provide the battery management system with a prediction of battery life, helping the system to formulate strategies for maintenance or replacement of batteries in advance. In addition, through linear regression, the system can also analyze the impact of different factors on battery performance, thereby optimizing battery use and management, so as to facilitate the subsequent prediction of faults or abnormal conditions and issue early warnings; The data analysis module uses the Z-score method to detect abnormal changes in battery parameters, such as a sudden increase or decrease in voltage or current. This helps the data analysis module quickly identify abnormal changes in battery parameters, thereby discovering possible battery problems or faults in advance, monitoring battery parameters in real time and providing early warnings, thereby improving the intelligent level of battery management. The algorithm formula is: , x is the original data value, is the mean of the data, is the standard deviation of the data; The user interface and interaction module uses a linear interpolation algorithm to smoothly display real-time status display and historical data query, intuitively displaying the key parameters of the battery pack voltage, current and temperature collected by the system, and uses a linear interpolation algorithm to estimate the continuous changes between these key data points and present them to the user in a graphical manner, making the real-time status and historical data display of the battery pack easy to understand, improving the user experience and enabling corresponding operations and adjustments as needed, namely: , and Represents battery status parameter values, such as voltage, current, and temperature.

2. The electric vehicle battery control system according to claim 1, characterized in that: The sensor module uses a voltage divider circuit and a resistor divider to measure the voltage of the battery pack, monitors the voltage data of the battery pack in real time, accurately evaluates the status of the battery pack, predicts the battery life, and formulates a more reasonable charging and discharging strategy, thereby improving the efficiency and safety of the battery. The algorithm formula is: , the voltage measured by the sensor is , the actual voltage of the battery pack is The resistance ratio of the voltage divider circuit is R1:R2. Adjust the resistance values ​​of R1 and R2 and change the ratio of the voltage divider circuit to adapt to the measurement requirements of different voltage ranges, and adjust the charging current and charging time according to the voltage changes to formulate a more reasonable charging and discharging strategy.

3. The electric vehicle battery control system according to claim 1, characterized in that: The control module has the following specific steps: Receive data analysis results: Receive processed data and analysis results from the data analysis module, including key information such as the working status of the battery pack and predicted battery life; Intelligent control strategy formulation: Based on the data analysis results, the control module formulates corresponding control strategies, including adjusting the charging current, limiting the discharge current, and activating the cooling system to ensure that the battery pack operates in a safe and efficient state; Automatic adjustment control strategy: According to the actual situation of the battery pack and user needs, the control strategy is automatically adjusted to adapt to different working environments and user needs; Send control instructions: The control module converts the formulated control strategy into specific control instructions and sends them to the actuator; Execution and control: Actuators (such as electronic switches, relays, and fans) perform corresponding operations and adjustments based on instructions from the control module to achieve intelligent control of the battery pack.

4. A battery control method based on the electric vehicle battery control system according to claim 1, characterized in that: The method includes: Real-time monitoring and data acquisition: The sensor module monitors the various parameters of the battery pack in real time, and the data acquisition module collects these data to provide raw data for subsequent analysis and control; Data analysis and prediction: Conduct in-depth analysis of the collected data to determine the working status of the battery pack and predict key information about the battery life, which will serve as the basis for formulating subsequent control strategies; Intelligent control strategy formulation: Based on the results of the data analysis module, corresponding control strategies are formulated, including adjusting charging current, limiting discharge current, and activating the cooling system. The control strategy can also be automatically adjusted according to the actual situation of the battery pack and user needs to adapt to different working environments and user needs; Execution and control: Intelligent control of the battery pack is achieved through actuators (electronic switches, relays, fans), and corresponding operations and adjustments are performed according to the instructions issued by the control module; User feedback and adjustments: Users can understand the working conditions of the battery pack through the user interface and interactive modules, and perform corresponding operations and adjustments as needed. At the same time, users can provide usage feedback and suggestions to the system so that the system can be continuously optimized and improved.

5. The electric vehicle battery control method according to claim 4, characterized in that: The control method further includes: after the control instruction is issued, diagnosing faults or potential problems in the battery pack through data analysis, such as abnormal single cell voltage or excessive temperature, attempting automatic repair, adjusting the charge and discharge strategy, activating the cooling system, and issuing an alarm to prompt the user to take further action.

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