Method and device for cooling battery pack of electric vehicle

Abstract

The invention discloses a method and a device for cooling a battery pack of an electric automobile, and relates to the technical field of electric automobiles, and the process method comprises the following specific steps: real-time monitoring: installing intelligent sensing equipment in and around each battery module of the battery pack, monitoring key parameters of temperature and pressure of the battery pack in real time, and simultaneously monitoring the temperature and the pressure of the battery pack; according to the invention, key parameters of temperature, pressure, current and voltage of the battery pack are monitored in real time through the intelligent sensing device, and thermal management data of the battery pack are processed and analyzed in real time in combination with a big data analysis technology. The battery performance reduction and the safety risk caused by too high or too low temperature are avoided, the thermal management requirements of the battery pack under different working conditions are better met, and the use efficiency and the safety of the battery pack are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicles, and in particular to a cooling method and device for an electric vehicle battery pack. Background Art

[0002] With the rapid development of electric vehicle technology and the continuous expansion of the market, the performance of battery packs, as the core components of electric vehicles, directly affects the overall performance and service life of electric vehicles. The thermal management of battery packs has always been one of the key points and difficulties in the design and manufacturing of electric vehicles. Traditional battery pack cooling methods cannot predict and prevent thermal management problems in advance. When thermal management problems occur in battery packs, manual inspection and maintenance are often required, which not only increases maintenance costs and time, but may also pose a threat to the driver's safety.

[0003] Traditional battery pack cooling methods are mostly based on fixed cooling strategies, such as using fans for air cooling or coolants for liquid cooling. Although these methods can reduce the temperature of the battery pack to a certain extent, they have many limitations. They cannot achieve precise control of the battery pack temperature, and the heat distribution between different battery modules is uneven. Traditional methods cannot perform personalized adjustments for each module. Secondly, traditional methods cannot predict thermal management problems that may arise in the battery pack in the future, so corresponding preventive measures cannot be taken in advance.

[0004] However, with the continuous advancement of battery technology and the improvement of electric vehicle performance, the heat generated by battery packs has also increased. Battery packs will inevitably generate heat during the charging and discharging process, especially under high load or high temperature environment. The temperature of the battery pack will rise rapidly. Excessive temperature will not only affect the performance and life of the battery, but may also lead to serious safety problems such as thermal runaway. Summary of the invention

[0005] The purpose of the present invention is to make up for the deficiencies of the prior art and to provide a method and device for cooling an electric vehicle battery pack. The method and device can monitor the key parameters of the battery pack's temperature, pressure, current and voltage in real time, and process and analyze the data in combination with big data analysis technology to achieve accurate prediction and intelligent regulation of the battery pack's thermal management problems. This intelligent predictive method and device for cooling an electric vehicle battery pack can not only improve the efficiency and safety of the battery pack, but also reduce the failure rate and maintenance costs, providing strong support for the sustainable development of electric vehicles.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a cooling method for an electric vehicle battery pack, the specific steps of the cooling method are:

[0007] S100, real-time monitoring: intelligent sensing devices are installed inside and around each battery module of the battery pack to monitor the key parameters of the battery pack's temperature and pressure in real time. At the same time, the charging and discharging status of the battery pack is monitored through current and voltage sensors;

[0008] S200, data processing and analysis: The monitored data is transmitted to the central controller, and the thermal management data of the battery pack is processed and analyzed in real time using big data analysis. By learning and comparing historical data, thermal management problems that may occur in the battery pack in the future are predicted;

[0009] S300, intelligent control: Based on the results of data processing and analysis, the central controller automatically adjusts the operating status of the cooling equipment, including adjusting the flow, temperature and pressure parameters of the cooling medium, and optimizing the layout and heat dissipation structure of the cooling device to achieve precise control of the battery pack temperature;

[0010] S400, Predictive Maintenance: Predicts thermal management problems in the battery pack, and the central controller issues an alarm in advance, notifying the driver and maintenance personnel to take appropriate maintenance measures, including adjusting the charging and discharging strategy, replacing the coolant, and cleaning the heat sink.

[0011] Furthermore, the S100 includes:

[0012] S101, deploy intelligent sensing devices: install intelligent sensing devices inside and around each battery module of the battery pack to monitor each part of the battery pack in real time;

[0013] S102, parameter data collection: the sensor continuously collects key parameter data of the battery pack, such as temperature, current, voltage and pressure, and records the charging and discharging status of the battery pack in real time;

[0014] S103, data preprocessing: cleaning and denoising the received raw data, and storing the processed data in the database of the central controller for subsequent analysis and processing;

[0015] S104, data transmission and verification: A stable data transmission channel is established between the sensor and the central controller. During the data transmission process, the data is verified to ensure the accuracy and integrity of the data.

[0016] Furthermore, the intelligent sensing device includes a temperature sensor, a pressure sensor, a current sensor and a voltage sensor.

[0017] Furthermore, the S103 uses a sliding average filtering algorithm to process the collected data such as temperature, current, voltage and pressure data information, and obtains a smooth output value by averaging the most recent input parameter data values, thereby reducing the impact of random noise and achieving the processing of noise and abnormal values. The algorithm formula is: Y n is the output value of the parameter data after filtering, X n-i It is the input value of the most recent N original parameter data, such as the original data value of current, voltage, temperature, and pressure.

[0018] Furthermore, the S200 includes:

[0019] S201, data reception: the central controller receives real-time data of battery pack temperature, pressure, current and voltage transmitted from the intelligent sensing device;

[0020] S202, real-time status monitoring: analyzing real-time data to understand the current status of the battery pack;

[0021] S203, historical data review: view the historical data of the battery pack, including temperature trend and number of charge and discharge cycles;

[0022] S204, Problem Identification: Identify potential thermal management issues, such as abnormal temperature rise and large temperature difference between batteries;

[0023] S205, thermal management prediction: using historical data and real-time data, combined with prediction algorithms, to predict future thermal management issues of the battery pack;

[0024] S206, result output: output the analysis results and prediction results to the central controller for intelligent control and predictive maintenance.

[0025] Furthermore, the S205 predicts the relationship between the battery pack temperature and other influencing factors (such as current, voltage, and time) through linear regression in the prediction algorithm, discovers potential thermal management problems, and takes corresponding measures to prevent them. Features closely related to the battery pack temperature, such as current, voltage, time (charging time and discharging time), and battery cycle times, are selected from real-time and historical data as independent variables (x1, x2, ..., x n ), the selected features and corresponding temperature data are organized into a data set for linear regression analysis, and the algorithm formula is: y = β0 + β1x1 + β2x2 + ... + β n x n , y is the predicted value of battery pack temperature, (β0, β1, ..., β n ) is the regression coefficient, (x1, x2, ..., x n) are independent variables, such as current, voltage, and time. Based on the prediction results, the central controller automatically adjusts the operating status of the cooling equipment, such as adjusting the parameters of the flow, temperature, and pressure of the cooling medium to achieve precise control of the battery pack temperature.

[0026] Furthermore, the S300 includes:

[0027] S301, receiving analysis data: receiving the data processing and analysis results from step S200, including the real-time status of the battery pack, historical data review, problem identification and prediction results;

[0028] S302, cooling demand assessment: based on the received data, assess the current cooling demand of the battery pack, including assessing factors such as the temperature, temperature difference, current density, and charge / discharge rate of the battery pack;

[0029] S303, control strategy formulation and instructions: based on the cooling demand assessment, formulate a control strategy for the cooling equipment, including determining the adjustment range and direction of the flow, temperature, and pressure parameters of the cooling medium, and generating specific control instructions according to the control strategy;

[0030] S304, the cooling device executes: after receiving the control instruction, the cooling device adjusts the flow rate, temperature and pressure parameters of the cooling medium according to the instruction, as well as the optimization of the cooling device layout and heat dissipation structure;

[0031] S305, feedback regulation: After the cooling device performs regulation, the central controller will receive the real-time data transmitted by the intelligent sensing device again, conduct a new round of evaluation and analysis, and fine-tune or re-formulate the regulation strategy based on the feedback results.

[0032] Furthermore, the S300 uses the PID control algorithm in the control algorithm to dynamically adjust the parameters of the cooling device, such as the flow rate and temperature of the cooling medium, according to the real-time monitored battery pack state by calculating the proportion, integral and differential terms of the error signal to achieve accurate control of the battery pack temperature. The algorithm formula is: Where u(t) represents the output control quantity of the controller, that is, the control signal on the cooling device, K p is the proportional gain, used to adjust the strength of the proportional action, K i is the integral gain, which is used to adjust the strength of the integral action, K d is the differential gain, which is used to adjust the intensity of the differential action. represents the error accumulation from the initial moment to the current moment, represents the rate of change of the error at the current moment, e(t) represents the error at the current moment, that is, the difference between the expected battery pack temperature and the real-time monitored battery pack temperature. By adjusting K p , K i and Kd The three parameters, PID control algorithm, ensures control accuracy while avoiding excessive oscillation or overshoot, and automatically adjusts the control strategy according to different battery pack states and working conditions to meet different control requirements.

[0033] A cooling device for an electric vehicle battery pack, characterized in that the cooling device comprises:

[0034] Intelligent sensing devices: installed inside and around each battery module of the battery pack to monitor key parameters of the battery pack, such as temperature, pressure, current and voltage, in real time;

[0035] Central controller: Receives data transmitted by intelligent sensing devices, uses big data analysis technology to process and analyze the thermal management data of the battery pack in real time, predicts thermal management problems that may arise in the battery pack in the future, and automatically adjusts the operating status of the cooling equipment;

[0036] Cooling equipment: According to the instructions of the central controller, the flow, temperature and pressure parameters of the cooling medium are adjusted to achieve precise control of the battery pack temperature;

[0037] Warning device: When a possible thermal management problem of the battery pack is predicted, it will alert the driver and maintenance personnel and prompt appropriate maintenance measures.

[0038] Furthermore, the working principle of the cooling device is:

[0039] Battery pack: It is composed of multiple battery modules, each of which has a temperature sensor and a pressure sensor installed inside or around it. The battery pack is used to store electrical energy and provide power for electric vehicles.

[0040] Intelligent sensing devices: including temperature sensors, pressure sensors, current sensors and voltage sensors, which are used to monitor various parameters of the battery pack in real time. These devices transmit the monitored data to the central controller for processing and analysis;

[0041] Central controller: Receives data transmitted by intelligent sensing devices and processes and analyzes the data using big data analysis technology. Based on the analysis results, the central controller automatically adjusts the operating status of the cooling device and issues predictive maintenance alarms;

[0042] Cooling device: Cools the battery pack according to the instructions of the central controller. The cooling device adopts a variety of technologies, such as immersion cooling technology, heat pipe cooling technology and phase change material cooling technology, which are combined and optimized according to actual needs to achieve the best cooling effect;

[0043] Alarm device: When a thermal management problem is predicted for the battery pack, the alarm will sound to remind the driver or maintenance personnel to take appropriate measures. The alarm device can provide prompts through sound, light and display.

[0044] Compared with the prior art, the electric vehicle battery pack cooling method and device have the following beneficial effects:

[0045] 1. The present invention uses intelligent sensing devices to monitor the key parameters of battery pack temperature, pressure, current and voltage in real time, and combines big data analysis technology to process and analyze the thermal management data of the battery pack in real time, avoiding battery performance degradation and safety risks caused by excessively high or low temperatures, better adapting to the thermal management requirements of the battery pack under different working conditions, and improving the use efficiency and safety of the battery pack.

[0046] 2. The intelligent predictive maintenance function of the present invention predicts possible thermal management problems of the battery pack in the future, and issues an alarm in advance before the problem occurs, notifying the driver and maintenance personnel to take corresponding maintenance measures. This predictive maintenance not only reduces the risk of damage to the battery pack due to overheating, but also reduces the maintenance cost and time caused by failures. Through timely intervention, it effectively extends the service life of the battery pack and improves the overall reliability of the electric vehicle.

[0047] 3. The intelligent control function of the present invention automatically adjusts the operating state of the cooling equipment according to the actual temperature and working state of the battery pack, and optimizes the parameters of the flow, temperature and pressure of the cooling medium. This intelligent control method can reduce unnecessary energy consumption and improve energy utilization efficiency. At the same time, by accurately controlling the temperature of the battery pack, it helps to reduce the energy loss of the battery pack during the charging and discharging process, thereby reducing the energy consumption and emissions of electric vehicles, which meets the current environmental protection requirements of energy conservation and emission reduction.

[0048] Other advantages, objectives and features of the present invention will be set forth 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 taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0050] Figure 1 The present invention is a flow chart of a method for cooling a battery pack of an electric vehicle.

[0051] Figure 2 The figure is a schematic diagram of the planar connection structure of a battery pack cooling device for an electric vehicle. DETAILED DESCRIPTION

[0052] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] Example 1

[0054] A cooling method for an electric vehicle battery pack, the method comprising the following steps: real-time monitoring, data processing and analysis, intelligent regulation, and predictive maintenance.

[0055] First, enter the real-time monitoring stage (S100). In the battery pack of the electric vehicle, intelligent sensing devices are installed inside and around each battery module, including temperature sensors, pressure sensors, current sensors and voltage sensors. These sensors monitor the key parameters of the battery pack's temperature, pressure, current and voltage in real time, and record the charge and discharge status of the battery pack in real time. The sliding average filtering algorithm is used to process the collected data. By taking the average of the most recent input parameter data values, a smooth output value is obtained, thereby reducing the impact of random noise and achieving the processing of noise and abnormal values. The algorithm formula is: Y n is the output value of the parameter data after filtering, X n-i It is the input value of the most recent N raw parameter data, such as the raw data value of current, voltage, temperature, and pressure. The processed data is stored in the database of the central controller for subsequent analysis and processing.

[0056] Then, the data processing and analysis stage (S200) is entered. After the central controller receives the real-time data from the intelligent sensing device, it first performs real-time status monitoring to understand the current status of the battery pack. Then, it reviews the historical data of the battery pack, including the temperature trend and the number of charge and discharge cycles, so as to have a more comprehensive understanding of the performance status of the battery pack. The big data analysis technology is used to combine the historical data and the real-time data to perform real-time processing and analysis on the thermal management data of the battery pack. The linear regression prediction algorithm is used to predict the relationship between the battery pack temperature and other influencing factors (such as current, voltage, and time), discover potential thermal management problems, and take corresponding measures to prevent them. Features closely related to the battery pack temperature, such as current, voltage, time (charging time and discharging time), and battery cycle times, are selected from the real-time and historical data as independent variables (x1, x2, ..., x n ), the selected features and the corresponding temperature data are organized into a data set for linear regression analysis, and the algorithm formula is: y = β0 + β1x1 + β2x2 + ... + β n x n , y is the predicted value of battery pack temperature, (β0, β1, ..., β n ) is the regression coefficient, (x1, x2, ..., x n ) are independent variables, such as current, voltage, and time. Based on the prediction results, the central controller automatically adjusts the operating status of the cooling equipment, such as adjusting the flow, temperature, and pressure parameters of the cooling medium to achieve precise control of the battery pack temperature, identify potential thermal management problems, such as abnormal temperature rise and excessive temperature difference between batteries, and predict future thermal management problems of the battery pack. The analysis results and prediction results are output to the central controller for intelligent regulation and predictive maintenance.

[0057] Subsequently, the intelligent control stage (S300) is entered, and the data processing and analysis results from step S200 are received, including the real-time status of the battery pack, historical data review, problem identification and prediction results, and the current cooling needs of the battery pack are evaluated, including the factors of the battery pack's temperature, temperature difference, current density, and charge / discharge rate. Based on the cooling demand evaluation, a control strategy for the cooling equipment is formulated, including determining the adjustment range and direction of the flow, temperature, and pressure parameters of the cooling medium, and generating specific control instructions according to the control strategy. After receiving the instructions, the central controller automatically adjusts the operating state of the cooling equipment, including adjusting the flow, temperature, and pressure parameters of the cooling medium, and optimizing the layout and heat dissipation structure of the cooling device, so as to achieve precise control of the battery pack temperature.

[0058] Finally, the predictive maintenance stage (S400) is entered. When it is predicted that the battery pack may have thermal management problems, the central controller will issue an alarm in advance and notify the driver and maintenance personnel to take corresponding maintenance measures, including adjusting the charging and discharging strategy, replacing the coolant, and cleaning the heat sink to prevent potential problems and ensure the safety and efficiency of the battery pack.

[0059] Through the implementation of the steps in the above embodiments, the electric vehicle battery pack cooling method of the present invention significantly improves the use efficiency and safety of the battery pack. Through the steps of real-time monitoring, data processing and analysis, intelligent regulation and predictive maintenance, it can timely discover and solve potential thermal management problems, avoid battery performance degradation and safety risks caused by excessively high or low temperatures, and at the same time, automatically adjust the operating state of the cooling equipment according to the thermal management requirements under different working conditions, realize precise control of the battery pack temperature, and improve the overall performance and service life of the battery pack.

[0060] Example 2

[0061] This embodiment aims to describe in detail an electric vehicle battery pack cooling device, which is based on the above-disclosed electric vehicle battery pack cooling method and aims to achieve efficient and precise cooling of the electric vehicle battery pack and improve the use efficiency and safety of the battery pack.

[0062] In the specific implementation process, first, intelligent sensing devices are installed inside and around each battery module of the battery pack, including temperature sensors, pressure sensors, current sensors and voltage sensors. These sensors monitor the key parameters of the battery pack's temperature, pressure, current and voltage in real time, and record the charge and discharge status of the battery pack in real time. The collected data is transmitted to the central controller through a stable data transmission channel. During the data transmission process, data analysis technology is used to ensure the accuracy and completeness of the data. In addition, the received raw data is pre-processed by cleaning and denoising to reduce the impact of random noise and improve the reliability of the data.

[0063] Secondly, the central controller receives real-time data from the battery pack and uses big data analysis technology to process and analyze the data in real time. Through real-time status monitoring and historical data review, it understands the current status and historical trends of the battery pack. At the same time, a prediction algorithm (linear regression) is used to predict future thermal management problems of the battery pack. The prediction algorithm is based on real-time and historical data, and selects features closely related to the battery pack temperature (such as current, voltage, time, and number of battery cycles) as independent variables. Through linear regression analysis, it predicts the changing trend of the battery pack temperature, thereby identifying potential thermal management problems.

[0064] Finally, the cooling equipment includes a cooling medium circulation structure and a heat dissipation structure. The cooling medium circulation structure is responsible for transporting the cooling medium (such as coolant) to the vicinity of the battery module, and adjusting the flow, temperature and pressure parameters of the cooling medium to achieve temperature regulation of the battery pack. The heat dissipation structure improves heat dissipation efficiency by optimizing the layout and heat sink design. According to the results of data processing and analysis, the central controller automatically adjusts the operating status of the cooling equipment, including adjusting the flow, temperature and pressure parameters of the cooling medium, and optimizing the layout and heat dissipation structure of the cooling device, so as to achieve precise control of the battery pack temperature.

[0065] In addition, when potential problems are predicted, the central controller will notify the driver and maintenance personnel to take corresponding maintenance measures, such as adjusting the charging and discharging strategies, replacing the coolant, and cleaning the heat sink, so as to detect and solve potential problems in advance, reduce downtime and maintenance costs caused by failures, and improve the reliability and operating efficiency of electric vehicles.

[0066] In summary, the electric vehicle battery pack cooling device provided in this embodiment realizes efficient and precise cooling of the electric vehicle battery pack, improves the use efficiency and safety of the battery pack, and at the same time, the device has the advantages of simple structure, easy operation and high reliability, and can meet the use requirements of electric vehicles in complex working environments.

[0067] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for cooling an electric vehicle battery pack, characterized in that: The specific steps of the cooling method are as follows: S100, real-time monitoring: intelligent sensing devices are installed inside and around each battery module of the battery pack to monitor the key parameters of the battery pack's temperature and pressure in real time. At the same time, the charging and discharging status of the battery pack is monitored through current and voltage sensors; S200, data processing and analysis: The monitored data is transmitted to the central controller, and the thermal management data of the battery pack is processed and analyzed in real time using big data analysis. By learning and comparing historical data, thermal management problems that may occur in the battery pack in the future are predicted; S300, intelligent control: Based on the results of data processing and analysis, the central controller automatically adjusts the operating status of the cooling equipment, including adjusting the flow, temperature and pressure parameters of the cooling medium, and optimizing the layout and heat dissipation structure of the cooling device to achieve precise control of the battery pack temperature; S400, Predictive Maintenance: Predicts thermal management problems in the battery pack, and the central controller issues an alarm in advance, notifying the driver and maintenance personnel to take appropriate maintenance measures, including adjusting the charging and discharging strategy, replacing the coolant, and cleaning the heat sink.

2. The electric vehicle battery pack cooling method according to claim 1, characterized in that: The S100 includes: S101, deploy intelligent sensing devices: install intelligent sensing devices inside and around each battery module of the battery pack to monitor each part of the battery pack in real time; S102, parameter data collection: the sensor continuously collects key parameter data of the battery pack, such as temperature, current, voltage and pressure, and records the charging and discharging status of the battery pack in real time; S103, data preprocessing: cleaning and denoising the received raw data, and storing the processed data in the database of the central controller for subsequent analysis and processing; S104, data transmission and verification: A stable data transmission channel is established between the sensor and the central controller. During the data transmission process, the data is verified to ensure the accuracy and integrity of the data.

3. The electric vehicle battery pack cooling method according to claim 2, characterized in that: The intelligent sensing device includes a temperature sensor, a pressure sensor, a current sensor and a voltage sensor.

4. A method for cooling an electric vehicle battery pack according to claim 2, characterized in that: The S103 uses a sliding average filtering algorithm to process the collected data such as temperature, current, voltage and pressure data information, and obtains a smooth output value by taking the average of the most recent input parameter data values, thereby reducing the impact of random noise and achieving the processing of noise and abnormal values. The algorithm formula is: Y n is the output value of the parameter data after filtering, X n-i It is the input value of the most recent N original parameter data, such as the original data value of current, voltage, temperature, and pressure.

5. The electric vehicle battery pack cooling method according to claim 1, characterized in that: The S200 includes: S201, data reception: the central controller receives real-time data of battery pack temperature, pressure, current and voltage transmitted from the intelligent sensing device; S202, real-time status monitoring: analyzing real-time data to understand the current status of the battery pack; S203, historical data review: view the historical data of the battery pack, including temperature trend and number of charge and discharge cycles; S204, Problem Identification: Identify potential thermal management issues, such as abnormal temperature rise and large temperature difference between batteries; S205, thermal management prediction: using historical data and real-time data, combined with prediction algorithms, to predict future thermal management issues of the battery pack; S206, result output: output the analysis results and prediction results to the central controller for intelligent control and predictive maintenance.

6. A method for cooling an electric vehicle battery pack according to claim 5, characterized in that: The S205 predicts the relationship between the battery pack temperature and other influencing factors (such as current, voltage, and time) through linear regression in the prediction algorithm, discovers potential thermal management problems, and takes corresponding measures to prevent them. Features closely related to the battery pack temperature, such as current, voltage, time (charging time and discharging time), and battery cycle times, are selected from real-time and historical data as independent variables (x1, x2, ..., x n ), the selected features and the corresponding temperature data are organized into a data set for linear regression analysis, and the algorithm formula is: y = β0 + β1x1 + β2x2 + ... + β n x n , y is the predicted value of battery pack temperature, (β0, β1, ..., β n ) is the regression coefficient, (x1, x2, ..., x n ) are independent variables, such as current, voltage, and time. Based on the prediction results, the central controller automatically adjusts the operating status of the cooling equipment, such as adjusting the parameters of the flow, temperature, and pressure of the cooling medium to achieve precise control of the battery pack temperature.

7. The electric vehicle battery pack cooling method according to claim 1, characterized in that: The S300 includes: S301, receiving analysis data: receiving the data processing and analysis results from step S200, including the real-time status of the battery pack, historical data review, problem identification and prediction results; S302, cooling demand assessment: based on the received data, assess the current cooling demand of the battery pack, including assessing factors such as the temperature, temperature difference, current density, and charge / discharge rate of the battery pack; S303, control strategy formulation and instructions: based on the cooling demand assessment, formulate a control strategy for the cooling equipment, including determining the adjustment range and direction of the flow, temperature, and pressure parameters of the cooling medium, and generating specific control instructions according to the control strategy; S304, the cooling device executes: after receiving the control instruction, the cooling device adjusts the flow rate, temperature and pressure parameters of the cooling medium according to the instruction, as well as the optimization of the cooling device layout and heat dissipation structure; S305, feedback regulation: After the cooling device performs regulation, the central controller will receive the real-time data transmitted by the intelligent sensing device again, conduct a new round of evaluation and analysis, and fine-tune or re-formulate the regulation strategy based on the feedback results.

8. The electric vehicle battery pack cooling method according to claim 7, characterized in that: The S300 uses the PID control algorithm in the control algorithm to dynamically adjust the parameters of the cooling device, such as the flow rate and temperature of the cooling medium, according to the real-time monitored battery pack state by calculating the proportion, integral and differential terms of the error signal to achieve accurate control of the battery pack temperature. The algorithm formula is: Where u(t) represents the output control quantity of the controller, that is, the control signal on the cooling device, K p is the proportional gain, used to adjust the strength of the proportional action, K i is the integral gain, which is used to adjust the strength of the integral action, K d is the differential gain, which is used to adjust the intensity of the differential action. represents the error accumulation from the initial moment to the current moment, represents the rate of change of the error at the current moment, e(t) represents the error at the current moment, that is, the difference between the expected battery pack temperature and the real-time monitored battery pack temperature. By adjusting K p , K i and K d The three parameters, PID control algorithm, ensures control accuracy while avoiding excessive oscillation or overshoot, and automatically adjusts the control strategy according to different battery pack states and working conditions to meet different control requirements.

9. An electric vehicle battery pack cooling device, characterized in that: The cooling device comprises: Intelligent sensing devices: installed inside and around each battery module of the battery pack to monitor key parameters of the battery pack, such as temperature, pressure, current and voltage, in real time; Central controller: Receives data transmitted by intelligent sensing devices, uses big data analysis technology to process and analyze the thermal management data of the battery pack in real time, predicts thermal management problems that may arise in the battery pack in the future, and automatically adjusts the operating status of the cooling equipment; Cooling equipment: According to the instructions of the central controller, the flow, temperature and pressure parameters of the cooling medium are adjusted to achieve precise control of the battery pack temperature; Warning device: When a possible thermal management problem of the battery pack is predicted, it will alert the driver and maintenance personnel and prompt appropriate maintenance measures.

10. The electric vehicle battery pack cooling device according to claim 9, characterized in that: The working principle of the cooling device is: Battery pack: It is composed of multiple battery modules, each of which has current, voltage, temperature and pressure sensors installed inside or around it. The battery pack is used to store electrical energy and provide power for electric vehicles; Intelligent sensing devices: including temperature sensors, pressure sensors, current sensors and voltage sensors, which are used to monitor various parameters of the battery pack in real time. These devices transmit the monitored data to the central controller for processing and analysis; Central controller: Receives data transmitted by intelligent sensing devices and processes and analyzes the data using big data analysis technology. Based on the analysis results, the central controller automatically adjusts the operating status of the cooling device and issues predictive maintenance alarms; Cooling device: Cools the battery pack according to the instructions of the central controller. The cooling device adopts a variety of technologies, such as immersion cooling technology, heat pipe cooling technology and phase change material cooling technology, which are combined and optimized according to actual needs to achieve the best cooling effect; Alarm device: When a thermal management problem is predicted for the battery pack, the alarm will sound to remind the driver or maintenance personnel to take appropriate measures. The alarm device can provide prompts through sound, light and display.

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