New energy automobile driving motor intelligent control system based on energy balance

Abstract

The invention discloses an intelligent control system for a driving motor of a new energy automobile based on energy balance, and relates to the technical field of new energy automobiles, the intelligent control system for the driving motor of the new energy automobile based on energy balance realizes comprehensive monitoring, accurate analysis and dynamic regulation and control of the running state of the motor through organic combination of a plurality of modules; compared with a traditional control system, the system has the advantages that the data processing precision, the real-time response capability and the intelligent level are remarkably improved, complex and changeable operation working conditions can be better dealt with through a double comparison mechanism and adaptive regulation and control, efficient and safe operation of the motor is ensured, and therefore the overall performance and reliability of the new energy automobile are improved, and the new energy automobile is suitable for popularization and application. Through comparative analysis, the system can identify the optimal control strategy, and dynamically adjust in the real-time driving process to ensure that the motor is always in the optimal state.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy vehicles, and in particular to an intelligent control system for a new energy vehicle drive motor based on energy balance. Background Art

[0002] With the increasing demand for environmental protection and sustainable energy development, new energy vehicles are becoming a major development direction in the automotive industry. Their power systems primarily rely on electric motors, so efficiently managing and controlling the energy consumption of electric motors is key to improving the performance and range of new energy vehicles.

[0003] Existing intelligent control technologies for drive motors in new energy vehicles primarily include real-time monitoring, current and voltage data processing, energy management, optimization control, feedback regulation, and fault diagnosis. However, these technologies suffer from several flaws and deficiencies in practical applications. Specifically, the system's slow response speed prevents it from responding quickly to emergencies, leading to increased energy consumption. Insufficient control precision makes it difficult to accurately predict energy demand under complex operating conditions, impacting optimization effectiveness. The system also suffers from poor reliability and is prone to failure during long-term operation or under extreme conditions, impacting vehicle stability. Furthermore, existing systems lack adaptability and are unable to flexibly respond to a variety of driving conditions and habits, resulting in inflexible and incomplete control strategies. Summary of the Invention

[0004] (1) Technical problems solved

[0005] In view of the deficiencies in the prior art, the present invention provides an intelligent control system for a new energy vehicle drive motor based on energy balance, which solves the problems mentioned in the above background technology.

[0006] (2) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent control system for a new energy vehicle drive motor based on energy balance, comprising a data acquisition module, a data processing module, a data calculation module, a data analysis module, an alarm module and a feedback module;

[0008] The data acquisition module is used to collect multi-source data when the drive motor is running;

[0009] The data processing module is used to preprocess the multi-source data set collected by the data collection module, and reclassify the multi-source data after preprocessing according to the timestamp to form a first data set, a second data set and a third data set;

[0010] The data calculation module is used to integrate and calculate the first data set, the second data set and the third data set to generate the control intervention coefficient TKX;

[0011] The data analysis module is used to compare the control intervention coefficient TKX with a preset first threshold value Y to generate a first comparison result, determine whether intervention control of the drive motor is required, and if the judgment result is that intervention is required, integrate and calculate the control intervention coefficient TKX with the preset first threshold value Y to generate an intervention value LJX, and compare the intervention value LJX with a preset second threshold value R to generate second and third comparison results;

[0012] The alarm module is used to generate a corresponding alarm prompt according to the second comparison result and the third comparison result;

[0013] The feedback module is used to send the alarm prompts generated by the alarm module to the control panel and perform adaptive intervention and regulation.

[0014] Preferably, the data acquisition module includes a first acquisition unit, a second acquisition unit and a third acquisition unit;

[0015] The first acquisition unit is used to collect electrical parameters of the drive motor during operation, including voltage, current and power values;

[0016] The second acquisition unit is used to collect the mechanical parameters of the drive motor during operation, including speed, torque and mechanical efficiency;

[0017] The third acquisition unit is used to acquire thermal parameters of the motor during operation, including temperature, heat dissipation rate, and thermal resistance.

[0018] Preferably, the data processing module includes a preprocessing unit and a sorting unit, and the preprocessing unit is used to preprocess the multi-source data collected by the data collection module;

[0019] The arranging unit is used to re-arrange the pre-processed multi-source data into a first data set, a second data set, and a third data set according to timestamps.

[0020] Preferably, the first data set includes voltage values, current values, and power;

[0021] The voltage values ​​are recorded as DY1, DY2, DY3, ..., DYn according to the timestamp;

[0022] The current values ​​are recorded as DL1, DL2, DL3, ..., DLn according to the timestamp;

[0023] The power is recorded as GL1, GL2, GL3, ..., GLn according to the timestamp;

[0024] The second data set includes speed, torque, and mechanical efficiency;

[0025] The speeds are recorded as ZS1, ZS2, ZS3, ..., ZSn according to the timestamps;

[0026] The torques are recorded as ZJ1, ZJ2, ZJ3, ..., ZJn according to the timestamps;

[0027] The mechanical efficiency is recorded as XL1, XL2, XL3, ..., XLn according to the time stamp;

[0028] The third data set includes temperature, heat dissipation rate, and thermal resistance;

[0029] The temperatures are recorded as WD1, WD2, WD3, ..., WDn according to the timestamp;

[0030] The heat dissipation rates are recorded as SR1, SR2, SR3, ..., SRn according to the timestamp;

[0031] The thermal resistances are recorded as RZ1, RZ2, RZ3, ..., RZn according to the timestamps.

[0032] Preferably, the data calculation module calculates and obtains the control intervention coefficient TKX through the following formula:

[0033] TKX=a1×S1+a2×S2+a3×S3;

[0034] Where: S1 is the first-stage value obtained by integrating and calculating the first data set, S2 is the second-stage value obtained by integrating and calculating the second data set, S3 is the third-stage value obtained by integrating and calculating the third data set, a1, a2, and a3 are weight values, and a1, a2, and a3 are adjusted and set by the user.

[0035] Preferably, the first stage value S1, the second stage value S2 and the third stage value S3 are respectively calculated and obtained by the following formulas:

[0036] S1=DYX+DLX+GLX;

[0037] S2=ZSX+ZJX+XLX;

[0038] S3=WDX+SRX+RZX;

[0039] Where: DYX is the voltage difference variation coefficient, DLX is the current difference variation coefficient, and GLX is the power difference variation coefficient, which are obtained by integrating and calculating the first data set.

[0040] ZSX is the speed difference coefficient, ZJX is the torque difference coefficient, and XLX is the mechanical efficiency difference coefficient, which are obtained by integrating and calculating the second data set.

[0041] WDX is the temperature difference coefficient, SRX is the heat dissipation difference coefficient, and RZX is the thermal resistance difference coefficient, which are respectively obtained by integrating and calculating the third data set.

[0042] Preferably, the data analysis module includes a first analysis unit and a second analysis unit. The first analysis unit is used to generate a first comparison result, and the second analysis unit is used to generate a second comparison result and a third comparison result.

[0043] Preferably, the first comparison result is:

[0044] When TKX < Y × 95%, it means that the drive motor is below the normal working state;

[0045] When Y × 95% ≤ TKX ≤ Y × 105%, it means that the drive motor is in the normal working state;

[0046] When TKX > Y × 105%, it means that the drive motor is above the normal working state.

[0047] Preferably, the second comparison result is as follows:

[0048] When R ≤ LJX ≤ R × 103%, it means that the drive motor is in the first-level overload working state; [[ID=二十二]]

[0049] When R × 103% ≤ LJX ≤ R × 105%, it means that the drive motor is in the first-level overload working state;

[0050] When LJX > R × 10​​​​​​​​​​​​​​​​​​​​1. This intelligent control system for new energy vehicle drive motors, based on energy balance, achieves comprehensive monitoring, precise analysis, and dynamic regulation of the motor's operating status through the organic combination of multiple modules. Compared with traditional control systems, this system has significantly improved data processing accuracy, real-time response capabilities, and intelligence levels. Through a dual comparison mechanism and adaptive regulation, it can better cope with complex and changing operating conditions, ensuring the efficient and safe operation of the motor, thereby improving the overall performance and reliability of new energy vehicles.

[0058] 2. This intelligent control system for new energy vehicle drive motors, based on energy balance, identifies the optimal control strategy through comparative analysis and dynamically adjusts it during real-time driving to ensure the motor is always in optimal condition. This precise data analysis and comparison helps improve system stability and reliability, reduce energy consumption, and extend the service life of the motor and vehicle system. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a structural schematic diagram of the present invention.

[0060] In the figure: 1. Data acquisition module; 11. First acquisition unit; 12. Second acquisition unit; 13. Third acquisition unit; 2. Data processing module; 21. Preprocessing unit; 22. Sorting unit; 3. Data calculation module; 4. Data analysis module; 41. First analysis unit; 42. Second analysis unit; 5. Alarm module; 6. Feedback module. DETAILED DESCRIPTION

[0061] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0062] Example 1

[0063] See also Figure 1 , an intelligent control system for a new energy vehicle drive motor based on energy balance, comprising a data acquisition module 1, a data processing module 2, a data calculation module 3, a data analysis module 4, an alarm module 5 and a feedback module 6;

[0064] The data acquisition module 1 is used to collect multi-source data when the drive motor is running;

[0065] The data processing module 2 is used to preprocess the multi-source data set collected by the data collection module 1, and reclassify the multi-source data after preprocessing according to the timestamp to form a first data set, a second data set and a third data set;

[0066] The data calculation module 3 is used to integrate and calculate the first data set, the second data set and the third data set to generate the control intervention coefficient TKX;

[0067] The data analysis module 4 is configured to compare the control intervention coefficient TKX with a preset first threshold value Y to generate a first comparison result, and determine whether intervention control of the drive motor is required. If the determination result is that intervention is required, the control intervention coefficient TKX is integrated and calculated with the preset first threshold value Y to generate an intervention value LJX, and the intervention value LJX is compared with a preset second threshold value R to generate second and third comparison results.

[0068] The alarm module 5 is used to generate a corresponding alarm prompt according to the second comparison result and the third comparison result;

[0069] The feedback module 6 is used to send the alarm prompt generated by the alarm module 5 to the control panel and perform adaptive intervention regulation.

[0070] In this embodiment, data acquisition module 1 is used to collect multi-source data from the drive motor during operation. Using various sensors and data input devices, it monitors the motor's operating status in real time, including key parameters such as voltage, current, temperature, and speed. This module's beneficial effect lies in its ability to capture comprehensive and detailed data during motor operation, providing rich data support for subsequent processing and analysis. This precise data acquisition ensures real-time monitoring of the motor's status, thereby improving the control system's response speed and accuracy, achieving comprehensive monitoring and basic data collection.

[0071] Data Processing Module 2 is responsible for preprocessing the multi-source data collected by Data Acquisition Module 1. This preprocessing includes steps such as data cleaning, noise removal, and format conversion. The processed data is then reclassified according to timestamps to form first, second, and third datasets. This module's beneficial effect is that preprocessing improves data quality and consistency, reducing data redundancy and errors. Timestamp classification allows for better tracking of data trends, laying a solid foundation for subsequent calculations and analysis, and achieving data standardization and normalization.

[0072] Data calculation module 3 integrates and calculates the preprocessed first, second, and third data sets to generate the control intervention coefficient TKX. This module's beneficial effect lies in its ability to comprehensively consider information from different data sets through integrated calculations, thereby more accurately reflecting the motor's operating status. The control intervention coefficient TKX is a key parameter for subsequent judgment and regulation, providing a comprehensive and dynamic control basis for the system, enhancing its ability to accurately judge and regulate the motor's status, achieving the goal of comprehensive calculation and dynamic adjustment.

[0073] Data analysis module 4 compares the control intervention coefficient TKX with a preset first threshold Y, generating a first comparison result to determine whether intervention control of the drive motor is necessary. If intervention is necessary, it further calculates and generates an intervention value LJX, which is then compared with a preset second threshold R to generate second and third comparison results. This module's dual comparison mechanism improves the accuracy of motor status assessment. It can dynamically adjust control strategies based on real-time data to ensure optimal motor operation, achieving both intelligent analysis and precise control.

[0074] Alarm module 5 generates corresponding alarms based on the second and third comparison results generated by data analysis module 4. This module's beneficial effect lies in its ability to promptly detect abnormalities in motor operation and, through alarms, prompt operators or the system to intervene. This early warning mechanism enables proactive action to avoid potential failures and safety hazards, ensuring the safe operation of the motor and system, thus achieving both abnormality detection and early warning.

[0075] Feedback module 6 is responsible for transmitting the alarms generated by alarm module 5 to the control panel and implementing adaptive intervention and control. This module's beneficial effect lies in its ability to transmit detected abnormalities and control recommendations to the operator or the automatic control system through a feedback mechanism, enabling timely response and adjustment. Adaptive intervention and control dynamically adjusts the control strategy based on real-time conditions, ensuring that the motor maintains optimal operating conditions under different operating conditions. This enhances the system's intelligence and adaptability, achieving the goal of real-time feedback and dynamic control.

[0076] Through the organic combination of multiple modules, this system realizes comprehensive monitoring, precise analysis and dynamic regulation of the motor's operating status. Compared with traditional control systems, this system has significantly improved data processing accuracy, real-time response capabilities and intelligence levels. Through the dual comparison mechanism and adaptive regulation, it can better cope with complex and changeable operating conditions, ensure the efficient and safe operation of the motor, and thus improve the overall performance and reliability of new energy vehicles.

[0077] Example 2

[0078] See also Figure 1 , the data acquisition module 1 includes a first acquisition unit 11, a second acquisition unit 12 and a third acquisition unit 13;

[0079] The first acquisition unit 11 is used to collect electrical parameters of the drive motor during operation, including voltage, current and power values;

[0080] The second acquisition unit 12 is used to collect mechanical parameters of the drive motor during operation, including speed, torque and mechanical efficiency;

[0081] The third acquisition unit 13 is used to acquire thermal parameters of the motor during operation, including temperature, heat dissipation rate, and thermal resistance.

[0082] In this embodiment: a traditional system may only focus on a single or a few parameters, while this system comprehensively collects the electrical, mechanical and thermal parameters of the drive motor during operation through the first acquisition unit 11, the second acquisition unit 12, and the third acquisition unit 13. The comprehensive data acquisition method can more accurately reflect the operating status of the motor, providing comprehensive and detailed data support for the system, ensuring that the system can promptly detect potential problems and make adjustments, thereby improving the operating efficiency and safety of the motor.

[0083] The first acquisition unit 11 collects voltage values, current values, and power values, providing real-time data on the electrical status of the motor. Accurate electrical parameter monitoring helps analyze the electrical performance and energy efficiency of the motor, detect electrical anomalies in a timely manner, prevent electrical failures, and improve the electrical operation reliability of the motor.

[0084] The second acquisition unit 12 collects speed, torque and mechanical efficiency, providing detailed data on the motor's mechanical performance. Real-time monitoring of mechanical parameters can quickly detect mechanical failures or abnormal operating conditions, providing data support for motor performance optimization and control strategies, ensuring that the motor operates in the best mechanical state, and improving the motor's mechanical reliability and efficiency.

[0085] The third acquisition unit 13 collects temperature, heat dissipation rate and thermal resistance, providing key data on the thermal performance of the motor. Real-time monitoring of temperature changes can prevent the motor from overheating, avoid thermal damage and efficiency reduction, provide data support for the optimization of the thermal management system, ensure that the motor operates within a safe temperature range, and extend the life of the motor.

[0086] Through the first, second, and third acquisition units, comprehensive electrical, mechanical, and thermal parameters are collected, enabling comprehensive monitoring of the motor's operating status. Compared to traditional technologies, this system significantly improves the comprehensiveness of data collection, the real-time nature of monitoring, and the accuracy of data processing. Comprehensive and accurate data collection not only enhances the motor's operating efficiency and safety but also provides a solid foundation for the system's intelligent analysis and dynamic control, thereby promoting the overall performance of new energy vehicles.

[0087] Example 3

[0088] See also Figure 1 , the data processing module 2 includes a preprocessing unit 21 and a sorting unit 22, the preprocessing unit 21 is used to preprocess the multi-source data collected by the data collection module 1;

[0089] The arranging unit 22 is used to re-arrange the pre-processed multi-source data into a first data set, a second data set, and a third data set according to timestamps.

[0090] In this embodiment, through detailed data collection, the system can obtain real-time information about the motor's operating status and make rapid adjustments based on this data, reducing energy consumption and increasing range. Accurate monitoring and real-time adjustments of electrical parameters help the system quickly respond to emergencies and ensure efficient motor operation.

[0091] Monitoring and analyzing mechanical parameters can help the system optimize control strategies under various operating conditions, ensuring the motor maintains optimal performance under different driving conditions. Simultaneously, monitoring and adjusting thermal parameters can prevent motor overheating, improving system reliability and service life.

[0092] By comprehensively analyzing electrical, mechanical, and thermal parameter data, the system can develop a more rational energy allocation strategy to ensure efficient energy utilization. This not only improves vehicle performance, but also extends driving range and provides a better user experience.

[0093] Example 4

[0094] See also Figure 1 , the first data set includes voltage value, current value and power;

[0095] The voltage values ​​are recorded as DY1, DY2, DY3, ..., DYn according to the timestamp;

[0096] The current values ​​are recorded as DL1, DL2, DL3, ..., DLn according to the timestamp;

[0097] The power is recorded as GL1, GL2, GL3, ..., GLn according to the timestamp;

[0098] The second data set includes speed, torque, and mechanical efficiency;

[0099] The speeds are recorded as ZS1, ZS2, ZS3, ..., ZSn according to the timestamps;

[0100] The torques are recorded as ZJ1, ZJ2, ZJ3, ..., ZJn according to the timestamps;

[0101] The mechanical efficiency is recorded as XL1, XL2, XL3, ..., XLn according to the time stamp;

[0102] The third data set includes temperature, heat dissipation rate, and thermal resistance;

[0103] The temperatures are recorded as WD1, WD2, WD3, ..., WDn according to the timestamp;

[0104] The heat dissipation rates are recorded as SR1, SR2, SR3, ..., SRn according to the timestamp;

[0105] The thermal resistances are recorded as RZ1, RZ2, RZ3, ..., RZn according to the timestamps.

[0106] In this embodiment, users can adjust weight values ​​based on specific vehicle and driving conditions, making the motor's operational control more aligned with actual needs, thereby improving driving performance and energy efficiency. By comprehensively considering the influence of electrical, mechanical, and thermal parameters, the calculation module can effectively optimize the motor's operating state. This comprehensive optimization not only improves the motor's efficiency and reliability, but also helps extend the service life of the motor and the entire vehicle system. The intelligent control system based on energy balance can adjust the control strategy based on real-time data, thereby achieving more efficient energy management and driving performance. This intelligent control strategy not only enhances the user's driving experience, but also helps reduce energy consumption, further increasing the market competitiveness of new energy vehicles.

[0107] Example 5

[0108] See also Figure 1 , the data calculation module 3 calculates and obtains the control intervention coefficient TKX through the following formula;

[0109] TKX=a1×S1+a2×S2+a3×S3;

[0110] Where: S1 is the first-stage value obtained by integrating and calculating the first data set, S2 is the second-stage value obtained by integrating and calculating the second data set, S3 is the third-stage value obtained by integrating and calculating the third data set, a1, a2, and a3 are weight values, and a1, a2, and a3 are adjusted and set by the user.

[0111] The first stage value S1, the second stage value S2 and the third stage value S3 are calculated and obtained by the following formulas respectively:

[0112] S1=DYX+DLX+GLX;

[0113] S2=ZSX+ZJX+XLX;

[0114] S3=WDX+SRX+RZX;

[0115] Where: DYX is the voltage difference variation coefficient, DLX is the current difference variation coefficient, and GLX is the power difference variation coefficient, which are obtained by integrating and calculating the first data set.

[0116] ZSX is the speed difference coefficient, ZJX is the torque difference coefficient, and XLX is the mechanical efficiency difference coefficient, which are obtained by integrating and calculating the second data set.

[0117] WDX is the temperature difference coefficient, SRX is the heat dissipation difference coefficient, and RZX is the thermal resistance difference coefficient, which are obtained by integrating and calculating the third data set.

[0118] In this embodiment, S1 is calculated by integrating the voltage difference variation coefficient DYX, the current difference variation coefficient DLX, and the power difference variation coefficient GLX, directly reflecting the real-time state changes in the motor's electrical performance. This precise electrical parameter monitoring helps the system dynamically adjust power distribution strategies based on actual needs, reducing energy consumption and improving energy efficiency and range.

[0119] S2 integrates the speed differential coefficient ZSX, torque differential coefficient ZJX, and mechanical efficiency differential coefficient XLX to reflect the changes in the motor's mechanical performance under different loads and operating conditions. By carefully monitoring and analyzing these mechanical parameters, the system optimizes the motor's operating control strategy, ensuring optimal power output and efficiency under various operating conditions.

[0120] S3 is calculated by integrating the temperature difference coefficient WDX, the heat dissipation difference coefficient SRX, and the thermal resistance difference coefficient RZX. It reflects the motor's thermal state and heat dissipation efficiency. By monitoring and analyzing these thermal parameters in real time, the system can effectively prevent motor overheating and improve system reliability and stability.

[0121] Through the above formula calculation and data integration, the system can be further optimized and improved, which improves the accuracy and stability of motor operation, while enhancing the system's adaptability and energy efficiency, providing users with a better driving experience and more reliable vehicle performance.

[0122] The voltage difference variation coefficient DYX, current difference variation coefficient DLX, and power difference variation coefficient GLX are calculated using the following formulas:

[0123]

[0124]

[0125] Where: DYn-2, DLn-2, ​​GLn-2, ​​ZSn-2, ZJn-2, XLn-2, ​​WDn-2, SRn-2, and RZn-2 are the voltage, current, power, speed, torque, mechanical efficiency, temperature, heat dissipation rate, and thermal resistance at timestamp n-2, respectively.

[0126] DYn-1, DLn-1, GLn-1, ZSn-1, ZJn-1, XLn-1, WDn-1, SRn-1, and RZn-1 are the voltage, current, power, speed, torque, mechanical efficiency, temperature, heat dissipation rate, and thermal resistance at timestamp n-1, respectively;

[0127] DYn, DLn, GLn, ZSn, ZJn, XLn, WDn, SRn and RZn are the voltage value, current value, power value, speed, torque, mechanical efficiency, temperature, heat dissipation rate and thermal resistance at timestamp n respectively.

[0128] Example 6

[0129] See also Figure 1 The data analysis module 4 includes a first analysis unit 41 and a second analysis unit 42. The first analysis unit 41 is used to generate a first comparison result, and the second analysis unit 42 is used to generate a second comparison result and a third comparison result.

[0130] In this embodiment, the first analysis unit 41 focuses on generating a first comparison result. By analyzing electrical, mechanical, and thermal parameters in detail, the system can accurately assess the motor's performance under different operating conditions. This refined analysis helps identify potential performance bottlenecks and optimization opportunities, enabling the system to quickly respond to varying driving demands and environmental conditions, thereby improving the overall driving experience and energy efficiency.

[0131] The second analysis unit 42 generates the second and third comparison results, further comparing the motor's performance under different operating conditions and strategies. Through this comparative analysis, the system identifies the optimal control strategy and dynamically adjusts it during real-time driving to ensure the motor maintains optimal performance. This precise data analysis and comparison helps improve system stability and reliability, reduce energy consumption, and extend the service life of the motor and vehicle system.

[0132] The application of Data Analysis Module 4 not only optimizes the control strategy and performance of the new energy vehicle drive motor, but also enhances the system's intelligent management capabilities, bringing users a better driving experience and more reliable vehicle performance guarantee.

[0133] Example 7

[0134] See also Figure 1 , the first comparison result is:

[0135] When TKX < Y × 95%, it indicates that the drive motor is below the normal operating state;

[0136] When Y × 95% ≤ TKX ≤ Y × 105%, it indicates that the drive motor is in the normal operating state;

[0137] When TKX > Y × 105%, it indicates that the drive motor is above the normal operating state.

[0138] The second comparison result is as follows:

[0139] When R ≤ LJX ≤ R × 103%, it indicates that the drive motor is in the first-level overload operating state;

[0140] When R × 103% ≤ LJX ≤ R × 105%, it indicates that the drive motor is in the first-level overload operating state;

[0141] When LJX > R × 105%, it indicates that the drive motor is in the third-level overload operating state.

[0142] The third comparison result is as follows;

[0143] When R ≤ LJX ≤ R × 103%, it indicates that the drive motor is in the first-level overload operating state;

[0144] When R × 103% ≤ LJX ≤ R × 105%, it indicates that the drive motor is in the first-level overload operating state;

[0145] When LJX > R × 105%, it indicates that the drive motor is in the third-level overload operating state.

[0146] In this embodiment: When the value of TKX is lower than the lower limit Y × 95% of the preset normal operating state, the system can accurately identify and prompt the driver that the motor may be in an inefficient or abnormal state, so as to take adjustment measures in time to avoid potential energy efficiency losses and equipment damage risks. When TKX is within the normal operating state range of Y × 95% to Y × 105%, the system can confirm the stable operating state of the motor, providing users with a reassuring driving experience and reliable energy output. When TKX exceeds the upper limit Y × 105% of the normal operating state, the system can quickly respond and issue a warning, prompting the driver that there may be overload or abnormal operation risks to protect the safe operation of the motor and the vehicle system.

[0147] When the load factor LJX is within the set first-level overload working state range of R to R×103%, the system can issue an alarm in time to remind the driver to pay attention to the working load of the motor to avoid the risk of equipment overheating or damage due to long-term high-load operation. When LJX is in the second-level overload working state range of R×103% to R×105%, the system can still effectively monitor and prompt to ensure that the motor operates within a higher load but still acceptable working range, thereby ensuring stability and safety during driving. When the load factor LJX exceeds the set third-level overload working state upper limit R×105%, the system immediately issues an emergency alarm to remind the driver that the motor is already at the extreme load state and it is recommended to stop the car immediately for inspection or take emergency measures to prevent possible equipment damage and safety risks.

[0148] When the load factor LJX is within the set first-level overload working state range of R to R×103%, the system can remind the driver in real time that the motor may be in a higher load operating state, prompting the driver to pay attention and take appropriate load management measures to ensure that the motor operates within a safe and efficient range. When LJX is within the second-level overload working state range of R×103% to R×105%, the system can still effectively monitor and prompt to ensure that the motor is in a higher load but acceptable working range in a short period of time, providing the driver with stable energy output while improving the reliability and durability of the system. When the load factor LJX exceeds the set third-level overload working state upper limit R×105%, the system immediately issues a serious alarm, prompting the driver that the motor is already in an extreme load state, and it is recommended to take emergency measures immediately to prevent damage to the equipment or possible safety risks.

[0149] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent control system for a new energy vehicle drive motor based on energy balance, characterized in that: It comprises a data acquisition module (1), a data processing module (2), a data calculation module (3), a data analysis module (4), an alarm module (5) and a feedback module (6); The data acquisition module (1) is used to collect multi-source data when the drive motor is running; The data processing module (2) is used to pre-process the multi-source data set collected by the data collection module (1), and reclassify the multi-source data after pre-processing according to the timestamp to form a first data set, a second data set and a third data set; The data calculation module (3) is used to integrate and calculate the first data set, the second data set and the third data set to generate a control intervention coefficient TKX; The data analysis module (4) is used to compare the control intervention coefficient TKX with a preset first threshold value Y, thereby generating a first comparison result, and judging whether intervention control of the drive motor is required. If the judgment result is that intervention is required, the control intervention coefficient TKX and the preset first threshold value Y are integrated and calculated to generate an intervention value LJX, and the intervention value LJX is compared with a preset second threshold value R, thereby generating a second comparison result and a third comparison result. The alarm module (5) is used to generate a corresponding alarm prompt according to the second comparison result and the third comparison result; The feedback module (6) is used to send the alarm prompt generated by the alarm module (5) to the control panel and perform adaptive intervention regulation.

2. The intelligent control system for driving motor of new energy vehicle based on energy balance according to claim 1 is characterized in that: The data acquisition module (1) comprises a first acquisition unit (11), a second acquisition unit (12) and a third acquisition unit (13); The first acquisition unit (11) is used to acquire electrical parameters of the drive motor when it is running, including voltage value, current value and power value; The second acquisition unit (12) is used to acquire mechanical parameters of the driving motor during operation, including rotation speed, torque and mechanical efficiency; The third acquisition unit (13) is used to acquire thermal parameters of the motor during operation, including temperature, heat dissipation rate and thermal resistance.

3. The intelligent control system for driving motor of new energy vehicle based on energy balance according to claim 2 is characterized in that: The data processing module (2) comprises a preprocessing unit (21) and a sorting unit (22), wherein the preprocessing unit (21) is used to preprocess the multi-source data collected by the data collection module (1); The sorting unit (22) is used to sort the multi-source data after preprocessing into a first data set, a second data set and a third data set according to timestamps.

4. The intelligent control system for driving motor of new energy vehicle based on energy balance according to claim 3 is characterized in that: The first data set includes voltage value, current value and power; The voltage values ​​are recorded as DY1, DY2, DY3, ..., DYn according to the timestamps; The current values ​​are recorded as DL1, DL2, DL3, ..., DLn according to the timestamps; The power is recorded as GL1, GL2, GL3, ..., GLn according to the timestamp; The second data set includes speed, torque, and mechanical efficiency; The speeds are recorded as ZS1, ZS2, ZS3, ..., ZSn according to the timestamps; The torques are recorded as ZJ1, ZJ2, ZJ3, ..., ZJn according to the timestamps; The mechanical efficiency is recorded as XL1, XL2, XL3, ..., XLn according to the time stamp; The third data set includes temperature, heat dissipation rate, and thermal resistance; The temperatures are respectively recorded as WD1, WD2, WD3, ..., WDn according to the timestamps; The heat dissipation rates are respectively recorded as SR1, SR2, SR3, ..., SRn according to the timestamps; The thermal resistances are respectively recorded as RZ1, RZ2, RZ3, ..., RZn according to the timestamps.

5. The intelligent control system for driving motor of new energy vehicle based on energy balance according to claim 4 is characterized in that: The data calculation module (3) calculates and obtains the regulation intervention coefficient TKX through the following formula; TKX = a1 × S1 + a2 × S2 + a3 × S3; In the formula: S1 is the first-stage value obtained by integrating and calculating from the first data set, S2 is the second-stage value obtained by integrating and calculating from the second data set, S3 is the third-stage value obtained by integrating and calculating from the third data set, and a1, a2, and a3 are weight values, and a1, a2, and a3 are adjusted and set by the user.

6. The intelligent control system for driving motor of new energy vehicle based on energy balance according to claim 5 is characterized in that: The first-stage value S1, the second-stage value S2, and the third-stage value S3 are respectively obtained by calculating through the following formulas; S1 = DYX + DLX + GLX; S2 = ZSX + ZJX + XLX; S3 = WDX + SRX + RZX; In the formula: DYX is the voltage difference change coefficient, DLX is the current difference change coefficient, GLX is the power difference change coefficient, and they are respectively obtained by integrating and calculating from the first data set; ZSX is the rotational speed difference coefficient, ZJX is the torque difference coefficient, XLX is the mechanical efficiency difference coefficient, and they are respectively obtained by integrating and calculating from the second data set; WDX is the temperature difference coefficient, SRX is the heat dissipation difference coefficient, RZX is the thermal resistance difference coefficient, and they are respectively obtained by integrating and calculating from the third data set.

7. The intelligent control system for driving motor of new energy vehicle based on energy balance according to claim 6 is characterized in that: The data analysis module (4) includes a first analysis unit (41) and a second analysis unit (42). The first analysis unit (41) is used to generate a first comparison result, and the second analysis unit (42) is used to generate a second comparison result and a third comparison result.

8. The intelligent control system for driving motor of new energy vehicle based on energy balance according to claim 7 is characterized in that: The first comparison result is: When TKX < Y × 95%, it means that the drive motor is below the normal operating state; When Y × 95% ≤ TKX ≤ Y × 105%, it means that the drive motor is in the normal operating state; When TKX > Y × 105%, it means that the drive motor is above the normal operating state.

9. The intelligent control system for driving motor of new energy vehicle based on energy balance according to claim 8 is characterized in that: The second comparison result is as follows: When R ≤ LJX ≤ R × 103%, it means that the drive motor is in the first-level overload operating state; When R × 103% ≤ LJX ≤ R × 105%, it means that the drive motor is in the first-level overload operating state; When LJX > R × 105%, it means that the drive motor is in the third-level overload operating state.

10. The intelligent control system for driving motor of new energy vehicle based on energy balance according to claim 9 is characterized in that: The third comparison result is as follows; When R ≤ LJX ≤ R × 103%, it means that the drive motor is in the first-level overload operating state; When R × 103% ≤ LJX ≤ R × 105%, it means that the drive motor is in the first-level overload operating state; When LJX > R × 105%, it means that the drive motor is in the third-level overload operating state.

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