High-safety new energy automobile motor control method and system

Abstract

The invention discloses a high-safety new energy automobile motor control method and system, and relates to the technical field of motor control, a data acquisition module of the system acquires real-time data of a new energy automobile through an integrated sensor group, and preprocesses the real-time data; the analysis module analyzes the collected data through a machine learning algorithm, and calculates a motor winding temperature coefficient Das and a motor output power coefficient Jsc; the comprehensive analysis module performs normalization processing on the motor winding temperature coefficient Das and the motor output power coefficient Jsc, and calculates a motor comprehensive control coefficient DJK; the prediction evaluation module evaluates the control system state of the motor by comparing the motor comprehensive control coefficient DJK with a preset threshold value; the intelligent integration module carries out intelligent control and fault monitoring on the motor according to the evaluation result, and reports and early warns to a vehicle owner in real time; the system can improve the operation safety and reliability of the new energy automobile motor.

Description

Technical Field

[0001] The present invention relates to the technical field of motor control, and in particular to a high-safety motor control method and system for new energy vehicles. Background Art

[0002] With the rapid development of new energy vehicles, the motor, as one of its core components, directly affects the performance and safety of the vehicle; however, due to the complex working environment of the motor, which is easily affected by factors such as temperature and load, the motor control system needs to have high safety and high reliability; most of the existing motor control systems on the market lack real-time monitoring and intelligent evaluation functions, making it difficult to fully and accurately grasp the operating status of the motor, and there are potential safety hazards. Therefore, it is particularly important to develop a high-safety new energy vehicle motor control system that can monitor, analyze and predict the operating status of the motor in real time.

[0003] At present, the shortcomings of new energy vehicle motor control systems in comprehensive analysis of temperature and power are mainly due to the limitations of system design and insufficient technical means; this deficiency is mainly reflected in the following aspects: first, data collection is not comprehensive enough, and it is not easy to fully reflect the actual operating status of the motor; second, data processing and analysis capabilities are insufficient, and it is not easy to effectively identify and prevent complex fault conditions; finally, the early warning and control mechanisms are relatively simple, and it is not easy to respond to various abnormal situations in a timely and effective manner; these shortcomings may cause the motor to fail under extreme conditions such as high temperature and high load, affecting the safety and reliability of the vehicle. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a highly secure new energy vehicle motor control method and system, which solves the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a highly safe new energy vehicle motor control method and system, including a data acquisition module, an analysis module, a comprehensive analysis module, a prediction and evaluation module and an intelligent integration module;

[0006] The data acquisition module is used to monitor the new energy vehicle in real time based on the integrated sensor group installed on the new energy vehicle, collect the motor temperature data group and the motor operation data group, and pre-process the collected data group;

[0007] The analysis module is used to perform in-depth analysis and prediction on the collected data, use machine learning algorithms to analyze the data, calculate and analyze the motor winding temperature coefficient Das through the motor temperature data group, and calculate and analyze the motor output power coefficient Jsc through the motor operation data group;

[0008] The comprehensive analysis module is used to perform correlation and summary calculation and analysis on the motor winding temperature coefficient Das and the motor output power coefficient Jsc calculated and analyzed by the analysis module during the operation of the motor of the new energy vehicle, and obtain the motor comprehensive control coefficient DJK;

[0009] The prediction and evaluation module is used to perform a preliminary comparative evaluation of the preset temperature threshold value A and the obtained motor winding temperature coefficient Das, analyze the temperature change of the motor during operation, and then further perform a secondary comparative evaluation of the first preset standard threshold value M and the preset second standard threshold value N with the analyzed motor comprehensive control coefficient DJK when abnormalities are monitored, evaluate the control system of the motor, and establish a control system evaluation model;

[0010] The intelligent integrated module is used to perform intelligent control and fault monitoring on the motor of the new energy vehicle according to the evaluation result of the motor comprehensive control coefficient DJK, and report the results in real time and warn the owner through the vehicle system.

[0011] Preferably, the data acquisition module includes a data acquisition unit and a data processing unit;

[0012] The data acquisition unit is used to arrange monitoring points around the motor of the new energy vehicle and install an integrated sensor group to monitor the motor temperature data group and the motor operation data group in real time, and collect and send the monitored motor temperature data group and the motor operation data group to the central control system of the vehicle by wireless transmission;

[0013] The motor temperature data set includes the winding temperature T and the number of windings n of the motor;

[0014] The winding temperature T of the motor is measured and obtained by a temperature sensor;

[0015] The number of windings n is the total number of windings inside the motor designed and produced;

[0016] The motor operation data set includes the motor torque NJ, the driving speed SD, the vehicle load ZZ, the battery discharge capacity FD and the motor rotor speed DZ;

[0017] The torque NJ of the motor is obtained by a torque tester device;

[0018] The driving speed SD is directly obtained through a speedometer;

[0019] The vehicle load ZZ is measured and obtained by a weighing sensor;

[0020] The battery discharge capacity FD is obtained by a battery discharge tester;

[0021] The rotational speed DZ of the motor rotor is obtained through a resolver sensor installed inside the motor.

[0022] Preferably, the data processing unit is used to extract data from the preprocessed first data set and second data set, eliminate the errors in the data collected by the sensor, perform smoothing processing on the calibrated data, remove noise, and perform normalization processing to convert the data to a unified scale, remove outliers from the data, obtain high-quality input data, further analyze and preprocess the data, provide input for the subsequent data calculation unit, train a machine learning algorithm model using historical data, and analyze real-time data.

[0023] Preferably, the analysis module includes a motor temperature analysis unit and a motor power calculation unit;

[0024] The motor temperature analysis unit calculates the motor winding temperature coefficient Das by performing normalization and summary calculation based on the preprocessed first data set, and analyzes the winding temperature uniformity of the motor in real time;

[0025] The motor winding temperature coefficient Das is calculated through the following formula:

[0026]

[0027] In the formula, Ti represents the winding temperature T of the i-th winding, T i represents the average value of the winding temperatures T of the i windings, and n represents the number of windings.

[0028] Preferably, the motor power calculation unit calculates the motor output power coefficient Jsc by performing normalization and summary calculation based on the preprocessed motor power data set, and analyzes the winding temperature uniformity of the motor in real time;

[0029] The motor output power coefficient Jsc is calculated through the following formula:

[0030]

[0031] In the formula, 0 < w1 < 1, 0 < w2 < 1, 0 < w3 < 1, 0 < w4 < 1, 0 < w5 < 1, and w1 + w2 + w3 + w4 + w5 = 1. w1, w2, w3, w4, and w5 respectively represent the preset weight values of the motor torque NJ, driving speed SD, battery discharge capacity FD, motor rotor speed DZ, and vehicle load ZZ.

[0032] Preferably, the comprehensive analysis module is used to perform normalization processing on the obtained motor winding temperature coefficient Das and motor output power coefficient Jsc, and then perform summary calculation to obtain the motor comprehensive control coefficient DJK.

[0033] The motor comprehensive control coefficient DJK is calculated through the following formula:

[0034] DJK=Das*α+Jsc*β+Z;

[0035] In the formula, 0<α<1, 0<β<1, and α+β=1, α and β respectively represent the preset weight values ​​of the motor winding temperature coefficient Das and the motor output power coefficient Jsc, and the specific values ​​are set by the user according to actual conditions, and Z represents the first correction constant.

[0036] Preferably, the prediction and evaluation module includes a first evaluation unit and a second evaluation unit;

[0037] The first evaluation unit performs a preliminary evaluation by presetting a temperature threshold value A based on the historical temperature safety value of the motor and then performing a preliminary evaluation with the obtained motor winding temperature coefficient Das to analyze the temperature of the generator during the driving of the current new energy vehicle. The specific evaluation scheme is as follows;

[0038] When the motor winding temperature coefficient Das is compared with the preset temperature threshold value A, a first prediction result is obtained;

[0039] When the motor winding temperature coefficient Das is less than the preset temperature threshold A, it means that the motor temperature is normal;

[0040] When the motor winding temperature coefficient Das ≥ the preset temperature threshold A, it indicates that the motor temperature is abnormal. At this time, a secondary evaluation is further performed to comprehensively analyze the causes of the motor abnormality, and the results are transmitted to the second evaluation unit to evaluate the specific causes.

[0041] Preferably, the second evaluation unit performs a second evaluation based on the historical comprehensive control safety value of the motor, a preset first standard threshold M and a preset second standard threshold N, and then performs a secondary evaluation with the obtained motor comprehensive control coefficient DJK to analyze the power and safe operation of the generator during the driving of the current new energy vehicle. The specific evaluation scheme is as follows;

[0042] When the motor comprehensive control coefficient DJK ≤ the preset first standard threshold M, it means that the motor is operating normally;

[0043] When the preset first standard threshold M is less than the motor comprehensive control coefficient DJK and less than the preset second standard threshold N, it indicates that there is an abnormality in the current motor operation. At this time, the first warning information is generated to prompt the driver to perform maintenance, and the motor is automatically controlled to switch to low power mode, reducing the motor power by 50% and improving the working efficiency of the motor cooling system by 70%;

[0044] When the motor comprehensive control coefficient DJK ≥ the preset second standard threshold N, it means that the current motor is in a dangerous state during operation and measures must be taken immediately to adjust it. At this time, the second warning information is generated to prompt the driver to pull over and automatically call the relevant department through the vehicle computer to file a report, call a tow truck and mobile vehicle for maintenance.

[0045] Preferably, the intelligent integrated module includes an instruction execution unit and an early warning unit;

[0046] The instruction execution unit is used to generate control instructions in real time according to the evaluation results provided by the prediction and evaluation module, adjust the operating parameters of the motor, optimize the motor performance and safety, and is responsible for executing preset safety measures and automatically slowing down or shutting down the motor when an abnormality is detected;

[0047] The early warning unit is used to monitor the operating results of the instruction execution unit and the operating status of the motor, promptly identify any abnormalities and potential faults, report the abnormalities and potential faults to the owner in real time through the vehicle system, and remind the owner to take corresponding safety measures.

[0048] A highly safe new energy vehicle motor control method comprises the following steps:

[0049] S1. Based on the integrated sensor group installed on the new energy vehicle, real-time monitoring of the new energy vehicle is performed, and a motor temperature data group and a motor operation data group are collected, and the collected data groups are pre-processed;

[0050] S2. Perform in-depth analysis and prediction on the collected data, use machine learning algorithms to analyze the data, calculate and analyze the motor temperature data group to obtain the motor winding temperature coefficient Das, and calculate and analyze the motor operation data group to obtain the motor output power coefficient Jsc;

[0051] S3, through the analysis module, during the operation of the new energy vehicle motor, the motor winding temperature coefficient Das and the motor output power coefficient Jsc calculated and analyzed, and the motor comprehensive control coefficient DJK is obtained by correlation summary calculation and analysis;

[0052] S4, perform preliminary comparative evaluation on the preset temperature threshold A and the obtained motor winding temperature coefficient Das, analyze the temperature change of the motor during operation, and then further perform secondary comparative evaluation on the first preset standard threshold M and the preset second standard threshold N with the analyzed motor comprehensive control coefficient DJK when abnormality is detected, evaluate the control system of the motor, and establish a control system evaluation model;

[0053] S5. Based on the evaluation results of the motor comprehensive control coefficient DJK, the new energy vehicle motor is intelligently controlled and fault problems are monitored, and the results are reported and warned to the car owner in real time through the vehicle system.

[0054] The present invention provides a highly safe new energy vehicle motor control method and system, which has the following beneficial effects:

[0055] (1) The system uses sensors installed on new energy vehicles to collect motor temperature and operating data in real time, ensuring the accuracy and real-time nature of the data. The analysis module provides reliable basic data for subsequent comprehensive analysis through deep data analysis and machine learning algorithms.

[0056] (2) The system can more comprehensively evaluate the operating status of the motor. The predictive assessment module evaluates the temperature and overall operating status of the motor in stages by comparing it with the preset thresholds, and promptly detects and warns of potential faults and abnormalities. After the initial assessment, if a temperature abnormality is found, the system will further conduct a secondary assessment using the first and second preset standard thresholds to determine the severity of the problem and take appropriate safety measures, such as reducing power or prompting the vehicle to stop.

[0057] (3) Based on the evaluation results, the system generates and executes control instructions in real time to optimize motor performance and safety. The system can not only automatically adjust motor parameters to prevent failures, but also provide real-time reports and warnings to vehicle owners through the vehicle computer system. Compared with traditional technical means, this greatly improves the intelligence and safety of motor control systems, and can monitor and handle potential problems earlier and more accurately, avoiding significant losses caused by motor failures, thereby greatly improving the overall safety performance and operating efficiency of new energy vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 This is a schematic diagram of a high-security new energy vehicle motor control system process of the present invention;

[0059] Figure 2 The figure is a schematic diagram of the steps of a highly safe new energy vehicle motor control method of the present invention. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0061] Example 1

[0062] See also Figure 1, the present invention provides a highly secure new energy vehicle motor control system, including a data acquisition module, an analysis module, a comprehensive analysis module, a prediction and evaluation module and an intelligent integration module;

[0063] The data acquisition module is used to monitor the new energy vehicle in real time based on the integrated sensor group installed on the new energy vehicle, collect the motor temperature data group and the motor operation data group, and pre-process the collected data group;

[0064] The analysis module is used to perform in-depth analysis and prediction on the collected data, use machine learning algorithms to analyze the data, calculate and analyze the motor winding temperature coefficient Das through the motor temperature data group, and calculate and analyze the motor output power coefficient Jsc through the motor operation data group;

[0065] The comprehensive analysis module is used to perform correlation and summary calculation and analysis on the motor winding temperature coefficient Das and the motor output power coefficient Jsc calculated and analyzed by the analysis module during the operation of the motor of the new energy vehicle, and obtain the motor comprehensive control coefficient DJK;

[0066] The prediction and evaluation module is used to perform a preliminary comparative evaluation of the preset temperature threshold value A and the obtained motor winding temperature coefficient Das, analyze the temperature change of the motor during operation, and then further perform a secondary comparative evaluation of the first preset standard threshold value M and the preset second standard threshold value N with the analyzed motor comprehensive control coefficient DJK when abnormalities are monitored, evaluate the control system of the motor, and establish a control system evaluation model;

[0067] The intelligent integrated module is used to perform intelligent control and fault monitoring on the motor of the new energy vehicle according to the evaluation result of the motor comprehensive control coefficient DJK, and report the results in real time and warn the owner through the vehicle system.

[0068] In this embodiment, the system achieves significant technical improvements and benefits through a data acquisition module, an analysis module, a comprehensive analysis module, a prediction and evaluation module, and an intelligent integration module; first, the data acquisition module realizes real-time monitoring and preprocessing of the motor temperature and operating data through an integrated sensor group on the new energy vehicle; this process ensures the high accuracy and integrity of the data; then, the analysis module uses a machine learning algorithm to perform an in-depth analysis of the collected data, calculates the motor winding temperature coefficient Das and the motor output power coefficient Jsc, and provides important parameters for subsequent analysis; the comprehensive analysis module obtains the motor comprehensive control coefficient DJK through the correlation calculation of these two coefficients, thereby comprehensively evaluating the operating status of the motor; the prediction and evaluation module preliminarily and secondarily evaluates the safety of the motor control system by comparing with a preset threshold, and establishes a scientific and systematic control system evaluation model; finally, the intelligent integration module performs intelligent control and fault monitoring of the motor based on the evaluation result of the motor comprehensive control coefficient DJK, and warns the owner in real time.

[0069] Example 2

[0070] This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the data acquisition module includes a data acquisition unit and a data processing unit;

[0071] The data acquisition unit is used to arrange monitoring points around the motor of the new energy vehicle and install an integrated sensor group to monitor the motor temperature data group and the motor operation data group in real time, and collect and send the monitored motor temperature data group and the motor operation data group to the central control system of the vehicle by wireless transmission;

[0072] The motor temperature data set includes the winding temperature T and the number of windings n of the motor;

[0073] The winding temperature T of the motor is measured and obtained by a temperature sensor;

[0074] The number of windings n is the total number of windings inside the motor designed and produced;

[0075] The motor operation data set includes the motor torque NJ, the driving speed SD, the vehicle load ZZ, the battery discharge capacity FD and the motor rotor speed DZ;

[0076] The torque NJ of the motor is obtained by a torque tester device;

[0077] The driving speed SD is directly obtained through a speedometer;

[0078] The vehicle load ZZ is measured and obtained by a weighing sensor;

[0079] The battery discharge capacity FD is obtained by a battery discharge tester;

[0080] The motor rotor speed DZ is obtained through a resolver sensor installed inside the motor.

[0081] The data processing unit is used to extract data from the preprocessed first data group and the second data group, eliminate errors in the sensor collected data, smooth the calibrated data, remove noise, and perform standardization processing, convert the data to a uniform scale, remove outliers in the data, obtain high-quality input data, further analyze and preprocess the data, provide input for subsequent data calculation units, use historical data to train a machine learning algorithm model, and analyze real-time data.

[0082] In this embodiment, the system collects motor temperature and operating data in real time through integrated sensors, and transmits the data wirelessly to the central control system. The data is pre-processed by the data processing unit to ensure high data quality and uniformity, and then combined with historical data to train the machine learning model for real-time analysis, thereby achieving efficient and safe motor intelligent control and fault warning, effectively improving the safety and operating efficiency of new energy vehicles.

[0083] Example 3

[0084] This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the analysis module includes a motor temperature analysis unit and a motor power calculation unit;

[0085] The motor temperature analysis unit normalizes and summarizes the preprocessed first data group to obtain the motor winding temperature coefficient Das, and analyzes the motor winding temperature uniformity in real time;

[0086] The motor winding temperature coefficient Das is calculated by the following formula:

[0087]

[0088] Wherein, Ti represents the winding temperature T of the i-th winding, Ti represents the average value of the i-th winding temperatures T, and n represents the number of windings.

[0089] The motor power calculation unit normalizes and summarizes the preprocessed motor power data group to obtain the motor output power coefficient Jsc, and analyzes the motor winding temperature uniformity in real time;

[0090] The motor output power coefficient Jsc is calculated by the following formula:

[0091]

[0092] Wherein, w1, w2, w3, w4 and w5 represent the preset weight values ​​of the motor torque NJ, the driving speed SD, the battery discharge capacity FD, the motor rotor speed DZ and the vehicle load ZZ respectively.

[0093] The comprehensive analysis module is used to normalize the motor winding temperature coefficient Das and the motor output power coefficient Jsc, and then perform summary calculation to obtain the motor comprehensive control coefficient DJK.

[0094] The motor comprehensive control coefficient DJK is calculated by the following formula:

[0095] DJK=Das*α+Jsc*β+Z;

[0096] In the formula, α and β represent the preset weight values ​​of the motor winding temperature coefficient Das and the motor output power coefficient Jsc respectively, and the specific values ​​are set by the user according to the actual situation, and Z represents the first correction constant.

[0097] In this embodiment, the motor temperature analysis unit and the motor power calculation unit are used to realize real-time monitoring and evaluation of the motor operation status, thereby improving the performance and safety of the whole vehicle; firstly, the motor temperature analysis unit uses the preprocessed data to calculate the motor winding temperature coefficient Das through normalization processing; this coefficient reflects the uniformity of the temperature of each winding inside the motor and the overall temperature distribution, and can timely monitor potential overheating problems and protect the motor from damage or performance degradation; secondly, the motor power calculation unit calculates the motor output power coefficient Jsc after normalization processing based on the preprocessed motor power data group, such as torque NJ, driving speed SD, battery discharge capacity FD, motor rotor speed DZ and vehicle load ZZ; the comprehensive control coefficient DJK comprehensively considers the two key factors of motor temperature uniformity and power output level, and provides an important basis for the comprehensive control of the vehicle motor; through real-time monitoring and analysis, the system can quickly respond to changes in the motor operation status, implement intelligent adjustment and optimization, and ensure the safety and performance stability of the vehicle under various road conditions and usage scenarios.

[0098] Example 4

[0099] This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the prediction and evaluation module includes a first evaluation unit and a second evaluation unit;

[0100] The first evaluation unit performs a preliminary evaluation by presetting a temperature threshold value A based on the historical temperature safety value of the motor and then performing a preliminary evaluation with the obtained motor winding temperature coefficient Das to analyze the temperature of the generator during the driving of the current new energy vehicle. The specific evaluation scheme is as follows;

[0101] When the motor winding temperature coefficient Das is compared with the preset temperature threshold value A, a first prediction result is obtained;

[0102] When the motor winding temperature coefficient Das is less than the preset temperature threshold A, it means that the motor temperature is normal;

[0103] When the motor winding temperature coefficient Das ≥ the preset temperature threshold A, it indicates that the motor temperature is abnormal. At this time, a secondary evaluation is further performed to comprehensively analyze the causes of the motor abnormality, and the results are transmitted to the second evaluation unit to evaluate the specific causes.

[0104] The second evaluation unit performs a second evaluation based on the historical comprehensive control safety value of the motor, a preset first standard threshold M and a preset second standard threshold N, and then performs a secondary evaluation with the obtained motor comprehensive control coefficient DJK to analyze the power and safe operation of the generator of the current new energy vehicle during driving. The specific evaluation scheme is as follows;

[0105] When the motor comprehensive control coefficient DJK ≤ the preset first standard threshold M, it means that the motor is operating normally;

[0106] When the preset first standard threshold M is less than the motor comprehensive control coefficient DJK and less than the preset second standard threshold N, it indicates that there is an abnormality in the current motor operation. At this time, the first warning information is generated to prompt the driver to perform maintenance, and the motor is automatically controlled to switch to low power mode, reducing the motor power by 50% and improving the working efficiency of the motor cooling system by 70%;

[0107] When the motor comprehensive control coefficient DJK ≥ the preset second standard threshold N, it means that the current motor is in a dangerous state during operation and measures must be taken immediately to adjust it. At this time, the second warning information is generated to prompt the driver to pull over and automatically call the relevant department through the vehicle computer to file a report, call a tow truck and mobile vehicle for maintenance.

[0108] In this embodiment, the system can monitor the operation of the generator of the new energy vehicle in real time, and conduct multi-stage evaluation through preset temperature and comprehensive control safety values, effectively predicting and responding to abnormal temperature and safety issues of the generator. The first evaluation unit preliminarily judges the normal and abnormal state of the motor temperature based on the comparison between the motor winding temperature coefficient and the preset temperature threshold; and the second evaluation unit further provides more refined safety evaluation and response measures based on the comparison between the motor comprehensive control coefficient and the preset first standard threshold M and second standard threshold N, including automatic control of the motor to switch to low power mode and emergency call service; this multi-level prediction and evaluation mechanism effectively ensures the safety and reliability of the motor during vehicle operation and improves the overall safety level of the driver and the vehicle.

[0109] Example 5

[0110] This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the intelligent integrated module includes an instruction execution unit and an early warning unit;

[0111] The instruction execution unit is used to generate control instructions in real time according to the evaluation results provided by the prediction and evaluation module, adjust the operating parameters of the motor, optimize the motor performance and safety, and is responsible for executing preset safety measures and automatically slowing down or shutting down the motor when an abnormality is detected;

[0112] The early warning unit is used to monitor the operating results of the instruction execution unit and the operating status of the motor, promptly identify any abnormalities and potential faults, report the abnormalities and potential faults to the owner in real time through the vehicle system, and remind the owner to take corresponding safety measures.

[0113] In this embodiment, the system combines the command execution unit and the early warning unit to achieve comprehensive monitoring and management of the operation of the motor of the new energy vehicle. The command execution unit generates and executes the corresponding control instructions in real time according to the real-time evaluation results provided by the prediction and evaluation module, adjusts the operating parameters of the motor to optimize performance and safety, and ensures that the motor can be automatically decelerated or shut down when an abnormality is detected, effectively reducing the risk of failure; the early warning unit is responsible for monitoring the operation of the command execution unit and the operating status of the motor, promptly identifying and reporting any abnormalities and potential faults, and conveying alarm information to the owner in real time through the vehicle system, reminding the owner to take appropriate safety measures, thereby greatly improving the safety of the vehicle and the comfort of the driver.

[0114] Example 6

[0115] See also Figure 1 and Figure 2 , a high-safety new energy vehicle motor control method, comprising the following steps:

[0116] S1. Based on the integrated sensor group installed on the new energy vehicle, real-time monitoring of the new energy vehicle is performed, and a motor temperature data group and a motor operation data group are collected, and the collected data groups are pre-processed;

[0117] S2. Perform in-depth analysis and prediction on the collected data, use machine learning algorithms to analyze the data, calculate and analyze the motor temperature data group to obtain the motor winding temperature coefficient Das, and calculate and analyze the motor operation data group to obtain the motor output power coefficient Jsc;

[0118] S3, performing correlation and summary calculation and analysis on the motor winding temperature coefficient Das and the motor output power coefficient Jsc calculated and analyzed by the analysis module during the operation of the motor of the new energy vehicle, and obtaining the motor comprehensive control coefficient DJK;

[0119] S4, perform preliminary comparative evaluation on the preset temperature threshold A and the obtained motor winding temperature coefficient Das, analyze the temperature change of the motor during operation, and then further perform secondary comparative evaluation on the first preset standard threshold M and the preset second standard threshold N with the analyzed motor comprehensive control coefficient DJK when abnormality is detected, evaluate the control system of the motor, and establish a control system evaluation model;

[0120] S5. Based on the evaluation results of the motor comprehensive control coefficient DJK, the new energy vehicle motor is intelligently controlled and fault problems are monitored, and the results are reported and warned to the car owner in real time through the vehicle system.

[0121] Although 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 the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A highly secure new energy vehicle motor control system, characterized in that: It includes data acquisition module, analysis module, comprehensive analysis module, prediction and evaluation module and intelligent integration module; The data acquisition module is used to monitor the new energy vehicle in real time based on the integrated sensor group installed on the new energy vehicle, collect the motor temperature data group and the motor operation data group, and pre-process the collected data group; The analysis module is used to perform in-depth analysis and prediction on the collected data, use machine learning algorithms to analyze the data, calculate and analyze the motor winding temperature coefficient Das through the motor temperature data group, and calculate and analyze the motor output power coefficient Jsc through the motor operation data group; The comprehensive analysis module is used to perform correlation and summary calculation and analysis on the motor winding temperature coefficient Das and the motor output power coefficient Jsc calculated and analyzed by the analysis module during the operation of the motor of the new energy vehicle, and obtain the motor comprehensive control coefficient DJK; The prediction and evaluation module is used to perform a preliminary comparative evaluation of the preset temperature threshold value A and the obtained motor winding temperature coefficient Das, analyze the temperature change of the motor during operation, and then further perform a secondary comparative evaluation of the first preset standard threshold value M and the preset second standard threshold value N with the analyzed motor comprehensive control coefficient DJK when abnormalities are monitored, evaluate the control system of the motor, and establish a control system evaluation model; The intelligent integrated module is used to perform intelligent control and fault monitoring on the motor of the new energy vehicle according to the evaluation result of the motor comprehensive control coefficient DJK, and report the results in real time and warn the owner through the vehicle system.

2. A highly secure new energy vehicle motor control system according to claim 1, characterized in that: The data acquisition module includes a data acquisition unit and a data processing unit; The data acquisition unit is used to arrange monitoring points around the motor of the new energy vehicle and install an integrated sensor group to monitor the motor temperature data group and the motor operation data group in real time, and collect and send the monitored motor temperature data group and the motor operation data group to the central control system of the vehicle by wireless transmission; The motor temperature data set includes the winding temperature T and the number of windings n of the motor; The winding temperature T of the motor is measured and obtained by a temperature sensor; The number of windings n is the total number of windings inside the motor designed and produced; The motor operation data set includes the motor torque NJ, the driving speed SD, the vehicle load ZZ, the battery discharge capacity FD and the motor rotor speed DZ; The torque NJ of the motor is obtained by a torque tester device; The driving speed SD is directly obtained through a speedometer; The vehicle load ZZ is measured and obtained by a weighing sensor; The battery discharge capacity FD is obtained by a battery discharge tester; The motor rotor speed DZ is obtained through a resolver sensor installed inside the motor.

3. A highly secure new energy vehicle motor control system according to claim 2, characterized in that: The data processing unit is used to extract data from the preprocessed first data group and the second data group, eliminate errors in sensor collected data, smooth the calibrated data, remove noise and standardize the data, convert the data to a uniform scale, remove outliers in the data, obtain high-quality input data, further analyze and preprocess the data, provide input for subsequent data calculation units, use historical data to train machine learning algorithm models, analyze real-time data, further analyze and preprocess the data, and provide input for subsequent analysis modules.

4. A highly secure new energy vehicle motor control system according to claim 1, characterized in that: The analysis module includes a motor temperature analysis unit and a motor power calculation unit; The motor temperature analysis unit normalizes and summarizes the preprocessed first data group to obtain the motor winding temperature coefficient Das, and analyzes the motor winding temperature uniformity in real time; The motor winding temperature coefficient Das is calculated by the following formula: Where Ti represents the winding temperature T of the i-th winding, represents the average temperature T of i windings, and n represents the number of windings.

5. A highly secure new energy vehicle motor control system according to claim 4, characterized in that: The motor power calculation unit normalizes and summarizes the preprocessed motor power data group to obtain the motor output power coefficient Jsc and analyzes the output power of the motor in real time; The motor output power coefficient Jsc is calculated by the following formula: Wherein, w1, w2, w3, w4 and w5 represent the preset weight values ​​of the motor torque NJ, the driving speed SD, the battery discharge capacity FD, the motor rotor speed DZ and the vehicle load ZZ respectively.

6. A highly secure new energy vehicle motor control system according to claim 1, characterized in that: The comprehensive analysis module is used to normalize the motor winding temperature coefficient Das and the motor output power coefficient Jsc, and then perform summary calculation to obtain the motor comprehensive control coefficient DJK. The motor comprehensive control coefficient DJK is calculated by the following formula: DJK=Das*α+Jsc*β+Z; In the formula, α and β represent the preset weight values ​​of the motor winding temperature coefficient Das and the motor output power coefficient Jsc respectively, and the specific values ​​are set by the user according to the actual situation, and Z represents the first correction constant.

7. A highly secure new energy vehicle motor control system according to claim 1, characterized in that: The prediction and evaluation module includes a first evaluation unit and a second evaluation unit; The first evaluation unit performs a preliminary evaluation by presetting a temperature threshold value A based on the historical temperature safety value of the motor and then performing a preliminary evaluation with the obtained motor winding temperature coefficient Das to analyze the temperature of the generator during the driving of the current new energy vehicle. The specific evaluation scheme is as follows; When the motor winding temperature coefficient Das is compared with the preset temperature threshold value A, a first prediction result is obtained; When the motor winding temperature coefficient Das is less than the preset temperature threshold A, it means that the motor temperature is normal; When the motor winding temperature coefficient Das ≥ the preset temperature threshold A, it indicates that the motor temperature is abnormal. At this time, a secondary evaluation is further performed to comprehensively analyze the causes of the motor abnormality, and the results are transmitted to the second evaluation unit to evaluate the specific causes.

8. A highly secure new energy vehicle motor control system according to claim 7, characterized in that: The second evaluation unit performs a second evaluation based on the historical comprehensive control safety value of the motor, a preset first standard threshold M and a preset second standard threshold N, and then performs a secondary evaluation with the obtained motor comprehensive control coefficient DJK to analyze the power and safe operation of the generator of the current new energy vehicle during driving. The specific evaluation scheme is as follows; When the motor comprehensive control coefficient DJK ≤ the preset first standard threshold M, it means that the motor is operating normally; When the preset first standard threshold M is less than the motor comprehensive control coefficient DJK and less than the preset second standard threshold N, it indicates that there is an abnormality in the current motor operation. At this time, the first warning information is generated to prompt the driver to perform maintenance, and the motor is automatically controlled to switch to low power mode, reducing the motor power by 50% and improving the working efficiency of the motor cooling system by 70%; When the motor comprehensive control coefficient DJK ≥ the preset second standard threshold N, it means that the current motor is in a dangerous state during operation and measures must be taken immediately to adjust it. At this time, the second warning information is generated to prompt the driver to pull over and automatically call the relevant department through the vehicle computer to file a report, call a tow truck and mobile vehicle for maintenance.

9. A highly secure new energy vehicle motor control system according to claim 1, characterized in that: The intelligent integrated module includes an instruction execution unit and an early warning unit; The instruction execution unit is used to generate control instructions in real time according to the evaluation results provided by the prediction and evaluation module, adjust the operating parameters of the motor, optimize the motor performance and safety, and is responsible for executing preset safety measures and automatically slowing down or shutting down the motor when an abnormality is detected; The early warning unit is used to monitor the operating results of the instruction execution unit and the operating status of the motor, promptly identify any abnormalities and potential faults, report the abnormalities and potential faults to the owner in real time through the vehicle system, and remind the owner to take corresponding safety measures.

10. A high-security new energy vehicle motor control method, comprising a high-security new energy vehicle motor control system according to any one of claims 1 to 9, characterized in that: The following steps are included: S1. Based on the integrated sensor group installed on the new energy vehicle, real-time monitoring of the new energy vehicle is performed, and a motor temperature data group and a motor operation data group are collected, and the collected data groups are pre-processed; S2. Perform in-depth analysis and prediction on the collected data, use machine learning algorithms to analyze the data, calculate and analyze the motor temperature data group to obtain the motor winding temperature coefficient Das, and calculate and analyze the motor operation data group to obtain the motor output power coefficient Jsc; S3, through the analysis module, during the operation of the new energy vehicle motor, the motor winding temperature coefficient Das and the motor output power coefficient Jsc calculated and analyzed, and the motor comprehensive control coefficient DJK is obtained by correlation summary calculation and analysis; S4, perform preliminary comparative evaluation on the preset temperature threshold A and the obtained motor winding temperature coefficient Das, analyze the temperature change of the motor during operation, and then further perform secondary comparative evaluation on the first preset standard threshold M and the preset second standard threshold N with the analyzed motor comprehensive control coefficient DJK when abnormality is detected, evaluate the control system of the motor, and establish a control system evaluation model; S5. Based on the evaluation results of the motor comprehensive control coefficient DJK, the new energy vehicle motor is intelligently controlled and fault problems are monitored, and the results are reported and warned to the car owner in real time through the vehicle system.

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