Intelligent electric control system based on new energy automobile motor control

Abstract

The invention discloses an intelligent electric control system based on new energy automobile motor control, and relates to the technical field of automobile control, the intelligent electric control system comprises a monitoring center, and the monitoring center is in communication and / or electrical connection with a multi-source data acquisition module, a data co-processing module, a motor operation analysis module and a motor health assessment module; different data storage amount thresholds are set for different data acquisition terminals, and the evaluation frequency of motor operation is defined by using the different data storage amount thresholds of the different data acquisition terminals, so that high-frequency evaluation on active data and low-frequency evaluation on inert data are realized; according to the method, the data use efficiency and pertinence in the motor operation evaluation process are improved, the problem of unreasonable evaluation frequency or excessive evaluation in the data evaluation process caused by a traditional fixed evaluation period is avoided, the data processing load of an automobile monitoring center is reduced, and the data processing efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of automobile control technology, and in particular to an intelligent electric control system based on new energy automobile motor control. Background Art

[0002] The automotive electrical system consists of a generator as an energy converter, a battery as an energy storage device, and electrical devices. As the requirements for automotive comfort and safety continue to increase, the requirements for electrical system performance have also increased significantly. More and more automotive parts are showing a trend of electrification, such as electric seat adjustment, electric parking brake, electric steering assist, etc. In new energy vehicles, the corresponding motors will generate a large amount of relevant operating data during the operation of the vehicle, and different operating data also have different importance. How to implement corresponding periodic evaluation of the motor's operation process according to the different importance of the data, so as to achieve effective monitoring of the motor's operating status, is a problem we need to solve. To this end, we now provide an intelligent electronic control system based on new energy vehicle motor control. Summary of the invention

[0003] The purpose of the present invention is to provide an intelligent electric control system based on new energy vehicle motor control.

[0004] The object of the present invention can be achieved by the following technical solutions: an intelligent electric control system based on new energy vehicle motor control, comprising a monitoring center, wherein the monitoring center is communicatively and / or electrically connected to a multi-source data acquisition module, a data collaborative processing module, a motor operation analysis module, and a motor health assessment module; The multi-source data acquisition module is composed of a plurality of data acquisition terminals, and each vehicle operation parameter in the vehicle startup state is acquired through each data acquisition terminal, and the vehicle operation parameters acquired by each data acquisition terminal are synchronized to the data collaborative processing module; The data collaborative processing module is used to perform data integration processing on the vehicle operating parameters synchronized by various data acquisition terminals, and generate a periodic performance benchmark data set corresponding to the vehicle according to the data integration processing result; The motor operation analysis module is used to analyze the operation status of the motor according to the periodic performance benchmark data set, and determine whether the motor operation parameters of each dimension exist according to the analysis results, and if so, generate a corresponding abnormal diagnosis data set; The motor health assessment module is used to perform health assessment on the health of the motor according to the abnormal diagnosis data set.

[0005] Furthermore, the process of the multi-source data acquisition module acquiring various vehicle operating parameters in the vehicle startup state includes: Acquiring the vehicle operating parameters of the vehicle in the startup state through a data acquisition terminal arranged in the vehicle, wherein the vehicle operating parameters include motor operating parameters and vehicle driving parameters; The time when the car is started is recorded as the initial start time of the car, and the motor operation parameters and the vehicle operation parameters after the initial start time are obtained in real time; The motor operating parameters and vehicle driving parameters include several parameter items.

[0006] Furthermore, the process of synchronizing the vehicle operation parameters acquired by each data acquisition terminal to the data collaborative processing module includes: Set a corresponding data storage volume threshold for each data collection terminal; Sort the data collection terminals according to the data storage thresholds corresponding to them from small to large, set a corresponding gradient label for each data collection terminal according to the sorting result, and set a corresponding gradient value for each gradient label; Set each gradient label to the corresponding initial state; Generate a corresponding binary sequence according to the number of gradient labels; When the data volume of the vehicle operating parameters acquired by the data acquisition terminal reaches the corresponding data storage volume threshold, the initial state volume of the corresponding gradient label is changed; When the initial state quantity corresponding to the set gradient label changes, a corresponding data check pointer is generated, and the generated data check pointer is associated with the corresponding data acquisition terminal; The vehicle operation parameters acquired by the data acquisition terminal corresponding to the gradient tag and the generated data verification pointer are synchronized to the data collaborative processing module.

[0007] Furthermore, the data collaborative processing module performs data integration processing on the vehicle operating parameters synchronized by each data acquisition terminal, and generates a periodic performance benchmark data set corresponding to the vehicle according to the data integration processing result, including: According to the synchronized data check pointer, a data storage sub-library corresponding to the data acquisition terminal associated with the data check pointer is constructed; Importing the vehicle operating parameters acquired by the data acquisition terminal into the data storage sub-library; Obtaining the gradient value corresponding to the gradient label corresponding to the data check pointer, and determining the corresponding data integration range according to the gradient value; Marking the corresponding data storage sub-library according to the generated data integration range, and summarizing all vehicle operating parameters in the marked data storage sub-library to obtain a periodic performance benchmark data set corresponding to the data verification pointer; When the last gradient label generates a data check pointer, all vehicle operating parameters in the corresponding periodic performance benchmark data set are summarized to obtain the total set of periodic data, and the binary sequence is reset periodically.

[0008] Furthermore, the motor operation analysis module is used to analyze the operation state of the motor according to the periodic performance benchmark data set, and the process of judging whether the motor operation parameters of each dimension are abnormal according to the analysis result includes: Extracting all vehicle operating parameters in the periodic performance benchmark data set, and obtaining an acquisition time period for each parameter item in the extracted vehicle operating parameters; Mapping the obtained vehicle operating parameters to the constructed time axis according to the acquisition time period to obtain the corresponding parameter change curve; The common time period of each parameter change curve is calibrated according to the acquisition time period in the time axis, and each parameter change curve is intercepted and selected according to the calibrated common time period; Eliminate the unselected parameter change curve part; The retained parameter variation curve is divided into a motor operation parameter variation curve and a vehicle operation parameter variation curve according to the type of the vehicle operation parameter; Marking the parameter items of the vehicle operating parameters acquired by the data acquisition terminal corresponding to the periodic performance benchmark data set; Recording the parameter items of the motor operating parameters in the marked vehicle operating parameters as reference items, respectively obtaining the vehicle operating parameters affecting each reference item, and recording the corresponding parameter items as control items; Then obtain the parameter performance evaluation coefficient corresponding to each benchmark item corresponding to the periodic performance benchmark data set; Set the corresponding performance evaluation threshold for each gradient label; Compare the obtained parameter performance evaluation coefficient of each benchmark item with the performance evaluation threshold of the gradient label corresponding to the periodic performance benchmark data set; If the motor performance evaluation coefficient is higher than the performance evaluation threshold, it means that the motor operating parameters corresponding to the benchmark item are abnormal, otherwise it means that the motor operating parameters corresponding to the benchmark item are normal; The motor operating parameters corresponding to the abnormal benchmark items and the vehicle operating parameters corresponding to the control items corresponding to the abnormal benchmark items are summarized to obtain an abnormal diagnosis data set corresponding to the gradient label.

[0009] Furthermore, the process of the motor health assessment module performing health assessment on the health of the motor according to the abnormal diagnosis data set includes: After the corresponding periodic data set is obtained, each benchmark item in the abnormal diagnosis data set corresponding to each gradient label is marked in the periodic data set; Reading parameter performance evaluation coefficients corresponding to all benchmark items in the periodic data set; Then, the periodic evaluation value corresponding to each benchmark item is obtained; According to the periodic evaluation values ​​of each benchmark item obtained, a health score of the motor is obtained; Set several health score intervals, and associate each health score interval with the corresponding motor health status; The obtained health score of the motor is matched with each health score interval respectively, the health score interval in which the health score of the motor is located is calibrated according to the matching result, and the motor health state associated with the health score interval is output.

[0010] Compared with the prior art, the present invention has the following beneficial effects: By setting different data storage thresholds for different data acquisition terminals and utilizing the different data storage thresholds for different data acquisition terminals, the frequency of motor operation evaluation is defined, thereby achieving high-frequency evaluation of "active data" and low-frequency evaluation of "inert data", thereby improving the efficiency and pertinence of data utilization in the motor operation evaluation process, while also avoiding the problems of unreasonable evaluation frequency or excessive evaluation in the data evaluation process caused by the traditional fixed evaluation cycle, and reducing the data processing load of the vehicle monitoring center and improving data processing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0012] Figure 1 It is a schematic diagram of the present invention. DETAILED DESCRIPTION

[0013] like Figure 1 As shown, the intelligent electric control system based on the motor control of new energy vehicles includes a monitoring center, which is communicated and / or electrically connected to a multi-source data acquisition module, a data collaborative processing module, a motor operation analysis module, and a motor health assessment module; The multi-source data acquisition module is composed of a plurality of data acquisition terminals, and each vehicle operation parameter in the vehicle startup state is acquired through each data acquisition terminal, and the vehicle operation parameters acquired by each data acquisition terminal are synchronized to the data collaborative processing module; The data collaborative processing module is used to perform data integration processing on the vehicle operating parameters synchronized by various data acquisition terminals, and generate a periodic performance benchmark data set corresponding to the vehicle according to the data integration processing result; The motor operation analysis module is used to analyze the operation status of the motor according to the periodic performance benchmark data set, and determine whether the motor operation parameters of each dimension exist according to the analysis results, and if so, generate a corresponding abnormal diagnosis data set; The motor health assessment module is used to perform health assessment on the health of the motor according to the abnormal diagnosis data set.

[0014] It should be further explained that, in the specific implementation process, the process of the multi-source data acquisition module acquiring various vehicle operating parameters in the vehicle startup state includes: Acquiring the vehicle operating parameters of the vehicle in the startup state through a data acquisition terminal arranged in the vehicle, wherein the vehicle operating parameters include motor operating parameters and vehicle driving parameters; The time when the car is started is recorded as the initial start time of the car, and the motor operation parameters and the vehicle operation parameters after the initial start time are obtained in real time; The motor operating parameters and vehicle driving parameters include several parameter items; Each parameter item in the motor operating parameters is marked as i, where i=1, 2, ..., n, n is an integer, and n≥1; The motor operating parameters corresponding to each parameter item are recorded as ; Each parameter item in the vehicle driving parameters is marked as j, where j=1, 2, ..., m, m is an integer, and m≥1; The vehicle driving parameter corresponding to each parameter item is recorded as .

[0015] It should be further explained that, in the specific implementation process, the process of synchronizing the vehicle operating parameters obtained by each data acquisition terminal to the data collaborative processing module includes: A corresponding data storage volume threshold is set for each data collection terminal; it should be further explained that, in a specific implementation process, the data storage volume threshold corresponding to different data collection terminals depends on the type of vehicle operating parameters obtained by the data collection terminal, and the data storage volume threshold of each data collection terminal is different; According to the data storage thresholds corresponding to the various data acquisition terminals, the data acquisition terminals are sorted from small to large, and a corresponding gradient label is set for each data acquisition terminal according to the sorting result, and a corresponding gradient value is set for each gradient label; it should be further explained that, in the specific implementation process, the gradient value of the gradient label corresponds to the sorting of the data acquisition terminals; Set the corresponding initial state quantity for each gradient label, where the initial state quantity is recorded as “0”; Generate a binary sequence consisting of a corresponding number of binary codes “0” according to the number of gradient labels; When the data volume of the vehicle operating parameters acquired by the data acquisition terminal reaches the corresponding data storage volume threshold, the initial state volume of the corresponding gradient label is changed, and the changed initial state volume is recorded as "1", and the binary code of the corresponding position is changed to "0" at the same time; When the initial state quantity corresponding to the set gradient label changes, a corresponding data check pointer is generated, and the generated data check pointer is associated with the corresponding data acquisition terminal; The vehicle operation parameters acquired by the data acquisition terminal corresponding to the gradient tag and the generated data verification pointer are synchronized to the data collaborative processing module, and the initial state quantity of the gradient tag is reset to "0".

[0016] It should be further explained that, in the specific implementation process, the data collaborative processing module performs data integration processing on the vehicle operating parameters synchronized by each data acquisition terminal, and the process of generating a periodic performance benchmark data set corresponding to the vehicle according to the data integration processing result includes: According to the synchronized data check pointer, a data storage sub-library corresponding to the data acquisition terminal associated with the data check pointer is constructed; Importing the vehicle operating parameters acquired by the data acquisition terminal into the data storage sub-library; Obtaining the gradient value corresponding to the gradient label corresponding to the data check pointer, and determining the corresponding data integration range according to the gradient value; It should be further explained that, in the specific implementation process, the process of determining the scope of data integration is as follows: According to the gradient value corresponding to the gradient label, the data check pointers corresponding to the gradient label and all gradient labels with a gradient value smaller than the gradient label are marked; Mark the data storage sub-libraries corresponding to all marked data verification pointers as data integration ranges; For example: Set gradient labels A=1, B=2, and C=3; Data check pointers a1, a2, a3 corresponding to the gradient label A have been generated; Data check pointers b1 and b2 corresponding to the gradient label B have been generated; A data check pointer c1 corresponding to the gradient label C has been generated; When the data check pointer a2 is used as the reference, the corresponding data integration range is a1 and a2; When the data check pointer b1 is used as the reference, the corresponding data integration range is the data storage sub-library corresponding to a1, a2, a3, and b1; When the data check pointer c1 is used as a reference, the data integration range is a1, a2, a3, b1, b2 and the data storage sub-library corresponding to c1, and so on.

[0017] Marking the corresponding data storage sub-library according to the generated data integration range, and summarizing all vehicle operating parameters in the marked data storage sub-library to obtain a periodic performance benchmark data set corresponding to the data verification pointer; When the last gradient label generates a data check pointer, all vehicle operating parameters in the corresponding periodic performance benchmark data set are summarized to obtain the total set of periodic data, and the binary sequence is reset periodically; After the cycle is reset to when all binary codes in the binary sequence become "1", all binary codes in the binary sequence are reset to "0"; By setting a binary sequence for each gradient label to perform statistics on the state quantity, the periodic performance benchmark data set can cover all data acquisition terminals.

[0018] It should be further explained that, in the specific implementation process, the motor operation analysis module is used to analyze the operating state of the motor according to the periodic performance benchmark data set, and the process of judging whether there is an abnormality in the motor operating parameters of each dimension according to the analysis results includes: Extracting all vehicle operating parameters in the periodic performance benchmark data set, and obtaining an acquisition time period for each parameter item in the extracted vehicle operating parameters; Mapping the obtained vehicle operating parameters to the constructed time axis according to the acquisition time period to obtain the corresponding parameter change curve; The common time period of each parameter change curve is calibrated according to the acquisition time period in the time axis, and each parameter change curve is intercepted and selected according to the calibrated common time period, and the common time period is recorded as [t1, t2]; Eliminate the unselected parameter change curve part; The retained parameter variation curve is divided into a motor operation parameter variation curve and a vehicle operation parameter variation curve according to the type of the vehicle operation parameter; Marking the parameter items of the vehicle operating parameters acquired by the data acquisition terminal corresponding to the periodic performance benchmark data set; Recording the parameter items of the motor operating parameters in the marked vehicle operating parameters as reference items, respectively obtaining the vehicle operating parameters affecting each reference item, and recording the corresponding parameter items as control items; Each reference item is labeled as Di, and the motor operating parameters corresponding to the reference item labeled Di are recorded as ; The control item affecting the reference item labeled Di is labeled as Cj, and the vehicle operating parameter corresponding to the reference item labeled Cj is recorded as ; Then obtain the parameter performance evaluation coefficient corresponding to each benchmark item of the periodic performance benchmark data set, recorded as DX Di ,in: ; in, It represents the influence coefficient of the control item labeled Cj on the reference item labeled Di; Set the corresponding performance evaluation threshold K for each gradient label Di ; Compare the obtained parameter performance evaluation coefficient of each benchmark item with the performance evaluation threshold of the gradient label corresponding to the periodic performance benchmark data set; If the motor performance evaluation coefficient is higher than the performance evaluation threshold, it means that the motor operating parameters corresponding to the benchmark item are abnormal, otherwise it means that the motor operating parameters corresponding to the benchmark item are normal; The motor operating parameters corresponding to the abnormal benchmark items and the vehicle operating parameters corresponding to the control items corresponding to the abnormal benchmark items are summarized to obtain an abnormal diagnosis data set corresponding to the gradient label.

[0019] It should be further explained that, in a specific implementation process, the process of the motor health assessment module performing health assessment on the health of the motor according to the abnormal diagnosis data set includes: After the corresponding periodic data set is obtained, each benchmark item in the abnormal diagnosis data set corresponding to each gradient label is marked in the periodic data set; Label all benchmark items in the total set of periodic data, denoted as k, where k=1, 2, ..., s; The parameter performance evaluation coefficient corresponding to each benchmark item is recorded as DX k ; Obtain the periodic evaluation value corresponding to each benchmark item, and record the obtained periodic evaluation value as Zp k ,in: ; in, is a constant, K k represents the performance evaluation threshold of the gradient label corresponding to the benchmark item numbered k in the total set of periodic data; According to the periodic evaluation values ​​of each benchmark item, the health score of the motor is obtained, which is recorded as Dp, where: ; Set several health score intervals, and associate each health score interval with the corresponding motor health status; The obtained health score of the motor is matched with each health score interval respectively, the health score interval in which the health score of the motor is located is calibrated according to the matching result, and the motor health state associated with the health score interval is output.

[0020] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments with equivalent changes without departing from the scope of the technical solution of the present invention. However, any modification or equivalent replacement of the above embodiments made according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still falls within the scope of the technical solution of the present invention.

Claims

1. An intelligent electric control system based on new energy vehicle motor control, including a monitoring center, characterized in that: The monitoring center is communicatively and / or electrically connected to a multi-source data acquisition module, a data collaborative processing module, a motor operation analysis module, and a motor health assessment module; The multi-source data acquisition module is composed of a plurality of data acquisition terminals, and each vehicle operation parameter in the vehicle startup state is acquired through each data acquisition terminal, and the vehicle operation parameters acquired by each data acquisition terminal are synchronized to the data collaborative processing module; The data collaborative processing module is used to perform data integration processing on the vehicle operating parameters synchronized by various data acquisition terminals, and generate a periodic performance benchmark data set corresponding to the vehicle according to the data integration processing result; The motor operation analysis module is used to analyze the operation status of the motor according to the periodic performance benchmark data set, and determine whether the motor operation parameters of each dimension exist according to the analysis results. If so, a corresponding abnormal diagnosis data set is generated; The motor health assessment module is used to perform health assessment on the health of the motor according to the abnormal diagnosis data set.

2. The intelligent electric control system based on new energy vehicle motor control according to claim 1 is characterized in that: The process of the multi-source data acquisition module acquiring various vehicle operating parameters in the vehicle startup state includes: Acquiring the vehicle operating parameters of the vehicle in the startup state through a data acquisition terminal arranged in the vehicle, wherein the vehicle operating parameters include motor operating parameters and vehicle driving parameters; The time when the car is started is recorded as the initial start time of the car, and the motor operation parameters and the vehicle operation parameters after the initial start time are obtained in real time; The motor operating parameters and vehicle driving parameters include several parameter items.

3. The intelligent electric control system based on new energy vehicle motor control according to claim 2 is characterized in that: The process of synchronizing the vehicle operating parameters obtained by each data acquisition terminal to the data collaborative processing module includes: Set a corresponding data storage volume threshold for each data collection terminal; Sort the data collection terminals according to the data storage thresholds corresponding to them from small to large, set a corresponding gradient label for each data collection terminal according to the sorting result, and set a corresponding gradient value for each gradient label; Set each gradient label to the corresponding initial state; Generate a corresponding binary sequence according to the number of gradient labels; When the data volume of the vehicle operating parameters acquired by the data acquisition terminal reaches the corresponding data storage volume threshold, the initial state volume of the corresponding gradient label is changed; When the initial state quantity corresponding to the set gradient label changes, a corresponding data check pointer is generated, and the generated data check pointer is associated with the corresponding data acquisition terminal; The vehicle operation parameters acquired by the data acquisition terminal corresponding to the gradient tag and the generated data verification pointer are synchronized to the data collaborative processing module.

4. The intelligent electric control system based on new energy vehicle motor control according to claim 3 is characterized in that: The data collaborative processing module performs data integration processing on the vehicle operating parameters synchronized by each data acquisition terminal, and generates a periodic performance benchmark data set corresponding to the vehicle according to the data integration processing result, including: According to the synchronized data check pointer, a data storage sub-library corresponding to the data acquisition terminal associated with the data check pointer is constructed; Importing the vehicle operating parameters acquired by the data acquisition terminal into the data storage sub-library; Obtaining the gradient value corresponding to the gradient label corresponding to the data check pointer, and determining the corresponding data integration range according to the gradient value; Marking the corresponding data storage sub-library according to the generated data integration range, and summarizing all vehicle operating parameters in the marked data storage sub-library to obtain a periodic performance benchmark data set corresponding to the data verification pointer; When the last gradient label generates a data check pointer, all vehicle operating parameters in the corresponding periodic performance benchmark data set are summarized to obtain the total set of periodic data, and the binary sequence is reset periodically.

5. The intelligent electric control system based on new energy vehicle motor control according to claim 4 is characterized in that: The motor operation analysis module is used to analyze the operation state of the motor according to the periodic performance benchmark data set, and the process of judging whether the motor operation parameters of each dimension are abnormal according to the analysis result includes: Extracting all vehicle operating parameters in the periodic performance benchmark data set, and obtaining an acquisition time period for each parameter item in the extracted vehicle operating parameters; Mapping the obtained vehicle operating parameters to the constructed time axis according to the acquisition time period to obtain the corresponding parameter change curve; The common time period of each parameter change curve is calibrated according to the acquisition time period in the time axis, and each parameter change curve is intercepted and selected according to the calibrated common time period; Eliminate the unselected parameter change curve part; The retained parameter variation curve is divided into a motor operation parameter variation curve and a vehicle operation parameter variation curve according to the type of the vehicle operation parameter; Marking the parameter items of the vehicle operating parameters acquired by the data acquisition terminal corresponding to the periodic performance benchmark data set; Recording the parameter items of the motor operating parameters in the marked vehicle operating parameters as reference items, respectively obtaining the vehicle operating parameters affecting each reference item, and recording the corresponding parameter items as control items; Then obtain the parameter performance evaluation coefficient corresponding to each benchmark item corresponding to the periodic performance benchmark data set; Set the corresponding performance evaluation threshold for each gradient label; Compare the obtained parameter performance evaluation coefficient of each benchmark item with the performance evaluation threshold of the gradient label corresponding to the periodic performance benchmark data set; If the motor performance evaluation coefficient is higher than the performance evaluation threshold, it means that the motor operating parameters corresponding to the benchmark item are abnormal, otherwise it means that the motor operating parameters corresponding to the benchmark item are normal; The motor operating parameters corresponding to the abnormal benchmark items and the vehicle operating parameters corresponding to the control items corresponding to the abnormal benchmark items are summarized to obtain an abnormal diagnosis data set corresponding to the gradient label.

6. The intelligent electric control system based on new energy vehicle motor control according to claim 5 is characterized in that: The process of the motor health assessment module performing health assessment on the motor health according to the abnormal diagnosis data set includes: After the corresponding periodic data set is obtained, each benchmark item in the abnormal diagnosis data set corresponding to each gradient label is marked in the periodic data set; Reading parameter performance evaluation coefficients corresponding to all benchmark items in the periodic data set; Then, the periodic evaluation value corresponding to each benchmark item is obtained; According to the periodic evaluation values ​​of each benchmark item obtained, a health score of the motor is obtained; Set several health score intervals, and associate each health score interval with the corresponding motor health status; The obtained health score of the motor is matched with each health score interval respectively, the health score interval in which the health score of the motor is located is calibrated according to the matching result, and the motor health state associated with the health score interval is output.

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