Motor control method and device of electric automobile

Abstract

The invention discloses a motor control method and device for an electric vehicle, and the device comprises a data collection module, a data processing center, a storage module, an invalid data module, a motor control terminal module and a data display terminal. Comprising real-time road condition data, battery state data and motor operation data. According to the motor control method and device for the electric vehicle, the motor control strategy is formulated and dynamically adjusted by collecting and classifying various driving data, and the energy utilization efficiency and the motor operation stability of the electric vehicle under different working conditions are improved. And meanwhile, a motor fault intelligent diagnosis and self-adaptive control mechanism is introduced, so that the safety and the reliability of the vehicle are enhanced. By means of the analysis and calculation method of the strategy chain, the efficiency and accuracy of control strategy generation are improved, the unnecessary calculation amount is reduced, and the overall control performance is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of motor control, and in particular relates to a motor control method and device for an electric vehicle. Background Art

[0002] With the widespread use of electric vehicles, higher requirements are placed on the efficiency, accuracy and safety of motor control. At present, most electric vehicle motor control methods are mainly based on fixed control strategies, and do not fully consider the impact of factors such as real-time road conditions and battery status on motor performance. For example, under different road conditions, such as congested sections and highways, the motor still operates according to preset fixed parameters, resulting in energy waste and increased motor loss. At the same time, when a motor fails, the existing control methods are often unable to diagnose the fault in a timely and accurate manner and make adaptive adjustments, affecting the normal driving and safety of the vehicle. There are also speed regulation methods for motors in the prior art, but they do not comprehensively consider the impact of multiple complex factors on motor control, and cannot meet the diverse needs of electric vehicles during actual driving. Summary of the invention

[0003] The present invention aims to provide a motor control method and device for an electric vehicle to solve the technical problems raised in the background technology.

[0004] To achieve the above object, the specific technical solution of the present invention is as follows: A motor control method for an electric vehicle, characterized in that it specifically includes the following steps:

[0005] Step 1: Collect various data information during the driving process of the electric vehicle, including real-time road condition data, battery status data, and motor operation data, and mark these data as source data, extract feature data of the source data, classify the source data according to different features, identify the classified difference data QB, and obtain pre-processed data Yc through analysis of associated data;

[0006] Step 2: According to the pre-processed data Yc obtained by different classifications, a corresponding motor control strategy is formulated. At the same time, different control strategies are bundled to form a pre-processing strategy bundle package, and different control strategy chains within the pre-processing strategy bundle package are analyzed and calculated, and the control strategy is dynamically adjusted according to real-time data;

[0007] Step 3: The result template data after analysis and calculation is transmitted to the storage module, which stores and records the control strategy results and performs matching and verification operations on the result template to ensure that the stored control strategy is accurate and meets the actual operation requirements of the vehicle;

[0008] Step 4: The control system of the electric vehicle obtains the processed control strategy data, sends the control command to the motor through the motor controller to achieve precise control of the motor, and at the same time, monitors the running status of the motor in real time, and transmits the feedback data back to the data acquisition module to form a closed-loop control so as to adjust the control strategy in time.

[0009] Preferably, the specific method of classifying the acquired data in step 1 is as follows:

[0010] S1. Extracting feature data from the acquired source data. Feature extraction includes a series of distinguishing factors such as the degree of traffic congestion, the size of the slope, the specific value of the battery power, the speed change rate of the motor, etc., to obtain distinguishing data of the source data, and integrate the distinguishing data to generate a feature data classification library;

[0011] S2. Match the generated feature data classification library with the classification matching template. The classification matching template is a pre-set standard for different data types. The data in the feature database is classified according to the classification matching template. For data types that do not exist in the matching template, the system automatically learns and updates the template, and performs different labeling on the feature data in the classification library. The labeling method is to use the same number as the subscript for the same type of data;

[0012] S3. Analyze the associated data within the identified distinguishing data QB. The associated data includes data of the same or similar type. The premise of screening the associated data is the feature data with the same label, and obtain the preprocessed data Yc.

[0013] Preferably, the analysis method of different policy chains within the policy bundle in step 2 is as follows:

[0014] T1. The policy bundle is transmitted. The system groups the policy chains according to the different types in the bundle. The policy connections of the same type are transmitted through the policy identification channel. The policy chains of the same type are transmitted to the storage unit. The storage unit matches the policy chains through the template.

[0015] T2. Analyze the policy chains in the storage unit through template matching, and sort the complexity of the policy chains. The sorting method is to sort the policy chains from low to high in order according to the complexity of the multiple groups of policy chains, and transmit the sorted policy chains to the storage unit for storage;

[0016] T3, matching the strategy chains sorted by complexity again, and then feeding back the matched data chains to the storage unit for secondary storage;

[0017] T4. Calculate the matched strategy chains, calculate the execution priority of each strategy chain under the current vehicle state, transfer the calculation results to the storage unit for storage, and generate the corresponding result template.

[0018] Preferably, the specific calculation method in step T4 is as follows:

[0019] Start calculating the strategy chain with the lowest complexity after sorting and matching, calculate the execution priority of the strategy chain with the lowest complexity under the current vehicle state and transmit it to the result template to store and memorize the result;

[0020] After the calculation with the lowest complexity is completed, the strategy chains with increasing complexity are calculated in sequence. If the strategy chain calculated later contains the same key parameter combination as the previous calculation, there is no need to fully calculate this set of strategies again. The priority can be adjusted directly by referring to the previous calculation results.

[0021] Preferably, after the calculation of the same type of strategy chain is completed: the system generates a corresponding matching template based on the calculation results, the memory storage unit memorizes and stores the matching template, the calculated strategy priority is matched and checked through the storage unit, and the checked control strategy is directly transmitted to the motor control terminal module.

[0022] Preferably, during the operation of the motor, various operating parameters of the motor are monitored in real time. When abnormal parameters are detected, the fault diagnosis module is started, and the fault characteristic data is extracted and analyzed, and matched with a preset fault mode library to quickly and accurately diagnose the motor fault type. At the same time, the fault information is transmitted to the data display terminal to inform the driver of the fault condition.

[0023] Preferably, the invalid data module is used to process and store the input invalid data, and directly transmit the invalid data to the data display terminal for the operator to observe. The data storage module stores the processed data results, and matches and verifies the processed data results under the action of the data storage module, and then transmits the matched and verified data results to the data display terminal for the operator to view.

[0024] Preferably, a motor control method for an electric vehicle comprises:

[0025] Data acquisition module: used to collect real-time road condition data, battery status data and motor operation data of electric vehicles during driving, and transmit the acquired data to the data processing center;

[0026] Data processing center: used to analyze and process the transmitted data, including data grouping unit, data sorting unit, data analysis and calculation unit, memory storage unit and fault diagnosis unit. The data grouping unit classifies the collected data; the data sorting unit sorts the strategy chain by complexity; the data analysis and calculation unit calculates the execution priority of the strategy chain; the memory storage unit stores the relevant templates and calculation results; the fault diagnosis unit diagnoses the motor fault and generates an adaptive control strategy;

[0027] Storage module: used to store and memorize the results of input data processing, including control strategies, fault diagnosis results, etc.;

[0028] Invalid data module: used to process invalid input data;

[0029] Motor control terminal module: precisely controls the motor according to the control strategy transmitted by the data processing center;

[0030] Data display terminal: used to display the results of input data processing, fault information, etc. for the driver to view.

[0031] The electric vehicle motor control method and device of the present invention have the following advantages:

[0032] The electric motor control method and device of an electric vehicle, through the collection and classification of a variety of driving data, formulates and dynamically adjusts the motor control strategy, thereby improving the energy utilization efficiency and motor operation stability of the electric vehicle under different working conditions. At the same time, the introduction of intelligent diagnosis and adaptive control mechanism of motor faults enhances the safety and reliability of the vehicle. The analysis and calculation method of the strategy chain is used to improve the efficiency and accuracy of the control strategy generation, reduce unnecessary calculations, and improve the overall control performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0034] Figure 1 This is a diagram of the motor control method of the electric vehicle of the present invention. DETAILED DESCRIPTION

[0035] In the following, only some exemplary embodiments are briefly described. As those skilled in the art will appreciate, the described embodiments may be modified in various ways without departing from the spirit or scope of the embodiments of the present invention. Therefore, the drawings and descriptions are considered to be exemplary and non-restrictive in nature.

[0036] In the description of the embodiments of the present invention, it should be understood that the terms "length", "vertical", "horizontal", "top", "bottom", etc. indicating the orientation or positional relationship are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the embodiments of the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the embodiments of the present invention.

[0037] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0038] In the embodiments of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, an electrical connection, or a communication; it can be a direct connection, or an indirect connection through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the embodiments of the present invention can be understood according to specific circumstances.

[0039] The disclosure below provides many different embodiments or examples to implement different structures of the embodiments of the present invention. In order to simplify the disclosure of the embodiments of the present invention, the parts and settings of specific examples are described below. Of course, they are only examples, and the purpose is not to limit the embodiments of the present invention. In addition, the embodiments of the present invention can repeat reference numbers and / or reference letters in different examples, and this repetition is for the purpose of simplification and clarity, which itself does not indicate the relationship between the various embodiments and / or settings discussed.

[0040] In order to better understand the purpose, structure and function of the present invention, the motor control method and device of an electric vehicle of the present invention are further described in detail below with reference to the accompanying drawings.

[0041] like Figure 1As shown, a motor control method of an electric vehicle of the present invention is characterized in that it specifically comprises the following steps:

[0042] Step 1: Collect various data information during the driving process of the electric vehicle, including real-time road condition data, battery status data, and motor operation data, and mark these data as source data, extract feature data of the source data, classify the source data according to different features, identify the classified difference data QB, and obtain pre-processed data Yc through analysis of associated data;

[0043] The specific method of classifying the acquired data in step 1 is as follows:

[0044] S1. Extracting feature data from the acquired source data. Feature extraction includes a series of distinguishing factors such as the degree of traffic congestion, the size of the slope, the specific value of the battery power, the speed change rate of the motor, etc., to obtain distinguishing data of the source data, and integrate the distinguishing data to generate a feature data classification library;

[0045] The labeling process is as follows: In the above technical solution, assuming that we use sets to represent different types of data, for each type of data set, we can express the labeling process with a formula in the following way: Let T be the set of all data types, ti∈T represents the i-th data type, and Dti represents the set of all data belonging to data type ti;

[0046] For each data d_ij in Dti (j represents the index in the data set of this type), we assign it a label L(dij), satisfying: L(dij)=dij+si, where si is the data type, and the specific subscript value corresponding to ti is used to identify the type of the data. In this way, after the data dij of the same type ti is labeled, they all have the same subscript value si, thus achieving label processing. For example, if s1=1 corresponding to the data of type t1, then the data d11 in Dt1 is labeled as d 11 1,d 12 The number is followed by d 12 1, and so on, just like 1231211, 3412321, etc. in the example have the same subscript 1, indicating that they belong to the same type of data.

[0047] S2. Match the generated feature data classification library with the classification matching template. The classification matching template is a pre-set standard for different data types. The data in the feature database is classified according to the classification matching template. For data types that do not exist in the matching template, the system automatically learns and updates the template, and performs different labeling on the feature data in the classification library. The labeling method is to use the same number as the subscript for the same type of data;

[0048] S3. Analyze the associated data within the identified distinguishing data QB. The associated data includes data of the same or similar type. The premise of screening the associated data is the feature data with the same label, and obtain the preprocessed data Yc.

[0049] Step 2: According to the pre-processed data Yc obtained by different classifications, a corresponding motor control strategy is formulated. At the same time, different control strategies are bundled to form a pre-processing strategy bundle package, and different control strategy chains within the pre-processing strategy bundle package are analyzed and calculated, and the control strategy is dynamically adjusted according to real-time data;

[0050] The analysis of different policy chains within the policy bundle in step 2 is as follows:

[0051] T1. The policy bundle is transmitted. The system groups the policy chains according to the different types in the bundle. The policy connections of the same type are transmitted through the policy identification channel. The policy chains of the same type are transmitted to the storage unit. The storage unit matches the policy chains through the template.

[0052] T2. Analyze the policy chains in the storage unit through template matching, and sort the complexity of the policy chains. The sorting method is to sort the policy chains from low to high in order according to the complexity of the multiple groups of policy chains, and transmit the sorted policy chains to the storage unit for storage;

[0053] T3, matching the strategy chains sorted by complexity again, and then feeding back the matched data chains to the storage unit for secondary storage;

[0054] T4, calculate the matched strategy chains, calculate the execution priority of each strategy chain under the current vehicle state, transfer the calculation results to the storage unit for storage, and generate the corresponding result template. The specific calculation method in step T4 is as follows:

[0055] Start calculating the strategy chain with the lowest complexity after sorting and matching, calculate the execution priority of the strategy chain with the lowest complexity under the current vehicle state and transmit it to the result template to store and memorize the result;

[0056] After the calculation with the lowest complexity is completed, the strategy chains with increasing complexity are calculated in sequence. If the strategy chain calculated later contains the same key parameter combination as the previous calculation, there is no need to fully calculate this set of strategies again. The priority can be adjusted directly by referring to the previous calculation results.

[0057] The system generates a corresponding matching template based on the calculated results. The memory storage unit memorizes and stores the matching template. The calculated strategy priority is matched and checked through the storage unit, and the checked control strategy is directly transmitted to the motor control terminal module. The various operating parameters of the motor are monitored in real time. When abnormal parameters are detected, the fault diagnosis module is started. By extracting and analyzing the fault feature data, it is matched with the preset fault mode library to quickly and accurately diagnose the motor fault type. At the same time, the fault information is transmitted to the data display terminal to inform the driver of the fault situation. The invalid data module is used to process and store the input invalid data, and directly transmit the invalid data to the data display terminal for the operator to observe. The data storage module stores the processed data results, and matches and checks the processed data results under the action of the data storage module, and then transmits the matched and checked data results to the data display terminal for the operator to watch.

[0058] Step 3: The result template data after analysis and calculation is transmitted to the storage module, which stores and records the control strategy results and performs matching and verification operations on the result template to ensure that the stored control strategy is accurate and meets the actual operation requirements of the vehicle;

[0059] Step 4: The control system of the electric vehicle obtains the processed control strategy data, sends the control command to the motor through the motor controller to achieve precise control of the motor, and at the same time, monitors the running status of the motor in real time, and transmits the feedback data back to the data acquisition module to form a closed-loop control so as to adjust the control strategy in time.

[0060] The present invention also provides a motor control device for an electric vehicle, comprising:

[0061] Data acquisition module: used to collect real-time road condition data, battery status data and motor operation data of electric vehicles during driving, and transmit the acquired data to the data processing center;

[0062] Data processing center: used to analyze and process the transmitted data, including data grouping unit, data sorting unit, data analysis and calculation unit, memory storage unit and fault diagnosis unit. The data grouping unit classifies the collected data; the data sorting unit sorts the strategy chain by complexity; the data analysis and calculation unit calculates the execution priority of the strategy chain; the memory storage unit stores the relevant templates and calculation results; the fault diagnosis unit diagnoses the motor fault and generates an adaptive control strategy;

[0063] Storage module: used to store and memorize the results of input data processing, including control strategies, fault diagnosis results, etc.;

[0064] Invalid data module: used to process invalid input data;

[0065] Motor control terminal module: precisely controls the motor according to the control strategy transmitted by the data processing center;

[0066] Data display terminal: used to display the results of input data processing, fault information, etc. for the driver to view.

[0067] It is to be understood that the present invention is described by some embodiments, and it is known to those skilled in the art that various changes or equivalent substitutions may be made to these features and embodiments without departing from the spirit and scope of the present invention. In addition, under the teachings of the present invention, these features and embodiments may be modified to adapt to specific circumstances and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are within the scope of protection of the present invention.

Claims

1. A motor control method for an electric vehicle, characterized in that: The specific steps include: Step 1: Collect various data information during the driving process of the electric vehicle, including real-time road condition data, battery status data, and motor operation data, and mark these data as source data, extract feature data of the source data, classify the source data according to different features, identify the classified difference data QB, and obtain pre-processed data Yc through analysis of associated data; Step 2: According to the pre-processed data Yc obtained by different classifications, a corresponding motor control strategy is formulated. At the same time, different control strategies are bundled to form a pre-processing strategy bundle package, and different control strategy chains within the pre-processing strategy bundle package are analyzed and calculated, and the control strategy is dynamically adjusted according to real-time data; Step 3: The result template data after analysis and calculation is transmitted to the storage module, which stores and records the control strategy results and performs matching and verification operations on the result template to ensure that the stored control strategy is accurate and meets the actual operation requirements of the vehicle; Step 4: The control system of the electric vehicle obtains the processed control strategy data, sends the control command to the motor through the motor controller to achieve precise control of the motor, and at the same time, monitors the running status of the motor in real time, and transmits the feedback data back to the data acquisition module to form a closed-loop control so as to adjust the control strategy in time.

2. The motor control method of an electric vehicle according to claim 1, characterized in that: The specific method of classifying the acquired data in step 1 is as follows: S1. Extracting feature data from the acquired source data. Feature extraction includes a series of distinguishing factors such as the degree of traffic congestion, the size of the slope, the specific value of the battery power, the speed change rate of the motor, etc., to obtain distinguishing data of the source data, and integrate the distinguishing data to generate a feature data classification library; S2. Match the generated feature data classification library with the classification matching template. The classification matching template is a pre-set standard for different data types. The data in the feature database is classified according to the classification matching template. For data types that do not exist in the matching template, the system automatically learns and updates the template, and performs different labeling on the feature data in the classification library. The labeling method is to use the same number as the subscript for the same type of data; S3. Analyze the associated data within the identified distinguishing data QB. The associated data includes data of the same or similar type. The premise of screening the associated data is the feature data with the same label, and obtain the preprocessed data Yc.

3. The motor control method of an electric vehicle according to claim 1, characterized in that: The analysis of different policy chains within the policy bundle in step 2 is as follows: T1. The policy bundle is transmitted. The system groups the policy chains according to the different types in the bundle. The policy connections of the same type are transmitted through the policy identification channel. The policy chains of the same type are transmitted to the storage unit. The storage unit matches the policy chains through the template. T2. Analyze the policy chains in the storage unit through template matching, and sort the complexity of the policy chains. The sorting method is to sort the policy chains from low to high in order according to the complexity of the multiple groups of policy chains, and transmit the sorted policy chains to the storage unit for storage; T3, matching the strategy chains sorted by complexity again, and then feeding back the matched data chains to the storage unit for secondary storage; T4. Calculate the matched strategy chains, calculate the execution priority of each strategy chain under the current vehicle state, transfer the calculation results to the storage unit for storage, and generate the corresponding result template.

4. The motor control method of an electric vehicle according to claim 3, characterized in that: The specific calculation method in step T4 is as follows: Start calculating the strategy chain with the lowest complexity after sorting and matching, calculate the execution priority of the strategy chain with the lowest complexity under the current vehicle state and transmit it to the result template to store and memorize the result; After the calculation with the lowest complexity is completed, the strategy chains with increasing complexity are calculated in sequence. If the strategy chain calculated later contains the same key parameter combination as the previous calculation, there is no need to fully calculate this set of strategies again. The priority can be adjusted directly by referring to the previous calculation results.

5. The motor control method of an electric vehicle according to claim 4, characterized in that: After the calculation of the same type of strategy chain is completed: the system generates a corresponding matching template based on the calculation results, the memory storage unit memorizes and stores the matching template, and matches and verifies the calculated strategy priority through the storage unit, and transmits the verified control strategy directly to the motor control terminal module.

6. The motor control method of an electric vehicle according to claim 5, characterized in that: During the operation of the motor, the various operating parameters of the motor are monitored in real time. When abnormal parameters are detected, the fault diagnosis module is started. By extracting and analyzing the fault feature data and matching it with the preset fault mode library, the motor fault type can be diagnosed quickly and accurately. At the same time, the fault information is transmitted to the data display terminal to inform the driver of the fault situation.

7. The motor control method of an electric vehicle according to claim 6, characterized in that: The invalid data module is used to process and store the input invalid data, and directly transmit the invalid data to the data display terminal for the operator to observe. The data storage module stores the processed data results, and matches and verifies the processed data results under the action of the data storage module, and then transmits the matched and verified data results to the data display terminal for the operator to view.

8. A motor control device for an electric vehicle, applied to a motor control method for an electric vehicle according to any one of claims 1 to 7, characterized in that: include: Data acquisition module: used to collect real-time road condition data, battery status data and motor operation data of electric vehicles during driving, and transmit the acquired data to the data processing center; Data processing center: used to analyze and process the transmitted data, including data grouping unit, data sorting unit, data analysis and calculation unit, memory storage unit and fault diagnosis unit. The data grouping unit classifies the collected data; the data sorting unit sorts the strategy chain by complexity; the data analysis and calculation unit calculates the execution priority of the strategy chain; the memory storage unit stores the relevant templates and calculation results; the fault diagnosis unit diagnoses the motor fault and generates an adaptive control strategy; Storage module: used to store and memorize the results of input data processing, including control strategies, fault diagnosis results, etc.; Invalid data module: used to process invalid input data; Motor control terminal module: precisely controls the motor according to the control strategy transmitted by the data processing center; Data display terminal: used to display the results of input data processing, fault information, etc. for the driver to view.

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