Three-phase current bias self-learning method
By performing three-phase current bias self-learning during power-on initialization and normal operation of the motor controller, the problem of inaccurate current bias detection in the prior art is solved, and the current bias self-learning and fault treatment under different working conditions is realized, ensuring the normal driving function of the vehicle.
Patent Information
- Application Number
- CN202510150897.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-11
- Publication Date
- 2025-04-18
AI Technical Summary
The prior art performs current bias detection when the motor controller is initialized when power-on, and the detection is inaccurate or there is an abnormal fault, and the current bias self-learning cannot be performed during normal operation, which affects the vehicle's driving.
The three-phase current bias self-learning method is adopted, including current bias self-learning during the initialization phase of the power-on motor controller and during normal operation, using two current bias self-learning schemes to cover different working conditions, and using the default bias value in case of failure.
It realizes the automatic learning of current bias during power-on initialization and normal operation of the motor controller, ensuring that there are also available current bias values in the fault condition, and ensuring the vehicle driving function.
Smart Images
Figure CN119853549A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to the technical field of vehicle drive motor controllers, and in particular to a three-phase current bias self-learning method. Background Art
[0002] The power system of new energy vehicles is the electric drive system, which mainly includes motors, reducers and motor controllers. The motor controller controls the output torque of the motor by adjusting the output of the three-phase current of the motor to drive the car forward, backward, accelerate and decelerate. At the same time, the electric drive system will be equipped with a three-phase current sensor to collect the actual three-phase current of the motor to estimate whether the actual torque is consistent with the target torque and perform closed-loop adjustment of the torque. If the electric drive system needs to achieve accurate and stable torque control, it needs to collect the three-phase current of the motor in real time and accurately. If it needs to obtain accurate three-phase current, it needs to obtain the voltage value of each phase current sensor under no-current conditions, that is, the three-phase current offset value.
[0003] The bias detection method currently used in electric vehicles is to perform bias detection only when the vehicle is powered on, and to determine the detection range. When the bias exceeds the range, a fault is triggered. This solution is simple and easy to implement and is therefore widely used, but it also has many limitations. In the prior art, detection is only performed when the vehicle is powered on and initialized. The complex working conditions of power-on initialization can also lead to inaccurate bias detection or abnormal fault reporting, and the detection cannot be continued after a fault occurs, affecting the normal driving of the vehicle. Summary of the invention
[0004] In view of the above-mentioned deficiencies in the prior art, the technical problem to be solved by the present invention is: how to provide a three-phase current bias self-learning method which can perform current bias self-learning not only in the power-on initialization stage of the motor controller, but also in the normal operation of the motor controller, and has a current bias value that can be used when the current bias self-learning fails or is not triggered normally, so as to ensure the vehicle driving function under fault conditions.
[0005] In order to solve the above technical problems, the present invention adopts the following technical solutions:
[0006] A three-phase current bias self-learning method comprises the following steps:
[0007] Step 1) reading the operating condition signal of the motor controller;
[0008] Step 2) judging whether the first entry condition of bias self-learning is met according to the operating condition signal of the motor controller, if so, executing step 3), if not, executing step 5);
[0009] Step 3) executing the first scheme of three-phase current offset value self-learning;
[0010] Step 4) determining whether the self-learning result of the current bias value is within the fault threshold, if so, executing step 9), if not, executing step 5);
[0011] Step 5) judging whether the second access condition of current bias self-learning is met according to the operating condition signal of the motor controller, if so, executing step 6), if not, executing step 8);
[0012] Step 6) executing a second scheme for self-learning of three-phase current offset values;
[0013] Step 7) determining whether the self-learning result of the current bias value is within the fault threshold, if so, executing step 9), if not, executing step 8) after the output current bias fault occurs;
[0014] Step 8) Use the default current bias data to perform current calculation, and then return to step 1);
[0015] Step 9) The bias self-learning is successful, and the bias value after self-learning is used to calculate the current, and then step 10) is executed;
[0016] Step 10) ends.
[0017] Preferably, the operating condition signal of the motor controller read in step 1) includes: the actual motor speed signal, the motor controller power-on operation time cycle signal and the bias self-learning state signal.
[0018] Preferably, the motor controller power-on operation time cycle signal is: a motor controller program operation time cycle signal obtained by monitoring the power supply enable signal of the current motor controller as a starting point and counting the program operation cycle;
[0019] The bias self-learning status is: read the current current bias self-learning status, and enter the subsequent bias self-learning stage only when the current bias self-learning status is failed, and the current bias self-learning status is failed. The situations where the motor controller is in the power-on initialization stage and the bias self-learning process has not been triggered, the result of the bias self-learning is not within the fault threshold, and the first entry condition and the second entry condition of the bias self-learning are not met.
[0020] Preferably, the first access condition in step 2) includes: setting the minimum start time t of bias self-learning under the first access condition min and the maximum start time t max , the current motor controller operation time cycle signal is t i , and t min <t i <t max , so that the current moment is after the current sensor is powered on and initialized and before the vehicle torque is output.
[0021] Preferably, the first entry condition in step 2) further includes: setting a motor speed threshold r max , the actual speed of the current motor is r i , and r i <r max , so that the current motor is in a stationary state and has no current output.
[0022] Preferably, the calculation method of the first scheme in step 3) is: filtering is performed by taking the average value after multi-cycle accumulation, and abnormal sampling points with a sampling value of 0 are removed in the middle to improve the accuracy and stability of the current bias self-learning of the first scheme;
[0023] The calculation method of the three-phase current bias self-learning of the first scheme is:
[0024]
[0025] Where: I n1 is the sampling value of each operation cycle during the bias self-learning process of the first scheme;
[0026] n1 is the number of sampling cycles accumulated during the bias self-learning process of the first scheme.
[0027] Preferably, the second entry condition in step 5) includes: the actual speed of the current motor is r i , and r i <r max ;
[0028] The current running time cycle signal of the motor controller is t i , and t i >t max ;
[0029] The actual control mode of the current motor is high voltage standby mode or torque control mode, but the current motor control output torque is 0N.m;
[0030] The difference between the maximum and minimum values of the current three-phase current sensor sampling setting cycle is less than the set change threshold I StpMax .
[0031] Preferably, the calculation method of the second solution in step 6) is: accumulating and collecting the current sensor signals within a set n2 time period, and then filtering by taking the average value;
[0032] The calculation method of the three-phase current bias self-learning of the second scheme is:
[0033]
[0034] Where: I n2is the sampling value of each operation cycle during the bias self-learning process of the second scheme;
[0035] n2 is the number of sampling cycles accumulated during the bias self-learning process of the second scheme.
[0036] Compared with the prior art, the present invention uses two current bias self-learning schemes to cover current bias self-learning under different working conditions, wherein the first scheme of current bias value self-learning is for power-on working conditions, and the second scheme of current bias value self-learning covers other working conditions after the whole vehicle is running, and when the working conditions cannot meet the first and second schemes of current bias self-learning, there are alternative default bias values to meet the normal calculation function of three-phase current. As a result, the present invention not only performs current bias self-learning in the power-on initialization stage of the motor controller, but also can perform current bias self-learning when the motor controller is running normally, and there is a current bias value that can be used when the current bias self-learning fails or fails to be triggered normally, so as to ensure the vehicle driving function under fault conditions. BRIEF DESCRIPTION OF THE DRAWINGS
[0037] Attached Figure 1 The figure is a flow chart of the three-phase current bias self-learning method of the present invention. DETAILED DESCRIPTION
[0038] In order to make the purpose, technical scheme and advantages of the embodiments of the present invention clearer, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.
[0039] As attached Figure 1 As shown, in this specific embodiment, a three-phase current bias self-learning method is provided, comprising the following steps:
[0040] Step 1) reading the operating condition signal of the motor controller;
[0041] Step 2) judging whether the first entry condition of bias self-learning is met according to the operating condition signal of the motor controller, if so, executing step 3), if not, executing step 5);
[0042] Step 3) executing the first scheme of three-phase current offset value self-learning;
[0043] Step 4) determining whether the self-learning result of the current bias value is within the fault threshold, if so, executing step 9), if not, executing step 5);
[0044] Step 5) judging whether the second access condition of current bias self-learning is met according to the operating condition signal of the motor controller, if so, executing step 6), if not, executing step 8);
[0045] Step 6) executing a second scheme for self-learning of three-phase current offset values;
[0046] Step 7) determining whether the self-learning result of the current bias value is within the fault threshold, if so, executing step 9), if not, executing step 8) after the output current bias fault occurs;
[0047] Step 8) Use the default current bias data to perform current calculation, and then return to step 1);
[0048] Step 9) bias self-learning is successful, the bias value after self-learning is used to calculate the current, and then step 10) is executed. After the current bias self-learning result is judged to be successful, the current current bias self-learning state will be changed to a successful state, and then the bias self-learning process will be exited and will not be re-entered;
[0049] Step 10) ends.
[0050] In this specific embodiment, the operating condition signal of the motor controller read in step 1) includes: the actual motor speed signal, the motor controller power-on operation time cycle signal and the bias self-learning state signal.
[0051] Wherein: the motor controller power-on operation time cycle signal is: the motor controller program operation time cycle signal obtained by monitoring the power supply enable signal of the current motor controller as the starting point and counting the program operation cycle;
[0052] The bias self-learning state will change to a successful state only after the bias self-learning is triggered and succeeds. After the bias self-learning state becomes successful, step 1) and the subsequent current bias self-learning process will not be entered. If the motor controller is in the power-on initialization stage and the bias self-learning process has not been triggered, the bias self-learning state will not be displayed as a successful state. Specifically, the bias self-learning state is: read the current current bias self-learning state, and only when the current bias self-learning state is a failure can the subsequent bias self-learning stage be entered, and the current bias self-learning state is a failure. The situation includes: the motor controller is in the power-on initialization stage and the bias self-learning process has not been triggered, the result of the bias self-learning is not within the fault threshold, and the first and second access conditions of the bias self-learning are not met.
[0053] In this specific embodiment, the first access condition in step 2) includes: setting the minimum start time t of bias self-learning under the first access condition min and the maximum start time t max , the current motor controller operation time cycle signal is t i , and t min <t i <t max , so that the current moment is after the current sensor is powered on and initialized and before the vehicle torque is output; set the motor speed threshold r max , the actual speed of the current motor is r i , and r i <r max , so that the current motor and the vehicle are in a stationary state and have no current output, so as to eliminate the influence of the complex operating environment on the current bias self-learning accuracy; and the current bias self-learning state is not a successful state.
[0054] In this specific embodiment, the calculation method of the first scheme in step 3) is: filtering is performed by taking the average value after multi-cycle cyclic accumulation, and abnormal sampling points with a sampling value of 0 are eliminated in the middle to improve the accuracy and stability of the current bias self-learning of the first scheme.
[0055] Specifically, the sum of the current sensor signal values ∑I is continuously collected within a fixed time period n1 Divided by the number of sampling cycles n1, the calculation method of the three-phase current bias self-learning of the first scheme is obtained as follows:
[0056]
[0057] Where: I n1 is the sampling value of each operation cycle during the bias self-learning process of the first scheme;
[0058] n1 is the number of sampling cycles accumulated during the bias self-learning process of the first scheme.
[0059] In this specific embodiment, the second access condition in step 5) is another biased self-learning scheme that does not meet the first access condition of biased self-learning. The working condition implemented is mainly the working condition where the whole vehicle is driven and then stopped, or the biased self-learning result performed according to the first scheme is judged to be a failure. The access conditions of this scheme include:
[0060] The actual speed of the current motor is r i , and r i <r max .
[0061] The current running time cycle signal of the motor controller is t i , and t i >tmax .
[0062] The actual control mode of the current motor is high-voltage standby mode or torque control mode, but the current motor control output torque is 0N.m; in this case, it is necessary to satisfy that the current motor controller can be in the upper high voltage working condition or in the torque control working condition, but the motor controller currently has no torque output, that is, the three-phase current value output under the current working condition should be 0A.
[0063] The difference between the maximum and minimum values of the current three-phase current sensor sampling setting cycle is less than the set change threshold I StpMax ; That is, the fluctuation value of the current three-phase current sensor sampling is the difference between the maximum and minimum values of the current sensor sampling value within the program running set cycle, and the size of the difference must be less than the set change threshold I StpMax , which indicates that the current three-phase current is in a stable state.
[0064] In this specific embodiment, the calculation method of the second solution in step 6) is: accumulating and collecting the current sensor signal within a set n2 time period, and then filtering by taking the average value;
[0065] The calculation method of the three-phase current bias self-learning of the second scheme is:
[0066]
[0067] Where: I n2 is the sampling value of each operation cycle during the bias self-learning process of the second scheme;
[0068] n2 is the number of sampling periods accumulated during the bias self-learning process of the second solution. The n2 time periods may be continuous sampling periods or discontinuous sampling periods.
[0069] In this specific embodiment, when determining whether the self-learning result of the current bias value is within the fault threshold in step 4) and step 7), the set fault threshold is obtained according to the accuracy range of the three-phase current sensor and the statistical actual sensor sampling value. The value obtained by bias self-learning must be greater than the minimum allowable range threshold I ofsetMin And less than the maximum allowable range threshold I ofsetMax If the self-learning result of the current bias value is within the fault threshold, it means that the bias self-learning is successful. In the state of successful bias self-learning, the bias self-learning will no longer be entered. The bias value after learning will be directly used for subsequent three-phase current calculations.
[0070] In this specific embodiment, the default current bias value is used in step 8). When the self-learning result of the detected current bias value is outside the fault threshold or does not meet the first and second entry conditions of the current bias self-learning, the default three-phase current bias value will be used for current calculation. However, the bias self-learning status will not be displayed as a successful status at this time, and the process can be returned to step 1) to enter the bias self-learning process again until the current bias learning is successful.
[0071] Compared with the prior art, the present invention uses two current bias self-learning schemes to cover current bias self-learning under different working conditions, wherein the first scheme of current bias value self-learning is for power-on working conditions, and the second scheme of current bias value self-learning covers other working conditions after the whole vehicle is running, and when the working conditions cannot meet the first and second schemes of current bias self-learning, there are alternative default bias values to meet the normal calculation function of three-phase current. As a result, the present invention not only performs current bias self-learning in the power-on initialization stage of the motor controller, but also can perform current bias self-learning when the motor controller is running normally, and there is a current bias value that can be used when the current bias self-learning fails or fails to be triggered normally, so as to ensure the vehicle driving function under fault conditions.
[0072] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit the technical solution. Those skilled in the art should understand that those modifications or equivalent substitutions of the technical solution of the present invention that do not depart from the purpose and scope of the technical solution should be included in the scope of the claims of the present invention.
Claims
1. A three-phase current bias self-learning method, characterized in that: The following steps are involved: Step 1) reading the operating condition signal of the motor controller; Step 2) judging whether the first access condition of bias self-learning is met according to the operating condition signal of the motor controller, if so, executing step 3), if not, executing step 5); Step 3) executing the first scheme of three-phase current offset value self-learning; Step 4) determining whether the self-learning result of the current bias value is within the fault threshold, if so, executing step 9), if not, executing step 5); Step 5) judging whether the second access condition of current bias self-learning is met according to the operating condition signal of the motor controller, if so, executing step 6), if not, executing step 8); Step 6) executing the second scheme of three-phase current offset value self-learning; Step 7) determining whether the self-learning result of the current bias value is within the fault threshold, if so, executing step 9), if not, executing step 8) after the output current bias fault; Step 8) Use the default current bias data to perform current calculation, and then return to step 1); Step 9) The bias self-learning is successful, and the bias value after self-learning is used to calculate the current, and then step 10) is executed; Step 10) ends.
2. The three-phase current bias self-learning method according to claim 1, characterized in that: The operating condition signals of the motor controller read in step 1) include: the actual motor speed signal, the motor controller power-on operation time cycle signal and the bias self-learning state signal.
3. The three-phase current bias self-learning method according to claim 2, characterized in that: The motor controller power-on operation time cycle signal is: the motor controller program operation time cycle signal obtained by monitoring the power supply enable signal of the current motor controller as the starting point and counting the program operation cycle; The bias self-learning status is: read the current current bias self-learning status, and enter the subsequent bias self-learning stage only when the current bias self-learning status is failed, and the current bias self-learning status is failed. The situations where the motor controller is in the power-on initialization stage and the bias self-learning process has not been triggered, the result of the bias self-learning is not within the fault threshold, and the first entry condition and the second entry condition of the bias self-learning are not met.
4. The three-phase current bias self-learning method according to claim 1, characterized in that: The first access condition in step 2) includes: setting the minimum start time t of bias self-learning under the first access condition min and the maximum start time t max , the current motor controller operation time cycle signal is t i , and t min <t i <t max , so that the current moment is after the current sensor is powered on and initialized and before the vehicle torque is output.
5. The three-phase current bias self-learning method according to claim 4, characterized in that: The first entry condition in step 2) also includes: setting the motor speed threshold r max , the actual speed of the current motor is r i , and r i <r max , so that the current motor is in a stationary state and has no current output.
6. The three-phase current bias self-learning method according to claim 1, characterized in that: The calculation method of the first scheme in step 3) is: filtering is performed by taking the average value after multi-cycle accumulation, and abnormal sampling points with a sampling value of 0 are removed in the middle to improve the accuracy and stability of the current bias self-learning of the first scheme; The calculation method of the three-phase current bias self-learning of the first scheme is: Where: I n1 is the sampling value of each operation cycle in the bias self-learning process of the first scheme; n1 is the number of sampling cycles accumulated during the bias self-learning process of the first scheme.
7. The three-phase current bias self-learning method according to claim 5, characterized in that: The second entry condition in step 5) includes: the actual speed of the current motor is r i , and r i <r max ; The current running time cycle signal of the motor controller is t i , and t i >t max ; The actual control mode of the current motor is high voltage standby mode or torque control mode, but the current motor control output torque is 0N.m; The difference between the maximum and minimum values of the current three-phase current sensor sampling setting cycle is less than the set change threshold I StpMax .
8. The three-phase current bias self-learning method according to claim 1, characterized in that: The calculation method of the second solution in step 6) is: accumulating and collecting the current sensor signal within a set n2 time period, and then filtering by taking the average value; The calculation method of the three-phase current bias self-learning of the second scheme is: Where: I n2 is the sampling value of each operation cycle during the bias self-learning process of the second scheme; n2 is the number of sampling cycles accumulated during the bias self-learning process of the second scheme.