Motor stalling detection method and computer equipment

Abstract

The invention discloses a motor stalling detection method, which comprises the steps of S1, acquiring a current waveform of a motor in a known stable operation state, and calculating a standard deviation sigma 1; s2, calculating the standard deviation sigma1'of t and n moments before t, and judging whether the standard deviation sigma1 'is abnormal or not; s3, acquiring a current waveform of the motor in a locked-rotor state, and calculating a current slope mean value K1; s4, on the basis of the abnormal state of the motor in the S2, obtaining a current slope k at the moment t and a current slope mean value K1'at the moments t and p moments after t; and S5, locked-rotor or stop is judged. In the invention, whether the running state of the motor is abnormal or not is monitored in real time by using the smoothness of the current waveform, and the change trend of the current slope is combined to further judge whether the motor is abnormal to be a locked-rotor state or a stopped state, so that the real-time performance and accuracy of motor locked-rotor state identification are improved, and the monitoring capability of the running state of the motor is enhanced; and the sensitivity to external environment change is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of BMS for new energy vehicles, and in particular to a motor stall detection method and computer equipment. Background Art

[0002] There are many reasons why new energy vehicle motors stall, whether it is because the rotor and stator are stuck in contact, because of bearing damage, or because the equipment load is too heavy and the motor cannot be driven, etc., all of which can cause the motor to stall.

[0003] At present, motor stall detection technology mainly relies on monitoring parameters such as current, vibration or temperature to determine the running status of the motor. However, the existing technology has certain limitations in determining the stall status, especially it is not sensitive enough to the change of current waveform and is easily affected by external environmental interference, resulting in insufficient accuracy and real-time performance. Summary of the invention

[0004] The present invention provides a motor stall detection method and computer equipment with more accurate and real-time judgment, which can solve at least one of the above technical problems.

[0005] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0006] A motor stall detection method comprises the following steps:

[0007] S1, obtaining the current waveform of the motor in a known stable running state, obtaining the continuous current value in any time period from the current waveform, calculating the smoothness of the current waveform in the time period, that is, the standard deviation σ1, and setting it as the threshold D1;

[0008] S2, calculate the standard deviation σ1' of n times before and before t, if the standard deviation σ1'<threshold D1, it is judged that the motor is in a stable state at time t, if the standard deviation σ1'>threshold D1, it is judged that the motor is in an abnormal state at time t;

[0009] S3, obtaining a current waveform of the motor in a known stalled state, obtaining a continuous current value in any time period from the current waveform, calculating a current slope mean K1 in the time period, and setting it as a threshold D2;

[0010] S4, based on the abnormal state of the motor in S2, obtaining the current slope k at time t, and the current slope average K1' at p times t and after t;

[0011] S5. If threshold D1>current slope average K1'≥threshold D2, it is determined that the motor is in a stalled state at time t; if threshold D1<current slope average K1'<threshold D2, it is determined that the motor is about to stop or has stopped at time t.

[0012] Furthermore, in S1 and S2, the calculation formula of the standard deviation is:

[0013] σ=sqrt[Σ(x-μ)2 / N]

[0014] Among them, x represents the value of each data point, μ represents the average of all data points, N represents the number of data points, Σ represents summation, and sqrt represents square root.

[0015] Furthermore, in S3 and S4, the calculation formula of the current slope mean K is:

[0016] K=(k1+k2+...+kn) / n

[0017] Wherein, k1, k2, ..., kp represent the current slope at each moment, and n represents the number of time points. Further, in S4, the calculation formula of the current slope k is:

[0018] k=(A-A1) / (t-t1)

[0019] Wherein, t represents the current moment, t1 represents the moment before t, A represents the sampled current value at t, and A1 represents the sampled current value at t1.

[0020] Furthermore, the values ​​of the threshold D1 in S1 and the threshold D2 in S3 are both adjustable.

[0021] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the motor stall detection method.

[0022] The beneficial effects of the present invention are embodied in:

[0023] In the present invention, the smoothness of the current waveform, that is, the standard deviation, is used to monitor in real time whether the motor operating state is abnormal, and combined with the changing trend of the current slope to further judge whether the motor abnormality is a stalled state or a stopped state. This not only improves the real-time and accuracy of the motor stalled state identification, but also enhances the monitoring capability of the motor operating state, while reducing the sensitivity to external environmental changes, reducing the cost and complexity of monitoring, and helping to take corresponding measures in time to prevent motor failure or damage. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The drawings described herein are used to provide further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute improper limitations on the present application.

[0025] Figure 1 It is a flowchart of the overall steps of an embodiment of the present invention.

[0026] Figure 2 Schematic diagram of a circuit according to an embodiment of the present invention.

[0027] Figure 3 It is a schematic diagram of the structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. In the absence of conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. It should be noted that the meaning of "and / or" appearing in the full text includes three parallel schemes. Taking "A and / or B" as an example, it includes scheme A, scheme B, or schemes that A and B meet at the same time. In addition, it can be understood by ordinary technicians in this field that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. In addition, the type of "motor" in the present application is not limited.

[0029] See also Figure 1 The embodiment of the present invention provides a motor stall detection method, comprising the following steps:

[0030] S1, obtaining the current waveform of the motor in a known stable running state, obtaining the continuous current value in any time period from the current waveform, calculating the smoothness of the current waveform in the time period, that is, the standard deviation σ1, and setting it as the threshold D1;

[0031] S2, calculate the standard deviation σ1' of n times before and before t, if the standard deviation σ1'<threshold D1, it is judged that the motor is in a stable state at time t, if the standard deviation σ1'>threshold D1, it is judged that the motor is in an abnormal state at time t;

[0032] S3, obtaining a current waveform of the motor in a known stalled state, obtaining a continuous current value in any time period from the current waveform, calculating a current slope mean K1 in the time period, and setting it as a threshold D2;

[0033] S4, based on the abnormal state of the motor in S2, obtaining the current slope k at time t, and the current slope average K1' at p times t and after t;

[0034] S5. If threshold D1>current slope average K1'≥threshold D2, it is determined that the motor is in a stalled state at time t; if threshold D1<current slope average K1'<threshold D2, it is determined that the motor is about to stop or has stopped at time t.

[0035] In the present invention, the smoothness of the current waveform is used to monitor in real time whether the motor operating state is abnormal, and the change trend of the current slope is combined to further determine whether the motor abnormality is a stalled state or a stopped state, thereby reducing the sensitivity to changes in the external environment and improving the real-time and accuracy of identifying the motor stalled state.

[0036] In this embodiment, in S1 and S2, the calculation formula of the standard deviation is:

[0037] σ=sqrt[Σ(x-μ)2 / N]

[0038] Among them, x represents the value of each data point, that is, the current value corresponding to each time point, μ represents the average value of all data points, that is, the average value of all current values, N represents the number of data points, that is, the number of time points in the monitored time period, Σ represents summation, and sqrt represents square root.

[0039] With this design, by calculating the standard deviation of the current waveform when the motor is running at the current moment and in the previous moment, and comparing it with the standard deviation of the current waveform in a known stable operating state, it is possible to determine whether the current motor operating state is normal or abnormal.

[0040] In this embodiment, in S3 and S4, the calculation formula of the current slope mean K is:

[0041] K=(k1+k2+...+kn) / n

[0042] Wherein, k1, k2, ..., kp represent the current slope at each moment, and n represents the number of time points.

[0043] With this design, by calculating the average slope of the current waveform when the motor is running at the current moment and in the next period of time, and comparing it with the average slope of the current waveform in a known stalled state, the abnormal state can be further identified as stalled, stopped or other based on the abnormal state that has been determined.

[0044] In this embodiment, in S4, the calculation formula of the current slope k is:

[0045] k=(A-A1) / (t-t1)

[0046] Wherein, t represents the current moment, t1 represents the moment before t, A represents the sampled current value at t, and A1 represents the sampled current value at t1.

[0047] It should be noted that the current waveform and current value in the present application are directly measured by sensors or data acquisition equipment in the prior art.

[0048] In this embodiment, the values ​​of the threshold D1 in S1 and the threshold D2 in S3 are both adjustable. In this design, since different motors have different currents when running under different conditions, the same motor is selected, and a period of time is intercepted in its stable running state and stalled state, and the corresponding threshold D1 and threshold D2 are calculated. The stalled state can be achieved by continuously increasing the motor load or other forms.

[0049] See also Figure 2 The present invention provides a circuit design diagram of a motor stall detection method, wherein the stall current monitoring point is CS. When INA is pulled high and INB is pulled low, the motor rotates forward, which corresponds to a normal operating state. Conversely, when INB is pulled high and INA is pulled low, the motor rotates reversely, which corresponds to an abnormal operating state.

[0050] See also Figure 3 The embodiment of the present invention further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the motor stall detection method. The steps of the motor stall detection method here may be the steps of the motor stall detection method in the above-mentioned embodiments.

[0051] In summary, in the present invention, the smoothness of the current waveform, that is, the standard deviation, is used to monitor in real time whether the motor operating state is abnormal or not, and the changing trend of the current slope is combined to further judge whether the motor abnormality is a stalled state or a stopped state. This not only improves the real-time and accuracy of the motor stall state identification, but also enhances the monitoring capability of the motor operating state, while reducing the sensitivity to changes in the external environment, reducing the cost and complexity of monitoring, and helping to take corresponding measures in time to prevent motor failure or damage.

[0052] It should be understood that the examples and implementation modes described herein are for illustrative purposes only and are not intended to limit the present invention. Those skilled in the art may make various modifications or changes based on the examples and implementation modes. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A motor stall detection method, characterized in that: The following steps are involved: S1, obtaining the current waveform of the motor in a known stable running state, obtaining the continuous current value in any time period from the current waveform, calculating the smoothness of the current waveform in the time period, that is, the standard deviation σ1, and setting it as the threshold D1; S2, calculate the standard deviation σ1' of n times before and before t, if the standard deviation σ1'<threshold D1, it is judged that the motor is in a stable state at time t, if the standard deviation σ1'>threshold D1, it is judged that the motor is in an abnormal state at time t; S3, obtaining a current waveform of the motor in a known stalled state, obtaining a continuous current value in any time period from the current waveform, calculating a current slope mean K1 in the time period, and setting it as a threshold D2; S4, based on the abnormal state of the motor in S2, obtaining the current slope k at time t, and the current slope average K1' at p times t and after t; S5. If threshold D1>current slope average K1'≥threshold D2, it is determined that the motor is in a stalled state at time t; if threshold D1<current slope average K1'<threshold D2, it is determined that the motor is about to stop or has stopped at time t.

2. The motor stall detection method according to claim 1, characterized in that: In the S1 and S2, the calculation formula for the standard deviation is: σ=sqrt[Σ(x-μ)2 / N] Among them, x represents the value of each data point, μ represents the average of all data points, N represents the number of data points, Σ represents summation, and sqrt represents square root.

3. The motor stall detection method according to claim 1, characterized in that: In S3 and S4, the calculation formula of the current slope mean K is: K=(k1+k2+...+kn) / n Wherein, k1, k2, ..., kp represent the current slope at each moment, and n represents the number of time points.

4. The motor stall detection method according to claim 1, characterized in that: In S4, the calculation formula of the current slope k is: k=(A-A1) / (t-t1) Wherein, t represents the current moment, t1 represents the moment before t, A represents the sampled current value at t, and A1 represents the sampled current value at t1.

5. The motor stall detection method according to claim 1, characterized in that: The values ​​of the threshold value D1 in S1 and the threshold value D2 in S3 are both adjustable.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method according to any one of claims 1 to 5.

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