A functional hybrid control method for an electric drive motor of a special vehicle

Abstract

The application provides a function type hybrid control method of an electric special vehicle driving motor, which combines a fuzzy PID algorithm and a conventional PID control algorithm through a switching function to obtain the comprehensive advantages of the two control algorithms. Meanwhile, in order to reduce the calculation burden and execution time of the controller and facilitate engineering implementation, a fuzzy proportional algorithm with the transient performance of the fuzzy PID algorithm is adopted instead. According to the operating conditions of the special vehicle, the operating state of the motor is segmented for research, and a set of rules or a separate fuzzy algorithm is used to determine the weight of the outputs of the two control algorithms. However, an additional fuzzy calculation is required, which increases the calculation time and gain constant adjustment, reduces the switching frequency of the control system, causes higher torque fluctuation, and further designs a switching function to calculate the output weight control of the two algorithm outputs, which not only reduces the torque fluctuation caused by redundant calculation, but also ensures that the vehicle driving motor works in the optimal control state.

Description

Technical Field

[0001] This invention relates to the field of motor control technology, and specifically to a functional hybrid control method for electric special vehicle drive motors. Background Technology

[0002] Permanent magnet synchronous motors (PMSMs) and their control systems are widely used in the electric vehicle field due to their high efficiency, high control precision, high torque density, good torque stability, and low vibration and noise. Electric vehicles equipped with PMSMs operate in complex environments, requiring frequent starts and significant acceleration and deceleration, while also considering driving range. This necessitates a control system with high efficiency and strong adaptability. Applying high-performance control strategies to the motor control system allows the motor's various potential capabilities to be fully utilized, making its performance more suitable for the application requirements. Electric special vehicles, as a special type of electric vehicle, face new demands regarding the stability, efficiency, and driving range due to the unique characteristics of their operation and work processes.

[0003] Traditional motor control technology has significant drawbacks in terms of energy consumption, performance, and controllability, which is not conducive to improving the range, driving safety, operational safety, and comfort of electric special vehicles. Optimizing motors and their control technologies is also key to improving the operating performance of electric vehicles and promoting the development of the electric vehicle industry. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this patent considers the superior performance of intelligent algorithms under transient conditions and the superior performance of PID controllers under steady-state conditions. Combining fuzzy PID algorithms and conventional PID control algorithms through a switching function yields the combined advantages of both. To reduce the computational burden and execution time of the controller and facilitate engineering implementation, this patent proposes using a fuzzy proportional algorithm with the transient performance of fuzzy PID algorithms instead of the fuzzy method. Furthermore, considering that using a set of rules or a single fuzzy algorithm to determine the output weights of the two control algorithms requires additional fuzzy computation, more computation time, and more gain constant adjustments. The increased computational load reduces the switching frequency of the control system, leading to higher torque fluctuations. Therefore, this patent further proposes a switching function-based hybrid control strategy, designing a switching function to calculate the output weights and control the proportion of the two outputs.

[0005] This invention proposes a functional hybrid control method for the drive motor of an electric special vehicle, comprising the following steps:

[0006] Step 1: The vehicle starts, the motor controller is powered on, and the motor controller reads the throttle signal through the capture unit and converts it into a given speed n through calculation. * (k); At the same time, the motor controller collects the motor speed n(k) transmitted back through the rotary transformer;

[0007] Step 2: Calculate the speed deviation e using a fuzzy proportional controller. n (k)=n * (k)-n(k), Change in velocity deviation ec n (k)=e n (k)-e n (k-1); where n * (k) represents the given rotational speed at time k, e n (k) represents the velocity difference at time k, e n (k-1) represents the velocity difference at time (k-1);

[0008] The current regulation output at time k is calculated using formula (1).

[0009]

[0010] Among them, K e This is the quantization factor for the fuzzy proportional controller;

[0011] Step 3: The PID controller determines the speed deviation e from step 2. n (k) and velocity deviation change ec n (k) The current regulation output is calculated using formula (2).

[0012]

[0013] Where, k p k i k d These are the control parameters for the PID controller; e represents the current value output by the PID controller at time (k-1); n (k), 2e n (k-1), e n (k-2) represent the speed deviations output at times k, (k-1), and (k-2), respectively.

[0014] Step 4: According to e in Step 2 n (k) Calculate the current regulation output using the switching functions of formulas (3) and (4). and The output weight is used to calculate the final current regulation output iq according to formula (5). * (k);

[0015]

[0016] f2(x)=1-f1(x) (4)

[0017]

[0018] Where x takes the value of velocity deviation e n (k), f1(x) is The weights, f2(x) are The weights are: a and b are constants, selected based on practical experience.

[0019] Step 5: Acquire the three-phase motor currents ia, ib, and ic fed back from the current Hall sensor via an analog-to-digital (A / D) converter interrupt. After digital filtering, first obtain the vector stationary coordinate current i through Park transformation. α i β Then, the quadrature-axis current iq and the direct-axis current id are obtained through Clarke transformation;

[0020] Step 6: Given the quadrature-axis current iq * The quadrature axis current deviation e is obtained by comparing it with the feedback quadrature axis current iq. iq (k)=iq * (k)-iq(k) and the change in cross-axis current deviation ec iq (k)=e iq (k)-e iq (k-1), e iq (k-1) represents the quadrature-axis current deviation at time k-1; the quadrature-axis voltage regulation output u at time k is calculated using equation (6). q (k);

[0021]

[0022] In the formula, u q (k) represents the quadrature-axis voltage and u output at time k. q (k-1) represents the quadrature-axis voltage of the output at time k-1, e iq (k), e iq (k-1), e iq (k-2) represent the speed deviations output at times k, (k-1), and (k-2), respectively.

[0023] Control the direct axis given current id * =0; Direct axis given current id * The direct-axis current deviation e is obtained by comparing it with the feedback direct-axis current id. id (k)=id * (k)-id(k) and the change in direct-axis current deviation ec id (k)=e id (k)-e id(k-1); The direct-axis voltage regulation output u at time k is calculated using equation (7). d (k);

[0024] u d (k)=u d (k-1)+k p ec id (k)+k i e id (k)+k d [e id (k)-2e id (k-1)+e id (k-2)] (7)

[0025] In the formula, u d (k) represents the direct-axis voltage at time k, u d (k-1) represents the direct-axis voltage at time k-1, e id (k), e id (k-1), e id (k-2) represents the current deviation at times k, (k-1), and (k-2), respectively.

[0026] Step 7: Put u q (k), u d (k) After Clarke inverse transform, the αβ axis voltage u is obtained. α u β Then, u is calculated using the inverse Park transform. a u b u c Finally, the controller determines the three-phase voltage u based on the three-phase voltage u. a u b u c The values ​​of CCR1, CCR2, and CCR3 in the TIM1 register are adjusted to control the SVPWM module to output six PWM wave control signals, drive the inverter to work, and output variable amplitude and frequency to the three-phase stator windings of the motor to achieve speed control.

[0027] The beneficial effects of this invention are:

[0028] 1) High-performance control strategies applied to motor control systems can fully utilize the various potential capabilities of the motor, making its performance more in line with usage requirements. Given the increased high-performance demands of users for special vehicles and the complex operating conditions of their drive motors, a single control strategy is insufficient for effective control. This invention proposes an improved functional hybrid control method. Based on the operating conditions of special vehicles, the motor's operating state is studied in segments, and two control algorithms suitable for each stage are integrated through a switching function to maximize the motor's high-performance output.

[0029] 2) The key to the functional hybrid control method lies in the establishment of the switching function. The switching function needs to allocate the output timing or weights of each control algorithm, so its accuracy determines the control performance of the control system. This invention establishes the switching function through calculation and analysis of each control algorithm. This reduces torque fluctuations caused by redundant calculations and accurately determines the output proportion of each algorithm controller. Attached Figure Description

[0030] Figure 1 This is a block diagram of the functional hybrid controller for the drive motor of the electric special vehicle in this invention;

[0031] Figure 2 This is a schematic diagram of the speed regulation system for vector control of electric vehicle drive motor in this invention.

[0032] Figure 3 This is a graph showing the relationship between output weights and velocity deviation in this invention.

[0033] Figure 4 The flowchart shows the functional hybrid control method for the drive motor of electric special vehicles in this invention, which is based on a DSP-based controller software implementation. Detailed Implementation

[0034] The invention will be further explained below with reference to the accompanying drawings and specific implementation examples.

[0035] When electric special vehicles are in operation, their drive motors frequently operate in various states, including starting, accelerating, maintaining speed, and decelerating, accompanied by various disturbances. A single control algorithm is insufficient to meet the optimal control requirements of each operating state. To ensure smooth operation of electric special vehicles in various working states such as starting, lifting and transporting, lifting operations, and unloaded operation, avoiding vibrations, and enhancing work efficiency, it is necessary to consider the motor's output characteristics under each working state and design a speed control algorithm suitable for each stage to maximize the motor's optimal output performance. Considering the superior performance of intelligent algorithms under transient conditions and PID controllers under steady-state conditions, combining fuzzy PID algorithms and conventional PID control algorithms through a switching function can achieve the combined advantages of both algorithms. Simultaneously, to reduce the controller's computational burden and execution time, and to facilitate engineering implementation, and based on the output of the fuzzy speed controller, which initially approaches the maximum permissible output value and decreases as the speed error decreases, a fuzzy proportional controller is designed to replace the fuzzy PID controller to further reduce the computational load of the fuzzy algorithm. The fuzzy proportional controller is a proportional control controller whose gain adjustment is achieved under the constraint of a limiter. The output of the fuzzy proportional controller is equivalent to the output of the fuzzy controller at the beginning of the transient.

[0036] Determining the weights of the outputs of two control algorithms using a set of rules or a single fuzzy algorithm requires additional fuzzy computation, resulting in more computation time and more gain constant adjustments. This increased computational load reduces the switching frequency of the control system, leading to higher torque ripple. Therefore, a switching function is designed to calculate the output weights and control the proportion of the two outputs. The control principle block diagram is shown below. Figure 1 As shown.

[0037] The permanent magnet synchronous motor control of electric special vehicles adopts space vector control. The principle of its speed regulation system is as follows: Figure 2 As shown, the speed control system consists of the following five parts: a speed loop (functional hybrid controller), a two-current loop controller (current PID controller); a coordinate transformation module; a space vector pulse width modulation module; an inverter module; and a position and speed detection module.

[0038] Control process: Given a speed signal, the difference between it and the detected speed is calculated. This difference is then adjusted by a fuzzy proportional controller and a conventional PID controller before being output. Simultaneously, a switching function determines the output weights of the two controllers based on the slip, calculates the quadrature-axis current component, and uses it as the given signal i for the q-axis current PID controller. qref Simultaneously, after coordinate transformation, the stator feedback current changes from i abc Become i d i q Given the quadrature-axis current i qref With the transformed quadrature-axis current i q The difference is calculated, and the quadrature-axis reference voltage u is output after the machine is regulated by a current PID controller. qref Control the direct-axis given current i dref =0, and the i obtained by transformation d The difference is calculated, and after current PID regulation, the direct-axis reference voltage u is output. dref ; will u dref u qref After a 2r / s transformation, the αβ axis voltage is obtained, and then u is calculated using the inverse Park transform. a u b u c Finally, the SVPWM module outputs six PWM wave control signals to drive the inverter, providing variable amplitude and frequency to the three-phase stator windings of the motor.

[0039] The design concept of this invention is as follows: To ensure the smooth operation and optimal output performance at each stage of operation of electric special vehicles, control algorithms suitable for each stage must be designed. When the drive motor is running at a steady speed, conventional PID control exhibits superior performance due to its low computational load and simple control structure. When the drive motor experiences significant speed changes or disturbances, fuzzy proportional control algorithms can adjust control parameters according to the changing environment, resulting in superior control performance. Combining fuzzy proportional control and conventional PID control algorithms yields the combined advantages of both. Using a set of rules or a single fuzzy algorithm to determine the output weights of the two control algorithms requires additional fuzzy computation, resulting in more computation time and more gain constant adjustments. This increased computational load reduces the switching frequency of the control system, leading to higher torque fluctuations and reduced vehicle smoothness. To address this issue, a switching function is designed to calculate the output weights and control the output ratio of the two algorithm controllers. Ultimately, this achieves optimal control output performance of the drive motor in each state, ensuring the acceleration, smoothness, and efficiency of the electric vehicle.

[0040] like Figure 4 As shown, a functional hybrid control method for a drive motor of an electric special vehicle includes the following steps:

[0041] Step 1: The vehicle starts, the motor controller is powered on, and the motor controller reads the throttle signal through the capture unit and converts it into a given speed n through calculation. * (k); At the same time, the motor controller collects the motor speed n(k) transmitted back through the rotary transformer;

[0042] Step 2: Calculate the speed deviation e using a fuzzy proportional controller. n (k)=n * (k)-n(k), Change in velocity deviation ec n (k)=e n (k)-e n (k-1); where n * (k) represents the given rotational speed at time k, e n (k) represents the velocity difference at time k, e n (k-1) represents the velocity difference at time (k-1);

[0043] The current regulation output at time k is calculated using formula (1).

[0044]

[0045] Among them, K e This is the quantization factor for the fuzzy proportional controller;

[0046] Step 3: The PID controller determines the speed deviation e from step 2. n (k) and velocity deviation change ec n (k) The current regulation output is calculated using formula (2).

[0047]

[0048] Where, k p k i k d These are the control parameters for the PID controller; e represents the current value output by the PID controller at time (k-1); n (k), 2e n (k-1), e n (k-2) represent the speed deviations output at times k, (k-1), and (k-2), respectively.

[0049] Step 4: According to e in Step 2 n (k) Calculate the current regulation output using the switching functions of formulas (3) and (4). and The output weights and the curve showing the relationship between output weights and velocity deviation are shown below. Figure 3 As shown, the final current regulation output iq is calculated according to formula (5). * (k);

[0050]

[0051] f2(x)=1-f1(x) (4)

[0052]

[0053] Where x takes the value of velocity deviation e n (k), f1(x) is The weights, f2(x) are The weights are: a and b are constants, selected based on practical experience.

[0054] Step 5: Acquire the three-phase motor currents ia, ib, and ic fed back from the current Hall sensor via an analog-to-digital (A / D) converter interrupt. After digital filtering, first obtain the vector stationary coordinate current i through Park transformation. α i β Then, the quadrature-axis current iq and the direct-axis current id are obtained through Clarke transformation;

[0055] Step 6: Given the quadrature-axis current iq * The quadrature axis current deviation e is obtained by comparing it with the feedback quadrature axis current iq.iq (k)=iq * (k)-iq(k) and the change in cross-axis current deviation ec iq (k)=e iq (k)-e iq (k-1), e iq (k-1) represents the quadrature-axis current deviation at time k-1; the quadrature-axis voltage regulation output u at time k is calculated using equation (6). q (k);

[0056]

[0057] In the formula, u q (k) represents the quadrature-axis voltage and u output at time k. q (k-1) represents the quadrature-axis voltage of the output at time k-1, e iq (k), e iq (k-1), e iq (k-2) represent the speed deviations output at times k, (k-1), and (k-2), respectively.

[0058] Control the direct axis given current id * =0; Direct axis given current id * The direct-axis current deviation e is obtained by comparing it with the feedback direct-axis current id. id (k)=id * (k)-id(k) and the change in direct-axis current deviation ec id (k)=e id (k)-e id (k-1); The direct-axis voltage regulation output u at time k is calculated using equation (7). d (k);

[0059] u d (k)=u d (k-1)+k p ec id (k)+k i e id (k)+k d [e id (k)-2e id (k-1)+e id (k-2)] (7)

[0060] In the formula, u d (k) represents the direct-axis voltage at time k, u d (k-1) represents the direct-axis voltage at time k-1, e id (k), e id (k-1), e id(k-2) represents the current deviation at times k, (k-1), and (k-2), respectively.

[0061] Step 7: Put u q (k), u d (k) After Clarke inverse transform, the αβ axis voltage u is obtained. α u β Then, u is calculated using the inverse Park transform. a u b u c Finally, the controller determines the three-phase voltage u based on the three-phase voltage u. a u b u c The values ​​of CCR1, CCR2, and CCR3 in the TIM1 register are adjusted to control the SVPWM module to output six PWM wave control signals, drive the inverter to work, and output variable amplitude and frequency to the three-phase stator windings of the motor to achieve speed control.

Claims

1. A functional hybrid control method for a drive motor of an electric special vehicle, characterized in that, include: Step 1: The vehicle starts, the motor controller is powered on, and the motor controller reads the throttle signal through the capture unit and converts it into a given speed through calculation. Simultaneously, the motor controller collects the motor speed transmitted back via the rotary transformer. ; Step 2: Calculate the speed deviation using a fuzzy proportional controller. Change in speed deviation ;in, This represents the given rotational speed at time k. This represents the velocity deviation at time k. This represents the velocity deviation at time (k-1); The current regulation output at time k is calculated using formula (1). ; （1） Among them, K e This is the quantization factor for the fuzzy proportional controller; Step 3: The PID controller determines the speed deviation e from step 2. n (k) and the change in velocity deviation ec n (k) The current regulation output is calculated using equation (2). ; （2） Where, k p k i k d These are the control parameters for the PID controller; This represents the current value output by the PID controller at time (k-1). The velocity deviations output at times k, (k-1), and (k-2) are respectively. Step 4: Determine the current regulation output. and The weights are used to calculate the final current regulation output. ; （3） （4） Final current regulation output Represented as: （5） in The value is the speed deviation. , for The weight, for The weights; a and b are constants; Step 5: Acquire the three-phase motor currents ia, ib, and ic fed back from the current Hall sensor via current analog-to-digital interrupt. After digital filtering, first obtain the vector stationary coordinate current i through Park transformation. α i β Then, the quadrature-axis current iq and the direct-axis current id are obtained through Clarke transformation; Step 6: Given the quadrature-axis current iq * The quadrature axis current deviation e is obtained by comparing it with the quadrature axis current iq. iq (k)=iq * (k)-iq(k) and the change in cross-axis current deviation ec iq (k) =e iq (k)-e iq (k-1), e iq (k-1) represents the quadrature-axis current deviation at time k-1; the quadrature-axis voltage regulation output at time k is calculated. ; Control the direct axis given current id * =0; Direct axis given current id * The direct-axis current deviation e is obtained by comparing it with the direct-axis current id. id (k)=id * (k)-id(k) and the change in direct-axis current deviation ec id (k)=e id (k)-e id (k-1); The direct-axis voltage regulation output at time k is calculated. ; Step 7: [The text appears to be incomplete and contains several grammatical errors. A more accurate translation would require the full context.] , After Clarke inverse transform, we obtain Axis voltage u α u β Then, u is calculated using the inverse Park transform. a u b u c Finally, the motor controller determines the three-phase voltage u based on the three-phase voltage u. a u b u c The values ​​of CCR1, CCR2, and CCR3 in the TIM1 register are adjusted to control the SVPWM module to output six PWM wave control signals, drive the inverter to work, and output variable amplitude and frequency to the three-phase stator windings of the motor to achieve speed control.

2. The functional hybrid control method for a drive motor of an electric special vehicle according to claim 1, characterized in that, In step 6, the quadrature-axis voltage is adjusted to adjust the output. Represented as: u q k = u q k - 1 + k p e c i q k + k i e i q k + k d e i q k - 2 e i q k - 1 + e i q k - 2 （6） In the formula, u q (k) represents the quadrature-axis voltage regulation output at time k, u q (k-1) represents the quadrature-axis voltage regulation output at time k-1. These are the quadrature-axis current deviations output at times k, (k-1), and (k-2), respectively. The direct-axis voltage regulation output Represented as: （7） In the formula, u d (k) represents the direct-axis voltage regulation output at time k, u d (k-1) represents the direct-axis voltage regulation output at time k-1. These are the direct-axis current deviations output at times k, (k-1), and (k-2), respectively.

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