Self-adaptive voltage control method for wireless electric energy transmission system of electric vehicle

Abstract

The invention discloses a self-adaptive voltage control method for a wireless electric energy transmission system of an electric vehicle. Comprising the steps that a self-adaptive model prediction controller is constructed in combination with an RBF neural network, and the self-adaptive model prediction controller outputs a control quantity at the moment k and acts on a wireless electric energy transmission system of the electric vehicle; the adaptive model prediction controller performs adaptive adjustment of state deviation according to the k + 1 moment state quantity of the wireless electric energy transmission system of the electric vehicle and the fixed model reference system, outputs the next control quantity, acts on the wireless electric energy transmission system of the electric vehicle, continuously acquires the state quantity of the system and adjusts the control quantity; therefore, online self-adaptive control of the wireless electric energy transmission system of the electric vehicle is completed. According to the invention, accurate control of the dynamic wireless power transmission system of the electric vehicle is realized, the dynamic response performance and the anti-interference performance of the system are obviously improved, the model can be dynamically adjusted especially for various external interferences of the system, and the stability of the control system is enhanced.

Description

Technical Field

[0001] The invention relates to a charging voltage control method in the field of wireless power transmission of electric vehicles, and in particular to an adaptive voltage control method and device for a wireless power transmission system of an electric vehicle. Background Art

[0002] The core problem of electric vehicles lies in their power storage technology. The battery pack must meet multiple standards such as high energy density, low cost, long life, and high safety. Vehicles that achieve high endurance often need to be equipped with high-cost, heavy battery packs, which directly affects the cost and performance of the vehicle. The application of wireless power transmission technology in electric vehicles not only facilitates users' daily use, but also solves safety issues such as potential electric shock risks in traditional wired charging methods. The dynamic wireless power transmission system allows electric vehicles to charge while driving, which helps to improve the vehicle's endurance and reduce battery capacity to save costs. It solves the problem of long charging time in wired mode and static wireless mode, ensures the safety and convenience of power transmission, shows great potential, and is expected to become an important development direction in the field of electric vehicle charging in the future.

[0003] In the dynamic electric vehicle wireless power transmission system, when the car passes between two adjacent transmitting coils, the coupling coefficient of the transmitting and receiving coils will change, which will cause the output voltage to fluctuate. Therefore, a control algorithm is needed to stabilize the output voltage. Currently, various corresponding control methods exist, but these existing technologies still have the following problems:

[0004] First, current control methods usually include transmitter control and dual-end control. Transmitter control requires the installation of a controller at the road-end coil. Since there are a large number of transmitter coils in the dynamic charging system, this significantly increases the cost of installation, adjustment, and maintenance. Dual-end control relies on communication between the transmitter and receiver, and is susceptible to signal interference in the strong magnetic field environment generated by the energy transmission coil, which makes it difficult to ensure the stability of the control system.

[0005] Second, current control methods such as PI control and model predictive control only adjust the output voltage through state feedback, and do not fully consider the drastic changes in the coupling coefficient during dynamic wireless power transmission, which leads to limitations in the system's anti-interference performance.

[0006] Third, in the current model predictive control method, the modeling of the control object is fixed, but in actual applications, the system will be affected by a variety of internal and external factors, including changes in the coupling coefficient of the transceiver coil, small electronic components that are not considered during modeling, signal sampling interference, etc. These factors may lead to a mismatch between the modeling and the system, thereby reducing the control accuracy of the system. Summary of the invention

[0007] In order to solve the technical challenges faced by the wireless power transmission system of electric vehicles, the present invention aims to propose an adaptive voltage control method and system for the wireless power transmission system of electric vehicles. The method controls the receiving end of the energy transmission system, and uses the RBF neural network to supplement and improve the mathematical model of the control circuit, predict the disturbance term of the system, and realize the precise control of the dynamic wireless power transmission system of electric vehicles, which significantly improves the dynamic response performance and anti-interference performance of the system, and enhances the anti-interference ability of the coupling coefficient of the transceiver coil in dynamic scenarios, especially in the face of various external interferences of the system, the model can be dynamically adjusted, and the stability of the control system is enhanced.

[0008] To achieve the above objectives, the present invention proposes the following technical solutions:

[0009] 1. An adaptive voltage control method for wireless power transmission system of electric vehicle

[0010] An adaptive model predictive controller is constructed in combination with the RBF neural network. The adaptive model predictive controller outputs the control quantity at time k and acts on the electric vehicle wireless power transmission system. The adaptive model predictive controller adaptively adjusts the state deviation based on the state quantity at time k+1 of the electric vehicle wireless power transmission system and the fixed model reference system, then outputs the next control quantity and acts on the electric vehicle wireless power transmission system. The system state quantity is continuously collected and the control quantity is adjusted, thereby completing the online adaptive control of the electric vehicle wireless power transmission system.

[0011] The adaptive model predictive controller comprises a model predictive controller, a fixed model reference system and an RBF adaptive model optimizer. The fixed model reference system estimates the k+1 moment state quantity of the electric vehicle wireless power transmission system according to the k moment state quantity of the electric vehicle wireless power transmission system and the k moment control quantity output by the model predictive controller, and obtains the state deviation by subtracting the estimated k+1 moment state quantity from the actual k+1 moment state quantity. The RBF adaptive model optimizer performs single-step training according to the training samples consisting of the control quantity and the state deviation at the corresponding moment and outputs the estimated state deviation. The model predictive controller performs adaptive adjustment of the control quantity in combination with the fixed model reference system and the RBF adaptive model optimizer and outputs the next control quantity.

[0012] The RBF adaptive model optimizer trains the RBF neural network according to the state deviation calculated by the system sampling signal during the system operation, and updates the trained RBF neural network to the model predictive controller, thereby realizing adaptive model optimization and adaptive model predictive control.

[0013] During the training process, the gradient descent method is used to optimize the network parameters of the RBF adaptive model.

[0014] The optimization objective in the model predictive controller satisfies the following formula:

[0015]

[0016]

[0017] Where N represents the step size of the prediction interval and the control interval, U k represents the predictive control sequence within the control interval, f(U k ) represents the optimization objective in model predictive control, Q, P, and F represent the weight matrices of the three performance requirements: control deviation within the prediction interval, system energy output, and control deviation at the final moment. represents the square of the quadratic norm of the weighted matrix Q, represents the square of the quadratic norm of the weighted matrix P, represents the square of the quadratic norm of the weighted matrix F, e k+i represents the predicted value of the system control deviation at time k+i under the system state at time k, u k+i represents the predicted value of the system control signal at time k+i under the system state at time k, i = 0, ..., N-1, e k+N represents the predicted value of the system control deviation at time k+N under the system state at time k, x k+1 and x k are the system states at time k+1 and time k respectively, u k represents the system input at time k, y k represents the output at time k, u min and u max Indicates the minimum and maximum values ​​of the input, y ref represents the expected output, e k is the deviation at time k, RBF(x k ,u k ) is the compensation part of the RBF neural network in the model, A is the state matrix of the system state space expression, B is the input matrix, C is the output matrix, and D is the direct matrix. represents the matrix discretization calculation of the continuous state space system, T s is the system sampling time, is the discretized state matrix, is the discretized input matrix, T represents the transpose, i Ld Indicates the inductance L in the receiving end control circuit d The current, R L Represents the load resistance at the receiving end of the power transmission system, L d Represents the inductance of the receiving end control circuit, C d2represents the capacitance of the receiving end control circuit; μ represents the controller output signal; V in Represents the input voltage of the receiving end control circuit, V out Represents the system output voltage dropped across the load resistor.

[0018] 2. An adaptive voltage control device for wireless power transmission system of electric vehicle

[0019] A fixed model reference system is used to estimate the state quantity of the electric vehicle wireless power transmission system at time k+1 according to the state quantity of the electric vehicle wireless power transmission system at time k and the control quantity output by the model prediction controller at time k;

[0020] The RBF adaptive model optimizer is used to train the RBF neural network according to the state deviation calculated by the system sampling signal during the system operation. The trained RBF neural network is updated to the model predictive controller, thereby realizing adaptive model optimization and adaptive model predictive control.

[0021] The model predictive controller is used to combine the fixed model reference system and the RBF adaptive model optimizer to perform adaptive adjustment of the control quantity and output the next control quantity.

[0022] 3. A computer device

[0023] The device includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method for adaptive voltage control of a wireless power transmission system for an electric vehicle when executing the computer program.

[0024] 4. A computer-readable storage medium

[0025] The medium stores a computer program, and when the computer program is executed by the processor, the steps of the method for adaptive voltage control of a wireless power transmission system for an electric vehicle are implemented.

[0026] V. A computer program product

[0027] The product includes a computer program / instruction, which, when executed by a processor, implements the steps of the method for adaptive voltage control of a wireless power transmission system for an electric vehicle.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] First, the present invention adopts a receiving end control method and installs a controller at the receiving end of the electric vehicle, thereby avoiding the complex communication problems caused by double-end control. Compared with installing a controller at each transmitting end, it significantly reduces the installation and maintenance costs, and the calibration of the controller is more convenient.

[0030] Second, the present invention adopts a model predictive control method, which can predict the changing trend of the coupling coefficient of the transceiver coil while obtaining state feedback, thereby obtaining more system information and showing excellent anti-interference performance when the coupling coefficient changes drastically.

[0031] Third, the present invention introduces the RBF neural network, which predicts the system uncertainty information based on the phase difference between the reference system model and the real system, thereby dynamically updating the system modeling in the model predictive control during the system operation, so as to adaptively deal with the situation where the modeling does not match the system, effectively improve the control accuracy and anti-interference performance of the system, and provide technical support for the feasibility of the dynamic wireless power transmission system of electric vehicles, promote the development and application of wireless power transmission technology, and is expected to have a significant impact on the future direction of electric vehicle charging. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a circuit diagram of the dynamic wireless power transmission system for electric vehicles.

[0033] Figure 2 It is the structural diagram of the proposed adaptive model predictive control method.

[0034] Figure 3 This is the effect diagram of the output voltage dynamic response.

[0035] Figure 4 This is a diagram showing the output voltage response during the dynamic wireless charging process.

[0036] Figure 5 This is a diagram showing the effect of the input voltage change at the receiving end.

[0037] Figure 6 It is the effect diagram of RBF prediction uncertainty. DETAILED DESCRIPTION

[0038] The present invention is further described in detail below with reference to the accompanying drawings. The circuit diagram of the electric vehicle dynamic wireless power transmission system designed by the present invention is shown in FIG. Figure 1 The energy transmitting end is located on one side of the road, and the energy receiving end is located at the bottom of the electric vehicle. The transmitting and receiving coils of the two will be relatively misaligned as the vehicle travels. The framework diagram of the method proposed by the present invention is shown in Figure 2 As shown in the figure. This control method uses RBF neural network to improve the mathematical model of the control circuit and enhance the control performance. The goal of this method is to ensure that the output voltage of the electric vehicle can stably track the expected value during dynamic driving and maintain excellent performance when the coupling coefficient of the transceiver coil changes significantly.

[0039] The input-output relationship of the receiving-end control circuit of the electric vehicle dynamic wireless power transmission system can be described by the following differential equations:

[0040]

[0041] Among them, R L Represents the load resistance at the receiving end of the power transmission system, L d Represents the inductance of the receiving end control circuit, C d2 represents the capacitance of the receiving end control circuit; μ represents the controller output signal; V in Indicates the input voltage of the receiving end control circuit, and its value will change due to external interference. V out It represents the system output voltage dropped on the load resistance and the capacitance C d2 The voltage, i Ld Indicates the inductance L d of current.

[0042] The specific process of the control method is as follows:

[0043] An adaptive model predictive controller is constructed in combination with the RBF neural network. The adaptive model predictive controller outputs the control quantity at time k and acts on the electric vehicle wireless power transmission system. The adaptive model predictive controller adaptively adjusts the state deviation based on the state quantity at time k+1 of the electric vehicle wireless power transmission system and the fixed model reference system, then outputs the next control quantity and acts on the electric vehicle wireless power transmission system. The system state quantity is continuously collected and the control quantity is adjusted, thereby completing the online adaptive control of the electric vehicle wireless power transmission system.

[0044] The adaptive model predictive controller includes a model predictive controller, a fixed model reference system and an RBF adaptive model optimizer. The fixed model reference system estimates the k+1 moment state quantity of the electric vehicle wireless power transmission system according to the k moment state quantity of the electric vehicle wireless power transmission system and the k moment control quantity output by the model predictive controller, and obtains the state deviation by subtracting the estimated k+1 moment state quantity from the actual k+1 moment state quantity. The RBF adaptive model optimizer performs single-step training according to the training samples consisting of the control quantity and the state deviation at the corresponding moment and outputs the estimated state deviation. The model predictive controller combines the fixed model reference system and the RBF adaptive model optimizer to perform adaptive adjustment of the control quantity and output the next control quantity.

[0045] The RBF adaptive model optimizer trains the RBF neural network according to the state deviation calculated by the system sampling signal during the system operation. The trained RBF neural network is updated to the model predictive controller to improve the modeling of the wireless power transmission system control circuit. The single-step training ensures that each execution of the model predictive controller is the adaptation model at the current moment, thereby realizing adaptive model optimization and adaptive model predictive control. During the training process, the gradient descent method is used to optimize the network parameters of the RBF adaptive model.

[0046] The optimization objective in the model predictive controller satisfies the following formula:

[0047]

[0048] Where N represents the step size of the prediction interval and the control interval, U k represents the predictive control sequence within the control interval, f(U k ) represents the optimization objective in model predictive control. Q, P, and F are all constant matrices, which represent the weight matrices of the three performance requirements: control deviation within the prediction interval, system energy output, and control deviation at the final moment. represents the square of the quadratic norm of the weighted matrix Q, represents the square of the quadratic norm of the weighted matrix P, represents the square of the quadratic norm of the weighted matrix F, e k+i represents the predicted value of the system control deviation at time k+i under the system state at time k, u k+i represents the predicted value of the system control signal at time k+i under the system state at time k, i = 0, ..., N-1, e k+N represents the predicted value of the system control deviation at time k+N under the system state at time k, x k+1 and x k are the system states at time k+1 and time k respectively, u k represents the system input at time k, i.e., the control quantity, y k represents the output at time k, u min and u max Indicates the minimum and maximum values ​​of the input, y ref represents the expected output, e k is the deviation at time k, RBF(x k ,u k ) is the compensation part of the RBF network in the model, A is the state matrix of the system state space expression, which represents the influence of the state quantity at the previous moment on the state quantity at the next moment, B is the input matrix, which represents the influence of the input quantity on the change of the state quantity, C is the output matrix, which represents the influence of the system state quantity on the output quantity, and D is the through matrix, which represents the influence of the system input quantity on the output quantity. represents the matrix discretization calculation of the continuous state space system, T s is the system sampling time, is the discretized state matrix, is the discretized input matrix, T represents the transpose, i Ld Indicates the inductance L in the receiving end control circuit d The current, R L Represents the load resistance at the receiving end of the power transmission system, Ld Represents the inductance of the receiving end control circuit, C d2 represents the capacitance of the receiving end control circuit; μ represents the controller output signal; V in It represents the input voltage of the control circuit at the receiving end. Its value will change due to external interference, that is, it is a variable that changes with time. V out It represents the system output voltage dropped on the load resistance and the capacitance C d2 voltage.

[0049] The simulation results are shown in Figures 3 to 6 middle. Figure 3 The rising step tracking effect of the output voltage of the dynamic wireless power transmission system of electric vehicles is demonstrated. When the expected output voltage undergoes a rising step change, the adaptive model predictive control method proposed in the present invention can respond to this change quickly, and the overshoot and convergence time are significantly better than the traditional PID control method. Figure 4 The tracking effect of the system output voltage when the coupling coefficient changes drastically during the dynamic wireless charging process of electric vehicles is demonstrated. Compared with the conventional model predictive control method, the method proposed in the present invention uses RBF neural network to compensate for model uncertainties and can track the expected output more effectively. Figure 5 The change of the input voltage at the receiving end is presented. The system fluctuation caused by the misalignment of the transmitting and receiving coils is shown between 0.2 and 0.25 seconds of simulation time. Figure 6 The RBF network accurately predicts the uncertainty of system modeling in the dynamic power transmission system of electric vehicles, where Figures A and B represent the prediction of uncertain information in two state quantities. These simulation results strongly confirm the effectiveness and superior performance of the present invention, and further prove the application potential of the proposed adaptive model predictive control method in the dynamic wireless power transmission system of electric vehicles.

[0050] The present invention proposes an adaptive voltage control device for a wireless power transmission system of an electric vehicle, comprising:

[0051] A fixed model reference system unit is used to estimate the state quantity of the electric vehicle wireless power transmission system at time k+1 according to the state quantity of the electric vehicle wireless power transmission system at time k and the control quantity output by the model prediction controller at time k;

[0052] The RBF adaptive model optimizer is used to train the RBF neural network according to the state deviation calculated by the system sampling signal during the system operation. The trained RBF neural network is updated to the model predictive controller to improve the modeling of the wireless power transmission system control circuit. The single-step training ensures that each execution of the model predictive controller is the adaptation model at the current moment, thereby realizing adaptive model optimization and adaptive model predictive control.

[0053] The model predictive controller is used to combine the fixed model reference system unit and the RBF adaptive model optimizer to perform adaptive adjustment of the control quantity and output the next control quantity.

[0054] The present invention provides a computer device, comprising a memory and a processor. The memory stores a computer program, and the processor implements the steps of an adaptive voltage control method for a wireless power transmission system of an electric vehicle when executing the computer program.

[0055] The present invention provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of an adaptive voltage control method for a wireless power transmission system of an electric vehicle are implemented.

[0056] The present invention provides a computer program product, comprising a computer program / instruction, which implements the steps of an adaptive voltage control method for a wireless power transmission system of an electric vehicle when the computer program / instruction is executed by a processor.

[0057] The present invention is not limited to this embodiment, and any equivalent concepts or changes within the technical scope disclosed by the present invention are included in the protection scope of the present invention.

Claims

1. An adaptive voltage control method for an electric vehicle wireless power transmission system, characterized in that: The following steps are involved: An adaptive model predictive controller is constructed in combination with the RBF neural network. The adaptive model predictive controller outputs the control quantity at time k and acts on the electric vehicle wireless power transmission system. The adaptive model predictive controller adaptively adjusts the state deviation based on the state quantity at time k+1 of the electric vehicle wireless power transmission system and the fixed model reference system, then outputs the next control quantity and acts on the electric vehicle wireless power transmission system. The system state quantity is continuously collected and the control quantity is adjusted, thereby completing the online adaptive control of the electric vehicle wireless power transmission system.

2. The adaptive voltage control method of the wireless power transmission system of an electric vehicle according to claim 1, characterized in that: The adaptive model predictive controller comprises a model predictive controller, a fixed model reference system and an RBF adaptive model optimizer. The fixed model reference system estimates the k+1 moment state quantity of the electric vehicle wireless power transmission system according to the k moment state quantity of the electric vehicle wireless power transmission system and the k moment control quantity output by the model predictive controller, and obtains the state deviation by subtracting the estimated k+1 moment state quantity from the actual k+1 moment state quantity. The RBF adaptive model optimizer performs single-step training according to the training samples consisting of the control quantity and the state deviation at the corresponding moment and outputs the estimated state deviation. The model predictive controller performs adaptive adjustment of the control quantity in combination with the fixed model reference system and the RBF adaptive model optimizer and outputs the next control quantity.

3. The adaptive voltage control method of the wireless power transmission system of an electric vehicle according to claim 2, characterized in that: The RBF adaptive model optimizer trains the RBF neural network according to the state deviation calculated by the system sampling signal during the system operation, and updates the trained RBF neural network to the model predictive controller, thereby realizing adaptive model optimization and adaptive model predictive control.

4. The adaptive voltage control method of the electric vehicle wireless power transmission system according to claim 2 is characterized in that: During the training process, the gradient descent method is used to optimize the network parameters of the RBF adaptive model.

5. The method for adaptive voltage control of wireless power transmission system of electric vehicle according to claim 1, characterized in that: The optimization objective in the model predictive controller satisfies the following formula: Where N represents the step size of the prediction interval and the control interval, U k represents the predictive control sequence within the control interval, f(U k ) represents the optimization objective in model predictive control, Q, P, and F represent the weight matrices of the three performance requirements: control deviation within the prediction interval, system energy output, and control deviation at the final moment. represents the square of the quadratic norm of the weighted matrix Q, represents the square of the quadratic norm of the weight matrix P, ‖‖ 2 F represents the square of the quadratic norm of the weighted matrix F, e k+i represents the predicted value of the system control deviation at time k+i under the system state at time k, u k+i represents the predicted value of the system control signal at time k+i under the system state at time k, i = 0, ..., N-1, e k+N represents the predicted value of the system control deviation at time k+N under the system state at time k, x k+1 and x k are the system states at time k+1 and time k respectively, u k represents the system input at time k, y k represents the output at time k, u min and u max Indicates the minimum and maximum values ​​of the input, y ref represents the expected output, e k is the deviation at time k, RBF(x k ,u k ) is the compensation part of the RBF neural network in the model, A is the state matrix of the system state space expression, B is the input matrix, C is the output matrix, and D is the direct matrix. represents the matrix discretization calculation of the continuous state space system, T s is the system sampling time, is the discretized state matrix, is the discretized input matrix, T represents the transpose, i Ld Indicates the inductance L in the receiving end control circuit d The current, R L Represents the load resistance at the receiving end of the power transmission system, L d Represents the inductance of the receiving end control circuit, C d2 represents the capacitance of the receiving end control circuit; μ represents the controller output signal; V in Represents the input voltage of the receiving end control circuit, V out Represents the system output voltage dropped across the load resistor.

6. An adaptive voltage control device for an electric vehicle wireless power transmission system, characterized in that: include: A fixed model reference system is used to estimate the state quantity of the electric vehicle wireless power transmission system at time k+1 according to the state quantity of the electric vehicle wireless power transmission system at time k and the control quantity output by the model prediction controller at time k; RBF adaptive model optimizer, used to train the RBF neural network according to the state deviation calculated by the system sampling signal during the system operation, and update the trained RBF neural network to the model predictive controller, so as to realize adaptive model optimization and adaptive model predictive control; The model predictive controller is used to combine the fixed model reference system and the RBF adaptive model optimizer to perform adaptive adjustment of the control quantity and output the next control quantity.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the adaptive voltage control method of the electric vehicle wireless power transmission system as described in any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the adaptive voltage control method of a wireless power transmission system for an electric vehicle as described in any one of claims 1 to 5 are implemented.

9. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the steps of the adaptive voltage control method of a wireless power transmission system for an electric vehicle as described in any one of claims 1 to 5 are implemented.

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