Electric vehicle wireless charging system control method based on adaptive voltage filter

Abstract

The invention discloses an electric vehicle wireless charging system control method based on a self-adaptive voltage filter. According to the invention, the adaptive filter is introduced, so that the system voltage is accurately filtered and controlled without an additional sensor. Furthermore, an input gain online identification and self-adaptive filter module is introduced, and only single sensor data is used, so that the output voltage regulation of the dynamic wireless charging system is realized. Therefore, according to the method provided by the invention, the precision and the setting performance of the voltage are remarkably improved, the initial implementation cost and the later maintenance cost of the system are reduced, and the stability of the control system is greatly enhanced.

Description

Technical Field

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

[0002] As a core component of future transportation, electric vehicles have shown strong competitiveness and promoted the transformation and upgrading of the automotive industry with their significant advantages in zero emissions, no dependence on fossil fuels, efficient energy utilization, convenient maintenance and quiet driving. However, electric vehicles still face the major challenge of limited range in long-distance driving. Traditional battery charging methods have many disadvantages, such as high infrastructure construction costs, limited coverage of charging stations, long charging time and safety hazards during charging. These problems restrict the widespread application of electric vehicles, especially in long-distance travel or long-distance driving scenarios. With the continuous advancement of technology, wireless charging technology has developed rapidly and has become a potential solution to the above problems. In particular, dynamic wireless charging technology can continuously provide electric vehicles with power while the vehicle is driving, which has attracted widespread attention. This technology transmits power to the vehicle through sensors and energy transmitters laid on the road without stopping or plugging in charging equipment, which greatly improves the convenience of electric vehicles and expands the diversity of travel methods. Therefore, dynamic wireless charging technology is regarded as an important direction for the future development of electric vehicles, which can effectively solve the bottleneck of range and provide a more flexible solution for sustainable transportation.

[0003] During the driving of electric vehicles, the coupling coefficient of the energy transmission coil changes significantly with the movement of the vehicle, and the system output voltage often fluctuates. Although the prior art has proposed a variety of control methods for controlling the output voltage of electric vehicle wireless charging systems, there are still the following shortcomings:

[0004] First, existing wireless charging control methods fail to effectively suppress output voltage fluctuations during dynamic wireless charging. These methods mainly rely on feedback control of measurement signals. However, feedback control cannot suppress disturbances in a timely and complete manner. In dynamic wireless charging systems, the coupling coefficients of the transceiver coils often vary widely, and are superimposed with external interference in a strong magnetic field environment. These factors pose a huge challenge to the output voltage control of the system.

[0005] Second, the output voltage control method of the existing dynamic wireless charging system for electric vehicles usually requires multiple voltage, current sensors or coupling coefficient sensors to obtain system status information. These additional sensors not only significantly increase the installation cost, but also increase the risk of system failure. As the number of sensors increases, the subsequent maintenance cost of the system will also increase significantly. Therefore, designing an adaptive control method based on low sensor requirements is more conducive to coping with complex wireless charging application scenarios.

[0006] Third, existing control methods fail to fully address the adverse effects of sensor measurement noise on system performance. Measurement noise affects the accuracy of the controller. In the traditional ADRC structure, the use of a high-gain extended state observer further amplifies the impact of measurement noise, resulting in high-frequency jitter in the control signal and output voltage. This defect not only increases the energy consumption of the dynamic wireless charging system, but also accelerates the wear of the actuator and shortens the service life of the system. Summary of the invention

[0007] In order to overcome the technical difficulties faced by the current control methods of electric vehicle wireless charging systems, the present invention proposes a voltage control method and system for an electric vehicle dynamic wireless charging system based on an adaptive filter. The present invention can not only accurately control the output voltage of the wireless charging system, but also effectively suppress the voltage fluctuation and high-frequency jitter of the system during the dynamic charging process. Through the adaptive input gain identification technology, the need for additional sensors is eliminated, significantly reducing the implementation cost of the system. The present invention greatly improves the performance of dynamic wireless charging of electric vehicles in complex environments by proposing an innovative control scheme.

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

[0009] 1. A control method for electric vehicle wireless charging system based on adaptive voltage filter

[0010] First, an auto-disturbance rejection control model is constructed. The expected output voltage value is given as the input signal of the auto-disturbance rejection control model. The control signal μ output by the auto-disturbance rejection control model acts on the dynamic wireless charging system of the electric vehicle. Then, an adaptive filter is constructed. The output voltage measurement signal V1* of the dynamic wireless charging system of the electric vehicle is sent to the adaptive filter. The adaptive filter combines the control signal μ to generate a predicted value V1 of the output voltage and sends it to the auto-disturbance rejection control model. The auto-disturbance rejection control model estimates the disturbance according to the predicted value of the output voltage and obtains the estimated value of the derivative of the predicted value of the output voltage. and the estimated value of the total disturbance of the system Then, combined with the control signal μ, the estimated value of the derivative of the output voltage prediction value is After the input gain is identified online with the estimated value of the total disturbance of the system, the system input gain is obtained. And feed back to the anti-disturbance control model to complete the voltage control of the dynamic wireless charging system of electric vehicles.

[0011] The active disturbance rejection control model includes an extended state observer and an active disturbance rejection control law; the extended state observer satisfies the following formula:

[0012]

[0013] Where V1 represents the output voltage prediction value obtained by the adaptive filter, V2 represents the derivative of the output voltage prediction value V1, σ represents the total disturbance of the system, Represents the estimated value of the output voltage prediction value Estimated value of the derivative of the predicted output voltage and the estimated value of the total disturbance of the system The derivative of , ω0 represents the positive observer gain coefficient;

[0014] The active disturbance rejection control law module satisfies the following formula:

[0015]

[0016] Among them, K c1 ,K c2 Respectively represent the positive first constant gain and the second constant gain, V1 r Indicates the desired output voltage value.

[0017] The estimated value of the derivative of the output voltage prediction value is the combined control signal μ. After the input gain is identified online with the estimated value of the total disturbance of the system, the system input gain is obtained. Specifically, the following formula is used for online identification of input gain:

[0018]

[0019] in, Indicates the input gain to be updated, K c represents the update error gain, ε0 represents the input gain update error, Represents the estimated value of the derivative of the predicted output voltage The derivative of represents the state transition parameter to be updated, P k represents the updated state transition parameter, and λ represents the forgetting factor.

[0020] The formula of the adaptive filter is as follows:

[0021]

[0022] in, represents the updated actual system state prediction value, I1, V1 represent the updated actual current prediction value and output voltage prediction value respectively; T represents the transpose of the matrix; A0 represents the state gain of the adjustment state information, represents the voltage state prediction value to be updated, A1 represents the control gain of the adjustment control signal; μ represents the output control signal, V1 * represents the output voltage measurement signal of the dynamic wireless charging system measured by the voltage sensor, C m represents a constant gain matrix, satisfying C m =[1,0], K p represents the prediction gain, P k-1 represents the state error covariance to be updated, P k represents the updated state error covariance, R represents the measurement noise variance, and I2 represents the unit matrix of size 2; Represents the prior information of the covariance matrix; Q p represents the state transition covariance.

[0023] 2. A control system for electric vehicle wireless charging system based on adaptive voltage filter

[0024] The control system includes an input gain online identification module, an adaptive filter module and an anti-disturbance control unit;

[0025] The control signal μ output by the ADRC control unit is sent to the input gain online identification module, the electric vehicle dynamic wireless charging system and the adaptive filter module. The ADRC control unit also estimates the derivative of the output voltage prediction value. and the estimated value of the total disturbance of the system Sent to the input gain online identification module, the input gain online identification module outputs the system input gain The output voltage prediction value V1 output by the adaptive filter module is sent to the ADRC control unit and the adaptive filter module. The output voltage measurement signal V1 of the dynamic wireless charging system of the electric vehicle is sent to the ADRC control unit. * The signal is sent to the adaptive filter module to complete the voltage control of the dynamic wireless charging system of the electric vehicle.

[0026] The active disturbance rejection control unit includes an extended state observer module and an active disturbance rejection control law module. The adaptive filter module sends the output voltage prediction value V1 to the extended state observer module, and the input gain online identification module sends the system input gain The output of the ADRC module is recorded as the control signal μ and sent to the extended state observer module. The extended state observer module converts the estimated value of the output voltage prediction value into Estimated value of the derivative of the predicted output voltage and the estimated value of the total disturbance of the system The extended state observer module sends the estimated value of the derivative of the output voltage prediction value to the active disturbance rejection control law module. and the estimated value of the total disturbance of the system

[0027] 3. A computer device

[0028] The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the control method of the electric vehicle wireless charging system based on the adaptive voltage filter when executing the computer program.

[0029] 4. A computer-readable storage medium

[0030] The medium stores a computer program, and when the computer program is executed by the processor, the steps of the control method of the electric vehicle wireless charging system based on the adaptive voltage filter are implemented.

[0031] V. A computer program product

[0032] The product includes a computer program / instruction, which, when executed by a processor, implements the steps of the control method of the electric vehicle wireless charging system based on the adaptive voltage filter.

[0033] Compared with the prior art, the present invention has the following significant beneficial effects:

[0034] First, the present invention fully considers the application scenarios of dynamic wireless charging and effectively copes with the complex uncertainties and external disturbances in the dynamic wireless charging system by introducing an extended state observer. This technical innovation provides the controller with stronger anti-interference capabilities and significantly improves the stability of the system in the face of various disturbance factors.

[0035] Second, the present invention adopts the total disturbance model and online parameter identification technology, eliminating the dependence on additional input voltage, current sensors and coupling coefficient sensors. The proposed method only requires one voltage sensor to achieve accurate control of the output voltage. This innovation not only significantly reduces the implementation and maintenance costs, but also enhances the adaptability of the system in complex environments.

[0036] Third, through the design of the adaptive filter, the present invention effectively eliminates the interference of sensor measurement noise on the system and overcomes the measurement noise amplification problem caused by the high gain characteristics in the traditional extended state observer. This technology not only improves the performance of the controller, but also avoids the extra energy consumption and excessive wear of the actuator, further improving the stability and service life of the dynamic wireless charging system. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is the structure diagram of the designed self-disturbance rejection control based on adaptive voltage filter.

[0038] Figure 2 This is the total disturbance estimation effect diagram of the dynamic wireless charging system.

[0039] Figure 3 It is the control output signal effect diagram.

[0040] Figure 4 This is the input gain online identification effect diagram.

[0041] Figure 5 This is a diagram of the output voltage filtering effect of the dynamic wireless charging system. DETAILED DESCRIPTION

[0042] The present invention is described in detail below in conjunction with the accompanying drawings.

[0043] The electric vehicle dynamic wireless charging system proposed in the present invention adopts an anti-disturbance control structure based on an adaptive filter, such as Figure 1 As shown. The system can not only accurately control the output voltage of the wireless charging system, but also effectively suppress the voltage fluctuation and high-frequency jitter of the system during dynamic charging. The adaptive input gain identification module eliminates the need for additional sensors, significantly reducing the implementation cost of the system. It consists of an input gain online identification module, an adaptive filter module, an anti-disturbance control unit, and a voltage sensor; specifically:

[0044] The control signal μ output by the ADRC control unit is sent to the input gain online identification module, the electric vehicle dynamic wireless charging system and the adaptive filter module. The ADRC control unit also estimates the derivative of the output voltage prediction value. and the estimated value of the total disturbance of the system Sent to the input gain online identification module, the input gain online identification module outputs the system input gain The output voltage prediction value V1 output by the adaptive filter module is sent to the extended state observer module, the active disturbance rejection control law module and the adaptive filter module of the active disturbance rejection control unit. The output voltage measurement signal V1 of the dynamic wireless charging system obtained by the dynamic wireless charging system of the electric vehicle is measured by the voltage sensor. * The signal is sent to the adaptive filter module to complete the voltage control of the dynamic wireless charging system of the electric vehicle.

[0045] The ADRC control unit includes an extended state observer module and an ADRC control law module. The adaptive filter module sends the output voltage prediction value V1 to the extended state observer module, and the input gain online identification module sends the system input gain The output of the ADRC module is recorded as the control signal μ and sent to the extended state observer module. The extended state observer module converts the estimated value of the output voltage prediction value into Estimated value of the derivative of the predicted output voltage and the estimated value of the total disturbance of the system The extended state observer module sends the estimated value of the derivative of the output voltage prediction value to the active disturbance rejection control law module. and the estimated value of the total disturbance of the system

[0046] The present invention also proposes an adaptive voltage filtering and control method for a dynamic wireless charging system for electric vehicles, which aims to achieve accurate tracking of the output voltage to the desired voltage signal while effectively filtering the sensor measurement noise. Its control goal is to achieve stable control of the desired output voltage under the condition of satisfying each equation, and effectively suppress the voltage and control signal jitter during the dynamic wireless charging process. The control method specifically includes the following steps:

[0047] First, an ADRC model is constructed. The desired output voltage value is given as the input signal of the ADRC model. The control signal μ output by the ADRC model acts on the dynamic wireless charging system of electric vehicles. Then, an adaptive filter is constructed. The output voltage measurement signal V1 of the dynamic wireless charging system of electric vehicles * The adaptive filter generates a predicted value of the output voltage in combination with the control signal μ and sends it to the ADRC model. The ADRC model estimates the disturbance based on the predicted value of the output voltage and obtains the estimated value of the derivative of the predicted value of the output voltage. and the estimated value of the total disturbance of the system Then, combined with the control signal μ, the estimated value of the derivative of the output voltage prediction value is After the input gain is identified online with the estimated value of the total disturbance of the system, the system input gain is obtained. And feed back to the anti-disturbance control model to complete the voltage control of the dynamic wireless charging system of electric vehicles.

[0048] The control circuit of the electric vehicle dynamic wireless charging system is described by the following differential equations:

[0049]

[0050] Among them, V in Indicates unknown input voltage, L d Represents the circuit inductance, C d2 Represents the circuit capacitance, R L Represents the load resistance, I Ld Represents the current flowing through the inductor L d The current, V out represents the output voltage, μ represents the control signal of the system, Respectively represent the inductor current I Ld and output voltage V out Differentiation of time.

[0051] The active disturbance rejection control model includes an extended state observer and an active disturbance rejection control law; the extended state observer satisfies the following formula:

[0052]

[0053] Where V1 represents the output voltage prediction value obtained by the adaptive filter, V2 represents the derivative of the output voltage prediction value V1, σ represents the total disturbance of the system, Represents the estimated value of the output voltage prediction value Estimated value of the derivative of the predicted output voltage and the estimated value of the total disturbance of the system The derivative of , ω0 represents the positive definite observer gain coefficient;

[0054] The active disturbance rejection control law module satisfies the following formula:

[0055]

[0056] Among them, K c1 ,K c2 Respectively represent the positive first constant gain and the second constant gain, V1 r Indicates the desired output voltage value.

[0057] Combined with the control signal μ, the estimated value of the derivative of the output voltage prediction value is After the input gain is identified online with the estimated value of the total disturbance of the system, the system input gain is obtained. Specifically, the following formula is used for online input gain identification:

[0058]

[0059] in, Indicates the input gain to be updated, K c represents the update error gain, ε0 represents the input gain update error, Represents the estimated value of the derivative of the predicted output voltage The derivative of represents the state transition parameter to be updated, P k represents the updated state transition parameter, and λ represents the forgetting factor. According to this formula, the system input gain Get online updates.

[0060] The formula for the adaptive filter is as follows:

[0061]

[0062] in, represents the updated actual system state prediction value, I1, V1 represent the updated actual current prediction value and output voltage prediction value respectively; T represents the transpose of the matrix; A0 represents the state gain of the adjustment state information, represents the voltage state prediction value to be updated, A1 represents the control gain of the adjustment control signal; μ represents the output control signal, V1 * represents the output voltage measurement signal of the dynamic wireless charging system measured by the voltage sensor, C m represents a constant gain matrix, satisfying C m =[1,0], K p represents the prediction gain, P k-1 represents the state error covariance to be updated, P k represents the updated state error covariance, R represents the measurement noise variance, and I2 represents the unit matrix of size 2; Represents the prior information of the covariance matrix; Q p represents the state transition covariance.

[0063] The solution proposed in the present invention is expected to overcome the challenges of voltage control in the current wireless charging system of electric vehicles, improve the performance and stability of the system, while reducing hardware costs, and provide a more reliable solution for power transmission in electric vehicles.

[0064] The experimental results of this embodiment are as follows Figures 2 to 5As shown in the figure. ADRC represents the traditional auto-disturbance rejection control method without filter, LPFC represents the auto-disturbance rejection control method combined with the traditional low-pass filter, PBC adopts the auto-disturbance rejection control method based on improved filter structure, and AKFC is the auto-disturbance rejection control method based on adaptive filter. Compared with the PBC method, the main difference of AKFC is that the input gain online identification module is designed, which can realize adaptive filtering and control and eliminate the dependence on additional sensors.

[0065] Figure 2 The extended state observer is used to estimate the total disturbance. No matter how the disturbance changes during the dynamic wireless charging process, the extended state observer used by the four control methods can accurately estimate the total disturbance. However, since the traditional ADRC method does not introduce a filter, the observation information contains high-frequency jitter, which will be transmitted through the control law, causing the control signal to produce high-frequency oscillations. Figure 3 The control signal effects of four methods are shown, among which Figure 3 The a shows that the conventional ADRC produces too much high-frequency vibration, which increases the energy consumption and wear of the actuator. Figure 3 b. Figure 3 C and Figure 3 d respectively show that the LPFC, PBC and AKFC methods (ie, the method of the present invention) successfully suppress the chattering due to the introduction of filters. Figure 4 The online identification effect of the AKFC method (i.e., the method of the present invention) on the input gain is demonstrated. During the dynamic wireless charging process, the system input gain changes significantly, but the proposed method can accurately identify the time-varying input gain. In addition, Figure 5 a, Figure 5 b. Figure 5 C and Figure 5 Figures d and d respectively show the output voltage control and filtering effects of the four methods during the dynamic wireless charging process. Compared with the traditional ADRC, AKFC successfully achieves noise filtering of the output voltage and suppresses voltage fluctuations during the dynamic charging process. At the same time, compared with the LPFC and PBC methods, the proposed method achieves output voltage fluctuation suppression during the dynamic wireless charging process and achieves stable and continuous voltage output. In contrast, the LPFC and PBC methods produce large voltage fluctuations and it is difficult to maintain a stable voltage output. These results show that the proposed method effectively suppresses the output voltage fluctuations during the dynamic wireless charging process while achieving sensor noise filtering. The experimental figures strongly support the effectiveness and performance superiority of the present invention, and further prove that the proposed adaptive voltage filtering and control method has wide application potential in electric vehicle wireless charging systems.

[0066] 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. A control method for an electric vehicle wireless charging system based on an adaptive voltage filter, characterized in that: The following steps are involved: First, an ADRC model is constructed. The desired output voltage value is given as the input signal of the ADRC model. The control signal μ output by the ADRC model acts on the dynamic wireless charging system of electric vehicles. Then, an adaptive filter is constructed. The output voltage measurement signal of the dynamic wireless charging system of electric vehicles The adaptive filter generates a predicted value V1 of the output voltage in combination with the control signal μ and sends it to the ADRC model. The ADRC model estimates the disturbance based on the predicted value of the output voltage and obtains the estimated value of the derivative of the predicted value of the output voltage. and the estimated value of the total disturbance of the system Then, combined with the control signal μ, the estimated value of the derivative of the output voltage prediction value is After the input gain is identified online with the estimated value of the total disturbance of the system, the system input gain is obtained. And feed back to the anti-disturbance control model to complete the voltage control of the dynamic wireless charging system of electric vehicles.

2. The control method of an electric vehicle wireless charging system based on an adaptive voltage filter according to claim 1, characterized in that: The active disturbance rejection control model includes an extended state observer and an active disturbance rejection control law; the extended state observer satisfies the following formula: Where V1 represents the output voltage prediction value obtained by the adaptive filter, V2 represents the derivative of the output voltage prediction value V1, σ represents the total disturbance of the system, Represents the estimated value of the output voltage prediction value Estimated value of the derivative of the predicted output voltage and the estimated value of the total disturbance of the system The derivative of , ω0 represents the positive definite observer gain coefficient; The active disturbance rejection control law module satisfies the following formula: Among them, K c1 ,K c2 denote the positive definite first constant gain and the second constant gain, respectively, Indicates the desired output voltage value.

3. The control method of an electric vehicle wireless charging system based on an adaptive voltage filter according to claim 1, characterized in that: The estimated value of the derivative of the output voltage prediction value is the combined control signal μ After the input gain is identified online with the estimated value of the total disturbance of the system, the system input gain is obtained. Specifically, the following formula is used for online identification of input gain: in, Indicates the input gain to be updated, K c represents the update error gain, ε0 represents the input gain update error, Represents the estimated value of the derivative of the predicted output voltage The derivative of represents the state transition parameter to be updated, P k represents the updated state transition parameter, and λ represents the forgetting factor.

4. The control method of an electric vehicle wireless charging system based on an adaptive voltage filter according to claim 1, characterized in that: The formula of the adaptive filter is as follows: in, represents the updated actual system state prediction value, I1, V1 represent the updated actual current prediction value and output voltage prediction value respectively; T represents the transpose of the matrix; A0 represents the state gain of the adjustment state information, represents the voltage state prediction value to be updated, A1 represents the control gain of the adjustment control signal; μ represents the output control signal, represents the output voltage measurement signal of the dynamic wireless charging system measured by the voltage sensor, C m represents a constant gain matrix, satisfying C m =[1,0], K p represents the prediction gain, P k-1 represents the state error covariance to be updated, P k represents the updated state error covariance, R represents the measurement noise variance, and I2 represents the unit matrix of size 2; Represents the prior information of the covariance matrix; Q p represents the state transition covariance.

5. A control system for an electric vehicle wireless charging system based on an adaptive voltage filter, characterized in that: It includes an input gain online identification module, an adaptive filter module and an anti-disturbance control unit; The control signal μ output by the ADRC control unit is sent to the input gain online identification module, the electric vehicle dynamic wireless charging system and the adaptive filter module. The ADRC control unit also estimates the derivative of the output voltage prediction value. and the estimated value of the total disturbance of the system Sent to the input gain online identification module, the input gain online identification module outputs the system input gain The output voltage prediction value V1 output by the adaptive filter module is sent to the ADRC control unit and the adaptive filter module. The output voltage measurement signal of the dynamic wireless charging system of the electric vehicle is sent to the ADRC control unit. The signal is sent to the adaptive filter module to complete the voltage control of the dynamic wireless charging system of the electric vehicle.

6. The electric vehicle wireless charging system control system based on adaptive voltage filter according to claim 5, characterized in that: The active disturbance rejection control unit includes an extended state observer module and an active disturbance rejection control law module. The adaptive filter module sends the output voltage prediction value V1 to the extended state observer module, and the input gain online identification module sends the system input gain The output of the ADRC module is recorded as the control signal μ and sent to the extended state observer module. The extended state observer module converts the estimated value of the output voltage prediction value into Estimated value of the derivative of the predicted output voltage and the estimated value of the total disturbance of the system The extended state observer module sends the estimated value of the derivative of the output voltage prediction value to the active disturbance rejection control law module. and the estimated value of the total disturbance of the system 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 control method of the electric vehicle wireless charging system based on the adaptive voltage filter as described in any one of claims 1 to 4 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 control method of an electric vehicle wireless charging system based on an adaptive voltage filter as described in any one of claims 1 to 4 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 control method of an electric vehicle wireless charging system based on an adaptive voltage filter as described in any one of claims 1 to 4 are implemented.

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