A dynamic wireless charging bilateral optimization coordination control system and method for electric vehicles

Abstract

The application discloses a dynamic wireless charging double-side optimization coordination control system and method for electric vehicles, and belongs to the technical field of wireless charging of electric vehicles. The system comprises a multi-criterion coordination matching network module, which is used for sensing driving lateral deviation and user expected average power, dynamically fusing three criteria of charging completion rate, average efficiency and total harmonic distortion, performing normalization processing on the three criteria, performing weighted fusion by using dynamic weights, obtaining a comprehensive target value, and outputting a reference inverter phase angle corresponding to a working point with the maximum comprehensive target value and a reference trajectory of a direct current converter. A road side unit comprises a road side controller and an inverter. The road side controller adopts phase shift coordination control and adjusts the output voltage of the inverter according to the reference inverter phase angle. A vehicle side unit comprises a vehicle side controller and a direct current converter. The vehicle side controller adopts feedback linearization control and makes the direct current converter track the reference trajectory according to the reference trajectory of the direct current converter.

Description

Technical Field

[0001] This invention belongs to the field of wireless charging technology for electric vehicles, and particularly relates to a dynamic wireless charging bilateral optimization coordination control system and method for electric vehicles. Background Technology

[0002] With the development of human society, increasingly prominent energy and environmental issues have prompted electric vehicles to gradually become an important development direction for the automotive industry. Dynamic wireless charging technology allows electric vehicles to receive electrical energy in real time while driving, which can effectively alleviate range anxiety and improve traffic efficiency, and has therefore attracted widespread attention.

[0003] Currently, electric vehicle charging methods can be categorized based on application scenarios into onboard emergency charging, home or public charging, charging pile charging, and charging station charging. Based on the different power transmission methods, they can be divided into two main categories: conductive charging and wireless charging. Among these, wireless charging, with its advantages of not requiring physical plugging and unplugging, good environmental adaptability, and suitability for automated charging, has become an important development direction for electric vehicle charging technology. Dynamic wireless charging, as an advanced form of wireless charging, uses transmitting coils buried in the road to allow the vehicle to continuously acquire power while driving, significantly reducing reliance on onboard battery capacity. Existing dynamic wireless charging control methods are mainly divided into two categories: single-sided control and double-sided control. Single-sided control independently controls only the roadside inverter or vehicle-side DC-DC converter, offering advantages such as low complexity and minimal communication requirements, but lacking adaptability to changes in driving behavior. Double-sided control simultaneously coordinates the adjustment of both the roadside and vehicle sides, resulting in a wider power adjustment range and higher control precision, demonstrating greater application potential in dynamic wireless charging systems. However, the double-sided control method still faces the following problems in practical applications:

[0004] Existing methods are not adaptable to driving offset: During the dynamic wireless charging process of electric vehicles, due to the uncertainty of the driving trajectory, longitudinal offset along the driving direction and lateral offset perpendicular to the driving direction will inevitably occur between the receiving coil and the transmitting coil. Both offsets will affect the coupling coefficient between the coils, thereby causing periodic fluctuations or directional disturbances in the transmitted power. Most existing bilateral control methods are designed based on ideal alignment conditions and lack precise power compensation strategies for different offset scenarios, resulting in unstable power output during actual charging and affecting the charging effect. Existing methods struggle to achieve synergistic optimization of multiple performance metrics: In practical operation, dynamic wireless charging systems need to simultaneously consider multiple metrics such as charging completion rate, system efficiency, and power quality, including total harmonic distortion (THD). Users' focus on these metrics varies across different charging scenarios. For example, in emergency charging scenarios, users prioritize charging speed, while in regular charging scenarios, they may prioritize efficiency and power quality. Existing control methods typically optimize only a single metric, such as maximum power output, lacking a mechanism for dynamic trade-offs and coordination among multiple metrics, making it difficult to adapt to diverse charging needs.

[0005] Therefore, it is necessary to provide a bilateral optimization and coordination control system and method for dynamic wireless charging of electric vehicles. It is essential to achieve coordinated optimization of multiple performance indicators of the dynamic wireless charging system while fully considering the impact of driving offset. Summary of the Invention

[0006] In view of this, the present invention proposes a dynamic wireless charging bilateral optimization and coordination control system and method for electric vehicles, which can adapt control parameters in a timely manner when the vehicle moves and the user's expected power changes, independently control the roadside and vehicle side respectively, and coordinate the multi-objective balance relationship of charging completion rate, energy conversion efficiency and inverter output harmonic distortion.

[0007] On one hand, the present invention provides a dynamic wireless charging bilateral optimization and coordination control system for electric vehicles, comprising: A multi-criteria coordination and matching network module is used to sense the lateral offset of the driver and the user's expected average power. It dynamically integrates three criteria: charging completion rate, average efficiency, and total harmonic distortion (THD). The three criteria are normalized and weighted by dynamic weights to obtain a comprehensive target value. The operating point with the largest comprehensive target value is used as the optimal control parameter under the current operating condition. The module outputs the reference inverter phase angle and the reference trajectory of the DC-DC converter. The charging completion rate is used to characterize the total charging capacity completion rate within the strategy cycle, the average efficiency is used to characterize the average energy conversion efficiency within the strategy cycle, and the total harmonic distortion (THD) is used to characterize the harmonic content of the inverter output voltage. The roadside unit includes a roadside controller and an inverter. The roadside controller is electrically connected to the control terminal of the inverter. The roadside controller adopts phase-shift coordination control to receive the reference inverter phase angle output by the multi-criteria coordination matching network module and adjust the output voltage of the inverter. The vehicle-side unit includes a vehicle-side controller and a DC-DC converter. The vehicle-side controller is electrically connected to the control terminal of the DC-DC converter. The vehicle-side controller adopts feedback linearization control to obtain the reference trajectory of the DC-DC converter output by the multi-criteria coordination matching network module, so that the DC-DC converter tracks the reference trajectory.

[0008] Based on the above technical solutions, preferably, the roadside unit further includes a DC bus, a roadside LCC resonant network, and a transmitting coil. The DC bus is electrically connected to the inverter, the output terminal of the inverter is electrically connected to the input terminal of the roadside LCC resonant network, and the output terminal of the roadside LCC resonant network is electrically connected to the transmitting coil. The multi-criteria coordination and matching network module is integrated in the roadside controller.

[0009] Preferably, the roadside controller adopts phase-shift coordination control, which generates an inverter drive signal based on the reference inverter phase angle output by the multi-criteria coordination matching network module and the relationship between the DC bus voltage and the inverter output voltage, so that the effective value of the inverter output voltage reaches the set target value.

[0010] Preferably, the vehicle-side unit further includes a receiving coil, a vehicle-side LCC resonant network, a rectifier and voltage regulator circuit, and a load. The receiving coil and the transmitting coil transmit energy through electromagnetic coupling. The output terminal of the receiving coil is electrically connected to the input terminal of the vehicle-side LCC resonant network, and the output terminal of the vehicle-side LCC resonant network is electrically connected to the input terminal of the rectifier and voltage regulator circuit. The rectifier and voltage regulator circuit includes a rectifier bridge and an input filter capacitor. C dc1 DC inductor L dc Output filter capacitor C dc2 Switching transistor Q and diodes D The first output terminal of the rectifier bridge is connected to the input filter capacitor. C dc1 one end and the switching transistor Q The drain electrical connection of the switching transistor Q The source terminals are respectively connected to the DC inductor L dc one end and diode D Cathode electrical connection, DC inductor L dc The other end is connected to the output filter capacitor. C dc2 One end of the rectifier bridge is electrically connected, and the second output terminal of the rectifier bridge is connected to the input filter capacitor. C dc1 The other end, diode D anode and output filter capacitor C dc2 The other end is electrically connected; output filter capacitor. C dc2 The two ends of the circuit are electrically connected to the two ends of the load, serving as the output terminals of the rectifier and voltage regulator circuit.

[0011] Preferably, the vehicle-side controller employs feedback linearization control, which involves establishing a nonlinear state-space model of the DC-DC converter and using the input filter capacitor. C dc1 Input voltage at both ends u cd1 DC inductor L dc Inductor current i Ld and output filter capacitor C dc2 Output voltage at both ends u out Let the input filter capacitor be a state variable. C dc1 voltage at both ends u cd1 For output variables y Calculate the Lie derivative based on the Lie derivative, reference trajectory, and output variables. y Compared with reference trajectory y r To address the tracking error between inputs, a feedback linearization control law is designed to obtain the virtual control input. u Convert virtual control input into target duty cycle d f The vehicle-side controller operates according to the target duty cycle. d f Generate control signals and input them to the switching transistor. Q The gate of the DC-DC converter makes the input voltage... u cd1 Tracking reference trajectory y r .

[0012] Preferably, the multi-criteria coordination and matching network module includes a charging completion rate criterion layer, an average efficiency criterion layer, a total harmonic distortion (THD) criterion layer, and a comprehensive target layer. The charging completion rate criterion layer is used to calculate the total charging capacity completion rate within the strategy cycle; the average efficiency criterion layer is used to calculate the average energy conversion efficiency within the strategy cycle; the THD criterion layer is used to calculate the harmonic content of the inverter output voltage; and the comprehensive target layer is used to normalize the criterion results output by the charging completion rate criterion layer, the average efficiency criterion layer, and the THD criterion layer, and then use dynamic weights for weighted fusion to obtain a comprehensive target value.

[0013] Preferably, the charging completion rate is calculated based on the expected average power, strategy cycle, inverter output voltage, DC-DC converter input voltage, mutual inductance, and resonant network parameters; the average efficiency is calculated based on mutual inductance, load resistance, and parasitic parameters; the total harmonic distortion is calculated based on the fundamental amplitude of the inverter output voltage and the amplitude of each harmonic; and the dynamic weights are adaptively determined based on the vectors normalized according to each criterion.

[0014] Preferably, when the calculated result of any one of the criteria—charging completion rate, average efficiency, and total harmonic distortion—is lower than the corresponding criterion's set threshold, the weighting coefficient of the calculated result of the corresponding criterion is smoothly taken from the lower limit value to the upper limit value through the Sigmoid function.

[0015] On the other hand, the present invention provides a bilateral optimization and coordinated control method for dynamic wireless charging of electric vehicles, comprising the following steps: S1: Configure the above-mentioned electric vehicle dynamic wireless charging bilateral optimization coordination control system; detect and obtain the current driving lateral offset and the user's expected average power; S2: The multi-criteria coordination matching network module is used to perform global optimization of candidate operating points. The charging completion rate, average efficiency and total harmonic distortion are calculated and normalized. The calculation results of the three criteria after normalization are weighted and fused using dynamic weights to obtain a comprehensive target value. The operating point with the largest comprehensive target value is taken as the optimal operating point under the current operating condition. The reference inverter phase angle and DC converter reference trajectory are output. S3: The roadside controller receives the reference inverter phase angle output by the multi-criteria coordination matching network module and adjusts the inverter's output voltage. The vehicle-side controller, based on the reference trajectory of the DC-DC converter output by the multi-criteria coordination matching network module, enables the DC-DC converter to track the reference trajectory. S4: During wireless charging, the system detects changes in the lateral offset of the driver and the user's expected average power in real time. If changes are detected, the system returns to steps S1-S3 to recalculate the results of each criterion, update the dynamic weights, and reselect the optimal operating point to achieve dynamic online switching of the control strategy. If the driver offset and expected power do not change in real time, the system continues to execute the current control strategy until charging is complete.

[0016] Thirdly, the present invention provides a computer storage medium for storing a computer program, which, when executed, implements the above-described dynamic wireless charging bilateral optimization coordination control method for electric vehicles.

[0017] The present invention provides a dynamic wireless charging bilateral optimization coordination control system and method for electric vehicles, which has the following advantages compared with the prior art: 1. The main objective of this invention is to address the shortcomings of existing technologies, such as insufficient adaptability to driving deviation and difficulty in coordinating the optimization of multiple performance indicators, by providing a bilateral optimization and coordination control system and method for dynamic wireless charging of electric vehicles, thereby overcoming the deficiencies of the prior art. This invention's bilateral optimization and coordination control system for dynamic wireless charging of electric vehicles employs a technical solution combining a multi-criteria coordinated matching network module with bilateral control. The roadside inverter uses phase-shift coordinated control, and the vehicle-side DC-DC converter uses feedback linearization control. The optimal control strategy is dynamically selected based on driving deviation and charging demand, achieving an effective trade-off between multiple indicators such as charging completion rate, system efficiency, and total harmonic distortion. This reduces algorithm complexity and communication interaction frequency, resulting in a simple structure, high control precision, and strong adaptability.

[0018] 2. The roadside unit also includes a DC bus, an LCC resonant network, and a transmitting coil. The multi-criteria coordinated matching network module is integrated into the roadside controller, and specifically adopts phase-shift coordinated control. Based on the reference phase angle and DC bus voltage, the mapping relationship between the phase angle and the output voltage amplitude is revealed to obtain the inverter's drive signal. By using phase shifting instead of frequency modulation, soft switching failure and harmonic deterioration can be avoided.

[0019] 3. The structure of the rectifier and voltage regulator circuit on the DC-DC converter side is provided, the physical quantities are defined as parameters for linearization modeling, the feedback linearization control law is designed, the virtual control input is obtained and converted into the target duty cycle, thereby forming the corresponding duty cycle control signal to achieve accurate tracking of the input voltage.

[0020] 4. It provides the specific network hierarchy structure of the multi-criteria coordination and matching network module, as well as the content of weighted fusion criteria combined with dynamic weights, to realize reliable online measurement and dynamic online switching of control strategies.

[0021] 5. Existing methods are not adaptable to driving offset: During the dynamic wireless charging process of electric vehicles, due to the uncertainty of the driving trajectory, longitudinal offset along the driving direction and lateral offset perpendicular to the driving direction will inevitably occur between the receiving coil and the transmitting coil. Both offsets will affect the coupling coefficient between the coils, thereby causing periodic fluctuations or directional disturbances in the transmitted power. Most existing bilateral control methods are designed based on ideal alignment conditions. In view of the lack of accurate power compensation strategies for different offset scenarios in existing technologies, the power output is unstable during actual charging, which affects the charging effect. This application obtains the current lateral offset and the user's expected average power, and calculates three criteria to find the optimal operating point, which can adapt well to the real-time changes in driving offset. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a schematic diagram of the system structure of a dynamic wireless charging bilateral optimization coordination control system and method for electric vehicles according to the present invention. Figure 2 This is a schematic diagram of the structure of a multi-criteria coordination matching network for a dynamic wireless charging bilateral optimization coordination control system and method for electric vehicles according to the present invention. Figure 3 This is a flowchart of a dynamic wireless charging bilateral optimization coordination control system and method for electric vehicles according to the present invention. Detailed Implementation

[0024] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0025] Existing bilateral control methods have high algorithm complexity and communication burden: In order to achieve precise coordination between the roadside and the vehicle side, traditional bilateral control methods often require high-frequency data interaction and complex synchronization control algorithms, which place high demands on processor performance and communication links. This not only increases the system cost, but also limits its engineering practicality and scalability in dynamic wireless charging scenarios to a certain extent.

[0026] In view of this, such as Figure 1 As shown, on one hand, the present invention provides a dynamic wireless charging bilateral optimization and coordination control system for electric vehicles, comprising: A multi-criteria coordination and matching network module is used to sense the lateral offset of the driver and the user's expected average power. It dynamically integrates three criteria: charging completion rate, average efficiency, and total harmonic distortion (THD). The three criteria are normalized and weighted by dynamic weights to obtain a comprehensive target value. The operating point with the largest comprehensive target value is used as the optimal control parameter under the current operating condition. The module outputs the reference inverter phase angle and the reference trajectory of the DC-DC converter. The charging completion rate is used to characterize the total charging capacity completion rate within the strategy cycle, the average efficiency is used to characterize the average energy conversion efficiency within the strategy cycle, and the total harmonic distortion (THD) is used to characterize the harmonic content of the inverter output voltage. The roadside unit includes a roadside controller and an inverter. The roadside controller is electrically connected to the control terminal of the inverter. The roadside controller adopts phase-shift coordination control to receive the reference inverter phase angle output by the multi-criteria coordination matching network module and adjust the output voltage of the inverter. The vehicle-side unit includes a vehicle-side controller and a DC-DC converter. The vehicle-side controller is electrically connected to the control terminal of the DC-DC converter. The vehicle-side controller adopts feedback linearization control to obtain the reference trajectory of the DC-DC converter output by the multi-criteria coordination matching network module, so that the DC-DC converter tracks the reference trajectory and dynamically selects the optimal control strategy according to driving deviation and charging demand.

[0027] like Figure 1 As shown, Figure 1 The lower section is the roadside unit. Figure 1 The upper part is the vehicle-side unit. Between the two are induction coils for wireless energy exchange, such as transmitting and receiving coils. The roadside unit is fixed to the roadside, and the vehicle-side unit moves with the vehicle. When the vehicle-side unit is within the linearly distributed area of ​​the roadside units, it exchanges energy with at least the nearest induction coil via wireless transmission, realizing contactless charging.

[0028] In addition to the roadside controller and inverter, the roadside unit also includes a DC bus, a roadside LCC resonant network, and a transmitting coil. The DC bus is electrically connected to the inverter, the output terminal of the inverter is electrically connected to the input terminal of the roadside LCC resonant network, and the output terminal of the roadside LCC resonant network is electrically connected to the transmitting coil. The multi-criteria coordination matching network module is integrated in the roadside controller.

[0029] The roadside controller adopts phase-shifting coordination control, which uses the reference inverter phase angle output by the multi-criteria coordination matching network module and the relationship between the DC bus voltage and the inverter output voltage to generate an inverter drive signal, so that the effective value of the inverter output voltage reaches the set target value.

[0030] Specifically, let the phase angle of the reference inverter be... For any inverter, the target output voltage value V target With respect to DC bus voltage VDC and reference inverter phase angle The relationship is: Based on this, the roadside controller generates inverter drive signals to regulate the roadside output voltage.

[0031] like Figure 1As shown, the vehicle-side unit, in addition to the vehicle-side controller and DC-DC converter, also includes a receiving coil, a vehicle-side LCC resonant network, a rectifier and voltage regulator circuit, and a load. The receiving coil and transmitting coil transfer energy via electromagnetic coupling. The output terminal of the receiving coil is electrically connected to the input terminal of the vehicle-side LCC resonant network, which includes a capacitor. C T_I , C Tf_I and capacitor L Tf_I The output of the vehicle-mounted LCC resonant network is electrically connected to the input of the rectifier and voltage regulator circuit, which includes a rectifier bridge and an input filter capacitor. C dc1 DC inductor L dc Output filter capacitor C dc2 Switching transistor Q and diodes D The first output terminal of the rectifier bridge is connected to the input filter capacitor. C dc1 one end and the switching transistor Q The drain electrical connection of the switching transistor Q The source terminals are respectively connected to the DC inductor L dc one end and diode D Cathode electrical connection, DC inductor L dc The other end is connected to the output filter capacitor. C dc2 One end of the rectifier bridge is electrically connected, and the second output terminal of the rectifier bridge is connected to the input filter capacitor. C dc1 The other end, diode D anode and output filter capacitor C dc2 The other end is electrically connected; output filter capacitor. C dc2 The two ends of the circuit are electrically connected to the two ends of the load, serving as the output terminals of the rectifier and voltage regulator circuit.

[0032] In one embodiment, the vehicle-side controller employs feedback linearization control, which involves establishing a nonlinear state-space model of the DC-DC converter, using the input filter capacitor... C dc1 Input voltage of the DC-DC converter at both ends u cd1 DC inductor L dc Inductor current i Ld and output filter capacitor Cdc2 Output voltage at both ends u out Let the input filter capacitor be a state variable. C dc1 voltage at both ends u cd1 For output variables y Calculate the Lie derivative based on the Lie derivative, reference trajectory, and output variables. y Compared with reference trajectory y r To address the tracking error between inputs, a feedback linearization control law is designed to obtain the virtual control input. u Convert virtual control input into target duty cycle d f The vehicle-side controller operates according to the target duty cycle. d f Generate control signals and input them to the switching transistor. Q The gate of the DC-DC converter makes the input voltage... u cd1 Tracking reference trajectory y r .

[0033] The nonlinear state-space model of a DC-DC converter can be expressed as: The state variables are Smooth vector field A ( x )and F ( x The specific form of the Lie derivative is determined by the DC-DC converter topology. Calculate the Lie derivative: ,in This is the input current of the DC-DC converter. C dc1 For input filter capacitors, x 2 Given the inductor current state change variable, design the feedback linearization control law as follows: The point on the parameter represents the first derivative. y r For reference trajectory, = y - y r To track errors, k This is the feedback gain coefficient. From this, the control duty cycle of the DC-DC converter can be obtained. d f The explicit expression of the control law. This control law enables the input voltage of the DC-DC converter to accurately track the reference trajectory. y r This counteracts the nonlinear interference caused by mutual inductance fluctuations.

[0034] like Figure 2 As shown, the multi-criteria coordination and matching network module includes a charging completion rate criterion layer, an average efficiency criterion layer, a total harmonic distortion (THD) criterion layer, and a comprehensive target layer. The charging completion rate criterion layer is used to calculate the total charging capacity completion rate within the strategy cycle; the average efficiency criterion layer is used to calculate the average energy conversion efficiency within the strategy cycle; the THD criterion layer is used to calculate the harmonic content of the inverter output voltage; and the comprehensive target layer is used to normalize the criterion results output by the charging completion rate criterion layer, the average efficiency criterion layer, and the THD criterion layer, and then use dynamic weights for weighted fusion to obtain the comprehensive target value.

[0035] Charging completion rate is based on expected average power. P e Strategy cycle T c Inverter output voltage U AB DC-DC converter input voltage u Cd1 Mutual induction M ( x 0 , t The resonant network parameters include the roadside resonant inductance. L Tf Resonant inductor on the vehicle side L Rf Calculated: Charging completion rate s f It is used to reflect the degree to which the actual output energy of the system meets the user's expected power within a control cycle. Its calculation formula is: ,in, P e It is the user's expected average power. T c It controls the update cycle. t o It can be any starting time. The integral term contains the expression for the instantaneous output power of the system. This indicator essentially measures the ratio of the total energy actually output by the system to the total energy expected by the user within a policy cycle. ω ω is the angular frequency.

[0036] Average efficiency is based on mutual inductance M ( x 0 , t ), load resistor R L The parasitic parameters are calculated from the parasitic parameters, including parasitic resistance. R a and R LT The efficiency coefficient is ηsys ; Mutual induction M ( x 0 , t In ) x 0 It is the lateral offset of the driver. t This formula, derived from the equivalent circuit model of the LCC-LCC resonant network, is an analytical expression for the system efficiency. It comprehensively considers mutual inductance. M The effect of load resistance, parasitic parameters, etc. on efficiency is a specific formula for this particular topology.

[0037] Total harmonic distortion (THD) is calculated based on the fundamental amplitude and the amplitudes of each harmonic of the inverter's output voltage. ,in, U AB(1) This represents the fundamental amplitude of the inverter's output voltage. U AB(i) For the first i The amplitude of the subharmonic.

[0038] Overall target value Obj It is calculated by normalizing the performance values ​​of each criterion layer through the comprehensive target layer and then using dynamic weighted fusion: Dynamic weights ω (x) The vectors are adaptively determined based on the normalization of each criterion: , This indicates the weights of the three criteria. ,in σ u (x) and σ t (x) The first x The upper and lower limits of the weights of each criterion. o s (x) The threshold for weight changes, k σ The coefficients are constants. The operating point with the largest overall objective value ( k r , This refers to the optimal control parameters under the current operating conditions.

[0039] When the calculated result of any one of the criteria—charging completion rate, average efficiency, and total harmonic distortion—is lower than the corresponding criterion's set threshold, the weighting coefficient of the calculated result of the corresponding criterion is smoothly taken from the lower limit value to the upper limit value through the Sigmoid function.

[0040] On the other hand, the present invention provides a bilateral optimization and coordinated control method for dynamic wireless charging of electric vehicles, comprising the following steps: S1: Configure the above-mentioned electric vehicle dynamic wireless charging bilateral optimization coordination control system; detect and obtain the current driving lateral offset and the user's expected average power; S2: The multi-criteria coordination matching network module is used to perform global optimization of candidate operating points. The charging completion rate, average efficiency and total harmonic distortion are calculated and normalized. The calculation results of the three criteria after normalization are weighted and fused using dynamic weights to obtain a comprehensive target value. The operating point with the largest comprehensive target value is taken as the optimal operating point under the current operating condition. The reference inverter phase angle and DC converter reference trajectory are output. S3: The roadside controller receives the reference inverter phase angle output by the multi-criteria coordination matching network module and adjusts the inverter's output voltage. The vehicle-side controller, based on the reference trajectory of the DC-DC converter output by the multi-criteria coordination matching network module, enables the DC-DC converter to track the reference trajectory. S4: During wireless charging, the system detects changes in the lateral offset of the driver and the user's expected average power in real time. If changes are detected, the system returns to steps S1-S3 to recalculate the results of each criterion, update the dynamic weights, and reselect the optimal operating point to achieve dynamic online switching of the control strategy. If the driver offset and expected power do not change in real time, the system continues to execute the current control strategy until charging is complete.

[0041] The following is combined Figure 3 This paper describes the implementation process of the bilateral optimization coordination control method of the present invention in a specific example.

[0042] After system power-on initialization, parameters such as strategy period, dynamic weight upper and lower limits, and thresholds for each criterion are set. The position detection module acquires the current lateral offset, and the user sets the desired average power through the on-board terminal. The multi-criteria coordinated matching network module receives the above information and power feedback in each strategy period, globally optimizes candidate operating points, calculates the target value of the operating point according to the aforementioned comprehensive target value formula, selects the operating point corresponding to the maximum value, and outputs the reference inverter phase angle and DC-DC converter reference trajectory.

[0043] The roadside controller calculates the inverter drive signal based on the reference phase angle and the phase-shift coordination control relationship, and adjusts the inverter output voltage. The vehicle-side controller calculates the DC-DC converter duty cycle based on the reference trajectory and the feedback linearization control law, so that the DC-DC converter input voltage tracks the reference trajectory. The actual output power of the system is thus stabilized near the desired power.

[0044] During charging, the system continuously monitors whether the lateral offset and desired power change. If they do, the multi-criteria coordinated matching network module is triggered to recalculate and update the output; if they do not change, the current control strategy is maintained. When charging is complete or the vehicle leaves, the system stops outputting the inverter and DC-DC converter drive signals and terminates its operation.

[0045] Thirdly, the present invention provides a computer storage medium for storing a computer program, which, when executed, implements the above-described dynamic wireless charging bilateral optimization coordination control method for electric vehicles.

[0046] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A dynamic wireless charging bilateral optimization and coordination control system for electric vehicles, characterized in that, include: The multi-criteria coordination and matching network module is used to sense the lateral offset of the driver and the user's expected average power. It dynamically integrates three criteria: charging completion rate, average efficiency, and total harmonic distortion. The three criteria are normalized and weighted by dynamic weights to obtain a comprehensive target value. The operating point with the largest comprehensive target value is used as the optimal control parameter under the current operating condition. The module outputs the reference inverter phase angle and the reference trajectory of the DC converter. The charging completion rate is used to characterize the total charging capacity completion rate within the strategy cycle, the average efficiency is used to characterize the average energy conversion efficiency within the strategy cycle, and the total harmonic distortion is used to characterize the harmonic content of the inverter output voltage. The roadside unit includes a roadside controller and an inverter. The roadside controller is electrically connected to the control terminal of the inverter. The roadside controller adopts phase-shift coordination control to receive the reference inverter phase angle output by the multi-criteria coordination matching network module and adjust the output voltage of the inverter. The vehicle-side unit includes a vehicle-side controller and a DC-DC converter. The vehicle-side controller is electrically connected to the control terminal of the DC-DC converter. The vehicle-side controller adopts feedback linearization control to obtain the reference trajectory of the DC-DC converter output by the multi-criteria coordination matching network module, so that the DC-DC converter tracks the reference trajectory.

2. The bilateral optimization and coordination control system for dynamic wireless charging of electric vehicles according to claim 1, characterized in that, The roadside unit also includes a DC bus, a roadside LCC resonant network, and a transmitting coil. The DC bus is electrically connected to the inverter, the output terminal of the inverter is electrically connected to the input terminal of the roadside LCC resonant network, and the output terminal of the roadside LCC resonant network is electrically connected to the transmitting coil. The multi-criteria coordination matching network module is integrated in the roadside controller.

3. The bilateral optimization and coordination control system for dynamic wireless charging of electric vehicles according to claim 2, characterized in that, The roadside controller employs phase-shift coordination control, which generates an inverter drive signal based on the reference inverter phase angle output by the multi-criteria coordination matching network module and the relationship between the DC bus voltage and the inverter's output voltage, so that the effective value of the inverter's output voltage reaches the set target value.

4. The bilateral optimization and coordination control system for dynamic wireless charging of electric vehicles according to claim 2, characterized in that, The vehicle-side unit also includes a receiving coil, a vehicle-side LCC resonant network, a rectifier and voltage regulator circuit, and a load. The receiving coil and the transmitting coil transfer energy through electromagnetic coupling. The output terminal of the receiving coil is electrically connected to the input terminal of the vehicle-side LCC resonant network, and the output terminal of the vehicle-side LCC resonant network is electrically connected to the input terminal of the rectifier and voltage regulator circuit. The rectifier and voltage regulator circuit includes a rectifier bridge and an input filter capacitor. C dc1 DC inductor L dc Output filter capacitor C dc2 Switching transistor Q and diodes D The first output terminal of the rectifier bridge is connected to the input filter capacitor. C dc1 one end and the switching transistor Q The drain electrical connection of the switching transistor Q The source terminals are respectively connected to the DC inductor L dc one end and diode D Cathode electrical connection, DC inductor L dc The other end is connected to the output filter capacitor. C dc2 One end of the rectifier bridge is electrically connected, and the second output terminal of the rectifier bridge is connected to the input filter capacitor. C dc1 The other end, diode D anode and output filter capacitor C dc2 The other end is electrically connected; output filter capacitor. C dc2 The two ends of the circuit are electrically connected to the two ends of the load, serving as the output terminals of the rectifier and voltage regulator circuit.

5. The bilateral optimization and coordination control system for dynamic wireless charging of electric vehicles according to claim 4, characterized in that, The vehicle-side controller employs feedback linearization control, which establishes a nonlinear state-space model of the DC-DC converter, using the input filter capacitor... C dc1 Input voltage at both ends u cd1 DC inductor L dc Inductor current i Ld and output filter capacitor C dc2 Output voltage at both ends u out Let the input filter capacitor be a state variable. C dc1 voltage at both ends u cd1 For output variables y Calculate the Lie derivative based on the Lie derivative, reference trajectory, and output variables. y Compared with reference trajectory y r To address the tracking error between inputs, a feedback linearization control law is designed to obtain the virtual control input. u Convert virtual control input into target duty cycle d f The vehicle-side controller operates according to the target duty cycle. d f Generate control signals and input them to the switching transistor. Q The gate of the DC-DC converter makes the input voltage... u cd1 Tracking reference trajectory y r .

6. The bilateral optimization and coordination control system for dynamic wireless charging of electric vehicles according to claim 4, characterized in that, The multi-criteria coordination and matching network module includes a charging completion rate criterion layer, an average efficiency criterion layer, a total harmonic distortion (THD) criterion layer, and a comprehensive target layer. The charging completion rate criterion layer is used to calculate the total charging capacity completion rate within the strategy cycle; the average efficiency criterion layer is used to calculate the average energy conversion efficiency within the strategy cycle; the THD criterion layer is used to calculate the harmonic content of the inverter output voltage; and the comprehensive target layer is used to normalize the criterion results output by the charging completion rate criterion layer, the average efficiency criterion layer, and the THD criterion layer, and then use dynamic weights for weighted fusion to obtain the comprehensive target value.

7. The bilateral optimization and coordination control system for dynamic wireless charging of electric vehicles according to claim 4, characterized in that, The charging completion rate is calculated based on the expected average power, strategy cycle, inverter output voltage, DC-DC converter input voltage, mutual inductance, and resonant network parameters; the average efficiency is calculated based on mutual inductance, load resistance, and parasitic parameters; the total harmonic distortion is calculated based on the fundamental amplitude of the inverter output voltage and the amplitude of each harmonic; the dynamic weights are adaptively determined based on the vectors normalized according to each criterion.

8. The bilateral optimization and coordination control system for dynamic wireless charging of electric vehicles according to claim 7, characterized in that, When the calculated result of any one of the criteria—charging completion rate, average efficiency, and total harmonic distortion—is lower than the corresponding criterion's set threshold, the weighting coefficient of the calculated result of the corresponding criterion is smoothly adjusted from the lower limit to the upper limit using the Sigmoid function.

9. A bilateral optimization and coordinated control method for dynamic wireless charging of electric vehicles, characterized in that, Includes the following steps: S1: Configure the electric vehicle dynamic wireless charging bilateral optimization coordination control system as described in any one of claims 1-8; detect and obtain the current driving lateral offset and the user's expected average power; S2: The multi-criteria coordination matching network module is used to perform global optimization of candidate operating points. The charging completion rate, average efficiency and total harmonic distortion are calculated and normalized. The calculation results of the three criteria after normalization are weighted and fused using dynamic weights to obtain a comprehensive target value. The operating point with the largest comprehensive target value is taken as the optimal operating point under the current operating condition. The reference inverter phase angle and DC converter reference trajectory are output. S3: The roadside controller receives the reference inverter phase angle output by the multi-criteria coordination matching network module and adjusts the inverter's output voltage. The vehicle-side controller, based on the reference trajectory of the DC-DC converter output by the multi-criteria coordination matching network module, enables the DC-DC converter to track the reference trajectory. S4: During wireless charging, the system detects changes in the lateral offset of the driver and the user's expected average power in real time. If changes are detected, the system returns to steps S1-S3 to recalculate the results of each criterion, update the dynamic weights, and reselect the optimal operating point to achieve dynamic online switching of the control strategy. If the driver offset and expected power do not change in real time, the system continues to execute the current control strategy until charging is complete.

10. A computer storage medium for storing a computer program, wherein when the computer program in the storage medium is executed, it implements the bilateral optimization coordination control method for dynamic wireless charging of electric vehicles as described in claim 9.

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