Wireless charging pavement system fusing vehicle perception and health diagnosis and working method thereof

Abstract

The application belongs to the technical field of intelligent transportation, and discloses a wireless charging pavement system fusing vehicle perception and health diagnosis and a coordination method thereof.The system takes an embedded wireless charging transmitting coil under the pavement as a multifunctional sensing unit, realizes online monitoring and diagnosis of vehicle non-inductive identification and structural health state of the charging pavement on a single hardware platform in parallel through real-time collection of electrical parameters (voltage, current, impedance and other characteristic values), and has an intelligent scheduler built-in to preferentially guarantee vehicle perception and dynamic charging, and automatically execute pavement health diagnosis during a no-vehicle period to complete full-process closed-loop management from data collection, feature extraction, fault identification to early warning pushing.The application breaks through the limitation of traditional separate perception and detection, realizes high reuse of hardware resources, intelligent coordination of system functions and active prediction of operation and maintenance management, significantly reduces system deployment and operation and maintenance costs, and improves the reliability and intelligent level of the road wireless charging system.

Description

Technical Field

[0001] This invention belongs to the field of intelligent transportation technology, and more specifically, relates to a wireless charging road surface system that integrates vehicle perception and health diagnosis, and its working method. Background Technology

[0002] Dynamic wireless charging pavement technology is a novel transportation infrastructure technology that integrates wireless charging transmitting coils into the road surface structure to provide real-time, uninterrupted power replenishment for electric vehicles in motion. This technology is considered a key path to support future green transportation systems, alleviate "range anxiety" for electric vehicles, and reduce onboard battery capacity, demonstrating significant social benefits and market potential. In recent years, with the continuous growth of electric vehicle ownership and the rapid development of intelligent connected vehicles, dynamic wireless charging pavement technology has gradually become a research hotspot in the field of intelligent transportation. Among its applications, using wireless charging pavement systems for vehicle speed measurement and health diagnostics has become one of the main applications.

[0003] Currently, the mainstream methods for speed measurement and health diagnosis of wireless charging road systems mainly include the following three types: The first type is based on external sensors, which involves laying wireless charging transmitting coils under the road surface and adding external sensors such as radar and cameras to identify and measure the speed of vehicles; the second type relies on periodic offline detection or manual inspection to assess the health status of the road surface and charging facilities; the third type is based on charging coils or electrical parameters, which uses charging coils for vehicle detection or electrical parameters for fault diagnosis.

[0004] However, the aforementioned methods for speed measurement and health diagnosis of wireless charging road systems all have some significant drawbacks: First, while existing external sensor-based implementations can achieve vehicle detection, they increase the hardware complexity and overall deployment cost of the system. They also present technical challenges in multi-sensor fusion and calibration, resulting in low system integration and poor economic efficiency. Second, the existing implementation methods that rely on periodic offline detection or manual inspection cannot achieve real-time and online monitoring. They are often only discovered after a fault occurs, and once a fault occurs, destructive excavation is often required for detection, resulting in high operation and maintenance costs, long repair cycles, and difficulty in achieving preventive maintenance. Third, existing implementations based on charging coils or electrical parameters are mostly limited to a single function and fail to achieve deep synergy between vehicle perception and facility health diagnosis from the perspectives of system architecture, signal fusion and control strategies, thus failing to fully leverage the integration potential of the same hardware platform. Summary of the Invention

[0005] To address the aforementioned deficiencies or improvement needs of existing technologies, this invention provides a wireless charging road surface system and its operating method that integrates vehicle perception and health diagnosis. The aim is to solve the technical problems of existing technologies, such as system complexity and high cost due to reliance on external sensors; the technical problems of inability to achieve real-time online monitoring and high maintenance costs due to reliance on periodic offline detection or manual inspection; and the technical problems of limited functionality in existing technologies, failing to achieve deep synergy between vehicle perception and health diagnosis, thus failing to fully realize the integration potential of the hardware platform.

[0006] To achieve the above objectives, according to one aspect of the present invention, a wireless charging road surface system integrating vehicle perception and health diagnosis is provided, comprising a dual-function transmitting coil array, a sensor circuit, a synchronous acquisition circuit, a programmable excitation source, and an intelligent fusion controller, wherein the dual-function transmitting coil array is buried at a predetermined depth below the road surface. The sensor circuit, synchronous acquisition circuit, programmable excitation source, and intelligent fusion controller are housed in a comprehensive cabinet next to the road. The dual-function transmitting coil array is connected to the integrated chassis via wires embedded in the roadbed. The dual-function transmitting coil array is electrically connected to the sensor circuit. The sensor circuit and the synchronous acquisition circuit are electrically connected; The synchronous acquisition circuit is electrically connected to the intelligent fusion controller; The programmable excitation source is electrically connected to the intelligent fusion controller; The programmable excitation source is electrically connected to the dual-function transmitting coil array.

[0007] Preferably, the preset depth ranges from 2cm to 15cm, and more preferably 5cm.

[0008] Preferably, the dual-function transmitting coil array uses a 30mm×15mm rectangular coil, which is wound with 15 turns of Litz wire with a diameter of 0.2mm. Each Litz wire has a resistance of 0.45Ω, a self-resonant frequency of 1.2MHz, an inductance of L=28±2μH, and a quality factor of Q≥80. The sensor circuit includes a high-precision current sensor and a voltage sensor connected in series, wherein the current sensor is an integrated current sensor and the voltage sensor is an optically isolated voltage sensor; The programmable excitation source includes a programmable waveform generator and an audio power amplifier connected in series.

[0009] According to another aspect of the present invention, a method for operating the above-mentioned wireless charging road system integrating vehicle perception and health diagnosis is provided, comprising the following steps: (1) The intelligent fusion controller determines whether the continuous period of no vehicles passing through the road exceeds the preset value, or whether the road has reached the fixed diagnostic cycle. If so, proceed to step (8); otherwise, proceed to step (2). (2) The sensor circuit uses a sampling rate f s Continuously monitor the current time-domain waveform i of the j-th coil loop in the dual-function transmitting coil array at time t. j (t); and send the acquired current time-domain waveform to the intelligent fusion controller, where j∈[1, the total number of coil loops in the dual-function transmitting coil array]; (3) The intelligent fusion controller measures the current time-domain waveform i of the j-th coil loop from the sensor circuit at time t. j (t) The current time-domain waveform is extracted to obtain the current envelope E of the j-th coil circuit at time t. j (t), and determine the current envelope E j If (t) is greater than the vehicle detection dynamic threshold T, then notify the sensor circuit to generate a trigger signal and send the trigger signal to the intelligent fusion controller, and then proceed to step (4); otherwise, return to step (2). (4) The intelligent fusion controller obtains the first trigger time t of the j-th coil circuit based on the trigger signal from the sensor circuit. j and the second trigger time t j+1 The difference between the two is then processed to obtain the triggering time difference Δt=t between adjacent coils. j+1 -t j And obtain the instantaneous velocity v of the j-th coil circuit based on the trigger time difference Δt. j =d / Δt, then obtain the second triggering time t of the j-th coil circuit. j+1 and the third trigger time t j+2 ..., obtain the Nth trigger time t of the j-th coil circuit. j+N and the (N+1)th trigger time t j+N+1 The above process is repeated to obtain N instantaneous velocities. The least squares method is used to perform linear fitting on all N instantaneous velocities to obtain the velocity estimate v and confidence level C, where d represents the distance between adjacent coils. (5) The intelligent fusion controller generates a power adjustment command based on the received speed estimate v and confidence level C, and sends the power adjustment command to the programmable excitation source; (6) The programmable excitation source parses the power adjustment command from the intelligent fusion controller to obtain the corresponding power, and outputs the excitation signal of the power to the dual-function transmitting coil array to perform dynamic charging of vehicles passing through the road, and at the same time sends a vehicle charging notification to the intelligent fusion controller. (7) After receiving the vehicle charging notification from the programmable excitation source, the intelligent fusion controller performs statistical processing on the traffic data of vehicles passing through the road to generate a vehicle traffic report, uploads the vehicle traffic report to the cloud operation and maintenance platform, and then returns to step (1). (8) The intelligent fusion controller connects the programmable excitation source to the dual-function transmitting coil array and sends a frequency sweep command to the programmable excitation source; (9) The programmable excitation source generates a frequency sweep excitation signal according to the frequency sweep command from the intelligent fusion controller and sends the frequency sweep excitation signal to the dual-function transmitting coil array; (10) The dual-function transmitting coil performs frequency sweeping according to the frequency sweeping excitation signal from the programmable excitation source to generate voltage steady-state response V(f) and current steady-state response I(f) at different frequencies f at both ends of the dual-function coil, and sends the voltage steady-state response V(f) and current steady-state response I(f) to the synchronous acquisition circuit. (11) The synchronous acquisition circuit performs analog-to-digital conversion on the steady-state voltage response V(f) from the dual-function transmitting coil array to obtain voltage data, and performs analog-to-digital conversion on the steady-state current response I(f) from the dual-function transmitting coil array to obtain current data, and sends the voltage data and current data to the intelligent fusion controller. (12) The intelligent fusion controller performs fast Fourier transform on the voltage and current data from the synchronous acquisition circuit to obtain the steady-state voltage response V(f) and steady-state current response I(f) respectively, and performs a division operation on the two to obtain the complex impedance Z(f) = V(f) / I(f), and obtains multiple characteristics based on the complex impedance Z(f), including the resonant frequency f. r Peak impedance Z p =|Z(f r Quality factor Q = f r / Δf, and impedance asymmetry A s =|Z(f r+δ )|-|Z(f r-δ )| / |Z(f r )|, and determine whether a fault has occurred based on each obtained feature. If so, query the pre-established fault feature library to obtain the specific fault type, the location of the fault, and the severity of the fault. Generate a health assessment report based on the specific fault type, the location of the fault, and the severity of the fault, and proceed to step (13). Otherwise, proceed to step (14), where Δf represents the half-power bandwidth of the resonant peak, that is, the impedance amplitude drops to 1 / of the peak impedance. The difference between the upper and lower cutoff frequencies corresponding to a value of 0.707 (i.e., 0.707 times) is calculated using the following formula: f1 and f2 satisfy δ represents the frequency offset, which is 3 to 5 times the sweep step size, preferably 5 times; (13) The intelligent fusion controller stores the health assessment report and the vehicle passage report in a unified manner to generate a historical operation database, and sends the historical operation database to the cloud platform, and then returns to step (1). (14) The intelligent fusion controller sends a standby command to the dual-function transmitting coil array so that the dual-function transmitting coil array enters a low-power standby state, and the process ends.

[0010] Preferably, the vehicle detection dynamic threshold T is obtained through the following steps: (A) After the dual-function transmitting coil array has been running continuously for 3 minutes, the intelligent fusion controller sends an initialization command to the programmable excitation source; (B) After receiving the initialization command from the intelligent fusion controller, the programmable excitation source inputs a sweep frequency excitation signal to the dual-function transmitting coil array; (C) After receiving the sweep frequency excitation signal from the programmable excitation source, the dual-function transmitting coil array generates voltage response signal and current response signal and sends them to the synchronous acquisition circuit; (D) The synchronous acquisition circuit performs fast Fourier transform on the voltage response signal and current response signal from the dual-function transmitting coil array to obtain the complex impedance spectrum at each frequency point, and sends the complex impedance spectrum of all frequency points to the intelligent fusion controller. (E) The intelligent fusion controller performs feature extraction on all complex impedance spectra from the synchronous acquisition circuit to obtain multiple feature values, including the resonant frequency f. r Peak impedance Z p Quality factor Q and impedance asymmetry A s ; (F) The sensor circuit collects background electrical noise signals of the road at fixed time intervals when there are no vehicles passing by, and calculates the mean and standard deviation of the collected background electrical noise signals to obtain the mean of the background electrical noise signal. With standard deviation σ noise and the mean With standard deviation σ noise Send to the intelligent fusion controller; (G) The intelligent fusion controller uses the average value from the sensor circuitry. With standard deviation σ noise Obtain the vehicle detection dynamic threshold T= +k*σ noise The value of k ranges from 3 to 5, with k=3 being preferred.

[0011] Preferably, if the rate of change of the resonant frequency Δf rIt is in the range of 3% to 6%, and the rate of change of peak impedance ΔZ p If the range is -20% to -10%, it indicates that the specific fault type is a short circuit fault between coil turns, the location of the fault is between coil turns, and the severity of the fault is mild. If the rate of change of the resonant frequency Δf r satisfy And the rate of change of peak impedance ΔZ p satisfy This indicates that the specific fault type is a short circuit fault between coil turns, the location of the fault is between coil turns, and the severity of the fault is severe. If the rate of change of the resonant frequency Δf r satisfy And the rate of change of peak impedance This indicates that the specific fault type is a microcrack in the magnetic core, the location of the fault is the magnetic core, and the severity of the fault is mild. If the rate of change of the resonant frequency Δf r satisfy And the rate of change of peak impedance This indicates that the specific fault type is a deep crack in the magnetic core, the fault location is the magnetic core, and the severity of the fault is severe.

[0012] Preferably, if the rate of change of peak impedance This indicates that the specific fault type is minor road surface cracks, the location of the fault is the road surface, and the severity of the fault is mild. If the rate of change of peak impedance This indicates that the specific fault type is moderate road surface crack, the fault location is the road surface, and the fault severity is moderate. If the rate of change of peak impedance This indicates that the specific fault type is severe road surface cracks, the fault location is the road surface, and the severity of the fault is severe.

[0013] Preferably, if the rate of change of the resonant frequency satisfies And the rate of change of peak impedance satisfies This indicates that the specific fault type is a metal foreign object smaller than 3mm, the location of the fault is on the road surface, and the severity of the fault is minor foreign object. If the rate of change of the resonant frequency satisfies And the rate of change of peak impedance satisfies This indicates that the specific fault type is a 3-5mm metal foreign object, the fault location is on the road surface, and the severity of the fault is a moderate foreign object. If the rate of change of the resonant frequency satisfies And the rate of change of peak impedance satisfies This indicates that the specific fault type is a metal foreign object larger than 5mm, the fault location is on the road surface, and the severity of the fault is a severe foreign object.

[0014] Preferably, if the rate of change of impedance asymmetry satisfy This indicates that the specific fault type is road surface water seepage fault, the fault location is the road surface overlay layer, and the fault severity is mild water seepage. If the rate of change of impedance asymmetry satisfy This indicates that the specific fault type is road surface water seepage fault, the fault location is the road surface overlay layer, and the fault severity is moderate water seepage. If the rate of change of impedance asymmetry satisfy This indicates that the specific fault type is road surface water seepage fault, the fault location is the road surface overlay layer, and the fault severity is severe water seepage.

[0015] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects: (1) This invention adopts a system architecture that uses a dual-function transmitting coil array as a multi-functional sensing probe. By deeply mining and fusing the electrical characteristic values ​​of the wireless charging coil, it realizes precise vehicle speed measurement and online road health diagnosis on a single hardware platform. Therefore, it can solve the technical problems of existing external sensor-based implementation methods that increase the hardware complexity and overall deployment cost of the system, and also have technical challenges in multi-sensor fusion and calibration, resulting in low system integration and poor economic efficiency. (2) This invention employs the vehicle perception and dynamic charging process in steps (2) to (7), which achieves accurate identification and real-time speed measurement of vehicles by continuously monitoring the coil current waveform and extracting the envelope and making dynamic threshold judgments. Therefore, it can solve the technical problem that most existing implementation methods based on charging coils or electrical parameters are limited to a single function and fail to achieve deep collaboration between vehicle perception and facility health diagnosis from the perspectives of system architecture, signal fusion and control strategy, thus failing to fully leverage the integration potential of the same hardware platform. (3) This invention employs the road health diagnosis process in steps (8) to (12). By automatically performing frequency sweeping excitation and impedance spectrum analysis during periods without vehicles, it extracts characteristic values ​​such as resonant frequency, peak impedance, and quality factor, and compares them with the fault feature database. This enables online, non-destructive, and accurate diagnosis of faults of different parts and degrees, such as coils, magnetic cores, and road surfaces. This solves the technical problem that existing methods relying on periodic offline detection or manual inspection cannot achieve real-time and online monitoring, often only discovering faults after they occur. Moreover, once a fault occurs, destructive excavation is often required for detection, resulting in high maintenance costs, long repair cycles, and difficulty in achieving preventive maintenance. (4) Since the present invention adopts the intelligent scheduling method described in step (1), by judging the duration of continuous vehicle passage or fixed diagnostic cycle, it prioritizes vehicle perception and dynamic charging, and automatically performs road health diagnosis during periods without vehicles. Therefore, it achieves deep collaboration between vehicle perception and facility health diagnosis, and fully leverages the integration potential of the same hardware platform. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the wireless charging road surface system integrating vehicle perception and health diagnosis according to the present invention. Figure 2 This is a flowchart of the working method of the wireless charging road surface system integrating vehicle perception and health diagnosis according to the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0018] This invention provides a wireless charging road surface system and its working method that integrates vehicle perception and health diagnosis. The core idea is to use the wireless charging transmitting coil laid under the road surface as a multi-functional sensing probe. By performing multi-dimensional and multi-mode analysis on the characteristic values ​​(voltage, current, impedance, etc.) in its circuit, the two major functions of vehicle perception and road surface health diagnosis are realized in parallel on the same hardware platform, and the task coordination is carried out through an intelligent scheduler.

[0019] like Figure 1 As shown, the present invention provides a wireless charging road surface system that integrates vehicle perception and health diagnosis, including a dual-function transmitting coil array 1, a sensor circuit 2, a synchronous acquisition circuit 3, a programmable excitation source 4, and an intelligent fusion controller 5.

[0020] The dual-function transmitting coil array 1 is buried at a preset depth below the road surface (the preset depth ranges from 2cm to 15cm, preferably 5cm). The sensor circuit 2, synchronous acquisition circuit 3, programmable excitation source 4, and intelligent fusion controller 5 are set in the integrated box next to the road. The dual-function transmitting coil array 1 is connected to the integrated box through wires buried in the road base layer.

[0021] The dual-function transmitting coil array 1 is electrically connected to the sensor circuit 2, the sensor circuit 2 is electrically connected to the synchronous acquisition circuit 3, the synchronous acquisition circuit 3 is electrically connected to the intelligent fusion controller 5, the programmable excitation source 4 is electrically connected to the intelligent fusion controller 5, and the programmable excitation source 4 is electrically connected to the dual-function transmitting coil array 1.

[0022] The dual-function transmitting coil array 1 uses a 30mm×15mm rectangular coil, which is wound with 15 turns of 0.2mm diameter Litz wire (each strand is made of multiple strands of fine enameled wire). The resistance of each Litz wire is 0.45Ω (25℃), the self-resonant frequency is 1.2MHz, the inductance is L=28±2μH (at 1kH), and the quality factor Q≥80 (100kHz). Sensor circuit 2 includes a high-precision current sensor and a voltage sensor connected in series. The current sensor is an integrated current sensor of model ACS37002 manufactured by Allegro MicroSystems, and the voltage sensor is an optically isolated voltage sensor of model ACPL-C87B manufactured by Broadcom. The high-precision synchronous acquisition circuit 3 uses an AD7606BSTZ acquisition circuit manufactured by Analog Devices. The programmable excitation source 4 includes a programmable waveform generator 41 and an audio power amplifier 42 connected in series; the programmable waveform generator is an AD9833BRMZ waveform generator manufactured by Analog Devices, and the audio power amplifier is a TPA3116D2DAD power amplifier manufactured by Texas Instruments. The intelligent fusion controller 5 uses a microcontroller manufactured by STMicroelectronics, model STM32F407VET6.

[0023] like Figure 2 As shown, the present invention also provides a method for operating the above-mentioned wireless charging road system integrating vehicle perception and health diagnosis, including the following steps: (1) The intelligent fusion controller determines whether the continuous period of no vehicle passage on the road exceeds the preset value (the value range is 15 seconds to 60 seconds, preferably 30 seconds), or whether the road has reached the fixed diagnosis cycle (usually 3-15 days, preferably 7 days). If so, proceed to step (8) (i.e., perform road health diagnosis); otherwise, proceed to step (2) (i.e., perform vehicle perception and dynamic charging). The advantage of this step (1) is that by prioritizing vehicle perception and dynamic charging functions through intelligent scheduling, it automatically switches to health diagnosis mode during periods without vehicles or at fixed intervals, realizing time-division multiplexing and collaborative work of the two major functions, avoiding functional conflicts, and improving the overall operating efficiency of the system. (2) The sensor circuit uses a sampling rate f s (500kHz~2MHz, preferably 1MHz) Continuously monitor the current time-domain waveform i of the j-th coil loop in the dual-function transmitting coil array at time t. j (t). The acquired current time-domain waveform is sent to the intelligent fusion controller, where j∈[1, the total number of coil loops in the dual-function transmitting coil array]; The advantage of this step (2) is that by using a high sampling rate to continuously monitor the coil current waveform, it is possible to capture the transient electromagnetic coupling changes caused by the passing of a vehicle, providing the original data basis for subsequent high-precision vehicle detection and speed measurement.

[0024] (3) The intelligent fusion controller measures the current time-domain waveform i of the j-th coil loop from the sensor circuit at time t. j (t) The current time-domain waveform is extracted to obtain the current envelope E of the j-th coil circuit at time t. j (t), and determine the current envelope E j If (t) is greater than the vehicle detection dynamic threshold T, then notify the sensor circuit to generate a trigger signal and send the trigger signal to the intelligent fusion controller, and then proceed to step (4); otherwise, return to step (2). The vehicle detection dynamic threshold T in this step is obtained through the following steps: (A) The dual-function transmitting coil array runs continuously for 3 minutes (to bring the circuit system to thermal equilibrium and eliminate the influence of temperature drift on the measurement), and the intelligent fusion controller sends an initialization command to the programmable excitation source; (B) After receiving the initialization command from the intelligent fusion controller, the programmable excitation source inputs a sweep frequency excitation signal to the dual-function transmitting coil array; (C) After receiving the sweep frequency excitation signal from the programmable excitation source, the dual-function transmitting coil array generates voltage response signal and current response signal and sends them to the synchronous acquisition circuit; (D) The synchronous acquisition circuit performs fast Fourier transform on the voltage response signal and current response signal from the dual-function transmitting coil array to obtain the complex impedance spectrum at each frequency point, and sends the complex impedance spectrum of all frequency points to the intelligent fusion controller. (E) The intelligent fusion controller performs feature extraction on all complex impedance spectra from the synchronous acquisition circuit to obtain multiple feature values, including the resonant frequency f. r Peak impedance Z p Quality factor Q and impedance asymmetry A s ; (F) The sensor circuit collects background electrical noise signals of the road when there are no vehicles passing by at fixed time intervals (10 seconds in this invention), and calculates the mean and standard deviation of the collected background electrical noise signals to obtain the mean of the background electrical noise signal. With standard deviation σ noise and the mean With standard deviation σ noise Send to the intelligent fusion controller; (G) The intelligent fusion controller uses the average value from the sensor circuitry. With standard deviation σ noise Obtain the vehicle detection dynamic threshold T= +k*σ noise The value of k ranges from 3 to 5, with k=3 being preferred; The advantage of the above sub-steps (A) to (G) is that by establishing the initial benchmark of the system through frequency sweeping excitation and dynamically calculating the detection threshold by collecting background noise in real time, the vehicle detection threshold can adapt to environmental changes, effectively suppress false triggering, and improve the reliability of vehicle recognition.

[0025] (4) The intelligent fusion controller obtains the first trigger time t of the j-th coil circuit based on the trigger signal from the sensor circuit. j and the second trigger time t j+1 The difference between the two is then processed to obtain the triggering time difference Δt=t between adjacent coils. j+1 -t j And obtain the instantaneous velocity v of the j-th coil circuit based on the trigger time difference Δt. j =d / Δt, then obtain the second triggering time t of the j-th coil circuit. j+1 and the third trigger time t j+2 ..., obtain the Nth trigger time t of the j-th coil circuit. j+N and the (N+1)th trigger time t j+N+1The above process is repeated to obtain N instantaneous velocities. The least squares method is used to perform linear fitting on all N instantaneous velocities to obtain the velocity estimate v and confidence level C, where d represents the distance between adjacent coils. The advantage of this step (4) is that by using the triggering times of multiple coils to perform least squares fitting, the single measurement error can be effectively reduced and the speed measurement accuracy can be improved. At the same time, the output confidence level can be used as the basis for subsequent charging power adjustment.

[0026] (5) The intelligent fusion controller generates a power adjustment command based on the received speed estimate v and confidence level C, and sends the power adjustment command to the programmable excitation source; (6) The programmable excitation source parses the power adjustment command from the intelligent fusion controller to obtain the corresponding power, and outputs the excitation signal of the power to the dual-function transmitting coil array to perform dynamic charging of vehicles passing through the road, and at the same time sends a vehicle charging notification to the intelligent fusion controller. (7) After receiving the vehicle charging notification from the programmable excitation source, the intelligent fusion controller performs statistical processing on the traffic data (speed, trigger time, identification result) of vehicles passing through the road to generate a vehicle traffic report, and uploads the vehicle traffic report to the cloud operation and maintenance platform, and then returns to step (1). (8) The intelligent fusion controller connects the programmable excitation source to the dual-function transmitting coil array and sends a frequency sweep command to the programmable excitation source (frequency range 1kHz~200kHz, step size 500Hz). (9) The programmable excitation source generates a frequency sweep excitation signal according to the frequency sweep command from the intelligent fusion controller and sends the frequency sweep excitation signal to the dual-function transmitting coil array; (10) The dual-function transmitting coil performs frequency sweeping according to the frequency sweeping excitation signal from the programmable excitation source to generate voltage steady-state response V(f) and current steady-state response I(f) at different frequencies f at both ends of the dual-function coil, and sends the voltage steady-state response V(f) and current steady-state response I(f) to the synchronous acquisition circuit. (11) The synchronous acquisition circuit performs analog-to-digital conversion on the steady-state voltage response V(f) from the dual-function transmitting coil array to obtain voltage data, and performs analog-to-digital conversion on the steady-state current response I(f) from the dual-function transmitting coil array to obtain current data, and sends the voltage data and current data to the intelligent fusion controller. (12) The intelligent fusion controller performs fast Fourier transform on the voltage and current data from the synchronous acquisition circuit to obtain the steady-state voltage response V(f) and steady-state current response I(f) respectively, and performs a division operation on the two to obtain the complex impedance Z(f) = V(f) / I(f), and obtains multiple characteristics based on the complex impedance Z(f), including the resonant frequency f. r (i.e., the maximum amplitude of the complex impedance), peak impedance Z p =|Z(f r Quality factor Q = f r / Δf, and impedance asymmetry A s =|Z(f r+δ )|-|Z(f r-δ )| / |Z(f r The system determines whether a fault has occurred based on each obtained feature. If so, it queries the pre-established fault feature library to obtain the specific fault type, the location of the fault (e.g., coil, magnetic core, coil turns, road surface, etc.), and the severity of the fault (based on the degree of deviation from the preset threshold). A health assessment report is generated based on the specific fault type, the location of the fault, and the severity of the fault, and the process proceeds to step (13). Otherwise, the process proceeds to step (14), where Δf represents the half-power bandwidth of the resonant peak, i.e., the impedance amplitude drops to 1 / of the peak impedance. The difference between the upper and lower cutoff frequencies corresponding to a value of 0.707 (i.e., 0.707 times) is calculated using the following formula: f1 and f2 satisfy δ represents the frequency offset, which is 3 to 5 times the sweep step size, preferably 5 times; The advantage of this step (12) is that by extracting multi-dimensional features (resonant frequency, peak impedance, quality factor, asymmetry) from the complex impedance spectrum and establishing a detailed fault feature library, it is possible to achieve accurate online diagnosis of different fault types, locations and severity, providing a quantitative basis for predictive maintenance.

[0027] Specifically, if the rate of change of the resonant frequency It is in the range of 3% to 6%, and the rate of change of peak impedance If the value is between -20% and -10%, it indicates that the specific fault type is a short circuit fault between coil turns, the location of the fault is between coil turns, and the severity of the fault is mild. If the rate of change of the resonant frequency satisfy And the rate of change of peak impedance ΔZ p satisfy This indicates that the specific fault type is a short circuit fault between coil turns, the fault location is between coil turns, and the severity of the fault is severe.

[0028] If the rate of change of the resonant frequency And the rate of change of the quality factor This indicates that the specific fault type is a microcrack in the magnetic core, the fault location is the magnetic core, and the severity of the fault is mild.

[0029] If the rate of change of the resonant frequency And the rate of change of the quality factor This indicates that the specific fault type is a deep crack in the magnetic core, the fault location is the magnetic core, and the severity of the fault is severe.

[0030] If the rate of change of the quality factor This indicates that the specific fault type is minor road surface cracks, the fault location is the road surface, and the severity of the fault is mild.

[0031] If the rate of change of the quality factor This indicates that the specific fault type is moderate road surface crack, the fault location is the road surface, and the fault severity is moderate.

[0032] If the rate of change of the quality factor This indicates that the specific fault type is severe road surface cracks, the fault location is the road surface, and the severity of the fault is severe.

[0033] If the rate of change of peak impedance And the rate of change of the quality factor This indicates that the specific fault type is a metal foreign object smaller than 3mm, the fault location is on the road surface, and the severity of the fault is a minor foreign object.

[0034] If the rate of change of peak impedance And the rate of change of the quality factor This indicates that the specific fault type is a 3-5mm metal foreign object, the fault location is on the road surface, and the severity of the fault is a moderate foreign object.

[0035] If the rate of change of peak impedance And the rate of change of the quality factor This indicates that the specific fault type is a metal foreign object larger than 5mm, the fault location is on the road surface, and the severity of the fault is a severe foreign object.

[0036] If the rate of change of impedance asymmetry satisfy This indicates that the specific fault type is road surface water seepage fault, the fault location is the road surface overlay layer, and the severity of the fault is mild water seepage.

[0037] If the rate of change of impedance asymmetry satisfy This indicates that the specific fault type is road surface water seepage fault, the fault location is the road surface overlay layer, and the fault severity is moderate water seepage.

[0038] If the rate of change of impedance asymmetry satisfy This indicates that the specific fault type is road surface water seepage fault, the fault location is the road surface overlay layer, and the fault severity is severe water seepage.

[0039] (13) The intelligent fusion controller stores the health assessment report and the vehicle passage report in a unified manner to generate a historical operation database, and sends the historical operation database to the cloud platform, and then returns to step (1). The advantages of the above steps (8) to (13) are: automatic frequency sweeping excitation and impedance analysis are performed during periods without vehicles, completing the closed-loop health diagnosis of the entire process from data acquisition, feature extraction, fault identification to report generation, and realizing online, non-destructive, and real-time monitoring of the charging road structure.

[0040] (14) The intelligent fusion controller sends a standby command to the dual-function transmitting coil array so that the dual-function transmitting coil array enters a low-power standby state, and the process ends.

[0041] The advantage of this step (14) is that it puts the system into a low-power standby mode when there is no vehicle and during non-diagnostic periods, which effectively reduces system energy consumption and extends the service life of the equipment.

[0042] Example 1 (Vehicle Speed ​​Measurement and Charging) The simulated road surface is 1.5m long, 0.4m wide, and 0.3m high, and consists of a surface layer, a middle layer, and a base layer.

[0043] The surface layer is 20mm asphalt concrete, simulating the wear-resistant layer of the road surface; the middle layer is a 20cm thick foamed PVC board with an embedded coil array, simulating the base layer of the road surface; the base layer is a 10mm thick aluminum plate, providing mechanical support and electromagnetic shielding. Ten dual-function transmitting coils are laid at equal intervals along the driving direction in the intermediate layer, with a spacing of 10cm. They are encapsulated in epoxy resin and buried 5mm from the surface. The dual-function transmitting coil array 1 is connected to the sensor circuit 2, synchronous acquisition circuit 3, programmable excitation source 4, and intelligent fusion controller 5, which are located in the roadside integrated control box, via wires buried in the roadbed layer.

[0044] The transmitting coil in the dual-function transmitting coil is a rectangular Litz wire coil with 15 turns, 0.2mm wire diameter, and 30mm×15mm. Each coil is connected to an independent sensor and acquisition board, and a half-bridge inverter topology synchronous acquisition circuit is used.

[0045] The magnetic core is made of 30mm×30mm×5mm JF95 magnetic sheet.

[0046] The sensor circuit 2 includes a high-precision current sensor (ACS37002) and a voltage sensor (ACPL-C87B); the synchronous voltage acquisition circuit is AD7606BSTZ, used for synchronous acquisition of voltage and current; the intelligent fusion controller uses STM32F407VET6, and a switchable power amplifier as the excitation source.

[0047] After the system was built, the speed measurement system was tested using a small car with the receiver installed. The car was made of 5mm thick acrylic sheet and its dimensions were 100mm (length) × 50mm (width) × 50mm (height). The total weight including the battery and load was 220g. It was equipped with a DC geared motor and achieved speed adjustment in the range of 0.1-0.3m / s through PWM speed regulation. The receiving coil in the dual-function transmitting coil had the same size and material as the transmitting coil and was located in the middle of the car chassis.

[0048] To ensure the stable and reliable operation of this system in complex real-world road environments, the following strategies were adopted in the design to improve system robustness: a) Coping with environmental interference (extreme water seepage, road surface cracking, magnetic core damage): The coil and acquisition circuit are encapsulated with high-protection materials such as epoxy resin; the signal processing algorithm integrates a temperature compensation model and an adaptive background noise filtering mechanism to ensure the stability of feature value measurement.

[0049] b) Counteracting electromagnetic interference: Employing methods such as synchronous acquisition, digital filtering (bandpass filtering, notch filtering), and frequency domain analysis (e.g., impedance spectrum) to effectively separate characteristic signals caused by vehicles or malfunctions, and suppress electromagnetic noise from other electrical equipment or the wireless charging itself.

[0050] c) Handling Communication Anomalies and Data Loss: The vehicle perception module and intelligent fusion controller possess edge storage and computing capabilities, allowing them to continue operating locally and cache critical data during network interruptions. After communication is restored, they support breakpoint resumption and data verification retransmission mechanisms to ensure data integrity. The state machine design of the multi-task scheduler is fault-tolerant, automatically recovering in the next cycle even if a single diagnostic task is interrupted.

[0051] In this specific embodiment, the specific process of acquiring this characteristic disturbance is as follows: a high-precision current sensor uses a sampling rate... Continuously acquire the time-domain waveform of the current in each coil circuit. (subscript) Indicates the first (Each coil). When the vehicle chassis passes over the coil, the current waveform generates a characteristic disturbance due to changes in electromagnetic coupling. The system marks the trigger time of the vehicle's arrival at each coil by detecting the zero-crossing point, peak value, or energy abrupt change in the disturbance signal.

[0052] It should be noted that a typical method for extracting the trigger moment involves bandpass filtering the current time-domain waveform and comparing it with the current time-domain waveform when no vehicle is passing. The trigger moment is determined when the current time-domain waveform first exceeds a preset dynamic threshold. Record this moment as the trigger moment. The threshold can be adaptively set according to the background noise level. in and The mean and standard deviation of the current time-domain waveform during periods without vehicles are given, and k is a constant (usually taken as 3 to 5), which is taken as 3 in this embodiment.

[0053] Based on the fixed spacing between adjacent coils (In this example, the measurement is 10cm). When the vehicle triggers the [number]th [time / time] in sequence... and the When there are multiple coils, the instantaneous velocity can be calculated: To measure average speed and reduce single-measurement error, multiple coil pairs can be used for speed averaging or linear fitting. The vehicle continuously triggers N coils (N≥3), and the trigger time sequence is recorded. and corresponding coil position Fitting a straight line using the least squares method , obtain speed The estimated value is fitted by the following formula: The experimental preparation work is as follows: 1) System preheating: Run continuously for 3 minutes after power-on to allow the power module, signal conditioning circuit and power amplifier to reach thermal equilibrium.

[0054] 2) Background noise measurement: Record the system background electrical noise for 10 seconds when there are no vehicles to eliminate the influence of environmental electromagnetic interference and sensor inherent noise.

[0055] 3) Template creation: Collect feature values ​​of a standard vehicle passing by at a speed of 0.2 m / s as a template.

[0056] Each experiment follows these testing steps: 1) System monitoring: All charging coils are in standby mode, and the high-precision current / voltage sensing unit continuously monitors the time-domain waveform of the current in each coil circuit.

[0057] 2) Identification: The vehicle chassis causes a characteristic disturbance when passing over the coil, and the system captures this feature. Each speed point is tested 5 times, with a 30-second interval between each test.

[0058] 3) Speed ​​measurement and charging control: Based on the recorded trigger time, calculated speed, error and traffic flow, precise dynamic power supply control is achieved.

[0059] 4) Data statistics and cloud upload: Statistical analysis of vehicle indicators, generation of reports, upload to the cloud, and real-time display of road conditions.

[0060] The test results are shown in Table 1.

[0061] Example 2 (Road Health Diagnosis) The simulated road surface is 1.5m long, 0.4m wide, and 0.3m high, and consists of a surface layer, a middle layer, and a base layer.

[0062] The surface layer is 20mm asphalt concrete, simulating the wear-resistant layer of the road surface; the middle layer is a 20cm thick foamed PVC board with an embedded coil array, simulating the base layer of the road surface; the base layer is a 10mm thick aluminum plate, providing mechanical support and electromagnetic shielding. Ten dual-function transmitting coils, spaced 10cm apart, are laid at equal intervals along the driving direction in the intermediate layer. They are encapsulated in epoxy resin and buried 5mm from the surface. The dual-function transmitting coil array 1 is connected to the sensor circuit 2, synchronous acquisition circuit 3, programmable excitation source 4, and intelligent fusion controller 5, all located in the roadside integrated control box, via wires embedded in the roadbed layer. The transmitting coils are 15-turn, 0.2mm diameter, 30mm × 15mm rectangular Litz wire coils. Each coil connects to an independent sensor and acquisition board, employing a half-bridge inverter topology.

[0063] The magnetic core is made of 30mm×30mm×5mm JF95 magnetic sheet.

[0064] The sensor circuit 2 includes a high-precision current sensor (ACS37002) and a voltage sensor (ACPL-C87B); the synchronous voltage acquisition circuit is AD7606BSTZ, used for synchronous acquisition of voltage and current; the intelligent fusion controller uses (STM32F407VET6) and a switchable power amplifier as the excitation source.

[0065] In this specific embodiment, the specific process of obtaining the feature value is as follows: high-precision current / voltage sensors are deployed on both sides of the transmitting coil to collect the waveform signals of voltage and current at each measuring point on the road surface in real time.

[0066] It should be noted that the time-domain waveforms of voltage and current at each measurement point are obtained through multiple sensors. N data points are extracted from the steady-state response segment at each frequency point, and a Fast Fourier Transform (FFT) is performed to obtain the frequency-domain complex representation: ; ; The complex impedance of the coil at this frequency is defined as: ; in This is the impedance amplitude. This is the impedance phase angle.

[0067] It should also be noted that, regarding the frequency sweep range Impedance spectrum sequences are calculated for each discrete frequency point. Based on this sequence, the following feature values ​​are extracted for health diagnosis: The resonant frequency is equal to: ; Peak impedance equals: ; The quality factor equals: ; ; Impedance asymmetry equals: ; The aforementioned feature values ​​form a multidimensional vector, which is compared with a pre-stored health benchmark model (obtained through experimental calibration or simulation). When the deviation of a certain feature value exceeds a preset threshold, the system determines the corresponding fault type and issues a warning.

[0068] To verify the system's diagnostic capabilities, the following disease simulation experiment scheme was designed: After the system is built, perform the following steps: 1) System preheating: Run continuously for 3 minutes after power-on to allow the power module, signal conditioning circuit and power amplifier to reach thermal equilibrium.

[0069] 2) Background noise measurement: Record the system background electrical noise for 10 seconds when there are no vehicles to eliminate the influence of environmental electromagnetic interference and sensor inherent noise.

[0070] 3) Template establishment: In the frequency range of 1kHz-200kHz, with a step size of 500Hz, each measurement is performed 3 times and the average feature value is taken as the template.

[0071] Each experiment follows these testing steps: 1) Excitation and data acquisition: During idle periods when there is no vehicle traffic, or at fixed intervals, the cloud-based operation and maintenance system switches to health diagnosis mode and controls one or more selected coils to perform frequency sweeps in the frequency range of 1kHz-200kHz with a step size of 500Hz. Each measurement is taken 3 times and the average characteristic value is taken simultaneously.

[0072] 2) Feature extraction: Calculate and store the input feature values.

[0073] 3) Health Assessment: The extracted feature values ​​are intelligently compared with a pre-stored health benchmark model. Diagnosis is made based on the degree of deviation.

[0074] 4) Early warning and reporting: Generate health status assessment reports, conduct fault early warning and location, and upload them to the cloud to update and store the road health status, providing a basis for predictive maintenance.

[0075] The test results are shown in Table 2. Table 1. Vehicle Perception Function Test Results Table 2. Accuracy Test of Various Disease Identification Rates Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A wireless charging road surface system integrating vehicle perception and health diagnosis, comprising a dual-function transmitting coil array, a sensor circuit, a synchronous acquisition circuit, a programmable excitation source, and an intelligent fusion controller, characterized in that, The dual-function transmitting coil array is buried at a predetermined depth below the road surface; The sensor circuit, synchronous acquisition circuit, programmable excitation source, and intelligent fusion controller are housed in a comprehensive cabinet next to the road. The dual-function transmitting coil array is connected to the integrated chassis via wires embedded in the roadbed. The dual-function transmitting coil array is electrically connected to the sensor circuit. The sensor circuit and the synchronous acquisition circuit are electrically connected; The synchronous acquisition circuit is electrically connected to the intelligent fusion controller; The programmable excitation source is electrically connected to the intelligent fusion controller; The programmable excitation source is electrically connected to the dual-function transmitting coil array.

2. The wireless charging road system integrating vehicle perception and health diagnosis according to claim 1, characterized in that, The preset depth ranges from 2cm to 15cm, with 5cm being the preferred value.

3. The wireless charging road system integrating vehicle perception and health diagnosis according to claim 1 or 2, characterized in that, The dual-function transmitting coil array uses a 30mm×15mm rectangular coil, which is wound with 15 turns of Litz wire with a diameter of 0.2mm. The resistance of each Litz wire is 0.45Ω, the self-resonant frequency is 1.2MHz, the inductance is L=28±2μH, and the quality factor is Q≥80. The sensor circuit includes a high-precision current sensor and a voltage sensor connected in series, wherein the current sensor is an integrated current sensor and the voltage sensor is an optically isolated voltage sensor; The programmable excitation source includes a programmable waveform generator and an audio power amplifier connected in series.

4. A method for operating a wireless charging pavement system integrating vehicle perception and health diagnosis according to any one of claims 1 to 3, characterized in that, Includes the following steps: (1) The intelligent fusion controller determines whether the continuous period of no vehicles passing through the road exceeds the preset value, or whether the road has reached the fixed diagnostic cycle. If so, proceed to step (8); otherwise, proceed to step (2). (2) The sensor circuit uses a sampling rate f s Continuously monitor the current time-domain waveform i of the j-th coil loop in the dual-function transmitting coil array at time t. j (t); and send the acquired current time-domain waveform to the intelligent fusion controller, where j∈[1, the total number of coil loops in the dual-function transmitting coil array]; (3) The intelligent fusion controller measures the current time-domain waveform i of the j-th coil loop from the sensor circuit at time t. j (t) The current time-domain waveform is extracted to obtain the current envelope E of the j-th coil circuit at time t. j (t), and determine the current envelope E j If (t) is greater than the vehicle detection dynamic threshold T, then notify the sensor circuit to generate a trigger signal and send the trigger signal to the intelligent fusion controller, and then proceed to step (4); otherwise, return to step (2). (4) The intelligent fusion controller obtains the first trigger time t of the j-th coil circuit based on the trigger signal from the sensor circuit. j and the second trigger time t j+1 The difference between the two is then processed to obtain the triggering time difference Δt=t between adjacent coils. j+1 -t j And obtain the instantaneous velocity v of the j-th coil circuit based on the trigger time difference Δt. j =d / Δt, then obtain the second triggering time t of the j-th coil circuit. j+1 and the third trigger time t j+2 ..., obtain the Nth trigger time t of the j-th coil circuit. j+N and the (N+1)th trigger time t j+N+1 The above process is repeated to obtain N instantaneous velocities. The least squares method is used to perform linear fitting on all N instantaneous velocities to obtain the velocity estimate v and confidence level C, where d represents the distance between adjacent coils. (5) The intelligent fusion controller generates a power adjustment command based on the received speed estimate v and confidence level C, and sends the power adjustment command to the programmable excitation source; (6) The programmable excitation source parses the power adjustment command from the intelligent fusion controller to obtain the corresponding power, and outputs the excitation signal of the power to the dual-function transmitting coil array to perform dynamic charging of vehicles passing through the road, and at the same time sends a vehicle charging notification to the intelligent fusion controller. (7) After receiving the vehicle charging notification from the programmable excitation source, the intelligent fusion controller performs statistical processing on the traffic data of vehicles passing through the road to generate a vehicle traffic report, uploads the vehicle traffic report to the cloud operation and maintenance platform, and then returns to step (1). (8) The intelligent fusion controller connects the programmable excitation source to the dual-function transmitting coil array and sends a frequency sweep command to the programmable excitation source; (9) The programmable excitation source generates a frequency sweep excitation signal according to the frequency sweep command from the intelligent fusion controller and sends the frequency sweep excitation signal to the dual-function transmitting coil array; (10) The dual-function transmitting coil performs frequency sweeping according to the frequency sweeping excitation signal from the programmable excitation source to generate voltage steady-state response V(f) and current steady-state response I(f) at different frequencies f at both ends of the dual-function coil, and sends the voltage steady-state response V(f) and current steady-state response I(f) to the synchronous acquisition circuit. (11) The synchronous acquisition circuit performs analog-to-digital conversion on the steady-state voltage response V(f) from the dual-function transmitting coil array to obtain voltage data, and performs analog-to-digital conversion on the steady-state current response I(f) from the dual-function transmitting coil array to obtain current data, and sends the voltage data and current data to the intelligent fusion controller. (12) The intelligent fusion controller performs fast Fourier transform on the voltage and current data from the synchronous acquisition circuit to obtain the steady-state voltage response V(f) and steady-state current response I(f) respectively, and performs a division operation on the two to obtain the complex impedance Z(f) = V(f) / I(f), and obtains multiple characteristics based on the complex impedance Z(f), including the resonant frequency f. r Peak impedance Z p =|Z(f r Quality factor Q = f r / Δf, and impedance asymmetry A s =|Z(f r+δ )|-|Z(f r-δ )| / |Z(f r )|, and determine whether a fault has occurred based on each obtained feature. If so, query the pre-established fault feature library to obtain the specific fault type, the location of the fault, and the severity of the fault. Generate a health assessment report based on the specific fault type, the location of the fault, and the severity of the fault, and proceed to step (13). Otherwise, proceed to step (14), where Δf represents the half-power bandwidth of the resonant peak, that is, the impedance amplitude drops to 1 / of the peak impedance. The difference between the upper and lower cutoff frequencies corresponding to a value of 0.707 (i.e., 0.707 times) is calculated using the following formula: f1 and f2 satisfy δ represents the frequency offset, which is 3 to 5 times the sweep step size, preferably 5 times; (13) The intelligent fusion controller stores the health assessment report and the vehicle passage report in a unified manner to generate a historical operation database, and sends the historical operation database to the cloud platform, and then returns to step (1). (14) The intelligent fusion controller sends a standby command to the dual-function transmitting coil array so that the dual-function transmitting coil array enters a low-power standby state, and the process ends.

5. The wireless charging road system integrating vehicle perception and health diagnosis according to claim 4, characterized in that, The vehicle detection dynamic threshold T is established through the following steps: (A) After the dual-function transmitting coil array has been running continuously for 3 minutes, the intelligent fusion controller sends an initialization command to the programmable excitation source; (B) After receiving the initialization command from the intelligent fusion controller, the programmable excitation source inputs a sweep frequency excitation signal to the dual-function transmitting coil array; (C) After receiving the sweep frequency excitation signal from the programmable excitation source, the dual-function transmitting coil array generates voltage response signal and current response signal and sends them to the synchronous acquisition circuit; (D) The synchronous acquisition circuit performs fast Fourier transform on the voltage response signal and current response signal from the dual-function transmitting coil array to obtain the complex impedance spectrum at each frequency point, and sends the complex impedance spectrum of all frequency points to the intelligent fusion controller. (E) The intelligent fusion controller performs feature extraction on all complex impedance spectra from the synchronous acquisition circuit to obtain multiple feature values, including the resonant frequency f. r Peak impedance Z p Quality factor Q and impedance asymmetry A s ; (F) The sensor circuit collects background electrical noise signals of the road at fixed time intervals when there are no vehicles passing by, and calculates the mean and standard deviation of the collected background electrical noise signals to obtain the mean of the background electrical noise signal. With standard deviation σ noise and the mean With standard deviation σ noise Send to the intelligent fusion controller; (G) The intelligent fusion controller uses the average value from the sensor circuitry. With standard deviation σ noise Obtain the vehicle detection dynamic threshold T= +k*σ noise The value of k ranges from 3 to 5, with k=3 being preferred.

6. The wireless charging road system integrating vehicle perception and health diagnosis according to claim 5, characterized in that, If the rate of change of the resonant frequency Δf r It is in the range of 3% to 6%, and the rate of change of peak impedance ΔZ p If the range is -20% to -10%, it indicates that the specific fault type is a short circuit fault between coil turns, the location of the fault is between coil turns, and the severity of the fault is mild. If the rate of change of the resonant frequency Δf r satisfy And the rate of change of peak impedance ΔZ p satisfy This indicates that the specific fault type is a short circuit fault between coil turns, the location of the fault is between coil turns, and the severity of the fault is severe. If the rate of change of the resonant frequency Δf r satisfy And the rate of change of peak impedance This indicates that the specific fault type is a microcrack in the magnetic core, the location of the fault is the magnetic core, and the severity of the fault is mild. If the rate of change of the resonant frequency Δf r satisfy And the rate of change of peak impedance This indicates that the specific fault type is a deep crack in the magnetic core, the fault location is the magnetic core, and the severity of the fault is severe.

7. The wireless charging road system integrating vehicle perception and health diagnosis according to claim 6, characterized in that, If the rate of change of peak impedance This indicates that the specific fault type is minor road surface cracks, the location of the fault is the road surface, and the severity of the fault is mild. If the rate of change of peak impedance This indicates that the specific fault type is moderate road surface crack, the fault location is the road surface, and the fault severity is moderate. If the rate of change of peak impedance This indicates that the specific fault type is severe road surface cracks, the fault location is the road surface, and the severity of the fault is severe.

8. The wireless charging road system integrating vehicle perception and health diagnosis according to claim 7, characterized in that, If the rate of change of the resonant frequency satisfies And the rate of change of peak impedance satisfies This indicates that the specific fault type is a metal foreign object smaller than 3mm, the location of the fault is on the road surface, and the severity of the fault is minor foreign object. If the rate of change of the resonant frequency satisfies And the rate of change of peak impedance satisfies This indicates that the specific fault type is a 3-5mm metal foreign object, the fault location is on the road surface, and the severity of the fault is a moderate foreign object. If the rate of change of the resonant frequency satisfies And the rate of change of peak impedance satisfies This indicates that the specific fault type is a metal foreign object larger than 5mm, the fault location is on the road surface, and the severity of the fault is a severe foreign object.

9. The wireless charging road system integrating vehicle perception and health diagnosis according to claim 8, characterized in that, If the rate of change of impedance asymmetry satisfy This indicates that the specific fault type is road surface water seepage fault, the fault location is the road surface overlay layer, and the fault severity is mild water seepage. If the rate of change of impedance asymmetry satisfy This indicates that the specific fault type is road surface water seepage fault, the fault location is the road surface overlay layer, and the fault severity is moderate water seepage. If the rate of change of impedance asymmetry satisfy This indicates that the specific fault type is road surface water seepage fault, the fault location is the road surface overlay layer, and the fault severity is severe water seepage.

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