New energy automobile wireless power supply soft switching control system based on segmentation strategy

Abstract

The invention discloses a new energy automobile wireless power supply soft switching control system based on a segmentation strategy, and particularly relates to the technical field of new energy automobile wireless power supply, and the control system comprises a resonance current zero-cross detection module which detects a resonance current output square wave signal of a resonance network in real time; the segmented control decision module is used for selecting a strong control strategy or a weak control strategy according to a comparison result of the smoothed frequency difference value and a preset threshold value; the segmented control execution module is used for adjusting the inverter driving frequency by adopting a corresponding control algorithm according to the output of the segmented control decision module; according to the new energy automobile wireless power supply soft switching control system based on the segmentation strategy, the segmentation control logic and the model and algorithm of the special module are designed, so that the driving frequency of the new energy automobile is adjusted, and the driving frequency of the new energy automobile is adjusted to be within the preset frequency locking range. And wide-range frequency locking, rapid dynamic correction and high-precision steady-state control are realized.

Description

Technical Field

[0001] This application relates to the field of wireless power supply technology for new energy vehicles, and more specifically, to a soft-switching control system for wireless power supply of new energy vehicles based on a segmented strategy. Background Technology

[0002] With the rapid development of the new energy vehicle industry, wireless power supply technology has become a research hotspot in this field due to its advantages such as no physical contact and convenient operation. In wireless power supply systems, the soft-switching state of the resonant network is crucial to ensuring energy transmission efficiency, which depends on the matching of the inverter drive frequency and the resonant network's inherent frequency. However, when a new energy vehicle is in motion, factors such as changes in the relative position of the coupling coils, load fluctuations, and temperature drift can alter the parameters of the resonant network, causing a shift in the inherent frequency, disrupting the soft-switching conditions, and consequently increasing switching losses, reducing efficiency, and even making the system unstable.

[0003] Existing soft-switching control technologies mainly employ a single control strategy: one type uses a phase-locked loop (PLL) as its core, achieving frequency locking by tracking the phase of the resonant current or voltage, but its dynamic response is slow and it is prone to losing lock under rapid operating conditions; the other type uses fixed-frequency offset control, which has a faster response but low steady-state accuracy and a narrow frequency locking range. Furthermore, traditional control strategies do not differentiate between different degrees of frequency offset, making it difficult to balance rapid correction under large offsets with steady-state accuracy under small offsets, resulting in poor system adaptability.

[0004] Therefore, a wireless power supply soft-switching control system for new energy vehicles based on a segmented strategy is proposed to address the above problems. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, this application provides a wireless power supply soft-switching control system for new energy vehicles based on a segmented strategy to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, this application provides the following technical solution: a wireless power supply soft-switching control system for new energy vehicles based on a segmented strategy, the control system comprising:

[0007] The resonant current zero-crossing detection module detects the resonant current of the resonant network in real time and performs zero-crossing detection, outputting a square wave signal;

[0008] The voltage and current acquisition module is used to synchronously acquire the primary-side inverter output voltage and resonant current at the zero-crossing point of the drive signal;

[0009] The real-time natural frequency calculation module calculates the real-time natural frequency of the resonant network based on the data collected by the voltage and current acquisition module and the square wave signal through online dynamic time compensation.

[0010] The frequency difference analysis module calculates the difference between the real-time inherent frequency and the current inverter drive frequency, and performs smoothing processing.

[0011] The segmented control decision module selects a strong or weak control strategy based on the comparison between the smoothed frequency difference and a preset threshold.

[0012] The segmented control execution module adjusts the inverter drive frequency using a corresponding control algorithm based on the output of the segmented control decision module.

[0013] The frequency locking range judgment module determines whether the adjusted drive frequency is within the preset frequency locking range and performs abnormal protection.

[0014] Preferably, the resonant current zero-crossing detection module is based on a detection circuit composed of a second-order Butterworth low-pass filter circuit and a high-speed comparator to detect the resonant current of the resonant network. The second-order Butterworth low-pass filter circuit uses a transfer function... High-frequency noise is filtered out from the current signal, where s is a complex variable and ω c This is the cutoff angular frequency.

[0015] Preferably, the resonant current zero-crossing detection module (1) adopts an adaptive threshold zero-crossing detection algorithm, and the specific strategy of the adaptive threshold zero-crossing detection algorithm is as follows:

[0016] Let the resonant current sampling sequence be i(t), and after filtering, we obtain i f (t), where the zero-crossing threshold is defined as ±k×I. max Where k is the adaptive coefficient, and its value ranges from 0.01 to k to 0.05. max This represents the peak current value for the current cycle.

[0017] when i f (t) When the threshold is crossed, a zero-crossing signal is triggered, and a square wave signal S is generated. z (t), the triggering condition is:

[0018] when i f (t) When S crosses the threshold from negative to positive z (t) = 1, indicating that the square wave signal is in a high-level state;

[0019] when i f (t) When S crosses the threshold from positive to negative, z (t) = 0 indicates that the square wave signal is in a low-level state.

[0020] Preferably, the output terminal of the resonant current zero-crossing detection module is electrically connected to the input terminal of the voltage and current acquisition module. The voltage and current acquisition module uses a Hall voltage sensor and a Hall current sensor, and converts the analog signal to a digital signal using a 16-bit analog-to-digital converter (ADC). The voltage and current acquisition module employs a synchronous sampling triggering algorithm, and the strategy of this algorithm is as follows: using the square wave signal S output by the resonant current zero-crossing detection module... z (t) The transition edge serves as a synchronization trigger source, driving the ADC at t = t z Sampling is performed at ±Δt time to lock the instantaneous values ​​of voltage and current at the zero-crossing point of the drive, where t z Square wave signal S z (t) The time corresponding to the transition edge, Δt is the trigger delay, and Δt≤1μs.

[0021] Preferably, the output terminal of the voltage and current acquisition module is electrically connected to the input terminal of the real-time natural frequency calculation module. The real-time natural frequency calculation module adopts a dynamic compensation algorithm, and the dynamic compensation algorithm strategy is as follows: the period T of the detected resonant current is determined by the period of the square wave signal output by the resonant current zero-crossing detection module. det The time is corrected by using the phase difference between voltage and current to obtain the compensation time ΔT. The correction formula is as follows: in The inverter output voltage u inv (t) and resonant current i ies The phase difference of (t) is obtained by synchronously acquiring u inv with i ies Perform calculations; This is the phase compensation coefficient, and its value range is...

[0022] The system's period T0 and real-time natural frequency f0 are calculated using the following formulas: T0 = T det +ΔT,

[0023] Preferably, the output of the real-time natural frequency calculation module is electrically connected to the input of the frequency difference analysis module, and the frequency difference analysis module calculates the real-time natural frequency f0 and the current inverter drive frequency f0. d The absolute difference Δf, i.e., Δf = |f d -f0|; The frequency difference analysis module filters Δf using a three-period moving average filtering algorithm. The calculation formula is as follows: Where n is the current period number, Δf avgΔf(n) represents the average frequency difference after three-cycle moving average filtering in the nth cycle, Δf(n) represents the original frequency difference in the nth cycle without filtering, Δf(n-1) represents the original frequency difference in the (n-1)th cycle without filtering, and Δf(n-2) represents the original frequency difference in the (n-2)th cycle without filtering.

[0024] Preferably, the output of the frequency difference analysis module is electrically connected to the input of the segmented control decision module, and the segmented control decision module adopts three strategies: strong control, over-control, and weak control. Let the strong control threshold be Δf1 and 5kHz≤Δf1≤10kHz, and the weak control threshold be Δf2 and 1kHz≤Δf1≤3kHz. The decision logic of the segmented control decision module is as follows:

[0025] When the average frequency difference Δf avg When the value is greater than Δf1, a strong control strategy is activated.

[0026] When Δf2≤Δf avg If ≤Δf1, then the transition control strategy is activated;

[0027] When Δf avg If the value is less than Δf2, then a weak control strategy is enabled.

[0028] Preferably, the output of the segmented control decision module is electrically connected to the input of the segmented control execution module, and the control algorithm in the segmented control execution module includes a strong control algorithm, a weak control algorithm, and a transition control algorithm, wherein the strong control algorithm minimizes the objective function min|f d -f0| represents the frequency regulation Δf output by the PID controller, which is the target of the variable frequency PID control strategy. d1 It consists of three terms: proportion, integral, and differential. Where the proportionality coefficient K p1 ∈[5,10], integral coefficient K i1 ∈

[0029] [0.1, 0.5], differential coefficient K d1 ∈[0.01,0.1], the updated output frequency is f d，new =f d +Δf d1 ;

[0030] The weak control algorithm employs a sliding mode variable structure control mechanism, specifically: defining the sliding surface as s = Δf avg +λ∫Δf avg dt, where λ is the sliding mode coefficient and its value ranges from 0.5 to 1.5. The control law adopts the form of a sign function: Δf d2 =k s×sign(s), k s As the sliding mode gain, and its value range is 100≤k s ≤500, the final output frequency update formula is: f d,new =f d +Δf d2 , where Δf d2 The frequency adjustment of the sliding mode variable structure control output is given by sign(s), where sign(s) is the sign function.

[0031] The transient control algorithm employs a weighted PID-sliding mode control mechanism, which in turn uses a weighted hybrid control algorithm: Δf d3 =α×Δf d1 +(1-α)×Δf d2 The weighting coefficient α is dynamically adjusted based on the frequency deviation. Δf d3 This refers to the frequency adjustment amount of the weighted hybrid control output.

[0032] Preferably, the output terminal of the segmented control execution module is electrically connected to the input terminal of the frequency locking range determination module, and the preset frequency locking range in the frequency locking range determination module is [f min ,f max The judgment strategy of the frequency locking range judgment module is as follows:

[0033] When f d,new <f min When the system automatically performs forced correction, then f d,new The value is fixed as f min ;

[0034] When f d,new >f max When the system forces automatic correction, then f d,new The value is fixed as f max ;

[0035] When f min <f d,new <f max If so, the current frequency value remains unchanged;

[0036] The abnormal protection mechanism of the frequency locking range determination module is as follows: when f d,new If the frequency exceeds the lockout range for three consecutive cycles, the system triggers a power reduction mode until the frequency returns to the normal range.

[0037] The technical effects and advantages of this application are as follows:

[0038] Compared with existing technologies, this new energy vehicle wireless power supply soft-switching control system based on segmented strategy achieves wide-range frequency locking, fast dynamic correction and high-precision steady-state control by designing segmented control logic and dedicated module models and algorithms.

[0039] A segmented control strategy is adopted, which dynamically switches between strong and weak control by frequency difference, taking into account both fast response under large offset (strong control) and steady-state accuracy under small offset (weak control), thus solving the performance bottleneck of traditional single strategy.

[0040] The real-time inherent frequency calculation module introduces dynamic phase difference compensation, which improves frequency detection accuracy, reduces errors, ensures accurate judgment of soft switching conditions, has a wide frequency locking range, and has an abnormal protection mechanism, which enhances the reliability of the system under complex working conditions. The algorithms of each module are calculated online in real time, and the response delay is significantly reduced, meeting the real-time requirements of dynamic wireless power supply for new energy vehicles. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the system flow of this application.

[0042] The attached diagram is labeled as follows: 1. Resonant current zero-crossing detection module; 2. Voltage and current acquisition module; 3. Real-time natural frequency calculation module; 4. Frequency difference analysis module; 5. Segmented control decision module; 6. Segmented control execution module; 7. Frequency lock range judgment module. Detailed Implementation

[0043] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0044] Example 1

[0045] like Figure 1 The illustrated wireless power supply soft-switching control system for new energy vehicles based on a segmented strategy includes:

[0046] The resonant current zero-crossing detection module 1 detects the resonant current of the resonant network in real time and performs zero-crossing detection, outputting a square wave signal;

[0047] The resonant current zero-crossing detection module 1, based on a second-order Butterworth low-pass filter circuit and a high-speed comparator, is used to detect the resonant current of the resonant network. The second-order Butterworth low-pass filter circuit uses a transfer function... High-frequency noise is filtered out from the current signal, where s is a complex variable and ω cThis is the cutoff angular frequency.

[0048] The resonant current zero-crossing detection module 1 adopts an adaptive threshold zero-crossing detection algorithm, and the specific strategy of the adaptive threshold zero-crossing detection algorithm is as follows:

[0049] Let the resonant current sampling sequence be i(t), and after filtering, we obtain i f (t), where the zero-crossing threshold is defined as ±k×I. max Where k is the adaptive coefficient, and its value ranges from 0.01 to k to 0.05. max This represents the peak current value for the current cycle.

[0050] when i f (t) When the threshold is crossed, a zero-crossing signal is triggered, and a square wave signal S is generated. z (t), the triggering condition is:

[0051] when i f (t) When S crosses the threshold from negative to positive z (t) = 1, indicating that the square wave signal is in a high-level state;

[0052] when i f (t) When S crosses the threshold from positive to negative, z (t) = 0 indicates that the square wave signal is in a low-level state.

[0053] Voltage and current acquisition module 2 is used to synchronously acquire the primary-side inverter output voltage and resonant current at the zero-crossing point of the drive signal;

[0054] The output terminal of the resonant current zero-crossing detection module 1 is electrically connected to the input terminal of the voltage and current acquisition module 2. The voltage and current acquisition module 2 uses a Hall voltage sensor and a Hall current sensor to convert the analog signal into a digital signal through a 16-bit analog-to-digital converter (ADC). The voltage and current acquisition module 2 adopts a synchronous sampling triggering algorithm, and the strategy of the synchronous sampling triggering algorithm is: using the square wave signal S output by the resonant current zero-crossing detection module... z (t) The transition edge serves as a synchronization trigger source, driving the ADC at t = t z Sampling is performed at ±Δt time to lock the instantaneous values ​​of voltage and current at the zero-crossing point of the drive, where t z Square wave signal S z (t) The time corresponding to the transition edge, Δt is the trigger delay, and Δt≤1μs;

[0055] The real-time natural frequency calculation module 3 calculates the real-time natural frequency of the resonant network based on the data collected by the voltage and current acquisition module 2 and the square wave signal through online dynamic time compensation.

[0056] The output of voltage and current acquisition module 2 is electrically connected to the input of real-time natural frequency calculation module 3. Real-time natural frequency calculation module 3 adopts a dynamic compensation algorithm, and the dynamic compensation algorithm strategy is as follows: the period T of the detected resonant current is determined by the period of the square wave signal output by resonant current zero-crossing detection module 1. det The time is corrected by using the phase difference between voltage and current to obtain the compensation time ΔT. The correction formula is as follows: in The inverter output voltage u inv (t) and resonant current i ies The phase difference of (t) is obtained by synchronously acquiring u inv with i ies Perform calculations; This is the phase compensation coefficient, and its value range is...

[0057] The system's period T0 and real-time natural frequency f0 are calculated using the following formulas: T0 = T det +ΔT,

[0058] Frequency difference analysis module 4 calculates the difference between the real-time inherent frequency and the current inverter drive frequency, and performs smoothing processing.

[0059] The output of the real-time natural frequency calculation module 3 is electrically connected to the input of the frequency difference analysis module 4, and the frequency difference analysis module 4 calculates the real-time natural frequency f0 and the current inverter drive frequency f. d The absolute difference Δf, i.e., Δf = |f d -f0|; The frequency difference analysis module 4 filters Δf using a three-period moving average filtering algorithm. The calculation formula is: Δf avg (n)=

[0060] Where n is the current period number, Δf avg Δf(n) represents the average frequency difference after three-cycle moving average filtering in the nth cycle, Δf(n) represents the original frequency difference in the nth cycle without filtering, Δf(n-1) represents the original frequency difference in the (n-1)th cycle without filtering, and Δf(n-2) represents the original frequency difference in the (n-2)th cycle without filtering.

[0061] The segmented control decision module 5 selects a strong control or weak control strategy based on the comparison result between the smoothed frequency difference and the preset threshold.

[0062] The output of the frequency difference analysis module 4 is electrically connected to the input of the segmented control decision module 5. The segmented control decision module 5 employs three strategies: strong control, over-control, and weak control. The strong control threshold is set to Δf1, where 5kHz ≤ Δf1 ≤ 10kHz, and the weak control threshold is set to Δf2, where 1kHz ≤ Δf1 ≤ 3kHz. The decision logic of the segmented control decision module 5 is as follows:

[0063] When the average frequency difference Δf avg When the value is greater than Δf1, a strong control strategy is activated.

[0064] When Δf2≤Δf avg If ≤Δf1, then the transition control strategy is activated;

[0065] When Δf avg If the value is less than Δf2, then a weak control strategy is enabled.

[0066] The segmented control execution module 6 adjusts the inverter drive frequency using a corresponding control algorithm based on the output of the segmented control decision module 5.

[0067] The output of the segmented control decision module 5 is electrically connected to the input of the segmented control execution module 6. The control algorithms in the segmented control execution module 6 include a strong control algorithm, a weak control algorithm, and a transition control algorithm. The strong control algorithm minimizes the objective function min|f. d -f0| represents the frequency regulation Δf output by the PID controller, which is the target of the variable frequency PID control strategy. d1 It consists of three terms: proportion, integral, and differential. Where the proportionality coefficient K p1 ∈[5,10], integral coefficient K i1 ∈[0.1,0.5], differential coefficient K d1 ∈[0.01,0.1], the updated output frequency is f d，new =f d +Δf d1 ;

[0068] The weak control algorithm employs a sliding mode variable structure control mechanism, specifically: defining the sliding surface as s = Δf avg +λ∫Δf avg dt, where λ is the sliding mode coefficient and its value ranges from 0.5 to 1.5. The control law adopts the form of a sign function: Δf d2 =k s ×sign(s), k s As the sliding mode gain, and its value range is 100≤k s ≤500, the final output frequency update formula is: f d,new =f d +Δfd2 , where Δf d2 The frequency adjustment of the sliding mode variable structure control output is given by sign(s), where sign(s) is the sign function.

[0069] The transient control algorithm employs a weighted PID-sliding mode control mechanism, which in turn uses a weighted hybrid control algorithm: Δf d3 =α×Δf d1 +(1-α)×Δf d2 The weighting coefficient α is dynamically adjusted based on the frequency deviation. Δf d3 This refers to the frequency adjustment amount of the weighted hybrid control output;

[0070] The frequency locking range judgment module 7 determines whether the adjusted drive frequency is within the preset frequency locking range and performs abnormal protection. The output terminal of the segmented control execution module 6 is electrically connected to the input terminal of the frequency locking range judgment module 7, and the preset frequency locking range in the frequency locking range judgment module 7 is [f min ,f max Furthermore, the judgment strategy of the frequency locking range judgment module 7 is as follows:

[0071] When f d,new <f min When the system automatically performs forced correction, then f d,new The value is fixed as f min ;

[0072] When f d,new >f max When the system forces automatic correction, then f d,new The value is fixed as f max ;

[0073] When f min <f d,new <f max If so, the current frequency value remains unchanged;

[0074] The abnormal protection mechanism of the frequency locking range judgment module 7 is as follows: when f d,new If the frequency exceeds the lockout range for three consecutive cycles, the system triggers a power reduction mode until the frequency returns to the normal range.

[0075] Example 2

[0076] This embodiment is based on a wireless power supply system for new energy vehicles with a rated power of 11kW. The primary side adopts a full-bridge inverter topology, the secondary side is a rectifier and filter circuit, and the resonant network is an LCL structure, wherein the resonant inductor L... res =150μH, resonant capacitance C res=15nF, nominal drive frequency 100kHz. This system is applied to static charging scenarios for new energy vehicles. During charging, the vehicle may experience a positional shift of ±5cm due to uneven ground. The ambient temperature range is -10℃ to 40℃.

[0077] Implementation details for each module:

[0078] Resonant current zero-crossing detection module 1:

[0079] Hardware configuration: The second-order Butterworth low-pass filter circuit has resistors R1 and R2 of 3.3kΩ, capacitors C1 and C2 of 1nF, and a cutoff frequency of 50kHz; the high-speed comparator is an LMV7219 with a response time of 8ns.

[0080] Algorithm execution: When the resonant current sampling sequence i(t) is filtered to obtain i f (t), if the current cycle current peak value I max =8A, adaptive coefficient k = 0.03, then the zero-crossing threshold is ±0.24A. When i f When (t) rises from -0.3A to 0.1A, a zero-crossing signal is triggered, and the square wave signal S z (t) jumps from 0 to 1; when i f When (t) decreases from 0.2A to -0.1A, S z (t) jumps from 1 to 0.

[0081] Voltage and current acquisition module 2:

[0082] Hardware configuration: The Hall voltage sensor is LV28-P, with an accuracy of 0.5% and a measurement range of 0 to 500V; the Hall current sensor is LA28-NP, with an accuracy of 0.5% and a measurement range of 0 to 20A; the ADC chip is ADS8364, with 16-bit resolution and a sampling rate of 1MHz.

[0083] Synchronous sampling: with S z The rising and falling edges of (t) are the trigger signals, with a trigger delay ΔT = 0.5 μs, at t = t z At +0.5μs, u was synchronously acquired. inv (t) = 320V and resonant current i ies (t) = 0.05A.

[0084] Real-time intrinsic frequency calculation module 3:

[0085] Periodic detection: based on S z The period of (t) is calculated to obtain T. det = 10μs (corresponding to a frequency of 100kHz).

[0086] Phase difference calculation: through synchronously acquired u inv(t) and i ies (t), the phase difference is calculated. (Converted to 0.087 radians).

[0087] Dynamic compensation: Phase compensation coefficient Compensation time

[0088] Frequency calculation: Real-time period T0 = 10 + 0.0174 = 10.0174 μs, real-time natural frequency.

[0089] Frequency difference analysis module 4:

[0090] Difference calculation: Current inverter drive frequency f d =100kHz, frequency difference Δf = |100-99.826| = 0.174kHz.

[0091] Moving average: If the Δf of the first two periods are 0.18kHz and 0.16kHz respectively, then

[0092] Segmented control decision module 5:

[0093] Threshold settings: Strong control threshold Δf1 = 8kHz, weak control threshold Δf2 = 2kHz.

[0094] Decision result: due to Δf avg =0.171kHz < Δf2 = 2kHz, so a weak control strategy is selected.

[0095] Segmented control execution module 6:

[0096] Weak control algorithm: sliding mode coefficient λ = 1.0, sliding surface s = 0.171 + 1.0 × ∫0.171dt. At t = 10 μs, ∫0.171dt = 1.71 × 10 -6 Then s≈0.171+0.00171+0.1727.

[0097] Control law output: sliding mode gain k s =300, sign(s)=1, Δf is calculated. d2 =300×1=300Hz.

[0098] Frequency adjustment: Adjusted drive frequency f d,new =100000+300=100300Hz.

[0099] Frequency locking range judgment module 7:

[0100] Range determination: The frequency locking range is 80kHz~120kHz / 100-300Hz. Since f falls within this range... d,new Maintain 100300Hz.

[0101] Anomaly monitoring: f for 3 consecutive periods d,new All events occurred within the frequency locking range, and no abnormal protection was triggered. Dynamic adjustment of scenario implementation:

[0102] When the resonant network parameters change due to vehicle position displacement, the specific handling procedure is as follows:

[0103] Parameter changes: Upon testing, the resonant inductance L... res The initial value was adjusted to 155μH, and the resonant capacitance C... res It becomes 14.5nF, and the detection period T det The phase difference reached 10.2 μs. It is 10° (corresponding to a radian value of 0.174 rad).

[0104] Real-time frequency calculation:

[0105] First, the time compensation is calculated based on the phase difference: ΔT = 0.2 × 0.174 = 0.0348 μs;

[0106] The corrected period is then obtained as: (T0 = 10.2 + 0.0348 = 10.2348 μs);

[0107] Therefore, the real-time frequency is calculated to be approximately 97.69 kHz.

[0108] Frequency difference analysis:

[0109] Given target frequency f d =100300Hz, calculate the frequency deviation: Δf=|100.3-97.69|=2.61kHz;

[0110] After processing with moving average, the average frequency deviation Δf is obtained. avg =2.5kHz.

[0111] Control strategy decision-making and implementation:

[0112] Given that Δf2=2kHz≤Δf avg =2.5kHz≤Δf1=8kHz, the system determines to adopt a transition control strategy;

[0113] Calculate the weighting coefficients

[0114] Strong control PID output:

[0115] Weak control output:

[0116] Let s = 2.5 + 1.0 × ∫2.5dt ≈ 2.5, then (sign(s) = 1), (Δf d2 =300×sign(s) = 300Hz;

[0117] Overall frequency adjustment amount: Δf d3 =0.083×20+(1-0.083)×300≈1.66+275.1=276.76Hz;

[0118] Updated target frequency f d_new =100300+276.76≈100576.76Hz, which is within the system's frequency locking range.

[0119] In the above implementation scenario, the system frequency adjustment response time is 1.8ms, the steady-state frequency error is 0.3kHz, the soft-switching success rate is 100%, and the system efficiency reaches 93.5%, which is better than the traditional PLL control strategy, verifying the effectiveness and superiority of the present invention.

Claims

1. A wireless power supply soft-switching control system for new energy vehicles based on a segmented strategy, characterized in that, The control system includes: The resonant current zero-crossing detection module (1) detects the resonant current of the resonant network in real time and performs zero-crossing detection, and outputs a square wave signal; Voltage and current acquisition module (2) is used to synchronously acquire the primary side inverter output voltage and resonant current at the zero-crossing point of the drive signal; The real-time natural frequency calculation module (3) calculates the real-time natural frequency of the resonant network based on the data collected by the voltage and current acquisition module (2) and the square wave signal through online dynamic time compensation. The frequency difference analysis module (4) calculates the difference between the real-time inherent frequency and the current inverter drive frequency, and performs smoothing processing. The segmented control decision module (5) selects a strong control or weak control strategy based on the comparison result between the smoothed frequency difference and the preset threshold. The segmented control execution module (6) adjusts the inverter drive frequency using a corresponding control algorithm based on the output of the segmented control decision module (5). The frequency locking range judgment module (7) determines whether the adjusted drive frequency is within the preset frequency locking range and performs abnormal protection.

2. The new energy vehicle wireless power supply soft-switching control system based on a segmented strategy according to claim 1, characterized in that: The resonant current zero-crossing detection module (1) is a detection circuit composed of a second-order Butterworth low-pass filter circuit and a high-speed comparator, used to detect the resonant current of the resonant network. The second-order Butterworth low-pass filter circuit uses a transfer function... High-frequency noise is filtered out from the current signal, where s is a complex variable and ω c This is the cutoff angular frequency.

3. The new energy vehicle wireless power supply soft-switching control system based on a segmented strategy according to claim 2, characterized in that: The resonant current zero-crossing detection module (1) adopts an adaptive threshold zero-crossing detection algorithm, and the specific strategy of the adaptive threshold zero-crossing detection algorithm is as follows: Let the resonant current sampling sequence be i(t), and after filtering, we obtain i f (t), where the zero-crossing threshold is defined as ±k×I. max Where k is the adaptive coefficient, and its value ranges from 0.01 to k to 0.

05. max This represents the peak current value for the current cycle. when i f (t) When the threshold is crossed, a zero-crossing signal is triggered, and a square wave signal S is generated. z (t), the triggering condition is: when i f (t) When S crosses the threshold from negative to positive z (t) = 1, indicating that the square wave signal is in a high-level state; when i f (t) When S crosses the threshold from positive to negative, z (t) = 0 indicates that the square wave signal is in a low-level state.

4. The new energy vehicle wireless power supply soft-switching control system based on a segmented strategy according to claim 3, characterized in that: The output terminal of the resonant current zero-crossing detection module (1) is electrically connected to the input terminal of the voltage and current acquisition module (2). The voltage and current acquisition module (2) uses a Hall voltage sensor and a Hall current sensor to convert the analog signal into a digital signal through a 16-bit analog-to-digital converter (ADC). The voltage and current acquisition module (2) adopts a synchronous sampling triggering algorithm. The strategy of the synchronous sampling triggering algorithm is: using the square wave signal S output by the resonant current zero-crossing detection module... z (t) The transition edge serves as a synchronization trigger source, driving the ADC at t = t z Sampling is performed at ±Δt time to lock the instantaneous values ​​of voltage and current at the zero-crossing point of the drive, where t z Square wave signal S z (t) The time corresponding to the transition edge, Δt is the trigger delay, and Δt≤1μs.

5. The new energy vehicle wireless power supply soft-switching control system based on a segmented strategy according to claim 4, characterized in that: The output terminal of the voltage and current acquisition module (2) is electrically connected to the input terminal of the real-time natural frequency calculation module (3). The real-time natural frequency calculation module (3) adopts a dynamic compensation algorithm, and the dynamic compensation algorithm strategy is as follows: the period T of the detected resonant current is determined by the period of the square wave signal output by the resonant current zero-crossing detection module (1). det The time is corrected by using the phase difference between voltage and current to obtain the compensation time ΔT. The correction formula is as follows: in The inverter output voltage u inv (t) and resonant current i ies The phase difference of (t) is obtained by synchronously acquiring u inv with i ies Perform calculations; This is the phase compensation coefficient, and its value range is... The system's period T0 and real-time natural frequency f0 are calculated using the following formulas: T0 = T det +ΔT, 6. The new energy vehicle wireless power supply soft-switching control system based on a segmented strategy according to claim 5, characterized in that: The output of the real-time natural frequency calculation module (3) is electrically connected to the input of the frequency difference analysis module (4), and the frequency difference analysis module (4) calculates the real-time natural frequency f0 and the current inverter drive frequency f d The absolute difference Δf, i.e., Δf = |f d -f0|; The frequency difference analysis module (4) filters Δf using a three-period moving average filtering algorithm. The calculation formula is as follows: Where n is the current period number, Δf avg Δf(n) represents the average frequency difference after three-cycle moving average filtering in the nth cycle, Δf(n) represents the original frequency difference in the nth cycle without filtering, Δf(n-1) represents the original frequency difference in the (n-1)th cycle without filtering, and Δf(n-2) represents the original frequency difference in the (n-2)th cycle without filtering.

7. The new energy vehicle wireless power supply soft-switching control system based on a segmented strategy according to claim 6, characterized in that: The output of the frequency difference analysis module (4) is electrically connected to the input of the segmented control decision module (5). The segmented control decision module (5) adopts three strategies: strong control, over-control, and weak control. The strong control threshold is set to Δf1 and 5kHz≤Δf1≤10kHz, and the weak control threshold is set to Δf2 and 1kHz≤Δf1≤3kHz. The decision logic of the segmented control decision module (5) is as follows: When the average frequency difference Δf avg When the value is greater than Δf1, a strong control strategy is activated. When Δf2≤Δf avg If ≤Δf1, then the transition control strategy is activated; When Δf avg If the value is less than Δf2, then a weak control strategy is enabled.

8. The new energy vehicle wireless power supply soft-switching control system based on a segmented strategy according to claim 7, characterized in that: The output of the segmented control decision module (5) is electrically connected to the input of the segmented control execution module (6), and the control algorithm in the segmented control execution module (6) includes a strong control algorithm, a weak control algorithm, and a transition control algorithm. The strong control algorithm minimizes the objective function min|f. d -f0| represents the frequency regulation Δf output by the PID controller, which is the target of the variable frequency PID control strategy. d1 It consists of three terms: proportion, integral, and differential. Where the proportionality coefficient K p1 ∈[5,10], integral coefficient K i1 ∈[0.1,0.5], differential coefficient K d1 ∈[0.01,0.1], the updated output frequency is f d，new =f d +Δf d1 ; The weak control algorithm employs a sliding mode variable structure control mechanism, specifically: defining the sliding surface as s = Δf avg +λ∫Δf avg dt, where λ is the sliding mode coefficient and its value ranges from 0.5 to 1.

5. The control law adopts the form of a sign function: Δf d2 =k s ×sign(s), k s As the sliding mode gain, and its value range is 100≤k s ≤500, the final output frequency update formula is: f d,new =f d +Δf d2 , where Δf d2 The frequency adjustment of the sliding mode variable structure control output is given by sign(s), where sign(s) is the sign function. The transient control algorithm employs a weighted PID-sliding mode control mechanism, which in turn uses a weighted hybrid control algorithm: Δf d3 =α×Δf d1 +(1-α)×Δf d2 The weighting coefficient α is dynamically adjusted based on the frequency deviation. Δf d3 This refers to the frequency adjustment amount of the weighted hybrid control output.

9. The new energy vehicle wireless power supply soft-switching control system based on a segmented strategy according to claim 8, characterized in that: The output of the segmented control execution module (6) is electrically connected to the input of the frequency locking range judgment module (7), and the preset frequency locking range in the frequency locking range judgment module (7) is [f min ,f max ], and the judgment strategy of the frequency locking range judgment module (7) is: When f d,new <f min When the system automatically performs forced correction, then f d,new The value is fixed as f min ; When f d,new >f max When the system forces automatic correction, then f d,new The value is fixed as f max ; When f min <f d,new <f max If so, the current frequency value remains unchanged; The abnormal protection mechanism of the frequency locking range judgment module (7) is as follows: when f d,new If the frequency exceeds the lockout range for three consecutive cycles, the system triggers a power reduction mode until the frequency returns to the normal range.

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