Composite control method and system for wireless charging system

Abstract

The invention belongs to the technical field of wireless charging, and discloses a composite control method and system for a wireless charging system, which is characterized in that the mutual inductance of the system is estimated by using a proposed improved estimation model, and a Buck-Boost circuit is controlled according to estimated parameters and PI closed-loop control, so that maximum efficiency tracking is realized, and a phase shift angle is adjusted to realize constant voltage control. According to the method, the wireless charging system of the LCC-S topology is established, an improved estimation model and dynamic adjustment of PI parameters are combined with the wireless charging technology, the charging system of maximum efficiency tracking and constant voltage compound control is designed and developed, on one hand, the actual requirements of some special application occasions can be met, and the application field of the wireless charging technology is expanded; and on the other hand, energy conservation and environmental protection are achieved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of new energy wireless charging, and particularly relates to a composite control method and system for a wireless charging system. BACKGROUND

[0002] The construction and maintenance cost of a traditional wired charging station is high, and the charging process is limited by physical connection, so it is difficult to meet the efficient and convenient charging demand after large-scale popularization of electric vehicles. The wireless charging technology is based on the principle of magnetic resonance, and can realize energy transmission without physical contact, eliminating the safety hazards of traditional methods in harsh environments, and significantly improving reliability and convenience. However, when the existing wireless power transmission system (WPT) is working, the changes of the vehicle parking position, ground condition and suspension height will cause the spatial position of the coupling coil to deviate, thereby causing the mutual inductance of the system to fluctuate obviously; the changes of the state of charge (SOC) of the battery, temperature drift and load characteristics will also significantly change the equivalent load characteristics of the system. The dynamic changes of mutual inductance and load at the same time make it difficult for a single control target system to consider the optimization of transmission efficiency and the constant stability of output voltage in the whole working condition range, so a composite control method with mutual inductance self-adaptation, efficiency optimization and voltage stability control needs to be developed to improve the dynamic performance and practical level of the WPT system. SUMMARY

[0003] The application aims to provide a composite control method and system for a wireless charging system, to solve the problem of certain limitations in adaptability and transmission performance of the wireless charging design in the background art.

[0004] To achieve the above-mentioned purpose, the application provides the following technical scheme:

[0005] A composite control method for a wireless charging system, the wireless charging system being an LCC-S wireless charging system, comprising a full-bridge inverter, a rectifier bridge, a compensation resonant circuit, a Buck-Boost circuit and a load R L , characterized in that it comprises the following steps:

[0006] S1, based on circuit analysis and resonance conditions, establishing an equivalent mathematical model and a mutual inductance estimation model of the LCC-S wireless charging system, the mutual inductance estimation model estimating the mutual inductance of the system and introducing a correction model to eliminate the system deviation, correcting the estimated mutual inductance, and obtaining M * ;

[0007] S2, according to the mutual inductance M *and the measured load resistance, the duty ratio d1 at the optimal load is calculated; the Buck-Boost circuit is controlled through PI closed-loop control to adjust the duty ratio d1 of the Buck-Boost circuit, so that the system maximum efficiency is tracked in real time, wherein the PI parameters are dynamically set based on the PI control parameter setting method of online model identification and performance index optimization, and the specific process is as follows: first, the recursive least square method RLS is used to identify the first-order inertia delay discrete model of the system online, and the parameter estimation is updated in real time; second, a performance index function containing error segmented weighting and error change rate penalty is designed to balance small error stability and large error fast convergence; third, the particle swarm optimization PSO is used to dynamically optimize the PI parameters to minimize the performance index; finally, an anti-disturbance mechanism is introduced, when the error mutation is detected to exceed the threshold, the learning rate is accelerated or the parameters are reset to cope with load mutation or external disturbance;

[0008] S3, mutual inductance M estimated according to S1 * , the measured load resistance and the duty ratio d1 calculated by S2, the phase shift angle θ at the constant voltage of the system is calculated, and the constant voltage control of the system full-bridge inverter control is realized.

[0009] Further, in step S1, the equivalent mathematical model of the LCC-S wireless charging system is used to describe the relationship between the system voltage, current and element parameters, the mutual inductance estimation model is based on the mathematical model, and the mutual inductance is estimated by measuring the system electrical parameters and introducing a linear correction method to correct the mutual inductance,

[0010] The mutual inductance estimation model is derived from the fundamental component of the system input voltage, the fundamental component of the output voltage and the input power of the system, and the mutual inductance estimation value is calculated through the mutual inductance estimation model The formula is as follows:

[0011]

[0012] Where, U c is the voltage after rectification of the system, R Leq is the equivalent load after rectification of the system, U in is the input voltage of the system, R s is the parasitic resistance of the compensation inductance L s in the resonant circuit, and L f is the compensation inductance in the resonant circuit.

[0013] Further, in step S1, the estimated mutual inductance is corrected by a linear correction model, which is represented as follows:

[0014]

[0015] Where, M *M is the estimated mutual inductance, Q and b are the slope and intercept, respectively, indicating the trend and offset of system error;

[0016] To obtain the values of Q and b, the estimated values under n different conditions are measured and the corresponding actual mutual inductance M i , n sets of mutual inductances are fitted, and the following weighted formula is used to obtain online dynamic update:

[0017]

[0018] wherein, α ∈ (0, 1) is an exponential decay coefficient; is the weighted mean of M i , ;

[0019] wherein, i represents the serial number of the i-th mutual inductance, α n-i represents the exponential decay coefficient of the n-i-th mutual inductance, and W is the sum of the exponential decay coefficients of the n sets of mutual inductances.

[0020] Further, in step S2, the Buck-Boost circuit is controlled by PI closed loop control, and the specific method is as follows:

[0021] The input voltage Uc, output voltage Uo and load current Io of the Buck-Boost circuit are collected by the DSP controller, and the PWM signal control with duty ratio d1 is outputted, and the specific control steps are as follows:

[0022] S21: dynamically setting PI parameters, setting switching frequency, and initializing duty ratio;

[0023] S22: obtaining the input voltage Uc, output voltage Uo and load current Io of the Buck-Boost circuit by sampling; S23: calculating the load R L and the equivalent load R Leq using the collected information;

[0024] S24: correcting the estimated mutual inductance using the correction model to obtain the corrected mutual inductance M * ;

[0025] S25: calculating the optimal equivalent load R ηmax based on M * obtained in step S24;

[0026] S26: introducing PI closed loop control to adjust the duty ratio d1 of the Buck-Boost circuit, and adjusting the equivalent load R Leq to the optimal equivalent load R ηmax .

[0027] Further, in step S2, based on the PI control parameter setting method of online model identification and performance index optimization, the specific steps are as follows:

[0028] Step 1: Online model identification

[0029] A first-order inertial delay model is used to approximate the controlled object:

[0030]

[0031] Where G(s) is the system transfer function, K is the system static gain, γ is the time constant of the system, L is the pure time delay of the system, and s is the Laplace variable;

[0032] The recursive least squares (RLS) method is used to estimate the model discrete transfer function online:

[0033] y(k) = a1y(k-1) + b0u(k-1);

[0034] Where k is the current discrete sampling time, y(k) is the output value of the system at the current time, y(k-1) is the system output at the last sampling time, u(k-1) is the control input at the last sampling time, i.e. the PI controller output, a1 and b0 are model parameters that need to be estimated online;

[0035] The parameters are updated continuously through the RLS formula:

[0036]

[0037] Where k is the current sampling time in the recursive process, θ(k) = [a1, b0] T ; φ(k) = [y(k-1), u(k-1)] T ; λ is the forgetting factor, usually set to 0.98-0.995; θ(k) is the parameter estimation value at the current time; θ(k-1) is the parameter estimation at the last time; P(k-1) is the covariance matrix; φ(k) is the regression vector; y(k) is the true output value at the current time; ∈(k) = y(k)-φ T (k)θ(k-1) is the prediction error;

[0038] Step 2: Construct performance index function

[0039] Define the instantaneous error performance index:

[0040]

[0041] Wherein, J is the performance index function value; τ is the discrete time index in the performance evaluation time window, that is, the number of sampling points; N is the total number of sampling points, or the sampling length in the performance evaluation time window; e(τ) is the control error at the τth sampling time, that is, the difference between the set value and the output value; w1, w2 represent the weight factor, which represents the degree of punishment corresponding to the error interval; ε is the threshold value of the error; Different error response functions and weights are set to balance the small error stability and large error convergence of the system, and the overall regulation performance of the system is improved; Δe(τ) = e(τ)-e(τ-1) is the error change rate β disturbance penalty weight coefficient;

[0042] The objective function is to minimize J, that is:

[0043]

[0044] Wherein, k p is a proportional control parameter, k i is an integral control parameter, which is used to control the regulation of the Buck-Boost circuit switching duty cycle;

[0045] Step 3: Online optimization of PI parameters

[0046] Optimize using particle swarm algorithm PSO: ①Initialize multiple particles, each particle represents a(k p ,k i ); ②Each particle evaluates its fitness(J); ③Adjust the speed and position according to the individual optimum and global optimum; ④Iterate until convergence or reach the maximum number of iterations;

[0047] Step 4: Anti-disturbance regulation mechanism

[0048] After detecting the disturbance, the learning rate can be accelerated or the fast reset mode can be enabled:

[0049] Detect the disturbance threshold:

[0050] If |e(k)-e(k-1)|>δ, trigger disturbance detection;

[0051] Wherein, e(k) is the control error at the current sampling time k; e(k-1) is the control error at the previous sampling time k-1; δ is the disturbance threshold, a preset constant, used to judge whether the error mutation exceeds the acceptable range;

[0052] When the absolute value of the difference between the two errors exceeds the threshold δ, the system determines that an external disturbance or load mutation has occurred, and should enter the anti-disturbance strategy, which will reset the optimization strategy or add a disturbance filter.

[0053] Further, in S25 and S26, the optimal equivalent load R ηmaxThe formula of d1 of Buck-Boost is as follows:

[0054] The transmission efficiency of the wireless charging system without Buck-Boost circuit in resonance is calculated as follows:

[0055]

[0056] Where, P in is the input power of the system, P o is the output power of the system, ω is the angular frequency of the system, M is the actual mutual inductance, R Leq is the equivalent load after rectification of the system, R s is the parasitic resistance of the secondary coil inductance L s in the resonance circuit, R p is the parasitic resistance of the primary coil inductance L p in the resonance circuit, L f is the compensation inductance in the resonance circuit.

[0057] The partial derivative of the efficiency is obtained, and the optimal equivalent load when the system has the maximum efficiency is:

[0058]

[0059] And the equivalent load of the system with Buck-Boost circuit is:

[0060]

[0061] The optimal duty ratio when the system has the maximum efficiency is calculated by equating the equivalent load to the optimal load when the system has the maximum efficiency, that is, R Leq_η =R ηmax The duty ratio d1 is obtained by solving:

[0062]

[0063] When the coil is offset or the charging object is changed, the optimal duty ratio d1 is calculated by estimating M * and measuring the load R L .

[0064] Further, in step S3, specifically comprising:

[0065] S31: According to the mutual inductance M * estimated in step S1, the measured parameters and d1 calculated in S2, the phase shift angle θ of the full-bridge inverter in constant voltage control mode is calculated by using the following relationship:

[0066]

[0067] Wherein, V set is a system preset constant voltage value, Z in is the input impedance of the system, Z s is the secondary impedance of the system, Z r is Z s the mapping impedance of the primary side;

[0068] S32: the calculated phase shift angle θ is taken as the phase input of the full-bridge inverter, four inverter switching devices are driven, the inverter bridge output voltage generates a corresponding delay in each half switching period, so as to realize accurate tracking of the reference value of the output DC side voltage;

[0069] S33: at the end of each switching period, the rectification side output voltage is sampled in real time, compared with the estimated and calculated voltage, and the phase shift angle correction amount Δθ is updated by a PI controller II according to the error, and is superimposed with the original phase shift angle θ to obtain the phase shift angle of the next period:

[0070] θ new = θ + K p (e) + K i ∫edt;

[0071] Wherein, e is the difference between the measured voltage and the estimated and calculated voltage;

[0072] S34: the parameters K p , K i of the PI controller II are adaptively adjusted by the PI control parameter setting method of online model identification and performance index optimization, so as to ensure that the system can quickly recover to a stable constant voltage state when the load suddenly changes;

[0073] S35: through the above steps, the closed-loop control of the full-bridge inverter of the LCC-S wireless charging system in the constant voltage mode is realized, the rectification output voltage is ensured to closely track the target reference value, so as to meet the accurate constant voltage power supply demand for different charging devices.

[0074] Further, the full-bridge inverter includes switching tubes S1, S2, S3 and S4, wherein the switching tube S1 and the switching tube S2 are connected in series as the leading front arm of the full-bridge inverter, and the switching tube S3 and the switching tube S4 are connected in series as the lagging back arm of the full-bridge inverter;

[0075] The compensation resonance circuit includes a primary side compensation resonance circuit and a secondary side compensation resonance circuit; wherein the primary side compensation resonance circuit is composed of a series branch of a primary side compensation capacitor C p and a primary side coil inductance L p and a parallel capacitor C f parallelly connected, and a compensation inductance L f is connected in series, and the parasitic resistance of the primary side coil inductance L p is R pThe auxiliary side compensation resonant circuit is composed of an auxiliary side series compensation capacitor C s The auxiliary side coil inductance L s The auxiliary side coil inductance L s The parasitic resistance is denoted as R s The front arm and the rear arm of the full-bridge inverter are connected to the two ends of the primary side compensation resonant circuit respectively after being connected in parallel;

[0076] The rectifier bridge comprises diodes D1, D2, D3 and D4, wherein the diodes D1 and D2 are connected in series as the front arm of the rectifier bridge, the diodes D3 and D4 are connected in series as the rear arm of the rectifier bridge, and the front arm and the rear arm of the rectifier bridge are connected to the two ends of the auxiliary side compensation resonant circuit respectively after being connected in parallel;

[0077] The Buck-Boost circuit comprises a switch tube S5, an inductor L and a diode D5; and a capacitor C o The load R L is connected in parallel to the capacitor C o .

[0078] The application further provides a wireless charging system composite control system for realizing the method, comprising an LCC-S wireless charging system, an estimation module, a maximum efficiency tracking module and a constant voltage control module, the estimation module is used for estimating the mutual inductance of the LCC-S wireless charging system, introducing a correction model to eliminate system deviation, correcting the estimated mutual inductance, obtaining M * , and inputting the maximum efficiency tracking module and the constant voltage control module; the maximum efficiency tracking module is used for calculating the duty cycle d1 at the optimal load according to the mutual inductance M * estimated by the estimation module and the measured load resistance; the Buck-Boost circuit is controlled through PI closed-loop control, the duty cycle d1 of the Buck-Boost circuit is adjusted, and the maximum efficiency of the system is tracked in real time; the constant voltage control module is used for calculating the phase shift angle θ at the constant voltage of the LCC-S wireless charging system according to the mutual inductance M * estimated by the estimation module, the measured load resistance and the duty cycle d1 calculated by the maximum efficiency tracking module, and realizing the constant voltage control of the LCC-S wireless charging system full-bridge inverter control.

[0079] Compared with the prior art, the application has the following beneficial effects:

[0080] 1. The introduction of wireless charging technology into electric vehicle charging systems can effectively solve the safety problems such as overheating and overcharging that are prone to occur in traditional charging methods. Traditional wired charging methods require repeated plugging and unplugging of charging cables, which can cause wear and tear on the charging connectors and sockets, leading to poor contact and potential risks such as overheating and overcharging. Wireless charging uses principles such as electromagnetic induction or electromagnetic resonance to transfer energy, eliminating the need for charging cables and making the energy transfer process less susceptible to environmental factors such as moisture, dust, and temperature changes. Therefore, wireless charging is more secure and reliable than traditional plug-in charging. In addition, the application of wireless charging technology also improves the durability of the charging system. Since there is no physical contact, wireless charging avoids mechanical wear and tear, extending the service life of the charging equipment. The design of the wireless charging system also eliminates potential safety hazards such as electrical sparks, reducing the risk of fires and other accidents. The convenience of wireless charging also eliminates the need for manual plugging and unplugging of cables for electric vehicle users, improving user experience while enhancing the safety and reliability of the charging process.

[0081] 2. The improved mutual inductance estimation model and composite control wireless charging system is faster and more accurate than traditional estimation methods. By estimating mutual inductance and dynamically adjusting PI parameters, the system can more accurately identify and adjust key parameters during the charging process, thereby improving charging efficiency and stability. Secondly, the maximum efficiency tracking and constant voltage control functions ensure that the charging system always operates at its best, reducing energy loss and extending the service life of the equipment. In addition, this improvement enhances the system's adaptability, allowing it to maintain high efficiency under different environmental and load conditions. In summary, this system has significantly improved in terms of energy efficiency, reliability, and adaptability, providing strong support for the development of wireless charging technology. BRIEF DESCRIPTION OF DRAWINGS

[0082] Figure 1 is the schematic diagram of the wireless charging system circuit of the present application;

[0083] Figure 2 is the block diagram of the wireless charging system estimation module of the present application;

[0084] Figure 3 is the flow chart of the maximum efficiency tracking working principle of the system of the present application;

[0085] Figure 4 is the flow chart of the constant voltage control working principle of the system of the present application;

[0086] Figure 5 is the structural diagram of the composite control system of the wireless charging system of the present application;

[0087] Figure 6is the estimated result when the actual mutual inductance in the embodiment of the application is 30uH; wherein (a) is the estimated mutual inductance when the load is 30Ω, (b) is the estimated mutual inductance when the load is 10Ω;

[0088] Figure 7 is the output voltage and output current waveform of the inverter side in the embodiment of the application; wherein (a) is the inverter output current and voltage, (b) is the output current and voltage when the load changes. DETAILED DESCRIPTION

[0089] The technical solutions in the embodiments of the application will be clearly and completely described with reference to the embodiments of the application and the accompanying drawings of the specification. Obviously, the described embodiments are only some of the embodiments of the application, but not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the application.

[0090] The application designs a composite control method for a wireless charging system, and the whole system comprises an LCC-S wireless charging system, an estimation module, a maximum efficiency tracking module and a constant voltage control module, and specifically comprises the following steps:

[0091] S1, based on circuit analysis and resonance conditions, an equivalent mathematical model and a mutual inductance estimation model of the LCC-S wireless charging system are established, the mutual inductance estimation model estimates the mutual inductance of the system and introduces a correction model to eliminate the system deviation, correct the estimated mutual inductance, and obtain M * ;

[0092] S2, according to the mutual inductance M * estimated by S1 and the measured load resistance, the duty cycle d1 at the optimal load is calculated; the Buck-Boost circuit is controlled through PI closed-loop control to adjust the duty cycle d1 of the Buck-Boost circuit, so that the system maximum efficiency is tracked in real time, wherein the PI parameters are dynamically set based on the PI control parameter setting method of online model identification and performance index optimization, and the specific process is as follows: first, the recursive least square method RLS is used to identify the first-order inertia delay discrete model of the system online to update the parameter estimation in real time; second, a performance index function containing error segmented weighting and error change rate penalty is designed to take into account small error stability and large error fast convergence; third, the particle swarm optimization algorithm PSO is used to dynamically optimize the PI parameters to minimize the performance index; finally, an anti-disturbance mechanism is introduced, when the error mutation is detected to exceed the threshold, the learning rate is accelerated or the parameters are reset to cope with load mutation or external disturbance;

[0093] S3, according to the mutual inductance M *The measured load resistance and the duty cycle d1 calculated by S2 are used to calculate the phase shift angle θ when the system is under constant voltage, and the system is controlled under constant voltage by the full-bridge inverter.

[0094] The system has significantly improved in terms of energy efficiency, reliability, and adaptability, providing strong support for the development of wireless charging technology.

[0095] The LCC-S wireless charging system includes a full-bridge inverter, a rectifier bridge, a compensated resonant circuit, a Buck-Boost circuit, and a load R. L The compensated resonant circuit includes a primary-side compensated resonant circuit and a secondary-side compensated resonant circuit; the primary-side compensated resonant circuit consists of a primary-side compensation capacitor C. p With the primary coil L p The series branch and the parallel capacitor C f After parallel connection with compensation inductor L f Composed of series connection, primary coil inductance L p The parasitic resistance is denoted as R. p The secondary-side compensated resonant circuit consists of a secondary-side compensation capacitor C connected in series. s With secondary coil inductance L s Series connection, secondary coil inductance L s Parasitic resistance is denoted as R s The front arm A and rear arm B of the full-bridge inverter are connected in parallel and then connected to the two ends of the primary-side compensation resonant circuit, respectively.

[0096] The rectifier bridge includes diodes (D1, D2, D3, D4), wherein diodes D1 and D2 are connected in series as arm A of the rectifier bridge, and D3 and D4 are connected in series as arm B of the rectifier bridge. Arms A and B of the rectifier bridge are connected in parallel and then connected to the two ends of the secondary compensation resonant circuit respectively.

[0097] The Buck-Boost circuit includes a switching transistor S5, an inductor L, and a diode D5; capacitor C o Connected in parallel across the Buck-Boost circuit, load R L Connected in parallel to capacitor C o Both ends.

[0098] like Figure 1The diagram shows the schematic of a wireless charging system circuit. One side of the circuit structure consists of a full-bridge inverter and an LCC topology, containing four switching transistors. The secondary side consists of a rectifier and a DC-DC converter. The transmitting end is connected to the resonant network via the full-bridge circuit, and the input voltage is provided by the power supply. The full-bridge inverter consists of switching transistors S1, S2, S3, and S4. Switches S1 and S2 are connected in series as the leading arm of the full-bridge inverter, and switches S3 and S4 are connected in series as the lagging arm. Constant voltage control is achieved using PWM phase-shifting technology. When switches S1 and S4 are turned on, the current flows through the compensating inductor L... f and primary-side compensation capacitor C p C f The LCC resonant network is constructed as follows: During commutation, after S1 and S4 are turned off, the current does not stop abruptly, but continues through the reverse parallel diodes of S2 and S3 to ensure a constant current freewheeling path. After commutation, S2 and S3 are turned on, while S1 and S4 remain off, continuing to inject energy into the LCC resonant network. A full-bridge inverter circuit converts the DC voltage to AC voltage, which serves as the driving voltage for the resonant network. The transmitting LCC structure wirelessly transmits electrical energy to the receiving end using resonance. The AC voltage received at the receiving end is rectified into DC voltage by a full-bridge rectifier composed of four diodes (D1-D4), then passed through a Buck-Boost circuit for maximum efficiency tracking, and finally smoothed by a filter capacitor C0 to output a stable DC voltage U. o , for load R L Provides electrical energy.

[0099] The equivalent mathematical model of the LCC-S wireless charging system is used to describe the relationship between system voltage, current and component parameters. The system mutual inductance estimation model is based on the mathematical model, which estimates the mutual inductance by measuring the system electrical parameters and introduces a linear correction method to correct the mutual inductance.

[0100] like Figure 2 The diagram shows the system estimation principle block diagram. Using Uc, output voltage Uo, and output current Io, M is estimated according to the estimation formula, and then a more accurate estimated value is obtained after correction.

[0101] The fundamental components of the system input and output voltages are as follows:

[0102]

[0103] Among them U in It is the system's input voltage, U o ω is the system's output voltage, ω is the system's angular frequency, and t is the system's running time.

[0104] The input power is as follows:

[0105]

[0106] where I f is the inverse current in the system, U in is the input voltage of the system, ω is the angular frequency of the system, R Leq is the equivalent load after rectification of the system.

[0107] The output power of the system is as follows:

[0108]

[0109] The mutual inductance estimation model is derived from the fundamental component of the input voltage of the system, the fundamental component of the output voltage and the input power of the system, and the mutual inductance estimation value is calculated through the mutual inductance estimation model The formula is as follows:

[0110]

[0111] where, U c is the voltage after rectification of the system, R Leq is the equivalent load after rectification of the system, U in is the input voltage of the system, R s is the parasitic resistance of the inductance L s of the secondary coil in the resonance circuit, L f is the compensation inductance in the resonance circuit.

[0112] The estimation value has a systematic deviation from the actual mutual inductance M, in order to reduce the estimation error, a linear correction model is proposed to correct the estimated mutual inductance:

[0113]

[0114] where, M * is the corrected mutual inductance estimation value, is the mutual inductance estimation value, Q and b are the slope and intercept respectively, indicating the trend and deviation of the system error.

[0115] In order to obtain the values of Q and b, n sets of different conditions of and the corresponding actual mutual inductance M i are measured, the n sets of mutual inductances are fitted, and the following weighted formula is used to dynamically update online:

[0116]

[0117] where, α ∈ (0, 1) is the exponential decay coefficient; is the weighted mean of M i , . Where i represents the sequence number of the i-th group of mutual inductance, α n-i Let represent the exponential decay coefficient of the ni-th mutual inductance, and W be the sum of the exponential decay coefficients of the n-th mutual inductance.

[0118] like Figure 3 The flowchart shown illustrates the system's maximum efficiency tracking. As a preferred implementation, to better achieve maximum efficiency tracking, PI closed-loop control is used to control the Buck-Boost circuit. The specific method is as follows:

[0119] The DSP controller acquires the input voltage Uc, output voltage Uo, and load current Io of the Buck-Boost circuit, and outputs a PWM signal with a duty cycle of d1 for control. The specific control steps are as follows:

[0120] S21: Dynamically tune PI parameters, set switching frequency, and initialize duty cycle;

[0121] S22: Obtain the Buck-Boost circuit input voltage Uc, output voltage Uo, and load current Io through sampling; S23: Calculate the load R using the collected information. L and equivalent load R Leq ;

[0122] S24: Correct the estimated mutual inductance using the correction model to obtain the corrected mutual inductance estimate M. * ;

[0123] S25: M obtained based on step S24 * Calculate the optimal equivalent load R ηmax ;

[0124] S26: Introduce PI closed-loop control to adjust the duty cycle d1 of the Buck-Boost circuit, thereby reducing the equivalent load R. Leq Adjust to the optimal equivalent load R ηmax .

[0125] In step S21, to achieve dynamic tuning of the PI parameters, a PI control parameter tuning method based on online model identification and performance index optimization is proposed. The details are as follows:

[0126] Step 1: Online Model Recognition

[0127] The controlled object is approximated using a first-order inertial delay model:

[0128]

[0129] Where G(s) is the system transfer function, K is the system static gain, γ is the system time constant, L is the system pure time delay, and s is the Laplace variable.

[0130] The model discrete transfer function form is estimated online using recursive least squares (RLS):

[0131] y(k) = a1y(k-1) + b0u(k-1);

[0132] where k is the current discrete sampling time in the recursive process, y(k) is the output value of the system at the current time, y(k-1) is the system output at the last sampling time, u(k-1) is the control input (PI controller output) at the last sampling time, a1 and b0 are model parameters that need to be estimated online.

[0133] The parameters are constantly updated by the RLS formula:

[0134]

[0135] where θ(k) = [a1, b0] T ; φ(k) = [y(k-1), u(k-1)] T ; λ is the forgetting factor, usually set to 0.98-0.995; θ(k) is the current parameter estimation value; θ(k-1) is the parameter estimation at the last time; P(k-1) is the covariance matrix (reflecting the uncertainty of parameter estimation); φ(k) is the regression vector (containing the input and output at the last time); y(k) is the true output value at the current time; ∈(k) = y(k) - φ T (k)θ(k-1) is the prediction error.

[0136] Step 2: Construct performance index function

[0137] Define the instantaneous error performance index:

[0138]

[0139] where J is the performance index function value; τ is the discrete time index (sampling point number) within the performance evaluation time window; N is the total number of sampling points, or the sampling length within the performance evaluation time window; e(τ) is the control error at the τth sampling time, i.e., the difference between the set value and the output value; w1 and w2 represent the weight factors, indicating the penalty degree of the corresponding error interval; ε is the error threshold; different error response functions and weights are set to balance the small error stability and large error convergence of the system, improving the overall regulation performance of the system. Δe(τ) = e(τ) - e(τ-1) is the error change rate β disturbance penalty weight coefficient.

[0140] The objective function is to minimize J, i.e.:

[0141]

[0142] where kp is a proportional control parameter, k i is an integral control parameter, used to control the adjustment of Buck-Boost circuit switch duty cycle.

[0143] Step 3: Online optimization of PI parameters

[0144] Optimization using PSO: ① Initialize multiple particles, each particle represents a (k p , k i ); ② Evaluate the fitness (J) of each particle; ③ Adjust the speed and position according to the individual optimal and global optimal; ④ Iterate until convergence or reach the maximum number of iterations.

[0145] Step 4: Anti-disturbance adjustment mechanism

[0146] After detecting disturbances (such as sudden load), the learning rate can be accelerated or a fast reset mode can be enabled:

[0147] Detect disturbance threshold:

[0148] If |e(k) - e(k-1)| > δ, trigger disturbance detection;

[0149] Where e(k) is the control error at the current sampling time k; e(k-1) is the control error at the previous sampling time k-1; δ is the disturbance threshold, a preset constant, used to judge whether the error mutation exceeds the acceptable range;

[0150] When the absolute value of the difference between the two errors exceeds the threshold δ, the system judges that an external disturbance or load mutation has occurred, and should enter the anti-disturbance strategy, reset the optimization strategy or add a disturbance filter.

[0151] As a preferred embodiment, the optimal equivalent load R ηmax and the formula of d1 of Buck-Boost are calculated in S25 and S26 as follows:

[0152] The transmission efficiency of the wireless charging system without Buck-Boost circuit at resonance is calculated as follows:

[0153]

[0154] Where P in is the input power of the system, P o is the output power of the system, ω is the angular frequency of the system, M is the actual mutual inductance, R Leq is the equivalent load after rectification of the system, R s is the parasitic resistance to compensate for the secondary side coil inductance L s in the resonance circuit, R p is the parasitic resistance to compensate for the primary side coil inductance Lp parasitic resistance, L f To compensate for the inductance in the resonant circuit.

[0155] For efficiency Taking the partial derivative, we obtain the optimal equivalent load for maximizing system efficiency:

[0156]

[0157] The equivalent load of adding a Buck-Boost circuit system is:

[0158]

[0159] The optimal duty cycle for maximum efficiency can be calculated from the optimal load when the system's equivalent load equals the optimal load for maximum efficiency, i.e., let R... Leq_η =R ηmax Solving for the duty cycle d1:

[0160]

[0161] When the coil shifts or the object being charged changes, the estimated M is used. * and the load R calculated by measurement L The optimal duty cycle d1 is calculated, and maximum efficiency tracking is achieved by setting the duty cycle of the Buck-Boost circuit. That is, by adjusting the duty cycle d1 of the Buck-Boost circuit, the equivalent load R is optimized. Leq_η The optimal load R required to match the system's maximum efficiency point ηmax .

[0162] like Figure 4 The flowchart shown below illustrates the constant pressure control process of the system, which specifically includes:

[0163] S31: Based on the mutual inductance M estimated in step S1 * Using the measured parameters and d1 calculated in S2, calculate the phase shift angle θ of the full-bridge inverter under constant voltage control mode using the following relationship:

[0164]

[0165] Among them, V set It is the system's preset constant pressure value, Z. in It is the system's input impedance, Z s It is the secondary impedance of the system, Z r It is Z s Mapped impedance on the primary side.

[0166] S32: the calculated phase shift angle θ is taken as the phase input of the full-bridge inverter, four inverter switching devices are driven, the inverter bridge output voltage generates a corresponding delay in each half switching period, so that the output DC side voltage accurately tracks the reference value;

[0167] S33: at the end of each switching period, the rectifier side output voltage is sampled in real time, compared with the estimated and calculated voltage, and the phase shift angle correction amount Δθ is updated by a PI controller II according to the error, and is superposed with the original phase shift angle θ to obtain the phase shift angle of the next period:

[0168] θ new = θ + K p (e) + K i ∫edt;

[0169] Wherein, e is the difference between the measured voltage and the estimated and calculated voltage;

[0170] S34: the parameters K p , K i of the PI controller II are adaptively adjusted by using the PI control parameter setting method of online model identification and performance index optimization, so that the system can quickly recover to a stable constant voltage state when the load suddenly changes;

[0171] S35: through the above steps, the closed-loop control of the full-bridge inverter of the LCC-S wireless charging system in the constant voltage mode is realized, the rectifier output voltage is ensured to closely track the target reference value, so that the accurate constant voltage power supply demand for different charging devices is met.

[0172] The application designs two PI controllers: one is placed in the Buck-Boost stage, and the duty cycle d1 is closed-loop regulated, so that the equivalent load R Leq of the converter tracks and converges to the optimal equivalent load R ηmax corresponding to the maximum efficiency point in real time. The second PI controller adjusts the phase shift angle θ to keep the output voltage constant, so as to realize the constant of the load voltage.

[0173] As another embodiment of the application, a wireless charging system composite control system is provided, as shown in Figure 5 The method is realized by using the wireless charging system composite control system, including an LCC-S wireless charging system, an estimation module, a maximum efficiency tracking module and a constant voltage control module. The estimation module is used for estimating the mutual inductance of the LCC-S wireless charging system and introducing a correction model to eliminate system deviation, correcting the estimated mutual inductance to obtain M * , and inputting the maximum efficiency tracking module and the constant voltage control module. The maximum efficiency tracking module calculates the duty cycle d1 at the optimal load according to the mutual inductance M * estimated by the estimation module and the measured load resistance, and adjusts the duty cycle d1 through PI closed-loop control.

[0174] The Buck-Boost circuit is used for control, adjusting its duty cycle d1 to achieve real-time tracking of maximum system efficiency; the constant voltage control module uses the mutual inductance M estimated by the estimation module. * The measured load resistance and the duty cycle d1 calculated by the maximum efficiency tracking module are used to calculate the phase shift angle θ of the LCC-S wireless charging system under constant voltage, thereby achieving constant voltage control of the LCC-S wireless charging system through full-bridge inverter control.

[0175] The functions and implementation processes of each module can be found in the previous records, and will not be repeated here.

[0176] experiment:

[0177] When the actual mutual inductance is 30μH, the estimated values ​​and correction values ​​of the mutual inductance under different loads and duty cycles are as follows: Figure 6 As shown, the estimated mutual inductance value, after correction, is closer to the measured mutual inductance value, verifying the feasibility of the mutual inductance identification method proposed in this paper.

[0178] The system performance was tested under the experimental conditions of using a 30V DC voltage source to power the system input side, a transmission distance of 13cm, a mutual inductance of 30μH, and a load resistance of 10Ω.

[0179] Figure 7 (a) shows the output voltage and current waveforms on the inverter side. The peak voltage is 30V, the maximum current is 1.35A, and the phase shift angle is approximately 101°. The current waveform is close to a sine wave, verifying the system's good voltage and current output characteristics under high-frequency operating conditions.

[0180] Figure 7 (b) shows the dynamic response curves of the system output voltage and current when the load suddenly changes. When the load resistance suddenly decreases from 10Ω to 5Ω, the system output voltage remains stable at about 10V without significant fluctuations, demonstrating good voltage stability; while the output current can be quickly adjusted according to the load change, rising instantly from 0.89A to 1.9A, indicating that the system has good dynamic response capability and the ability to adapt to different load conditions.

[0181] In summary, the present application estimates the mutual inductance of the system by using the improved estimation model, and dynamically adjusts the PWM wave of S5 according to the estimated parameters and dynamic PI parameters to realize maximum efficiency tracking and constant voltage control. The present application establishes a wireless charging system with LCC-S topology, combines the improved estimation model and dynamic adjustment PI parameters with wireless charging technology, and designs and develops a charging system with maximum efficiency tracking and constant voltage composite control. On the one hand, it can meet the actual needs of some special application occasions and expand the application field of wireless charging technology. On the other hand, it has the advantages of energy saving and environmental protection.

[0182] The basic principles, main features and advantages of the present application are shown and described above. Those skilled in the art should understand that the present application is not limited by the above examples, and the above examples and descriptions in the specification are only preferred examples of the present application and are not intended to limit the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A composite control method for a wireless charging system, wherein the wireless charging system is an LCC-S wireless charging system, comprising a full-bridge inverter, a rectifier bridge, a compensated resonant circuit, a Buck-Boost circuit, and a load R. L Its characteristics are, Includes the following steps: S1. Based on circuit analysis and resonance conditions, an equivalent mathematical model and a mutual inductance estimation model for the LCC-S wireless charging system are established. The mutual inductance estimation model estimates the mutual inductance of the system and introduces a correction model to eliminate system deviations, correcting the estimated mutual inductance to obtain M. * ; S2, mutual inductance M estimated based on S1 * The duty cycle d1 under optimal load is calculated based on the measured load resistance. The Buck-Boost circuit is controlled via PI closed-loop control to adjust the duty cycle d1, achieving real-time tracking of maximum system efficiency. The PI parameters are dynamically tuned using a PI control parameter tuning method based on online model identification and performance index optimization. The specific process is as follows: First, the first-order inertial delay discrete model of the system is identified online using the recursive least squares (RLS) method, and the parameter estimates are updated in real time. Second, a performance index function is designed that includes error piecewise weighting and error rate of change penalty to balance stability with small errors and rapid convergence with large errors. Next, the PI parameters are dynamically optimized using the particle swarm optimization (PSO) algorithm to minimize the performance index. Finally, an anti-disturbance mechanism is introduced; when a sudden error change exceeds a threshold, the learning rate is accelerated or the parameters are reset to cope with load changes or external disturbances. S3, mutual inductance M estimated based on S1 * The measured load resistance and the duty cycle d1 calculated by S2 are used to calculate the phase shift angle θ when the system is under constant voltage, and the system is controlled under constant voltage by the full-bridge inverter.

2. The composite control method for a wireless charging system according to claim 1, characterized in that, In step S1, the equivalent mathematical model of the LCC-S wireless charging system is used to describe the relationship between system voltage, current, and component parameters. The mutual inductance estimation model is based on the mathematical model, estimating the mutual inductance by measuring the system's electrical parameters and introducing a linear correction method to correct the mutual inductance model. A mutual inductance estimation model is derived from the fundamental components of the system input voltage and output voltage, and the system input power. The estimated mutual inductance value is then calculated using this model. The formula is as follows: Among them, U c It is the voltage after system rectification, R Leq It is the equivalent load after system rectification, U in It is the system input voltage, R s To compensate for the inductance L of the secondary coil in the resonant circuit s parasitic resistance, L f To compensate for the inductance in the resonant circuit.

3. The composite control method for a wireless charging system according to claim 1, characterized in that, In step S1, the estimated mutual inductance is corrected by modifying the model, as shown below: Among them, M * This is the corrected estimate of mutual inductance. Here, Q and b represent the estimated mutual inductance, respectively, and the slope and intercept represent the trend and offset of the system error. To obtain the values ​​of Q and b, it is necessary to measure n sets of estimated values ​​under different conditions. With the corresponding actual mutual inductance M i We fit the mutual inductance of n groups and dynamically update it online using the following weighted formula: Where α∈(0,1) is the exponential decay coefficient; For M i , The weighted mean; Where i represents the sequence number of the i-th group of mutual inductance, α n-i Let represent the exponential decay coefficient of the ni-th mutual inductance, and W be the sum of the exponential decay coefficients of the n-th mutual inductance.

4. The composite control method for a wireless charging system according to claim 1, characterized in that, In step S2, the Buck-Boost circuit is controlled using PI closed-loop control, as follows: The DSP controller acquires the input voltage Uc, output voltage Uo, and load current Io of the Buck-Boost circuit, and outputs a PWM signal with a duty cycle of d1 for control. The specific control steps are as follows: S21: Dynamically tune PI parameters, set switching frequency, and initialize duty cycle; S22: Obtain the Buck-Boost circuit input voltage Uc, output voltage Uo, and load current Io through sampling; S23: Calculate the load R using the collected information. L and equivalent load R Leq ; S24: Correct the estimated mutual inductance using the correction model to obtain the corrected mutual inductance estimate M. * ; S25: M obtained based on step S24 * Calculate the optimal equivalent load R ηmax ; S26: Introduce PI closed-loop control to adjust the duty cycle d1 of the Buck-Boost circuit, thereby reducing the equivalent load R. Leq Adjust to the optimal equivalent load R ηmax .

5. The composite control method for a wireless charging system according to claim 1, characterized in that, In step S2, the PI control parameter tuning method based on online model identification and performance index optimization is as follows: Step 1: Online Model Recognition The controlled object is approximated using a first-order inertial delay model: Where G(s) is the system transfer function, K is the system static gain, γ is the system time constant, L is the system pure time delay, and s is the Laplace variable. The discrete transfer function of the model is estimated online using the recursive least squares (RLS) method: y(k) = a1y(k-1) + b0u(k-1); Where k is the current discrete sampling time, y(k) is the system output value at the current time, y(k-1) is the system output at the previous sampling time, u(k-1) is the control input at the previous sampling time, i.e. the PI controller output, and a1 and b0 are model parameters that need to be estimated online. Parameters are continuously updated using the RLS formula: Where k is the current sampling time in the recursive process, and θ(k) = [a1, b0] T φ(k)=[y(k-1),u(k-1)] T λ is the forgetting factor, usually set to 0.98–0.995; θ(k) is the parameter estimate at the current time step; θ(k-1) is the parameter estimate at the previous time step; P(k-1) is the covariance matrix; φ(k) is the regression vector; y(k) is the true output value at the current time step; ∈(k) = y(k) - φ T (k)θ(k-1) is the prediction error; Step 2: Construct the performance index function Define instantaneous error performance metrics: Where J is the performance index function value; τ is the discrete-time index within the performance evaluation time window, i.e., the sampling point number; N is the total number of sampling points, or the sampling length within the performance evaluation time window; e(τ) is the control error at the τth sampling time, i.e., the difference between the set value and the output value; w1 and w2 represent weighting factors, indicating the degree of penalty for the corresponding error interval; ε is the error threshold; different error response functions and weights are set to balance the stability of the system with small errors and the convergence with large errors, thereby improving the overall system regulation performance; Δe(τ)=e(τ)-e(τ-1) is the error change rate β disturbance penalty weight coefficient; The objective function is to minimize J, that is: Where, k p It is a proportional control parameter, k i These are integral control parameters used to control and adjust the duty cycle of the Buck-Boost circuit switches; Step 3: Online PI parameter optimization Optimization using Particle Swarm Optimization (PSO): ① Initialize multiple particles, each representing a (k) p ,k i ); ② Evaluate the fitness J of each particle; ③ Adjust the velocity and position based on individual optimality and global optimality; ④ Iterate until convergence or the maximum number of generations is reached; Step 4: Disturbance Reduction Mechanism After detecting interference, you can speed up the learning rate or enable the fast resetting mode: Detection perturbation threshold: If |e(k)-e(k-1)|>δ, trigger perturbation detection; Where e(k) is the control error at the current sampling time k; e(k-1) is the control error at the previous sampling time k-1; δ is the disturbance threshold, a preset constant used to determine whether the error mutation exceeds the acceptable range; When the absolute value of the difference between two errors exceeds the threshold δ, the system determines that an external disturbance or load change has occurred and should enter the anti-disturbance strategy, which will either reset the optimization strategy or add a disturbance filter.

6. The composite control method for a wireless charging system according to claim 4, characterized in that, Calculate the optimal equivalent load R in S25 and S26 ηmax The specific formula for d1 in Buck-Boost is as follows: The transmission efficiency of a wireless charging system without Buck-Boost circuitry at resonance is calculated as follows: Among them, P in It is the system's input power, P o ω is the system's output power, M is the system's angular frequency, and R is the actual mutual inductance of the coils. Leq It is the equivalent load after system rectification, R s To compensate for the inductance L of the secondary coil in the resonant circuit s parasitic resistance, R p To compensate for the inductance L of the primary coil in the resonant circuit p parasitic resistance, L f To compensate for the inductance in the resonant circuit; For efficiency Taking the partial derivative, we obtain the optimal equivalent load for maximizing system efficiency: The equivalent load of adding a Buck-Boost circuit system is: The optimal duty cycle for maximum efficiency is calculated from the optimal load when the system's equivalent load equals the optimal load for maximum efficiency, i.e., let R... Leq_η =R ηmax Solving for the duty cycle d1: When the coil shifts or the object being charged changes, the estimated M is used. * and the load R calculated by measurement L The optimal duty cycle d1 is calculated.

7. The composite control method for a wireless charging system according to claim 1, characterized in that, Step S3 specifically includes: S31: Based on the mutual inductance M estimated in step S1 * Using the measured parameters and d1 calculated in S2, calculate the phase shift angle θ of the full-bridge inverter under constant voltage control mode using the following relationship: Among them, V set It is the system's preset constant pressure value, Z. in It is the system's input impedance, Z s It is the secondary impedance of the system, Z r It is Z s Mapped impedance on the primary side; S32: The calculated phase shift angle θ is used as the phase input of the full-bridge inverter to drive the four inverter switching devices, so that the output voltage of the inverter bridge generates a corresponding delay in each half-switching cycle, so as to achieve accurate tracking of the reference value of the output DC side voltage. S33: At the end of each switching cycle, the output voltage of the rectifier side is sampled in real time and compared with the estimated voltage. Based on the error, the phase shift angle correction Δθ is updated through a PI controller II and superimposed with the original phase shift angle θ to obtain the phase shift angle for the next cycle. i new =θ+K p (e)+K i ∫edt; Where e is the difference between the measured voltage and the estimated voltage; S34: Parameter K of the PI controller II p K i The PI control parameter tuning method, which uses online model identification and performance index optimization, is adaptively adjusted to ensure that the system can quickly recover to a stable constant voltage state when the load changes abruptly. S35: Through the above steps, closed-loop control of the full-bridge inverter of the LCC-S wireless charging system in constant voltage mode is achieved, ensuring that the rectified output voltage closely tracks the target reference value, thereby meeting the precise constant voltage power supply requirements of different charging devices.

8. The composite control method for a wireless charging system according to claim 1, characterized in that, The full-bridge inverter includes switching transistors S1, S2, S3, and S4, wherein switching transistors S1 and S2 are connected in series as the leading arm of the full-bridge inverter, and switching transistors S3 and S4 are connected in series as the lagging arm of the full-bridge inverter. The compensation resonant circuit includes a primary-side compensation resonant circuit and a secondary-side compensation resonant circuit; wherein the primary-side compensation resonant circuit consists of a primary-side compensation capacitor C. p With the primary coil inductance L p The series branch and the parallel capacitor C f After parallel connection with compensation inductor L f Composed of series connection, primary coil inductance L p The parasitic resistance is denoted as R. p The secondary-side compensated resonant circuit consists of a secondary-side compensation capacitor C connected in series. s With secondary coil inductance L s Series connection, secondary coil inductance L s The parasitic resistance is denoted as R. s The front and rear arms of the full-bridge inverter are connected in parallel and then connected to the two ends of the primary-side compensation resonant circuit, respectively. The rectifier bridge includes diodes D1, D2, D3, and D4. Diodes D1 and D2 are connected in series to form the front arm of the rectifier bridge, and D3 and D4 are connected in series to form the rear arm of the rectifier bridge. The front and rear arms of the rectifier bridge are connected in parallel and then connected to the two ends of the secondary compensation resonant circuit. The Buck-Boost circuit includes a switching transistor S5, an inductor L, and a diode D5; capacitor C o Connected in parallel across the Buck-Boost circuit, load R L Connected in parallel to capacitor C o Both ends.

9. A composite control system for a wireless charging system, characterized in that, The method for implementing the method as described in any one of claims 1-8 includes an LCC-S wireless charging system, an estimation module, a maximum efficiency tracking module, and a constant voltage control module. The estimation module is used to estimate the mutual inductance of the LCC-S wireless charging system and introduce a correction model to eliminate system bias, correcting the estimated mutual inductance to obtain M. * The maximum efficiency tracking module and the constant voltage control module are input; the maximum efficiency tracking module, based on the mutual inductance M estimated by the estimation module... * Based on the measured load resistance, the duty cycle d1 under optimal load is calculated; the Buck-Boost circuit is controlled via PI closed-loop control to adjust the duty cycle d1, achieving real-time tracking of maximum system efficiency; the constant voltage control module, based on the mutual inductance M estimated by the estimation module... * The measured load resistance and the duty cycle d1 calculated by the maximum efficiency tracking module are used to calculate the phase shift angle θ of the LCC-S wireless charging system under constant voltage, thereby achieving constant voltage control of the LCC-S wireless charging system through full-bridge inverter control.

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