Fractional order sliding mode prediction control method for V2G vehicle-mounted converter

Abstract

The invention discloses a fractional order sliding mode prediction control method for a V2G vehicle-mounted converter, and relates to the technical field of vehicle-mounted bidirectional converter control. According to the method, firstly, a fractional order mathematical model of a V2G (Vehicle-to-Grid) vehicle-mounted converter is established on the basis of a fractional order calculus theory; the method comprises the following steps: firstly, constructing a fractional order state space model of a system by introducing a fractional order differential operator, and deriving a fractional order state space equation of a vehicle-mounted bidirectional transducer; the invention provides a V2G vehicle-mounted converter control strategy combining a fractional order mathematical model, a sliding mode controller and a model prediction control method, and realizes accurate control of a front-stage totem-pole bridgeless PFC and a rear-stage CLLC resonant converter of the V2G vehicle-mounted converter. Through the control strategy, the limitation of modeling precision of traditional integer order modeling in V2G vehicle-mounted converter control is effectively solved, and the control precision and the dynamic response performance of the system are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of on-vehicle bidirectional converter control, and particularly to a fractional-order sliding mode predictive control method for a V2G on-vehicle converter. Background Art

[0002] As a key technology for realizing bidirectional energy interaction between electric vehicles and the power grid, the core architecture of the V2G system adopts a front-back two-stage power conversion topology. This architecture mainly consists of a front-stage AC / DC converter and a rear-stage DC / DC converter. The front-stage converter is responsible for realizing the energy conversion between the AC power grid and the DC bus, while the rear-stage converter is used to regulate the voltage and current during the battery charging and discharging process. These two converters achieve efficient conversion and precise transmission of electrical energy through a coordinated control strategy, thereby ensuring reliable and stable bidirectional energy flow between the power grid and the electric vehicle.

[0003] The front - end AC / DC converter, as the core power conversion unit of the V2G system, plays a crucial role in the energy interaction process between the power grid and electric vehicles. In the forward working mode, i.e., when the electric vehicle charges from the power grid, the AC / DC converter mainly realizes the power factor correction and rectification functions. Among them, the PFC function significantly reduces the harmonic pollution on the grid side by optimizing the input current waveform, improves the power utilization efficiency, and ensures the stable operation of the power grid. In the reverse working mode, i.e., when the electric vehicle feeds power back to the power grid, the AC / DC converter switches to the inverter grid - connection function, converting the direct current in the electric vehicle battery into alternating current synchronized with the grid voltage, frequency, and phase, thus realizing the reverse transmission of energy. This ability of bidirectional energy flow not only enables the V2G system to provide efficient charging services for electric vehicles but also endows it with the ability to assist in power supply during peak grid load periods, significantly enhancing the stability and reliability of the power grid, and providing important technical support for the flexible dispatching of the power system and the efficient consumption of renewable energy. The rear - end DC / DC converter is responsible for further voltage conversion of the direct current output by the front - end to meet the charging requirements of the electric vehicle battery or the grid - connection requirements of the power grid. Among them, the CLLC resonant converter has become one of the widely used DC / DC converters in the V2G system due to its unique symmetric topology structure, wide voltage gain range, and soft - switching characteristics in the full - load range. The CLLC resonant converter realizes efficient energy transmission through the resonant network, can maintain high efficiency in a wide voltage range, and achieves soft - switching in the full - load range, significantly reducing the switching losses, thus improving the overall efficiency of the system. In application scenarios such as new - energy electric vehicle charging systems and home energy storage systems, the dynamic response speed of the CLLC resonant converter is one of the key technical indicators for evaluating its performance. The speed of the dynamic response directly determines the stability of the system output voltage and the power quality. A faster dynamic response speed can quickly adjust the output voltage under working conditions such as load mutation or input voltage fluctuation, thus effectively maintaining the stable operation of the system. Therefore, optimizing the dynamic response speed of the CLLC resonant converter has important theoretical and practical significance for improving the overall performance of the system. Through a detailed analysis of the converter's working mode, its equivalent circuit model can be established, and then the relationship between the resonant network parameters and the system performance can be deduced. Based on these theoretical analyses, designers can optimize the resonant network parameters to ensure that the CLLC resonant converter can maintain efficient and stable operation under different working conditions.

[0004] The present invention adopts a fractional-order mathematical model to improve the problem of insufficient control accuracy of the V2G on-vehicle converter caused by the low modeling accuracy of traditional modeling methods. The present invention proposes a control method for a V2G on-vehicle converter based on fractional-order sliding mode predictive control. This method first discretizes the fractional-order mathematical models of the front-stage totem-pole bridgeless PFC and the rear-stage CLLC resonant converter of the on-vehicle V2G system to construct a high-precision prediction model. On this basis, a sliding mode controller is designed for the inner loop of the system, while the outer loop uses a model predictive controller for optimal control. Among them, the sliding mode controller has significant advantages compared with the traditional PI controller, including stronger robustness, faster dynamic response speed, adaptability to complex multi-input multi-output working conditions, and low dependence on model accuracy. During the control process, the predicted value generated by the prediction model is compared with the standard reference value given by the system to form a constraint control term of the objective function. Subsequently, by traversing all possible switching states, substituting them into the objective function for rolling optimization, and finally selecting the switching state that minimizes the objective function value and applying it to the switching tube, the high-performance control of the system is realized. This method significantly improves the control accuracy and dynamic performance of the V2G on-vehicle converter through the organic combination of fractional-order theory and sliding mode predictive control, providing an innovative solution for energy management under complex working conditions. Summary of the Invention

[0005] The object of the present invention is to provide a fractional-order sliding mode predictive control method for a V2G on-vehicle converter in order to solve the above problems.

[0006] To achieve the above object, the technical solution adopted by the present invention is as follows: including the following steps:

[0007] S1: Based on the fractional calculus theory, establish the fractional-order mathematical model of the V2G on-vehicle converter, including the fractional-order state space equations of the front-stage totem-pole bridgeless PFC and the rear-stage CLLC resonant converter;

[0008] S2: Discretize the fractional-order mathematical model to construct a high-precision prediction model;

[0009] S3: Design a sliding mode controller for the inner loop of the system, and use a model predictive controller for the outer loop for optimal control;

[0010] S4: Globally optimize the key parameters of the totem-pole bridgeless PFC and the CLLC resonant converter through a genetic algorithm to obtain the optimal control parameters to achieve the overall improvement of the system performance.

[0011] Further, the fractional-order mathematical model of the totem-pole bridgeless PFC includes:

[0012] The state variable x1 represents the average inductor current, x2 represents the capacitor voltage, and the system input u represents the duty cycle;

[0013] The state - space equations of the totem - pole bridgeless PFC are as follows:

[0014] (5).

[0015] Furthermore, the fractional - order mathematical model of the CLLC resonant converter includes:

[0016] Fractional - order non - linear state equation:

[0017] (6);

[0018] where \(i_1\) and \(i_2\) correspond to the resonant inductors \(L_1\) and \(L_2\) in the system, \(u_1\) and \(u_2\) correspond to the resonant capacitors \(C_1\) and \(C_2\) in the system, and \(0\lt\alpha,\beta\lt1\).

[0019] Large - signal model of the system:

[0020] (8);

[0021] Steady - state equation of the system:

[0022] (9).

[0023] Furthermore, the control strategy of the totem - pole bridgeless PFC includes:

[0024] The outer loop is a model - predictive controller. Taking the bus voltage as the reference value, after passing through the model - predictive outer - loop controller, a control quantity is generated as the reference given value of the inductor current;

[0025] The inner loop adopts a sliding - mode controller, which generates an SVPWM signal according to the error value of the inductor current to drive the power MOS transistors of the totem - pole bridgeless PFC converter.

[0026] Furthermore, the design of the model - predictive controller of the totem - pole bridgeless PFC includes:

[0027] Fractional - order state equation:

[0028] (12)

[0029] (13);

[0030] The state equation is discretized using the first - order Euler method to obtain the discretized - form formula:

[0031] (14);

[0032] The predictive formula for the inductor current is a known formula

[0033] (16);

[0034] The duty cycle prediction model expression is a known formula:

[0035] (18)

[0036] The above formula is the duty cycle prediction model expression of the totem-pole bridgeless PFC, is the current-related control quantity Pre_I, is the voltage quantity Pre_U.

[0037] Furthermore, the design of the sliding mode controller of the totem-pole bridgeless PFC includes:

[0038] According to the state space equation, define the reference value of the current loop as x1d, and obtain the error e and its differential expression:

[0039] (23);

[0040] The equation of the sliding mode surface:

[0041] (25);

[0042] The expression of the system input u, i.e., the duty cycle:

[0043] (28).

[0044] Furthermore, the control strategy of the CLLC resonant converter includes:

[0045] The outer loop is a model predictive controller. Taking the reference voltage as the reference value, after passing through the model predictive outer loop controller, a control quantity is generated as the reference given value of the resonant current;

[0046] The inner loop adopts a sliding mode controller, and a PFM signal is generated according to the error value of the resonant current to drive the power MOS tube of the CLLC resonant converter.

[0047] Furthermore, the design of the model predictive controller of the CLLC resonant converter includes:

[0048] The fractional-order state equation is:

[0049] (19);

[0050] Among them, i1 is the primary-side resonant current of the transformer, i m is the exciting inductor current, i2 is the secondary-side resonant current of the transformer, u 1、 u2 is the resonant capacitor voltage, V o is the output filter capacitor voltage.

[0051] The state equation is discretized using the first-order Euler method to obtain the discretized form:

[0052] (20);

[0053] Optimization objective function

[0054] (21)

[0055] where N is the prediction horizon, is the reference state, and are the weight matrices at times;

[0056] System constraint conditions:

[0057] (22)

[0058] The input voltage constraint is: .

[0059] Furthermore, the design of the sliding mode controller for the CLLC resonant converter includes:

[0060] The sliding surface function is defined as:

[0061] (31)

[0062] where Vo represents the error between the reference voltage and the output voltage, and M1 and M2 are the sliding surface coefficients;

[0063] Steady-state value of the output voltage and its correlation with the sliding mode coefficients:

[0064] (32);

[0065] Constraint conditions of the sliding mode function:

[0066] (34);

[0067] Transfer function of the sliding mode controller:

[0068] (36).

[0069] Compared with the prior art, the present invention has the following beneficial effects:

[0070] (1) The present invention accurately describes the dynamic characteristics of the V2G on-vehicle converter through a fractional-order mathematical model, breaks through the limitations of the traditional integer-order model, realizes a fast and stable steady-state response of the system, significantly improves the transient response and load mutation stability, and enhances the overall dynamic performance.

[0071] (2) The present invention adopts a double-closed-loop control strategy combining a sliding mode controller and a model predictive control to improve the robustness and anti-interference ability of the system. Under complex working conditions, the system can still operate with high performance, effectively suppressing the resonant current impact and ensuring stable and reliable energy transmission.

[0072] (3) The present invention realizes the precise control of the V2G on-vehicle converter, improves the two-way energy interaction efficiency between the electric vehicle and the power grid, reduces the switching loss, improves the overall efficiency of the system, reduces the operating cost, and provides support for the efficient operation of the new energy power system. Description of the Drawings

[0073] Figure 1 is the control structure diagram of the totem-pole bridgeless PFC converter of the present invention;

[0074] Figure 2 is the control structure diagram of the CLLC resonant converter of the present invention;

[0075] Figure 3 is the flow chart of the genetic algorithm for optimizing key parameters of the present invention;

[0076] Figure 4 is the fractional-order equivalent topology of the CLLC resonant converter of the present invention;

[0077] Figure 5 is the double-closed-loop control block diagram of the totem-pole bridgeless PFC of the present invention;

[0078] Figure 6 is the double-closed-loop control block diagram of the CLLC of the present invention. Detailed Embodiments

[0079] To make the technical means, creative features, achieved purposes and effects of the present invention easy to understand, the present invention will be further described below in conjunction with specific embodiments.

[0080] As shown in Figure 1-6 , the present invention proposes a fractional-order sliding mode predictive control method for a V2G on-vehicle converter, aiming to solve the problems of poor dynamic characteristics, large bus voltage fluctuations, poor load stability, and poor anti-interference ability of the current V2G on-vehicle converter. By adopting a more accurate mathematical model and state equation, the corresponding controller is designed to achieve precise control of the system voltage loop. Among them, the fractional-order mathematical model solves the problems of insufficient model accuracy established by traditional integer-order modeling methods, inability to obtain accurate mathematical model parameters of the V2G on-vehicle converter, and thus the system cannot reach a stable state quickly and smoothly. The current inner loop of the V2G on-vehicle converter adopts a sliding mode controller and the voltage outer loop samples the model predictive controller to improve the dynamic characteristics and stability of the on-vehicle V2G system.

[0081] The structure diagram of the model predictive control of the totem-pole bridgeless PFC is as Figure 1 shown in the figure. The model predictive controller of the PFC resonant converter uses the bus voltage as the reference value. After passing through the model predictive outer-loop controller, the control quantity is generated as the reference given value of the inductor current i L . The error value between the actual inductor current and it generates the SVPWM signal through the inner-loop sliding-mode controller to drive the silicon carbide MOSFET of the totem-pole bridgeless PFC converter, and the output can be a bus voltage that is adjustable within a wide range.

[0082] The structure diagram of the control of the CLLC resonant converter is as Figure 2 shown in the figure. The model predictive controller of the CLLC resonant converter uses the U ref voltage as the reference value. After passing through the model predictive outer-loop controller, the control quantity is generated as the reference given value of the resonant current i L of the inner-loop resonant converter. The error value between the actual resonant current and it generates the PFM signal through the inner-loop sliding-mode controller to drive the silicon carbide MOSFET of the CLLC resonant converter, and the output can be the charging voltage of the lithium battery or lithium iron phosphate battery of the new energy electric vehicle that is adjustable within a wide range.

[0083] The genetic algorithm (GA) is a heuristic global optimization algorithm based on the principles of biological evolution. It realizes the efficient solution of complex nonlinear problems by simulating biological mechanisms such as natural selection, genetic variation, and population evolution. With its powerful global search ability and robustness, this algorithm shows significant advantages in solving multi-modal optimization, high-dimensional nonlinear, and discontinuous problems. When optimizing the key parameters of the V2G on-vehicle converter, the genetic algorithm can effectively handle the optimization problems with multiple objectives and multiple constraints. First, clarify the key parameters of the front-stage totem-pole bridgeless PFC and the rear-stage CLLC resonant converter of the V2G on-vehicle converter. Among them, the key parameters of the front-stage converter include the inductor current and the output voltage, and this output voltage also serves as the input voltage of the rear-stage CLLC resonant converter. The key parameters of the rear-stage converter include the resonant inductor current and the output voltage. Secondly, based on the fractional calculus theory, establish the fractional-order mathematical model of the V2G on-vehicle converter and deduce its state-space equation. Finally, according to the optimization process as Figure 2 shown in the figure, use the global optimization algorithm to iteratively optimize the key parameters, so as to obtain the optimal parameter combination of the front-stage totem-pole bridgeless PFC and the rear-stage CLLC resonant converter to achieve the overall improvement of the system performance.

[0084] Modeling of V2G on-vehicle converter

[0085] Modeling of totem-pole bridgeless PFC

[0086] The totem-pole bridgeless power factor correction (PFC) circuit is a high-efficiency and high-power-density AC-DC converter topology. Due to its excellent power quality and energy conversion efficiency, it is widely used in fields such as electric vehicle chargers and server power supplies. To more accurately describe its dynamic characteristics, a fractional-order mathematical model can be adopted. Fractional calculus, by introducing non-integer-order differential operators, can more accurately characterize the nonlinear behavior and memory characteristics of the system, especially suitable for the modeling and analysis of complex power electronic systems. Based on the small-signal modeling theory, by analyzing the dynamic behavior of the bidirectional totem-pole converter in the on and off states, a fractional-order mathematical model of the front-stage totem-pole bridgeless PFC can be established. This model can more comprehensively reflect the dynamic response and steady-state characteristics of the system, providing a theoretical basis for the design and optimization of subsequent control strategies.

[0087] (1)

[0088] (2)

[0089] In the formula, A1, B1, A2, and B2 are the coefficients of the state equation; x(t) is the state variable of the system; u(t) is the output variable of the system; v o (t) is the output DC voltage; i L (t) is the inductor current. According to the state-averaging method, the state equation of the system over the entire period is obtained:

[0090] (3)

[0091] By converting the perturbation equation from the time domain to the complex frequency domain through fractional calculus, we can obtain:

[0092] (4)

[0093] Let the state variable x1 represent the average inductor current, the state variable x2 represent the capacitor voltage, and the system input u represent the duty cycle. The state-space equation of the totem-pole bridgeless PFC obtained is:

[0094] (5)

[0095] Modeling of the CLLC Resonant Converter

[0096] The fractional-order mathematical model of the CLLC resonant converter can more accurately describe the dynamic characteristics of the system by introducing the fractional-order calculus theory, and is especially suitable for the modeling and analysis of high-frequency and nonlinear power electronic systems. For the key devices of the CLLC resonant converter, a fractional-order resonant inductor model and a fractional-order resonant capacitor model are established to more accurately characterize their non-ideal characteristics. On this basis, the genetic algorithm is used to optimize the two key parameters of the resonant current and the output voltage to improve the system performance. Through the modal analysis of the CLLC bidirectional resonant converter, the fractional-order equivalent model of the system as shown in Figure 3 can be obtained. The fractional-order nonlinear state-space equation of the system is derived from this modal analysis, providing theoretical support for the accurate description of the system dynamic behavior and the design of control strategies. This method not only improves the accuracy of the model but also lays a foundation for the design and performance analysis of the resonant converter.

[0097] By analyzing the fractional-order forms of the inductor current and capacitor voltage of these electronic components, the fractional-order nonlinear state equation of the system can be obtained:

[0098] (6)

[0099] Taking the differentiation of the above equation with orders α and β, and 0 < α, β < 1, we can get: (7)

[0100] Based on the harmonic balance principle, by equivalently matching the coefficients of the sine and cosine components in the above equation respectively and through systematic analysis and arrangement, the large-signal model of the system can be derived. (8)

[0101] According to the definition of the Caputo fractional-order derivative, the steady-state equation of the system can be obtained: . The variable matrices in the formula are as follows:

[0102] (9)

[0103] (10)

[0104] (11)

[0105] Design of on-vehicle V2G converter controller

[0106] Design of totem-pole bridgeless PFC controller

[0107] The outer loop of the V2G front-end totem-pole bridgeless PFC is a model predictive controller, and the inner loop adopts a sliding mode controller. The control principle of the totem-pole bridgeless PFC is as follows Figure 4 shown. In order to improve the stability of the output voltage of the totem-pole bridgeless PFC and reduce the influence of the bus voltage fluctuation on the subsequent CLLC resonant converter. This scheme introduces a genetic algorithm for the two parameters of the inductor current and the output bus voltage of the totem-pole bridgeless PFC to realize the online identification of key parameters and obtain the optimal parameter identification results. Ensure that the model parameters can be updated in real time during the control process, so as to complete the precise control of the system.

[0108] Design of the Model Predictive Controller for the Totem-Pole Bridgeless PFC

[0109] The voltage loop controller of the totem-pole bridgeless PFC is a model predictive controller. i L is the inductor current on the totem-pole bridgeless PFC, and V o is the bus voltage of the V2G on-vehicle converter and also the output voltage of the totem-pole bridgeless PFC. i L1 is the current compensation amount, and then the inductor current obtained by the voltage loop is used as the reference value of the current quantity.

[0110] The fractional-order state equation of the totem-pole bridgeless PFC is as follows:

[0111] (12)

[0112] (13)

[0113] Based on the first-order Euler method, the state equations of the inductor current and the output voltage in the above equations are discretized between adjacent sampling points k and k + 1, and its discretized form is as follows:

[0114] (14)

[0115] Equation (13) is the initial inductor current value when the switch tube is excited at the moment. By sorting out the inductor current values at the two moments, we can get:

[0116] (15)

[0117] Substitute (13) into (14) to get the predicted formula of the inductor current at the

[0118] (16)

[0119] Based on the real-time sampling data of the grid voltage parameters, the DC output voltage parameters and the inductor input current, the predicted value range of the reference current can be deduced through the predictive control algorithm. (17)

[0120] The duty cycle expression of the predictive control can be obtained as follows:

[0121] (18)

[0122] The above formula is the duty cycle prediction model expression of the totem-pole bridgeless PFC, where Pre_I is the control quantity related to current, and Pre_U is the voltage quantity.

[0123] Design of the Sliding Mode Controller for Totem-Pole Bridgeless PFC

[0124] According to the state space equation of the totem-pole bridgeless PFC, the reference value of the current loop is defined as x 1d， The error e and its differential expression are obtained:

[0125] (23)

[0126] To make s converge, let:

[0127] (24)

[0128] The equation of the sliding mode surface can be obtained:

[0129] (25)

[0130] The error converges exponentially with a time constant of k2. By combining equations (23) to (25), the expression of the system input u, i.e., the duty cycle, can be obtained as:

[0131] (26)

[0132] The convergence equation of the error is:

[0133] (27)

[0134] By reasonably designing the values of k1 and k2, the constant gain of the convergence term can be changed to further improve the convergence speed. The expression of the system input at this time is:

[0135] (28)

[0136] Design of the Controller for CLLC Resonant Converter

[0137] Traditional CLLC resonant converters usually adopt a single-voltage closed-loop control strategy, which has inherent limitations such as slow system dynamic response speed, poor dynamic characteristics, and insufficient stability during load mutations. Although the double-closed-loop control strategy based on load current and load voltage improves the system response speed to a certain extent, it is still difficult to effectively suppress the negative impact of resonant current shock on system performance. To significantly improve the anti-interference ability, dynamic response speed, and output voltage regulation accuracy of CLLC resonant converters, the present invention proposes an innovative double-closed-loop control system structure. In this control system, the current inner loop composed of a sliding mode controller is mainly used to achieve precise control of the resonant current, while the voltage outer loop composed of a linear model predictive controller is responsible for the stable regulation of the output voltage. This control strategy effectively improves the dynamic performance and robustness of the system through the collaborative optimization of the inner and outer loops. This control structure can ensure that when the load mutates, the controller can effectively control the resonant current by quickly adjusting the switching frequency, thereby significantly reducing the resonant current deviation and improving the system dynamic response characteristics. The control process of the CLLC resonant converter is as Figure 5 shown.

[0138] Design of Model Predictive Controller for CLLC Resonant Converter

[0139] The following state equations are obtained through the fractional-order mathematical model of the above CLLC resonant converter:

[0140] (19)

[0141] The state equations of the resonant inductor current and output voltage in the above formula are discretized at two adjacent sampling points k and k + 1 using the first-order Euler method as follows:

[0142] (20)

[0143] Solve the optimal control problem, apply the first control input to the system, and establish an optimization objective function.

[0144] (21)

[0145] where N is the prediction horizon, is the reference state, and are the weight matrices at

[0146] The system constraint conditions are:

[0147] (22)

[0148] The input voltage constraint is:

[0149] Solve the above optimization problem to find the optimal control input sequence, and then use the obtained PFM signal for the MOS transistor control of the CLLC resonant converter. Design of the CLLC Resonant Converter Sliding Mode Controller To control the output voltage Vo, the sliding surface function can be defined as a linear combination of system variables:

[0150] (31)

[0151] where Vo represents the error between the reference voltage and the output voltage, and M1 and M2 are the sliding surface coefficients.

[0152] Convert the sliding mode function to the complex frequency domain. The steady-state value of the output voltage and its correlation with the sliding mode coefficients are as follows:

[0153] (32)

[0154] At the equilibrium point = 0, the equilibrium frequency will be obtained to minimize the steady-state error. Therefore, the actual output voltage at steady state in the s-domain is:

[0155] (33)

[0156] M1 and M2 should fully consider the dynamic performance of the system under specific conditions. The amplitude and phase of the output voltage provide the amplitude and phase information of the output voltage at steady state.

[0157] It is inferred from the constraint conditions of the sliding surface that the maximum slope amplitude appears at the minimum value of or . Therefore, the sliding mode function can be written as:

[0158] (34)

[0159] The output capacitor current can be used to represent the stability of the system, Lyapunov stability criterion:

[0160] (35)

[0161] The transfer function of the sliding mode controller is:

[0162] (36)

[0163] It is obvious to those skilled in the art that the present invention is not limited to the details of the above-described exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention. Therefore, in any aspect, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Accordingly, all changes that fall within the meaning and scope of the equivalent elements of the claims are intended to be embraced within the present invention. Any reference signs in the claims should not be construed as limiting the claims concerned.

[0164] In addition, it should be understood that although this specification is described in terms of embodiments, not every embodiment only contains an independent technical solution. This narrative manner of the specification is only for clarity. Those skilled in the art should regard the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A fractional-order sliding mode predictive control method for a V2G on-vehicle converter, characterized in that, It includes the following steps: S1: Based on the fractional calculus theory, establish the fractional-order mathematical model of the V2G on-vehicle converter, including the fractional-order state space equations of the front-stage totem-pole bridgeless PFC and the rear-stage CLLC resonant converter; S2: Discretize the fractional-order mathematical model to construct a high-precision prediction model; S3: Design a sliding mode controller for the inner loop of the system, and use a model predictive controller for the outer loop for optimal control; S4: Globally optimize the key parameters of the totem-pole bridgeless PFC and the CLLC resonant converter through a genetic algorithm to obtain the optimal parameter combination to achieve precise control of the system.

2. The fractional-order sliding mode predictive control method for the V2G on-vehicle converter according to claim 1, characterized in that The fractional-order mathematical model of the totem-pole bridgeless PFC includes: The state space equation of the totem-pole bridgeless PFC is: (5)； Among them, the state variable x1 represents the average inductor current, x2 represents the capacitor voltage, and the system input u represents the duty cycle.

3. The fractional-order sliding mode predictive control method for the V2G on-vehicle converter according to claim 1, characterized in that, The fractional-order mathematical model of the CLLC resonant converter includes: Fractional-order nonlinear state equation: （6）； where L m is the exciting inductance of the transformer, L1 is the resonant inductance, n is the turns ratio of the transformer, and V o is the output voltage; The large-signal model of the system: （8）； Among which V o is the output voltage, u 1c is the voltage across the resonant capacitor C1 in the system, u 2c is the resonant capacitor in the system The voltage across c2, and i1 and i2 correspond to the currents of the resonant inductors L1 and L2 in the system; The steady-state equation of the system: （9）。 4. The fractional-order sliding mode predictive control method for the V2G on-vehicle converter according to claim 1, wherein The control strategy of the totem-pole bridgeless PFC includes: The outer loop is a model predictive controller, taking the bus voltage as the reference value, and generating a control quantity as the reference given value of the inductor current after passing through the model predictive outer loop controller; The inner loop adopts a sliding mode controller, generating an SVPWM signal according to the error value of the inductor current to drive the power MOS transistors of the totem-pole bridgeless PFC converter.

5. The fractional-order sliding mode predictive control method for the V2G on-vehicle converter according to claim 4, characterized in that, The design of the model predictive controller of the totem-pole bridgeless PFC includes: Fractional-order state equation: （12） （13）； v i is the input voltage, v o is the output voltage, i L is the inductor current; Use the first-order Euler method to discretize the state equation to obtain the discretized form formula: （14）； The inductor current prediction formula is a known formula （16）； where L is the inductance value, and d k is the duty cycle; The expression of the duty cycle prediction model is: (18) The above formula is the duty cycle prediction model expression of the totem-pole bridgeless PFC, which is the current-related control quantity Pre_I, and the voltage quantity Pre_U.

6. The fractional-order sliding mode predictive control method for the V2G on-vehicle converter according to claim 4, characterized in that The design of the sliding mode controller of the totem-pole bridgeless PFC includes: According to the state-space equation, the reference value of the current loop is defined as x 1d , and the error e and its differential expression are obtained: （23）； The equation of the sliding surface: （25）； The expression of the system input u, that is, the duty cycle: （28）。 7. The fractional-order sliding mode predictive control method for the V2G on-vehicle converter according to claim 1, wherein The control strategy of the CLLC resonant converter includes: The outer loop is a model predictive controller, taking the given output voltage as the reference value, and generating a control quantity as the reference given value of the resonant current after passing through the model predictive outer loop controller; The inner loop adopts a sliding mode controller, generating a PFM signal according to the error value of the resonant current to drive the power MOS transistors of the CLLC resonant converter.

8. The fractional-order sliding mode predictive control method for the V2G on-vehicle converter according to claim 7, characterized in that The design of the model predictive controller of the CLLC resonant converter includes: The fractional-order state equation is: （19）； Among them, i1 is the primary-side resonant current of the transformer, u1 is the resonant capacitor voltage, i2 is the secondary-side resonant current of the transformer, u2 is the resonant capacitor voltage, 0 < α, β < 1; Use the first-order Euler method to discretize the state equation to obtain the discretized form: （20）； Optimization objective function （21） where N is the prediction horizon, is the reference state, and are the weight matrices at time System constraint conditions: （22） The input voltage is constrained as: .

9. The fractional-order sliding mode predictive control method for the V2G on-vehicle converter according to claim 7, wherein The design of the sliding mode controller of the CLLC resonant converter includes: The sliding surface function is defined as: （31） Among them, Vo represents the error between the reference voltage and the output voltage, and M1 and M2 are the sliding surface coefficients; The steady-state value of the output voltage and its correlation with the sliding mode coefficients: （32）； The constraint conditions of the sliding mode function: （34）； The transfer function of the sliding mode controller: （36）。

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