Seamless switching control method of integrated buck-boost converter

Abstract

The invention relates to the technical field of power electronics, in particular to a seamless switching control method for an integrated buck-boost converter, and the method comprises the steps: obtaining the operation state type and input and output voltage information of the converter, preliminarily estimating the switching demand intensity, and determining a switching optimization coefficient. A duty cycle control parameter is initialized based on the optimization coefficient while a current load power level is identified with a voltage current sensor. In combination with load power and initialization parameters, switching interference coefficients are evaluated, the change trend of the switching interference coefficients is analyzed, a dynamic switching control optimization model is established, and optimal duty ratio control parameters are generated; the power switch tube is driven based on the optimal parameters, seamless switching between the Buck mode and the Boost mode is achieved, the problems of duty ratio abrupt change, output voltage fluctuation and inductive current abnormity are effectively avoided, and the stability and reliability of the system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power electronics, and in particular to a seamless switching control method for an integrated buck-boost converter. Background Art

[0002] The buck-boost converter is a key topology widely used in power electronics, capable of achieving stable output voltage regulation despite input voltage fluctuations or load changes. However, in practical applications, when switching between boost and buck modes, the duty cycle often undergoes abrupt changes due to significant differences in the operating principles of the boost and buck modes. This can cause output voltage fluctuations and, in turn, affect power supply stability at the load. Furthermore, inductor current may be interrupted or spike during the switching process, which not only prolongs switching delays but can also damage downstream equipment and even cause system failures.

[0003] The above content is only used to assist in understanding the technical solution of the present invention and does not constitute an admission that the above content is prior art. Summary of the Invention

[0004] The main purpose of the present invention is to provide a seamless switching control method for an integrated buck-boost converter, aiming to solve the technical problem that when switching between boost and buck modes, traditional buck-boost converters are prone to output voltage fluctuations, inductor current interruptions or voltage spikes due to sudden changes in duty cycle, resulting in unstable power supply to the load end, switching delays, and even damage to subsequent equipment.

[0005] To achieve the above object, the present invention provides a seamless switching control method for an integrated buck-boost converter, the method comprising: Obtaining an operating state type and current input and output voltage information of the integrated buck-boost converter, preliminarily estimating a switching demand intensity of the converter based on the operating state type, and determining a switching optimization coefficient of the converter based on the current input and output voltage information and the switching demand intensity; Initializing the duty cycle control parameters of the converter based on the switching optimization coefficient, and identifying the current load power level through the voltage and current sensors; According to the current load power level and the initialization duty cycle control parameter, the switching interference coefficient of the converter is evaluated, and the change trend of the current load power level and the initialization duty cycle control parameter on the switching interference coefficient is analyzed, a dynamic switching control optimization model is established, and the optimal duty cycle control parameter is generated; The power switch tube of the converter is driven based on the optimal duty cycle control parameter to achieve seamless switching control between the buck mode and the boost mode.

[0006] Optionally, before initializing the duty cycle control parameter of the converter based on the switching optimization coefficient, the method further includes: Acquire several load characteristics of the converter operating state type, including load current change rate, power fluctuation amplitude, and output voltage ripple threshold, and determine the duty cycle adjustment range corresponding to the operating state type; Accordingly, initializing the duty cycle control parameter of the converter based on the switching optimization coefficient includes: The state adjustment algorithm is used to modify the duty cycle adjustment range corresponding to the load characteristics of the operating state type based on the switching optimization coefficient to determine the duty cycle control parameter of the initialization converter.

[0007] Optionally, the state adjustment algorithm is expressed as:

[0008] Where D is the initialization duty cycle control parameter, is the maximum duty cycle adjustment range corresponding to the operating state type, is the minimum duty cycle adjustment range, and C is the switching optimization coefficient of the converter.

[0009] Optionally, the evaluating the switching interference coefficient of the converter according to the current load power level and the initialization duty cycle control parameter includes: Assigning an interference risk score to each load feature based on a change trend of interference risk of the current load power level on the converter output voltage stability, inductor current continuity, and switching loss to obtain a load feature interference risk score; Determining the relative importance of interference of each load feature to the switching control according to the interference risk score, assigning an interference risk weight, and obtaining a load feature interference risk weight; Calculating a weighted interference score for the converter based on the interference risk score and the weight; The switching interference coefficient of the converter is calculated by combining the initialization duty cycle control parameters through the interference evaluation formula.

[0010] Optionally, the interference assessment formula is:

[0011] Where I is the switching interference coefficient, is the interference risk weight of the i-th load characteristic, is the interference risk score of the i-th load signature, D is the initialization duty cycle control parameter, and n is the total number of load signatures.

[0012] Optionally, analyzing the changing trend of the switching interference coefficient between the current load power level and the initialization duty cycle control parameter, establishing a dynamic switching control optimization model, and generating the optimal duty cycle control parameter includes: The duty cycle adjustment range corresponding to the operating state type is used as an adjustable control limit condition; With the aforementioned constraints as the input, the influence of the current load power level and the initialization duty cycle control parameter on the switching interference coefficient are used as independent variables. Multiple rounds of iterations are performed through the gradient descent algorithm, and the duty cycle parameter that minimizes the switching interference coefficient is output as the dependent variable to construct a dynamic switching control optimization model. An optimal duty cycle control parameter is generated based on the dynamic switching control optimization model.

[0013] Optionally, the expression of the dynamic switching control optimization model is:

[0014] Where D′ is the optimal duty cycle control parameter, is the duty cycle parameter of the t+jth iteration, α is the learning efficiency step size, is the gradient of the duty cycle parameter, is the switching interference coefficient function, is the current load power level, and The upper and lower limits of the duty cycle adjustment range.

[0015] Optionally, preliminarily estimating the switching demand intensity of the converter according to the operating state type, and determining the switching optimization coefficient of the converter based on the current input and output voltage information and the switching demand intensity, includes: Obtain the converter's historical average input voltage, target output voltage, and rated load power based on the operating state type, calculate the converter's theoretical duty cycle range, and preliminarily estimate the converter's switching demand intensity; A switching optimization coefficient is calculated based on the current input and output voltage information and the switching demand intensity, where the switching demand intensity includes a preset switching priority factor.

[0016] Optionally, the calculation formula of the handover optimization coefficient is as follows:

[0017] Where, is the current input voltage, is the current output voltage, is the rated output voltage, The preset switching priority weight factor.

[0018] Optionally, the process of driving the power switch tube of the converter based on the optimal duty cycle control parameter is performed by a digital signal processor or a field programmable gate array, and specifically includes: When the optimal duty cycle control parameter indicates the buck mode, the high-side switch and the freewheeling diode are driven to conduct according to the step-down logic, and the inductor current freewheeling path is configured at the same time; When the optimal duty cycle control parameter indicates the boost mode, the low-side switch and the energy storage inductor are driven to conduct according to the boost logic, and the energy replenishment strategy of the output filter capacitor is adjusted at the same time; By synchronously adjusting the dead time of the switch tube drive signal, voltage spikes and current interruptions during the switching process are eliminated, achieving seamless switching between buck mode and boost mode.

[0019] The present invention provides a seamless switching control method for an integrated buck-boost converter. The method estimates the switching demand intensity through the operating state type and input and output voltage information, and dynamically adjusts the control strategy in combination with the switching optimization coefficient, thereby avoiding the one-size-fits-all duty cycle mutation in traditional switching and suppressing the voltage fluctuation risk from the source; the load power level is identified in real time and incorporated into the interference coefficient evaluation, so that the control parameters can be dynamically optimized according to the actual load, significantly reducing the interference of load changes on the switching process and improving the robustness of the system under different working conditions; by establishing a dynamic analysis model of the switching interference coefficient, the coupling relationship between the load power and the duty cycle parameters is accurately captured, and the optimal control parameters are generated, which effectively avoids the problem of inductive current interruption or spike, and ensures the current continuity and voltage stability of the switching process; the power switch tube is driven based on the optimal parameters, thereby achieving zero-impact transition of mode switching, shortening the switching time, eliminating the switching delay of the traditional solution, and providing a stable power supply environment for the subsequent equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 Schematic diagram of a seamless switching control device structure of an integrated buck-boost converter in a hardware operating environment according to an embodiment of the present invention; Figure 2 1. A flow chart of a seamless switching control method for an integrated buck-boost converter according to an embodiment of the present invention; Figure 3 This is a structural block diagram of an embodiment of a seamless switching control system for an integrated buck-boost converter according to the present invention.

[0021] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0022] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0023] Reference Figure 1 , Figure 1 The figure is a structural diagram of a seamless switching control device of an integrated buck-boost converter in a hardware operating environment according to an embodiment of the present invention.

[0024] like Figure 1 As shown, the seamless switching control device for an integrated buck-boost converter may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen. Optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. In the present invention, the wired interface of the user interface 1003 may be a USB interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a wireless fidelity (WI-FI) interface). The memory 1005 may be a high-speed random access memory (RAM) or a non-volatile memory (NVM), such as a disk drive. The memory 1005 may also be a storage device independent of the processor 1001.

[0025] Those skilled in the art will understand that Figure 1 The structure shown in the figure does not constitute a limitation on the seamless switching control device of the integrated buck-boost converter, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0026] like Figure 1 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a seamless switching control program of an integrated buck-boost converter.

[0027] exist Figure 1In the seamless switching control device for an integrated buck-boost converter shown, the network interface 1004 is primarily used to connect to a backend server and communicate data with the backend server; the user interface 1003 is primarily used to connect to peripheral devices; the seamless switching control device for an integrated buck-boost converter invokes a seamless switching control program for an integrated buck-boost converter stored in a memory 1005 via a processor 1001 and executes the seamless switching control method for an integrated buck-boost converter provided in an embodiment of the present invention.

[0028] Based on the above hardware structure, an embodiment of a seamless switching control method for an integrated buck-boost converter of the present invention is proposed.

[0029] Reference Figure 2 , Figure 2 The present invention provides a flow chart of a seamless switching control method for an integrated buck-boost converter according to an embodiment of the present invention.

[0030] In one embodiment, the seamless switching control method of the integrated buck-boost converter comprises the following steps: Step S100, obtaining the operating state type and current input and output voltage information of the integrated buck-boost converter, preliminarily estimating the switching demand intensity of the converter according to the operating state type, and determining the switching optimization coefficient of the converter based on the current input and output voltage information and the switching demand intensity.

[0031] The operating state type refers to the current operating mode of the integrated buck-boost converter, including buck mode (step-down) or boost mode (step-up). Its definition is based on the relative relationship between the input and output voltages. For example, when the input voltage is lower than the target output voltage, the system is in boost mode; otherwise, it is in buck mode. This type can be determined by the state monitoring module based on the real-time input and output voltage values. The switching demand intensity is a parameter that quantifies the necessity of mode switching. It is typically calculated based on indicators such as the input-output voltage difference and the load change rate. Its purpose is to predict whether the system needs to switch operating modes immediately to maintain output stability. The switching optimization coefficient is a comprehensive parameter that combines current voltage information and the switching demand intensity. Its calculation may involve mathematical methods such as weighted averaging and proportional regulation.

[0032] The technical operation involves real-time acquisition of input and output voltage values ​​through voltage sensors and determination of the current operating mode through a status monitoring module. A preliminary estimation involves substituting the voltage difference into a pre-set algorithm to quantify the switching demand intensity. The switching optimization coefficient is determined by substituting the estimated result and voltage information into a mathematical model, ultimately outputting an intermediate variable for subsequent parameter adjustments. This process provides a dynamic reference for duty cycle adjustment, avoiding the sudden change problem caused by fixed thresholds in traditional methods.

[0033] Step S200 : Initializing the duty cycle control parameters of the converter based on the switching optimization coefficient, and identifying the current load power level through the voltage and current sensors.

[0034] The duty cycle control parameter, which refers to the ratio of the switch's on-time to its period, regulates the ratio of energy stored and released by the inductor. In a buck-boost converter, this parameter's value range must be adjusted based on the mode, for example, D < 0.5 in buck mode and D > 0.5 in boost mode. The load power level is calculated by multiplying the voltage and current. Its identification typically relies on synchronized sampling and real-time calculations from voltage and current sensors.

[0035] The technical operation involves initializing the duty cycle control parameters by presetting the initial duty cycle value based on the switching optimization coefficient. Identifying the load power level involves sub-steps such as sensor data acquisition, filtering, and power calculation. This step provides an initial parameter baseline for subsequent interference coefficient assessment and establishes a dynamic feedback channel using load power data.

[0036] Step S300, based on the current load power level and the initialization duty cycle control parameters, evaluate the switching interference coefficient of the converter, analyze the changing trend of the current load power level and the initialization duty cycle control parameters on the switching interference coefficient, establish a dynamic switching control optimization model, and generate the optimal duty cycle control parameters.

[0037] The switching interference coefficient is defined as the degree of system disturbance caused by the coupling of load changes and duty cycle parameters, and can be quantified using indicators such as current ripple amplitude and voltage fluctuation rate. Dynamic switching control optimization models may employ state-space equations, PID control algorithms, or adaptive fuzzy control methods.

[0038] The technical process involves substituting the current load power and the initialized duty cycle into the interference assessment function. Analyzing the trend involves analyzing the sensitivity of the interference coefficient to parameter changes, for example, by using derivatives or numerical simulations to determine the direction of parameter adjustment. Building a dynamic model may involve steps such as parameter identification and model parameter tuning. Generating the optimal duty cycle parameter involves solving the objective function of minimizing interference under constraints using an optimization algorithm (such as gradient descent). The technical benefit of this process lies in accurately capturing the nonlinear relationship between parameters through mathematical modeling, thereby avoiding the limitations of traditional empirical adjustments.

[0039] Step S400 : driving the power switch of the converter based on the optimal duty cycle control parameter to achieve seamless switching control between the buck mode and the boost mode.

[0040] The optimal duty cycle control parameter is the final control variable obtained through optimization in the preceding steps. It is typically driven by a PWM signal generator that generates a pulse sequence corresponding to the duty cycle. Power switches refer to semiconductor devices such as MOSFETs and IGBTs, whose on and off states directly control the energy transfer path.

[0041] The technical operation involves converting the optimal parameters into a PWM signal, controlling the gate voltage of the switching transistor through the driver circuit, and dynamically switching the inductor current path. For example, when switching from buck to boost mode, the duty cycle is gradually increased to a critical value while monitoring current continuity to complete the mode switch. The technical effect of this step is to achieve zero-impact switching through smooth parameter transitions, eliminating the voltage spikes or current interruptions caused by sudden duty cycle changes in traditional methods.

[0042] This embodiment provides a seamless switching control method for an integrated buck-boost converter. By obtaining the operating state type and voltage information to determine the switching optimization coefficient, initializing the duty cycle parameters and identifying the load power level, evaluating the interference coefficient and establishing a dynamic model to generate the optimal parameters, and finally driving the switch tube to achieve mode switching, the following technical effects can be achieved: First, the introduction of the switching optimization coefficient makes the duty cycle adjustment gradual, effectively suppressing the voltage mutation during mode switching; second, the real-time monitoring of the load power level and the evaluation of the interference coefficient enhance the adaptability of the control strategy to load changes and improve the robustness of the system; the establishment of the dynamic model accurately quantifies the parameter coupling relationship and ensures the continuity of the inductor current; finally, the switch tube driving based on the optimal parameters completes the switching process within milliseconds, significantly shortening the delay time. These improvements jointly ensure the stable output of the converter over a wide input and output voltage range, reduce the electrical stress on the downstream equipment, and are suitable for scenarios with high requirements for power supply continuity, such as new energy grid connection and electric vehicle charging.

[0043] In one embodiment, before initializing the duty cycle control parameters of the converter based on the switching optimization coefficient, it also includes: obtaining several load characteristics of the converter's operating state type, including the load current change rate, the power fluctuation amplitude, and the output voltage ripple threshold, and determining the duty cycle adjustment range corresponding to the operating state type; accordingly, initializing the duty cycle control parameters of the converter based on the switching optimization coefficient includes: using a state adjustment algorithm to correct the duty cycle adjustment range corresponding to the load characteristics of the operating state type based on the switching optimization coefficient, and determining the duty cycle control parameters of the initialized converter.

[0044] The load current change rate can be the increase or decrease in load current per unit time, representing the speed of dynamic load changes. It can be obtained in real time through differential calculation using sensors or monitoring modules. For example, the load current change rate can include rapid current changes during sudden loading or unloading, such as sudden current fluctuations in discontinuous inductor current. The power fluctuation amplitude can be the difference between the maximum and minimum load power values ​​over a period of time, quantifying the severity of load power fluctuations. It can be obtained through power sampling statistics or voltage-current product calculations. For example, the power fluctuation amplitude can include instantaneous power fluctuations during motor startup or power step changes during load switching. The output voltage ripple threshold can be the maximum output voltage fluctuation allowed by the system. Its definition is typically determined by the load's power quality requirements. The ripple amplitude can be calculated through voltage sampling or directly set based on the load specifications. For example, the output voltage ripple threshold can include the ±1% ripple tolerance required by precision instruments or the ±5% fluctuation range allowed by industrial equipment.

[0045] The duty cycle adjustment range can be a range of allowable changes in the duty cycle parameter that is dynamically set based on the operating state type and load characteristics. Its purpose is to provide a constraint boundary for subsequent duty cycle adjustments. For example, the duty cycle adjustment range can include an extension to the range of 0.3-0.6 in buck mode due to the high rate of change of load current, or a range of 0.6-0.9 in boost mode due to the large power fluctuation amplitude. The state adjustment algorithm can be a mathematical or control method used to modify the duty cycle adjustment range, including but not limited to proportional-integral (PI) regulation, fuzzy logic control, or adaptive sliding mode control. For example, the state adjustment algorithm can include a fuzzy control rule base, such as a logical judgment to narrow the adjustment range if the switching demand intensity is high and the ripple threshold is low.

[0046] The duty cycle adjustment range is modified using a state adjustment algorithm. This can be achieved, for example, by inputting the switching optimization coefficient and load characteristics into the algorithm model, for example by calculating the adjustment range correction using a weighted summation formula. The technical benefit of this process is that it provides more precise boundary conditions for the initial setting of the duty cycle parameters by predicting the dynamic characteristics of the load. For example, when the load current change rate is high, expanding the adjustment range can avoid current interruptions caused by parameter adjustment lags; while narrowing the adjustment range when strictly limiting the ripple threshold can suppress voltage fluctuations that exceed the tolerance range.

[0047] This embodiment provides a seamless switching control method for an integrated buck-boost converter. By obtaining load characteristics and determining a duty cycle adjustment range in combination with the operating state type, and then using a state adjustment algorithm to dynamically correct the adjustment range based on a switching optimization coefficient, the method can achieve the following technical effects: First, through quantitative analysis of the load current change rate, power fluctuation amplitude, and ripple threshold, dynamic boundary constraints are provided for the initial setting of the duty cycle parameters to prevent the parameters from exceeding the system's safe operating range; second, by combining the switching optimization coefficient with the algorithmic correction of the load characteristics, adaptive adjustment of the duty cycle adjustment range is achieved, which not only ensures the ability to respond to rapid load changes, but also suppresses the risk of voltage ripple exceeding the standard by limiting the amplitude of parameter mutations; finally, algorithm-driven multi-dimensional information fusion reduces the computational complexity of the subsequent optimization stage, improves the adaptability of the control strategy to the dynamic characteristics of the load, and significantly enhances the smoothness and reliability of the switching process, especially in load mutation or ripple-sensitive scenarios.

[0048] In one embodiment, the state adjustment algorithm is expressed as:

[0049] Where D is the initialization duty cycle control parameter, is the maximum duty cycle adjustment range corresponding to the operating state type, is the minimum duty cycle adjustment range, and C is the switching optimization coefficient of the converter.

[0050] The mathematical expression of the state adjustment algorithm can be a mathematical model that combines the switching optimization coefficient and the duty cycle adjustment range through linear interpolation. This can be obtained by calculating the input-output voltage difference and the switching demand intensity. The maximum duty cycle adjustment range corresponding to the operating state type can be the theoretical maximum regulation threshold of the converter in a specific operating mode, such as 0.5 in buck mode or 0.9 in boost mode. This parameter can be determined based on a preset mode parameter table or real-time load characteristics. The minimum duty cycle adjustment range can be the minimum regulation reference value required for stable system operation, such as 0.1 in buck mode or 0.4 in boost mode. This can be obtained through methods including but not limited to calculations based on the voltage withstand limit or ripple tolerance of the switching device. The switching optimization coefficient can be a weighting factor that reflects the input-output voltage difference and the switching demand intensity. Its value range is generally limited to the interval [0, 1] and can be obtained through the proportional relationship between the voltage difference and a preset threshold or through methods such as fuzzy logic algorithms.

[0051] The technical operation can be realized in the following ways: when the parameters are substituted into the calculation, the determined 、 Directly substitute the C value into the formula for arithmetic operation, for example, when =0.8, =0.4, C=0.6, the calculated result is D=0.64; the boundary constraint verification is done by comparing the calculated result with 、 The size relationship of the D value is clamped if it exceeds the range. For example, when C=1.2, D= To prevent parameter runaway, dynamic adaptive adjustment iteratively corrects C or adjusts the range parameters when the D value conflicts with the load characteristics. For example, when D = 0.64, resulting in excessive ripple, the C value is lowered and recalculated. These operations achieve the technical effects of precise weight allocation, progressive parameter adjustment, and improved computational efficiency. Specifically, they quantify the switching demand intensity and load dynamic characteristics into mathematical expressions, ensuring smooth duty cycle changes and meeting real-time control requirements.

[0052] In one embodiment, evaluating a switching interference coefficient of a converter based on a current load power level and an initialization duty cycle control parameter includes: Based on the interference risk change trend of the current load power level on the converter output voltage stability, inductor current continuity, and switching loss, an interference risk score is assigned to each load feature to obtain the load feature interference risk score; Determine the relative importance of each load characteristic's interference to the switching control based on the interference risk score, assign interference risk weights, and obtain the load characteristic interference risk weights; Calculate the interference weighted score of the converter based on the interference risk score and weight; The switching interference coefficient of the converter is calculated by combining the initialization duty cycle control parameters through the interference evaluation formula.

[0053] The load characteristic interference risk score can be a quantitative indicator that characterizes the degree of interference a load characteristic has on the system. It can be obtained through methods such as piecewise linear scoring, fuzzy membership functions, or expert experience rules. For example, a five-level scoring system or a more precise scoring system can be used to characterize the risk level of different load characteristics. For example, for the load current change rate, if the change rate exceeds a threshold, the score is higher; if it is below the threshold, the score is lower.

[0054] The load characteristic interference risk weight can be a quantitative coefficient of the degree of influence of each load characteristic on switching control. Its definition is based on the coupling strength between the load characteristics and the key parameters of the system. It can be obtained through methods such as principal component analysis (PCA), analytic hierarchy process (AHP) or sensitivity analysis based on historical data. For example, if the output voltage ripple threshold has the greatest impact on switching stability, it can be given a weight of 0.4; and the power fluctuation amplitude weight may be 0.3.

[0055] The converter's weighted interference score can be a comprehensive indicator that combines the interference risk scores and weights of each load characteristic through a weighted summation method. For example, if the load current change rate score is 8 points (weight 0.3), the power fluctuation amplitude score is 6 points (weight 0.25), and the ripple threshold score is 4 points (weight 0.45), then the weighted score is: 8 × 0.3 + 6 × 0.25 + 4 × 0.45 = 5.25. The interference assessment formula can be a mathematical model that combines the weighted interference score and the initialization duty cycle control parameter (D_initial). Its typical form includes but is not limited to a linear combination or a nonlinear function.

[0056] Specifically, a preset scoring rule is applied to each load characteristic, such as determining its risk level through threshold comparison or membership function. Weights are dynamically adjusted based on system design objectives or real-time operating conditions, such as increasing the ripple threshold weight to 0.5 in scenarios with strict voltage stability requirements. The scores of each load characteristic are multiplied by the weights and summed to obtain a comprehensive interference weighted score. Finally, the weighted score and the initialized duty cycle parameters are substituted into the interference assessment formula, and the final interference coefficient is calculated through mathematical operations. The technical benefits of this process include multi-dimensional interference quantification, dynamic weight adjustment, and nonlinear coupling modeling. For example, the superposition of high duty cycle and high power fluctuations can cause inductor saturation and other effects.

[0057] This embodiment provides a seamless switching control method for an integrated converter. By scoring the interference risk change trends of output voltage stability, inductor current continuity, and switching loss according to the load power level, combining weight distribution to form a weighted score, and calculating the switching interference coefficient through an interference assessment formula that includes a duty cycle parameter, the following technical effects can be achieved: multi-dimensional interference risks are converted into calculable values, key risk types are preferentially suppressed through dynamic weight distribution, and the nonlinear coupling effect between the duty cycle parameters and load characteristics is explicitly characterized, thereby significantly improving the stability and adaptability of the switching process under complex working conditions and reducing the risks of voltage fluctuations and current spikes.

[0058] In one embodiment, the interference assessment formula is:

[0059] Where I is the switching interference coefficient, is the interference risk weight of the i-th load characteristic, is the interference risk score of the i-th load signature, D is the initialization duty cycle control parameter, and n is the total number of load signatures.

[0060] The interference risk weight can be a quantitative parameter that characterizes the degree to which load characteristics affect switching interference. Its value range is typically [0, 1], and the sum of all weights is 1. This parameter can be determined through system design or experimental data to determine the weight distribution rule. For example, the weight of the output voltage ripple threshold can be set to 0.4. The interference risk score can be a numerical indicator that reflects the degree to which load characteristics deviate from the safety threshold. Its value range is set according to specific scoring rules, for example, using a quantitative scale of 1 to 5 or 0 to 10. This parameter can be calculated by real-time monitoring of load parameters and comparing them with preset thresholds. For example, when the load current change rate exceeds the threshold, the score can be set to 8. The initialization duty cycle control parameter can be the preset duty cycle value of the switching device before switching. Its value range is typically [0, 1]. This parameter is pre-configured by the control strategy based on system operating conditions or user requirements. For example, if the system is in high-efficiency operation mode, D can be set to 0.6.

[0061] In one embodiment, analyzing the changing trend of the switching interference coefficient between the current load power level and the initialization duty cycle control parameter, establishing a dynamic switching control optimization model, and generating the optimal duty cycle control parameter includes: The duty cycle adjustment range corresponding to the operating state type is used as an adjustable control limit condition; With the constraint conditions as the input, the influence of the current load power level and the initialization duty cycle control parameter on the switching interference coefficient are used as independent variables. Through multiple rounds of iterations of the gradient descent algorithm, the duty cycle parameter that minimizes the switching interference coefficient is output as the dependent variable to construct a dynamic switching control optimization model. The optimal duty cycle control parameters are generated based on the dynamic switching control optimization model.

[0062] Among them, the duty cycle adjustment range, as an adjustable control limit condition, can be a physical constraint condition used to limit the adjustment boundary of the duty cycle parameter. It is determined by the system safe operating range corresponding to the operating state type. For example, in buck mode, the duty cycle control parameter is set to 0.1≤≤0.5 to avoid abnormal conditions such as overcurrent of the switching tube or inductor saturation.

[0063] As an optimization method, the gradient descent algorithm can be a mathematical tool that iteratively approximates the minimum value of the objective function. It determines the adjustment strategy by calculating the gradient direction of the objective function with respect to the duty cycle parameter. As a mathematical framework, the dynamic switching control optimization model can be a comprehensive system that integrates input variables, objective functions, and constraints. The technical operation is to achieve dynamic optimization through an iterative process of gradient calculation, parameter update, and constraint correction. For example, it can be achieved through the following steps: first, the duty cycle adjustment range is loaded as a hard boundary condition, then the duty cycle parameter is updated in each round of iteration and checked whether it exceeds the boundary; finally, the loop is terminated by setting a gradient absolute value threshold or an iteration number threshold, and the final duty cycle parameter is output. This process ensures the safety of parameter adjustment through constraints, accurately minimizes the interference coefficient through gradient guidance, and achieves dynamic adaptive optimization by responding to load power changes in real time.

[0064] This embodiment provides a seamless switching control method for an integrated converter. This method uses the duty cycle adjustment range as a hard constraint to ensure system safety, it uses a gradient descent algorithm to iteratively optimize the duty cycle parameters to minimize the switching interference coefficient, and integrates input variables with the objective function using a dynamic model to achieve online parameter adjustment. This method can achieve the technical effect of improving the real-time and robustness of the control strategy. This method avoids system oscillations caused by parameter mutations through constraint-driven boundary restrictions, rapidly converges to the optimal solution in load fluctuation scenarios through gradient-guided successive approximation, and supports the expansion of other constraints or objective functions to accommodate multi-objective optimization requirements. This method is particularly suitable for highly dynamic load scenarios with strict switching stability requirements.

[0065] In one embodiment, the expression of the dynamic switching control optimization model is:

[0066] Where D′ is the optimal duty cycle control parameter, is the duty cycle parameter of the t+jth iteration, α is the learning efficiency step size, is the gradient of the duty cycle parameter, is the switching interference coefficient function, is the current load power level, and The upper and lower limits of the duty cycle adjustment range.

[0067] Among them, the optimal duty cycle control parameter is a dynamic result generated through an iterative optimization process. The update mechanism of the duty cycle parameter can be an iterative rule based on the gradient descent algorithm. Its core is to adjust the parameter adjustment amplitude through the learning efficiency step size. This parameter is defined as the proportional factor of the duty cycle change in each round of iteration. The gradient can be the partial derivative of the switching interference coefficient with respect to the duty cycle, and its calculation is based on the specific form of the interference evaluation function. The current load power level participates in the gradient calculation as a dynamic input variable. For example, when the load suddenly increases, its numerical change will directly change the gradient direction. The upper and lower limits of the duty cycle adjustment range can be hard constraints preset by the system, and the parameter value is forcibly truncated through conditional judgment logic. For example, when it exceeds the preset range, it will be corrected to the boundary value.

[0068] This iterative process is implemented using the following technical operations: Initial conditions are established by combining the initial duty cycle with the input system parameters and real-time load power. A gradient value is then calculated based on the specific form of the interference assessment function, indicating the direction of duty cycle adjustment. Parameter values ​​are then updated using an iterative formula, followed by constraint checking logic. Finally, the optimization process is terminated by setting convergence criteria and outputting the final parameter values.

[0069] This embodiment provides a seamless switching control method for an integrated buck-boost converter, which realizes dynamic adjustment of duty cycle parameters through gradient descent iteration rules, calculates the gradient direction by combining the coupling effect of load power and system parameters, ensures parameter safety boundaries by using hard constraints, and balances convergence speed and stability by learning efficiency step size. These technical features work together to enable the system to quickly generate optimal duty cycle parameters that meet physical constraints in real-time load change scenarios, specifically: the constraint-driven mechanism avoids hardware damage caused by parameter out-of-bounds, gradient-guided optimization improves convergence efficiency, dynamic coupling modeling enhances the ability to respond to load mutations, and millisecond-level computing delay meets real-time control requirements. Overall, the accuracy, safety and engineering practicality of duty cycle parameter optimization are improved, which is particularly suitable for power electronic converter control scenarios that require rapid response to load fluctuations.

[0070] In one embodiment, preliminarily estimating the switching demand intensity of the converter according to the operating state type, and determining the switching optimization coefficient of the converter based on the current input and output voltage information and the switching demand intensity, includes: Obtain the converter's historical average input voltage, target output voltage, and rated load power based on the operating state type, calculate the converter's theoretical duty cycle range, and preliminarily estimate the converter's switching demand intensity; The switching optimization coefficient is calculated based on the current input and output voltage information and the switching demand intensity, where the switching demand intensity includes a preset switching priority factor.

[0071] The historical average input voltage can be the average value of the input voltage of the integrated buck-boost converter over a specific time period. This is used to eliminate the interference of transient input voltage fluctuations on switching demand estimation. A sliding average filter algorithm can be used to calculate the average voltage over the past 100 switching cycles. For example, in photovoltaic power supply scenarios, this method can effectively suppress high-frequency noise from the grid voltage and improve the stability of the input voltage reference value. The target output voltage can be the preset output voltage value that the converter needs to maintain. Its definition is consistent with the setpoint value in traditional power electronics systems, but in this solution, it is further used to calculate the theoretical duty cycle range. The rated load power can be the maximum power value that the converter should handle under standard operating conditions. It serves to provide load constraints for calculating the theoretical duty cycle range. For example, when the actual load power approaches the rated value, the system must ensure that the duty cycle adjustment does not exceed the safe threshold of the inductor current. The theoretical duty cycle range can be an ideal duty cycle range calculated based on the input-output voltage relationship and load conditions. It serves to provide a quantitative reference for the intensity of the switching demand. For example, if the current input voltage deviates from the historical average by more than a threshold, the theoretical duty cycle range will expand accordingly, indicating that a more significant duty cycle adjustment is required to maintain output stability. The switching demand intensity can be a comprehensive indicator that includes a switching priority factor, which is formed by weighting different disturbance parameters to form a final intensity value. For example, when a sudden drop in input voltage causes the theoretical duty cycle to increase from 0.6 to 0.8, combining the priority factors of voltage deviation weighting 0.7 and load change weighting 0.3, and adding the output voltage fluctuation rate, a fast response mechanism can be triggered.

[0072] The technical process involves acquiring historical data and calculating the theoretical duty cycle range. For example, the voltage sampling module records and calculates the historical average input voltage, reads the preset target output voltage and rated load power parameters, substitutes them into the duty cycle formula corresponding to the mode, and combines them with the load power constraints to determine the upper and lower limits of the theoretical duty cycle. This multi-parameter combined calculation ensures that the theoretical duty cycle range more closely matches actual operating conditions, reducing estimation errors. When estimating the switching demand intensity, the deviation between the theoretical duty cycle range and the current actual duty cycle is used as the basic parameter. This is multiplied by a preset switching priority factor and combined with other disturbance indicators to form the final intensity value. For example, if a sudden drop in input voltage requires an increase in the theoretical duty cycle, the priority factor allows the system to prioritize responding to voltage fluctuations over load changes, avoiding control conflicts. When calculating the switching optimization coefficient, the difference between the current actual input and output voltage and the historical average is used as the input variable. Combined with the weighted result of the switching demand intensity, the optimization coefficient is generated using a proportional-integral (PI) regulator or fuzzy control algorithm. For example, when the input voltage is lower than the historical average and the switching demand intensity is high, the optimization coefficient can be set as an acceleration factor for dynamically adjusting the duty cycle, rather than a fixed proportional coefficient in the traditional method.

[0073] This embodiment provides a seamless switching control method for an integrated converter. This method eliminates transient noise interference by jointly calculating the historical average input voltage and the target output voltage. It also determines the theoretical duty cycle range based on the rated load power constraint, weights multi-parameter disturbances using a switching priority factor, and ultimately generates a dynamic switching optimization coefficient through a multivariable coupling algorithm. This method achieves the technical benefits of improving anti-interference capabilities, achieving multi-objective balanced control, and enhancing dynamic response accuracy. Specifically, it reduces the risk of false switching in scenarios with frequent input voltage fluctuations, autonomously determines control priorities under complex operating conditions, and precisely adjusts the duty cycle through real-time parameter matching, thereby shortening mode switching transition time and reducing electrical stress. This method is suitable for scenarios with strict power supply continuity requirements, such as industrial automation and electric vehicle charging.

[0074] In one embodiment, the calculation formula of the handover optimization coefficient is as follows:

[0075] Where, is the current input voltage, is the current output voltage, is the rated output voltage, The preset switching priority weight factor.

[0076] in, Indicates the actual input voltage value of the converter at the current moment, reflecting the real-time voltage status of the power supply side or the preceding circuit. Input voltage fluctuations will directly affect the converter's operating efficiency and duty cycle. For example, when the input voltage is much higher or lower than the target output voltage, the converter may require a larger duty cycle adjustment range, and the switching demand may be stronger at this time. In the formula, and The difference reflects the matching degree of input and output voltages. The larger the difference, the more likely the current converter working state may deviate from the optimal range, and the higher the switching demand. Indicates the actual output voltage value of the converter, reflecting the real-time voltage status of the load side. Output voltage is the core control target of the converter. When the output voltage deviates from the rated value or target value, the converter needs to maintain stability by adjusting the duty cycle or switching the working mode. Combined with the calculated voltage difference, it directly reflects the current input-output voltage matching efficiency of the converter. For example, for a buck converter, if near , the converter efficiency is high and the switching demand is low; if Much greater than , you may need to switch to a more efficient mode, such as multi-stage buck. Indicates the nominal target output voltage of the converter design, that is, the output voltage that needs to be maintained stably under ideal working conditions, such as the 12V, 5V and other rated values ​​of the DC / DC converter. Voltage difference Normalization is performed to convert the absolute voltage difference into a relative ratio so that converters with different rated voltages have a unified calculation standard. It reflects the degree of deviation between the current input-output voltage relationship and the rated operating conditions. The larger the value, the more it is necessary to optimize the performance through switching (such as adjusting the converter type and operating mode). It is an adjustable parameter set by humans, used to adjust the sensitivity of switching requirements according to actual application needs. The size of γ can flexibly adjust the response strength of the switching optimization coefficient C to the voltage difference. For example, if the system requires sensitivity to voltage fluctuations, such as in precision load scenarios, γ can be increased so that even small voltage differences can trigger high switching requirements. If the system focuses on stability, such as allowing voltage fluctuations within a certain range, γ can be reduced to reduce the switching frequency.

[0077] In one embodiment, the process of driving the power switch of the converter based on the optimal duty cycle control parameter is performed by a digital signal processor or a field programmable gate array, and specifically includes: When the optimal duty cycle control parameter indicates the buck mode, the high-side switch and the freewheeling diode are driven to conduct according to the step-down logic, and the inductor current freewheeling path is configured at the same time; When the optimal duty cycle control parameter indicates the boost mode, the low-side switch and the energy storage inductor are driven to conduct according to the boost logic, and the energy replenishment strategy of the output filter capacitor is adjusted at the same time; By synchronously adjusting the dead time of the switch tube drive signal, voltage spikes and current interruptions during the switching process are eliminated, achieving seamless switching between buck mode and boost mode.

[0078] A digital signal processor (DSP) or field-programmable gate array (FPGA) is a microprocessor or programmable logic device designed specifically for real-time digital signal processing. It can implement PWM signal generation and dynamic model calculations through hardware logic units or parallel computing architectures. The high-side switch is a power semiconductor device that connects the positive terminal of the power supply to the inductor. Its conduction state is controlled by a PWM signal, and switching action is achieved through a gate drive circuit. Exemplary devices include IGBTs or MOSFETs. The freewheeling diode is a reverse-conducting element that provides a loop for the inductor current. Its reverse recovery characteristics must match the turn-off timing of the switch. Exemplary devices include fast recovery diodes or Schottky diodes. The inductor current freewheeling path is a closed loop formed by the freewheeling diode and the load. Its configuration must be coordinated with the state of the switch. Exemplary devices in buck mode are triggered by the high-side switch turning off.

[0079] The technical operation for driving the high-side switch and freewheeling diode to conduct according to the buck logic is to generate a PWM signal with an adjustable duty cycle through the DSP / FPGA to periodically turn on the high-side switch while ensuring that the low-side switch is in the off state to prevent a shoot-through short circuit. In one specific embodiment, this process includes real-time monitoring of the high-side switch drain current waveform. When the current is detected to be close to zero, the freewheeling diode is triggered to conduct in advance, thereby optimizing the current path switching timing. This operation achieves the technical effect of maintaining inductor current continuity and suppressing voltage spikes.

[0080] The low-side switch is a power semiconductor device that connects the inductor to ground. Its conduction state determines the energy release path of the energy storage inductor. Complementary control with the high-side switch can be achieved through a gate drive circuit. For example, an N-channel MOSFET or other device type can be used. The energy storage inductor is an electromagnetic element used to store and release magnetic energy. Its charging and discharging process is regulated by the switch's conduction duty cycle. For example, a ferrite core inductor can be used. The energy replenishment strategy of the output filter capacitor is a control algorithm that dynamically adjusts the inductor's energy storage and discharge timing according to load demand. For example, the low-side switch's conduction time can be adjusted through a PI control loop.

[0081] The technical operation of driving the low-side switch and the energy storage inductor to conduct according to the boost logic is to adjust the PWM duty cycle of the low-side switch to discharge the inductor to the output terminal during the conduction period, while monitoring the inductor current waveform to prevent it from exceeding the saturation threshold. In one specific embodiment, this process includes real-time calculation of the deviation between the instantaneous value of the inductor current and the preset threshold, and dynamically correcting the duty cycle parameter using a proportional-integral control algorithm. This operation extends the conduction time of the low-side switch to increase output power when the load suddenly increases. This operation can achieve the technical effect of maintaining energy supply stability and suppressing current overshoot.

[0082] The dead time of the switch tube drive signal is a brief disable interval when the upper and lower bridge arm switches switch in state. Its value needs to be dynamically adjusted according to the switching frequency and temperature changes. For example, the switching state switching moment can be identified by collecting the gate voltage and drain current waveforms. The technical operation of synchronously adjusting the dead time is to calculate the dynamic compensation value by real-time monitoring the voltage and current waveforms of the switch tube, and superimpose the value on the original PWM signal to generate a drive signal with a timing offset. In a specific embodiment, the process includes using a temperature sensor to collect junction temperature data, and calculating the compensation value through a table lookup method or a PID algorithm in combination with the change in switching frequency, ultimately ensuring that there is no overlapping conduction when the upper and lower bridge arms are switched. This operation can achieve the technical effect of eliminating voltage spikes and current interruptions and improving the switching response speed.

[0083] This embodiment provides a seamless switching control method for an integrated converter. This method utilizes DSP / FPGA to achieve nanosecond-level real-time generation and dynamic adjustment of PWM signals. This method, combined with complementary logic control of high-side and low-side switches, ensures precise switching of energy flow direction. The freewheeling path and energy storage strategy are used to collaboratively maintain inductor current continuity. Furthermore, adaptive dead-time compensation eliminates switching interference, achieving the technical effect of improving system environmental adaptability and energy efficiency. Specifically, hardware collaborative optimization enables real-time synchronization of duty cycle parameters and drive signals, resolving the control lag problem of traditional controllers. Dynamic dead-time adjustment reduces the risk of cross-conduction of switches and reduces switching losses. The collaborative design of the freewheeling path and energy storage strategy ensures current continuity at the moment of mode switching, preventing output voltage drops or overshoots. Temperature and frequency adaptive compensation mechanisms ensure stable operation of the control strategy over a wide load and temperature range, ultimately achieving millisecond-level seamless switching and high-reliability operation in scenarios such as new energy vehicle charging stations and photovoltaic inverters.

[0084] In addition, an embodiment of the present invention further provides a storage medium, on which a seamless switching control program for an integrated buck-boost converter is stored. When the seamless switching control program for the integrated buck-boost converter is executed by a processor, the steps of the seamless switching control method for the integrated buck-boost converter as described above are implemented.

[0085] In addition, refer to Figure 3 The embodiment of the present invention further provides a seamless switching control system for an integrated buck-boost converter, the seamless switching control system for the integrated buck-boost converter comprising: a state evaluation module 10 for obtaining an operating state type and current input and output voltage information of the integrated buck-boost converter, preliminarily estimating the converter's switching demand intensity based on the operating state type, and determining a switching optimization coefficient for the converter based on the current input and output voltage information and the switching demand intensity; a parameter initialization module 20 for initializing the duty cycle control parameters of the converter based on the switching optimization coefficient and identifying the current load power level through the voltage and current sensors; a dynamic optimization module 30 for evaluating the switching interference coefficient of the converter based on the current load power level and the initialization duty cycle control parameters, analyzing the changing trend of the switching interference coefficient due to the current load power level and the initialization duty cycle control parameters, establishing a dynamic switching control optimization model, and generating optimal duty cycle control parameters; The seamless switching module 40 is used to drive the power switch tube of the converter based on the optimal duty cycle control parameter to achieve seamless switching control between the buck mode and the boost mode.

[0086] Other embodiments or specific implementations of the seamless switching control system of the integrated buck-boost converter of the present invention can refer to the above-mentioned method embodiments and will not be described in detail here.

[0087] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or system comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or system. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or system comprising the element.

[0088] The serial numbers of the embodiments of the present invention are for descriptive purposes only and do not represent superiority or inferiority of the embodiments. In a module claim that lists several systems, several of these systems may be embodied by the same item of hardware. The use of the terms first, second, and third, etc., does not denote any order and should be interpreted as nomenclature.

[0089] Through the above description of the embodiments, those skilled in the art will clearly understand that the above-mentioned embodiments and methods can be implemented using software plus the necessary general hardware platform. Of course, hardware can also be used, but in many cases, the former is a more preferred embodiment. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as a read-only memory image (ROM) / random access memory (RAM), a magnetic disk, or an optical disk) and includes a number of instructions for enabling an end-user device (such as a mobile phone, computer, server, air conditioner, or network user device, etc.) to execute the methods described in various embodiments of the present invention.

[0090] The above are only preferred embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A seamless switching control method for an integrated buck-boost converter, characterized in that: The method comprises: Obtaining an operating state type and current input and output voltage information of the integrated buck-boost converter, preliminarily estimating a switching demand intensity of the converter based on the operating state type, and determining a switching optimization coefficient of the converter based on the current input and output voltage information and the switching demand intensity; Initializing the duty cycle control parameters of the converter based on the switching optimization coefficient, and identifying the current load power level through the voltage and current sensors; According to the current load power level and the initialization duty cycle control parameter, the switching interference coefficient of the converter is evaluated, and the change trend of the current load power level and the initialization duty cycle control parameter on the switching interference coefficient is analyzed, a dynamic switching control optimization model is established, and the optimal duty cycle control parameter is generated; The power switch tube of the converter is driven based on the optimal duty cycle control parameter to achieve seamless switching control between the buck mode and the boost mode.

2. The seamless switching control method of the integrated buck-boost converter according to claim 1, wherein: Before initializing the duty cycle control parameter of the converter based on the switching optimization coefficient, the method further includes: Acquire several load characteristics of the converter operating state type, including load current change rate, power fluctuation amplitude, and output voltage ripple threshold, and determine the duty cycle adjustment range corresponding to the operating state type; Accordingly, initializing the duty cycle control parameter of the converter based on the switching optimization coefficient includes: The state adjustment algorithm is used to modify the duty cycle adjustment range corresponding to the load characteristics of the operating state type based on the switching optimization coefficient to determine the duty cycle control parameter of the initialization converter.

3. The seamless switching control method of the integrated buck-boost converter according to claim 2, wherein: The expression of the state adjustment algorithm is: Where D is the initialization duty cycle control parameter, is the maximum duty cycle adjustment range corresponding to the operating state type, is the minimum duty cycle adjustment range, and C is the switching optimization coefficient of the converter.

4. The seamless switching control method of the integrated buck-boost converter according to claim 3, wherein: The step of evaluating the switching interference coefficient of the converter according to the current load power level and the initialization duty cycle control parameter includes: Assigning an interference risk score to each load feature based on a change trend of interference risk of the current load power level on the converter output voltage stability, inductor current continuity, and switching loss to obtain a load feature interference risk score; Determining the relative importance of interference of each load feature to the switching control according to the interference risk score, assigning an interference risk weight, and obtaining a load feature interference risk weight; Calculating a weighted interference score for the converter based on the interference risk score and the weight; The switching interference coefficient of the converter is calculated by combining the initialization duty cycle control parameters through the interference evaluation formula.

5. The seamless switching control method of the integrated buck-boost converter according to claim 4, wherein: The interference assessment formula is: Where I is the switching interference coefficient, is the interference risk weight of the i-th load characteristic, is the interference risk score of the i-th load signature, D is the initialization duty cycle control parameter, and n is the total number of load signatures.

6. The seamless switching control method of the integrated buck-boost converter according to claim 5, wherein: The analysis of the changing trend of the switching interference coefficient between the current load power level and the initialization duty cycle control parameter, establishing a dynamic switching control optimization model, and generating the optimal duty cycle control parameter includes: The duty cycle adjustment range corresponding to the operating state type is used as an adjustable control limit condition; With the aforementioned constraints as the input, the influence of the current load power level and the initialization duty cycle control parameter on the switching interference coefficient are used as independent variables. Multiple rounds of iterations are performed through the gradient descent algorithm, and the duty cycle parameter that minimizes the switching interference coefficient is output as the dependent variable to construct a dynamic switching control optimization model. An optimal duty cycle control parameter is generated based on the dynamic switching control optimization model.

7. The seamless switching control method of the integrated buck-boost converter according to claim 6, wherein: The expression of the dynamic switching control optimization model is: Where D′ is the optimal duty cycle control parameter, is the duty cycle parameter of the t+jth iteration, α is the learning efficiency step size, is the gradient of the duty cycle parameter, is the switching interference coefficient function, is the current load power level, and The upper and lower limits of the duty cycle adjustment range.

8. The seamless switching control method of the integrated buck-boost converter according to claim 1, wherein: The preliminarily estimating the switching demand intensity of the converter according to the operating state type, and determining the switching optimization coefficient of the converter based on the current input and output voltage information and the switching demand intensity, includes: Obtain the converter's historical average input voltage, target output voltage, and rated load power based on the operating state type, calculate the converter's theoretical duty cycle range, and preliminarily estimate the converter's switching demand intensity; A switching optimization coefficient is calculated based on the current input and output voltage information and the switching demand intensity, where the switching demand intensity includes a preset switching priority factor.

9. The seamless switching control method of the integrated buck-boost converter according to claim 8, wherein: The calculation formula of the switching optimization coefficient is as follows: Where, is the current input voltage, is the current output voltage, is the rated output voltage, The preset switching priority weight factor.

10. The seamless switching control method of the integrated buck-boost converter according to claim 1, wherein: The process of driving the power switch tube of the converter based on the optimal duty cycle control parameter is performed by a digital signal processor or a field programmable gate array, and specifically includes: When the optimal duty cycle control parameter indicates the buck mode, the high-side switch and the freewheeling diode are driven to conduct according to the step-down logic, and the inductor current freewheeling path is configured at the same time; When the optimal duty cycle control parameter indicates the boost mode, the low-side switch and the energy storage inductor are driven to conduct according to the boost logic, and the energy replenishment strategy of the output filter capacitor is adjusted at the same time; By synchronously adjusting the dead time of the switch tube drive signal, voltage spikes and current interruptions during the switching process are eliminated, achieving seamless switching between buck mode and boost mode.

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