Optimization method of hybrid modulation strategy for OBC-DAB converter with HFV and ZVS constraints

Abstract

This invention relates to an optimization method for hybrid modulation strategy of OBC-DAB converters integrating HFV and ZVS constraints, belonging to the field of power electronic conversion technology. The method first establishes a frequency domain mathematical model of the DAB converter under hybrid five-variable HFV modulation, deriving the precise hard constraint conditions for zero-voltage switching (ZVS) of the switching transistors; then, it constructs a multivariable optimization model with minimizing the RMS value of the inductor current as the core objective and ZVS constraints as hard constraints, completing offline global optimization and generating a high-dimensional lookup table through an improved particle swarm optimization algorithm; finally, it designs an online lookup table interpolation control mechanism to achieve optimal modulation under all operating conditions. This invention can achieve full-range ZVS activation of all eight switching transistors in the full power range of the OBC-DAB converter, improving efficiency under all operating conditions and possessing strong engineering practical value.

Description

Technical Field

[0001] This invention belongs to the field of power electronic conversion technology and relates to a hybrid modulation strategy optimization method for a dual active bridge (DAB) DC / DC converter for an electric vehicle on-board charger (OBC) that integrates HFV and ZVS constraints. Background Technology

[0002] Isolated bidirectional DC / DC converters (IBDCs) play a crucial role in power systems because they have bidirectional power flow, soft switching capability, and electrical isolation characteristics, and are therefore widely used in electric vehicles, energy storage systems, and smart grids.

[0003] As a core component of electric vehicles, the on-board charger is responsible for converting AC grid power into DC power to charge the battery. Its performance directly determines the charging efficiency, range, and operational reliability of electric vehicles.

[0004] Mainstream on-board chargers adopt a two-stage architecture: a front-stage PFC (Power Factor Correction) rectifier and a rear-stage isolated DC / DC converter. The rear-stage DC / DC converter is the core component for energy transfer and control. Dual active bridge (DAB) converters, with their inherent support for bidirectional energy flow, low voltage stress on switching transistors, ease of soft switching, and high power density, have become the mainstream choice for the rear-stage DC / DC topology of on-board chargers (OBCs).

[0005] Currently, the modulation strategies for DAB converters mainly include single-phase shift (SPS), dual-phase shift (DPS), extended-phase shift (EPS), and triple-phase shift (TPS). Among them, SPS has a simple control structure, but under wide battery voltage and light load conditions at the OBC, it generates extremely high return current power and current stress, leading to a sharp deterioration in converter efficiency. DPS and EPS can suppress circulating current and widen the soft-switching range to some extent by increasing the degree of control freedom, but there is a theoretical upper limit to performance optimization under extreme voltage conversion ratio conditions. TPS achieves higher degree of control freedom through three independent phase shift angles and has become the mainstream modulation scheme for high-performance DAB converters. However, under light load conditions at the OBC, it still suffers from narrow soft-switching range and high switching loss ratio.

[0006] To further enhance the control freedom and optimization space of DAB converters, a Hybrid Five-Variable (HFV) modulation strategy has been proposed. This strategy achieves more refined shaping of the bridge arm output voltage waveform through five independent control variables. It is a unified form of traditional SPS, DPS, EPS, and TPS modulation strategies, possessing the theoretical advantage of being adaptable to all operating conditions. Existing HFV modulation research mostly focuses on minimizing the effective value (RMS) of the inductor current as the optimization objective, only verifying zero-voltage switching (ZVS) as the optimization result, without incorporating its realization conditions as hard constraints into the optimization model. This results in only 2-4 switches achieving ZVS under light load conditions of the OBC, with switching losses accounting for more than 30%, severely restricting efficiency improvement. At the same time, existing ZVS optimization research is mostly based on TPS modulation strategies, which have limited control freedom and cannot fully realize the performance potential of HFV modulation. Furthermore, it does not fully consider the impact of parasitic capacitance and secondary voltage on ZVS realization, resulting in insufficient ZVS constraint accuracy and easy soft-switching loss in engineering applications.

[0007] In summary, existing technologies cannot achieve coordinated optimization of conduction and switching losses in OBC-DAB converters under wide voltage and full power conditions. They suffer from drawbacks such as low efficiency under light loads, narrow soft-switching range, insufficient adaptability to wide operating conditions, and difficulty in engineering implementation. Summary of the Invention

[0008] In view of this, the purpose of this invention is to provide an optimization method for hybrid modulation strategy of OBC-DAB converter that integrates HFV and ZVS constraints, to solve the problems of limited soft switching range, low efficiency under light load, and inability to coordinate the optimization of conduction loss and switching loss of existing DAB converters in OBC application scenarios, so as to achieve efficient operation of OBC-DAB converter under all operating conditions.

[0009] To achieve the above objectives, the present invention provides the following technical solution: An optimization method for hybrid modulation strategy of OBC-DAB converter integrating HFV and ZVS constraints, specifically includes the following steps: S1: Establish the frequency domain mathematical model of the OBC-DAB converter under HFV modulation. Based on Fourier series decomposition, derive the analytical expressions for inductor current, RMS and average transmission power under HFV control variables. Wherein OBC represents the on-board charger, DAB represents the dual active bridge, HFV represents the hybrid five-variable, and RMS represents the effective value of inductor current. S2: Analyze the commutation process of the switching transistors in the DAB converter, derive the precise hard constraint condition of ZVS for the switching transistors, and transform it into an energy-quantifiable mathematical inequality constraint; where ZVS is zero-voltage switching. S3: Construct a multi-objective optimization model for HFV hybrid modulation that integrates ZVS hard constraints, with RMS minimization as the core optimization objective and power transmission constraints, ZVS exact hard constraints, and control variable boundary constraints as constraints, and establish a standard constraint optimization problem; S4: An improved particle swarm optimization (PSO) algorithm is used to perform offline global optimization of the optimization model. The algorithm traverses the entire operating range of the OBC-DAB converter, solves the optimal solution set of the HFV control variables corresponding to each operating point, and generates a high-dimensional offline lookup table (LUT). S5: Design an online real-time control mechanism to sample the input voltage, output voltage, and load power of the OBC-DAB converter in real time, and obtain the optimal control parameters under the current operating conditions through table lookup and linear interpolation; configure a dead time of 100 ns to 500 ns according to the switching device parameters to prevent bridge arm shoot-through.

[0010] Furthermore, in step S1, the HFV control variable includes the primary side inward phase shift angle. The angle corresponding to the duty cycle of the original side bridge arm. Secondary side inward shift phase angle Secondary side bridge arm conduction duty cycle corresponding angle Phase angle of the original secondary side bridge arm shift ; The HFV modulation is a hybrid of traditional phase-shift modulation and PWM modulation, and is a unified form of SPS, DPS, EPS, and TPS modulation strategies. When HFV degenerates into TPS modulation; when or When HFV degenerates into EPS modulation; when At that time, HFV degenerates into SPS modulation, where D i For the duty cycle form of the corresponding control variable SPS stands for single phase shift, DPS for dual phase shift, EPS for extended phase shift, and TPS for triple phase shift.

[0011] Furthermore, in step S1, a frequency domain mathematical model of the OBC-DAB converter under HFV modulation is established, specifically by selecting a time zero point. The phase angle shifted inward from the original side The midpoint, based on the Fourier transform of the output voltage of the primary and secondary bridge arms of the DAB converter. , v cd By decomposing the components, we obtain the expressions for each harmonic:

[0012] in, This is the primary input voltage of the DAB converter. This is the secondary output voltage. This refers to the turns ratio of the primary and secondary sides of the transformer. The switching angular frequency, For harmonic order, At time t; based on the time-domain relationship between inductor voltage and current, the inductor current is derived. The parsing expression:

[0013] in, This is the sum of the transformer's leakage inductance and auxiliary inductance. , The harmonic component coefficients are expressed as follows:

[0014] in, M Voltage conversion ratio, ; Based on the analytical expression for inductor current, the RMS expression containing the 8th harmonic component is derived as follows:

[0015] in, It is the normalized effective value of the inductor current that includes the first n harmonic components; Based on the average power transmission characteristics of linear networks, the average transmission power is derived. The expression is: .

[0016] Furthermore, in step S2, the derivation process of the exact hard constraint condition for ZVS is as follows: (1) Analyze the four stages of switching transistor commutation and clarify that ZVS normal turn-on must simultaneously meet the current direction constraint and the minimum commutation current constraint; (2) Based on the conduction timing and current path of the 8 switches of the DAB converter, they are divided into two groups of complementary switches, and the inductor current direction constraints for the ZVS turn-on of the two groups of switches are defined respectively:

[0017] In other words, when the switching transistor is turned on, the inductor current must meet the corresponding direction in order for the drain-source voltage of the switching transistor to be clamped to 0V through the anti-parallel diode, thus providing a prerequisite for ZVS turn-on; among which, ~ For primary-side full-bridge switching transistors, ~ For secondary-side full-bridge switching transistors; (3) Taking into account the parasitic capacitance of the switching transistor Based on the influence of secondary voltage on the commutation process, the minimum commutation current for ZVS turn-on is derived. Quantization current amplitude constraint:

[0018] in, This is the minimum commutation current at the moment the switching transistor is turned on. This refers to the turn-on time of the switching transistor; By combining current direction constraints and minimum commutation current constraints, a complete and precise hard constraint condition for ZVS realization is formed.

[0019] Furthermore, in step S3, the standard constraint optimization problem is established in the following form:

[0020] The constraints are:

[0021] in, For the target transmission power, For the first i The turn-on time of each switching transistor For the first i The minimum commutation current of each switching transistor, For the first i The minimum commutation current at the turn-on time of each switch transistor Compared to the shift in the turn-on time of the first switch tube of the original secondary side bridge arm.

[0022] Furthermore, step S4 specifically includes the following steps: S41: Determine the algorithm parameters, including a search space dimension of 5 and corresponding HFV control variables; set the particle swarm size to 100~1000, and the inertia weight. ω= 0.5~1, Individual learning factor C 1 = 1.5, Group Learning Factor C 2 = Version 1.5, with a maximum of 400 iterations; S42: Construct the fitness function, with RMS minimization as the core, and introduce a penalty term for the degree of ZVS constraint non-satisfaction. The expression is:

[0023] in, For fitness, This is a penalty factor, with a value ranging from 100 to 1000; S43: Initialize the position and velocity of the particle swarm, and randomly generate an initial solution within the control variable boundary; S44: Iterative optimization: Calculate the fitness value of each particle, update the individual historical best solution and the global best solution of the population, adjust the particle state based on the velocity and position update formula until the maximum number of iterations is reached, and output the optimal HFV control variable for the current working point. S45: Traverse the operating range of the OBC-DAB converter, including input voltage 220V / 380V, output voltage 200~450V, and 0~100% rated power, solve for the optimal HFV control variables at each operating point, and generate a voltage conversion ratio. M Average transmission power P A high-dimensional offline lookup table with an input index.

[0024] Furthermore, in step S43, an elite retention strategy is introduced, which retains the top 5% of particles with the best fitness in each iteration and directly enters the next iteration, thereby improving the convergence speed and optimization accuracy of the algorithm.

[0025] Furthermore, in step S5, an online real-time control mechanism is designed, specifically including the following steps: S51: Real-time sampling of the primary input voltage of the OBC-DAB converter V 1. Secondary output voltage V 2 and output current I o Calculate the current voltage conversion ratio M With average transmission power P ; S52: with M and P Using an index, perform a lookup and bilinear interpolation in the offline lookup table to obtain the optimal value of the HFV control variable under the current operating conditions; S53: Configure normal dead time to prevent bridge arm shoot-through; S54: Based on the optimal HFV control variables and the conventional dead time, it generates PWM drive signals for 8 switching transistors, which are output to the main circuit through the digital controller to complete the real-time execution of the hybrid modulation strategy.

[0026] Furthermore, in step S1, a collaborative optimization mechanism for frequency conversion and HFV modulation is established. By adjusting the switching frequency to adapt to the wide voltage conversion ratio operating conditions of the OBC-DAB converter, the switching frequency is incorporated into the control variables of the optimization model and the offline optimization process.

[0027] Option 2: An optimization system for hybrid modulation strategy of OBC-DAB converter integrating HFV and ZVS constraints, comprising: The sampling module is used to acquire the input voltage, output voltage, output current, and inductor current signals of the OBC-DAB converter in real time. The digital controller has a built-in offline lookup table and control program for performing online calculations of optimization methods, lookup interpolation, and PWM drive signal generation. The driver module is used to amplify the PWM signal output by the digital controller and drive the eight switching transistors of the DAB converter. The DAB main circuit is an isolated bidirectional DC / DC converter topology after the OBC stage, including a primary full-bridge, a high-frequency transformer, an auxiliary inductor, and a secondary full-bridge, which connects the output of the front-end PFC rectifier to the power battery; where PFC is power factor correction.

[0028] The beneficial effects of this invention are as follows: (1) This invention achieves the coordinated optimization of conduction loss and switching loss of DAB converter. For the first time, the exact ZVS realization condition is incorporated as a hard constraint into the optimization model of HFV five-variable modulation. While reducing conduction loss with the goal of minimizing RMS current, it mathematically ensures the full-range ZVS turn-on of 8 switching transistors in the full power range, greatly reducing switching loss. It solves the core contradiction in the traditional optimization strategy that "low current" and "wide ZVS" cannot be taken into account at the same time, and can improve the efficiency of OBC-DAB converter under all operating conditions.

[0029] (2) This invention significantly improves the wide operating condition adaptability of OBC scenarios. It provides the highest known control freedom through 5 independent control variables of HFV modulation. At the same time, it introduces frequency conversion technology and HFV modulation to form a collaborative optimization mechanism, adapting to the wide range of fluctuations of OBC input voltage 220V / 380V and output voltage 200-450V. It solves the problems of voltage mismatch, RMS current surge and ZVS loss under light load conditions of traditional modulation strategies, and realizes efficient and stable operation in the full power range from no load to full load.

[0030] (3) This invention solves the engineering implementation problem of high-dimensional optimization model. By improving the PSO algorithm, a high-dimensional lookup table is generated for offline global optimization. The optimal control parameters can be obtained in microseconds online by only looking up the table and bilinear interpolation. The complex non-convex multi-constraint optimization problem is transformed into an engineering solution that can be executed in real time.

[0031] (4) The modulation strategy of the present invention has the ability to switch smoothly across all operating conditions. As a unified form of traditional SPS, DPS, EPS and TPS modulation strategies, it can automatically degenerate into the corresponding traditional modulation strategy under heavy load conditions. While maintaining the best performance across all operating conditions, it achieves smooth and uninterrupted switching of different modulation modes, thereby improving the robustness and stability of the control system.

[0032] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0033] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein: Figure 1 This is the overall flowchart of the OBC-DAB converter hybrid modulation strategy optimization method that integrates HFV and ZVS constraints according to the present invention. Figure 2 This is a diagram of the main circuit topology of the OBC-DAB converter of the present invention; Figure 3 This is a typical working waveform diagram of HFV modulation of the present invention; Figure 4 This is a flowchart of the iterative optimization process of the improved PSO algorithm of this invention; Figure 5(a) shows the Irms convergence curve, the change in the number of feasible solutions, and the convergence process of the optimal control parameters of the PSO algorithm under the conditions of M=0.6, P=0.2 KW, f=50 kHz. Figure 5(b) shows the Irms convergence curve, the change in the number of feasible solutions, and the convergence process of the optimal control parameters of the PSO algorithm under the conditions of M=0.6, P=0.6 KW, f=50 kHz. Figure 5(c) shows the Irms convergence curve, the change in the number of feasible solutions, and the convergence process of the optimal control parameters of the PSO algorithm under the conditions of M=0.6, P=0.9 KW, f=50 kHz. Detailed Implementation

[0034] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0035] This embodiment uses a bidirectional DAB converter for a 1.2kW electric vehicle on-board charger as the implementation object to describe the specific implementation method of the present invention in detail. The main circuit parameters of the converter are shown in Table 1.

[0036] Table 1.2 Main Circuit Parameters of 1.2 kW OBC-DAB Converter

[0037] like Figures 1-4 As shown, the specific implementation steps of the OBC-DAB converter hybrid modulation strategy optimization method integrating HFV and ZVS constraints described in this invention are as follows: Step S1: Establish the frequency domain mathematical model of the OBC-DAB converter under HFV modulation. First, define the main circuit topology of the OBC-DAB converter, as shown in Figure 2. It consists of a primary full-bridge H1, a high-frequency transformer T, and an auxiliary inductor. L The primary and secondary full-bridges are composed of H1 and H2, respectively. H1 contains switching transistors S1 to S4, and H2 contains switching transistors Q1 to Q4. The primary and secondary full-bridges are connected to the auxiliary inductor through a high-frequency transformer to achieve bidirectional energy flow and electrical isolation.

[0038] The typical operating waveform of HFV modulation is shown in Figure 3. Taking the switching transistor S1 as the reference, five independent control variables are defined: the primary side inward phase shift angle. α 1 (Phase shift angle between switching transistors S1 and S4), angle corresponding to the duty cycle of the primary bridge arm. α 2 (The conduction time of switch S1 corresponds to the angle), secondary side inward phase shift angle α 3 (Phase shift angle between switching transistors Q1 and Q4), and the corresponding angle of the secondary bridge arm conduction duty cycle. α 4 (The conduction time of switch Q1 corresponds to the angle), the phase shift angle of the primary and secondary bridge arms. β (Phase shift angle between switching transistors S1 and Q1). Define the phase shift ratio between the turn-on times of the primary and secondary bridge arms S1 and Q1 as... D 0, corresponding angle α 0 = 2πD 0, β The output voltage of the primary and secondary bridge arms v ab , v cd The phase difference between the center point of the waveform and the fundamental component satisfies β = α 0 +(α 3 α 1 ) / 2. Among them To correspond to the duty cycle form of the control variable, HFV modulation is the unified form of traditional modulation strategies: when When HFV degenerates into TPS modulation; when or When HFV degenerates into EPS modulation; when At this time, HFV degenerates into SPS modulation.

[0039] A mathematical model is established using frequency domain analysis, and a time zero point is selected. t=0 The phase angle shifted inward from the original side α The midpoint of 1, based on the Fourier transform of the output voltage of the primary and secondary bridge arms. 、v cd By decomposing the components, we obtain the expressions for each harmonic:

[0040] in, ω s = 2πf s The switching angular frequency, n To determine the harmonic order, this embodiment uses the first 8 harmonic components for calculation, balancing calculation accuracy and computational complexity.

[0041] Based on the time-domain relationship between inductor voltage and current The inductor current is derived. The parsing expression:

[0042] Where A and B are harmonic component coefficients, expressed as:

[0043] in, M Voltage conversion ratio, M = NV 2 / V 1. In this embodiment N = 1, therefore M = V 2 / V 1. The range of variation is 0.526~1.184.

[0044] Based on the analytical expression for inductor current, the expression for the RMS current of the inductor, which includes the 8th harmonic component, is derived:

[0045] Based on the average power transmission characteristics of linear networks, the expression for average transmission power is derived as follows:

[0046] Step S2: Derive the precise hard constraint conditions for the ZVS implementation of the switching transistor. Analyze the commutation process of the switching transistor. For ZVS to be turned on normally, it is necessary to simultaneously satisfy the current direction constraint and the minimum commutation current constraint. Neither can be omitted.

[0047] First, based on the conduction timing and current path of the 8 switches, they are divided into two groups of complementary switches, and the inductor current direction constraints for ZVS turn-on of the two groups of switches are defined respectively:

[0048] In other words, when the switching transistor is turned on, the inductor current must meet the corresponding direction in order to clamp the drain-source voltage of the switching transistor to 0V through the anti-parallel diode, thus providing a prerequisite for ZVS turn-on. Among them, S1~S4 are primary-side full-bridge switching transistors, and Q1~Q4 are secondary-side full-bridge switching transistors.

[0049] For single-objective optimization strategies of these DAB converters, it is difficult to strike a balance between a wide ZVS range and a minimum current level. Modulation schemes that only aim at minimum current often sacrifice ZVS range, resulting in significant switching losses. Furthermore, multi-objective optimizations that do not consider precise current constraints are not efficient because soft switching cannot be guaranteed. To overcome these limitations and further improve the efficiency of DAB converters, a comprehensive optimization method incorporating precise ZVS constraints is proposed.

[0050] Secondly, the parasitic capacitance of the switching transistor should be taken into account. The influence of secondary voltage on the commutation process, and the derivation of the minimum commutation current for ZVS turn-on. Quantization current amplitude constraint:

[0051] in, This refers to the turn-on time of the switching transistor.

[0052] For the buck mode 0 in this embodiment M <1, the expression for the minimum commutation current of the primary-side switch S1 is:

[0053] For boost mode M >1. The same method is used to derive the minimum commutation current expression for each switching transistor to ensure the constraint accuracy across the entire voltage range.

[0054] Combining the current direction constraints and minimum commutation current constraints mentioned above, a complete and precise hard constraint condition is formed for the ZVS of the eight switching transistors, which is then transformed into a mathematical inequality and incorporated into the subsequent optimization model.

[0055] Step S3: Construct a multi-objective optimization model for HFV hybrid modulation that incorporates ZVS hard constraints. With minimizing the RMS current of the inductor as the core optimization objective, and power transfer constraints, ZVS exact hard constraints, and control variable boundary constraints as constraints, a standard constrained optimization problem is established, which takes the following form:

[0056] The constraints are:

[0057] in, The target transmission power varies from 0 to 1.2 kW. For the first i The turn-on time of each switching transistor For the first i The minimum commutation current of each switching transistor.

[0058] This optimization model uses the ZVS constraint as a hard constraint to ensure that all feasible solutions can achieve ZVS turn-on of the switch. At the same time, it aims to minimize the RMS current and thus minimize the conduction loss, thereby solving the problem of co-optimization of conduction loss and switching loss from the root.

[0059] Step S4: Use an improved particle swarm optimization algorithm to perform offline global optimization and generate an offline lookup table. For the aforementioned non-convex multi-constraint optimization problem, an improved particle swarm optimization (PSO) algorithm is used for offline global optimization. The algorithm flowchart is shown in Figure 4, and the specific execution process is as follows: 1) Determine the algorithm parameters: The search space dimension is 5, corresponding to the HFV five control variables. α 1. α 2. α 3. α 4. β Set the particle swarm size to 800 and the inertia weight. ω = 0.7, Individual Learning Factor C 1 = 1.5, Group Learning Factor C 2 = Version 1.5, with a maximum of 400 iterations; 2) Construct a fitness function, with RMS current minimization as the core, and introduce a penalty term for ZVS constraint non-satisfaction, the expression of which is:

[0060] Where λ is the penalty factor, which is set to 500 in this embodiment to ensure that solutions that do not meet the ZVS constraint are quickly eliminated; 3) Initialize the position and velocity of the particle swarm, randomly generate initial solutions within the control variable boundaries, and introduce an elite retention strategy to retain the top 5% of particles with the best fitness in each iteration and directly enter the next iteration; 4) Iterative optimization: Calculate the fitness value of each particle, update the individual historical best solution and the global best solution of the group, adjust the particle state based on the velocity and position update formula until the maximum number of iterations is reached, and output the optimal five control variables for the current working point; 5) Traverse the entire operating range of the OBC-DAB converter: voltage conversion ratio M from 0.5 to 1.2, step size 0.02; transmission power P from 0 to 1pu (rated power), step size 0.02pu; solve for the optimal five control variables at each operating point, generate a high-dimensional offline lookup table with M and P as input indices, and burn it into the DSP controller.

[0061] Step S5: Design online real-time control and complete the hybrid modulation strategy. The modulation strategy is executed based on the above-mentioned online real-time control mechanism. The specific process is as follows: 1) Real-time sampling: The primary input voltage of the OBC-DAB converter is acquired through voltage and current sensors. V 1. Secondary output voltage V 2. Output current I 0, calculate the current voltage conversion ratio. M = V 2 / V 1. Actual transmission power P = V 2 I 0; 2) Obtain the five control variables of HFV under the current operating conditions. α 1. α 2. α 3. α 4. β The optimal values ​​are shown in Figures 5(a) to 5(c). 3) Dead time configuration: Configure a normal dead time of 100 ns to 500 ns according to the switching device parameters to prevent bridge arm shoot-through; 4) Drive signal generation: Based on the optimal five control variables, the PWM drive signal of 8 switching transistors is generated by the ePWM module of the DSP. After being amplified by the drive module, it is output to the DAB main circuit to complete the real-time execution of the hybrid modulation strategy.

[0062] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for optimizing the hybrid modulation strategy of an OBC-DAB converter that integrates HFV and ZVS constraints, characterized in that, The method specifically includes the following steps: S1: Establish the frequency domain mathematical model of the OBC-DAB converter under HFV modulation. Based on Fourier series decomposition, derive the analytical expressions for inductor current, RMS and average transmission power under HFV control variables. Wherein OBC represents the on-board charger, DAB represents the dual active bridge, HFV represents the hybrid five-variable, and RMS represents the effective value of inductor current. S2: Analyze the commutation process of the switching transistors in the DAB converter, derive the precise hard constraint condition of ZVS for the switching transistors, and transform it into an energy-quantifiable mathematical inequality constraint; where ZVS is zero-voltage switching. S3: Construct a multi-objective optimization model for HFV hybrid modulation that integrates ZVS hard constraints, with RMS minimization as the core optimization objective and power transmission constraints, ZVS exact hard constraints, and control variable boundary constraints as constraints, and establish a standard constraint optimization problem; S4: An improved PSO algorithm is used to perform offline global optimization of the optimization model. The entire operating range of the OBC-DAB converter is traversed to find the optimal solution set of the HFV control variables corresponding to each operating point and generate a high-dimensional offline lookup table. Here, PSO stands for Particle Swarm Optimization Algorithm. S5: Design an online real-time control mechanism to sample the input voltage, output voltage, and load power of the OBC-DAB converter in real time, and obtain the optimal control parameters under the current operating conditions through table lookup and linear interpolation; configure the dead time according to the switching device parameters to prevent bridge arm shoot-through.

2. The method for optimizing the hybrid modulation strategy of the OBC-DAB converter integrating HFV and ZVS constraints according to claim 1, characterized in that, In step S1, the HFV control variables include the primary side inward phase shift angle. The angle corresponding to the duty cycle of the original side bridge arm. Secondary side inward shift phase angle Secondary side bridge arm conduction duty cycle corresponding angle Phase angle of the original secondary side bridge arm shift ; The HFV modulation is a hybrid of traditional phase-shift modulation and PWM modulation, and is a unified form of SPS, DPS, EPS, and TPS modulation strategies. When HFV degenerates into TPS modulation; when or When HFV degenerates into EPS modulation; when At that time, HFV degenerates into SPS modulation, where D i For the duty cycle form of the corresponding control variable ; SPS stands for single-phase shift, DPS for dual-phase shift, EPS for extended-phase shift, and TPS for triple-phase shift.

3. The method for optimizing the hybrid modulation strategy of the OBC-DAB converter integrating HFV and ZVS constraints according to claim 2, characterized in that, In step S1, a frequency domain mathematical model of the OBC-DAB converter under HFV modulation is established, specifically by selecting a time zero point. The phase angle shifted inward from the original side The midpoint, based on the Fourier transform of the output voltage of the primary and secondary bridge arms of the DAB converter. , v cd By decomposing the components, we obtain the expressions for each harmonic: in, This is the primary input voltage of the DAB converter. This is the secondary output voltage. This refers to the turns ratio of the primary and secondary sides of the transformer. The switching angular frequency, For harmonic order, At time t; based on the time-domain relationship between inductor voltage and current, the inductor current is derived. The parsing expression: in, This is the sum of the transformer's leakage inductance and auxiliary inductance. , The harmonic component coefficients are expressed as follows: in, M Voltage conversion ratio, ; Based on the analytical expression for inductor current, the RMS expression containing the 8th harmonic component is derived as follows: in, Is included before n Normalized RMS value of inductor current for subharmonic components; Based on the average power transmission characteristics of linear networks, the average transmission power is derived. The expression is: 。 4. The method for optimizing the hybrid modulation strategy of the OBC-DAB converter by integrating HFV and ZVS constraints according to claim 3, characterized in that, In step S2, the derivation process of the exact hard constraint conditions for ZVS is as follows: (1) Analyze the four stages of switching transistor commutation and clarify that ZVS normal turn-on must simultaneously meet the current direction constraint and the minimum commutation current constraint; (2) Based on the conduction timing and current path of the 8 switches of the DAB converter, they are divided into two groups of complementary switches, and the inductor current direction constraints for the ZVS turn-on of the two groups of switches are defined respectively: In other words, when the switching transistor is turned on, the inductor current must meet the corresponding direction in order for the drain-source voltage of the switching transistor to be clamped to 0V through the anti-parallel diode, thus providing a prerequisite for ZVS turn-on; among which, ~ For primary-side full-bridge switching transistors, ~ For secondary-side full-bridge switching transistors; (3) Taking into account the parasitic capacitance of the switching transistor Based on the influence of secondary voltage on the commutation process, the minimum commutation current for ZVS turn-on is derived. Quantization current amplitude constraint: in, This is the minimum commutation current at the moment the switching transistor is turned on. This refers to the turn-on time of the switching transistor; By combining current direction constraints and minimum commutation current constraints, a complete and precise hard constraint condition for ZVS realization is formed.

5. The method for optimizing the hybrid modulation strategy of the OBC-DAB converter integrating HFV and ZVS constraints according to claim 1, characterized in that, In step S3, the standard constraint optimization problem is established in the following form: The constraints are: in, For the target transmission power, For the first i The turn-on time of each switching transistor For the first i The minimum commutation current of each switching transistor, For the first i The minimum commutation current at the turn-on time of each switch transistor Compared to the shift in the turn-on time of the first switch tube of the original secondary side bridge arm.

6. The method for optimizing the hybrid modulation strategy of the OBC-DAB converter integrating HFV and ZVS constraints according to claim 5, characterized in that, Step S4 specifically includes the following steps: S41: Determine the algorithm parameters, including a search space dimension of 5 and corresponding HFV control variables; set the particle swarm size and inertia weights. ω Individual learning factor C 1. Group learning factor C 2. Maximum number of iterations; S42: Construct the fitness function, with RMS minimization as the core, and introduce a penalty term for the degree of ZVS constraint non-satisfaction. The expression is: in, For fitness, As a penalty factor; S43: Initialize the position and velocity of the particle swarm, and randomly generate an initial solution within the control variable boundary; S44: Iterative optimization: Calculate the fitness value of each particle, update the individual historical best solution and the global best solution of the population, adjust the particle state based on the velocity and position update formula until the maximum number of iterations is reached, and output the optimal HFV control variable for the current working point. S45: Traverse the operating range of the OBC-DAB converter, solve for the optimal HFV control variables at each operating point, and generate a voltage conversion ratio. M Average transmission power P A high-dimensional offline lookup table with an input index.

7. The method for optimizing the hybrid modulation strategy of the OBC-DAB converter integrating HFV and ZVS constraints according to claim 6, characterized in that, In step S43, an elite retention strategy is introduced, which retains the top 5% of particles with the best fitness in each iteration and directly enters the next iteration, thereby improving the convergence speed and optimization accuracy of the algorithm.

8. The method for optimizing the hybrid modulation strategy of the OBC-DAB converter integrating HFV and ZVS constraints according to claim 1, characterized in that, In step S5, an online real-time control mechanism is designed, which specifically includes the following steps: S51: Real-time sampling of the primary input voltage of the OBC-DAB converter V 1. Secondary output voltage V 2 and output current I o Calculate the current voltage conversion ratio M With average transmission power P ; S52: with M and P Using an index, perform a lookup and bilinear interpolation in the offline lookup table to obtain the optimal value of the HFV control variable under the current operating conditions; S53: Configure normal dead time to prevent bridge arm shoot-through; S54: Based on the optimal HFV control variables and the conventional dead time, it generates PWM drive signals for 8 switching transistors, which are output to the main circuit through the digital controller to complete the real-time execution of the hybrid modulation strategy.

9. A system for implementing the OBC-DAB converter hybrid modulation strategy optimization method integrating HFV and ZVS constraints as described in any one of claims 1 to 8, characterized in that, The system includes: The sampling module is used to acquire the input voltage, output voltage, output current, and inductor current signals of the OBC-DAB converter in real time. The digital controller has a built-in offline lookup table and control program for performing online calculations of optimization methods, lookup interpolation, and PWM drive signal generation. The driver module is used to amplify the PWM signal output by the digital controller and drive the eight switching transistors of the DAB converter. The DAB main circuit is an isolated bidirectional DC / DC converter topology after the OBC stage, including a primary full-bridge, a high-frequency transformer, an auxiliary inductor, and a secondary full-bridge, which connects the output of the front-end PFC rectifier to the power battery; where PFC is power factor correction.

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