An Optimized Control Method for a Three-Phase Dual Active Bridge Isolated DC-DC Converter System

Abstract

The present invention discloses an optimization control method for a three-phase dual-active bridge isolated DC-DC converter system. The method comprises the following steps: firstly constructing a steady-state model of a circuit, then constructing an optimization problem cost function, inputting optimization operating condition parameters, then selecting particle swarm optimization parameters, initializing a particle swarm, calculating a case circuit operating state and a target cost function, storing the current optimal cost function, sequentially determining whether exit particle swarm optimization conditions and optimization indicators are met, and finally obtaining the optimal control parameters under specific operating conditions by performing particle swarm optimization within the entire control interval on the model. The method can effectively optimize circuit operating efficiency or component voltage and current stresses, improve the power density of the converter, and then find the optimal control parameters under various operating conditions in a three-degree-of-freedom space through a particle swarm algorithm. Finally, piecewise linear interpolation is performed to ensure that the optimized controller parameters cover the entire required operating condition.

Description

Technical Field

[0001] The present invention belongs to the technical field of power electronics, and in particular relates to a method for calculating optimized control parameters applicable to a three-phase dual active bridge (3p-DAB) isolated DC-DC converter system. Background Art

[0002] The 3p-DAB converter has attracted widespread attention due to its advantages of galvanic isolation, bidirectional power conversion flow and easy implementation of zero voltage switching (ZVS). This technology can be applied to DC conversion devices, photovoltaic power generation systems, electric vehicle charging piles, etc.

[0003] The traditional three-phase DAB circuit uses a single phase shift control (SPS) method to control the output voltage and output power of the converter. This control method can achieve zero voltage switching of the input and output dual bridge arms under the condition of matching input and output voltages, so that the conversion efficiency of the converter can reach more than 95%.

[0004] However, when the input and output voltages do not match, this control method can only achieve full-bridge ZVS within a very narrow power range close to the converter's output limit. Furthermore, due to the generally high switching frequency of three-phase DABs and the large number of switches in the circuit topology, the SPS suffers from high switching losses and low efficiency under non-ideal operating conditions.

[0005] To solve the above problems and improve the working efficiency of the 3p-DAB converter under voltage mismatch conditions, choosing a control scheme with greater control freedom is a feasible option. DCC control adds two new control degrees of freedom: the duty cycle of the front bridge arm (input bridge arm) and the rear bridge arm (output bridge arm) on the basis of single phase-shift control, so as to effectively control the internal operating state of the circuit while controlling the output power.

[0006] However, due to the difficulty in analyzing 3p-DAB under DCC control and the high degree of control freedom of the control method, it is difficult to implement an analytical calculation formula for the optimal control parameters. It becomes very complicated to perform analytical analysis of the circuit and obtain the optimal control parameters. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings and deficiencies of the prior art and to propose an optimized control method for a three-phase dual active bridge isolated DC-DC converter system.

[0008] The technical solution adopted by the present invention to solve the technical problem is: an optimization control method for a three-phase dual active bridge isolated DC-DC converter system, wherein the three-phase dual active bridge isolated DC-DC converter is composed of a front bridge arm and a rear bridge arm through a transformer and an inductor L sa , L sb , L scThe front bridge arm is connected by the switch tube S 14 ~S 16 The rear bridge arm is composed of switch tube S 21 ~S 26 The six switching tubes constitute the upper and lower bridge arms respectively; the steps are as follows:

[0009] S1: Construct the cost function of the optimization problem and input the optimized operating parameters: Use the piecewise linearization analysis method to analyze the inductor in steady-state operation in sections. Calculate the inductor current in the entire interval based on the switching timestamps, the front and rear bridge arm switching functions, and the input and output voltages, and then calculate the output power. Use the piecewise linearization method to construct a three-phase DAB steady-state circuit model under DCC control.

[0010] S2, constructing a cost function composed of circuit parameters according to the optimization target: repackaging the steady-state circuit model to construct an optimizable function model whose input is a variable control parameter and output is the cost function corresponding to the optimization target;

[0011] S3, select particle swarm optimization parameters: select appropriate particle swarm optimization parameters according to the problem type to be optimized;

[0012] S4, initializing the particle swarm: performing particle swarm optimization on the optimizable function model under the set working conditions. If the cost function corresponding to the optimized control parameters is higher than the cost function corresponding to the SPS control parameters under the same working conditions, the optimization is considered invalid and the particle swarm optimization needs to be repeated.

[0013] S5, calculating the circuit model operation state and target cost function;

[0014] S6, store the current optimal cost function, determine whether the conditions for exiting the particle swarm optimization are met, otherwise perform a particle swarm iteration and return to S5;

[0015] S7, if yes, further determine whether the optimization index is met, otherwise return to S4;

[0016] S8, if yes, output the optimal control parameters and end the operation.

[0017] Furthermore, the three-phase DAB steady-state circuit model is constructed by the following steps:

[0018] (1) Generate a three-phase front and rear bridge arm switch action timestamp sequence within a single cycle based on the three-degree-of-freedom control input. The timestamp element consists of two parts: the action time value and the action type label. Then, the timestamp sequence is sorted according to the action time. The A-phase switch action time is shown as follows:

[0019]

[0020] Where t Ta1 Indicates the time between the upper arm switch tube of input A phase being turned on and the lower arm switch tube being turned off, t Ta2 Indicates the time between the lower bridge arm switch tube of input A phase being turned on and the upper bridge arm switch tube being turned off, t Sa1 Indicates the time between the output A phase upper arm switch tube turning on and the lower arm switch tube turning off, t Sa2 Indicates the time between the output A phase lower arm switch tube being turned on and the upper arm switch tube being turned off; T s Represents the switching cycle, D1 and D2 are the switching duty cycles of the front and rear bridge arms respectively, D f D is the ratio of the front bridge arm to the rear bridge arm. f =1 means that the front bridge arm lags behind the rear bridge arm by half a switching cycle, and n is the number of cycles from time zero to now; the switching action time of phase B lags behind phase A by 1 / 3 switching cycle, and the switching action time of phase C leads the switching action time of phase A by 1 / 3 switching cycle. The expression is:

[0021] (2) According to the switching action timestamp, the entire switching cycle is partitioned, and the segmented switching functions of the front and rear bridge arms are drawn according to the sorted switching action timestamp labels:

[0022]

[0023] Where δ(t) is the impulse function. When t0∈[t1,t2], for any f(x) At the same time if but

[0024] (3) Calculate the inductance L based on the segmented switching function sa , L sb , L sc The voltage at both ends is: Where U dc Represents the DC side voltage of the three-phase bridge arm, S a 、S b 、S c They represent the switching functions of the front and rear bridge arms of the three phases respectively, and their values ​​are the switching functions of the front bridge arm of the corresponding phase minus the switching functions of the rear bridge arm of the corresponding phase. For the front and rear bridge arm switching functions, when the upper bridge arm is turned on, it is 1, and when the lower bridge arm is turned on, it is -1.

[0025] (4) According to the inductance L sa , L sb , L sc The voltage across both ends and the duration of each small interval are used to calculate the inductor current change within each small interval. The inductor current change within the nth small interval formed by the A phase timestamp is: Where L is the equivalent inductance in the three-phase circuit, tn-1 and t n are the start time and end time of the cell respectively;

[0026] (5) Calculate the inductor current offset at the beginning of the switching cycle based on the inductor current change calculated in the previous step. The inductor current at the beginning of the cycle is Where t0 represents the cycle start time, I′(t) represents the inductor current at the beginning of the cycle, which is zero. Therefore, the inductor current in the entire switching interval is I(t)=I0+I′(t).

[0027] Furthermore, the output charge in a single switching cycle is calculated based on the inductor current and the switching function of the rear bridge arm. The output power expression is as follows:

[0028]

[0029] The above model parameters can be further transformed into an optimized target cost function.

[0030] Furthermore, the mathematical expression of the cost function optimization problem in step S5 is:

[0031] minimize f cost

[0032] subject to P o =P ref

[0033] others,

[0034] Where f cost Represents the cost function; the cost function constructed by integrating the constraints into the cost function is:

[0035] CF=target+M1×ε(P o -P ref |-P ref ×0.1%)+M2×ε[f cost ],

[0036] Where target represents a circuit indicator that needs to be optimized to the minimum value, that is, the original cost function f described in the above formula cost , M i It represents a maximum value, which is absolutely greater than any possible target value, so that the cost function of the control target will jump to a maximum value when the output power does not meet the requirements. f(x) represents other possible constraint expressions except output power.

[0037] The beneficial effects of the present invention are:

[0038] 1. The present invention is based on a 3p-DAB converter circuit under three-degree-of-freedom duty cycle control, which avoids complex analytical processes and complicated partitioning conclusions and can quickly obtain its steady-state operating state.

[0039] 2. Based on the simulation model, the present invention realizes a fully automated solution for generating optimal control parameters of the 3p-DAB circuit. The generated control parameters can effectively improve the working efficiency of the circuit under the working condition of input and output voltage mismatch. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 Flowchart of the optimization control method of the present invention;

[0041] Figure 2 This is a topological diagram of the three-phase dual active bridge isolated DC-DC converter system of the present invention;

[0042] Figure 3 The steady-state model of the three-phase dual active bridge isolated DC-DC converter system of the present invention;

[0043] Figure 4 This is a simulation diagram of the three-phase dual active bridge isolated DC-DC converter system of the present invention;

[0044] Figure 5 Schematic diagram of the switching functions of the front and rear bridge arms of a three-phase DAB circuit under three-degree-of-freedom control used in the steady-state model of the present invention: when the upper bridge arm is turned on, the switching function is 1, and when the lower bridge arm is turned on, the switching function is 0;

[0045] Figure 6 This is a diagram showing the simulated inductor current reduction effect of the present invention under full working conditions with 300V input and 600V output;

[0046] Figure 7 This is a diagram showing the simulated inductor current reduction effect of the present invention under full working conditions of 600V input / 300V output. DETAILED DESCRIPTION

[0047] The present invention will be further described in detail with reference to the accompanying drawings.

[0048] When the input and output voltages do not match, a three-phase dual-active bridge converter system using traditional SPS control methods struggles to maintain zero-voltage turn-on of all bridge-leg switches. Furthermore, current backflow is large and the topology has a high number of switches, resulting in low circuit efficiency under these conditions. Therefore, it is necessary to adopt a steady-state digital modeling method and an optimized control parameter calculation method for three-degree-of-freedom duty cycle control (DCC).

[0049] Reference Figure 1 As shown, the present invention discloses a method for calculating optimal control parameters of a three-phase dual active bridge isolated DC-DC converter system under DCC control, and the steps are as follows.

[0050] S1 starts running, constructs the optimization problem cost function, and inputs the optimization operating condition parameters: a piecewise linearization analysis method is used to perform a segmented analysis of the inductor in steady-state operation. The inductor current in the entire interval is calculated through the switching timestamps, the front and rear bridge arm switching functions, and the input and output voltages, and then the output power is calculated. Using the above piecewise linearization method, a three-phase DAB steady-state circuit model under DCC control is constructed.

[0051] Description of steady-state computer modeling of three-phase DAB circuit under DCC control: Figure 2 This shows the final form of the three-phase DAB steady-state circuit model. Its input parameters consist of circuit component parameters, input and output voltages, and control inputs. The basic output is output power, but other circuit operating parameters within this class can also be used. The detailed construction process is as follows.

[0052] 1) Generate a single-cycle three-phase front and rear bridge arm switching action timestamp sequence based on the three-degree-of-freedom control input. The timestamp element consists of two parts: the action time value and the action type label. Then, the timestamp sequence is sorted according to the action time. Taking phase A as an example, its switching action time is shown as follows:

[0053]

[0054] Among them, t Ta1 Indicates the time between the upper arm switch tube of input A phase being turned on and the lower arm switch tube being turned off, t Ta2 Indicates the time between the lower bridge arm switch tube of input A phase being turned on and the upper bridge arm switch tube being turned off, t Sa1 Indicates the time between the output A phase upper arm switch tube turning on and the lower arm switch tube turning off, t Sa2 Indicates the time between the output A phase lower arm switch tube being turned on and the upper arm switch tube being turned off; T s Represents the switching cycle, D1 and D2 are the switching duty cycles of the front and rear bridge arms respectively, D f D is the ratio of the front bridge arm to the rear bridge arm. f =1 means that the front bridge arm lags behind the rear bridge arm by half a switching cycle, and n is the number of switching cycles from time zero to the present. The switching action time of phase B and phase C lags behind and leads the switching action time of phase A by 1 / 3 of a switching cycle respectively. The expression is:

[0055] (2) According to the switching action timestamp, the entire switching cycle is partitioned. The segmented switching functions of the front and rear bridge arms are plotted based on the sorted switching action timestamp labels. When the corresponding upper bridge arm is turned on, the switching function is 1, and when the corresponding lower bridge arm is turned on, the switching function is -1. The expression is:

[0056]

[0057] Where δ(t) is the impulse function. When t0∈[t1,t2], for any f(x) At the same time if but

[0058] (3) Calculate the inductance L based on the segmented switching function sa , L sb , L sc The voltage across both ends, Figure 4 The figure shows the equivalent circuit of a three-phase DAB circuit. The three-phase bridge arms on the input and output sides and the input and output voltages are equivalent to forming two controllable voltage sources across the inductor. When performing circuit analysis, the virtual center potentials at both ends of the transformer are connected. Using the superposition principle, it can be concluded that in any switching state, the voltage across the equivalent three-phase inductor is controlled by the switching function of the three-phase bridge arms. Taking the equivalent voltage source of the front bridge arm of phase A as an example, its controlled voltage expression is Where U dc Represents the DC side voltage of the three-phase bridge arm, S a 、S b 、S c They represent the switching functions of the three-phase bridge arms respectively, and their values ​​are the switching functions of the corresponding front bridge arm minus the switching functions of the corresponding rear bridge arm. For the front and rear bridge arm switching functions, the corresponding upper bridge arm is 1 when it is turned on, and the corresponding lower bridge arm is -1 when it is turned on, as shown in Figure 5 shown.

[0059] (4) According to the inductance L sa , L sb , L sc The voltage across both ends and the duration of each small interval are used to calculate the inductor current change within each small interval. Taking phase A as an example, the inductor current change within the nth small interval formed by the timestamp is: Where L is the equivalent inductance in the three-phase circuit, t n-1 and t n They are the start time and end time of the cell respectively.

[0060] (5) Calculate the inductor current offset at the beginning of the switching cycle based on the inductor current change calculated in the previous step. The calculation is based on the fact that the inductor current should not have a DC bias in a single switching cycle, that is, the integral of the inductor current in the entire cycle should be 0. Because the three phases of this circuit should be symmetrical, if the three phase inductors have the same DC bias, it violates Kirchhoff's current law. Therefore, the inductor current at the beginning of the cycle is Where t0 represents the cycle start time, and I′(t) represents the inductor current value during the entire switching cycle, calculated based on the linear change of the inductor current, assuming the inductor current is zero at the start of the cycle. Therefore, the inductor current during the entire switching interval is I(t)=I0+I′(t).

[0061] S2, construct a cost function composed of circuit parameters according to the optimization target: repackage the steady-state circuit model to construct an optimizable function model whose input is a variable control parameter and output is the cost function corresponding to the optimization target.

[0062] The output charge within a single switching cycle is calculated based on the inductor current and the switching function of the lower bridge arm. This is because the inductor charge is transferred to the output filter capacitor and the output load only when the inductor current is positive and the corresponding upper bridge arm is conducting. Therefore, the output power expression is as follows:

[0063]

[0064] The above calculation process generates a sequence of inductor current magnitudes at each switching action. The inductor current in this sequence can be used to determine whether the switching action is in a soft switching state and calculate the effective value of the inductor current. The turn-on and turn-off losses and conduction losses of the switching element can also be calculated based on this. The above model parameters can be further transformed into an optimization target cost function.

[0065] S3, select particle swarm optimization parameters: select appropriate particle swarm optimization parameters according to the type of problem to be optimized. In this step, a more general particle swarm optimization parameter can also be used.

[0066] This patent uses a particle swarm optimization scheme to intelligently optimize the optimization objective cost function. The particle swarm optimization (PSO) algorithm is a swarm-based search algorithm based on intelligent optimization algorithms that mimic swarm intelligence. In PSO, individuals called particles "fly" in a hyperdimensional search space, and each particle represents a potential solution.

[0067] The pattern of particle position changes in the search space is based on the social psychological tendency of individuals to imitate the successful experiences of other individuals. Therefore, the changes in particles in the particle swarm are affected by the experience or knowledge of all particles or their neighbors. The result of modeling this social behavior is that particles tend to approach the known optimal point at a random speed during the iteration process. The particle swarm algorithm distributes the initialized particle swarm throughout the multidimensional control space, and uses a certain degree of randomness in the optimization process, so that it has better global optimization capabilities and has a better optimization effect on the piecewise nonlinear optimization model described in this patent. If the cost function corresponding to the control parameters optimized this time fails to be lower than the cost function corresponding to the SPS control, it means that the optimization is invalid and needs to be re-optimized. In this way, the optimal control parameters that meet the optimization objectives can be obtained.

[0068] S4, initialize particle swarm: perform particle swarm optimization on the optimizable function model under the set working conditions. If the cost function corresponding to the optimized control parameters is higher than the cost function corresponding to the SPS control parameters under the same working conditions, the optimization is considered invalid and particle swarm optimization needs to be performed again.

[0069] The particle position is changed by adding the velocity v to the current position. i (t) to achieve, that is:

[0070] x i (t+1)=x i (t)+v i (t+1),

[0071] Where t represents the discrete time step, x i (t) represents the position of particle i in the search space at time step t, v i (t) is the velocity vector of the optimization process, which is determined by the particle's own empirical knowledge and the exchange information from the particle's neighborhood (or all) particles. The particle's empirical knowledge is generally called the cognitive component, which is proportional to the distance from the particle's historical best position found since the first time step (called the particle's personal best position). The social exchange information is called the social component in the velocity equation and is proportional to the overall best component of the particle in the neighborhood or all particles. Its calculation formula is:

[0072]

[0073] Where V ij (t) represents the velocity of particle i in dimension j at time step t, y ij (t) and Respectively represent the best historical position of the particle itself and the field or the whole up to the tth step, r 1j (t) and r 2j(t) is a random value uniformly distributed between [0, 1] to enhance the randomness of the particle swarm algorithm, and ω is the inertia parameter.

[0074] Particle swarm optimization has two basic strategies, namely global optimal particle swarm optimization and neighborhood optimal particle swarm optimization. The difference is Does it represent the historical best position of n particles in the domain or the historical best position of the particle swarm as a whole? In comparison, the local best particle swarm algorithm has stronger exploration capabilities and can better avoid falling into the local optimal solution. Since the effective interval involved in this paper accounts for a small proportion of the overall space, in order to prevent the particle swarm from being prematurely attracted to the local optimal point outside the effective interval, this paper uses the neighborhood best particle swarm algorithm.

[0075] S5, calculate the case circuit operation status and target cost function.

[0076] Explanation of the cost function construction and parameter processing after optimization. The mathematical expression of the optimization problem of this patent method is:

[0077] minimize f cost

[0078] subject to P o =P ref

[0079] others,

[0080] Where f cost Represents the cost function. Since the use of full-precision constraints in digital optimization algorithms may result in excessive computation or difficulty in convergence, this patented method retains a certain error range when setting constraints and integrates the constraints into the cost function. The constructed cost function is as follows:

[0081] CF=target+M1×ε(P o -P ref |-P ref ×0.1%)+M2×ε[f cost ],

[0082] Where target represents a circuit indicator that needs to be optimized to the minimum value, that is, the original cost function f described in the above formula cost , M i represents a maximum value that is absolutely greater than any possible target value, causing the cost function to jump to a maximum value when the output power does not meet the requirements. f(x) represents the expression of other possible constraints besides output power.

[0083] S6, store the current optimal cost function, determine whether the conditions for exiting the particle swarm optimization are met, otherwise perform a particle swarm iteration and return to S5.

[0084] S7: If yes, further determine whether the optimization index is met; otherwise, return to S4.

[0085] S8, if yes, output the optimal control parameters and end the operation.

[0086] To optimize control parameters for a specific range of operating conditions, the aforementioned optimization process is performed by discretizing and sampling a subset of operating points within the operating condition. The more points sampled, the better the optimization results, but the initial optimization time is longer. If a large number of points are sampled, consider applying low-frequency filtering to the generated control parameters. Finally, a piecewise linear interpolation method is used to ensure that the optimized controller parameters cover the entire required operating range.

[0087] Figure 2 The topology diagram of the three-phase DAB circuit is shown, which consists of the front bridge arm and the rear bridge arm through the transformer and the inductor L sa , L sb , L sc The front bridge arm is connected by the switch tube S 14 ~S 16 The rear bridge arm is composed of switch tube S 21 ~S 26 The six switching transistors form the upper and lower bridge arms, respectively. The left-side input represents the control parameters for this model, with the switching frequency being a fixed control parameter. The upper bridge duty cycle and the phase shift ratio of the front and rear bridge arms represent the three controllable degrees of freedom in DCC control. The controllable range for the bridge arm duty cycle is [0, 1], and the controllable range for the phase shift ratio is [0, 0.5], corresponding to a phase shift from no phase shift to the rear bridge arm lagging the front bridge arm by 90°. The right-side output, namely the output power, is the constraint target for optimal control, ensuring that the selected control parameters meet certain output power operating conditions. The upper side represents the fixed component parameters and the current operating parameters in the circuit. The most important circuit component parameters for this model are the transformer ratio and the equivalent leakage inductance. The current operating state is determined by the input and output voltages and output power. The input and output voltages are the input parameters of the model, while the output power is the output parameter controlled by the control parameters shown on the left side of the model.

[0088] Figure 3The figure shows a steady-state model for a three-phase DAB circuit. The circuit operating state obtained during the intermediate process of calculating the output is centered on the operating state of the three-phase inductor in the topological center. This is because in this converter, the three-phase inductor is the core element for storing and transferring energy. The switch current at the switching moment can also be obtained through the inductor current and the switching function, and the circuit magnetic loss can also be measured using the effective value of the inductor current. These various circuit operating state parameters can be used to form our optimization objectives and construct a cost function with the goal of minimizing them. The above model can then be further processed into an optimization target cost function with three-degree-of-freedom control parameters as input and a cost function as output.

[0089] Explanation of the optimized parameter processing: Under a certain range of operating conditions, the parameters obtained after optimization may fluctuate slightly. In order to reduce the adverse effects of such fluctuations on control, it is possible to consider using low-frequency filtering to trim a few offset control parameters within the entire waveform.

[0090] After low-frequency filtering, a series of control parameter sequences for the entire operating range can be obtained. It is possible to consider performing a piecewise linear interpolation on the control parameters generated after optimization within the operating range so that the obtained control parameters can cover the entire operating range.

[0091] Figure 6 and Figure 7 The optimization results of optimizing the effective value of the inductor current using the patented method obtained through simulation are as follows: the control parameter calculation method of the patented optimization is used to optimize the control parameters of the three-phase DAB circuit with an input voltage of 300V, an output voltage of 600V, an equivalent leakage inductance of 6.5uH, a transformer ratio of 1:1, a switching frequency of 14kHz, and a maximum output power of 130kW. The control parameters under all working conditions of energy forward flow and energy reverse flow are optimized. The optimization target is the effective value of the inductor current. Studies have shown that the smaller the effective value of the inductor current, the smaller the overall conduction loss of the circuit. The conduction loss is composed of two parts: the magnetic loss of the magnetic component and the conduction loss of the switch.

[0092] After obtaining the optimized parameters, simulation verification was carried out. The circuit operation under various working conditions of 300V input and 600V output and 600V input and 300V output, with the output power ranging from no-load to 130kW with the output upper limit set, was simulated. The optimization effect of the optimized DCC controller on the effective value of the inductor current under various working conditions was recorded. The results are shown in the attached figure. Figure 4 shown.

[0093] From the simulation results, it can be seen that the optimization parameters calculated using the optimization method proposed in this patent can effectively reduce the effective value of the inductor current within the power output range of the sample converter, achieving the predetermined optimization goal.

[0094] The above embodiments are merely illustrative of the principles and effects of the present invention, as well as some embodiments of its application. A person skilled in the art may make several modifications and improvements without departing from the inventive concept of the present invention, and all of these modifications and improvements fall within the scope of protection of the present invention.

Claims

1. An optimization control method for a three-phase dual active bridge isolated DC-DC converter system, wherein the three-phase dual active bridge isolated DC-DC converter system comprises a front bridge arm and a rear bridge arm connected by a transformer and an inductor. L sa 、 L sb 、 L sc The front bridge arm is connected by the switch tube S 11 ~S 16 The rear bridge arm is composed of switch tube S 21 ~S 26 Composition; characterized by: The steps are as follows In S1, the piecewise linearization analysis method is used to analyze the inductor in steady-state operation in sections. The inductor current in the entire interval is calculated based on the switching timestamps, the front and rear bridge arm switching functions, and the input and output voltages. The output power is then calculated and the three-phase DAB steady-state circuit model is constructed: (1) Generate a three-phase front and rear bridge arm switch action timestamp sequence within a single cycle based on the three-degree-of-freedom control input. The timestamp element consists of two parts: the action time value and the action type label. Then, the timestamp sequence is sorted according to the action time. The A-phase switch action time is shown as follows: ， In the formula t Ta1 Indicates the time when the upper bridge arm switch tube of input A phase is turned on and the lower bridge arm switch tube is turned off. t Ta2 Indicates the time between the turn-on of the lower bridge arm switch tube and the turn-off of the upper bridge arm switch tube of input A phase. t Sa1 Indicates the time when the upper bridge arm switch tube of output A phase is turned on and the lower bridge arm switch tube is turned off. t Sa2 Indicates the time when the lower bridge arm switch of output phase A is turned on and the upper bridge arm switch is turned off; T s Represents the switching cycle, D1 and D2 are the switching duty cycles of the front and rear bridge arms respectively, D f is the shift ratio of the front bridge arm lagging behind the rear bridge arm, n is the number of cycles from time zero to the present; the switching action time of phase B lags behind phase A by 1 / 3 switching cycle, and the switching action time of phase C is ahead of phase A by 1 / 3 switching cycle. Its expression is: ; (2) Partition the entire switching cycle according to the switching action timestamp, and draw the segmented switching function of the front and rear bridge arms according to the sorted switching action timestamp labels ,in δ(t) is the impulse function, when When , for any f(x) , and if but ; (3) Calculate inductance based on the segmented switching function L sa 、 L sb 、 L sc The voltage at both ends is: , where U dc Represents the DC side voltage of the three-phase bridge arm, S a 、 S b 、 S c They represent the switching functions of the three-phase bridge arms respectively; (4) According to inductance L sa 、 L sb 、 L sc The voltage across both ends and the duration of each small interval are used to calculate the inductor current change within each small interval. The inductor current change within the nth small interval formed by the A phase timestamp is: , where L is the equivalent inductance in the three-phase circuit, t n and t n are the start time and end time of the cell respectively; (5) Calculate the inductor current offset at the beginning of the switching cycle based on the inductor current change. The inductor current at the beginning of the cycle is , where t 0 Indicates the cycle start time, I′(t) Indicates that the inductor current is 0 at the beginning of the cycle, and the inductor current in the entire switching interval is ; This step calculates the output charge in a single switching cycle based on the inductor current and the switching function of the rear bridge arm. The output power expression is: , further transformed to obtain the optimized target cost function; S2, repackage the steady-state circuit model to construct an optimizable function model whose input is a variable control parameter and output is a cost function corresponding to the optimization target; S3, select appropriate particle swarm optimization parameters; S4, performing particle swarm optimization on the optimizable function model. If the cost function corresponding to the optimized control parameters is higher than the cost function corresponding to the control parameters of the single-phase shift control method under the same working condition, the optimization is considered invalid and the particle swarm optimization is repeated. S5, calculating the circuit model operation state and target cost function; S6, store the current optimal cost function, determine whether the conditions for exiting the particle swarm optimization are met, otherwise perform a particle swarm iteration and return to S5; S7, if yes, further determine whether the optimization index is met, otherwise return to S4; S8, if yes, output the optimal control parameters and end the operation.

2. The optimization control method of a three-phase dual active bridge isolated DC-DC converter system according to claim 1, characterized in that: The mathematical expression of the cost function optimization problem in step S5 is: , In the formula f cost Represents the cost function; the cost function constructed by integrating the constraints into the cost function is: , where target Represents the cost function that needs to be optimized to the minimum value f cost , M i Indicates a target value, so that the cost function of the control target will jump to a very large value when the output power does not meet the requirements. f ( x ) represents other possible constraint expressions except output power.

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