WO2024159591A1 - 一种dab变换器的高频变压器漏感参数估计方法 - Google Patents

一种dab变换器的高频变压器漏感参数估计方法 Download PDF

Info

Publication number
WO2024159591A1
WO2024159591A1 PCT/CN2023/082638 CN2023082638W WO2024159591A1 WO 2024159591 A1 WO2024159591 A1 WO 2024159591A1 CN 2023082638 W CN2023082638 W CN 2023082638W WO 2024159591 A1 WO2024159591 A1 WO 2024159591A1
Authority
WO
WIPO (PCT)
Prior art keywords
leakage inductance
transformer
current
voltage
dab converter
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/CN2023/082638
Other languages
English (en)
French (fr)
Inventor
杨勇
朱易
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Suzhou University
Original Assignee
Suzhou University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Suzhou University filed Critical Suzhou University
Publication of WO2024159591A1 publication Critical patent/WO2024159591A1/zh
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R19/00—Arrangements for measuring currents or voltages or for indicating presence or sign thereof
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R19/00—Arrangements for measuring currents or voltages or for indicating presence or sign thereof
    • G01R19/003—Measuring mean values of current or voltage during a given time interval
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R25/00—Arrangements for measuring phase angle between a voltage and a current or between voltages or currents
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/10—Technologies improving the efficiency by using switched-mode power supplies [SMPS], i.e. efficient power electronics conversion e.g. power factor correction or reduction of losses in power supplies or efficient standby modes

Definitions

  • the present application belongs to the field of power electronic converters, and in particular relates to a method for estimating leakage inductance parameters of a high-frequency transformer of a DAB converter.
  • the dual active bridge DC-DC converter consists of a primary H-bridge, a high-frequency transformer and a secondary H-bridge. This DC-DC converter has the advantages of high efficiency, wide voltage gain range, bidirectional power transmission and electrical isolation.
  • the dual active bridge isolated DC-DC converter (DAB) is widely used in DC energy storage systems due to its wide voltage conversion range, electrical isolation and simple control method.
  • the dual active bridge DC-DC converter can be divided into two structures: two-level and multi-level. Compared with the two-level DAB converter, the voltage stress on a single device of the multi-level DAB is smaller, which is conducive to cost optimization in high-power applications.
  • the control methods of dual active bridge DC-DC converters can be divided into single phase shift (SPS) control, dual phase shift (DPS) control, and triple phase shift (TPS) control.
  • SPS single phase shift
  • DPS dual phase shift
  • TPS triple phase shift
  • Single phase shift control is widely used in the engineering field due to its single-dimensional control and simple control method.
  • the control dimensions of dual phase shift and triple phase shift control are two-dimensional and three-dimensional respectively. They can effectively suppress the reflux power of the dual active bridge converter and improve the efficiency of the system. They are used in high-power energy conversion scenarios.
  • the traditional control strategy of dual active bridge DC-DC converter is proportional integral (PI) control.
  • the commonly used controller structure is a structure in which the voltage outer loop and the current inner loop act simultaneously.
  • the reference value of the current inner loop is calculated by the voltage outer loop, and the phase shift angle acting on the converter is calculated by the current inner loop.
  • Zero-static tracking can be achieved by adjusting the parameters of the PI controller.
  • this traditional control strategy has many defects: First, it needs to adjust the PI parameters according to the system output. Since the actual system needs to meet various complex working conditions, the PI parameters usually need to be adjusted repeatedly, which is very time-consuming. Second, the dynamic response speed of the PI controller is slow, and the output will be distorted when carrying nonlinear loads.
  • model predictive control predicts the output of the system based on the modeling of the system, evaluates the future output through a predefined cost function, and determines the optimal phase shift angle on this basis.
  • the main control strategy of the dual active bridge DC-DC converter is to control the direction and magnitude of the transmission power by controlling the phase difference between the inverter output of the primary H-bridge of the high-frequency transformer and the secondary H-bridge of the high-frequency transformer.
  • the model predictive control predicts the output results of the converter under multiple phase shift angles in the current control cycle, and defines a value function to evaluate the similarity between different phase shift angles and the expected results, and selects the optimal phase shift angle based on this standard.
  • Model predictive control usually depends on the parameters of the system. When the system parameters change due to external reasons, the system output usually oscillates at high frequencies or has output amplitude errors. Online parameter estimation calculates the system variables based on the input and output parameters of the control loop and updates the parameters in the controller, reducing the output oscillation of the system.
  • system parameters will greatly affect the control performance of the system.
  • Parameter mismatch usually causes system output oscillation.
  • system parameters are usually affected by natural factors (such as temperature, humidity, altitude, working environment, etc.) and human factors (manufacturing accuracy, etc.). Therefore, in order to further improve system performance, online estimation of system parameters has become a current research hotspot.
  • the system parameter estimation mostly uses statistical methods such as least squares method and linear regression or modern intelligent algorithms such as particle swarm algorithm, neural network, genetic algorithm, etc., which are usually independent of the control loop and can estimate system parameters without affecting the control loop, greatly improving the performance and stability of the system.
  • the model predictive control strategy currently used in dual active bridge DC-DC converters has greatly improved dynamic performance and stability compared to traditional control strategies (such as proportional integral (PI) controllers).
  • PI proportional integral
  • Adding system parameters to the control strategy increases the complexity and maintenance cost of the controller to a certain extent.
  • measuring system parameters also consumes a lot of costs and has a loss of accuracy. Therefore, it is necessary to adjust system parameters online in the controller to improve system performance, which can greatly reduce costs.
  • a method for estimating leakage inductance parameters of a high-frequency transformer of a DAB converter comprising:
  • a gradient descent method is used to calculate the transformer leakage inductance
  • phase shift angle to be evaluated is calculated, and the phase shift angle is selected according to the principle of minimizing the cost function to control the DAB converter.
  • the online high-frequency transformer leakage inductance estimation method proposed in this application greatly reduces the output ripple of the dual active bridge DC-DC converter under model predictive control.
  • the model predictive control under online leakage inductance parameter estimation proposed in this application can accurately and quickly track the reference output voltage and has good steady-state performance and dynamic performance.
  • the DAB model predictive control method under online leakage inductance parameter estimation proposed in this application greatly simplifies the complexity of code parameters compared with the traditional DAB model predictive control method, is conducive to the modular implementation of the code, is conducive to the reuse and maintenance of the code, and can reduce the cost to a certain extent.
  • FIG1 shows a topology diagram of a dual active bridge DC-DC converter.
  • Figure 2 shows the typical drive and transformer current waveforms of a dual active bridge DC-DC converter.
  • FIG3 is a schematic diagram of the selection of an object to be evaluated by a processor under model predictive control.
  • FIG. 4 is a control flow chart of an embodiment of the present application.
  • FIG5 is a schematic diagram of the output results when the model parameters have errors of different orders of magnitude under a load of 600W.
  • FIG6 is a schematic diagram showing the output results of the DAB converter under various loads and the effect of transformer leakage inductance parameter identification.
  • FIG. 7 is a schematic diagram showing the output result of the DAB converter when the load changes suddenly.
  • FIG8 is a schematic diagram of transformer leakage inductance parameter identification values during load switching.
  • This application proposes a method for estimating the leakage inductance parameters of a high-frequency transformer of a dual active bridge DC-DC converter under model predictive control.
  • the leakage inductance of the transformer is calculated by sampling the system output using a gradient descent method in a microcontroller, and then applied to the calculation of the system output. On this basis, a suitable phase shift angle is selected for control according to the principle of minimizing the cost function.
  • the present application adopts a two-level dual active bridge topology structure, wherein V LV is the low voltage side voltage, V HV is the high voltage side voltage, the turns ratio of the transformer is 1:n , the capacitance of the output filter capacitor is Co , the leakage inductance of the high frequency transformer is L , the current flowing through the primary side of the transformer is i L, the load current is I L , and the current before the capacitor is I .
  • the output power and output current of the dual active bridge DC-DC converter can be calculated from the current waveform of the transformer.
  • the typical drive and inductor current waveforms of the DAB converter are shown in Figure 2, where V ab and V cd are the output voltages of the primary and secondary sides of the transformer.
  • the current flowing through the primary side of the transformer is a pure AC quantity. Therefore, when the high-frequency transformer has no DC bias, the transformer current should be symmetrical about 0, that is,
  • the output power and current of the converter can be calculated.
  • f is the current change rate.
  • AVG(x) Ts represents the average value of variable x in a control cycle. From (10), we can get
  • the load current can be considered unchanged, that is,
  • phase shift angle is discretized, that is,
  • the minimum resolution ⁇ D is set to 0.01. At the initial moment, the phase shift angle D is 0.
  • V Ref is the reference value of the output voltage
  • V m is the saturation value of the voltage gain in one cycle. Its setting purpose is to limit the voltage gain in one control cycle to prevent excessive gain from causing DC bias of the transformer.
  • V com is used to compensate for the difference between V Ref and the sampled value V HV , which can effectively improve the response speed of the DAB converter during the startup phase.
  • the model predictive control evaluates the phase shift angle in the control set by defining a cost function, and the phase shift angle that minimizes the cost function is the optimal phase shift angle.
  • the cost function defined in this embodiment is as follows
  • the purpose of the G1 part is to make the output voltage converge to V Ref
  • the purpose of the G2 part is to improve the performance of the DAB converter in steady state.
  • ⁇ 1 and ⁇ 2 are the weight coefficients of these two parts.
  • control set contains three objects to be evaluated, namely ⁇ D- ⁇ D, D, D+ ⁇ D ⁇ .
  • ⁇ D is calculated by formula (15), and then the three evaluation objects are substituted into the cost function described by formula (17) (18).
  • the object to be evaluated that minimizes the cost function will be applied to the DAB converter in the next control cycle.
  • Figure 4 shows the processor's selection of phase shift angles in four cycles.
  • the controller first calculates the output voltage of the DAB converter according to formula (13), and then calculates the phase shift angle to be evaluated according to formula (15). The most appropriate phase shift angle is selected based on the principle of minimizing the cost function and is finally applied to the DAB converter.
  • the dynamic response speed of the DAB converter under model predictive control depends greatly on the calculation of the average output current, which in turn depends on the leakage inductance of the high-frequency transformer. In practical engineering applications, it is very susceptible to the influence of external temperature. Therefore, it is necessary to introduce a method for estimating the leakage inductance of the high-frequency transformer in the microprocessor.
  • the leakage inductance of a high-frequency transformer mainly consists of two parts. One is the leakage inductance of the primary side of the transformer, and the other is the leakage inductance of the secondary side of the transformer converted to the leakage inductance of the primary side of the transformer through the turns ratio of the transformer, which can be expressed as
  • n1 is the number of turns of the primary side of the high-frequency transformer
  • n2 is the number of turns of the secondary side of the high-frequency transformer.
  • Formula (21) can be further used to calculate formula (22):
  • formula (23) can be simplified during implementation, so that the transformer leakage inductance update principle of formula (24) can be obtained:
  • ⁇ is the gradient factor.
  • the convergence speed of the estimated value can be controlled by adjusting ⁇ to meet different working conditions.
  • Figure 4 shows a control flow chart of the method for estimating leakage inductance parameters of a high-frequency transformer of a dual active bridge DC-DC converter under model predictive control proposed in the present application, which includes: sampling the output of the DAB converter to obtain a current current value and a current voltage value;
  • a gradient descent method is used to calculate the transformer leakage inductance
  • phase shift angle to be evaluated is calculated, and the phase shift angle is selected according to the principle of minimizing the cost function to control the DAB converter.
  • the parameters in the model are first corrected according to formula (24), then the output voltage is predicted according to formula (14), the phase shift angle to be evaluated is calculated according to formula (16) and formula (17), and the most appropriate phase shift angle is selected based on the principle of minimizing the cost function.
  • the independence of online parameter estimation and control loop can ensure that parameter estimation will not affect the control loop.
  • This application first analyzes the output of the dual active bridge when the system parameters are incorrect and correct through experiments, then conducts a steady-state performance analysis experiment, tests the output of the DAB converter under constant load and the transformer leakage inductance parameter identification effect under the control strategy proposed in this application, and finally conducts a dynamic performance analysis experiment to observe the dynamic response capability of the DAB converter and the transformer leakage inductance parameter identification effect when the output reference voltage suddenly changes.
  • Figure 5 (a), (b), and (c) show the output results of model parameters with different orders of magnitude errors under a 600W load.
  • 600W output, 2
  • 600W output, 10
  • 600W output, 1
  • d) 1200W output, 2
  • e) 1200W output, 10
  • f) 1200W output, 1.
  • Figure 6 shows the output results of the DAB converter and the transformer leakage inductance parameter identification effect under various loads. Among them, (a, b) 600W load (c, d) 800W load (e, f) 1000W load (g, h) 1200W.
  • the load test results show that the method proposed in this application can effectively identify the transformer leakage inductance at different power levels, and the identification value of the transformer leakage inductance reaches stability after 7s.
  • Figures 7 and 8 show the output results of the DAB converter and the transformer leakage inductance parameter identification effect when the load changes suddenly.
  • (a) the load switches between 600W and 1000W (b) the load switches from 600W to 1000W, and (c) the load switches from 1000W to 600W.
  • the experimental results show that the method proposed in this application is also applicable to the occasion of sudden load changes. It can recalculate the accurate value of the high-frequency transformer leakage inductance within 400ms when the load switches between 600W and 1000W.

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Data Mining & Analysis (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Computational Mathematics (AREA)
  • Pure & Applied Mathematics (AREA)
  • Databases & Information Systems (AREA)
  • Software Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Algebra (AREA)
  • Dc-Dc Converters (AREA)

Abstract

一种DAB变换器的高频变压器漏感参数估计方法，包括：对DAB变换器的输出进行采样，得到当前电流值和当前电压值；根据当前电流值计算得到平均电流值；根据平均电流值、当前电流值和当前电压值，使用梯度下降的方法计算得到变压器漏感；根据变压器漏感和平均电流值预测输出电压；计算待评估移相角，并根据使代价函数最小的原则选择移相角，控制DAB变换器。本方法大大减小了模型预测控制下双有源桥直流-直流变换器的输出纹波，能准确快速地跟踪参考输出电压，并具有良好的稳态性能和动态性能，简化了代码参数的复杂度，有利于代码的模块化实现，有利于代码的重用及维护，可以在一定程度下降低成本。

Description

一种DAB变换器的高频变压器漏感参数估计方法 技术领域
本申请属于电力电子变换器领域，具体涉及一种DAB变换器的高频变压器漏感参数估计方法。
 背景技术
在电力电子变换器领域，随着全球能源危机的不断加剧，新能源并网发电的趋势日益增长，带动了直流微电网技术的发展。蓄电池作为储能装置，是直流微电网系统中的关键一环，而连接储能蓄电池与直流母线的直流-直流变换器的性能及稳定性影响着直流微电网系统的性能。双有源桥直流-直流变换器(DAB)由原边H桥，高频变压器及副边H桥组成。这种直流-直流变换器具有效率高、电压增益范围广、功率双向传输、电气隔离的优点。双有源桥隔离式直流-直流变换器（DAB）以其电压变换范围广，电气隔离，控制方式简便的优点被广泛应用于直流储能系统中，因此双有源桥直流-直流变换器的拓扑结构及控制策略成为越来越受关注的方向。目前，根据原边H桥及副边H桥的电平数，双有源桥直流-直流变换器可分为两电平和多电平两种结构。与两电平DAB变换器相比，多电平DAB单个器件上承受的电压应力更小，有利于在大功率场合的成本优化。
双有源桥直流-直流变换器的控制方法可分为单移相(SPS，Single Phase Shift)控制，双移相(DPS，Dual Phase Shift)控制，三移相(TPS，Triple Phase Shift)控制，其中单移相控制以其单维度控制，控制方式简便的优点被广泛应用于工程领域，而双移相和三移相控制的控制维度分别为双维度和三维度，其能有效抑制双有源桥变换器的回流功率，提升系统的效率，被应用于大功率能源变换的场景。
 技术问题
双有源桥直流-直流变换器的传统控制策略为比例积分(PI)控制。通常采用的控制器结构为电压外环与电流内环同时作用的结构，电流内环的参考值由电压外环计算得到，而作用于变换器的移相角则由电流内环计算得到。通过调节PI控制器的参数可以实现无静差跟踪。然而，该传统控制策略存在诸多缺陷：第一，其需要根据系统输出调节PI参数。由于实际系统需要满足各种复杂工况，因此PI参数通常需要进行反复调节，十分消耗时间。第二，PI控制器的动态响应速度较慢，在带非线性负载时输出会发生畸变。因此，为了满足各种复杂的实际工况，学者提出了众多的非线性控制策略，例如边界控制等。在这些非线性控制策略中，模型预测控制（MPC）基于对系统的建模预测系统的输出，通过预先定义的代价函数评估未来输出，在此基础上确定最优移相角。双有源桥直流-直流变换器的主要控制策略为通过控制高频变压器原边H桥与高频变压器副边H桥逆变输出的相位差来控制传输功率的方向及数量级。模型预测控制在当前的控制周期内对在多个移相角下变换器的输出结果进行预测，同时定义价值函数评价不同移相角与期望结果的相似程度，并以此为标准选出最优移相角。
模型预测控制通常依赖于系统的参数。当系统参数因外界原因发生变化时，系统输出通常会发生高频振荡或输出幅值误差。在线参数估计依据控制环的输入输出参数计算系统变量，并在控制器内更新参数，减少了系统的输出振荡。
在MPC算法中，系统的参数将极大地影响系统的控制性能。参数不匹配通常会造成系统输出振荡，而在实际应用中，系统的参数通常会受到自然原因(如温度，湿度，海拔，工作环境等)及人为原因(制造精度等)的影响。因此，为了进一步提升系统性能，对系统的参数进行在线估计成为了当下的研究热点。目前，对系统进行参数估计多采用最小二乘法，线性回归等基于统计学的方法或粒子群算法，神经网络，遗传算法等现代智能算法，其通常独立于控制环，可以在不影响控制环的前提下估计系统参数，极大地提升了系统的性能及稳定性。
目前应用于双有源桥直流-直流变换器的模型预测控制策略相较于传统的控制策略（如比例积分（PI）控制器等）动态性能和稳定性得到了大幅提升。但是，其对系统参数的依赖较高，参数匹配失当将会造成系统输出振荡或失稳。而控制策略中加入系统参数则在一定程度上增加了控制器的复杂程度及维护成本。同时，对系统参数进行测量也需要消耗大量的成本，并且其有精度损失。因此，在控制器中在线调校系统参数以提升系统的性能能在很大程度上降低成本，是十分有必要的。
 技术解决方案
本发明的目的是通过以下技术方案实现的。
一种DAB变换器的高频变压器漏感参数估计方法，包括：
对所述DAB变换器的输出进行采样，得到当前电流值和当前电压值；
根据所述当前电流值计算得到平均电流值；
根据所述平均电流值、当前电流值和当前电压值，使用梯度下降的方法计算得到变压器漏感；
根据所述变压器漏感和平均电流值预测输出电压；
计算待评估移相角，并根据使代价函数最小的原则选择移相角，控制所述DAB变换器。
 有益效果
1、本申请提出的在线高频变压器漏感估计方法大大减小了模型预测控制下双有源桥直流-直流变换器的输出纹波。
2、本申请提出的在线漏感参数估计下的模型预测控制能准确快速地跟踪参考输出电压，并具有良好的稳态性能和动态性能。
3、本申请提出的在线漏感参数估计下的DAB模型预测控制方法较传统DAB模型预测控制方法而言大大简化了代码参数的复杂度，有利于代码的模块化实现，有利于代码的重用及维护，可以在一定程度下降低成本。
 附图说明
图1示出了双有源桥直流-直流变换器拓扑图。
图2为双有源桥直流-直流变换器典型驱动及变压器电流波形图。
图3为模型预测控制下处理器对待评价对象的选择示意图。
图4为本申请实施例的控制流程图。
图5为在600W负载下模型参数在不同数量级误差时的输出结果示意图。
图6为在多种负载下DAB变换器的输出结果及变压器漏感参数辨识效果示意图。
图7为当负载突变时DAB变换器的输出结果示意图。
图8为负载切换过程中变压器漏感参数辨识值示意图。
 本发明的实施方式
下面将参照附图更详细地描述本公开的示例性实施方式。虽然附图中显示了本公开的示例性实施方式，然而应当理解，可以以各种形式实现本公开而不应被这里阐述的实施方式所限制。相反，提供这些实施方式是为了能够更透彻地理解本公开，并且能够将本公开的范围完整的传达给本领域的技术人员。
本申请提出了一种应用于模型预测控制下的双有源桥直流-直流变换器的高频变压器漏感参数估计方法。通过对系统输出进行采样在微控制器中用梯度下降的方法计算得到变压器漏感，从而应用于系统输出的计算，在此基础上根据使代价函数最小的原则选择合适的移相角进行控制。
A．系统模型的搭建
如图1所示，本申请采用的是两电平双有源桥拓扑结构。其中， V LV 为低压侧电压， V HV 为高压侧电压，变压器的匝数比为 1:n，输出滤波电容的容值为 C o ，高频变压器的漏感为 L，流过变压器原边的电流为 i L 负载电流为 I L ，电容之前的电流为 I。
双有源桥直流-直流变换器的输出功率及输出电流可由变压器的电流波形计算得到。DAB变换器的典型驱动及电感电流波形如图2所示，其中 V ab 和 V cd 为变压器原边及变压器副边的输出电压。
根据图2中的变压器原边电流波形可以得到其表达式
流经变压器原边的电流为纯交流量，因此在高频变压器没有直流偏置的情况下，变压器电流应关于0对称，即
且有
由(2)(3)(4)可得
得到变压器原边的电流表达式后，即可计算变换器的输出功率及电流，f为电流变化率
B．构建预测模型
为了对双有源桥直流-直流变换器进行模型预测控制，首先要对其输出电压进行预测。DAB在连续时间内的电压输出可以由输出电容的动态特性方程得到
对公式(8)进行离散化可以得到
其中 AVG(x) Ts 表示变量 x在一个控制周期内的平均值。由(10)可以得到
在相邻的两个控制周期内，负载的电流可以被视作不变，即有
由以上分析可以得到预测输出电压的表达式
为了在数字控制器中应用模型预测控制的方法，对移相角进行离散化，即有
在本实施例中，最小分辨率ΔD被设置为了0.01。在初始时刻，移相角D为0。
模型预测控制的控制目标是控制输出电压，提升其动态性能，因此必须建立电压至移相角的传递函数，其表达式如下
其中k为当前的控制周期，μ，β为增益系数， V Ref 为输出电压的参考值。 V m 为在一个周期内电压增益的饱和值，其设置目的是限制在一个控制周期内的电压增益，防止过大的增益导致变压器直流偏置。 V com 是为了补偿 V Ref 和采样值 V HV 的差值，其能有效提升DAB变换器在启动阶段的响应速度。
为了选取控制集中的最优移相角，模型预测控制通过定义代价函数来评价控制集中的移相角，能使代价函数最小化的移相角即为最优移相角。本实施例中定义的代价函数如下
在该表达式中， G 1 部分的目的是使输出电压收敛到 V Ref ， G 2 部分的目的是提升稳态时DAB变换器的性能。α 1，α 2为这两个部分的权重系数。
在本实施例中，控制集内包含三个待评估对象，即为{D-ΔD，D，D+ΔD}。在每个控制周期内，ΔD由公式(15)计算得到，然后该三个评估对象被代入公式(17)(18)所描述的代价函数中，使该代价函数最小的待评估对象将会在下一个控制周期内被应用于DAB变换器中。
图4展示了在四个周期内处理器对移相角的选择。在每个控制周期内，控制器首先根据公式(13)计算DAB变换器的输出电压，在此基础上根据公式(15)计算得到待评价移相角，依据使得代价函数最小化的原则选择最合适的移相角，最终应用于DAB变换器中。
C．构建参数辨识方法
模型预测控制下DAB变换器的动态响应速度极大依赖于输出电流平均值的计算，而输出电流平均值的计算则依赖于高频变压器的漏感。在实际工程应用中，其极易受到外接温度的影响。因此，在微处理器中引入高频变压器漏感的估计方法是十分有必要的。
高频变压器的漏感主要包含两个部分，其一是变压器原边的漏感，其二是变压器副边的漏感通过变压器的匝数比折算到变压器原边的漏感，其可表示为
n 1 是高频变压器原边的匝数， n 2 是高频变压器副边的匝数。本申请所提出的变压器漏感估计策略独立于控制环。首先计算负载电流
同时定义估计误差 E(k)
高频变压器的漏感估计值记为 。在每个控制周期内， 依据公式(22)进行优化
由公式(21)可以进一步计算公式(22)
为了减轻数字控制器的计算负担，可以在实施过程中对公式(23)进行简化，从而可以得到公式(24)的变压器漏感值更新原则
η为梯度因子。在实际工程应用中可以通过调节η来控制估计值的收敛速度，从而满足不同的工况。
图4展示了本申请所提出的应用于模型预测控制下的双有源桥直流-直流变换器的高频变压器漏感参数估计方法的控制流程图。包括：对DAB变换器的输出进行采样，得到当前电流值和当前电压值；
根据所述当前电流值计算得到平均电流值；
根据所述平均电流值、当前电流值和当前电压值，使用梯度下降的方法计算得到变压器漏感；
根据所述变压器漏感和平均电流值预测输出电压；
计算待评估移相角，并根据使代价函数最小的原则选择移相角，控制所述DAB变换器。
在每个控制周期内，首先根据公式(24)对模型中的参数进行校正，然后根据公式(14)预测输出电压，根据公式(16)和公式(17)计算待评估移相角，并依据最小化代价函数的原则选择最合适的移相角。在线参数估计与控制环独立能保证参数估计不会对控制环造成影响。
实施例：
下面进行具体的实验验证，搭建了基于数字信号处理器(DSP)TMS320F28374S的实验平台。实验平台具体参数如表2所示。
本申请首先通过实验对比分析了在系统参数不正确及正确的情况下双有源桥的输出情况，接着进行了稳态性能分析实验，测试了在本申请提出的控制策略下DAB变换器在恒定负载情况下的输出及变压器漏感参数辨识效果，最后进行动态性能分析实验，观察输出参考电压突变的情况下DAB变换器的动态响应能力及变压器漏感参数辨识效果。
A．系统输出对系统参数敏感性实验
本申请进行了系统输出对系统参数的敏感性的实验。图5(a)(b)(c)依次展示了在600W负载下模型参数在不同数量级误差时的输出结果。其中，(a)600W输出， =2 (b) 600W 输出， =10 (c) 600W 输出， =1 (d) 1200W 输出， =2 (e) 1200W 输出， =10 (f) 1200W 输出， =1。当模型预测控制中变压器漏感为实际变压器漏感的50%时，DAB变换器的输出出现2V左右的静差；当模型预测控制中变压器漏感为实际变压器漏感的10%时，DAB变换器的输出出现低频振荡及较大的静差(22V)。(d)(e)(f)依次展示了在1200W负载下模型参数在不同数量级误差时的输出结果。当模型预测控制中变压器漏感为实际变压器漏感的50%时，DAB变换器的输出出现稳态误差；当模型预测控制中变压器漏感为实际变压器漏感的10%时，DAB变换器的输出出现较大幅度的低频振荡及稳态误差。这表明在微处理器中在线调校DAB变换器模型预测控制参数在实际工程中是十分有必要的。
B．稳态性能分析实验
图6给出了在多种负载下DAB变换器的输出结果及变压器漏感参数辨识效果。其中，(a,b) 600W负载 (c,d) 800W负载 (e,f)1000W 负载 (g,h) 1200W。负载实验结果表明本申请提出的方法在不同功率等级下均可以有效辨识变压器漏感，变压器漏感的辨识值在7s后达到稳定。
C．动态性能分析实验
图7和图8给出了当负载突变时DAB变换器的输出结果及变压器漏感参数辨识效果。图7中，(a)负载在600W与1000W之间切换 (b)负载从600W切换至1000W (c)负载从1000W切换至600W。实验结果表明本申请提出的方法也适用于负载突变的场合，其能在负载在600W和1000W间切换时在400ms内重新计算得到准确的高频变压器漏感的值。
以上所述，仅为本发明较佳的具体实施方式，但本发明的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本发明揭露的技术范围内，可轻易想到的变化或替换，都应涵盖在本发明的保护范围之内。因此，本发明的保护范围应以所述权利要求的保护范围为准。

Claims (9)

  1. 一种DAB变换器的高频变压器漏感参数估计方法，其特征在于，包括：
    对所述DAB变换器的输出进行采样，得到当前电流值和当前电压值；
    根据所述当前电流值计算得到平均电流值；
    根据所述平均电流值、当前电流值和当前电压值，使用梯度下降的方法计算得到变压器漏感；
    根据所述变压器漏感和平均电流值预测输出电压；
    计算待评估移相角，并根据使代价函数最小的原则选择移相角，控制所述DAB变换器。
  2. 根据权利要求1所述的一种DAB变换器的高频变压器漏感参数估计方法，其特征在于：
    所述平均电流值由所述DAB变换器的变压器的电流波形计算得到。
  3. 根据权利要求1或2所述的一种DAB变换器的高频变压器漏感参数估计方法，其特征在于：
    所述平均电流值的计算公式如下：
    ，
    其中 V LV 为低压侧电压， V HV 为高压侧电压，变压器的匝数比为 1:n，变压器的漏感为 L，D为移相角，f为电流变化率。
  4. 根据权利要求1所述的一种DAB变换器的高频变压器漏感参数估计方法，其特征在于：
    所述DAB在连续时间内的电压输出由输出电容的动态特性方程计算得到。
  5. 根据权利要求3所述的一种DAB变换器的高频变压器漏感参数估计方法，其特征在于：
    所述预测输出电压的表达式如下：
    ,
    k为当前的控制周期， C o 为输出滤波电容的容值， I L 为负载电流， I为变压器副边的电容之前的电流。
  6. 根据权利要求1所述的一种DAB变换器的高频变压器漏感参数估计方法，其特征在于：
    所述代价函数CF如下
    ,
    其中， V Ref 为输出电压的参考值， V com 补偿电压， V HV 为高压侧电压， G 1 部分的目的是使输出电压收敛到 V Ref ，k为当前的控制周期， G 2 部分的目的是提升稳态时DAB变换器的性能；α 1，α 2为 G 1 G 2 两个部分的权重系数。
  7. 根据权利要求6所述的一种DAB变换器的高频变压器漏感参数估计方法，其特征在于：
    所述变压器漏感包含两个部分，其一是变压器原边的漏感，其二是变压器副边的漏感通过变压器的匝数比折算到变压器原边的漏感。
  8. 根据权利要求7所述的一种DAB变换器的高频变压器漏感参数估计方法，其特征在于：
    所述梯度下降的公式为 ,
    为变压器的漏感估计值， E(k)为估计误差，η为梯度因子。
  9. 根据权利要求1所述的一种DAB变换器的高频变压器漏感参数估计方法，其特征在于：
    所述计算待评估移相角，并根据使代价函数最小的原则选择移相角，控制所述DAB变换器，包括：
    在每个控制周期内，将控制集内的三个待评估对象，被代入所述代价函数中，将该代价函数最小的待评估对象在下一个控制周期内用于DAB变换器中。
PCT/CN2023/082638 2023-02-01 2023-03-21 一种dab变换器的高频变压器漏感参数估计方法 Ceased WO2024159591A1 (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202310048939.4 2023-02-01
CN202310048939.4A CN116087595A (zh) 2023-02-01 2023-02-01 一种dab变换器的高频变压器漏感参数估计方法

Publications (1)

Publication Number Publication Date
WO2024159591A1 true WO2024159591A1 (zh) 2024-08-08

Family

ID=86205929

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2023/082638 Ceased WO2024159591A1 (zh) 2023-02-01 2023-03-21 一种dab变换器的高频变压器漏感参数估计方法

Country Status (2)

Country Link
CN (1) CN116087595A (zh)
WO (1) WO2024159591A1 (zh)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN119254027A (zh) * 2024-12-04 2025-01-03 浙江大学 基于人工智能和电路暂态行为的dab优化调制方法
CN119765888A (zh) * 2024-12-26 2025-04-04 山东大学 混合型isop直流变压器软启动限流控制方法及系统

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102023123180A1 (de) * 2023-08-29 2025-03-06 HELLA GmbH & Co. KGaA Gleichspannungswandler
FI20236449A1 (en) * 2023-12-29 2025-06-30 Vensum Power Oy Method and system for calibrating power converters

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170370977A1 (en) * 2016-06-24 2017-12-28 General Electric Company System and method for determining inductance in a power converter
CN113206499A (zh) * 2021-04-23 2021-08-03 西安理工大学 一种双闭环模型预测与pi复合控制的dab变换器控制方法
CN113394982A (zh) * 2021-06-23 2021-09-14 湘潭大学 一种级联h桥型电力电子变压器暂态电压与功率均衡方法
CN113765393A (zh) * 2021-08-06 2021-12-07 南京理工大学 一种dab变换器电流模式调制方法
CN114221553A (zh) * 2021-12-13 2022-03-22 山东大学 一种基于模型预测控制的dab变换器控制方法及系统
CN115473440A (zh) * 2022-08-29 2022-12-13 江苏科曜能源科技有限公司 面向双有源桥dcdc变换器的模型预测控制方法及系统
WO2022262066A1 (zh) * 2021-06-18 2022-12-22 苏州大学 T型三电平电压型的逆变器的输出控制方法及相关设备

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115276419B (zh) * 2022-08-22 2023-05-16 山东大学 耐采样噪声的双有源桥变换器模型预测控制方法及系统

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170370977A1 (en) * 2016-06-24 2017-12-28 General Electric Company System and method for determining inductance in a power converter
CN113206499A (zh) * 2021-04-23 2021-08-03 西安理工大学 一种双闭环模型预测与pi复合控制的dab变换器控制方法
WO2022262066A1 (zh) * 2021-06-18 2022-12-22 苏州大学 T型三电平电压型的逆变器的输出控制方法及相关设备
CN113394982A (zh) * 2021-06-23 2021-09-14 湘潭大学 一种级联h桥型电力电子变压器暂态电压与功率均衡方法
CN113765393A (zh) * 2021-08-06 2021-12-07 南京理工大学 一种dab变换器电流模式调制方法
CN114221553A (zh) * 2021-12-13 2022-03-22 山东大学 一种基于模型预测控制的dab变换器控制方法及系统
CN115473440A (zh) * 2022-08-29 2022-12-13 江苏科曜能源科技有限公司 面向双有源桥dcdc变换器的模型预测控制方法及系统

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN119254027A (zh) * 2024-12-04 2025-01-03 浙江大学 基于人工智能和电路暂态行为的dab优化调制方法
CN119765888A (zh) * 2024-12-26 2025-04-04 山东大学 混合型isop直流变压器软启动限流控制方法及系统

Also Published As

Publication number Publication date
CN116087595A (zh) 2023-05-09

Similar Documents

Publication Publication Date Title
WO2024159591A1 (zh) 一种dab变换器的高频变压器漏感参数估计方法
CN104734532B (zh) 一种Buck‑Boost矩阵变换器的复合控制方法及其装置
CN105552959B (zh) 基于扩张状态观测器的三相并网整流器预测直接功率控制方法
CN112953236B (zh) 一种双向dc/dc变换器的移相控制方法及系统
CN113765393B (zh) 一种dab变换器电流模式调制方法
CN117277758A (zh) 一种结合回流功率优化的超螺旋滑模控制方法
CN110118945A (zh) 一种能馈型直流电子负载系统及其自组织模糊控制方法
CN118611438B (zh) 一种双有源桥型变换器拓扑簇的控制方法、装置和设备
CN114744886A (zh) 一种双有源全桥变换器的智能pi控制方法
CN114844042A (zh) 一种微网系统电感设计方法及系统
Yang et al. Neural network control based on nonlinear autoregressive exogenous model and adaptive dynamic programming algorithm for high-gain DC-DC converter
Wang et al. Optimization of reflux power in triple active bridge with online particle swarm optimization and exponential weighted moving average algorithm
CN115149819A (zh) 基于分数阶pid控制的移相全桥变换器
CN119335851B (zh) 储能变流器控制器优化方法、设备、存储介质及产品
CN120498224A (zh) 一种四端口磁网络电能路由器的自抗扰控制方法、系统
CN110518625A (zh) 一种可变学习率bp-pid控制的并网逆变器直流分量抑制方法
Huang et al. Tps-mpc method with backflow power optimization for series resonant dab converter
CN117081030A (zh) 一种多母线直流微电网分布式预设时间优化与控制方法
CN113659624A (zh) 一种并网变流器无偏差预测控制方法及系统
Hatatah et al. Power and voltage regulation of a solid-state transformer based quad-active bridge dc–dc converter using adaptive linear quadratic regulator and nonlinear model predictive control
CN204597785U (zh) 一种Buck-Boost矩阵变换器的复合控制装置
CN116865586B (zh) 一种变流器参数自学习控制方法
CN118801722B (zh) 大功率牵引供电双向变流器控制方法及装置、系统及介质
Jin et al. Optimal Control of Efficiency and Dynamic Performance for DAB Converter in Electric Vehicle
CN116865532B (zh) 一种采用模型预测控制的交直变换器的控制方法

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 23919165

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 23919165

Country of ref document: EP

Kind code of ref document: A1