Incremental double-variable fuzzy control method for DC / DC converter of electric vehicle

Abstract

The invention discloses an incremental double-variable fuzzy control method for a DC / DC converter of an electric vehicle, and relates to the technical field of electric vehicles, and the method comprises the steps: obtaining an actual output voltage Vreal and an actual output current Ireal of the DC / DC converter; based on the actual output voltage Vreal and the target output voltage Vref, a voltage error is calculated; calculating a current error Ierror based on the actual output current Ireal and a target limiting current Iref; and on the basis of the voltage error Veror and the current error Ierror, an incremental fuzzy control strategy is adopted, and a PWM duty ratio adjustment amount delta D is calculated so as to adjust the output voltage and the output current of the DC / DC converter. The voltage and current overshoot and oscillation phenomena existing in a traditional mode can be effectively restrained, the control precision and the anti-interference performance are high, the response speed is high, and implementation is easy.

Description

Technical Field

[0001] The present application relates to the technical field of electric vehicles, and in particular to an incremental dual-variable fuzzy control method for a DC / DC converter of an electric vehicle. Background Art

[0002] As one of the main components of electric vehicle power control, the on-board power supply DC / DC converter is used to convert high-voltage power into 12 / 24V low voltage to power the auxiliary power supply of the on-board 12 / 24V system and electronic equipment. It has the advantages of small size, high power density, and high efficiency. As a stable power source, the output accuracy, efficiency, and dynamic response of the DC / DC converter are particularly important. If the output voltage and current accuracy are insufficient, the efficiency is low, and there are frequent overcurrent, overvoltage, and oscillation, it will affect the stability of the vehicle's operation, resulting in a decrease in the vehicle's power performance and mileage. Figure 1 As shown in the figure, the topology of the currently commonly used phase-shifted full-bridge DC / DC converter is combined with the technical advantages of phase-shifted PWM pulse width control and resonant soft switching, which improves the conversion efficiency while reducing the weight and volume of the DC / DC converter.

[0003] With the increasing diversification of customer needs, the increasing number of new energy vehicle functions and the increasing number of electronic module devices, the power of DC / DC converters has also increased, and higher requirements have been placed on the operating stability of DC / DC converters. Among them, the main control factors affecting the operating stability of DC / DC converters are the input and output voltage changes and the input and output current fluctuations. In order to make the PWM duty cycle of the controller output track the target voltage while avoiding the output current from overshoot, oscillation, overcurrent and other uncontrolled phenomena, the industry often uses a dual closed-loop control mode that combines the voltage loop control mode with the current loop control mode. The control structure is as follows: Figure 2 shown.

[0004] The control process of the series dual PID control is as follows: the actual output voltage V is collected by ADC real and the actual output current I real , the target voltage V ref The actual output voltage V real The error V error , after voltage loop PID adjustment, the output current limit value I ref , and then I ref The actual output current I real After subtraction, the current error I is obtained error , the current error I errorThrough current loop PID adjustment, the output system approaches the PWM duty cycle required by the target. This method has high accuracy in current control, but it will produce nearly sinusoidal voltage and current ripples, and the response time is long and prone to overshoot.

[0005] Through further improvement, parallel dual PID control is now commonly used, and the voltage error V error and current error I error The voltage loop and current loop are PID regulated separately, and the smaller value of the PWM duty cycle of the two is selected as the duty cycle required by the system. This method outputs smaller voltage and current ripples, but when adjusting one of the voltage and current loops, the other loop is prone to integral saturation and cause the system to lose control.

[0006] Moreover, in the series dual PID control and parallel dual PID control, since the output current of the DC / DC converter passes through the inductor and filter capacitor, it belongs to a hysteresis system, and the PID control tracking AC target response speed is slow, and it is easy to produce jitter overshoot when the load current changes. In addition, the proportional, integral, and differential values ​​of the PID control are usually fixed values. For example, the converter with the same PID value can stably adjust the capacitor voltage differential value on a voltage platform of 400V to 27.5V. The output voltage and current fluctuations caused by the change make the system run normally; and in the voltage platform from 700V to 27.5V, the voltage differential value of the capacitor The changes are large, and the output voltage and current experience oscillation and overshoot. Therefore, the fixed PID control is not suitable for the hysteresis system with a wider voltage control range, and the control effect is not ideal. The existing series dual PID control and parallel dual PID control cannot meet the needs well.

[0007] Therefore, if the traditional digital dual PI / PID linear control strategy of combining the voltage loop with the current loop is used to control the output of the DC / DC converter, there will be problems such as severe output voltage and current overshoot and oscillation, slow response speed, and difficulty in adjusting parameters, which will make it difficult to meet the increasingly complex and diverse needs of new energy vehicles. Summary of the invention

[0008] In order to solve the problems of severe output voltage and current overshoot and oscillation, slow response speed and difficulty in adjusting parameters in the traditional digital dual PI / PID linear control strategy of combining voltage loop with current loop, the present application provides an incremental dual-variable fuzzy control method for an electric vehicle DC / DC converter.

[0009] In the first aspect, the present application provides an incremental dual-variable fuzzy control method for a DC / DC converter of an electric vehicle, which adopts the following technical solution: The control method includes: Get the actual output voltage V of the DC / DC converter realand the actual output current I real ; Based on the actual output voltage V real and the target output voltage V ref , calculate the voltage error V error ; Based on the actual output current I real and the target limiting current I ref , calculate the current error I error ; Based on the voltage error V error and the current error I error , an incremental fuzzy control strategy is adopted to obtain a PWM duty cycle adjustment amount ΔD to adjust the output voltage and output current of the DC / DC converter.

[0010] By adopting the above technical scheme and using the incremental fuzzy control strategy, the error between the target voltage and the actual output voltage, and the error between the target limiting current and the actual output current are used as two inputs, and the PWM duty cycle adjustment amount is used as the output. By regulating the increment of the control object instead of directly controlling the absolute amount of the object, it has the advantage of eliminating steady-state errors. Compared with the traditional digital dual PI / PID linear control strategy of combining the voltage loop with the current loop, this incremental dual-variable fuzzy control method can effectively suppress the overshoot and oscillation of voltage and current in the traditional control method, and has high control accuracy and anti-interference, fast response speed, and is easy to implement.

[0011] In a specific implementation scheme, based on the voltage error V error and the current error I error , using the incremental fuzzy control strategy, the PWM duty cycle adjustment ΔD is obtained, which specifically includes: Based on the preset domain range, the voltage error V error and the current error I error After normalization, the voltage error e is obtained. V and current error e I ; Based on the pre-constructed fuzzy subsets and membership functions, the voltage error e V and the current error e I Through fuzzy processing, the voltage error e is obtained respectively. V The corresponding fuzzy subsets and membership degrees, the current error e I Corresponding fuzzy subsets and membership degrees; Based on the voltage error e V The corresponding fuzzy subsets and membership degrees, the current error e IThe corresponding fuzzy subsets and membership degrees, as well as the pre-built fuzzy rule base, are used to obtain the fuzzy subsets and membership degrees corresponding to the PWM duty cycle adjustment amount ΔD through fuzzy reasoning; Based on the fuzzy subset and the degree of membership corresponding to the PWM duty cycle adjustment amount ΔD, the PWM duty cycle adjustment amount ΔD is obtained through defuzzification processing.

[0012] In a specific implementation scheme, the pre-constructed fuzzy subsets include: {NL, NM, NS, ZO, PS, PM, PL}; The membership function includes: In a specific implementation scheme, in the fuzzy subset, NL is negative and large, NM is negative and medium, NM is negative and small, ZO is zero, PS is positive and small, PM is positive and medium, and PL is positive and large.

[0013] In a specific implementation scheme, the domain range is [-1, 1].

[0014] In a specific implementation scheme, the defuzzification process includes one or more of a maximum membership method, an area center method, a height method, and a maximum area method.

[0015] In a specific implementation scheme, the fuzzy rule base includes: If the first input variable is NL, NM or NS, and the second input variable is NL or NM, then the output variable is NL, and the membership of the output variable is the larger value of the membership of the first input variable and the membership of the second input variable; wherein the first input variable is the voltage error e V The corresponding fuzzy subset, the second input variable is the current error e I The corresponding fuzzy subset, the output variable is the fuzzy subset corresponding to the PWM duty cycle adjustment amount ΔD; If the first input variable is NL, NM or NS, and the second input variable is NS, then the output variable is NM, and the membership of the output variable is the larger value of the membership of the first input variable and the membership of the second input variable; If the first input variable is NL and the second input variable is ZO, the output variable is NL, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable; If the first input variable is NM and the second input variable is ZO, then the output variable is NM, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable; If the first input variable is NS and the second input variable is ZO, the output variable is NS, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable; If the first input variable is NL and the second input variable is PS, PM or PL, then the output variable is NL, and the membership of the output variable is the membership of the first input variable; If the first input variable is NM and the second input variable is PS, then the output variable is NM, and the membership of the output variable is the membership of the first input variable; If the first input variable is NM and the second input variable is PM or PL, the output variable is NL, and the membership of the output variable is the membership of the first input variable; If the first input variable is NS and the second input variable is PS, PM or PL, then the output variable is NM, and the membership of the output variable is the membership of the first input variable; If the first input variable is ZO and the second input variable is NL, then the output variable is NL, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable; If the first input variable is ZO and the second input variable is NM, then the output variable is NM, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable; If the first input variable is ZO and the second input variable is NS, then the output variable is NS, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable; If the first input variable is ZO and the second input variable is ZO, PS, PM or PL, then the output variable is ZO, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable; If the first input variable is PS, PM or PL, and the second input variable is NL or NM, then the output variable is NL, and the membership of the output variable is the membership of the second input variable; If the first input variable is PS, PM or PL, and the second input variable is NS, then the output variable is NM, and the membership of the output variable is the membership of the second input variable; If the first input variable is PS and the second input variable is ZO, PS or PM, then the output variable is PS, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable; If the first input variable is PS and the second input variable is PL, the output variable is PM, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable; If the first input variable is PM and the second input variable is ZO, then the output variable is PM, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable; If the first input variable is PM and the second input variable is PS, then the output variable is PS, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable; If the first input variable is PM and the second input variable is PM or PL, then the output variable is PM, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable; If the first input variable is PL and the second input variable is ZO, then the output variable is PL, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable; If the first input variable is PL and the second input variable is PS or PM, the output variable is PM, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable; If the first input variable is PL and the second input variable is PL, the output variable is PL, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable.

[0016] In a second aspect, the present application provides an incremental dual-variable fuzzy control system for an electric vehicle DC / DC converter, applying the incremental dual-variable fuzzy control method for an electric vehicle DC / DC converter in the above-mentioned first aspect or any one of the implementation schemes of the first aspect.

[0017] In a third aspect, the present application provides an electric vehicle, comprising the electric vehicle DC / DC converter incremental dual-variable fuzzy control system described in the second aspect above.

[0018] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. The incremental dual-variable fuzzy control method of the present application uses an incremental fuzzy control strategy, takes the error between the target voltage and the actual output voltage, and the error between the target limit current and the actual output current as two inputs, and takes the PWM duty cycle adjustment amount as the output. By regulating the increment of the control object instead of directly controlling the absolute amount of the control object, it has the advantage of eliminating steady-state errors; and compared with the traditional digital dual PI / PID linear control strategy of combining the voltage loop with the current loop, the incremental dual-variable fuzzy control method can effectively suppress the overshoot and oscillation of voltage and current in the traditional control method, and has high control accuracy and anti-interference, fast response speed, and is easy to implement; 2. A single fuzzy logic rule base for voltage and current dual variables is designed, and the strategy for determining the voltage and current control priority is based on the DC / DC converter regulation experience when the voltage is over-voltage or the current is over-current, thereby effectively reducing over-voltage and over-current phenomena. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a topology structure of a common phase-shifted full-bridge DC / DC converter in the prior art; Figure 2 It is a flow chart of the series dual PID control and parallel dual PID control in the prior art; Figure 3 This is a schematic diagram of the process corresponding to steps S1-S3 in the embodiment of the present application; Figure 4 It is a simplified flowchart corresponding to steps S1-S3 in the embodiment of the present application; Figure 5 This is a schematic diagram of the process corresponding to steps A1-A4 in the embodiment of the present application; Figure 6 Schematic diagram of each membership function in the embodiment of the present application; Figure 7 This is a schematic diagram of a fuzzy rule base in an embodiment of the present application; Figure 8 It is a schematic diagram of anti-blurring using the area center method in an embodiment of the present application; Fig. 9 It is an overall flow chart of the incremental dual-variable fuzzy control method of the DC / DC converter of the electric vehicle in the embodiment of the present application; Fig.10 Schematic diagram of voltage, current and duty cycle waveforms obtained by using simulation software in the embodiment of the present application; Fig.11 The voltage and current waveforms are obtained by using an oscilloscope in CC mode in the embodiment of the present application; Fig.12 These are the voltage and current waveforms obtained using an oscilloscope in CV mode in the embodiments of the present application. DETAILED DESCRIPTION

[0020] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0021] With the development of integrated chip technology, digital control technology has the advantages of more flexible configuration, stronger anti-interference ability and low cost. The switching converter solution using digital control technology has gradually replaced the traditional analog control technology. In the control of DC / DC converters, the traditional digital dual PI / PID linear control strategy of combining voltage loop with current loop has the problems of severe overshoot and oscillation of output voltage and current, slow response speed and difficulty in adjusting parameters, which makes it difficult to meet the increasingly complex and diverse needs of new energy vehicles.

[0022] If adaptive control is used, it is necessary to derive a specific mathematical model of the complex system, which lacks practicality. Fuzzy control (FLC), as a nonlinear model-free intelligent control technology, uses an artificial intelligence algorithm based on multi-valued fuzzy logic reasoning to control the system with human-like thinking and problem-solving methods, and can be applied to the output control of DC / DC converters.

[0023] However, if fuzzy control is used directly, there will be a certain steady-state error, resulting in low control accuracy. Therefore, fuzzy PID control strategy is also adopted at present, which combines fuzzy control and PID control, and uses fuzzy rules to adjust the proportional, integral and differential values ​​in PID online to improve the dynamic performance of DC / DC converter voltage loop control; however, this method uses two sets of control methods, which requires not only adjusting PID parameters but also building fuzzy logic rule bases for proportional, integral and differential parameters respectively. The system is highly complex and only considers voltage control, lacking effective monitoring of current mutations and overshoots. If fuzzy PID control of current is added, the required rule base and data parameters will increase greatly, which will affect the calculation efficiency and is not conducive to practical engineering applications.

[0024] Therefore, the embodiment of the present application provides an incremental dual-variable fuzzy control method for an electric vehicle DC / DC converter, and the control method includes steps S1-S3. Figure 3 , is a flow chart of steps S1-S3, refer to Figure 4 , which is a flowchart corresponding to steps S1-S3.

[0025] S1, obtain the actual output voltage V of the DC / DC converter real and the actual output current I real ; This step can be obtained through ADC sampling.

[0026] S2, based on the actual output voltage V real and the target output voltage V ref , calculate the voltage error V error ; Based on the actual output current I real and the target limiting current I ref , calculate the current error I error .

[0027] S3, based on the voltage error V error and the current error I error , using an incremental fuzzy control strategy to obtain a PWM duty cycle adjustment amount ΔD to adjust the output voltage and output current of the DC / DC converter; Specifically, according to the PWM duty cycle adjustment amount ΔD, the on-off time of the corresponding power switch device in the DC / DC converter is adjusted to adjust the output voltage and output current of the DC / DC converter. This step can use a / DC switch circuit to adjust the on-off time of the corresponding power switch device in the DC / DC converter.

[0028] Those skilled in the art can understand that, by adding the PWM duty cycle adjustment amount ΔD to the duty cycle at the current moment, the duty cycle at the next moment can be obtained, thereby achieving control of the duty cycle at the next moment.

[0029] That is: D(t)=D(t-1)+ΔD(t), D∈[0,1](1) In the formula, D(t-1) represents the duty cycle at the current moment, ΔD(t) represents the PWM duty cycle adjustment amount obtained by the incremental fuzzy control strategy, and D(t) represents the duty cycle at the next moment. The range of the duty cycle is [0,1].

[0030] In one possible implementation, refer to Figure 5 Step S3 is based on the voltage error V error and the current error I error , using an incremental fuzzy control strategy to obtain a PWM duty cycle adjustment amount ΔD, specifically including steps A1-A4.

[0031] A1, based on the preset domain range, converts the voltage error V error and the current error I error After normalization, the voltage error e is obtained. V and current error e I ; Specifically, the domain range can be [-1,1], and the voltage error V error and current error I error To standardize the voltage error V error and current error I error They are converted into a dimensionless value in the range of [-1,1] as the input variable of the subsequent fuzzy controller, which facilitates the unification of the controller interface and parameters.

[0032] A2, based on the pre-constructed fuzzy subsets and membership functions, the voltage error e V and the current error e I Through fuzzy processing, the voltage error e is obtained respectively. V The corresponding fuzzy subsets and membership degrees, the current error e I Corresponding fuzzy subsets and membership degrees.

[0033] Specifically, in this step, the pre-constructed fuzzy subsets include {NL, NM, NS, ZO, PS, PM, PL}, where NL represents large negative, NM represents medium negative, NS represents small negative, ZO represents zero, PS represents small positive, PM represents medium positive, and PL represents large positive; different fuzzy subsets represent different degrees of error. For example, NL indicates that the actual value is much higher than the target value; NM indicates that the actual value is higher than the target value, but the gap is not very large; NS indicates that the actual value is slightly higher than the target value, and there is a small gap between the two; ZO indicates that the error between the actual value and the target value is zero or close to zero; PS indicates that the actual value is slightly lower than the target value; PM indicates that the actual value is lower than the target value; PL indicates that the actual value is much lower than the target value.

[0034] Specifically, combined Figure 6 , each fuzzy subset and its membership function are expressed in coordinate form, and the membership function corresponding to each fuzzy subset includes: It can be obtained that the value range corresponding to negative large NL is: [-1,-0.5]; The value range corresponding to negative medium NM is: [-0.8,-0.2]; The value range corresponding to negative small NS is: [-0.5, 0]; The value range corresponding to zero ZO is: [-0.2, 0.2]; The value range corresponding to positive small PS is: [0,0.5]; The value range corresponding to the middle PM is: [0.2, 0.8]; The numerical range corresponding to the positive PL is: [0.5,1].

[0035] Those skilled in the art will appreciate that the membership function NL(x) corresponds to the fuzzy subset NL, the membership function NM(x) corresponds to the fuzzy subset NM, the membership function NS(x) corresponds to the fuzzy subset NS, the membership function ZO(x) corresponds to the fuzzy subset ZO, the membership function PS(x) corresponds to the fuzzy subset PS, the membership function PM(x) corresponds to the fuzzy subset PM, and the membership function PL(x) corresponds to the fuzzy subset PL.

[0036] Therefore, the voltage error e V and the current error e I Through fuzzy processing, the voltage error e is obtained respectively. V The corresponding fuzzy subsets and membership degrees, the current error e I The corresponding fuzzy subsets and membership degrees include: In determining the voltage error e V When the corresponding fuzzy subsets and membership degrees are calculated, the voltage error e VAs the independent variable x, determine the voltage error e V The corresponding fuzzy subset; let x = e V , substituting into the corresponding membership function, the voltage error e can be calculated. V The degree of membership under the corresponding fuzzy subset; Similarly, in determining the current error e I When the corresponding fuzzy subsets and membership degrees are obtained, the current error e I As the independent variable x, determine the current error e I The corresponding fuzzy subset; let x = e I , substituting into the corresponding membership function, the current error e can be calculated I The degree of membership under the corresponding fuzzy subset.

[0037] For example, if the voltage error e V = -0.4, then let x = -0.4, and we can get the voltage error e V The fuzzy subsets are NM and NS, and then x = -0.4 is substituted into the above membership functions NM (x) and NS (x), and the corresponding memberships can be calculated to be 0.6672 and 0.4 respectively. I = -0.3, then let x = -0.3, and we can get the current error e I The fuzzy subsets are NM and NS, and then x=-0.3 is substituted into the above membership functions NM(x) and NS(x), and the corresponding memberships can be calculated to be 0.3339 and 0.8 respectively.

[0038] Therefore, the present application adopts a triangular membership function, and each fuzzy subset corresponds to a different data range. Moreover, in order to improve the accuracy of the output voltage and current of the DC / DC converter and speed up the response speed, a symmetrical design with a narrow center and a wide outside is adopted for the membership functions of the input variables and the output variables, thereby reducing the width of the fuzzy subset with a control quantity of zero ZO and expanding the width of the fuzzy subset with a control quantity of positive large PL and negative large NL.

[0039] A3, based on the voltage error e V The corresponding fuzzy subsets and membership degrees, the current error e I The corresponding fuzzy subsets and membership degrees, as well as the pre-built fuzzy rule base, are used to obtain the fuzzy subsets and membership degrees corresponding to the PWM duty cycle adjustment amount ΔD through fuzzy reasoning.

[0040] In a possible implementation, the fuzzy rule base is as follows: Figure 7 As shown below, combined Figure 7 The fuzzy rule base is described in detail: If the first input variable is NL, NM or NS, and the second input variable is NL or NM, then the output variable is NL, and the membership of the output variable is the larger value of the membership of the first input variable and the membership of the second input variable; wherein the first input variable is e V The corresponding fuzzy subset, the second input variable is e I The corresponding fuzzy subset, the output variable is the fuzzy subset corresponding to the PWM duty cycle adjustment amount ΔD; If the first input variable is NL, NM or NS, and the second input variable is NS, then the output variable is NM, and the membership of the output variable is the larger value of the membership of the first input variable and the membership of the second input variable; If the first input variable is NL and the second input variable is ZO, the output variable is NL, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable; If the first input variable is NM and the second input variable is ZO, then the output variable is NM, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable; If the first input variable is NS and the second input variable is ZO, the output variable is NS, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable; If the first input variable is NL and the second input variable is PS, PM or PL, then the output variable is NL, and the membership of the output variable is the membership of the first input variable; If the first input variable is NM and the second input variable is PS, then the output variable is NM, and the membership of the output variable is the membership of the first input variable; If the first input variable is NM and the second input variable is PM or PL, the output variable is NL, and the membership of the output variable is the membership of the first input variable; If the first input variable is NS and the second input variable is PS, PM or PL, then the output variable is NM, and the membership of the output variable is the membership of the first input variable; If the first input variable is ZO and the second input variable is NL, then the output variable is NL, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable; If the first input variable is ZO and the second input variable is NM, then the output variable is NM, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable; If the first input variable is ZO and the second input variable is NS, then the output variable is NS, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable; If the first input variable is ZO and the second input variable is ZO, PS, PM or PL, then the output variable is ZO, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable; If the first input variable is PS, PM or PL, and the second input variable is NL or NM, then the output variable is NL, and the membership of the output variable is the membership of the second input variable; If the first input variable is PS, PM or PL, and the second input variable is NS, then the output variable is NM, and the membership of the output variable is the membership of the second input variable; If the first input variable is PS and the second input variable is ZO, PS or PM, then the output variable is PS, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable; If the first input variable is PS and the second input variable is PL, the output variable is PM, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable; If the first input variable is PM and the second input variable is ZO, then the output variable is PM, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable; If the first input variable is PM and the second input variable is PS, then the output variable is PS, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable; If the first input variable is PM and the second input variable is PM or PL, then the output variable is PM, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable; If the first input variable is PL and the second input variable is ZO, then the output variable is PL, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable; If the first input variable is PL and the second input variable is PS or PM, the output variable is PM, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable; If the first input variable is PL and the second input variable is PL, the output variable is PL, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable.

[0041] For ease of understanding, the following example is used to illustrate the above situation. V The corresponding fuzzy subsets are NM and NS, and their membership degrees are 0.6672 and 0.4 respectively, that is, the first input variable includes two cases, NM and NS; the current error e IThe corresponding fuzzy subsets are NM and NS, and the membership degrees are 0.3339 and 0.8 respectively, that is, the second input variable includes both NM and NS. Figure 7 , forming a 2*2 matrix, there are four cases: If the first input variable is NM and the second input variable is NM, then the output variable is NL, and the membership of the output variable is the larger value of the membership of the first input variable and the membership of the second input variable, that is, 0.6672; If the first input variable is NM and the second input variable is NS, then the output variable is NM, and the membership of the output variable is the larger value of the membership of the first input variable and the membership of the second input variable, that is, 0.8; If the first input variable is NS and the second input variable is NM, the output variable is NL, and the membership of the output variable is the larger value of the membership of the first input variable and the membership of the second input variable, that is, 0.4; If the first input variable is NS and the second input variable is NS, then the output variable is NM, and the membership of the output variable takes the larger value of the membership of the first input variable and the membership of the second input variable, that is, 0.8.

[0042] Furthermore, when the output variable includes multiple identical fuzzy subsets, the value with the largest membership among the multiple identical fuzzy subsets is taken as the membership of the output variable under the fuzzy subset. That is, combined with the above, since the output variable includes two NLs, the maximum value of the membership corresponding to the two NLs is taken, that is, 0.6672 is taken as the membership when the output variable is NL; similarly, if the output variable also includes two NMs, the maximum value of the membership corresponding to the two NMs is taken, that is, 0.8 is taken as the membership when the output variable is NM.

[0043] At this point, we can get that the fuzzy subsets corresponding to the PWM duty cycle adjustment amount are NL and NM, and their membership degrees are 0.6672 and 0.8 respectively.

[0044] A4, obtaining the PWM duty cycle adjustment amount ΔD through defuzzification processing based on the fuzzy subset and the degree of membership corresponding to the PWM duty cycle adjustment amount ΔD.

[0045] Specifically, the defuzzification process may adopt commonly used defuzzification methods such as maximum membership method, area center method, height method or maximum area method.

[0046] For ease of understanding, after calculating that the fuzzy subsets corresponding to the PWM duty cycle adjustment amount are NL and NM, and the membership degrees are 0.6672 and 0.8 respectively, the following is combined with Figure 8 , using the area center method as an example to explain: Step 1: First, discretize the domain and divide the x-axis into hundreds of points. This is the process of discretizing the continuous domain for numerical calculation. Step 2: Calculate the sum of the memberships. For each point under the contour where NL(x) takes a value of 0.6672 and NM(x) takes a value of 0.8, calculate the corresponding membership value and add it up, that is, the value on the y-axis and add it up, so as to get the sum of the membership proportions. This step is actually preparing for the subsequent weighted average. Step 3: Use the area integration method to calculate the area sum. For each point under the contour where NL(x) takes a value of 0.6672 and NM(x) takes a value of 0.8, multiply its x-axis value by the corresponding y-axis value, and then add up the multiplied values. Step 4: Divide the accumulated value obtained in step 3 by the sum of the membership proportions obtained in step 2 to obtain the total membership, thereby obtaining the weighted average x value, that is, the precise value ΔD after defuzzification.

[0047] According to the formula, it is: N is the number of points, hundreds of points.

[0048] In a possible implementation, those skilled in the art can flexibly adjust the performance of the fuzzy controller according to specific application requirements and control objectives by combining the adjustable gain factors of voltage error fuzzification, current error fuzzification, and PWM duty cycle variation defuzzification; Specifically, the voltage error e V When performing fuzzy processing, the adjustable gain factor k of the voltage error fuzzification is combined eV , so that the voltage error e V With adjustable gain factor k eV After multiplication, the new voltage error e is obtained V , and then use the new voltage error e V Fuzzy processing is performed, that is, according to the new voltage error e V The value of determines the corresponding fuzzy subset and calculates the membership degree; Similarly, when the current error e I When performing fuzzy processing, the adjustable gain factor k of the current error fuzzification is combined eI , so that the current error e I With adjustable gain factor k eI After multiplication, the new current error e is obtained I , and then use the new current error e I Fuzzy processing is performed, that is, according to the new current error e I The value of determines the corresponding fuzzy subset and calculates the membership degree; Similarly, during the defuzzification process, after obtaining the PWM duty cycle adjustment value ΔD, the PWM duty cycle adjustment value ΔD is then compared with the adjustable gain factor k. ΔD Multiplying them together, a new PWM duty cycle adjustment amount ΔD is obtained, and the output voltage and output current of the DC / DC converter are adjusted with the new PWM duty cycle adjustment amount ΔD.

[0049] For further understanding, refer to Fig. 9 , showing the overall flow chart of the control method of the present application.

[0050] The following is an explanation of the construction process of the fuzzy rule base: The output of incremental fuzzy control is the duty cycle adjustment, which needs to be added to the historical accumulated duty cycle to form the duty cycle at the next moment. Its essence is equivalent to introducing an integral link to eliminate steady-state errors. In addition, the DC / DC converter should try to avoid overvoltage and overcurrent during operation. Based on these characteristics, a fuzzy rule base is designed to convert the voltage error e V and current error e I Considering the cases of positive, zero, and negative numbers, and combining the knowledge and experience of converter operation and regulation, the constructed reasoning mechanism should follow the following logic: 1) When the voltage error e V and current error e I When both are positive, it means that the output voltage and output current of the DC / DC converter are lower than the actual demand; then the PWM duty cycle needs to be increased, and the output PWM duty cycle adjustment ΔD should be a positive number, such as positive small PS, positive medium PM or positive large PL.

[0051] 2) When the voltage error e V is a positive number, the current error e I When it is a negative number, it means that the output voltage of the DC / DC converter is lower than the actual demand and the output current is higher than the actual demand; therefore, in order to avoid output current overcurrent, it is necessary to reduce the PWM duty cycle, and the output PWM duty cycle adjustment amount ΔD should be a negative number, such as negative large NL, negative medium NM or negative small NS.

[0052] 3) When the voltage error e V is a negative number, the current error e I When it is a positive number, it means that the output voltage of the DC / DC converter is higher than the actual demand and the output current is lower than the actual demand; therefore, in order to avoid output voltage overvoltage, the PWM duty cycle needs to be reduced, and the output PWM duty cycle adjustment amount ΔD should be a negative number, such as negative large NL, negative medium NM or negative small NS.

[0053] 4) When the voltage error e V and current error e IWhen both are negative, it means that the output voltage and output current of the DC / DC converter are higher than the actual demand, and there is output overvoltage and overcurrent. In this case, the PWM duty cycle needs to be reduced, and the output PWM duty cycle adjustment ΔD should be a negative number, such as negative large NL, negative medium NM or negative small NS.

[0054] 5) When the voltage error e V is a positive number, the current error e I When it is zero, it is determined that the electronic load at the back end of the DC / DC converter output is in constant current output mode, that is, CC (constant current) output mode. The output current of the DC / DC converter is equal to the actual demand (that is, equal to the load current), and the output voltage is lower than the actual demand. In this case, when the output PWM duty cycle increases, the output current of the DC / DC converter will remain unchanged, and the voltage will gradually rise to the actual demand. In this case, the PWM duty cycle needs to be increased, and the output PWM duty cycle adjustment amount ΔD should be a positive number, such as positive small PS, positive medium PM or positive large PL.

[0055] 6) When the voltage error e V is zero, the current error e I When it is a positive number, it is determined that the electronic load at the back end of the DC / DC converter output is in constant voltage output mode, that is, CV (constant voltage) output mode. Because when the DC / DC converter charges the load, the target output voltage of the DC / DC converter needs to be slightly higher than the load voltage. The output current of the DC / DC converter is lower than the actual demand. If the PWM duty cycle of the DC / DC converter output remains unchanged, the output current can gradually increase to the actual demand current. Therefore, the PWM duty cycle needs to be kept unchanged, and the output PWM duty cycle adjustment amount ΔD is zero ZO.

[0056] 7) When the voltage error e V is a negative number, the current error e I When it is zero, it means that the output voltage of the DC / DC converter is higher than the actual demand, then the PWM duty cycle needs to be reduced, and the output PWM duty cycle adjustment amount ΔD should be a negative number, such as negative large NL, negative medium NM or negative small NS.

[0057] 8) When the voltage error e V is zero, the current error e I When it is a negative number, it means that the output current of the DC / DC converter is higher than the actual demand, then the PWM duty cycle needs to be reduced, and the output PWM duty cycle adjustment amount ΔD should be a negative number, such as negative large NL, negative medium NM or negative small NS.

[0058] 9) When the voltage error e V and current error e IWhen both are zero, it means that the output voltage and output current of the DC / DC converter meet the actual demand. Then it is necessary to keep the PWM duty cycle unchanged and the output PWM duty cycle adjustment ΔD is zero ZO.

[0059] Referring to the above nine logics, a fuzzy rule base design containing seven fuzzy subsets is initially constructed, as shown in Table 1: Table 1 Preliminary construction of fuzzy rule base Usually, the mapping of the two input membership degrees to the same output membership degree of fuzzy control usually takes the input with the lower membership value among the two inputs and sets it as the output membership value of the corresponding fuzzy subset, that is, the Mandani method: C(x1,x2)=min [A(x1),B(x2)] (3) In the formula, x1 and x2 are the fuzzy subsets corresponding to inputs A and B respectively, A(x1) is the degree of membership of item A in the x1 subset, and C(x1,x2) is the subset of C output obtained by the fuzzy rule base for the x1 and x2 subsets of inputs A and B.

[0060] Therefore, the above method can be applied to this solution. Taking the fuzzy control of the DC / DC converter as an example, the above formula is equivalent to formula (4): ΔD(x1,x2)=min [e V (x1),e I (x2)] (4) Where ΔD(x1,x2) represents the analog value of PWM duty cycle adjustment, e V (x1) represents the voltage error, e I (x2) represents the current error.

[0061] As an auxiliary power supply device, the output voltage and output current of the DC / DC converter should remain stable during operation, and avoid overvoltage and overcurrent phenomena that generate excess energy and cause system temperature rise, affecting the operating stability of the load power supply. Therefore, when there is overvoltage and overcurrent, that is, the voltage error e V and current error e I When any value is a negative number, such as negative large NL, negative medium NM, and negative small NS, it is necessary to focus on the impact of the negative input on the output, and give priority to the negative input for output control.

[0062] Furthermore, according to formula (4), the inference mechanism initially obtained in Table 1 is expanded and optimized in combination with different overcurrent and overvoltage conditions, and three mapping rules are designed: 1) When the voltage error e V and current error e IWhen both are negative, that is, when overvoltage and overcurrent occur at the same time, in order to speed up the response speed of the output PWM duty cycle adjustment, the voltage error e V The current error e I The one with the larger membership value is taken as the output membership value, e V (x1) and e I When the fuzzy subsets corresponding to (x2) are all negative numbers, the output satisfies the following formula: ΔD(x1,x2)=max[e V (x1),e I (x2)] (5) 2) When the voltage error e V is a negative number, the current error e I When it is a positive number, the output should respond to the voltage error e first. V , to solve the problem of overvoltage, that is, e V The fuzzy subset corresponding to (x1) is a negative number, e I When the fuzzy subset corresponding to (x2) is a positive number, the output satisfies the following formula: ΔD(x1,x2)=e V (x1) (6) 3) When the voltage error e V is a positive number, the current error e I When it is negative, the output should respond to the current error e first. I , solve the problem of overcurrent, that is, e V The fuzzy subset corresponding to (x1) is a positive number, e I When the fuzzy subset corresponding to (x2) is a negative number, the output satisfies the following formula: ΔD(x1,x2)=e I (x2) (7) According to the three mapping rules for overvoltage and overcurrent priority judgment, the above Table 1 is optimized to obtain a fuzzy rule base containing priority judgment, such as Figure 7 The fuzzy rule base shown in the figure: The input-output mapping rule in the red dashed box adopts the rule of taking the larger input value, that is, the membership degree of the PWM duty cycle adjustment value ΔD is e V Membership and e I The larger value in the membership degree, that is, formula (5); the mapping rule in the blue dashed box preferentially responds to the input voltage error e V The corresponding fuzzy subset, that is, the membership degree of the PWM duty cycle adjustment ΔD is e V Membership degree, that is, formula (6); the mapping rule in the green dashed box preferentially responds to the input current error e I The corresponding fuzzy subset, that is, the membership degree of the PWM duty cycle adjustment ΔD is e IMembership degree, that is, formula (7); for other mapping rules in the orange dashed box, the rule of taking the smaller input value is adopted, that is, the membership degree of the PWM duty cycle adjustment amount ΔD is e V Membership and e I The smaller value in the membership degree is formula (4).

[0063] Therefore, the incremental dual-variable fuzzy control method of the present application uses an incremental fuzzy control strategy, takes the error between the target voltage and the actual output voltage, and the error between the target limiting current and the actual output current as two inputs, and takes the PWM duty cycle adjustment amount as the output, and regulates the increment of the control object instead of directly controlling the absolute amount of the object, which has the advantage of eliminating steady-state errors; and the present application designs a single fuzzy logic rule base for the voltage and current dual variables, and determines the strategy for voltage and current control priority based on the regulation experience of the DC / DC converter when the voltage is over-voltage or the current is over-current, thereby effectively reducing over-voltage and over-current phenomena, and is easy to implement in engineering.

[0064] Since fuzzy control does not rely on the controlled object model, it performs fuzzy reasoning by imitating human thinking, and fuzzily quantifies the considerable system input and output into language variables described by humans, such as imprecise language conditions such as "large deviation", "not much difference", and "small deviation". It combines the logical rule system designed with expert knowledge and the precise value obtained by defuzzification to timely adjust the control quantity of the system. It has strong robustness and anti-interference ability and belongs to a nonlinear intelligent control. This application replaces the previous voltage and current dual-loop control scheme with incremental dual-variable fuzzy control. Since the DC / DC converter needs to control the output voltage and current at the same time within a wide range of input voltage, the incremental fuzzy control supports multiple inputs and outputs, and the fuzzy rule base size can be designed to adjust the control range and accuracy. Therefore, the voltage error between the target output voltage and the actual output voltage, and the current error between the target limit current and the actual output current are used as two input quantities, and the PWM duty cycle adjustment amount is calculated by the controller as the output. By analyzing the two-dimensional correspondence between the voltage error, the current error and the output PWM duty cycle, an incremental dual-variable fuzzy control method with two inputs and one output is constructed.

[0065] According to the control method of this embodiment, Matlab / Simulink is used to model a 3.3kW phase-shifted full-bridge DC / DC converter to verify the reliability of the incremental dual-variable fuzzy control method of this application. The following are the performance parameters of the DC / DC converter, the same as the actual experimental model, see Table 2: Table 2 DC / DC converter performance parameters by Figure 2The conventional parallel dual PID control shown in the figure is used as a comparative example, and a variable load simulation comparison experiment is carried out together with the incremental dual variable fuzzy control strategy of the present application. Among them, the input voltage is 540V, the target output voltage is set to 27.5V, the target limit current is set to 120A, and the load current increases from 0A to 120A and then decreases to 0A in sequence at a change interval of 30A. The comparison of the simulated voltage, current and duty cycle waveforms of the parallel dual PID control and the incremental dual variable fuzzy control is shown in Fig.10 .

[0066] From the comparison of the voltage waveforms, it can be seen that the output voltages of both control methods have reached the target voltage of 27.5V. However, due to the fixed values ​​of the proportional, differential, and integral terms of the parallel dual PID control, the maximum voltage error is more than 10A when the load changes suddenly, and the response time is about 0.07 seconds. The tracking performance is slow and the full-load output voltage accuracy is low. The incremental dual-variable fuzzy startup speed is slightly lower than that of PID control, but its nonlinear control characteristics can quickly adjust the voltage output when the load changes suddenly. The voltage error is 1.7A and the response time is between 0.03-0.06 seconds. In the second half of the load drop part, the incremental dual-variable fuzzy control has a shorter overvoltage period and strong anti-sudden change ability.

[0067] From the comparison chart of current waveforms, it can be seen that the current waveform of the parallel dual PID control oscillates when the load changes suddenly, it takes longer to adjust to the load voltage, and there is a current error of 2A at full load, and the output power cannot reach 3.3kW; the incremental dual-variable fuzzy control has a smoother current change when the load changes suddenly, and there is no oscillation or overshoot. There is no steady-state error at full load, and the full load power is achieved.

[0068] From the comparison diagram of duty cycle waveforms, it can be seen that since the parallel dual PID control directly outputs the duty cycle required by the system according to the error, its error integral needs to be accumulated over time. When the load changes suddenly, the effect of the error integral is accumulated for a long time, and the integral plays a very small role. The proportional and differential of the controller play a major role, resulting in a large decrease in the duty cycle. The logical reasoning process of the incremental dual-variable fuzzy control does not require the participation of the integral term, and there is no accumulation time for the error. It can directly respond to the change in duty cycle required for the mutation. Therefore, when the load changes suddenly, the voltage error becomes larger. In theory, the fuzzy control will not infer that the duty cycle should be reduced. The simulation results also show that there is no decrease in the duty cycle.

[0069] Therefore, the incremental dual-variable fuzzy control simulation model has the characteristics of fast voltage response, smooth current change without oscillation, and no decrease in duty cycle when the load changes suddenly, making its control performance better than the parallel dual PID control.

[0070] Of course, these two control methods can also be simulated and verified using real objects: In the physical simulation, the experimental object is the 3.3kW phase-shifted full-bridge DC / DC converter of the vehicle power supply. The parallel dual PID control and incremental dual-variable fuzzy control strategies are used to carry out the response experiment of the dynamic change of the resistive load. At the same time, the power supply is used to provide power for the DC / DC converter, the electronic load is connected to the output of the DC / DC converter, the host computer is used to adjust the parameters, and the oscilloscope is used to observe the waveform.

[0071] The performance parameters of the DC / DC converter still refer to the above Table 2. The control chip uses DSP TMS320F28035, the input voltage is set to 600V, and the switching frequency is 80Khz; the voltage loop PID control parameters are set to P = 0.012, I = 0.016, D = 0.0001, and the current loop PID control parameters are set to P = 0.006, I = 0.032, D = 0.00001; the incremental dual variable fuzzy control parameters are set to k eV =0.01, k eI =0.025, k ΔD =0.002.

[0072] The following is an experiment on the constant current CC working mode of the electronic load: The working current of the electronic load is set to 100A, and the target output voltage of the DC / DC converter is set to 27.5V for the load startup experiment. When the electronic load constant current CC working mode is started, the DC / DC converter responds to the output current to 100A first, and then adjusts the output voltage to 27.5V; after the voltage enters the steady state, the current of the electronic load is dynamically adjusted between 50A and 100A, and the load is dumped at 100A, that is, the electronic load is turned off, and the voltage waveform is observed using an oscilloscope. The experimental results are shown in Fig.11 .

[0073] Combination Fig.11 , Under the parallel dual PID control, the output current rises quickly, but the total time required for the actual output voltage to reach the target output voltage is longer; when the output current drops from 100A to 50A, there is a parallel PID integral saturation phenomenon, which causes a large voltage fluctuation; when the output current rises from 50A to 100A, the output voltage suddenly changes to -2.237V. When the electronic load is turned off, the output voltage exceeds the target by 4.707V; Under the incremental dual-variable fuzzy control, the output voltage rises faster, is smoother when approaching the target output voltage, and the startup to steady-state speed is 37.5% faster; when the output current drops from 100A to 50A, the output voltage exceeds the target by 1.901V, and when the output current rises from 50A to 100A, the output voltage suddenly changes to -2.159V, and when the electronic load is turned off, the output voltage exceeds the target by 3.931V. Compared with the parallel dual PID, when adjusting the output current, the incremental FLC has a faster dynamic response of output voltage, a smaller overshoot, and a strong ability to resist sudden changes.

[0074] The following is an experiment on the constant voltage CV working mode of the electronic load: Electric vehicle loads are usually in constant voltage CV mode. The target output voltage of the DC / DC converter is set to 27.5V. The 24V battery is charged to simulate the load of an electric vehicle. The operating voltage of the electronic load is set to 24.5V, the target output voltage of the DC / DC converter is set to 27.5V, and the target limit current is set to 100A. A load start experiment is performed. After the current enters a steady state, the output current limit value is dynamically adjusted between 50A and 100A. The experimental results are shown in Fig.12 .

[0075] Combination Fig.12 The output voltage response of the parallel dual PID control strategy is faster, but the dynamic regulation performance of the output current is poor; when the output current starts to reach the limit value of 100A, the current overshoot is close to 16A, there is an obvious overcurrent phenomenon, the output voltage fluctuates around the target voltage, and the control accuracy is low; when the limit current is adjusted to 50A, the current overshoot is 9.274A, and the adjustment time is long; there is a large current ripple in the steady state; The incremental dual-variable fuzzy control strategy has significantly improved the output current overshoot. When the current starts to reach the limit value of 100A, the actual current exceeds the limit current by 1.623A, with almost no overshoot, and the start-to-steady-state speed is 14.3% faster. When the limit current is adjusted to 50A, there is no current overshoot; the current ripple is small in steady state, and the control accuracy is high.

[0076] The above experimental results show that the incremental dual-variable fuzzy control strategy has a faster startup speed, smaller voltage and current overshoot, and higher control accuracy in the constant current CC working mode and constant voltage CV working mode of the electronic load, and is more suitable for actual repeated startup and load change conditions.

[0077] This application analyzes the control structure of the traditional digital voltage and current series / parallel dual PID control DC / DC converter to improve its overvoltage, overcurrent and current ripple problems, and newly designs and develops an incremental dual-variable fuzzy controller suitable for the DC / DC converter. For the overvoltage and overcurrent phenomena that occur in the series / parallel dual PID control, a two-in-one fuzzy logic rule base for voltage and current errors is proposed. Simulation and experimental results show that when the electronic load of the DC / DC converter changes dynamically, the overcurrent and oscillation phenomena can be effectively reduced, the current ripple is small, the anti-interference ability is strong, and the response speed is fast. Subsequently, the fast anti-fuzzy algorithm of the incremental dual-variable fuzzy controller can be optimized to further improve the operating efficiency, and the parameter setting law of the fuzzy gain factor can be studied.

[0078] The above simulation and experimental results show that, by adopting the control method of the present application, the overshoot of the system output voltage and current is significantly reduced compared with the traditional dual-loop PID control, and it has good control accuracy, dynamic response and anti-interference ability. The DC / DC converter system is stable and reliable, proving the effectiveness and practicality of the incremental dual-variable fuzzy control method.

[0079] It should be noted that the control strategy of this embodiment can be applied to a phase-shifted full-bridge DC / DC converter, and is also applicable to other types of converters such as an LLC resonant converter and a bidirectional DC / DC converter.

[0080] The embodiment of the present application provides an incremental dual-variable fuzzy control system for a DC / DC converter of an electric vehicle, and applies the incremental dual-variable fuzzy control method for a DC / DC converter of an electric vehicle.

[0081] An embodiment of the present application provides an electric vehicle, comprising the above-mentioned electric vehicle DC / DC converter incremental dual-variable fuzzy control system.

[0082] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. An incremental dual-variable fuzzy control method for an electric vehicle DC / DC converter, characterized in that: include: Get the actual output voltage V of the DC / DC converter real and the actual output current I real ; Based on the actual output voltage V real and the target output voltage V ref , calculate the voltage error V error ; Based on the actual output current I real and the target limiting current I ref , calculate the current error I error ; Based on the voltage error V error and the current error I error , an incremental fuzzy control strategy is adopted to obtain a PWM duty cycle adjustment amount ΔD to adjust the output voltage and output current of the DC / DC converter.

2. The incremental dual variable fuzzy control method for the DC / DC converter of an electric vehicle according to claim 1, characterized in that: Based on the voltage error V error and the current error I error , using the incremental fuzzy control strategy, the PWM duty cycle adjustment ΔD is obtained, which specifically includes: Based on the preset domain range, the voltage error V error and the current error I error After normalization, the voltage error e is obtained. V and current error e I ; Based on the pre-constructed fuzzy subsets and membership functions, the voltage error e V and the current error e I Through fuzzy processing, the voltage error e is obtained respectively. V The corresponding fuzzy subsets and membership degrees, the current error e I Corresponding fuzzy subsets and membership degrees; Based on the voltage error e V The corresponding fuzzy subsets and membership degrees, the current error e I The corresponding fuzzy subsets and membership degrees, as well as the pre-built fuzzy rule base, are used to obtain the fuzzy subsets and membership degrees corresponding to the PWM duty cycle adjustment amount ΔD through fuzzy reasoning; Based on the fuzzy subset and the degree of membership corresponding to the PWM duty cycle adjustment amount ΔD, the PWM duty cycle adjustment amount ΔD is obtained through defuzzification processing.

3. The incremental dual variable fuzzy control method for the DC / DC converter of an electric vehicle according to claim 2, characterized in that: The pre-constructed fuzzy subsets include: {NL, NM, NS, ZO, PS, PM, PL}; The membership function includes:

4. The incremental dual variable fuzzy control method for the DC / DC converter of an electric vehicle according to claim 3 is characterized in that: In the fuzzy subset, NL is negative and large, NM is negative and medium, NM is negative and small, ZO is zero, PS is positive and small, PM is positive and medium, and PL is positive and large.

5. The incremental dual variable fuzzy control method for the DC / DC converter of an electric vehicle according to claim 2, characterized in that: The domain range is [-1,1].

6. The incremental dual variable fuzzy control method for the DC / DC converter of an electric vehicle according to claim 2, characterized in that: The defuzzification process includes one or more of a maximum membership method, an area center method, a height method and a maximum area method.

7. The electric vehicle DC / DC converter incremental dual variable fuzzy control method according to claim 2, characterized in that: The fuzzy rule base includes: If the first input variable is NL, NM or NS, and the second input variable is NL or NM, then the output variable is NL, and the membership of the output variable is the larger value of the membership of the first input variable and the membership of the second input variable; wherein the first input variable is the voltage error e V The corresponding fuzzy subset, the second input variable is the current error e I The corresponding fuzzy subset, the output variable is the fuzzy subset corresponding to the PWM duty cycle adjustment amount ΔD; If the first input variable is NL, NM or NS, and the second input variable is NS, then the output variable is NM, and the membership of the output variable is the larger value of the membership of the first input variable and the membership of the second input variable; If the first input variable is NL and the second input variable is ZO, the output variable is NL, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable; If the first input variable is NM and the second input variable is ZO, then the output variable is NM, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable; If the first input variable is NS and the second input variable is ZO, the output variable is NS, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable; If the first input variable is NL and the second input variable is PS, PM or PL, then the output variable is NL, and the membership of the output variable is the membership of the first input variable; If the first input variable is NM and the second input variable is PS, then the output variable is NM, and the membership of the output variable is the membership of the first input variable; If the first input variable is NM and the second input variable is PM or PL, the output variable is NL, and the membership of the output variable is the membership of the first input variable; If the first input variable is NS and the second input variable is PS, PM or PL, then the output variable is NM, and the membership of the output variable is the membership of the first input variable; If the first input variable is ZO and the second input variable is NL, then the output variable is NL, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable; If the first input variable is ZO and the second input variable is NM, then the output variable is NM, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable; If the first input variable is ZO and the second input variable is NS, then the output variable is NS, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable; If the first input variable is ZO and the second input variable is ZO, PS, PM or PL, then the output variable is ZO, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable; If the first input variable is PS, PM or PL, and the second input variable is NL or NM, then the output variable is NL, and the membership of the output variable is the membership of the second input variable; If the first input variable is PS, PM or PL, and the second input variable is NS, then the output variable is NM, and the membership of the output variable is the membership of the second input variable; If the first input variable is PS and the second input variable is ZO, PS or PM, then the output variable is PS, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable; If the first input variable is PS and the second input variable is PL, the output variable is PM, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable; If the first input variable is PM and the second input variable is ZO, then the output variable is PM, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable; If the first input variable is PM and the second input variable is PS, then the output variable is PS, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable; If the first input variable is PM and the second input variable is PM or PL, then the output variable is PM, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable; If the first input variable is PL and the second input variable is ZO, then the output variable is PL, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable; If the first input variable is PL and the second input variable is PS or PM, the output variable is PM, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable; If the first input variable is PL and the second input variable is PL, the output variable is PL, and the membership of the output variable is the smaller value of the membership of the first input variable and the membership of the second input variable.

8. An incremental dual-variable fuzzy control system for an electric vehicle DC / DC converter, characterized in that: An incremental dual-variable fuzzy control method for a DC / DC converter of an electric vehicle is applied.

9. An electric vehicle, characterized in that: It includes the electric vehicle DC / DC converter incremental dual variable fuzzy control system as described in claim 8.

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