CN111404374A - Control method of bidirectional DC-DC converter optimized by using genetic algorithm - Google Patents

Control method of bidirectional DC-DC converter optimized by using genetic algorithm Download PDF

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CN111404374A
CN111404374A CN202010227562.5A CN202010227562A CN111404374A CN 111404374 A CN111404374 A CN 111404374A CN 202010227562 A CN202010227562 A CN 202010227562A CN 111404374 A CN111404374 A CN 111404374A
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付主木
陶发展
王永强
朱龙龙
司鹏举
高爱云
高晓博
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Henan University of Science and Technology
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02M—APPARATUS FOR CONVERSION BETWEEN AC AND AC, BETWEEN AC AND DC, OR BETWEEN DC AND DC, AND FOR USE WITH MAINS OR SIMILAR POWER SUPPLY SYSTEMS; CONVERSION OF DC OR AC INPUT POWER INTO SURGE OUTPUT POWER; CONTROL OR REGULATION THEREOF
    • H02M3/00—Conversion of dc power input into dc power output
    • H02M3/02—Conversion of dc power input into dc power output without intermediate conversion into ac
    • H02M3/04—Conversion of dc power input into dc power output without intermediate conversion into ac by static converters
    • H02M3/10—Conversion of dc power input into dc power output without intermediate conversion into ac by static converters using discharge tubes with control electrode or semiconductor devices with control electrode
    • H02M3/145—Conversion of dc power input into dc power output without intermediate conversion into ac by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal
    • H02M3/155—Conversion of dc power input into dc power output without intermediate conversion into ac by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal using semiconductor devices only
    • H02M3/156—Conversion of dc power input into dc power output without intermediate conversion into ac by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal using semiconductor devices only with automatic control of output voltage or current, e.g. switching regulators
    • B—PERFORMING OPERATIONS; TRANSPORTING
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    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/40—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for controlling a combination of batteries and fuel cells
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Abstract

The invention aims to provide a control method of a bidirectional DC-DC converter optimized by using a genetic algorithm, which comprises the following steps: s1, establishing a self-adaptive observer of the buck converter, and establishing an observer for the total interference; s2, establishing a finite-state machine controller based on self-adaptive nonsingular fast terminal sliding mode control aiming at the buck converter in the step S1, and obtaining a convergence condition of the finite-state machine controller based on self-adaptive nonsingular fast terminal sliding mode control; and S3, optimizing the adaptive fast terminal synovial membrane controller in the step S2 through a genetic algorithm. The method adopts the genetic algorithm to carry out parameter optimization on the self-adaptive fast terminal sliding film controller in the control strategy, so that the effect of the DC-DC converter on power control is better.

Description

Control method of bidirectional DC-DC converter optimized by using genetic algorithm
Technical Field
The invention belongs to the field of hybrid power supply electric automobiles, and particularly relates to a control method of a bidirectional DC-DC converter optimized by using a genetic algorithm.
Background
The new energy automobile has the advantages of zero emission, no pollution, low noise and the like, and the new energy automobile meets the social requirements at present with higher and higher environmental requirements. Nowadays, in order to increase the range of new energy vehicles, fuel cells are generally selected as their main energy source.
However, when the vehicle is accelerated and started, the fluctuation of the load power is severe, the internal electrochemical structure of the fuel cell is easily impacted, the service life of the fuel cell is shortened, and the fuel cell cannot absorb the braking energy, so that the energy conservation of the new energy automobile is not facilitated. Therefore, the lithium battery and the super capacitor are selected as auxiliary energy sources, additional power is provided when the vehicle is started and accelerated to reduce the impact of load power fluctuation on the fuel battery, and the braking energy is absorbed to improve the fuel economy of the whole vehicle.
However, due to the introduction of lithium batteries and super capacitors, the energy management of the whole vehicle is relatively complex, and the vehicle DC-DC converter as a key component of the energy management has an important role in the dynamic performance and the cruising ability of the vehicle.
In the prior art, for example, chinese patent CN106130125 discloses a fuzzy sliding mode feedback charging controller for an electric vehicle and a feedback charging control method thereof, which adopt fuzzy sliding mode variable structure control, solve the problem of performance degradation of the controller caused by uncertain input voltage and output load change due to uncertainty of vehicle parameters and vehicle speed change and equivalent load resistance change during battery charging in the conventional control method under various driving conditions, have strong robustness, and can recover more braking energy.
It can be seen that the research direction of the existing control method for the DC-DC converter mainly focuses on improving the robustness of the system. The research on the power regulation capability of the energy source for the speed of responding to the power demand of the vehicle is also the research direction in the field of new energy vehicles.
Disclosure of Invention
The invention aims to provide a control method of a bidirectional DC-DC converter optimized by a genetic algorithm, so that the energy conversion quality is better, the efficiency is high, and the advantages of prolonging the service lives of a fuel cell and a lithium battery and enhancing the energy use efficiency are achieved.
In order to achieve the above object, a control method of a bidirectional DC-DC converter optimized using a genetic algorithm includes the steps of:
s1, establishing a self-adaptive observer of the buck converter, solving the self-adaptive law of the power supply and the load resistance by utilizing the Lyapunov function, and establishing an observer for the total interference;
s2, establishing a finite-state machine controller based on self-adaptive nonsingular fast terminal sliding mode control aiming at the buck converter in the step S1, and obtaining a convergence condition of the finite-state machine controller based on self-adaptive nonsingular fast terminal sliding mode control;
s3, optimizing the adaptive fast terminal synovial controller in the step S2 by a genetic algorithm, wherein an objective function is designed as follows:
Figure BDA0002427062620000021
wherein the meaning of the individual parameters
Then, automatically optimizing the target function within the constraint range of the parameters according to the designed target function;
the method comprises the following specific steps:
s301, initializing parameters, and randomly generating a first generation population Pop;
s302, calculating the fitness of each individual in the population Pop, and initializing an empty population newPop;
s303, selecting 2 individuals from the population Pop according to the fitness by a proportional selection algorithm, performing cross operation and mutation operation on the 2 individuals, and then adding 2 new individuals into the population newPop;
s304, replacing the population Pop in the step S302 with the population newPop in the step S303 until the fitness function of any individual generated by the evolution exceeds Tf, and terminating the evolution process.
The S1 includes:
s1-1, establishing a differential equation under the controller starting structure according to the selected control variables, wherein the two selected control variables are respectively the inductive current
Figure BDA0002427062620000031
And an output voltage
Figure BDA0002427062620000032
According to the structural characteristics of the controller, the input and output differential equations of the controller are converted into
Figure BDA0002427062620000033
Obtaining two variable state observers, determining the self-adaptive rules of the input voltage and the load resistance by using a Lyapunov equation, and finally establishing an integral state observer;
s1-2, establishing a finite time controller based on self-adaptive nonsingular rapid terminal sliding control for the buck converter, carrying out error correction on estimated values and actual values of voltage and current of a circuit to obtain an error of the sliding mode controller, converting the error into a second-order form, and substituting the second-order form into a nonsingular rapid terminal sliding mode formula to obtain the controller.
The S1-1 comprises:
S1-A, obtaining an observer of current and voltage according to a mathematical model of a buck circuit:
Figure BDA0002427062620000034
Figure BDA0002427062620000035
Figure BDA0002427062620000036
to obtain the adaptive rule of input voltage and load conductance, the lyapunov function is derived to obtain the following equation:
Figure BDA0002427062620000041
order to
Figure BDA0002427062620000042
The adaptive rule is calculated as:
Figure BDA0002427062620000043
wherein L is inductor, C is capacitor, R is resistor, and VinIs the input voltage, G is the conductance.
The nonsingular fast terminal sliding mode second-order model in the S1-2 is as follows:
Figure BDA0002427062620000044
where C is the output capacitance, G is the load conductance, iLIs the inductor current, L is the inductance, u is the controller, and d is the external disturbance.
The S2 includes:
s2-1, setting an initial state, generating errors between actual values and estimated values of current and voltage when a circuit operates, obtaining the reciprocal of input voltage and load resistance conductance according to the errors, obtaining the input voltage and the load resistance conductance value through integration, substituting the input voltage and the load resistance conductance value into an output voltage reciprocal formula and an inductance reciprocal formula to obtain an input-output voltage reciprocal formula and an inductance reciprocal, and obtaining the estimated values of inductance current and output voltage through integration;
s2-2, obtained according to S1 with respect to iL，
Figure BDA0002427062620000045
Vo，
Figure BDA0002427062620000046
To obtain a second order nonsingular blockAnd a fast terminal sliding mode control surface adopts an sat function in order to avoid chattering during switching.
The S2-1 comprises:
S2-A, the input and output observer differential equation of the buck converter is as follows:
Figure BDA0002427062620000051
wherein the current state is determined by an energy management strategy, when a positive energy command is received, the bidirectional DC-DC converter is in a boost state, the current is positive, when a negative energy command is received, the bidirectional DC-DC converter is in a buck state, the current is negative, and the energy management strategy is characterized in that the bidirectional DC-DC converter is in a boost state, the current is positive, and when a negative energy command is received, the bidirectional DC-DC
Figure BDA0002427062620000052
The power conservation formula can judge the inductance reference current under the reference voltage.
The s2-2 comprises the following specific steps:
designing a second-order nonsingular fast terminal sliding mode control method through a difference value of an actual value and an observer estimated value:
Figure BDA0002427062620000053
wherein, the meaning of the individual parameters;
the switching conditions of the sat function are as follows:
Figure BDA0002427062620000054
Figure BDA0002427062620000055
where C is the output capacitance, G is the load conductance, iLIs the inductor current, L is the inductance, u is the controller, d is the external disturbance, VinIs the input voltage, is a normal number, α is an error amplification factor, β is the amplification factor of the estimator, theta is the sliding mode surface adjustment factor, D is the upper bound of the external disturbance, VoIs the output voltage.
Has the advantages that: the invention adopts self-adaptive control and fast terminal sliding mode control to carry out power control on the converter, so that the energy source has quicker response to the power required by the vehicle, more stable power regulation and more reasonable protection to the energy source, and adopts a genetic algorithm to carry out parameter optimization on the self-adaptive fast terminal sliding mode controller in the control strategy, so that the DC-DC converter has better effect on power control.
Drawings
FIG. 1 is a schematic diagram of a DC-DC converter according to the present invention.
FIG. 2 is a flow chart of genetic algorithm optimization.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
Further details of the present invention are provided below in conjunction with the appended drawings.
Embodiment I: the invention provides a control strategy of a DC-DC converter of a hybrid power electric vehicle, which comprises the following steps:
s1: and establishing a self-adaptive observer of the buck converter, solving the self-adaptive law of the power supply and the load resistor by utilizing the Lyapunov function, and establishing the observer for the total interference.
S2: establishing a finite-state machine controller based on self-adaptive nonsingular rapid terminal sliding mode control for the buck converter, setting an effective state and an initial state, enabling the effective state to correspond to the output quantity of the controller, analyzing a differential equation according to the selected control variable, and obtaining the convergence condition of the finite-state machine controller based on the self-adaptive nonsingular rapid terminal sliding mode control under the condition of no output overshoot.
S3: and generating a next generation solution through operations such as heredity, crossing, mutation, natural selection and the like of a genetic algorithm, gradually eliminating the solution with a low fitness function value, and increasing the solution with a high fitness function. Thus, after N generations of evolution, individuals with high fitness function values are likely to be evolved, and the target function is designed as follows:
Figure BDA0002427062620000071
and then, automatically optimizing the objective function within the constraint range of the parameters according to the designed objective function.
In step S1, a differential equation is established in the controller-on configuration according to the selected control variables of fig. 1, the selected two control variables being the inductor current respectively
Figure BDA0002427062620000072
And an output voltage
Figure BDA0002427062620000073
According to the structural characteristics of the controller, the input and output differential equations of the controller are converted into
Figure BDA0002427062620000074
The two variable state observers are obtained, then the Lyapunov equation is used for determining the self-adaptive rule of the input voltage and the load resistance, and finally the integral state observer is established to predict the four variables. And establishing a finite time controller based on self-adaptive nonsingular fast terminal sliding control for the buck converter, carrying out error difference on estimated values and actual values of voltage and current of a circuit to obtain an error of the sliding mode controller, converting the error into a second-order form, and substituting the second-order form into a nonsingular fast terminal sliding mode formula to obtain the controller. The control method of the bidirectional DC-DC (buck) converter based on the self-adaptive nonsingular fast terminal sliding control obtains an observer of current and voltage by a mathematical model of a buck circuit:
Figure BDA0002427062620000075
Figure BDA0002427062620000076
in order to obtain the adaptive rule of the input voltage and the load conductance, the lyapunov function is differentiated, and the formula (4) can be obtained by combining the formulas (1) and (2) and the formula (3):
Figure BDA0002427062620000077
Figure BDA0002427062620000081
order to
Figure BDA0002427062620000082
The adaptive rule is calculated as:
Figure BDA0002427062620000083
wherein L is inductor, C is capacitor, R is resistor, and VinIs the input voltage, G is the conductance.
In step S2, the method for controlling the bidirectional DC-DC (buck) converter based on the adaptive nonsingular fast terminal sliding mode control uses a nonsingular fast terminal sliding mode second-order model equation (5):
Figure BDA0002427062620000084
wherein the current state is determined by an energy management strategy, when a positive energy command is received, the bidirectional DC-DC converter is in a boost state, the current is positive, when a negative energy command is received, the bidirectional DC-DC converter is in a buck state, the current is negative, and the energy management strategy is characterized in that the bidirectional DC-DC converter is in a boost state, the current is positive, and when a negative energy command is received, the bidirectional DC-DC
Figure BDA0002427062620000085
The power conservation formula can judge the inductance reference current under the reference voltage, the self-adaptive observer mainly observes the difference value between the output voltage and the inductance current through the actual value and the observer estimated value, and a second-order nonsingular fast terminal sliding mode control method is designed:
Figure BDA0002427062620000086
in addition, severe jitter during sliding mode surface switching is avoided by designing the sat function, and the switching conditions are as follows:
Figure BDA0002427062620000087
Figure BDA0002427062620000091
in step S3, the objective function is automatically optimized using a genetic algorithm within the constraint range of the parameters according to the designed objective function. The template function is:
Figure BDA0002427062620000092
the flow chart is shown in fig. 2, and the steps are as follows:
initializing parameters such as Pm, Pc, M, G, Tf and the like. Randomly generating first generation population Pop
do { calculating the fitness f (i) of each individual in the population Pop. Initializing empty population newPop
do { 2 individuals are selected from the population Pop by a proportional selection algorithm according to fitness
if (random (0,1) < Pc) { performing crossover operation on 2 individuals with crossover probability Pc }
if (random (0,1) < Pm) { executing mutation operation on 2 individuals according to mutation probability Pm }
2 New individuals were added to the population newPop
} unitil (M descendants are created)
Substitution of Pop with newPop
Until (any chromosome score exceeds Tf, or reproduction passage number exceeds G)
Wherein Pc: probability of cross occurrence, Pm: probability of occurrence of mutation, M: population size, G:
algebraic termination of evolution, Tf: the evolution process may be terminated if the fitness function of any one individual resulting from the evolution exceeds Tf.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily change or replace the present invention within the technical scope of the present invention. Therefore, the protection scope of the present invention is subject to the protection scope of the claims.

Claims (7)

1. A method for controlling a bidirectional DC-DC converter optimized using a genetic algorithm, comprising the steps of:
s1, establishing a self-adaptive observer of the buck converter, solving the self-adaptive law of the power supply and the load resistance by utilizing the Lyapunov function, and establishing an observer for the total interference;
s2, establishing a finite-state machine controller based on self-adaptive nonsingular fast terminal sliding mode control aiming at the buck converter in the step S1, and obtaining a convergence condition of the finite-state machine controller based on self-adaptive nonsingular fast terminal sliding mode control;
s3, optimizing the adaptive fast terminal synovial controller in the step S2 by a genetic algorithm, wherein an objective function is designed as follows:
f(α，β，θ，D)＝min{∫0 t|Vo-Vo targ et|dt)；
wherein α is the error amplification factor, β is the amplification factor of the estimator, θ is the sliding mode surface adjustment factor, D is the upper bound of the external disturbance, VoIs the output voltage.
Then, automatically optimizing the target function within the constraint range of the parameters according to the designed target function;
the method comprises the following specific steps:
s301, initializing parameters, and randomly generating a first generation population Pop;
s302, calculating the fitness of each individual in the population Pop, and initializing an empty population newPop;
s303, selecting 2 individuals from the population Pop according to the fitness by a proportional selection algorithm, performing cross operation and mutation operation on the 2 individuals, and then adding 2 new individuals into the population newPop;
s304, replacing the population Pop in the step S302 with the population newPop in the step S303 until the fitness function of any individual generated by the evolution exceeds Tf, and terminating the evolution process.
2. The method for controlling a bidirectional DC-DC converter optimized using a genetic algorithm according to claim 1, wherein the S1 includes:
s1-1, establishing a differential equation under the controller starting structure according to the selected control variables, wherein the two selected control variables are respectively the inductive current
Figure FDA0002427062610000011
And an output voltage
Figure FDA0002427062610000012
According to the structural characteristics of the controller, the input and output differential equations of the controller are converted into
Figure FDA0002427062610000013
Figure FDA0002427062610000014
Obtaining two variable state observers, determining the self-adaptive rules of the input voltage and the load resistance by using a Lyapunov equation, and finally establishing an integral state observer;
s1-2, establishing a finite time controller based on self-adaptive nonsingular rapid terminal sliding control for the buck converter, carrying out error correction on estimated values and actual values of voltage and current of a circuit to obtain an error of the sliding mode controller, converting the error into a second-order form, and substituting the second-order form into a nonsingular rapid terminal sliding mode formula to obtain the controller.
3. The method for controlling a bidirectional DC-DC converter optimized using a genetic algorithm according to claim 2, wherein the S1-1 includes:
S1-A, obtaining an observer of current and voltage according to a mathematical model of a buck circuit:
Figure FDA0002427062610000021
Figure FDA0002427062610000022
Figure FDA0002427062610000023
to obtain the adaptive rule of input voltage and load conductance, the lyapunov function is derived to obtain the following equation:
Figure FDA0002427062610000024
order to
Figure FDA0002427062610000025
The adaptive rule is calculated as:
Figure FDA0002427062610000026
wherein L is inductor, C is capacitor, R is resistor, and VinIs the input voltage, G is the conductance.
4. The method for controlling a bidirectional DC-DC converter optimized by using genetic algorithm as claimed in claim 2, wherein the non-singular fast terminal sliding mode second order model in S1-2 is:
Figure FDA0002427062610000031
where C is the output capacitance, G is the load conductance, iLIs the inductor current, L is the inductance, u is the controller, and d is the external disturbance.
5. The method for controlling a bidirectional DC-DC converter optimized using a genetic algorithm according to claim 1, wherein the S2 includes:
s2-1, setting an initial state, generating errors between actual values and estimated values of current and voltage when a circuit operates, obtaining the reciprocal of input voltage and load resistance conductance according to the errors, obtaining the input voltage and the load resistance conductance value through integration, substituting the input voltage and the load resistance conductance value into an output voltage reciprocal formula and an inductance reciprocal formula to obtain an input-output voltage reciprocal formula and an inductance reciprocal, and obtaining the estimated values of inductance current and output voltage through integration;
s2-2, obtained according to S1 with respect to iL，
Figure FDA0002427062610000032
Vo，
Figure FDA0002427062610000033
The second-order nonsingular fast terminal sliding mode control surface is obtained through the differential equation, and in order to avoid flutter during switching, an sat function is adopted.
6. The method for controlling a bidirectional DC-DC converter optimized using a genetic algorithm according to claim 5, wherein the S2-1 comprises:
S2-A, the input and output observer differential equation of the buck converter is as follows:
Figure FDA0002427062610000034
wherein the current state is determined by an energy management strategy, when a positive energy command is received, the bidirectional DC-DC converter is in a boost state, the current is positive, when a negative energy command is received, the bidirectional DC-DC converter is in a buck state, the current is negative, and the energy management strategy is characterized in that the bidirectional DC-DC converter is in a boost state, the current is positive, and when a negative energy command is received, the bidirectional DC-DC
Figure FDA0002427062610000035
The power conservation formula can judge the inductance reference current under the reference voltage.
7. The method of claim 5, wherein the step s2-2 comprises the steps of:
designing a second-order nonsingular fast terminal sliding mode control method through a difference value of an actual value and an observer estimated value:
Figure FDA0002427062610000041
wherein z is1Is the output voltage error value, z2Is the derivative of the error, thetazIs the sliding mode adjusting coefficient of the sliding mode,
Figure FDA0002427062610000042
is the slip form adjustment index.
The switching conditions of the sat function are as follows:
Figure FDA0002427062610000043
Figure FDA0002427062610000044
；
wherein, VinIs the input voltage and is a normal number.
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