DC-DC converter control method using fixed time control theory and particle swarm algorithm

Abstract

The DC-DC converter control method using fixed-time control theory and particle swarm algorithm includes the following steps: S1, establishing a state space model based on the circuit topology of the DC-DC converter, and performing coordinate transformation on the average state space equation of the model; S2, establishing a fixed time controller based on Lyapunov function and fixed time control theory for the state space model; S3, optimizing the fixed time controller by particle swarm algorithm to obtain the optimal fixed time controller parameters. The present invention can make the output voltage quickly and stably track the required voltage, while also reducing the current ripple, improving the power quality, and thus improving the energy utilization rate and the service life of the vehicle battery.

Description

Technical Field

[0001] The invention relates to the field of composite power source electric vehicles, in particular to a DC-DC converter control method using fixed time control theory and particle swarm algorithm. Background Art

[0002] New energy vehicles with fuel cells have advantages over automobiles such as zero emissions, no pollution, and low noise. At the same time, they avoid the shortcomings of pure electric vehicles, such as frequent charging and regular battery replacement. Therefore, new energy vehicles are more environmentally friendly. However, since the output voltage of the fuel cell has a soft characteristic, that is, the voltage drops too quickly when the current increases, the stability of the bus voltage cannot be guaranteed. Existing Chinese patents such as CN113525106A disclose a DC / DC converter and its control method. The method uses a converter set between the fuel cell and the power battery to perform voltage matching and conversion processing according to the target drive signal; at the same time, a signal acquisition circuit is set to collect the target measured signal, so that it can perform voltage matching and conversion under different driving modes. The output voltage needs to pass through the DC-DC converter before it can give the bus voltage. Because the vehicle load fluctuates violently, the converter needs to have the ability to quickly track the vehicle's required power.

[0003] The existing control methods for DC-DC converters mostly use the addition of acquisition equipment to collect more signals to control the output of DC-DC. For example, real-time acquisition signals are added to meet the voltage matching under different driving modes, and the control methods also mostly use simple PID control. However, due to the nonlinear characteristics of the DC-DC structure, this method is difficult to meet the real-time and stability requirements of fuel cell new energy vehicles. Summary of the invention

[0004] In order to solve the problem of the output voltage rapidly and stably tracking the required voltage, the present invention provides a DC-DC converter control method using fixed time control theory and particle swarm algorithm, which can make the output voltage rapidly and stably track the required voltage, while also reducing the current ripple, improving the power quality, and thus improving the energy utilization rate and the service life of the battery of the whole vehicle.

[0005] In order to achieve the above object, the specific scheme adopted by the present invention is: a DC-DC converter control method using fixed time control theory and particle swarm algorithm, characterized in that it includes the following steps:

[0006] S1, establishing a state space model based on the circuit topology of the DC-DC converter, and performing coordinate transformation on the average state space equation of the model;

[0007] S2, a fixed-time controller based on Lyapunov function and fixed-time control theory is established for the state-space model;

[0008] S3, the particle swarm algorithm is used to optimize the fixed time controller to obtain the optimal fixed time controller parameters.

[0009] As a further optimization of the DC-DC converter control method using the fixed time control theory and the particle swarm algorithm, the S1 includes:

[0010] S1-1, establishing a state space model according to the circuit topology of the DC-DC converter;

[0011] S1-2, generating state space expressions based on the state space model;

[0012] S1-2, introduce the intermediate control rate to perform coordinate transformation on the state space expression.

[0013] As a further optimization of the DC-DC converter control method using fixed time control theory and particle swarm algorithm, the S1-1 includes:

[0014] According to the on-off of the switch, the average model of the converter in the continuous conduction mode is obtained:

[0015]

[0016] Where C is the capacitance value, L is the inductance value, R is the resistance value, i C is the capacitor current, V in is the input voltage, V0 is the output voltage, and u is the fixed time controller;

[0017] S1-2, assuming V ref is the reference output voltage, and the output voltage error is x1=V0-V ref , the output capacitor current is x2 = i C , transform the state space model to obtain the state space expression:

[0018]

[0019] The coordinate transformation in S1-3 is:

[0020]

[0021] Among them, α is the intermediate control rate.

[0022] As a further optimization of the DC-DC converter control method using the fixed time control theory and the particle swarm algorithm, the S2 includes:

[0023] S2-1, construct the Lyapunov function V(x) and determine the intermediate control rate using the backstepping method;

[0024] S2-2, based on the intermediate control rate obtained in S2-1 and in accordance with the fixed time theory, a fixed time controller satisfying the fixed time control theorem is obtained by using the backstepping method.

[0025] As a further optimization of the DC-DC converter control method using the fixed time control theory and the particle swarm algorithm: In S2-1, the Lyapunov function is:

[0026] V=V1+V2

[0027]

[0028]

[0029] Determine the intermediate control rate:

[0030]

[0031] Calculate the derivative of V1:

[0032]

[0033] The Lyapunov function with the derivative of V1 is:

[0034]

[0035] As a further optimization of the DC-DC converter control method using the fixed time control theory and particle swarm algorithm, the fixed time controller that satisfies the fixed time control theorem is:

[0036]

[0037] The optimization objective function in S3 is:

[0038] f(b / k,c / k,m3,m4)=min(∫|V0-V ref |d t )

[0039] Among them, b / k, c / k, m3, and m4 are the parameters of the fixed-time controller, and the optimal fixed-time controller parameters are obtained according to the objective function;

[0040] The specific steps are as follows:

[0041] S3-1, initialize fixed time controller parameters b / k, c / k, m3, m4;

[0042] S3-2, evaluate the fitness of each particle according to the objective function;

[0043] S3-3, update the particle's velocity and position by iterating the following two formulas:

[0044] G i =mG i +c1rand()(pbest i -x i )+c2rand()(gbest i -x i )

[0045] x i =x i +G i

[0046] S3-4, find the two extreme individual optimal (pbest) and group optimal (gbest).

[0047] Beneficial effect: The present invention uses fixed-time control theory to establish a fixed-time controller. When the required voltage of the automobile changes rapidly, the required voltage can be tracked quickly and stably, that is, the actual output voltage converges to the reference voltage within a fixed time. The particle swarm algorithm is used to optimize the parameters of the fixed-time controller, so that the DC-DC converter has a better effect, meets the real-time and stability of the power of fuel cell new energy vehicles, and improves the energy utilization rate and the service life of the battery of the whole vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is a schematic diagram of a DC-DC converter of the present invention;

[0049] Figure 2 It is the particle swarm algorithm optimization flow chart. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] The DC-DC converter control method using fixed time control theory and particle swarm algorithm is characterized by comprising the following steps:

[0052] S1, establishing a state space model based on the circuit topology of the DC-DC converter, and performing coordinate transformation on the average state space equation of the model;

[0053] S2, a fixed-time controller based on Lyapunov function and fixed-time control theory is established for the state-space model to achieve the output voltage converging to the reference voltage within a fixed time;

[0054] S3, the particle swarm algorithm is used to optimize the fixed time controller to obtain the optimal fixed time controller parameters.

[0055] The S1 includes:

[0056] S1-1, establishing a state space model according to the circuit topology of the DC-DC converter;

[0057] S1-2, generating state space expressions based on the state space model;

[0058] S1-2, introduce the intermediate control rate to perform coordinate transformation on the state space expression.

[0059] The S1-1 includes:

[0060] According to the on-off of the switch, the average model of the converter in the continuous conduction mode can be obtained;

[0061] When the circuit switch is turned on, we can get:

[0062]

[0063] When the circuit switch is turned off, we can get:

[0064]

[0065] Average model of the converter in continuous conduction mode:

[0066]

[0067] Where C is the capacitance value, L is the inductance value, R is the resistance value, i C is the capacitor current, V in is the input voltage, V0 is the output voltage, and u is the fixed time controller;

[0068] S1-2, assuming V ref is the reference output voltage, and the output voltage error is x1=V0-V ref , the output capacitor current is x2 = i C , transform the state space model to obtain the state space expression:

[0069]

[0070] Introducing the intermediate control rate, the coordinate transformation of the above state space expression is performed:

[0071]

[0072] Among them, α is the intermediate control law.

[0073] The S2 includes:

[0074] S2-1, construct the Lyapunov function V(x) and determine the intermediate control rate using the backstepping method;

[0075] S2-2, based on the intermediate control rate obtained in S2-1 and in accordance with the fixed time theory, a fixed time controller satisfying the fixed time control theorem is obtained by using the backstepping method.

[0076] In S2-1, the Lyapunov function V(x) is:

[0077]

[0078] Use the backstepping method to determine the appropriate intermediate control rate.

[0079]

[0080] Calculating the derivative of V1 yields:

[0081]

[0082] Substituting the derivative of V1 into the Lyapunov function, we get:

[0083]

[0084] The obtained intermediate control rate is obtained according to the fixed time theory and the backstepping method to obtain a fixed time controller that satisfies the fixed time control theorem:

[0085]

[0086] The fixed time control theorem is: that is, assuming that there is a Lyapunov function V(x), constants, κ, γ>0, 0<λ1<1, λ2>1, we can get: Then V(x) can converge to the area near the equilibrium point within a fixed time.

[0087] Further explanation of S2:

[0088] In 2-1, taking the derivative of V1, we get:

[0089]

[0090] After scaling the inequality, we get:

[0091]

[0092] From this design: Right now Substituting into the above inequality, V1 satisfies:

[0093]

[0094] The intermediate control rate is thus determined.

[0095] In 2-2, the determined intermediate control rate is substituted into the Lyapunov function:

[0096]

[0097] Bundle Substituting into the above formula, we get:

[0098]

[0099] Scaling by the inequality gives:

[0100]

[0101] Therefore, it can be obtained that when u satisfies the following conditions:

[0102]

[0103] The fixed controller can satisfy the fixed time theorem, and the output voltage converges quickly to the reference voltage within a fixed time.

[0104] The optimization objective function in S3 is:

[0105] f(b / k,c / k,m3,m4)=min(∫|V0-V ref |d t )

[0106] Among them, b / k, c / k, m3, and m4 are the parameters of the fixed-time controller, and the optimal fixed-time controller parameters are obtained according to the objective function;

[0107] like Figure 2 As shown, the specific steps are as follows:

[0108] S3-1, initialize fixed time controller parameters b / k, c / k, m3, m4;

[0109] S3-2, evaluate the fitness of each particle according to the objective function;

[0110] S3-3, update the particle's velocity and position by iterating the following two formulas:

[0111] G i =mG i +c1rand()(pbest i -x i )+c2rand()(gbest i -x i )

[0112] x i =x i +G i

[0113] S3-4, find the two extreme individual optimal (pbest) and group optimal (gbest).

[0114] The optimal values ​​of the individual optimum and the group optimum are compared to determine the global optimal value, thereby determining the optimal fixed-time controller, at which point the value of the objective function is minimized.

[0115] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A DC-DC converter control method using fixed time control theory and particle swarm algorithm, characterized in that: The following steps are involved: S1, establishing a state space model based on the circuit topology of the DC-DC converter, and performing coordinate transformation on the average state space equation of the model; S1-1, establishing a state space model according to the circuit topology of the DC-DC converter; The S1-1 includes: According to the on-off of the switch, the average model of the converter in the continuous conduction mode is obtained: Where C is the capacitance value, L is the inductance value, R is the resistance value, i C is the capacitor current, V in is the input voltage, V0 is the output voltage, and u is the fixed time controller; S1-2, generating state space expressions based on the state space model; S1-2, assuming V ref is the reference output voltage, and the output voltage error is x1=V0-V ref , the output capacitor current is x2 = i C , transform the state space model to obtain the state space expression: S1-3, introduce the intermediate control rate to transform the coordinates of the state space expression; The coordinate transformation in S1-3 is: Among them, a is the intermediate control rate; S2, establishing a fixed time controller based on Lyapunov function and fixed time control theory for the state space model; S2 includes: S2-1, construct the Lyapunov function V(x) and use the backstepping method to determine the intermediate control rate; S2-2, according to the intermediate control rate obtained in S2-1, based on the fixed time theory, a fixed time controller satisfying the fixed time control theorem is obtained by using the backstepping method; S3, the particle swarm algorithm is used to optimize the fixed time controller to obtain the optimal fixed time controller parameters.

2. The DC-DC converter control method using fixed time control theory and particle swarm algorithm as claimed in claim 1, characterized in that: In S2-1, the Lyapunov function is: V=V1+V2 Determine the intermediate control rate: Calculate the derivative of V1: Substitute the Lyapunov function of the derivative of V1: The fixed-time controller that satisfies the fixed-time control theorem is:

3. The DC-DC converter control method using fixed time control theory and particle swarm algorithm as claimed in claim 2, characterized in that: The optimization objective function in S3 is: f(b / k,c / k,m3,m4)=min(∫|V0-V ref |d t ) Among them, b / k, c / k, m3, and m4 are the parameters of the fixed-time controller, and the optimal fixed-time controller parameters are obtained according to the objective function; The specific steps are as follows: S3-1, initialize fixed time controller parameters b / k, c / k, m3, m4; S3-2, evaluate the fitness of each particle according to the objective function; S3-3, update the particle's velocity and position by iterating the following two formulas: G i =mG i +c1rand( )(pbest i -x i )+c2rand( )(gbest i -x i ) x i =xi+G i S3-4, find the two extreme individual optimal pbest and group optimal gbest.

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