A boost circuit double closed loop control method for fuel cell hybrid electric vehicle

Abstract

The application aims to provide a fuel cell hybrid electric vehicle Boost circuit double closed loop control method, comprising the following steps: S1, designing an adaptive load estimator to reduce the influence of load disturbance on voltage regulation; S2, using the load estimation value obtained in the S1 step, introducing a double loop feedback control technology based on the fixed time control theory to replace the virtual control rate in the backstepping method, using the fixed time control method to control the current regulation internal loop, and ensuring that the system output voltage is tracked to the set reference voltage within a fixed time; S3, using the particle swarm algorithm to optimize the fixed time controller in the S2 step, finding the global optimal value, and minimizing the target function value. According to the fixed time control theory, the fixed time double closed loop control circuit is designed, the rapid response capability of the Boost converter is improved, and the particle swarm algorithm is used to optimize the controller parameters in the fixed time control strategy, so that the power control effect of the Boost converter is better.

Description

Technical Field

[0001] This invention relates to the field of hybrid electric vehicles, specifically to a dual closed-loop control method for the Boost circuit of a fuel cell hybrid electric vehicle. Background Technology

[0002] Fuel cells are characterized by being pollution-free and having high energy density, and are thus known as "clean" energy. The fuel cell system in a fuel cell hybrid electric vehicle is a nonlinear multi-input multi-output power system with parameter uncertainties, external disturbances, and internal coupling variables.

[0003] Due to the soft characteristics of fuel cell systems, the voltage will decrease significantly when the output current increases, leading to unstable bus voltage. Currently, the main way to stabilize the bus voltage is by adding a boost circuit, which requires a precise boost circuit control strategy to achieve optimal operation.

[0004] Existing methods, such as Chinese patent CN202010659836.8, disclose a dual-mode predictive control scheme for a Boost circuit based on state estimation. This method designs a dimension-reduced unknown input state observer and uses error feedback and dual-mode predictive control to control unstable input voltage, thereby ensuring the robustness and stability of the system.

[0005] It is evident that existing control methods for Boost converters primarily consider the uncertainties of the system's mathematical model parameters to achieve robustness and stability against external inputs, while rarely addressing the system's convergence speed. The control methods are relatively simple. Due to the nonlinear characteristics of the Boost converter itself, using traditional control theory for modeling and control is insufficient to meet the requirements for the speed and stability of the output voltage in fuel cell hybrid electric vehicles. Summary of the Invention

[0006] The purpose of this invention is to provide a dual closed-loop control method for the Boost circuit of fuel cell hybrid electric vehicles, which enables the output voltage to quickly and stably track the reference voltage when the load and reference voltage change, thereby improving power quality, energy utilization, and the lifespan of the vehicle battery.

[0007] To achieve the above objectives, a dual closed-loop control method for a fuel cell hybrid electric vehicle Boost circuit includes the following steps:

[0008] S1. Considering the impact of unknown load changes on voltage tracking regulation, an adaptive load estimator is designed by combining immersion and invariance methods to reduce the impact of load disturbances on voltage regulation.

[0009] S2. Using the load estimate obtained in step S1, based on fixed-time control theory, a dual-loop feedback control technique is introduced: the proportional-integral (PI) control method controls the external loop of voltage regulation to achieve steady-state error compensation and provides the inner loop reference current to replace the virtual control law in the backstepping method; the fixed-time control method controls the internal loop of current regulation to ensure that the system output voltage tracks the set reference voltage within a fixed time.

[0010] S3. The fixed-time controller described in step S2 is optimized using the particle swarm optimization algorithm to find the global optimum, i.e. the optimal controller parameters, which minimizes the objective function value.

[0011] As a further optimization of the above-mentioned dual closed-loop control method for the Boost circuit of fuel cell hybrid electric vehicles: S1 includes:

[0012] S1-1, Establish a state-space model based on the circuit topology of the Boost converter;

[0013] S1-2, using the principles of immersion and invariance, establish the expression for the load error;

[0014] S1-3, construct the Lyapunov function, and obtain the load estimate according to Lyapunov's second method.

[0015] As a further optimization of the Boost converter control method using fixed-time control theory and particle swarm optimization, S1-1 includes:

[0016] Based on the analysis of the switch's on / off states, the average model of the converter in continuous conduction mode is as follows:

[0017]

[0018] Where C is the capacitance, L is the inductance, R is the resistance, and i L V is the inductor current. in V is the input voltage. o denoted as the output voltage, and u as the fixed-time controller.

[0019] Regarding S1-2, based on the principles of immersion and invariance, and combined with the converter average model, let φ = 1 / R. and The expression for load error can be obtained as follows:

[0020]

[0021] Therefore, when Substituting into the above formula, we can obtain...

[0022] S1-3, construct the positive definite Lyapunov function: When κ(V) O )=-CωV O When (ω>0), According to Lyapunov's second method, differentiate the Lyapunov function and... Substituting, we get:

[0023] Therefore, when χ satisfies hour, It will converge to a small neighborhood near 0. That is, in the steady state of the system, The estimated load value is: (in (This is the average value), and this estimated resistance value will be used to replace the unknown constant of the resistance value in the subsequent controller design.

[0024] As a further optimization of the above-mentioned dual closed-loop control method for the Boost circuit of fuel cell hybrid electric vehicles: S2 includes:

[0025] S2-1, Based on the load estimate obtained in S1, design the outer loop PI controller and obtain the reference current expression;

[0026] S2-2, Design the inner loop controller. Based on the system error equations and the fixed-time theory, obtain the fixed-time controller that satisfies the fixed-time control theorem.

[0027] As a further optimization of the dual closed-loop control method for the Boost circuit of the aforementioned fuel cell hybrid electric vehicle: In S2-1, based on the outer loop PI control principle, the expression for the reference current is:

[0028] The first and second derivative equations of the reference current:

[0029]

[0030] S2-2 defines the current tracking error x1:=I L -I REF ,make The error equations are as follows:

[0031]

[0032] Based on fixed-time control theory, we need to find a suitable control signal μ such that the system state error satisfies the following within a fixed time period. Where T is a variable that is independent of the system state.

[0033] Fixed-time control theorem: Suppose there exists a continuous positive definite Lyapunov V(x) if its derivative satisfies If α>0, β>0, 1>λ1>0, and λ2>1, then the system is stable within a fixed time interval.

[0034] Construct positive definite Lyapunov functions:

[0035] Differentiation yields:

[0036] Let u = 1 - μ, and substituting it into the above equation, we get:

[0037]

[0038] By scaling the inequality, we can obtain:

[0039]

[0040] Where α1 to α5 are positive integers, α6 is the minimum of α4 and α2, and α7 is the minimum of α3 and α5.

[0041] Therefore, when hour, According to the fixed-time control theorem, and combined with the estimated value of resistance R from the first step, in Under its influence, the output voltage tracks the reference voltage for a fixed time, and the current converges to the outer loop reference current within a fixed time.

[0042] As a further optimization of the above-mentioned dual closed-loop control method for the Boost circuit of fuel cell hybrid electric vehicles: the objective function for optimization in S3 is:

[0043] f(α1,α2,α3,α4,α5)=min(∫|V O -V REF |d t )

[0044] Where α1, α2, α3, α4, and α5 are the parameters of the fixed-time controller, and the optimal fixed-time controller parameters are obtained according to the objective function.

[0045] The specific steps are as follows:

[0046] S3-1, Initialize the fixed-time controller parameters α1, α2, α3, α4, α5;

[0047] S3-2, evaluate the fitness of each particle based on the objective function;

[0048] S3-3, update the particle's velocity and position by iterating over the following two formulas:

[0049] Gi =mG i +c1rand()(pbest i -α i )+c2rand()(gbest i -α i ),

[0050] α i =α i +G i .

[0051] S3-4: Find two extreme values, the individual optimal (pbest) and the group optimal (gbest). Compare the optimal values ​​of the individual optimal and the group optimal to find the global optimal value, thereby finding the optimal controller parameters that minimize the objective function value.

[0052] Beneficial effects

[0053] This invention establishes a dual closed-loop controller with fixed-time control using fixed-time control theory. When the load and reference voltage of the fuel cell hybrid electric vehicle change, it can quickly and stably track the required voltage, that is, the actual output voltage converges to the reference voltage within a fixed time. The particle swarm optimization algorithm is used to optimize the parameters of the fixed-time controller, which makes the Boost converter perform better, meets the real-time and stability requirements of the fuel cell hybrid electric vehicle power, and extends the service life of the fuel cell. Attached Figure Description

[0054] Figure 1 This is a schematic diagram of the Boost converter in this invention;

[0055] Figure 2 This is a flowchart of the particle swarm optimization algorithm of the present invention. Detailed Implementation

[0056] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] Please see Figure 1-2 A dual closed-loop control method for a fuel cell hybrid electric vehicle Boost circuit, the algorithm comprising the following steps:

[0058] S1. Considering the impact of unknown load changes on voltage tracking regulation, an adaptive load estimator is designed by combining immersion and invariance methods to reduce the impact of load disturbances on voltage regulation.

[0059] S2, based on the fixed-time control method, introduces dual-loop feedback control technology: the proportional-integral (PI) control method controls the external loop of voltage regulation to achieve steady-state error compensation and provides the inner loop reference current to replace the virtual control law in the backstepping method; the fixed-time control method controls the internal loop of current regulation to ensure that the system output voltage tracks the set reference voltage within a fixed time.

[0060] S3 uses the particle swarm optimization algorithm to obtain the optimal fixed-time controller parameters.

[0061] S1 includes:

[0062] S1-1, Establish a state-space model based on the circuit topology of the Boost converter;

[0063] S1-2, using the principles of immersion and invariance, establish the expression for the load error;

[0064] S1-3, construct the Lyapunov function, and obtain the load estimate according to Lyapunov's second method.

[0065] S1-1 includes:

[0066] Based on the on / off state of the switch, the average model of the converter in continuous conduction mode is as follows:

[0067]

[0068] Where C is the capacitance, L is the inductance, R is the resistance, and i L V is the inductor current. in V is the input voltage. o denoted as the output voltage, and u as the fixed-time controller.

[0069] S1-2, based on the principles of immersion and invariance, and combined with the converter average model, let φ = 1 / R, and The expression for load error can be obtained as follows:

[0070]

[0071] Therefore, when Substituting into the above formula, we can obtain...

[0072] S1-3, Constructing the positive definite Lyapunov function: When κ(V) O )=-CωV O When (ω>0), According to Lyapunov's second method, differentiate the Lyapunov function and... Substituting, we get:

[0073] Therefore, when χ satisfies hour, It will converge to a small neighborhood near 0. That is, in the steady state of the system, The estimated load value is: (in (This is the average value), and this estimated resistance value will be used to replace the unknown constant of the resistance value in the subsequent controller design.

[0074] S2 includes:

[0075] S2-1, Based on the load estimate obtained in S1, design the outer loop PI controller and obtain the reference current expression;

[0076] S2-2, Design the inner loop controller. Based on the system error equations and the fixed-time theory, obtain the fixed-time controller that satisfies the fixed-time control theorem.

[0077] As a further optimization of the dual closed-loop control method for the Boost circuit of the aforementioned fuel cell hybrid electric vehicle: In S2-1, based on the outer loop PI control principle, the expression for the reference current is:

[0078] The first and second derivative equations of the reference current:

[0079]

[0080] S2-2, Define the current tracking error x1:=I L -I REF ,make The error equations are as follows:

[0081]

[0082] Based on fixed-time control theory, we need to find a suitable control signal μ such that the system state error satisfies the following within a fixed time period. Where T is a variable independent of the system state.

[0083] Fixed-time control theorem: Suppose there exists a continuous positive definite Lyapunov V(x) if its derivative satisfies If α>0, β>0, 1>λ1>0, and λ2>1, then the system is stable within a fixed time interval.

[0084] Construct positive definite Lyapunov functions:

[0085] Differentiation yields:

[0086] Let u = 1 - μ, and substituting it into the above equation, we get:

[0087]

[0088] By scaling the inequality, we can obtain:

[0089]

[0090] Where α1 to α5 are positive integers, α6 is the minimum of α4 and α2, and α7 is the minimum of α3 and α5.

[0091] Therefore, when hour, According to the fixed-time control theorem, and combined with the estimated value of resistance R from the first step, in Under its influence, the output voltage tracks the reference voltage for a fixed time, and the current converges to the outer loop reference current within a fixed time.

[0092] The objective function in S3 is:

[0093] f(α1,α2,α3,α4,α5)=min(∫|V O -V REF |d t )

[0094] Where α1, α2, α3, α4, and α5 are the parameters of the fixed-time controller, and the optimal fixed-time controller parameters are obtained according to the objective function.

[0095] like Figure 2 As shown, the specific steps are as follows:

[0096] S3-1, Initialize the fixed-time controller parameters α1, α2, α3, α4, α5;

[0097] S3-2, evaluate the fitness of each particle based on the objective function;

[0098] S3-3, update the particle's velocity and position by iterating through the following two formulas:

[0099] G i =mG i +c1rand()(pbest i -α i )+c2rand()(gbest i -α i ),

[0100] α i =α i +G i .

[0101] S3-4: Find two extreme values, the individual optimal (pbest) and the group optimal (gbest). Compare the optimal values ​​of the individual optimal and the group optimal to find the global optimal value, thereby finding the optimal controller parameters that minimize the objective function value.

[0102] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those 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 invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A boost circuit double closed loop control method for fuel cell hybrid electric vehicle using fixed time control theory, characterized in that, The method comprises the following steps: S1, considering the influence of unknown load change on voltage tracking regulation, combining immersion and invariance method, an adaptive load estimator is designed to reduce the influence of load disturbance on voltage regulation; S2, using the load estimation value obtained in S1, based on fixed-time control theory, double-loop feedback control technology is introduced: proportional integral (PI) control method is used to control the external loop of voltage regulation, which is used to realize steady-state error compensation and provide inner loop reference current to replace the virtual control rate in backstepping method; fixed-time control method is used to control the internal loop of current regulation, which ensures that the output voltage of the system tracks the set reference voltage in fixed time; S3, the particle swarm optimization algorithm is used to optimize the fixed-time controller in S2 to find the global optimal value, i.e. the optimal controller parameters; The S1 comprises: S1-1, a state space model is established according to the circuit topology structure of the Boost converter; S1-2, a load error expression is established by using the immersion and invariance principle; S1-3, a Lyapunov function is constructed, and a load estimation value is obtained according to the Lyapunov second method; The S1-1 comprises: According to the analysis of the on-off state of the switch, the average model of the converter in the continuous conduction mode is obtained: wherein C is a capacitance value, L is an inductance value, and R is a resistance value, is an inductance current, is an input voltage, is an output voltage, is a fixed time controller output; S1-2, according to the immersion and invariance principle, combining the transformer average model, let , and , the load error expression: Thus, when , substituting the above equation can be obtained ; S1-3, construct a positive definite Lyapunov function: When And , ; according to the second Lyapunov method, the derivative of Lyapunov function is obtained and is substituted to obtain: ; Thus, when is satisfied , will converge to a small neighborhood around 0; that is, at the steady state of the system, , , the load estimate is: where is the average value, and in the following controller design, this resistance estimate is used to replace the unknown constant of the resistance value; The S2 comprises: S2-1, based on the load estimation value obtained in S1, the PI controller of the outer loop is designed, and the reference current expression is obtained; S2-2, the inner loop controller is designed, and the fixed-time controller satisfying the fixed-time control theorem is obtained according to the system error equation set and the fixed-time control theory; The S2-1 comprises: According to the outer loop PI control principle, the expression of the reference current is: ; The first-order derivative and second-order derivative equation sets of the reference current are as follows: ； S2-2, define current tracking error , let , , get error equations ； According to the fixed-time control theory, find a suitable control signal So that the system state error satisfies , where T is a variable independent of the system state Fixed-time control theorem: Assume that there exists a continuous positive definite Lyapunov function if the derivative of the function satisfies where , , , then the state of the system is stable in fixed time; Constructing a positive definite Lyapunov function: ; The derivative can be obtained as: ; Let , substituting the above formula can be obtained: ； The inequality scaling can obtain: ； wherein to is a positive integer, is and the minimum value of is and the minimum value of Therefore, when time, ; according to the fixed time control theorem, combined with the first step of the estimated value of the resistance R, under the action of the output voltage tracks the reference voltage in fixed time, and the current converges to the outer loop reference current in fixed time.

2. The method of claim 1, wherein the boost circuit dual closed loop control method of a fuel cell hybrid electric vehicle using fixed-time control theory is characterized by, The optimization objective function in S3 is as follows: wherein , , , , is a parameter of the fixed-time controller, the optimal fixed-time controller parameter being obtained according to the target function; The specific steps are as follows: S3-1, initializing fixed time controller parameters , , , , ; S3-2, the fitness of each particle is evaluated according to the objective function; S3-3, update the velocity and position of the particle by iterating the following two equations: velocity update equation position update equation ; S3-4, two extreme individual optimal (pbest) and group optimal (gbest) are found, the optimal values of the individual optimal and the group optimal are compared, the global optimal value is found, and the optimal controller parameters are found, so that the objective function value is minimized.

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