阅读说明：本技术 一种超级电容-燃料电池混合动力特种车辆功率协调方法 (Power coordination method for super-capacitor-fuel cell hybrid power special vehicle ) 是由 任毅龙 兰征兴 于海洋 王吉祥 付翔 余航 于 2021-07-20 设计创作，主要内容包括：本专利涉及一种超级电容-燃料电池混合动力特种车辆功率协调方法,其特征在于,包括：步骤100,建立混合动力系统结构的数学模型,包括：功率平衡模型、超级电容的数学模型以及燃料电池的数学模型,所述超级电容和燃料电池作为混合动力源,以得到超级电容的剩余电量以及燃料电池的电量消耗量；步骤200,根据得到的超级电容的剩余电量以及燃料电池的电量消耗量建立模型预测控制器系统,进行能量管理,最终得到最优控制序列；步骤300,将步骤200最终得到的最优控制序列中的第一个值附加给模型预测控制器,更新状态值,进行依次迭代。(The patent relates to a power coordination method for a super-capacitor-fuel cell hybrid special vehicle, which is characterized by comprising the following steps: step 100, establishing a mathematical model of a hybrid power system structure, including: the power balance model, the mathematical model of the super capacitor and the mathematical model of the fuel cell are used as a hybrid power source to obtain the residual electric quantity of the super capacitor and the electric quantity consumption of the fuel cell; step 200, establishing a model predictive controller system according to the obtained residual electric quantity of the super capacitor and the electric quantity consumption of the fuel cell, performing energy management, and finally obtaining an optimal control sequence; and step 300, adding the first value in the optimal control sequence finally obtained in the step 200 to the model predictive controller, updating the state value, and performing sequential iteration.)

1. A super capacitor-fuel cell hybrid special vehicle power coordination method is characterized by comprising the following steps:

step 100, establishing a mathematical model of a hybrid power system structure, including: the power balance model, the mathematical model of the super capacitor and the mathematical model of the fuel cell are used as a hybrid power source to obtain the residual electric quantity of the super capacitor and the electric quantity consumption of the fuel cell;

the step 100 further includes the following steps: step 101, starting from the angle of power flow, establishing a power balance model of a total power node of a whole vehicle; 102, establishing a mathematical model according to the working principle of the super capacitor and a circuit model, and obtaining an expression of the residual electric quantity of the super capacitor; 103, establishing a mathematical model according to the working principle and the circuit model of the fuel cell, and obtaining an expression of the electric quantity consumption of the fuel cell;

step 200, establishing a model predictive controller system according to the obtained residual electric quantity of the super capacitor and the electric quantity consumption of the fuel cell, performing energy management, and finally obtaining an optimal control sequence;

the step 200 comprises the following steps: step 201, establishing a mathematical model of a model predictive controller based on the super capacitor model and the fuel cell model obtained in step 200; step 202, based on the model predictive controller system, the system parameter of the drawing up system includes: state variables, control variables, measurement inputs, outputs, and indicator functions; step 203, obtaining a calculation formula of a target function of the super capacitor-fuel cell hybrid power system, and obtaining an optimal control input sequence;

and step 300, adding the first value in the optimal control sequence finally obtained in the step 200 to the model predictive controller, updating the state value, and performing sequential iteration.

2. The power coordination method for the super capacitor-fuel cell hybrid special vehicle according to claim 1, characterized in that the expression of the established power balance model of the total vehicle power sum node is as follows: p_C(t)＝P_uc(t)+P_req(t) wherein P_C(t) instantaneous output power of the fuel cell, P_uc(t) is the instantaneous input and output power of the super capacitor, which is the charging state when the value is positive number, P_req(t) instantaneous output Power for vehicle loadThe vehicle loads include electric motors and other vehicular electrical loads.

3. The power coordination method for the super capacitor-fuel cell hybrid special vehicle according to claim 2, characterized in that the expression of the residual capacity SOE (t) of the super capacitor is represented by the differential form SOE (t)':wherein E_capIs the maximum energy capacity, ξ_capIs the super capacitor power.

4. The power coordination method for super capacitor-fuel cell hybrid special vehicle according to claim 1, characterized in that the electric consumption B of the fuel cell is obtained_eThe expression of (a) is:wherein P is_CAs power of fuel-powered electric vehicles, t₀For the initial operation time of the fuel cell,the rate of power consumption.

5. The method of claim 4, wherein the model predictive controller system has a state variable x, a control variable u, a measurement input v, and a measurement output y, wherein,u＝P_uc，v＝P_req，P_ucis the output power of the super capacitor, P_reqWork output for vehicle loadAnd (4) rate.

6. The power coordination method for the super capacitor-fuel cell hybrid special vehicle as claimed in claim 1, wherein the index function of the model predictive controller system is obtained by performing linearization and discretization on the system.

7. The power coordination method for the super capacitor-fuel cell hybrid special vehicle as claimed in claim 1, wherein the state space form of the model predictive controller system after linearization and discretization is as follows:wherein k is time and belongs to a time set {1, 2, …, T }; x (k) is the state variable of the model predictive control system at time k; x (k +1) is a state variable of the model predictive control system at the moment of k + 1; u (k) is the output power of the super capacitor of the model predictive control system at the moment k; y (k) is the measured output of the model predictive control system at time k; a (k), B_u(k),B_v(k) And C (k) are each:

wherein the content of the first and second substances, is the amount of electricity consumed per unit time, m₁Is the slope of the linear function; m is₂Is a constant of the linear function.

8. The method as claimed in claim 7, wherein the indicator function J is a measured output of a processThe difference between the value and the reference track is minimum, and the expression is as follows:wherein N is the length of the prediction view; q is a state weight value; r is input penalty amount; y is the measurement output; y is_refIs a reference value; i is stage; y (k + i | k) is an output term; y is_ref(k + i) is a reference term; u (k + i-1) is a control input term; u is a control variable matrix, and the expression is as follows:N_cis the predicted length of the control variable.

9. The method of claim 6, wherein the objective function minB is passed through_eAnd the corresponding constraints: y is_min≤y(k)≤y_max,k＝1,2,...,N，u_min≤u(k)≤u_maxK 1, 2.. N, resulting in an optimal control sequence u₀,u₁,u₂,…,u_N-1。

10. The method of claim 9, wherein u is determined by comparing u with the power of the hybrid vehicle₀After the power is input into the model predictive controller, the next moment is entered, and the current load required power P of the vehicle is obtained_reqFor B at the next time_eAnd predicting by the SOE, correcting the predicted value at the previous moment, and repeating the steps of predicting, optimizing and correcting.

Technical Field

The invention belongs to the technical field of new energy hybrid electric vehicles, and relates to a power coordination method for a super capacitor-fuel cell hybrid special vehicle.

Background

In recent years, new energy automobiles are favored by people due to the advantages of environmental protection, energy conservation and the like, and are rapidly developed. In the field of civil aviation, with the starting of special trial work of 'oil to gas' special vehicles on the ground of airports, how to deploy new energy vehicles in the airport environment is attracting attention in the industry. Compared with a common application scene, the vehicle running condition is complex under the airport background, the working time is long, the working frequency is high, the working load is large, and the load change is frequent. In particular for aircraft-guided vehicles in airports, a smooth towing of the aircraft within a very short time is required, which places very high demands on the instantaneous change in power of the vehicle.

Based on the above scene requirements, a reliable power system belonging to the airport special vehicle is urgently needed to be designed to adapt to the airport working condition.

As a novel vehicle power source, the fuel cell has the characteristics of high energy density, high discharging speed, cleanness, environmental protection and the like, and provides a new idea for meeting the operation requirements of special vehicles under the airport working condition. However, the fuel cell cannot recover the excessive energy, and the fuel cell system is required to have high dynamic performance and reliability, which requires the fuel cell to be matched with other power sources to improve the above disadvantages of the fuel cell.

Disclosure of Invention

In view of the foregoing analysis, the present invention aims to provide a power coordination method for a super capacitor-fuel cell hybrid special vehicle, so as to solve the problems of response to a load change power system, energy waste of a fuel cell, and high requirements on a fuel cell system in the prior art.

The purpose of the invention is realized as follows:

the power coordination method of the super capacitor-fuel cell hybrid special vehicle comprises the following steps: step 100, establishing a mathematical model of a hybrid power system structure, including: the power balance model, the mathematical model of the super capacitor and the mathematical model of the fuel cell are used as a hybrid power source to obtain the residual electric quantity of the super capacitor and the electric quantity consumption of the fuel cell; the step 100 further includes the following steps: step 101, starting from the angle of power flow, establishing a power balance model of a total power node of a whole vehicle; 102, establishing a mathematical model according to the working principle of the super capacitor and a circuit model, and obtaining an expression of the residual electric quantity of the super capacitor; 103, establishing a mathematical model according to the working principle and the circuit model of the fuel cell, and obtaining an expression of the electric quantity consumption of the fuel cell; step 200, establishing a model predictive controller system according to the obtained residual electric quantity of the super capacitor and the electric quantity consumption of the fuel cell, performing energy management, and finally obtaining an optimal control sequence; the step 200 comprises the following steps: step 201, establishing a mathematical model of a model predictive controller based on the super capacitor model and the fuel cell model obtained in step 200; step 202, based on the model predictive controller system, the system parameter of the drawing up system includes: state variables, control variables, measurement inputs, outputs, and indicator functions; step 203, obtaining a calculation formula of a target function of the super capacitor-fuel cell hybrid power system, and obtaining an optimal control input sequence; and step 300, adding the first value in the optimal control sequence finally obtained in the step 200 to the model predictive controller, updating the state value, and performing sequential iteration. The functions of real-time electric quantity storage and peak clipping and valley filling of the super capacitor can be exerted, the quick response of the power system under the load change is realized, and the working stability and reliability of the vehicle power system are ensured.

Further, the expression of the established power balance model of the total power node of the whole vehicle is as follows: p_C(t)＝P_uc(t)+P_req(t) wherein P_C(t) instantaneous output power of the fuel cell, P_uc(t) is the instantaneous input and output power of the super capacitor, which is the charging state when the value is positive number, P_req(t) is the instantaneous output power of the vehicle load, including the electric motor and other electrical loads for the vehicle. Coordinating vehicle load, supercapacitor, and fuel-electricity from a system power flow perspectivePower distribution among the three pools.

Further, the expression of the remaining capacity soe (t) of the super capacitor is represented by its differential form soe (t)':wherein E_capIs the maximum energy capacity, ξ_capIs the super capacitor power. And providing a basis for calculating the optimal control sequence.

Further, the amount of electricity consumption B of the fuel cell is obtained_eThe expression of (a) is:wherein P is_CAs power of fuel-powered electric vehicles, t₀For the initial operation time of the fuel cell,the rate of power consumption. And providing a basis for calculating the optimal control sequence in the prediction time domain.

Further, the model predictive controller system has a state variable x, a control variable u, a measurement input v, and a measurement output y, wherein,u＝P_uc，v＝P_req， P_ucis the output power of the super capacitor, P_reqThe output power of the vehicle load.

Further, the index function of the model predictive controller system is obtained by carrying out linearization and discretization processing on the system.

Further, the state space form of the model predictive controller system after linearization and discretization is:where k is time and belongs to a time setSynthesis {1, 2, …, T }; x (k) is the state variable of the model predictive control system at time k; x (k +1) is a state variable of the model predictive control system at the moment of k + 1; u (k) is the output power of the super capacitor of the model predictive control system at the moment k; y (k) is the measured output of the model predictive control system at time k;

A(k),B_u(k),B_v(k) and C (k) are each:

wherein the content of the first and second substances, is the amount of electricity consumed per unit time, m₁Is the slope of the linear function; m is₂Is a constant of the linear function.

Further, the index function J is a minimum difference between the measured output value of the process and the reference trajectory, and its expression is:wherein N is the length of the prediction view; q is a state weight value; r is input penalty amount; y is the measurement output; y is_refIs a reference value; i is stage; y (k + i | k) is an output term; y is_ref(k + i) is a reference term; u (k + i-1) is a control input term; u is a control variable matrix, and the expression is as follows:N_cis the predicted length of the control variable.

Further, by the objective function minB_eAnd the corresponding constraints: y is_min≤y(k)≤y_max,k＝1,2,...,N，u_min≤u(k)≤u_maxK 1, 2.. N, resulting in an optimal control sequence u₀,u₁,u₂,…,u_N-1。

Further, u is₀After the power is input into the model predictive controller, the next moment is entered, and the current load required power P of the vehicle is obtained_reqFor B at the next time_eAnd predicting by the SOE, correcting the predicted value at the previous moment, and repeating the steps of predicting, optimizing and correcting.

Compared with the prior art, the invention can realize at least one of the following beneficial effects:

a) the energy management method based on the model predictive controller is provided, so that power coordination among hybrid power sources is guaranteed, and quick response of a power system is realized when the load changes.

b) The running performance of the power system is improved while the system is ensured to respond quickly under the condition of frequent load change.

c) The functions of real-time electric quantity storage and peak clipping and valley filling of the super capacitor are fully exerted, the quick response of the power system under the load change is realized, and the working stability and reliability of the vehicle power system are ensured.

d) From the angle of system power flow, power distribution among the vehicle load, the super capacitor and the fuel cell is coordinated, real-time control is achieved, and the optimal control effect is achieved.

Drawings

In order to more clearly illustrate the embodiments of the present specification or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the embodiments of the present specification, and other drawings can be obtained by those skilled in the art according to the drawings.

FIG. 1 is a flow chart of the steps of a power coordination method for a super capacitor-fuel cell hybrid special vehicle according to the present invention;

FIG. 2 is a schematic diagram of a power balance model of a super capacitor-fuel cell hybrid special vehicle according to the present invention;

FIG. 3 is a simplified model diagram of an equivalent RC of the super capacitor according to the present invention;

FIG. 4 is a schematic diagram of an equivalent circuit of a fuel cell Rint according to the present invention;

FIG. 5 is a step-by-step flow chart of step 100 of the present invention;

FIG. 6 is a step-by-step flow diagram of step 200 of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

For the purpose of facilitating understanding of the embodiments of the present application, the following description will be made in terms of specific embodiments with reference to the accompanying drawings, which are not intended to limit the embodiments of the present application.

Example 1

The embodiment provides a power coordination method for a super capacitor-fuel cell hybrid special vehicle.

As shown in FIG. 1, the power coordination method for the super capacitor-fuel cell hybrid special vehicle comprises the following steps:

step 100, establishing a mathematical model of a hybrid power system structure, including: the power balance model, the mathematical model of the super capacitor and the mathematical model of the fuel cell are used as a hybrid power source, so that the residual electric quantity of the super capacitor and the electric quantity consumption of the fuel cell are obtained.

Step 101, from the power flowing angle, establishing a power balance model of a total power node of the whole vehicle.

The power balance model includes: power distribution among fuel cells, supercapacitors, motors, and other electrical loads for vehicles.

Referring to fig. 2, the power balance model may be represented by the following expression:

P_C(t)＝P_uc(t)+P_req(t)

wherein, P_C(t) is the instantaneous output power of the fuel cell in kw; p_uc(t) is the instantaneous input and output power of the super capacitor, which is in a charging state when the value is a positive number, and the unit is kw; p_req(t) is the instantaneous output power of the vehicle load in kw; t is the time; p_reqMainly consists of an electric motor and other electric load requirements for vehicles.

Further, P can be deduced_ucConstraints that need to be met:

P_C-min-P_req(t)≤P_uc(t)≤P_C-max-P_req(t)

wherein, P_C-minIs the instantaneous minimum output power of the fuel cell; p_C-maxThe instantaneous maximum output power of the fuel cell.

In addition, in order to ensure the safe and stable operation of the whole vehicle power system, the output P of the super capacitor_ucAnd the SOE value of the super capacitor must meet the physical constraints under hardware conditions, namely:

P_uc-min≤P_uc(t)≤P_uc-max

SOE_min≤SOE(t)≤SOE_max

wherein, P_uc-maxRepresents the maximum output power of the super capacitor; p_uc-minIs the minimum output power of the super capacitor; as the SOE of the super capacitor is highest in charging and discharging efficiency within the range of 40% -80%, the maximum residual electric quantity SOE of the super capacitor SOE is_maxSet to 0.64; minimum remaining capacity SOE of super capacitor SOE_minSet to 0.16.

And 102, establishing a mathematical model according to the working principle of the super capacitor and the circuit model, and obtaining an expression of the residual electric quantity of the super capacitor.

According to the Resistive-capacitance (rp) simplified circuit of the super capacitor shown in fig. 3, where esr (equivalent Series resistance) is equivalent Series resistance, a mathematical model of the super capacitor can be obtained, and the expression is as follows:

P_uc(t)＝V_L(t)·I_cap(t)

wherein, P_uc(t) is the instantaneous input-output power of the super capacitor, in kw, when P_uc(t) a positive value indicates that the capacitor is in a charged state; v_L(t) is the terminal voltage of the super capacitor at time t, and the unit is V; i is_cap(t) is the real-time current flowing through the supercapacitor, in units of A;is the first derivative of the voltage across the equivalent capacitor at time t, in units of A/F; c is a supercapacitor, with the unit of F; SOC (t) (State Of Charge) is the real-time state Of charge Of the super capacitor; q (t) is the amount of charge stored in the capacitor, in units of C; q_maxThe maximum storable electric charge quantity of the super capacitor at the time t is represented by C; v_cap(t) is the voltage across the equivalent capacitance at time t, in units of V; v_maxIs the maximum volt-age of the supercapacitor, in V; SOE (t), (State Of energy) is the residual electric quantity Of the super capacitor at the time t; e (t) represents the energy stored in the super capacitor at the time t, and the unit is J; e_capIs the maximum energy capacity, in J.

The relationship between the differential of the SOE, the maximum energy capacity and the supercapacitor power is as follows:

wherein SOE (t)' is the differential of the SOE of the super capacitor at the time t; xi_capRefers to the efficiency of the super capacitor, and is set to 98%.

And 103, establishing a mathematical model according to the working principle of the fuel cell and the circuit model, and obtaining an expression of the electric quantity consumption of the fuel cell.

A mathematical model characterizing a fuel cell according to the Rint equivalent circuit model shown in fig. 4 can be represented by the following expression:

U′＝U_DC-i·R

P_C(t)＝U′(t)·i(t)

in the above equation, U' (t) is the terminal output voltage of the fuel cell at time t, in units of V; i (t) is the circuit current at time t, in units of A.

Defining the power consumption of the fuel cell per unit time asThe calculation formula is as follows:

in the formula, P_CRepresents the output power of the fuel cell, in kw;the specific energy consumption is expressed in V/(kw · h).

The amount of power consumption during the fuel cell operation time t can be expressed as:

wherein, B_eRepresents the amount of power consumption of the fuel cell; t is t₀Indicating the initial operating time of the fuel cell.

The DC/DC bidirectional converter is simplified, and its conversion efficiency is set to 1.

And 200, establishing a model predictive controller system according to the obtained residual electric quantity of the super capacitor and the electric quantity consumption of the fuel cell, performing energy management, and finally obtaining an optimal control sequence.

And step 201, establishing a mathematical model of the model predictive controller based on the super capacitor model and the fuel cell model obtained in the step 200.

The model predictive controller may be represented using the following equation:

wherein x is₁SOE represents the remaining capacity of the super capacitor; x is the number of₂＝B_eRepresents the power consumption of the fuel cell; u ═ P_ucThe control variable is represented and output as the power of the super capacitor.

Step 202, based on the model predictive controller system, the system parameter of the drawing up system includes: state variables, control variables, measurement inputs, outputs, and indicator functions.

Defining state variables, control variables, measurement inputs and outputs of the model predictive control system:

u＝P_uc

v＝P_req

in the formula, x is a state variable of the model predictive control system; u is a control variable; v is the measurement input; and y is the measurement output.

The model predictive controller system is subjected to linearization and discretization processing to obtain a state space form:

x(k+1)＝A(k)x(k)+B_u(k)u(k)+B_v(k)

y(k)＝C(k)x(k)

wherein k is time and belongs to a time set {1, 2, …, T }; x (k) is the state variable of the model predictive control system at time k; x (k +1) is a state variable of the model predictive control system at the moment of k + 1; u (k) is the output power of the super capacitor of the model predictive control system at the moment k; y (k) is the measured output of the model predictive control system at time k; a (k), B_u(k),B_v(k) And C (k) are each:

wherein, among others,then m is₁Is the slope of the linear function; m is₂Is a constant of the linear function.

Defining the metric function J of the model predictive controller system as the minimum difference between the measured output value of the process and the reference trajectory, the minimized metric function J can be described as the sum of the difference between the phased output term and the reference term and the weighted norm of the control input term:

wherein N is the length of the prediction view; q is a state weight value; r is input penalty amount; y is the measurement output; y is_refIs a reference value; i is stage; y (k + i | k) is an output term; y is_ref(k + i) is a reference term; u (k + i-1) is a control input term; u is a control variable matrix, and the expression is as follows:

wherein N is_cIs the predicted length of the control variable.

Is a weighted norm of the difference between the output term and the reference term,is the weighted norm of the control input term.

The expression for the weighted norm of the difference between the output term and the reference term is:

the expression for the weighted norm of the control input is:

and 203, obtaining a calculation formula of an objective function of the super capacitor-fuel cell hybrid power system, and obtaining an optimal control input sequence.

The target of the super capacitor-fuel cell hybrid power system is the electric quantity consumption B of the fuel cell_eThe minimum, the calculation formula is as follows:

due to the physical constraints under hardware conditions that the measurement output and the control variables must satisfy, namely:

y_min≤y(k)≤y_max,k＝1,2,...,N

u_min≤u(k)≤u_max,k＝1,2,...,N

calculating to obtain the optimal control sequence u of the control variable u under the given constraint condition₀,u₁,u₂,…,u_N-1。

And step 300, adding the first value in the optimal control sequence finally obtained in the step 200 to the model predictive controller, updating the state value, and performing sequential iteration.

Inputting the first numerical value in the optimal control sequence obtained by calculation into a model predictive controller, and then entering the next moment to continuously obtain the current load required power P of the vehicle_reqInformation on the power consumption B of the fuel cell at the next moment of the model predictive controller system_eAnd predicting the residual electric quantity SOE of the super capacitor, and correcting the predicted value at the previous moment. And repeating the steps of predicting, optimizing, and correcting.

The above-mentioned embodiments, objects, technical solutions and advantages of the present application are described in further detail, it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present application, and are not intended to limit the scope of the present application, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present application should be included in the scope of the present application.