Temperature control methods for fuel cell vehicles in high-temperature environments during summer

Abstract

This invention discloses a temperature control method for fuel cell vehicles operating in high-temperature summer environments, belonging to the field of fuel cell vehicle control technology. The purpose of this invention is to establish a thermally coupled model and design a Pontryagin minimum principle controller to control the temperature of fuel cell vehicles operating in high-temperature summer environments. The steps of this invention are: building a control model for the fuel cell and cabin thermal management; designing a Pontryagin minimum principle controller; using the Nelder-Mead algorithm to obtain suitable initial values ​​for costate variables; and then finding the optimal control sequence. This invention's controller design can consider the influence of the air conditioning system, simultaneously controlling both the air conditioning system and the fuel cell, making it more practical.

Description

Technical Field

[0001] This invention belongs to the field of fuel cell vehicle control technology. Background Technology

[0002] Fossil fuels produce large amounts of carbon dioxide and other harmful gases during use, exacerbating the greenhouse effect and environmental pollution. Therefore, finding suitable clean energy alternatives to fossil fuels is urgent. Fuel cells are an extremely clean energy source, converting the chemical energy of hydrogen and oxygen into electrical energy; the product of this reaction is water, making it extremely environmentally friendly. Therefore, fuel cell vehicles will be a key area of ​​future research.

[0003] Many factors influence the performance of fuel cell vehicles, with fuel cell temperature being a critical one. In high-temperature environments, if the generated heat cannot be dissipated in time, the fuel cell temperature will exceed its normal operating range. When the fuel cell temperature is too high, the proton exchange membrane inside the fuel cell will dry out and may even detach, ultimately causing a safety hazard. Therefore, fuel cell temperature control is extremely important. In fuel cell vehicles, there is thermal coupling between the fuel cell cooling system and the cabin cooling system; therefore, the influence of the cabin must be considered when controlling the fuel cell temperature. The thermal management system of a fuel cell vehicle is a complex, coupled, highly nonlinear system, and currently used proportional-integral-derivative (PID) controllers and rule-based controllers often struggle to achieve satisfactory control.

[0004] In summary, the main issues concerning the control of fuel cell thermal management systems are as follows:

[0005] 1. There is thermal coupling between the thermal management system and the air conditioning system of the fuel cell. The impact of the cabin on the fuel cell temperature must be considered when designing the controller.

[0006] 2. Existing integrated temperature control strategies for fuel cells all use PID and rule-based controllers, which are difficult to achieve good control results.

[0007] There is thermal coupling between the condenser of the air conditioning system and the radiator of the fuel cell cooling system. The condenser transfers heat from the cabin to the outside, so the air temperature after passing through the condenser is higher than the ambient temperature. This heated air then passes through the radiator of the fuel cell cooling system, thus affecting the heat dissipation of the fuel cell. Currently used PID controllers and rule-based controllers struggle to solve this complex and coupled temperature control problem. Summary of the Invention

[0008] The purpose of this invention is to establish a thermal coupling model and design a Pontryagin minimum principle controller to control the temperature of fuel cell vehicles in high-temperature summer environments.

[0009] The steps of this invention are:

[0010] S1. Establish a control model for fuel cell and cabin thermal management:

[0011]

[0012] Q cl =W cl C cl (T fc -T fcin (2)

[0013] in Q is the derivative of the fuel cell temperature. tot It is the total energy generated by the fuel cell, P fc Q is the electrical energy output by the fuel cell. cl It is the energy carried away by the coolant, T fcin It is the coolant temperature at the fuel cell inlet, W. cl It is the flow rate of the coolant, C cl It is the heat capacity of the coolant, m fc It is the mass of the fuel cell, C fc It is the heat capacity of the fuel cell;

[0014] Temperature of coolant at fuel cell inlet:

[0015]

[0016] in It is the derivative of the coolant temperature at the fuel cell inlet, W air It refers to the airflow at the radiator, C. air It is the specific heat capacity of air, T atm It is the ambient temperature. It is the air temperature output by the radiator, m s It's the quality of the radiator, C s It is the heat capacity of the radiator;

[0017] Thermal model of the cabin of a fuel cell vehicle:

[0018]

[0019] in Q is the derivative of the car cabin temperature. sol It is the radiation energy of the sun passing through the car window glass, Q car It is the heat exchange between the car body and the air inside the cabin, Q pasIt's for cooling the passengers and driver, Q ele It's about heat dissipation for the electronic components in the cockpit, Q eva It is the heat carried away by the refrigerant, m air It refers to the quality of the air inside the cabin;

[0020] The heat removed by the evaporator is as follows:

[0021] Q eva =a oe A oe (T we -T cab (5)

[0022] Where a oe It is the heat exchange coefficient of the outer surface of the radiator, A oe It is the surface area of ​​the outer surface of the radiator, T we It is the temperature of the radiator wall;

[0023] Evaporator temperature:

[0024]

[0025] Where m e It is the quality of the evaporator, c e It is the heat capacity of the evaporator, a ie It is the heat exchange coefficient inside the evaporator, D ie It is the diameter of the pipe inside the evaporator, l e T is the length of the two-phase refrigerant state inside the evaporator. re It is the critical temperature of the refrigerant at that pressure;

[0026] S2. The objective function for the Pontryagin minimum principle controller is designed as follows:

[0027]

[0028] Where L1, L2, and L3 are temperature weighting parameters, T fc * This is the ideal temperature for fuel cells, T fcin * This is the ideal temperature of the fuel cell inlet coolant, T. cab * This is the ideal temperature for a car cabin;

[0029] Constructed Hamiltonian function:

[0030]

[0031] Where H(x(t),u(t),λ(t),t) is the Hamiltonian function, and λ1,λ2,λ3 are the corresponding costate variables;

[0032] Costate variables should satisfy the following conditions:

[0033]

[0034] λ1(t f ) = 0

[0035] λ2(t f ) = 0

[0036] λ3(t f )=0 (9)

[0037] Where t f It is the final moment;

[0038] Optimal sequence of control variables:

[0039] H(x * (t),u * (t),λ * (t),t)≤H(x * (t),u(t),λ * (t),t) (10)

[0040] Where x * (t) is the optimal state variable at time t, u * (t) is the optimal control quantity at time t, λ * H(x) is the optimal costate variable at time t. * (t),u * (t),λ * H(x) is the Hamiltonian function value at time t, where the state variables, control variables, and costate variables are all at their optimal values. * (t),u(t),λ * (t),t) is the Hamiltonian function value of the optimal state value, optimal costate variable value, and optimal control variable value at time t;

[0041] The process of obtaining the initial values ​​of costate variables:

[0042] 1) Initial value setting: The sum of the absolute values ​​of the costate variables is selected as the objective function of the Nelder-Mead algorithm. Four sets of numbers are selected as the four initial values ​​of the costate variables. The values ​​of the objective function under the four initial values ​​are calculated and sorted in ascending order, denoted as l. min ,l1,l2,l max Sort the four initial values ​​according to the size of their corresponding objective functions, from smallest to largest, and denote them as a1, a2, a3, a4.

[0043] 2) The average value of the initial value a o :

[0044]

[0045] Calculate the reflection point:

[0046] a r =a o +α(a o -a1) (12)

[0047] Where α is the reflection point coefficient;

[0048] 3) Let the reflection point be the initial value of the costate variable, denoted as a. r The final objective function value is calculated and denoted as l. new Compare the obtained objective function value with the objective function value in step 1).

[0049] 4) If the objective function value in step 3) is l new Greater than or equal to the maximum value of the objective function in step 1) max Then calculate the point of contraction, denoted as a. s Using the contraction point as the initial value of the costate variable, the final objective function value is calculated and denoted as l. s ; will l s and l max Perform a size comparison; if l s Less than l max , with contraction point a s Instead of a4, use l s Replace l max ; a1, a2, a3, a s Sort the objects according to the size of their corresponding objective functions, and reassign them as a1, a2, a3, a4 from smallest to largest. Simultaneously, sort the recombined objective functions by size. Return to step 2) and restart the loop. The formula for calculating the contraction point is shown below:

[0050] a s =a o +δ(a4-a o (13)

[0051] Where δ is the contraction point coefficient;

[0052] If l s Greater than or equal to l max All initial values ​​except those corresponding to the minimum objective function must be recalculated to obtain a new sequence of initial values. Using this new sequence as the initial values ​​for the costate variables, the corresponding objective functions are calculated and sorted according to their values. The initial value sequence, from smallest to largest, is a1, a2, a3, a4, and the objective functions, from smallest to largest, are l. min ,l1,l2,l maxReturn to step 2) and restart the loop; the formula for recalculating the initial value is shown below:

[0053] b2 = a1 + δ(a2 - a1)

[0054] b3 = a1 + δ(a3 - a1)

[0055] b4=a1+δ(a4-a1) (14)

[0056] Where b2, b3, b4 are the newly obtained initial value sequence;

[0057] 5) If l in step 3) new Less than the minimum value of the objective function in step 1) min Calculate the point of expansion, denoted as a. e Using the expansion point as the initial value of the costate variable, the corresponding objective function is calculated and denoted as l. e The obtained l e and l in step 3) new Compare the sizes of l, if l e Less than l new , using a e Replace a1; if l e Greater than or equal to l new , using a r Replace a1; then sort according to the size of the corresponding objective function, the initial value sequence is denoted as a1, a2, a3, a4 from smallest to largest; return to step 2) and start the loop again; the formula for calculating the expansion point is as follows:

[0058] a e =a o +γ(a r -a o (15)

[0059] Where γ is the expansion point coefficient;

[0060] 6) If l in step 3) new The size in step 1) l min and l max When in between, use a r Replace a1 to obtain a new sequence of initial values, and sort them according to the size of their respective objective functions, from smallest to largest as a1, a2, a3, a4; return to step 2) and start the loop again;

[0061] 7) After the loop ends, the optimal control sequence and the corresponding initial values ​​of the costate variables are obtained.

[0062] This invention takes into account the coupling between temperatures, simultaneously controlling the fuel cell temperature, the coolant temperature at the fuel cell inlet, and the cabin temperature, with good control performance. The controller design of this invention can account for the impact of the air conditioning system, simultaneously controlling both the air conditioning system and the fuel cell, making it more practical. Attached Figure Description

[0063] Figure 1 This is a thermal coupling diagram of the fuel cell thermal management system and the air-conditioning cabin thermal management system;

[0064] Figure 2 It is an algorithm flowchart;

[0065] Figure 3 This is a diagram of the operating current of a fuel cell under NEDC conditions;

[0066] Figure 4 This is a speed chart of fuel cell vehicles under NEDC conditions;

[0067] Figure 5 This is a temperature diagram of a fuel cell;

[0068] Figure 6 This is a diagram showing the coolant temperature at the fuel cell inlet.

[0069] Figure 7 This is a cabin air temperature chart. Detailed Implementation

[0070] This invention addresses two existing problems by designing a temperature control strategy. Taking into account the thermal coupling between the fuel cell and the cabin, this invention establishes temperature models for both the fuel cell and the cabin, and designs a Pontryagin minimum principle controller based on these models.

[0071] Fuel cell vehicles dissipate heat from the cabin to the outside through the evaporator and condenser of the air conditioning system. During this process, the temperature of the air around the condenser rises. This heated air then passes through the radiator of the fuel cell cooling system, thus affecting the temperature control of the fuel cell. This invention establishes a thermal coupling model and designs a Pontryagin minimum principle controller to control the temperature.

[0072] The technical solution of the present invention is as follows:

[0073] 1. Establish a simulation model of the fuel cell and cabin in the commercial software AVL / Cruise M. The model mainly consists of two parts: the fuel cell cooling system and the cabin air conditioning system.

[0074] 2. Establish control models for the fuel cell and cabin in Matlab / Simulink. The fuel cell temperature model has four inputs: coolant flow rate, airflow rate at the radiator, fuel cell operating current, and air temperature at the radiator; it has two outputs: fuel cell temperature and coolant temperature at the fuel cell inlet. The cabin temperature model has four inputs: vehicle speed, refrigerant flow rate, airflow rate at the condenser, and airflow rate at the evaporator; it has two outputs: cabin air temperature and air temperature at the condenser outlet.

[0075] 3. Based on the established model, design a Pontryagin minimum principle controller. Its optimization problem can be described in the following form:

[0076]

[0077] stX k+1 =h(X) k U k )

[0078] U k ∈U

[0079]

[0080] Where J is the objective function, U k U is the control variable of the system, and X is the control constraint. k It is the system's state variable, f(X) k ) is the temperature control cost function, h(X) k U k ) is the solution function for the control quantity, L is the weighting coefficient, and N is the number of prediction steps.

[0081] 4. Output the Pontryagin Minimum Principle controller from Matlab / Simulink as a Functional Model Unit (FMU) file, import it into the AVL / Cruise M simulation model, and connect the ports of the controller and the model.

[0082] In fuel cell vehicles, the fuel cell cooling system is not independent. The fuel cell temperature is affected by the air conditioning system. The air conditioning condenser impacts the fuel cell's heat dissipation; under condenser interference, the fuel cell temperature can be 5 to 10 degrees Celsius higher than without a condenser. Therefore, the impact of the air conditioning system on the fuel cell is not negligible. Furthermore, the air conditioning condenser and the fuel cell radiator share a fan, so controlling the fan will affect both the fuel cell and the air conditioning system simultaneously. This model takes these two points into account. When designing a controller based on this model, the impact of the air conditioning system can be considered, allowing for simultaneous control of both the air conditioning system and the fuel cell, making it more realistic.

[0083] The following detailed description of the optimized control of fuel cell and cabin temperature according to the present invention, with reference to the accompanying drawings, illustrates that the present invention designs an optimized controller for controlling the temperature of fuel cell and cabin. Figure 1 This is a schematic diagram of the fuel cell and cabin thermal management system of a fuel cell vehicle. The thermal management system consists of two cooling cycles: a fuel cell cooling system and a cabin cooling system. The cooling pump is the power source for the fuel cell cooling system. Driven by the cooling pump, the coolant is sent to the fuel cell, where it exchanges heat with the fuel cell, carrying away heat and raising its temperature. Then, driven by the cooling pump, the coolant is sent to the radiator, where it exchanges heat with the air, dissipating heat and lowering its temperature. It then returns to the fuel cell to cool it down. The cabin cooling system is powered by a compressor. In the cabin cooling system, the evaporator is located inside the cabin, and the condenser is located outside. At the evaporator, the refrigerant undergoes a phase change, changing from a low-temperature, low-pressure gas-liquid mixture to a high-temperature, low-pressure gas. During this process, the refrigerant absorbs heat, thus cooling the cabin. The high-temperature, low-pressure gaseous refrigerant is then compressed by the compressor into a high-temperature, high-pressure gaseous refrigerant. At the condenser, the high-temperature, high-pressure gaseous refrigerant exchanges heat with the air, becoming a high-pressure, low-temperature liquid refrigerant. High-pressure, low-temperature liquid refrigerant is converted into a low-temperature, low-pressure gas-liquid mixture refrigerant through an expansion valve, and then reaches the evaporator to cool the cabin.

[0084] Figure 1 This is a structural diagram of the entire thermal management system. Figure 2 The algorithm flow of this invention is as follows, and the specific implementation steps are as follows: 1. Build a fuel cell cabin temperature simulation model in a commercial software. The commercial software used in this invention is AVL / CruiseM.

[0085] 2. Build a control model for fuel cell and cabin thermal management in Matlab / Simulink. The fuel cell temperature is mainly related to the fuel cell operating current and the coolant flow rate, as shown in the following formula:

[0086]

[0087] Q cl =W cl C cl (T fc -T fcin (2)

[0088] in Q is the derivative of the fuel cell temperature. tot It is the total energy generated by the fuel cell, P fc Q is the electrical energy output by the fuel cell. cl It is the energy carried away by the coolant, T fcin It is the coolant temperature at the fuel cell inlet, W. cl It is the flow rate of the coolant, C cl It is the heat capacity of the coolant, m fc It is the mass of the fuel cell, C fc It refers to the heat capacity of the fuel cell.

[0089] The temperature of the coolant at the fuel cell inlet is mainly related to the coolant flow rate and the airflow rate at the radiator, as shown in the following formula:

[0090]

[0091] in It is the derivative of the coolant temperature at the fuel cell inlet, W air It refers to the airflow at the radiator, C. air It is the specific heat capacity of air, T atm It is the ambient temperature. It is the air temperature output by the radiator, m s It's the quality of the radiator, C s It is the heat capacity of the radiator.

[0092] The thermal model of a fuel cell vehicle's cabin is quite complex due to its multiple energy sources. The cabin temperature is affected by solar radiation, environmental heat exchange, heat dissipation from the passenger body, and heat dissipation from in-vehicle electronic components. The formula is as follows:

[0093]

[0094] in Q is the derivative of the car cabin temperature. sol It is the radiation energy of the sun passing through the car window glass, Q car It is the heat exchange between the car body and the air inside the cabin, Q pas It's for cooling the passengers and driver, Q ele It's about heat dissipation for the electronic components in the cockpit, Q eva It is the heat carried away by the refrigerant, m air It refers to the quality of the air inside the cabin.

[0095] The air conditioning system exchanges heat with the air in the cabin through an evaporator. Inside the evaporator, the refrigerant changes from a low-temperature, gaseous mixture to a high-temperature gaseous state, absorbing a significant amount of heat in the process, thus lowering the evaporator's temperature. Further heat exchange between the evaporator and the hot air in the cabin further lowers the cabin temperature. The heat removed by the evaporator is as follows:

[0096] Q eva =a oe A oe (T we -T cab (5)

[0097] Where a oe It is the heat exchange coefficient of the outer surface of the radiator, A oe It is the surface area of ​​the outer surface of the radiator, T we It is the temperature of the radiator wall.

[0098] The temperature of the evaporator is mainly affected by two factors: heat exchange between the evaporator and the cabin air, and heat exchange between the refrigerant and the evaporator. Heat exchange between the refrigerant and the evaporator primarily occurs in the two-phase region, where the refrigerant is in a gas-liquid mixture. Therefore, the formula is as follows:

[0099]

[0100] Where m e It is the quality of the evaporator, c e It is the heat capacity of the evaporator, a ie It is the heat exchange coefficient inside the evaporator, D ie It is the diameter of the pipe inside the evaporator, l e T is the length of the two-phase refrigerant state inside the evaporator. re It is the critical temperature of the refrigerant at that pressure.

[0101] 3. Design a Pontryagin minimum principle controller based on the control model in Matlab / Simulink. This controller has three control variables: fuel cell coolant flow rate, airflow rate at the radiator, and refrigerant flow rate in the air conditioning system. The fuel cell temperature, coolant temperature at the fuel cell inlet, and cabin temperature are selected as state variables. The fuel cell operating current and the vehicle speed are disturbance variables. The objective function of the controller is as follows:

[0102]

[0103] Where L1, L2, and L3 are temperature weighting parameters, T fc * This is the ideal temperature for fuel cells, T fcin* This is the ideal temperature of the fuel cell inlet coolant, T. cab * This is the ideal temperature for a car cabin.

[0104] The constructed Hamiltonian function is as follows:

[0105]

[0106] Where H(x(t),u(t),λ(t),t) is the Hamiltonian function, and λ1,λ2,λ3 are the corresponding costate variables.

[0107] Costate variables should satisfy the following conditions:

[0108]

[0109] λ1(t f ) = 0

[0110] λ2(t f ) = 0

[0111] λ3(t f )=0 (9)

[0112] Where t f At the final moment, the derivative of the costate variable is the negative partial derivative of the Hamiltonian function with respect to the corresponding state variable, and the value of the costate variable at the final moment must be 0.

[0113] The control problem of this invention is to find the optimal sequence of control variables that minimizes the objective function. This optimization problem is transformed into finding the extremum of the Hamiltonian function, i.e., finding the optimal sequence of control variables that minimizes the Hamiltonian function, as shown in the following equation:

[0114] H(x * (t),u * (t),λ * (t),t)≤H(x * (t),u(t),λ * (t),t) (10)

[0115] Where x * (t) is the optimal state variable at time t, u * (t) is the optimal control quantity at time t, λ * H(x) is the optimal costate variable at time t. * (t),u * (t),λ * H(x) is the Hamiltonian function value at time t, where the state variables, control variables, and costate variables are all at their optimal values. * (t),u(t),λ *(t),t) is the Hamiltonian function value of the optimal state value, optimal costate variable value, and control variable value at time t.

[0116] The optimal control sequence can be obtained by taking the partial derivative of the Hamiltonian function with respect to the control variables. The current problem lies in how to find suitable initial values ​​for the costate variables. This invention uses the Nelder-Mead algorithm to find suitable initial values ​​for the costate variables, and then finds the optimal control sequence. This algorithm is a local minimum algorithm for multivariate functions. Its advantage is that it does not require the function to be differentiable and can converge to the local minimum relatively quickly.

[0117] Its workflow is as follows:

[0118] 1) Initial value setting. The sum of the absolute values ​​of the costate variables is chosen as the objective function of the Nelder-Mead algorithm. Four sets of numbers are selected as the four initial values ​​of the costate variables. The values ​​of the objective function under the four initial values ​​are calculated and sorted in ascending order, denoted as l. min ,l1,l2,l max The four initial values ​​are sorted according to the magnitude of their corresponding objective functions, from smallest to largest, and denoted as a1, a2, a3, a4.

[0119] Establish a loop. While in the loop, calculate the average value of the four initial values ​​for each particle, denoted as a. o The calculation formula is as follows:

[0120]

[0121] The formula for calculating the reflection point is as follows:

[0122] a r =a o +α(a o -a1) (12)

[0123] Where α is the reflection point coefficient.

[0124] 3) Let the reflection point be the initial value of the costate variable, denoted as a. r The final objective function value is calculated and denoted as l. new The obtained objective function value is compared with the objective function value in step 1).

[0125] If the objective function value in step 3) is l new Greater than or equal to the maximum value of the objective function in step 1) max Then calculate the point of contraction, denoted as a. s Using the contraction point as the initial value of the costate variable, the final objective function value is calculated and denoted as l. s . will l s and l maxPerform a size comparison; if l s Less than l max , with contraction point a s Instead of a4, use l s Replace l max . a1, a2, a3, a s Sort the objective functions according to their corresponding sizes, and reassign them as a1, a2, a3, a4 from smallest to largest. Simultaneously, sort the recombined objective functions by size. Return to step 2) and restart the loop. The formula for calculating the contraction point is shown below:

[0126] a s =a o +δ(a4-a o (13)

[0127] Where δ is the contraction point coefficient.

[0128] If l s Greater than or equal to l max All initial values ​​except those corresponding to the minimum objective function need to be recalculated to obtain a new sequence of initial values. Using this new sequence as the initial values ​​for the costate variables, the corresponding objective functions are calculated and sorted according to their values. The initial value sequence, from smallest to largest, is a1, a2, a3, a4, and the objective functions, from smallest to largest, are l. min ,l1,l2,l max Returning to step 2), the loop restarts. The formula for recalculating the initial value is shown below:

[0129] b2 = a1 + δ(a2 - a1)

[0130] b3 = a1 + δ(a3 - a1)

[0131] b4=a1+δ(a4-a1) (14)

[0132] Where b2, b3, b4 are the newly obtained initial value sequence.

[0133] If l in step 3) new Less than the minimum value of the objective function in step 1) min Calculate the point of expansion, denoted as a. e Using the expansion point as the initial value of the costate variable, the corresponding objective function is calculated, denoted as l. e The obtained l e and l in step 3) new Compare the sizes of l, if l e Less than l new , using a e Replace a1. If l e Greater than or equal to l new, using a r Replace a1. Then sort according to the magnitude of the corresponding objective function, and denote the initial value sequence from smallest to largest as a1, a2, a3, a4. Return to step 2) and restart the loop. The formula for calculating the inflation point is as follows:

[0134] a e =a o +γ(a r -a o (15)

[0135] Where γ is the expansion point coefficient.

[0136] If l in step 3) new The size in step 1) l min and l max When in between, use a r Replace a1 to obtain a new sequence of initial values, and sort them according to the size of their respective objective functions, from smallest to largest, denoted as a1, a2, a3, a4. Return to step 2) and restart the loop.

[0137] After the loop ends, the optimal control sequence and the corresponding initial values ​​of the costate variables are obtained.

[0138] Export the designed Pontryagin minimum principle controller as an FMU file and import it into the AVL / CruiseM software. Connect the FMU controller module and the model port via a bus. Set the appropriate simulation step size and simulation time, and perform simulation verification.

[0139] The simulation results lead to the following conclusions:

[0140] Figure 3 and Figure 4 These represent the operating current of the fuel cell under NEDC conditions and the driving speed of the fuel cell vehicle, respectively. Figure 5 , Figure 6 and Figure 7 These refer to the fuel cell temperature, the coolant temperature at the fuel cell inlet, and the temperature of the vehicle's passenger compartment under the specified operating conditions. In the controller of this invention, the ideal temperature for the fuel cell is 65°C, the ideal temperature for the coolant at the fuel cell inlet is 55°C, and the ideal temperature for the vehicle's passenger compartment is 25°C. Figure 5 As can be seen, the temperature of the fuel cell dropped rapidly from the initial 70°C to 65°C, and was then stably controlled at 65°C. Figure 6 The initial temperature of the coolant at the fuel cell inlet is 55℃, but this temperature fluctuates significantly. This is because the temperature of the air passing through the condenser varies considerably, affecting heat dissipation at the radiator and consequently the coolant temperature at the fuel cell inlet. Figure 7As can be seen, the temperature in the car cabin dropped from 40°C to 25°C, and the cooling process was relatively stable.

[0141] In summary, the Pontryagin minimum principle controller of this invention can achieve temperature control.

[0142] The innovation of this invention lies in:

[0143] 1. A fuel cell temperature model was established in the simulation software AVL / Cruise M and Matlab / Simulink. The model takes into account the influence of the air conditioning cabin system on the fuel cell temperature and reflects the coupling relationship between the two.

[0144] 2. A Pontryagin minimum principle controller was designed. This controller takes into account the coupling between temperatures and simultaneously controls the fuel cell temperature, the coolant temperature at the fuel cell inlet, and the cabin temperature, with good control performance.

Claims

1. A temperature control method for fuel cell vehicles operating in high-temperature environments during summer, characterized in that: S1. Establish a control model for fuel cell and cabin thermal management: in Q is the derivative of the fuel cell temperature. tot It is the total energy generated by the fuel cell, P fc Q is the electrical energy output by the fuel cell. cl It is the energy carried away by the coolant, T fcin It is the coolant temperature at the fuel cell inlet, W. cl It is the flow rate of the coolant, C cl It is the heat capacity of the coolant, m fc It is the mass of the fuel cell, C fc It is the heat capacity of the fuel cell; Temperature of coolant at fuel cell inlet: in It is the derivative of the coolant temperature at the fuel cell inlet, W air It refers to the airflow at the radiator, C. air It is the specific heat capacity of air, T atm It is the ambient temperature. It is the air temperature output by the radiator, m s It's the quality of the radiator, C s It is the heat capacity of the radiator; Thermal model of the cabin of a fuel cell vehicle: in Q is the derivative of the car cabin temperature. sol It is the radiation energy of the sun passing through the car window glass, Q car It is the heat exchange between the car body and the air inside the cabin, Q pas It's for cooling the passengers and driver, Q ele It's about heat dissipation for the electronic components in the cockpit, Q eva It is the heat carried away by the refrigerant, m air It refers to the quality of the air inside the cabin; The heat removed by the evaporator is as follows: Qeva=aoeAoe(Twe-Tcab) (5) Where a oe It is the heat exchange coefficient of the outer surface of the radiator, A oe It is the surface area of ​​the outer surface of the radiator, T we It is the temperature of the radiator wall; Evaporator temperature: Where m e It is the quality of the evaporator, c e It is the heat capacity of the evaporator, a ie It is the heat exchange coefficient inside the evaporator, D ie It is the diameter of the pipe inside the evaporator, l e T is the length of the two-phase refrigerant state inside the evaporator. re It is the critical temperature of the refrigerant; S2. The objective function for the Pontryagin minimum principle controller is designed as follows: Where L1, L2, and L3 are temperature weighting parameters, T fc * This is the ideal temperature for fuel cells, T fcin * This is the ideal temperature of the fuel cell inlet coolant, T. cab * This is the ideal temperature for a car cabin; Constructed Hamiltonian function: Where H(x(t),u(t),λ(t),t) is the Hamiltonian function, and λ1,λ2,λ3 are the corresponding costate variables; Costate variables should satisfy the following conditions: λ1(t f )=0 λ2(t f )=0 λ3(t f )=0 (9) Where t f It is the final moment; Optimal sequence of control variables: H(x*(t),u*(t),λ*(t),t)≤H(x*(t),u(t),λ*(t),t) (10) Where x * (t) is the optimal state variable at time t, u * (t) is the optimal control quantity at time t, λ * H(x) is the optimal costate variable at time t. * (t),u * (t),λ * H(x) is the Hamiltonian function value at time t, where the state variables, control variables, and costate variables are all at their optimal values. * (t),u(t),λ * (t),t) is the Hamiltonian function value of the optimal state value, optimal costate variable value, and optimal control variable value at time t; The process of obtaining the initial values ​​of costate variables: 1) Initial value setting: The sum of the absolute values ​​of the costate variables is selected as the objective function of the Nelder-Mead algorithm. Four sets of numbers are selected as the four initial values ​​of the costate variables. The values ​​of the objective function under the four initial values ​​are calculated and sorted in ascending order, denoted as l. min ,l1,l2,l max Sort the four initial values ​​according to the size of their corresponding objective functions, from smallest to largest, and denote them as a1, a2, a3, a4. 2) The average value of the initial value a o : Calculate the reflection point: ar=a o +α(a o -a1) (12) Where α is the reflection point coefficient; 3) Let the reflection point be the initial value of the costate variable, denoted as a. r The final objective function value is calculated and denoted as l. new Compare the obtained objective function value with the objective function value in step 1). 4) If the objective function value in step 3) is l new Greater than or equal to the maximum value of the objective function in step 1) max Then calculate the point of contraction, denoted as a. s Using the contraction point as the initial value of the costate variable, the final objective function value is calculated and denoted as l. s ; will l s and l max Perform a size comparison; if l s Less than l max , with contraction point a s Instead of a4, use l s Replace l max ; a1, a2, a3, a s Sort the objects according to the size of their corresponding objective functions, and reassign them as a1, a2, a3, a4 from smallest to largest. Simultaneously, sort the recombined objective functions by size. Return to step 2) and restart the loop. The formula for calculating the contraction point is shown below: as=a o +δ(a4-a o ) (13) Where δ is the contraction point coefficient; If l s Greater than or equal to l max All initial values ​​except those corresponding to the minimum objective function must be recalculated to obtain a new sequence of initial values. Using this new sequence as the initial values ​​for the costate variables, the corresponding objective functions are calculated and sorted according to their values. The initial value sequence, from smallest to largest, is a1, a2, a3, a4, and the objective functions, from smallest to largest, are l. min ,l1,l2,l max Return to step 2) and restart the loop; the formula for recalculating the initial value is shown below: b2 = a1 + δ(a2 - a1) b3 = a1 + δ(a3 - a1) b4=a1+δ(a4-a1) (14) Where b2, b3, b4 are the newly obtained initial value sequence; 5) If l in step 3) new Less than the minimum value of the objective function in step 1) min Calculate the point of expansion, denoted as a. e ; Using the expansion point as the initial value of the costate variable, the corresponding objective function is calculated and denoted as l. e The obtained l e and l in step 3) new Compare the sizes of l, if l e Less than l new , using a e Replace a1; if l e Greater than or equal to l new , using a r Replace a1; then sort according to the size of the corresponding objective function, the initial value sequence is denoted as a1, a2, a3, a4 from smallest to largest; return to step 2) and start the loop again; the formula for calculating the expansion point is as follows: ae=ao+γ(ar-ao) (15) Where γ is the expansion point coefficient; 6) If l in step 3) new The size in step 1) l min and l max When in between, use a r Replace a1 to obtain a new sequence of initial values, and sort them according to the size of their respective objective functions, from smallest to largest as a1, a2, a3, a4; return to step 2) and start the loop again; 7) After the loop ends, the optimal control sequence and the corresponding initial values ​​of the costate variables are obtained.

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