A thermal management control method for a fuel cell hybrid electric vehicle

Abstract

The invention relates to the field of thermal management technology for fuel cell hybrid electric vehicles, and discloses a thermal management control method for fuel cell hybrid electric vehicles. The thermal management control method for fuel cell hybrid electric vehicles uses MPC to control the coolant flow rate to ensure that each component is within a normal operating temperature range in a fuel cell hybrid electric vehicle with multiple inputs and multiple outputs and complex nonlinear systems, and then uses the inlet and outlet temperatures of each component as state variables, the coolant flow rate of each component water pump as a control variable, and uses an MPC controller to control and coordinate the temperatures of each component, and considers the correlation of each loop thermal management system to improve the overall performance of the fuel cell hybrid electric vehicle.

Description

Technical Field

[0001] The invention relates to the technical field of thermal management of fuel cell hybrid electric vehicles, and in particular to a thermal management control method for fuel cell hybrid electric vehicles. Background Art

[0002] Fuel cell hybrid vehicles have the advantages of fast hydrogen refueling, long driving range, high energy conversion efficiency, clean and environmentally friendly, etc., and have become the ultimate solution for transportation. Unfortunately, excessively high temperatures have a great impact on the performance, working efficiency, and life of fuel cells, power batteries, and motors. As the operating conditions are running, heat will inevitably be generated, and each component has its own normal operating temperature range, which makes heat dissipation more difficult. The accuracy of thermal management system control significantly affects the good output characteristics of fuel cell hybrid vehicles under steady / transient conditions. Therefore, efficient and reliable thermal management control strategies are crucial to the operation and safety of fuel cell hybrid vehicles.

[0003] At present, it is difficult to establish a comprehensive and accurate control system for the vehicle's multi-input and multi-output, complex and nonlinear system. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the deficiencies in the prior art, the present invention provides a thermal management control method for a fuel cell hybrid electric vehicle, which has the advantages of accurately controlling the temperature and solves the above-mentioned technical problems.

[0006] (II) Technical solution

[0007] To achieve the above object, the present invention provides the following technical solution: a fuel cell hybrid electric vehicle thermal management control method, comprising the following steps:

[0008] S1. Obtain the performance data of fuel cell hybrid electric vehicles, conduct cycle test on the whole vehicle, and record in real time the fuel cell current, stack output voltage, fuel cell coolant inlet / outlet temperature, stack cooling water flow, power battery inlet / outlet temperature, battery cooling water flow, chiller temperature, power battery output current, power battery voltage and power battery internal temperature, motor cooling water flow, passenger compartment inlet / outlet temperature, air conditioning refrigerant flow, motor inlet / outlet temperature, motor water pump flow, total motor power demand, equivalent efficiency of motor and power control unit, air flow, evaporation The air flow data of the device; obtain the solenoid valve switch signal: the stack coolant circulation switch signal, the power battery switch chiller circuit signal; obtain the ambient temperature and passenger compartment related parameters, including the window transmittance to solar radiation, solar radiation intensity, the area of ​​the windshield, rear window and side windows, shading factor, the angle between the window and the vertical direction, the convection heat transfer coefficient between the body shell and the passenger compartment, the heat transfer area of ​​the top cover, windshield, rear side window and side windows, the number of passengers in the passenger compartment, the air temperature of the passenger compartment, the air flow through the evaporator, and the specific heat capacity of the air in the passenger compartment;

[0009] S2. Constructing a vehicle thermodynamic model, specifically including the following steps:

[0010] S2.1. Calculate the heat generation power Q of the stack by using the stack output voltage and stack current in step S1 gen , the specific expression is as follows:

[0011] Q gen =(E nernst N FC -V st )I st

[0012] Among them, Q gen Indicates the heat generation power of the battery stack, E nernst Indicates Nents voltage, generally 1.2V, N FC Indicates the number of fuel cell stacks, V st Indicates the voltage of the battery stack, I st Indicates the current of the battery stack;

[0013] S2.2. Calculate the battery heat generation power Q by using the power battery output current, power battery voltage and power battery internal temperature in step S1. bat , the specific expression is as follows:

[0014]

[0015] Among them, Q bat Indicates the heat generation power of the battery, I bat Represents the current of the power battery, R esistance Represents resistance, Tamb Indicates the ambient temperature, represents the temperature influence coefficient;

[0016] S2.3, calculate the conduction heat load Q through the passenger compartment related parameters obtained in step S1 rb , radiant heat load Q cb , the heat generated by the passengers in the car Q h and ventilation system heat load Q n Get the passenger compartment heat generation power Q cab , the specific expression is as follows:

[0017] Q cab =Q rb +Q cb +Q h +Q n

[0018]

[0019] Q cb =∑h b F(T amb -T cabin，in )

[0020] Q h =1145+116n m

[0021] Q n =m e ξC cabin (T amb -T cabin，in )

[0022] Among them, Q rb represents the conduction heat load, ξ represents the transmittance of the window to solar radiation, I represents the intensity of solar radiation, and F g，j represents the area of ​​the roof, windshield, rear window and side windows, C represents the shading factor, θ j It is expressed as the angle between the window and the vertical direction. It means the calculation of top cover, windshield, rear window and side window are accumulated in sequence, Q cb represents the radiation heat load, h b It is expressed as the convective heat transfer coefficient between the body shell and the passenger compartment, F represents the heat transfer area of ​​the roof, windshield, rear side window and side window, T amb Indicates the ambient temperature, T cabin，in represents the cabin air temperature, Q h represents the heat generated by the occupants in the vehicle, n m represents the number of passengers in the cabin, Q n represents the heat load of the ventilation system, m erepresents the air flow through the evaporator, C cabin represents the specific heat capacity of the cabin air;

[0023] S2.4. The motor heat generation power is obtained by using the motor parameters obtained in step S1. The specific expression is as follows:

[0024] Q mot =P·(1-η mot / PCU )

[0025] Among them, Q mot represents the heat generation power of the motor, P is the total required power of the motor, η mot / PCU Indicates the equivalent efficiency of the motor and PCU, where PCU is the power control unit;

[0026] S2.5. The heat generation power of each component obtained through steps S2.1-S2.4 is used to build a vehicle thermodynamic model according to the first law of thermodynamics. The specific expression is as follows:

[0027] Q coolant =C p ·ρ·q·|T in -T out |

[0028]

[0029] Among them, Q coolant Indicates the heat output per unit time of the fluid, C p is the specific heat capacity of the fluid, ρ is the fluid density, q is the fluid flow rate, T in Indicates the temperature of the inlet fluid, T out Indicates the temperature of the outlet fluid, m Fi Indicates the mass of component Fi, C Fi represents the specific heat capacity of component Fi, represents the temperature of the component Fi, Q Fi Indicates the heat generated by the components. Components Fi include fuel cells, power batteries, motors, and passenger compartments;

[0030] S3. Build a nonlinear temperature dynamic model, combine the heat generation and heat dissipation of each key component during vehicle driving, and design the flow rate of each water pump based on the constructed vehicle thermodynamic model to output the expected temperature. The specific steps are as follows:

[0031] S3.1, the fuel cell coolant inlet / outlet temperature, power battery inlet / outlet temperature, motor inlet / outlet temperature, chiller temperature and passenger compartment inlet / outlet temperature obtained in step S1 are used to form a state variable x;

[0032] S3.2, the stack cooling water flow, battery cooling water flow, motor cooling water flow, air conditioning refrigerant flow, and air flow obtained in step S1 are used as input variables u;

[0033] S3.3, the heat generation power Q of the battery stack in step S2.1 gen , the power battery heat generation power Q in step S2.2 bat , the passenger compartment heat generation power Q in step S2.3 cab , the motor heat generation power Q in step S2.4 mot , the ambient temperature value, the stack coolant circulation switch signal and the power battery switch chiller circuit signal constitute the disturbance variable d;

[0034] S4. Linearize the nonlinear temperature dynamic model: The nonlinear system is linearly processed at the reference working point ref using the Tait series expansion and ignoring the high-order terms. Then the disturbance variable d in S3.3 is expanded into the nonlinear temperature dynamic model system in the form of a state variable. The specific expression is as follows:

[0035]

[0036]

[0037] in, represents the linearized state variable, represents the reference state variable, y represents the output variable of the system, represents the state variable, represents the input variable, u ref represents the reference input variable, u represents the input variable, The superscript T represents the transpose, A represents the system state matrix obtained by partial differentiation of the equation established in step S2.5, and represents the number of system state variables, B represents the system input matrix obtained by partial differentiation of the established equation step S2.5, represents the number of system input variables, C represents the output matrix of the constructed MPC system, n y Indicates the number of system output variables;

[0038] S5. Discretize the model after linearization in step S4, specifically using the forward Euler formula for discretization. The specific expression is as follows:

[0039]

[0040] A k,t =E+T ime A

[0041] B k,t =T ime B

[0042]

[0043] Among them, A k,t and B k,t They represent the discretized system matrices, express The predicted value at the next moment, E represents the state variable of the system at the kth moment, the input variable of the system at the kth moment, and the unit matrix, T ime represents the sampling time, A represents the system state matrix, and represents the number of system state variables, B represents the system input matrix, represents the number of system input variables, C represents the system output matrix, k represents the system running time,

[0044] S6. Design of MPC control system: Give the currently required state variable signal and target value signal to the MPC control system, obtain the corresponding input variable u, adjust the flow rate of the water pump of each component, and control the temperature;

[0045] S6.1, setting the objective function J(k) for the MPC control system established in step S6;

[0046] S6.2, setting constraints for limiting the input variable u and the control increment Δu in the control process of the MPC control system;

[0047] S6.3, transform the objective function in step S6.1 into a form that can be solved by quadratic programming. The specific expression is as follows:

[0048]

[0049] Among them, J, Y, Y T , They represent the objective function, the state variable matrix in the prediction time domain, the state variable matrix transpose in the prediction time domain, the output variable matrix transpose in the prediction time domain, and the output variable matrix in the prediction time domain, respectively. They represent the system prediction from time t to k+N p After the time is discretized, the state variable is They represent the discretized input variables from time k to time k+Nc-1, and Respectively represent N p ×N p and N c ×N c The identity matrix, represents the Kronecker product;

[0050] S6.4, the solvable quadratic programming solution expression obtained according to step S6.3 is as follows:

[0051]

[0052] in, H. f T They represent the output variable matrix transpose in the prediction time domain, the quadratic term matrix in the quadratic programming, the output variable matrix in the prediction time domain, and the linear term matrix in the quadratic programming, respectively.

[0053] S7, according to the objective function and constraint solving in step S6, obtain the optimal temperature control, specifically including the following steps:

[0054] S7.1. Optimize the solution by combining the prediction model, objective function and constraints;

[0055] S7.2, by obtaining the optimal control sequence at the current moment, inputting it into the fuel cell hybrid vehicle, the fuel cell hybrid vehicle executes the received control command and feeds back to the MPC control layer;

[0056] S7.3, input the state quantity at the current moment into the MPC controller, perform the optimization solution again, and obtain the flow control variable required by the fuel cell hybrid vehicle at the next moment.

[0057] As a preferred technical solution of the present invention, the specific expression of the objective function in step S4 is as follows:

[0058]

[0059] Among them, N p is the prediction time domain, N c is the control time domain, τ is the weight coefficient, ε is the relaxation factor, ΔU is the control change, k+i represents the prediction time, t represents the time, x(k+i|t) represents the prediction of the state variable at time k+i at time t, Indicates the system's ability to follow the reference target, represents the system's constraint on the control variation, τε 2represents soft constraints, Q and Z are weight matrices, ΔU(k+i|t) represents the prediction of the control change at time k+i at time t, and It represents the second norm under the adjustment of Q weight matrix and the second norm under the adjustment of Z weight matrix.

[0060] As a preferred technical solution of the present invention, the specific expression of the constraint condition in step S5 is as follows:

[0061] u min (t+k)≤u(t+k)≤u max (t+k)

[0062] Δu min (t+k)≤Δu(t+k)≤Δu max (t+k)

[0063] Among them, u(t+k), Δu(t+k), u min (t+k),u max (t+k), Δu min (t+k) and Δu max (t+k) represents the input variable value at time t+k, the control increment value at time t+k, the lower limit constraint of the input variable, the upper limit constraint of the input variable, the lower limit constraint of the control increment, and the upper limit constraint of the control increment, respectively. k=0,1,…,N c -1.

[0064] Compared with the prior art, the present invention provides a fuel cell hybrid electric vehicle thermal management control method, which has the following beneficial effects:

[0065] The present invention uses MPC to control the coolant flow rate in a fuel cell hybrid vehicle with multiple inputs and multiple outputs and complex nonlinear systems to ensure that each component is within a normal operating temperature range. The inlet and outlet temperatures of each component are used as state variables, the coolant flow rate of each component water pump is used as a control variable, and the MPC controller is used to control and coordinate the temperatures of each component. The correlation between the thermal management systems of each loop is taken into consideration to improve the overall performance of the fuel cell hybrid vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 This is the stack temperature effect obtained by controlling the flow rate in the present invention;

[0067] Figure 2 is a battery temperature curve during the implementation of the present invention;

[0068] Figure 3 is the temperature curve of the motor during the implementation of the present invention;

[0069] Figure 4is a passenger cabin temperature comparison curve obtained by implementing the present invention;

[0070] Figure 5 It is a schematic diagram of a control system of a MPC fuel cell hybrid vehicle based on an embodiment of the present disclosure; DETAILED DESCRIPTION

[0071] 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.

[0072] See also Figure 1-5 The present invention provides the following technical solution: A thermal management control method for a fuel cell hybrid electric vehicle, comprising the following steps:

[0073] S1. Obtain the performance data of fuel cell hybrid electric vehicles, conduct cycle test on the whole vehicle, and record in real time the fuel cell current, stack output voltage, fuel cell coolant inlet / outlet temperature, stack cooling water flow, power battery inlet / outlet temperature, battery cooling water flow, chiller temperature, power battery output current, power battery voltage and power battery internal temperature, motor cooling water flow, passenger compartment inlet / outlet temperature, air conditioning refrigerant flow, motor inlet / outlet temperature, motor water pump flow, total motor power demand, equivalent efficiency of motor and power control unit, air flow, evaporation The air flow data of the device; obtain the solenoid valve switch signal: the stack coolant circulation switch signal, the power battery switch chiller circuit signal; obtain the ambient temperature and passenger compartment related parameters, including the window transmittance to solar radiation, solar radiation intensity, the area of ​​the windshield, rear window and side windows, shading factor, the angle between the window and the vertical direction, the convection heat transfer coefficient between the body shell and the passenger compartment, the heat transfer area of ​​the top cover, windshield, rear side window and side windows, the number of passengers in the passenger compartment, the air temperature of the passenger compartment, the air flow through the evaporator, and the specific heat capacity of the air in the passenger compartment;

[0074] S2. Constructing a vehicle thermodynamic model, specifically including the following steps:

[0075] S2.1. Calculate the heat generation power Q of the stack by using the stack output voltage and stack current in step S1 gen , the specific expression is as follows:

[0076] Q gen =(E nernst N FC -V st )I st

[0077] Among them, Q gen Indicates the heat generation power of the battery stack, E nernst Indicates Nents voltage, generally 1.2V, N FC Indicates the number of fuel cell stacks, V st Indicates the voltage of the battery stack, I st Indicates the current of the battery stack;

[0078] S2.2. Calculate the battery heat generation power Q by using the power battery output current, power battery voltage and power battery internal temperature in step S1. bat , the specific expression is as follows:

[0079]

[0080] Among them, Q bat Indicates the heat generation power of the battery, I bat Represents the current of the power battery, R esistance Represents resistance, T amb Indicates the ambient temperature, represents the temperature influence coefficient;

[0081] S2.3, calculate the conduction heat load Q through the passenger compartment related parameters obtained in step S1 rb , radiant heat load Q cb , the heat generated by the passengers in the car Q h and ventilation system heat load Q n Get the passenger compartment heat generation power Q cab , the specific expression is as follows:

[0082] Q cab =Q rb +Q cb +Q h +Q n

[0083]

[0084] Q cb =∑h b F(T amb -T cabin，in )

[0085] Q h =145+116n m

[0086] Q n =m e ξC cabim (T amb -T cabin，in )

[0087] Among them, Q rb represents the conduction heat load, ξ represents the transmittance of the window to solar radiation, I represents the intensity of solar radiation, and F g，j represents the area of ​​the roof, windshield, rear window and side windows, C represents the shading factor, θ j It is expressed as the angle between the window and the vertical direction. It means the calculation of top cover, windshield, rear window and side window are accumulated in sequence, Q cb represents the radiation heat load, h b It is expressed as the convective heat transfer coefficient between the body shell and the passenger compartment, F represents the heat transfer area of ​​the roof, windshield, rear side window and side window, T amb Indicates the ambient temperature, T cabin，in represents the cabin air temperature, Q h represents the heat generated by the occupants in the vehicle, n m represents the number of passengers in the cabin, Q n represents the heat load of the ventilation system, m e represents the air flow through the evaporator, C cabin represents the specific heat capacity of the cabin air;

[0088] S2.4. The motor heat generation power is obtained by using the motor parameters obtained in step S1. The specific expression is as follows:

[0089] Q mot =P·(1-η mot / PCU )

[0090] Among them, Q mot represents the heat generation power of the motor, P is the total required power of the motor, η mot / PCU Indicates the equivalent efficiency of the motor and PCU, where PCU is the power control unit;

[0091] S2.5. The heat generation power of each component obtained through steps S2.1-S2.4 is used to build a vehicle thermodynamic model according to the first law of thermodynamics. The specific expression is as follows:

[0092] Q coolant =C p ·ρ·q·|T in -T out |

[0093]

[0094] Among them, Q coolant Indicates the heat output per unit time of the fluid, C p is the specific heat capacity of the fluid, ρ is the fluid density, q is the fluid flow rate, T in Indicates the temperature of the inlet fluid, T out Indicates the temperature of the outlet fluid, mFi Indicates the mass of component Fi, C Fi represents the specific heat capacity of component Fi, represents the temperature of the component Fi, Q Fi Indicates the heat generated by the components. Components Fi include fuel cells, power batteries, motors, and passenger compartments;

[0095] S3. Build a nonlinear temperature dynamic model, combine the heat generation and heat dissipation of each key component during vehicle driving, and design the flow rate of each water pump based on the constructed vehicle thermodynamic model to output the expected temperature. The specific steps are as follows:

[0096] S3.1, the fuel cell coolant inlet / outlet temperature, power battery inlet / outlet temperature, motor inlet / outlet temperature, chiller temperature and passenger compartment inlet / outlet temperature obtained in step S1 are used to form a state variable x;

[0097] S3.2, the stack cooling water flow, battery cooling water flow, motor cooling water flow, air conditioning refrigerant flow, and air flow obtained in step S1 are used as input variables u;

[0098] S3.3, the heat generation power Q of the battery stack in step S2.1 gen , the power battery heat generation power Q in step S2.2 bat , the passenger compartment heat generation power Q in step S2.3 cab , the motor heat generation power Q in step S2.4 mot , the ambient temperature value, the stack coolant circulation switch signal and the power battery switch chiller circuit signal constitute the disturbance variable d;

[0099] S4. Linearize the nonlinear temperature dynamic model: The nonlinear system is linearly processed at the reference working point ref using the Tait series expansion and ignoring the high-order terms. Then the disturbance variable d in S3.3 is expanded into the nonlinear temperature dynamic model system in the form of a state variable. The specific expression is as follows:

[0100]

[0101]

[0102] in, represents the linearized state variable, represents the reference state variable, y represents the output variable of the system, represents the state variable, represents the input variable, u ref represents the reference input variable, u represents the input variable, The superscript T indicates transposition, and A indicates partial differentiation of the equation established in step S2.5. The resulting system state matrix is represents the number of system state variables, and B represents the partial differentiation of the established equation step S2.5. The resulting system input matrix, represents the number of system input variables, x represents the output matrix of the constructed MPC system, n y Indicates the number of system output variables;

[0103] S5. Discretize the model after linearization in step S4, specifically using the forward Euler formula for discretization. The specific expression is as follows:

[0104]

[0105] A k,t =E+T ime A

[0106] B k,t =T ime B

[0107]

[0108] Among them, A k,t and B k,t They represent the discretized system matrices, express The predicted value at the next moment, E represents the state variable of the system at the kth moment, the input variable of the system at the kth moment, and the unit matrix, T ime represents the sampling time, A represents the system state matrix, and represents the number of system state variables, B represents the system input matrix, represents the number of system input variables, C represents the system output matrix, k represents the system running time,

[0109] S6. Design of MPC control system: Give the current required state variable signal and target value signal to the MPC control system, obtain the corresponding input variable u, adjust the flow of each component pump, and control the temperature; during the control process, there is a desired reference trajectory;

[0110] The core idea is that the thermal management control of fuel cell hybrid electric vehicles is completely based on MPC model predictive control, rather than traditional PID control or algorithm optimization. It consists of three parts: predictive model, rolling optimization and feedback correction, each of which has different functions. By controlling the temperature status information of each component of the current system provided by the fuel cell hybrid electric vehicle in the control system, plus the future control input pump flow variable, the state of thermal management temperature control of the fuel cell hybrid electric vehicle in the future is predicted. In addition, at each sampling moment, according to the objective function and constraints, the optimal control rate in the control time domain from that moment is solved. By comparing the output with the estimated value of the model, the prediction error of the model is obtained, and then the model prediction error is used to correct the predicted value of the model, so as to obtain a more accurate prediction value of the future output.

[0111] S6.1, the objective function J(k) of the model, its specific expression is as follows:

[0112]

[0113] Among them, N p is the prediction time domain, N c is the control time domain, τ is the weight coefficient, ε is the relaxation factor, ΔU is the control change, k+i represents the prediction time, t represents the time, x(k+i|t) represents the prediction of the state variable at time k+i at time t, Indicates the system's ability to follow the reference target, represents the system's constraint on the control variation, τε 2 represents soft constraints, Q and Z are weight matrices, ΔU(k+i|t) represents the prediction of the control change at time k+i at time t, and represents the second norm under the adjustment of Q weight matrix and the second norm under the adjustment of Z weight matrix;

[0114] S6.2. Set constraints to limit the input variable u and control increment Δu in the control process of the MPC control system. The specific expressions are as follows:

[0115] u min (t+k)≤u(t+k)≤u max (t+k)

[0116] Δu min (t+k)≤Δu(t+k)≤Δu max (t+k)

[0117] Among them, u(t+k), Δu(t+k), u min (t+k),u max (t+k), Δu min(t+k) and Δu max (t+k) represents the input variable value at time t+k, the control increment value at time t+k, the lower limit constraint of the input variable, the upper limit constraint of the input variable, the lower limit constraint of the control increment, and the upper limit constraint of the control increment, respectively. k=0,1,…,N c -1;

[0118] S6.3, transform the objective function in step S6.1 into a form that can be solved by quadratic programming. The specific expression is as follows:

[0119]

[0120] Among them, J, Y, Y T , They represent the objective function, the state variable matrix in the prediction time domain, the state variable matrix transpose in the prediction time domain, the output variable matrix transpose in the prediction time domain, and the output variable matrix in the prediction time domain, respectively. They represent the system prediction from time k to time k+N respectively. p After the time is discretized, the state variable is They represent the discretized input variables from time k to time k+Nc-1, and Respectively represent N p ×N p and N c ×N c The identity matrix, represents the Kronecker product;

[0121] S6.4, the solvable quadratic programming solution expression obtained according to step S6.3 is as follows:

[0122]

[0123] in, H. f T They represent the output variable matrix transpose in the prediction time domain, the quadratic term matrix in the quadratic programming, the output variable matrix in the prediction time domain, and the linear term matrix in the quadratic programming, respectively. represents the system matrix discretized to the first power, second power, ..., Np power; N p -1th power, N p -2,...,N p -N c The discretized system matrix.

[0124] S7, according to the objective function and constraint solving in step S6, obtain the optimal temperature control, specifically including the following steps:

[0125] S7.1. Optimize the solution by combining the prediction model, objective function and constraints;

[0126] S7.2, by obtaining the optimal control sequence at the current moment, inputting it into the fuel cell hybrid vehicle, the fuel cell hybrid vehicle executes the received control command and feeds back to the MPC control layer;

[0127] S7.3, input the state quantity at the current moment into the MPC controller, perform optimization solution again, and obtain the flow control variable required by the fuel cell hybrid vehicle at the next moment;

[0128] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A thermal management control method for a fuel cell hybrid electric vehicle, characterized in that: The following steps are involved: S1. Obtain the performance data of fuel cell hybrid electric vehicles, conduct cycle test on the whole vehicle, and record in real time the fuel cell current, stack output voltage, fuel cell coolant inlet / outlet temperature, stack cooling water flow, power battery inlet / outlet temperature, battery cooling water flow, refrigerator temperature, power battery output current, power battery voltage and power battery internal temperature, motor cooling water flow, passenger compartment inlet / outlet temperature, air conditioning refrigerant flow, motor inlet / outlet temperature, motor water pump flow, total motor power demand, equivalent efficiency of motor and power control unit, air flow, evaporation The air flow data of the device; obtain the solenoid valve switch signal: the stack coolant circulation switch signal, the power battery switch refrigerator circuit signal; obtain the ambient temperature and the relevant parameters of the passenger compartment, which include the transmittance of the window to solar radiation, the intensity of solar radiation, the area of ​​the windshield, the rear window and the side windows, the shading factor, the angle between the window and the vertical direction, the convection heat transfer coefficient between the body shell and the passenger compartment, the heat transfer area of ​​the top cover, windshield, rear side window and side window, the number of passengers in the passenger compartment, the air temperature of the passenger compartment, the air flow through the evaporator, and the specific heat capacity of the air in the passenger compartment; S2. Constructing a vehicle thermodynamic model, specifically including the following steps: S2.

1. Calculate the heat generation power Q of the stack by using the stack output voltage and stack current in step S1 gen , the specific expression is as follows: Q gen =(E nernst N FC -V st )I st Among them, Q gen Indicates the heat generation power of the battery stack, E nernst Indicates Nents voltage, generally 1.2V, N FC Indicates the number of fuel cell stacks, V st Indicates the voltage of the battery stack, I st Indicates the current of the battery stack; S2.

2. Calculate the battery heat generation power Q by using the power battery output current, power battery voltage and power battery internal temperature in step S1. bat , the specific expression is as follows: Among them, Q bat Indicates the heat generation power of the battery, I bat Represents the current of the power battery, R esistance Represents resistance, T amb Indicates the ambient temperature, represents the temperature influence coefficient; S2.3, calculate the conduction heat load Q through the passenger compartment related parameters obtained in step S1 rb , radiant heat load Q cb , the heat generated by the passengers in the car Q h and ventilation system heat load Q n Get the passenger compartment heat generation power Q cab , the specific expression is as follows: Q cab =Q rb +Q cb +Q h +Q n Q cb =∑h b F(T amb -T cabin，in ) Q h =145+116n m Q n =m e ξC cabin (T amb -T cabin，in ) Among them, Q rb represents the conduction heat load, ξ represents the transmittance of the window to solar radiation, I represents the intensity of solar radiation, and F g，j represents the area of ​​the roof, windshield, rear window and side windows, C represents the shading factor, θ j It is expressed as the angle between the window and the vertical direction. It means the calculation of top cover, windshield, rear window and side window are accumulated in sequence, Q cb is the radiation heat load, h b It is expressed as the convective heat transfer coefficient between the body shell and the passenger compartment, F represents the heat transfer area of ​​the roof, windshield, rear side window and side window, T amb Indicates the ambient temperature, T cabin，in represents the cabin air temperature, Q h represents the heat generated by the occupants in the vehicle, n m represents the number of passengers in the cabin, Q n represents the heat load of the ventilation system, m e represents the air flow through the evaporator, C cabin represents the specific heat capacity of the cabin air; S2.

4. The motor heat generation power is obtained by using the motor parameters obtained in step S1. The specific expression is as follows: Q mot =P·(1-η mot / PCU ) Among them, Q mot represents the heat generation power of the motor, P is the total required power of the motor, η mot / PCU Indicates the equivalent efficiency of the motor and PCU, where PCU is the power control unit; S2.

5. The heat generation power of each component obtained through steps S2.1-S2.4 is used to build a vehicle thermodynamic model according to the first law of thermodynamics. The specific expression is as follows: Q coolant =C p ·ρ·q·|T in -T out | Among them, Q coolant Indicates the heat output per unit time of the fluid, C p is the specific heat capacity of the fluid, ρ is the fluid density, q is the fluid flow rate, T in Indicates the temperature of the inlet fluid, T out Indicates the temperature of the outlet fluid, m Fi Indicates component F i The quality of C Fi represents the specific heat capacity of component Fi, represents the temperature of the component Fi, Q Fi Indicates the heat generated by the components. Components Fi include fuel cells, power batteries, motors, and passenger compartments; S3. Build a nonlinear temperature dynamic model, combine the heat generation and heat dissipation of each key component during vehicle driving, and design the flow rate of each water pump based on the constructed vehicle thermodynamic model to output the expected temperature. The specific steps are as follows: S3.1, the fuel cell coolant inlet / outlet temperature, power battery inlet / outlet temperature, motor inlet / outlet temperature, chiller temperature and passenger compartment inlet / outlet temperature obtained in step S1 are used to form a state variable x; S3.2, the stack cooling water flow, battery cooling water flow, motor cooling water flow, air conditioning refrigerant flow, and air flow obtained in step S1 are used as input variables u; S3.3, the heat generation power Q of the battery stack in step S2.1 gen , the power battery heat generation power Q in step S2.2 bat , the passenger compartment heat generation power Q in step S2.3 cab , the motor heat generation power Q in step S2.4 mot , the ambient temperature value, the stack coolant circulation switch signal and the power battery switch chiller circuit signal constitute the disturbance variable d; S4. Linearize the nonlinear temperature dynamic model: The nonlinear system is linearly processed at the reference working point ref using the Tait series expansion and ignoring the high-order terms. Then the disturbance variable d in S3.3 is expanded into the nonlinear temperature dynamic model system in the form of a state variable. The specific expression is as follows: in, represents the linearized state variable, represents the reference state variable, y represents the output variable of the system, represents the state variable, represents the input variable, u ref represents the reference input variable, u represents the input variable, The superscript T represents the transpose, A represents the system state matrix obtained by partial differentiation of the equation established in step S2.5, and represents the number of system state variables, B represents the system input matrix obtained by partial differentiation of the established equation step S2.5, represents the number of system input variables, C represents the output matrix of the constructed MPC system, n y Indicates the number of system output variables; S5. Discretize the model after linearization in step S4, specifically using the forward Euler formula for discretization. The specific expression is as follows: A k,t =E+T ime A B k,t =T ime B Among them, A k,t and B k,t They represent the discretized system matrices, express The predicted value at the next moment, E represents the state variable of the system at the kth moment, the input variable of the system at the kth moment, and the unit matrix, T ime represents the sampling time, A represents the system state matrix, and represents the number of system state variables, B represents the system input matrix, represents the number of system input variables, C represents the system output matrix, k represents the system running time, S6. Design of MPC control system: Give the currently required state variable signal and target value signal to the MPC control system, obtain the corresponding input variable u, adjust the flow rate of the water pump of each component, and control the temperature; S6.1, setting the objective function J(k) for the MPC control system established in step S6; The specific expression of the objective function in step S6.1 is as follows: Among them, N p is the prediction time domain, N c is the control time domain, τ is the weight coefficient, ε is the relaxation factor, ΔU is the control change, k+i represents the prediction time, t represents the time, x(k+i|t) represents the prediction of the state variable at time k+i at time t, Indicates the system's ability to follow the reference target, represents the system's constraint on the control variation, τε 2 represents soft constraints, Q and Z are weight matrices, ΔU(k+i|t) represents the prediction of the control change at time k+i at time t, and represents the second norm under the adjustment of Q weight matrix and the second norm under the adjustment of Z weight matrix; S6.2, setting constraints for limiting the input variable u and controlling the increment Δu during the control process of the MPC control system. The specific expression of the constraints in step S6.2 is as follows: u min (t+k)≤u(t+k)≤u max (t+k) Δu min (t+k)≤Δu(t+k)≤Δu max (t+k) Among them, u(t+k), Δu(t+k), u min (t+k),u max (t+k), Δu min (t+k) and Δu max (t+k) represents the input variable value at time t+k, the control increment value at time t+k, the lower limit constraint of the input variable, the upper limit constraint of the input variable, the lower limit constraint of the control increment, and the upper limit constraint of the control increment, respectively. k=0,1,…,N c -1, N c To control the time domain; S7, according to the objective function and constraint solving in step S6, obtain the optimal temperature control, specifically including the following steps: S7.

1. Optimize the solution by combining the prediction model, objective function and constraints; S7.2, by obtaining the optimal control sequence at the current moment, inputting it into the fuel cell hybrid vehicle, the fuel cell hybrid vehicle executes the received control command and feeds back to the MPC control system; S7.3, input the state quantity at the current moment into the MPC control system, perform the optimization solution again, and obtain the flow control variable required by the fuel cell hybrid vehicle at the next moment.

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