A power optimization control method for a hybrid fuel cell vehicle considering speed optimization

Abstract

The present invention discloses a power optimization control method for a hybrid fuel cell vehicle considering speed optimization. In order to improve the real-time performance of the speed optimization method, the present invention designs a speed optimization method based on a hierarchical framework. The upper layer module of speed optimization optimizes the switching boundary conditions of the vehicle driving mode, and the lower layer module of speed optimization optimizes the wheel torque distribution to improve the energy efficiency utilization rate and reduce the vehicle driving energy consumption. The target vehicle speed can be obtained through the speed optimization module, and thus the desired load current can be calculated. Based on the constructed multi-objective optimization function, the adaptive factor can be calculated. Based on the state space model of the hybrid fuel cell vehicle power system and the adaptive factor, the power optimization control method based on port-Hamiltonian theory is used to track the desired load current in real time, and at the same time optimize and adjust the power distribution of the hybrid energy system to improve the economy, durability and comfort of the hybrid energy system.

Description

Technical Field

[0001] The present invention relates to an optimization control method for a hybrid fuel cell vehicle, and more particularly to a power optimization control method for a hybrid fuel cell vehicle considering speed optimization. Background Art

[0002] The increasingly serious environmental problems have prompted automobile manufacturers to promote the research on clean energy vehicles. Hybrid fuel cell vehicles have the advantages of zero pollution, high energy conversion efficiency, long driving range, short refueling time, etc. They are important members of future clean transportation vehicles and can meet the needs of green and sustainable development. Current research shows that the power optimization control method has a huge impact on the economy and durability of hybrid fuel cell vehicles. The key problem lies in how to efficiently optimize the power distribution of the power system of hybrid fuel cell vehicles to save energy and improve the service life of the system. However, most current power optimization control methods only focus on the economy of hydrogen consumption. Different from traditional fuel vehicles, hybrid fuel cell vehicles still have bottlenecks before large-scale commercial promotion, such as high energy system cost and short service life. Therefore, when designing a power optimization control method for hybrid fuel cell vehicles, the durability of the energy system must be considered to extend their service life, which is ignored in most power optimization control methods. In addition, longitudinal control considering speed optimization can effectively reduce the mechanical energy consumption and motor energy consumption during vehicle driving. However, there is rarely research considering the integration of speed optimization in current power optimization control methods, which may waste potential opportunities to improve the energy efficiency of the driving system. Therefore, in order to improve the energy utilization efficiency of hybrid fuel cell vehicles, it is very necessary to design a power optimization control method for hybrid fuel cell vehicles considering speed optimization. Summary of the Invention

[0003] In order to solve the problems and requirements in the background art, the present invention provides a power optimization control method for a hybrid fuel cell vehicle considering speed optimization, which improves the economy, durability and comfort of the hybrid fuel cell vehicle while real-time tracking the desired load. To improve the real-time performance of the speed optimization method, the present invention designs a speed optimization method based on a hierarchical framework. The upper layer module of speed optimization optimizes the switching boundary conditions of the vehicle driving mode, and the lower layer module of speed optimization optimizes the wheel torque distribution to improve the energy efficiency utilization rate and reduce the vehicle driving energy consumption. The target vehicle speed can be obtained through the speed optimization module, and thus the desired load current can be calculated. Based on the constructed multi-objective optimization function, the adaptive factor can be calculated. Based on the state space model of the power system of the hybrid fuel cell vehicle and the adaptive factor, the power optimization control method based on port-Hamiltonian theory is used to real-time track the desired load current while optimizing and adjusting the power distribution of the hybrid energy system, improving the economy, durability and comfort of the hybrid energy system.

[0004] The technical solution of the present invention is as follows:

[0005] Step 1: Establish a hybrid fuel cell vehicle longitudinal dynamics model, a fuel cell system unidirectional DC / DC converter model, a fuel cell system hydrogen consumption model, a fuel cell system aging model, a lithium battery system dynamic model, and a lithium battery system aging model. Then, based on the fuel cell system unidirectional DC / DC converter model and the lithium battery system dynamic model, establish a state space model of the hybrid fuel cell vehicle power system.

[0006] Step 2: Based on the longitudinal dynamics model of the hybrid fuel cell vehicle, a hierarchical speed optimization method is constructed. In this speed optimization method, the upper-level speed optimization module determines the switching timing and combination of the four driving modes, and the lower-level speed optimization module optimizes the wheel torque distribution in the acceleration and deceleration modes to obtain the target speed trajectory and the corresponding expected load current.

[0007] Step 3: Quantify the hydrogen consumption, fuel cell system aging status, and lithium battery system aging status of the hybrid fuel cell vehicle using the fuel cell system's hydrogen consumption model, fuel cell system aging model, and lithium battery system aging model. Optimize the total battery cost based on the hydrogen consumption, fuel cell system aging status, and lithium battery system aging status of the hybrid fuel cell vehicle to obtain an adaptive factor.

[0008] Step 4: Based on the state space model and adaptive factors of the hybrid fuel cell vehicle power system, a power optimization control method based on the port Hamiltonian theory is used to track the expected load current in real time, and at the same time, power optimization regulation of the hybrid fuel cell vehicle power system is achieved.

[0009] In the speed optimization method based on the hierarchical framework of step 2, the formulas for the overall objective optimization function and the constraint conditions of the upper module for speed optimization are as follows:

[0010] min J h =J A +J C -J B

[0011] Among them, min means taking the minimum value operation, J h The overall objective optimization function value of the upper module is to optimize the speed, J A is the energy consumption function value of the acceleration mode, J C is the energy consumption function value of the constant speed mode, J B is the energy consumption function value of the braking mode;

[0012] The upper module constraints include longitudinal dynamic constraints and vehicle motion constraints. The formula for the vehicle speed constraint is as follows:

[0013] v a ≤v lim ,v b (t f )=v f ,a a ≤a a,max ,a b ≤a b,max

[0014] t0≤t a ≤t c ≤t b ≤t f ,S a +S c +S d +S b =S f

[0015] Among them, v a is the speed of the vehicle in acceleration mode, v lim is the maximum speed limit, v b () is the speed of the vehicle in braking mode, t f is the end time of the vehicle, v f is the terminal speed of the vehicle, a a is the acceleration of the vehicle in acceleration mode, a a,max is the maximum acceleration of the vehicle acceleration mode, a b is the acceleration of the vehicle in braking mode, a b,max is the maximum acceleration of the vehicle in braking mode, t0 is the starting time of the vehicle, t a is the end time of vehicle acceleration mode, t c is the end time of the vehicle constant speed mode, t b is the end time of vehicle braking mode, S a is the driving distance of the vehicle in acceleration mode, S c is the distance traveled by the vehicle in constant speed mode, S d is the driving distance of the vehicle in acceleration mode, S b is the distance traveled in vehicle braking mode, S f is the total distance traveled by the vehicle;

[0016] The formula of the objective optimization function of the speed optimization lower module is as follows:

[0017]

[0018] Among them, min means taking the minimum value operation, J l is the target optimization function value of the speed optimization lower module, i is the serial number of the wheel, T di is the driving torque of the i-th wheel, ω iis the angular velocity of the i-th wheel, η di is the driving efficiency of the i-th wheel, T bi is the braking torque of the i-th wheel, η bi is the braking efficiency of the i-th wheel.

[0019] In step 3, the hydrogen consumption cost C is calculated based on the hydrogen consumption of the hybrid fuel cell vehicle, the aging state of the fuel cell system, and the aging state of the lithium battery system. e , fuel cell system aging loss cost C fc and the loss cost of lithium battery system aging C bat , add the three together to get the total cost C, then derive the total cost C and establish the total cost optimization function and solve the minimum value to get the minimum total cost optimization function value. The formula is as follows:

[0020]

[0021] Among them, P fc is the output power of the fuel cell system, P bat is the output power of the lithium battery system, A f is the control factor of the quadratic term of the fuel cell system output power, B f is the control factor of the first-order output power of the fuel cell system, A b is the control factor of the quadratic term of the output power of the lithium battery system, B b is the control factor of the primary term of the output power of the lithium battery system;

[0022] After deriving the minimum total cost optimization function value, the fuel cell system output power is obtained The formula is as follows:

[0023]

[0024] Among them, P d The vehicle requires power;

[0025] Finally, based on the fuel cell system output power Constructing the adaptive factor k b , the formula is as follows:

[0026]

[0027] Among them, k fc is the adjustment factor of the battery system output power, P fc,max is the maximum output power of the fuel cell system.

[0028] The step 4 is specifically as follows:

[0029] Firstly, an actual port Hamiltonian model of the hybrid fuel cell vehicle power system is established based on the state space model of the hybrid fuel cell vehicle power system. Then, the actual port Hamiltonian model is converted into an expected port Hamiltonian model using the port Hamiltonian theory. Then, combined with the adaptive factor, the output current of the fuel cell system is obtained, and the expected load current is tracked in real time, while achieving power optimization regulation of the hybrid fuel cell vehicle power system.

[0030] In step 4, the formula of the actual port Hamiltonian model is as follows:

[0031]

[0032] Among them, x is the system state quantity, is the derivative of the system state, J(x) is the interconnection matrix, R(x) is the damping matrix, is the partial derivative of the Hamiltonian function H(x) with respect to the state quantity x, k is the control gain, is the desired fuel cell system output current, ε is the first external disturbance;

[0033] The formula of the expected port Hamiltonian model is as follows:

[0034]

[0035] in, is the derivative of the system state quantity deviation, x e is the deviation of the system state quantity, J(x) is the interconnection matrix, R(x) is the damping matrix, x is the system state quantity, is the expected Hamiltonian function H d (x e ) relative to the deviation of the system state quantity x e The partial derivative of , ε1 is the second external disturbance;

[0036] Output current of the fuel cell system The formula is as follows:

[0037]

[0038] Among them, I ld is the desired virtual load current, k b is the adaptive factor, V b is the output voltage of the lithium battery system, V ocd is the desired open circuit voltage of the lithium battery system, k r is the regulation factor determined by the duty cycle of the DC / DC converter.

[0039] The beneficial effects of the present invention are:

[0040] The present invention realizes the multi-objective optimization of the performance of a hybrid fuel cell vehicle, and the designed control method has good real-time performance. The speed optimization module designed based on the hierarchical framework can effectively improve the economy and comfort of the driving vehicle. The target speed generated by the speed optimization module can be used to calculate the desired load current. Combining the adaptive factor and the power optimization control method based on port Hamiltonian can ensure that the hybrid fuel cell vehicle optimizes the power distribution of the power system while tracking the desired load in real time, effectively improving the economy, durability and comfort of the hybrid fuel cell vehicle. Description of the Drawings

[0041] Figure 1 System block diagram of the power optimization control method considering speed optimization;

[0042] Figure 2 Speed optimization trajectories of each method in Case 1;

[0043] Figure 3 Speed optimization trajectories of each method in Case 2;

[0044] Figure 4 Speed trajectory of the UDDS driving cycle;

[0045] Figure 5 Variations of the required power and the output power of the lithium battery system under the UDDS driving cycle;

[0046] Figure 6 Variations of the output power of the fuel cell system of each method under the UDDS driving cycle;

[0047] Figure 7 Variation of the adaptive factor under the UDDS driving cycle;

[0048] Figure 8 Variation of the state of charge of the lithium battery system under the UDDS driving cycle; Detailed Description of the Invention

[0049] The present invention will be further described below in conjunction with specific examples. It should be understood that these examples are only used to illustrate the present invention and not to limit the scope of the present invention.

[0050] The present invention designs a verification work of the power optimization control method based on a software simulation platform, and compares it with the speed optimization method and the economic driving mode method based on the overall framework to verify the effectiveness of the designed speed optimization method based on the hierarchical framework. Under the standard UDDS driving cycle, the effectiveness of the designed power optimization control method considering the adaptive factor is verified.

[0051] As Figure 1This is a system block diagram of the power optimization control method for speed optimization provided by the present invention. The power optimization control method's workflow is as follows: Based on target setting information, the speed optimization module generates the target speed online. The desired load current is calculated from the target speed. Then, by combining an adaptive factor with a power optimization control method based on port Hamiltonian theory, the desired load current is tracked, while the power distribution of the hybrid energy system is optimized and adjusted, improving the system's economy and durability.

[0052] The method comprises the following steps:

[0053] Step 1: Establish a hybrid fuel cell vehicle longitudinal dynamics model, a fuel cell system unidirectional DC / DC converter model, a fuel cell system hydrogen consumption model, a fuel cell system aging model, a lithium battery system dynamic model, and a lithium battery system aging model. Then, based on the fuel cell system unidirectional DC / DC converter model and the lithium battery system dynamic model, establish a state space model of the hybrid fuel cell vehicle power system.

[0054] Step 2: Fixed-form driving tasks on a single road section typically involve four driving modes: acceleration, constant speed, deceleration, and braking. Based on the target settings, a hierarchical speed optimization method is constructed based on the longitudinal dynamics model of the hybrid fuel cell vehicle. In this speed optimization method, the upper-level speed optimization module determines the switching timing and combination of the four driving modes, while the lower-level speed optimization module optimizes the wheel torque distribution in the acceleration and deceleration modes, thereby obtaining the target speed trajectory and the corresponding expected load current.

[0055] In the hierarchical framework-based speed optimization method of step 2, the upper-level module of speed optimization is expressed as a nonlinear optimization problem for solving the driving mode boundary switching. The overall objective optimization function and the constraint conditions of the upper-level module of speed optimization are formulated as follows:

[0056] min J h =J A +J C -J B

[0057] Among them, min means taking the minimum value operation, J h The overall objective optimization function value of the upper module is to optimize the speed, J A is the energy consumption function value of the acceleration mode, J C is the energy consumption function value of the constant speed mode, J B is the energy consumption function value of the braking mode;

[0058] The constraints of the upper module of speed optimization include longitudinal dynamics constraints and vehicle motion constraints. The formula of vehicle speed constraint is as follows:

[0059] v a ≤v lim ,v b (t f )=v f ,a a ≤a a,max ,a b ≤a b,max

[0060] t0≤t a ≤t c ≤t b ≤t f ,S a +S c +S d +S b =S f

[0061] Among them, v a is the speed of the vehicle in acceleration mode, v lim is the maximum speed limit, v b () is the speed of the vehicle in braking mode, t f is the end time of the vehicle, v f is the terminal speed of the vehicle, a a is the acceleration of the vehicle in acceleration mode, a a,max is the maximum acceleration of the vehicle acceleration mode, a b is the acceleration of the vehicle in braking mode, a b,max is the maximum acceleration of the vehicle in braking mode, t0 is the starting time of the vehicle, t a is the end time of vehicle acceleration mode, t c is the end time of the vehicle constant speed mode, t b is the end time of vehicle braking mode, S a is the driving distance of the vehicle in acceleration mode, S c is the distance traveled by the vehicle in constant speed mode, S d is the driving distance of the vehicle in acceleration mode, S b is the distance traveled in vehicle braking mode, S f is the total distance traveled by the vehicle.

[0062] The speed optimization upper module can determine the switching boundary conditions of each driving mode, and then obtain the speed trajectories of the constant speed mode and the deceleration mode.

[0063] The speed optimization lower-level module is used to optimize the speed trajectory of the acceleration mode and the braking mode. The formula of the target optimization function of the speed optimization lower-level module is as follows:

[0064]

[0065] Among them, min means taking the minimum value operation, J l is the target optimization function value of the speed optimization lower module, i is the serial number of the wheel, T di is the driving torque of the i-th wheel, ω i is the angular velocity of the i-th wheel, η di is the driving efficiency of the i-th wheel, T bi is the braking torque of the i-th wheel, η bi is the braking efficiency of the i-th wheel. By solving the optimization problem of the speed optimization module, the torque of the vehicle in acceleration and braking modes can be obtained. Then, through the longitudinal dynamics model, the velocity trajectory of the vehicle in acceleration and braking modes can be obtained.

[0066] Step 3: Quantify the hydrogen consumption, fuel cell system aging state and lithium battery system aging state of the hybrid fuel cell vehicle through the hydrogen consumption model of the fuel cell system, the fuel cell system aging model and the lithium battery system aging model respectively; obtain the corresponding costs based on the hydrogen consumption, fuel cell system aging state and lithium battery system aging state of the hybrid fuel cell vehicle, and then establish a multi-objective optimization function composed of the corresponding costs of the three. After optimizing the total battery cost of the multi-objective optimization function, the adaptive factor k is obtained. b The design takes into account the economy and durability of the hybrid energy system;

[0067] In step 3, the hydrogen consumption cost C is calculated based on the hydrogen consumption of the hybrid fuel cell vehicle, the aging state of the fuel cell system, and the aging state of the lithium battery system. e , fuel cell system aging loss cost C fc and the loss cost of lithium battery system aging C bat , add the three together to get the total cost C, then derive the total cost C and establish the total cost optimization function and solve the minimum value to get the minimum total cost optimization function value. The formula is as follows:

[0068]

[0069] Among them, A f is the control factor of the quadratic term of the fuel cell system output power, B f is the control factor of the first-order output power of the fuel cell system, A b is the control factor of the quadratic term of the output power of the lithium battery system, B b is the control factor of the primary term of the lithium battery system output power.

[0070] After deriving the minimum total cost optimization function value, the fuel cell system output power that minimizes the target optimization is obtained. The formula is as follows:

[0071]

[0072] Among them, P d is the vehicle demand power;

[0073] Finally, an adaptive factor k is constructed based on the output power of the fuel cell system , and the formula is as follows: b

[0074]

[0075] Among them, k fc is the adjustment factor of the output power of the fuel cell system, and P fc,max is the maximum output power of the fuel cell system.

[0076] Among them, the total cost C for measuring economy and durability is calculated by the following expression

[0077] C = C e + C fc + C bat

[0078] Among them, C e is the hydrogen consumption cost, C fc is the loss cost of the aging of the fuel cell system, and C bat is the loss cost of the aging of the lithium battery system. Their calculation expressions are specifically as follows

[0079]

[0080] C fc = c fc · Δ fc · P fc,r

[0081] C bat = c bat · Δ bat · Q n

[0082] Among them, is the consumption cost per gram of hydrogen, is the mass of hydrogen consumed, c fc is the consumption cost per unit energy of the fuel cell system, Δ fc is the aging state quantity of the fuel cell system, P fc,r is the rated power of the fuel cell system, c bat is the consumption cost per unit energy of the lithium battery system, Δ bat is the aging state quantity of the lithium battery system, and Q n is the nominal capacity of the lithium battery system.​

[0083] Hydrogen consumption rate The specific expression is as follows

[0084]

[0085] Among them, N fc is the number of cells in the fuel cell system, is the molar mass of hydrogen, F is the Faraday constant, V fco is the open circuit voltage of the fuel cell system, P fc is the output power of the fuel cell system, R fc is the equivalent internal resistance of the fuel cell system.

[0086] Fuel cell system aging state Δ fc The specific expression is as follows:

[0087]

[0088] Among them, N s is the number of starts and stops of the fuel cell system, δ s is the start-stop coefficient of the fuel cell system, δ0 and α0 are the first and second attenuation coefficients of the fuel cell system, T fc is the operating time of the fuel cell system. The aging state of the lithium battery system Δ bat The specific expression is as follows:

[0089]

[0090] Among them, L e is the expected service life of the lithium battery system, T bat is the operating time of the lithium battery system, SoC is the state of charge of the lithium battery system, H(SoC) is the life decay function related to the state of charge of the lithium battery system, I b is the output current of the lithium battery system, G(I b ) is the life degradation function related to the output current of the lithium battery system.

[0091] Step 4: Based on the state-space model and adaptive factors of the hybrid fuel cell vehicle powertrain, a power optimization control method based on the port Hamiltonian theory is used to track the desired load current in real time. This method optimizes the power of the hybrid fuel cell vehicle powertrain, improving its economy and durability. In the implementation, Lyapunov's theorem is used to prove the convergence and stability of the designed power optimization control method.

[0092] Step 4 is as follows:

[0093] First, based on the state space model of the hybrid fuel cell vehicle power system, an actual port Hamiltonian model of the hybrid fuel cell vehicle power system is established. Then, the actual port Hamiltonian model is converted into an expected port Hamiltonian model using the port Hamiltonian theory. Then, combined with the adaptive factor, the output current of the fuel cell system is obtained, and the expected load current is tracked in real time. At the same time, the power optimization regulation of the hybrid fuel cell vehicle power system is realized, thereby improving the economy and durability of the power system.

[0094] In step 4, the formula of the actual port Hamiltonian model is as follows:

[0095]

[0096] Among them, x is the system state quantity, is the derivative of the system state, J(x) is the interconnection matrix, R(x) is the damping matrix, is the partial derivative of the Hamiltonian function H(x) with respect to the state quantity x, k is the control gain, is the desired fuel cell system output current, ε is the first external disturbance;

[0097] The formula of the expected port Hamiltonian model is as follows:

[0098]

[0099] in, is the derivative of the system state quantity deviation, x e is the deviation of the system state quantity, J(x) is the interconnection matrix, R(x) is the damping matrix, x is the system state quantity, is the expected Hamiltonian function H d (x e ) relative to the deviation of the system state quantity x e The partial derivative of , ε1 is the second external disturbance;

[0100] Output current of the fuel cell system The formula is as follows:

[0101]

[0102] Among them, I ld is the desired virtual load current, k b is the adaptive factor, V b is the output voltage of the lithium battery system, V ocd is the desired open circuit voltage of the lithium battery system, k r It is a positive parameter and a regulation factor determined by the duty cycle of the DC / DC converter.

[0103] The method of the present invention has been verified on a simulation platform. Compared with the speed optimization method based on the overall framework and the economic driving mode method, the effectiveness of the designed speed optimization method is verified. Under the standard UDDS driving condition, compared with the power optimization control method without considering the adaptive factor, the effectiveness of the designed power optimization control method considering the adaptive factor is verified. By selecting two different target settings, the speed trajectories of the speed optimization method based on the overall framework, the economic driving mode method, and the speed optimization method based on the hierarchical framework under the two different target settings can be obtained. The generated speed optimization trajectory is as Figure 2 and Figure 3 shown. The energy consumption comparison of each method is shown in Table 1. The speed trajectory of the standard UDDS driving condition is as Figure 4 shown. The required power and the output power of the lithium battery system are as Figure 5 shown. The total costs C of the power optimization control method without considering the adaptive factor and the power optimization control method considering the adaptive factor are $0.5719 and $0.5427 respectively. Their fuel cell system output powers are as Figure 6 shown. The change of the adaptive factor is as Figure 7 shown. The state of charge of the lithium battery system is as Figure 8 shown.

[0104] Table 1 Energy consumption cost comparison of each speed optimization method

[0105]

[0106] The simulation results show that compared with the speed optimization method based on the overall framework and the economic driving mode method, the designed speed optimization method based on the hierarchical framework can effectively reduce energy consumption while ensuring real-time performance. Compared with the power optimization control method without considering the adaptive factor, the designed power optimization control method considering the adaptive factor can reduce the total cost that measures economy and durability, indicating that the designed method can balance the conflict between the economy and durability of hybrid fuel cell vehicles. In addition, the designed method can also keep the state of charge of the lithium battery system fluctuating within a small range, thus ensuring the working efficiency of the energy system.

Claims

1. A power optimization control method for a hybrid fuel cell vehicle considering speed optimization, characterized in that It includes the following steps: Step 1: Establish a longitudinal dynamics model of a hybrid fuel cell vehicle, a unidirectional DC / DC converter model of a fuel cell system, a hydrogen consumption model of a fuel cell system, an aging model of a fuel cell system, a dynamic model of a lithium battery system, and an aging model of a lithium battery system respectively. Then, based on the unidirectional DC / DC converter model of the fuel cell system and the dynamic model of the lithium battery system, establish a state space model of the power system of the hybrid fuel cell vehicle; Step 2: Construct a speed optimization method based on a hierarchical framework according to the longitudinal dynamics model of the hybrid fuel cell vehicle. In the speed optimization method, the upper layer module of speed optimization determines the switching timing and combination mode of four driving modes, and the lower layer module of speed optimization optimizes the wheel torque distribution in the acceleration and deceleration modes, so as to obtain the target speed trajectory and then obtain the corresponding desired load current; Step 3: Quantify the hydrogen consumption, the aging state of the fuel cell system, and the aging state of the lithium battery system of the hybrid fuel cell vehicle through the hydrogen consumption model of the fuel cell system, the aging model of the fuel cell system, and the aging model of the lithium battery system respectively; After optimizing the total battery cost based on the hydrogen consumption, the aging state of the fuel cell system, and the aging state of the lithium battery system of the hybrid fuel cell vehicle, obtain an adaptive factor; Step 4: Based on the state space model of the power system of the hybrid fuel cell vehicle and the adaptive factor, use the power optimization control method based on port-Hamiltonian theory to track the desired load current in real time, and at the same time realize the power optimization regulation of the power system of the hybrid fuel cell vehicle.

2. The power optimization control method for a hybrid fuel cell vehicle considering speed optimization according to claim 1, wherein, In the speed optimization method based on the hierarchical framework in Step 2, the formula of the total target optimization function and the upper layer module constraint conditions of the upper layer module of speed optimization are as follows: min J h = J A + J C - J B Among them, min represents the minimum value operation, and J h is the total target optimization function value of the upper layer module for speed optimization, J A is the energy consumption function value of the acceleration mode, J C is the energy consumption function value of the constant speed mode, J B is the energy consumption function value of the braking mode; The upper layer module constraint conditions include longitudinal dynamics constraints and vehicle motion constraints, and the formula of the vehicle speed constraint is as follows: v a ≤ v lim , v b (t f ) = v f , a a ≤ a a,max , a b ≤ a b,max t0 ≤ t a ≤ t c ≤ t b ≤ t f , S a + S c + S d + S b =S f Among them, v a is the speed in the vehicle acceleration mode, v lim is the maximum speed limit, v b () is the speed in the vehicle braking mode, t f is the end time of vehicle travel, v f is the end speed of vehicle travel, a a is the acceleration in the vehicle acceleration mode, a a,max is the maximum acceleration in the vehicle acceleration mode, a b is the acceleration in the vehicle braking mode, a b,max is the maximum acceleration in the vehicle braking mode, t0 is the start time of vehicle travel, t a is the end time of the vehicle acceleration mode, t c is the end time of the vehicle constant speed mode, t b is the end time of the vehicle braking mode, S a is the travel distance in the vehicle acceleration mode, S c is the travel distance in the vehicle constant speed mode, S d is the travel distance in the vehicle acceleration mode, S b is the travel distance in the vehicle braking mode, S f is the total travel distance of the vehicle; The formula of the target optimization function of the lower layer module of speed optimization is as follows: Among them, min represents the operation of taking the minimum value, and J l is the target optimization function value of the lower layer module for speed optimization, i is the serial number of the wheel, and T di is the driving torque of the i-th wheel, ω i is the angular velocity of the i-th wheel, and η di is the driving efficiency of the i-th wheel, and T bi is the braking torque of the i-th wheel, and η bi is the braking efficiency of the i-th wheel.

3. A power optimization control method for a hybrid fuel cell vehicle considering speed optimization according to claim 1, characterized in that, In step 3, the hydrogen consumption cost C is calculated based on the hydrogen consumption, the aging state of the fuel cell system, and the aging state of the lithium battery system of the hybrid fuel cell vehicle. e The loss cost C fc of the fuel cell system aging and the loss cost C bat of the lithium battery system aging are added together to obtain the total cost C. Then, the derivative of the total cost C is taken to establish the total cost optimization function and solve for the minimum value, obtaining the minimum total cost optimization function value. The formula is as follows: Among them, P fc is the output power of the fuel cell system, P bat is the output power of the lithium battery system, A f is the control factor of the quadratic term of the output power of the fuel cell system, B f is the control factor of the linear term of the output power of the fuel cell system, A b is the control factor of the quadratic term of the output power of the lithium battery system, B b is the control factor of the linear term of the output power of the lithium battery system; After taking the derivative of the minimum total cost optimization function value, the output power of the fuel cell system is obtained The formula is as follows: Among them, P d is the vehicle demand power; Finally, based on the output power of the fuel cell system an adaptive factor k is constructed b , and the formula is as follows: Among them, k fc is the adjustment factor of the output power of the fuel cell system, and P fc,max is the maximum output power of the fuel cell system.

4. A power optimization control method for a hybrid fuel cell vehicle considering speed optimization according to claim 1, characterized in that, The specific content of Step 4 is as follows: First, establish an actual port-Hamiltonian model of the power system of the hybrid fuel cell vehicle based on the state space model of the power system of the hybrid fuel cell vehicle, then use port-Hamiltonian theory to convert the actual port-Hamiltonian model into a desired port-Hamiltonian model, and then combine the adaptive factor to obtain the output current of the fuel cell system, and then track the desired load current in real time, and at the same time realize the power optimization regulation of the power system of the hybrid fuel cell vehicle.

5. A power optimization control method for a hybrid fuel cell vehicle considering speed optimization according to claim 4, characterized in that, In Step 4, the formula of the actual port-Hamiltonian model is as follows: where \(x\) is the system state variable, is the derivative of the system state variable, \(J(x)\) is the interconnection matrix, and \(R(x)\) is the damping matrix, is the partial derivative of the Hamiltonian function \(H(x)\) with respect to the state variable \(x\), \(k\) is the control gain, is the desired output current of the fuel cell system, and \(\varepsilon\) is the first external disturbance; The formula of the desired port-Hamiltonian model is as follows: Among them, is the derivative of the system state quantity deviation, x e is the deviation of the system state quantity, J(x) is the interconnection matrix, R(x) is the damping matrix, x is the system state quantity, is the desired Hamiltonian function H d (x e ) with respect to the deviation x e of the system state quantity, and ε1 is the second external disturbance; Output current of the fuel cell system The formula is as follows: Where, I ld is the desired virtual load current, k b is the adaptive factor, V b is the output voltage of the lithium battery system, V ocd is the desired open-circuit voltage of the lithium battery system, k r is the adjustment factor determined by the duty cycle of the DC / DC converter.

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