Hybrid electric vehicle double-layer energy management method integrating rapid heat management

Abstract

The invention relates to a hybrid electric vehicle double-layer energy management method integrating rapid heat management, and belongs to the technical field of hybrid electric vehicle energy management. The method comprises the following steps: finding the optimal working temperature of a fuel cell by utilizing the relationship between the temperature of the fuel cell and the output power, and designing a fixed-time active-disturbance-rejection controller to track the optimal working temperature; meanwhile, an upper-layer DDPG energy management strategy fusing temperature factors and a lower-layer DC / DC composite converter fixed time control double-layer energy management method are established, deep fusion of heat management and energy management is achieved, hydrogen consumption is reduced, dependence of a traditional control strategy on the initial state of a hybrid power system is overcome, and the performance of the power system of the whole vehicle is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management for hybrid vehicles, and particularly to a dual-layer energy management method for hybrid vehicles integrating rapid thermal management. Background Art

[0002] As a truly "zero-emission, pollution-free" means of transportation, hydrogen fuel cell hybrid vehicles are one of the main development directions of future new energy clean power vehicles. With the development of the automotive industry towards electrification and intelligence, the content covered by vehicle thermal management and energy management has increased, and it has gradually shifted from a rough design to a refined design: the temperature control range of the fuel cell thermal management system is more refined, the upper-layer energy management and thermal management design are more complex, and the response speed requirements for the DC / DC converter in the lower-layer energy management are higher. The dual-layer energy management technology integrating rapid thermal management, which conducts overall energy management of the system from the vehicle level, has become the future development trend of hybrid vehicles.

[0003] The traditional fuel cell thermal management controls the stack temperature within a certain safe range. However, in the actual operation process, there is an optimal operating temperature point for the output power of the fuel cell. How to keep the fuel cell working at the optimal operating temperature all the time and achieve more precise temperature control to further reduce energy consumption is a challenge. The upper-layer energy management strategy can utilize the characteristics of each energy source to effectively allocate the output power of each energy source. However, the output power of the fuel cell is closely related to temperature control. The current lack of thermal management fineness results in a low utilization rate of the fuel cell energy source. At the same time, the executor of the distribution result of the lower-layer energy management is the DC / DC converter, and the speed of its response directly affects the accurate execution of the energy management strategy. However, there is also room for improvement in the current response speed of the DC / DC converter, which makes the execution accuracy of the vehicle energy management strategy to be improved. In summary, the current hybrid vehicles have the technical problem that the performance of the vehicle's power system is poor due to the deficiencies in temperature management and energy management strategies. Summary of the Invention

[0004] The present invention aims to provide a dual-layer energy management method for hybrid vehicles integrating rapid thermal management. By using the relationship between the fuel cell temperature and the output power, the optimal operating temperature is found, and a fixed-time active disturbance rejection controller is designed to track the optimal operating temperature. At the same time, a dual-layer energy management method of the upper-layer DDPG energy management strategy integrating temperature factors - the fixed-time control of the lower-layer DC / DC composite converter is established to achieve the deep integration of thermal management and energy management, reduce hydrogen consumption, overcome the dependence of the traditional control strategy on the initial state of the hybrid system, and improve the performance of the vehicle's power system, so as to solve the technical problem that the performance of the vehicle's power system is poor due to the deficiencies in temperature management and energy management strategies in the current hybrid vehicles.

[0005] To solve the above technical problems, the present invention provides a double-layer energy management method for a hybrid vehicle integrated with rapid thermal management. The method includes the following steps:

[0006] S1: Obtain the optimal operating temperature of the fuel cell, design a heat dissipation regulation strategy based on fixed-time active disturbance rejection control to track the optimal operating temperature of the fuel cell, and complete the construction of the fuel cell thermal management model;

[0007] S2: Add the temperature difference between the temperature of the fuel cell and the optimal operating temperature as a state to the state space, integrate the power of the coolant pump and the temperature penalty factor into the reward function to obtain a DDPG energy management strategy integrating rapid thermal management, and then design an adaptive update mechanism based on differential error network parameters to train the DDPG energy management strategy integrating rapid thermal management, and complete the construction of the energy management framework for upper-layer integrated thermal management;

[0008] S3: Establish a global model of the composite energy system according to the voltage-current relationship of the single / double converter topology of the hybrid vehicle, and construct a fixed-time hybrid power controller based on the global model of the composite converter according to the dual tracking objectives of the bus voltage and the reference current, and complete the construction of the fixed-time hybrid power control scheme for the lower layer based on the global model of the composite energy system.

[0009] Further, the S1 includes:

[0010] S1-1: Collect the corresponding relationship between the temperature and power of the fuel cell, and determine the optimal operating temperature of the fuel cell;

[0011] S1-2: Adopt the heat dissipation regulation strategy based on fixed-time active disturbance rejection control to track the optimal operating temperature.

[0012] Further, the S1-1 includes:

[0013] The upper computer sends a fuel cell temperature acquisition experiment instruction to the controller of the fuel cell. The controller of the fuel cell controls the hydrogen pump, the air compressor, and the coolant pump to work. The sampling circuit collects the temperature and voltage of the fuel cell and transmits them to the upper computer. The upper computer fits the relationship between the optimal temperature and power as follows:

[0014]

[0015] where P optimal is the maximum power corresponding to the optimal operating temperature, T st-optimal is the optimal operating temperature, and α, β, and λ are fitting coefficients.

[0016] Furthermore, the process of fixed-time active disturbance rejection control in S1-2 is as follows:

[0017] According to the law of conservation of energy, the energy of the fuel cell is expressed as follows:

[0018]

[0019] Among them, Q st represents the energy generated by the reaction of hydrogen and oxygen, P st represents the electric power consumed by the stack load, Q cool represents the thermal power carried away by the coolant, Q amb represents the thermal power radiated from the stack to the environment, M st is the mass of the stack, C st is the specific heat capacity of the stack, T st is the stack temperature;

[0020] Then, a fixed-time active disturbance rejection controller and a fixed-time extended state observer are introduced to replace the traditional nonlinear state error feedback and extended observer. The coolant flow rate is adjusted by the fixed-time active disturbance rejection controller to control the stack temperature, and the total disturbance is compensated by the fixed-time extended state observer. Specifically:

[0021] First, according to the model of the fuel cell thermal management system, the closed-loop control system of temperature is expressed as Among them, x1 is T st , f total is the disturbance term of the fuel cell thermal management system, and u is the flow rate of the coolant;

[0022] Second, the first-order temperature dynamic system is extended to a second-order system as follows:

[0023]

[0024] Finally, through inequality scaling and the Cauchy-Schwarz inequality, the fixed-time active disturbance rejection controller and the fixed-time extended state observer are designed to satisfy the Lyapunov fixed-time lemma

[0025] Furthermore, the construction method of the reward function is as follows:

[0026] The reward function is used to evaluate the actions in the current state, so as to guide the learning process of the reinforcement learning algorithm. The reward function is established by using the equivalent minimum fuel consumption strategy method:

[0027]

[0028] In the formula, Reward is the reward function, C total(t) is the instantaneous total hydrogen consumption; ρ BAT is the battery SoC penalty coefficient; γ T is the temperature penalty coefficient; ΔSoC BAT (t) is the difference between the battery SoC and the SoC reference value.

[0029] Furthermore, the design trains the DDPG energy management strategy integrating fast thermal management based on the adaptive update mechanism of differential error network parameters, including:

[0030] The adaptive soft update process of the network parameter adaptive soft update mechanism based on TD error is as follows:

[0031]

[0032] In the formula, ω old and ω new are the current network weights before parameter update and the newly calculated network weights, ω' new is the current network weight after parameter update, TD max is the maximum TD error during learning;

[0033] The traditional target participant and key network parameter update process is as follows:

[0034]

[0035] Replace the update rate in the above formula with as follows:

[0036]

[0037] First, use the Actor network to generate actions, observe the environmental feedback after executing the actions; then store the experience in the experience pool, then sample data from the experience pool to calculate the target Q value, update the parameters of the Actor and Critic networks according to the TD error; finally, update the parameters of the target network through the soft update strategy, and repeat the iteration continuously until the specified number of iterations is reached or convergence occurs.

[0038] Furthermore, establishing the global model of the composite energy system according to the voltage - current relationship of the single / double - way converter topology of the hybrid electric vehicle includes:

[0039] The global model of the bilinear switch is established from the topologies of the unidirectional converter and the bidirectional converter, that is:

[0040]

[0041] In the formula, i pf is the fuel cell current, i ucis the capacitive current, i0 is the load current, L1 and R1 are the inductor and resistor in the fuel cell boost circuit, L2 and R2 are the inductor and resistor in the lithium battery and supercapacitor bidirectional conversion circuit, v bus is the bus voltage, V pf is the fuel cell voltage, u1 is the switch of the fuel cell boost converter, u 23 is the only input control variable of the buck-boost converter, defined as:

[0042] u 23 = k(1 - u2) + (1 - k)u3

[0043] Therefore, the average global model within the switching period is as follows:

[0044]

[0045] where x1 is the average value of i pf ; x2 is the average value of i uc ; x3 is the average value of v bus ; μ1 and μ 23 are the duty cycles and also the average values of u1 and u 23 .

[0046] Furthermore, based on the dual tracking objectives of the bus voltage and the reference current, a fixed-time hybrid power controller based on the global model of the composite converter is constructed, including:

[0047] Firstly, design the state variable errors e1, e2, and e3, where e1 = x1 - i pf-ref , e2 = x2 - i uc-ref , e3 = x3 - x 3d , and establish the Lyapunov function as

[0048] Secondly, design u a and u b to satisfy the following conditions:

[0049]

[0050] According to the inequality scaling and the Cauchy-Schwarz inequality, the following formula is obtained:

[0051]

[0052] where m7 = min(m1, m3, m5), m8 = min(m2, m4, m6).

[0053] Compared with the prior art, the beneficial effects of the present invention are as follows: First, a fixed-time active disturbance rejection controller is designed to track the optimal operating temperature of the fuel cell, so that the fuel cell always operates at the best efficiency point, which plays a role in reducing the hydrogen consumption of the fuel cell. Then, using the obtained optimal operating temperature, the temperature difference between the fuel cell temperature and the optimal temperature is added as a state to the state space, the power of the cooling pump and the temperature penalty factor are integrated into the reward function, and an upper-layer DDPG energy management strategy that integrates fast thermal management is designed. Finally, a fixed-time controller for the lower-layer DC / DC composite converter is designed, thus completing a two-layer energy management method for a hybrid vehicle that integrates fast thermal management, which has better effects in improving the efficiency of the fuel cell and reducing the hydrogen consumption of the fuel cell. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 FIG. is a schematic flow chart of a two-layer energy management method for a hybrid vehicle that integrates fast thermal management provided by an embodiment of the present invention;

[0055] Figure 2 FIG. is a schematic diagram of a fuel cell thermal management model provided by an embodiment of the present invention

[0056] Figure 3 FIG. is a schematic diagram of an energy management framework for upper-layer integrated thermal management provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0057] The following provides a further detailed description of the present invention with reference to the accompanying drawings.

[0058] As Figure 1 shown, FIG. is a schematic flow chart of a two-layer energy management method for a hybrid vehicle that integrates fast thermal management provided by Embodiment 1 of the present invention. It can be seen from the figure that the method includes the following steps:

[0059] S1: Construct a fuel cell thermal management model as Figure 2 shown. Obtain the optimal operating temperature of the fuel cell through experiments, and design a fixed-time active disturbance rejection control strategy to quickly track the optimal operating temperature of the battery, so that the operating efficiency of the fuel cell is at a relatively high level;

[0060] S2: Construct an energy management framework for upper-layer integrated thermal management as Figure 3 shown. Add the temperature difference between the fuel cell temperature and the optimal temperature as a state to the state space, integrate the power of the cooling pump and the temperature penalty factor into the reward function, and design a DDPG energy management strategy that integrates fast thermal management. Design an adaptive update mechanism based on the differential error network parameters to shorten the training time and solve the problem of deep fusion optimization of thermal management and energy management in high-dimensional spaces;

[0061] S3: Design a fixed-time hybrid power control scheme for the global model of the lower-layer composite energy system. Deeply explore the voltage-current relationship of the single / double-way converter topology of the whole vehicle, establish the global model of the composite energy system, design the dual tracking objectives of the bus voltage and reference current, and construct a fixed-time hybrid power controller based on the global model of the composite converter to overcome the inherent dependence of traditional control strategies on the initial state of the hybrid power system and improve the dynamic performance of the whole vehicle.

[0062] The specific implementation process in step S1:

[0063] S1-1: The topology structure diagram of the fuel cell hybrid vehicle is as Figure 2 shown. It can be seen from the figure that a fuel cell thermal management model is constructed, the points of the fuel cell temperature and power are collected, and the corresponding relationship between the optimal working temperature and power is obtained. The fuel cell temperature acquisition experiment sends instructions from the upper computer to the fuel cell controller, and the controller controls the hydrogen pump, air compressor, and cooling pump; the temperature and voltage of the fuel cell are transmitted to the upper computer through the sampling circuit. Multiple different working points are uniformly selected, and these working points are maintained at a specific time under the condition of high battery temperature. During this period, the output power values are recorded at different temperatures. The relationship between the fitted optimal temperature and power based on experimental measurements is as follows:

[0064] Among them, P optimal is the maximum power corresponding to the optimal working temperature, T st-optimal is the optimal working temperature, and α, β, λ are fitting coefficients.

[0065] S1-2: According to the law of conservation of energy, the energy generated and dissipated during the operation of the fuel cell stack is divided into the following parts: the energy Q st generated by the reaction of hydrogen and oxygen, the electric power P st consumed by the fuel cell stack load, the thermal power Q cool carried away by the coolant, and the thermal power Q amb radiated from the stack to the environment. Thus, there is the following equation:

[0066] In the formula, M st is the mass of the stack, C st is the specific heat capacity of the stack, and T stis the stack temperature. Since the fuel cell thermal management goal is to accurately and quickly track the optimal temperature point, a fixed-time active disturbance rejection control strategy is adopted, and a fixed-time controller and a fixed-time extended state observer are introduced to replace the traditional nonlinear state error feedback and extended observer. The fixed-time active disturbance rejection controller controls the temperature of the stack by adjusting the cooling water flow rate u, and compensates for the total disturbance through the fixed-time observer to achieve good robustness and fast tracking performance. First, according to the fuel cell thermal management system model, the closed-loop control system of temperature can be expressed as (where x1 is T st , f total is the disturbance term of the fuel cell thermal management system, and u is the flow rate of the cooling pump); Secondly, the first-order temperature dynamic system is extended to a second-order system as follows:

[0067] Finally, by introducing inequality scaling and Cauchy-Schwarz inequality techniques, the controller and observer are designed to satisfy the Lyapunov fixed-time lemma

[0068] The specific implementation process in step S2 is as follows:

[0069] S2-1: The temperature difference between the fuel cell temperature and the optimal temperature is added to the state space as a state, and the power of the cooling pump and the temperature penalty factor are integrated into the reward function;

[0070] Reward function: The reward function is used to evaluate the action in the current state, so as to guide the learning process of the reinforcement learning algorithm. The higher the reward value in the energy management strategy, the better the power distribution strategy. The equivalent minimum fuel consumption strategy method is used to establish the reward function, which is described as:

[0071] where C total (t) is the instantaneous total hydrogen consumption; ρ BAT is the battery SoC penalty coefficient; γ T is the temperature penalty coefficient; ΔSoC BAT (t) is the difference between the battery SoC and the SoC reference value. Since the policy goal is to keep the difference between the total hydrogen consumption and ΔSoC BAT (t) and the optimal temperature as small as possible in the ECMS to maximize the return, the negative value of their sum is used as the reward.

[0072] S2-2: During the learning process of the traditional DDPG algorithm, the method of soft-updating network parameters with fixed weights is prone to slow network convergence. This project intends to propose an adaptive soft-updating mechanism for network parameters based on TD error. This method can dynamically adjust the update rate and direction of network parameters, enabling the network to more flexibly adapt to different learning situations and effectively improving the adaptability and learning efficiency of the network during the learning process. The adaptive soft-updating process is as follows:

[0073] Where ω old and ω new are the current network weights before parameter update and the newly calculated network weights, ω' new is the current network weight after parameter update, and TD max is the maximum TD error during learning.

[0074] The traditional target actor and key network parameter update processes are as follows:

[0075] The update rate in the above formula can be replaced by as follows:

[0076] Therefore, the improvement of the update rate of the traditional DDPG algorithm mainly adopts the following methods. First, the Actor network is used to generate actions, and after executing the actions, the environmental feedback is observed; then the experience is stored in the experience pool, and then data is sampled from the experience pool to calculate the target Q value, and the parameters of the Actor and Critic networks are updated according to the TD error; finally, the parameters of the target network are updated through the soft-update strategy, and the whole process is continuously repeated until the specified number of iterations or convergence is reached.

[0077] The specific implementation process in step S3 is as follows

[0078] S3-1: Establish a global model of the hybrid energy system; the global model can uniformly describe and analyze the single-direction and bi-direction converters. Based on the local module modeling, according to its own topological structure, through the circuit analysis of voltage and current characteristics, a global system model is established.

[0079] The global model of the bilinear switch can be established from the topological structures of the unidirectional converter and the bidirectional converter, that is:

[0080] Where i pf is the fuel cell current, i ucis the capacitive current, i0 is the load current, L1 and R1 are the inductor and resistor in the fuel cell boost circuit, L2 and R2 are the inductor and resistor in the lithium battery and supercapacitor bidirectional conversion circuit, and v bus is the bus voltage, and V pf is the fuel cell voltage, u1 is the switch of the fuel cell boost converter, and u 23 is the only input control variable of the buck-boost converter, defined as u 23 = k(1 - u2) + (1 - k)u3.

[0081] Therefore, the average global model within the switching period is as follows:

[0082] In the formula, x1 is the average value of i pf ; x2 is the average value of i uc ; x3 is the average value of v bus ; μ1 and μ 23 are the duty cycles and also the average values of u1 and u 23 . Since the above equation is a multi-input multi-output system, it is difficult to achieve stable control with traditional control methods. Therefore, the fixed-time control theory will be adopted to design the fixed-time allocation strategy for the hybrid power.

[0083] S3-2: Design a fixed-time hybrid power controller based on the global model of the composite converter. To achieve global fixed-time stability in the hybrid power control system, the state variable errors (e1, e2, e3) are designed to be determined, where e1 = x1 - i pf-ref , e2 = x2 - i uc-ref , e3 = x3 - x 3d . The Lyapunov function is established as

[0084] Design u a , u b to satisfy the following conditions:

[0085] According to the Cauchy-Schwarz inequality, the following formula can be obtained:

[0086] In the formula, m7 = min(m1, m3, m5), m8 = min(m2, m4, m6), the system satisfies the fixed-time lemma, and the state variables of the system are stable within a fixed time.

[0087] The foregoing description of the disclosed embodiments enables those skilled in the art to practice or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A double-layer energy management method for a hybrid vehicle integrated with fast thermal management, characterized in that, It includes the following steps: S1. Obtain the optimal operating temperature of the fuel cell, design a heat dissipation regulation strategy based on fixed-time active disturbance rejection control to track the optimal operating temperature of the fuel cell, and complete the construction of the fuel cell thermal management model; S2. Add the temperature difference between the temperature of the fuel cell and the optimal operating temperature as a state to the state space, integrate the power of the coolant pump and the temperature penalty factor into the reward function to obtain a DDPG energy management strategy integrating fast thermal management, and then design an adaptive update mechanism based on differential error network parameters to train the DDPG energy management strategy integrating fast thermal management, and complete the construction of the energy management framework for upper-layer integrated thermal management; S3. According to the voltage-current relationship of the single / double converter topology of the hybrid electric vehicle, establish a global model of the composite energy system, and construct a fixed-time hybrid power controller based on the global model of the composite converter according to the dual tracking objectives of the bus voltage and the reference current, and complete the construction of the fixed-time hybrid power control scheme based on the global model of the composite energy system at the lower layer.

2. The integrated fast thermal management hybrid vehicle double-layer energy management method according to claim 1, wherein The S1 includes: S1-1. Collect the corresponding relationship between the temperature and power of the fuel cell, and determine the optimal operating temperature of the fuel cell; S1-2. Use the heat dissipation regulation strategy based on fixed-time active disturbance rejection control to track the optimal operating temperature.

3. The integrated fast thermal management hybrid vehicle double-layer energy management method according to claim 2, wherein The S1-1 includes: The upper computer sends a fuel cell temperature acquisition experiment instruction to the controller of the fuel cell. The controller of the fuel cell controls the hydrogen pump, the air compressor and the coolant pump to work. The sampling circuit collects the temperature and voltage of the fuel cell and transmits them to the upper computer. The upper computer fits the relationship between the optimal temperature and power as follows: Among them, P optimal is the maximum power corresponding to the optimal operating temperature, T st-optimal is the optimal operating temperature, and α, β, and λ are fitting coefficients.

4. A dual-layer energy management method for a hybrid vehicle integrating rapid thermal management according to claim 2, characterized in that, The process of fixed-time active disturbance rejection control in S1-2 is as follows: According to the law of conservation of energy, the energy of the fuel cell is expressed as follows: Among them, Q st represents the energy generated by the reaction of hydrogen and oxygen, and P st represents the electric power consumed by the fuel cell stack load. Q cool represents the heat power carried away by the coolant. Q amb represents the heat power radiated from the fuel cell stack to the environment. M st is the mass of the fuel cell stack, and C st is the specific heat capacity of the fuel cell stack, and T st is the temperature of the fuel cell stack; Then, introduce a fixed-time active disturbance rejection controller and a fixed-time extended state observer to replace the traditional nonlinear state error feedback and extended observer. Adjust the coolant flow rate through the fixed-time active disturbance rejection controller to control the temperature of the stack, and compensate for the total disturbance through the fixed-time extended state observer. Specifically: First, according to the model of the fuel cell thermal management system, the closed-loop control system of temperature is expressed as where x1 is T st , f total is the interference term of the fuel cell thermal management system, and u is the flow rate of the coolant; Secondly, expand the first-order temperature dynamic system into a second-order system as follows: Finally, by using inequality scaling and the Cauchy–Schwarz inequality, the fixed-time active disturbance rejection controller and the fixed-time extended state observer are designed and satisfy the Lyapunov fixed-time lemma.

5. The integrated fast thermal management hybrid vehicle double-layer energy management method according to claim 1, characterized in that, The construction method of the reward function is: The reward function is used to evaluate the action in the current state, so as to guide the learning process of the reinforcement learning algorithm. The reward function is established by using the equivalent minimum fuel consumption strategy method: where Reward is the reward function, C total (t) is the instantaneous total hydrogen consumption; ρ BAT is the battery SoC penalty coefficient; γ T is the temperature penalty coefficient; ΔSoC BAT (t) is the difference between the battery SoC and the SoC reference value.

6. A dual-layer energy management method for a hybrid vehicle integrating rapid thermal management according to claim 1, characterized in that The design of the adaptive update mechanism based on differential error network parameters to train the DDPG energy management strategy integrating fast thermal management includes: The adaptive soft update process of the network parameter adaptive soft update mechanism based on TD error is as follows: where ω old and ω new are the current network weights before parameter update and the newly calculated network weights, ω' new is the current network weight after parameter update, and TD max is the maximum TD error during learning; The update process of the traditional target actor and key network parameters is as follows: Substitute the update rate in the above formula with as follows: First, use the Actor network to generate actions. After executing the actions, observe the environmental feedback. Then, store the experiences in the experience pool. Next, sample data from the experience pool to calculate the target Q value, and update the parameters of the Actor and Critic networks according to the TD error. Finally, update the parameters of the target network through a soft update strategy, and continuously repeat the iteration until the specified number of iterations is reached or convergence occurs.

7. The integrated fast thermal management hybrid vehicle double-layer energy management method according to claim 1, characterized in that, The establishment of the global model of the composite energy system according to the voltage-current relationship of the single / double converter topology of the hybrid electric vehicle includes: The global model of the bilinear switch is established from the topologies of the unidirectional converter and the bidirectional converter, that is: where i pf is the fuel cell current, i uc is the capacitor current, i0 is the load current, L1, R1 are the inductor and resistor in the fuel cell boost circuit, L2, R2 are the inductor and resistor in the bidirectional conversion circuit of the lithium battery and the super capacitor, v bus is the bus voltage, V pf is the fuel cell voltage, u1 is the switch of the fuel cell boost converter, u 23 is the only input control variable of the buck-boost converter, defined as: u 23 = k(1 - u^2)+(1 - k)u^3 Therefore, the average global model within the switching period is as follows: where x1 is the average value of i pf ; x2 is the average value of i uc ; x3 is the average value of v bus ; μ1 and μ 23 are the duty cycles and also the average values of u1 and u 23 .

8. A dual-layer energy management method for a hybrid electric vehicle integrating rapid thermal management according to claim 1, characterized in that, The construction of the fixed-time hybrid power controller based on the global model of the composite converter according to the dual tracking objectives of the bus voltage and the reference current includes: First, design the state variable errors e1, e2, and e3, where e1 = x1 - i pf-ref , e2 = x2 - i uc-ref , e3 = x3 - x 3d , and establish the Lyapunov function as Secondly, design u a and u b satisfy the following conditions: The following formula is obtained based on inequality scaling and the Cauchy-Schwarz inequality: In the formula, m7 = min(m1, m3, m5), and m8 = min(m2, m4, m6).

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