Fuel cell hybrid vehicle energy management method, system and hybrid vehicle

Abstract

The present invention discloses a fuel cell hybrid vehicle energy management method, system, and hybrid vehicle. The management method includes: optimizing thresholds based on a genetic algorithm, encoding the upper and lower thresholds of the lithium battery SOC, and the output power of a single fuel cell stack as decision variables, combining equivalent hydrogen consumption for the same mileage and constraints introduced as penalty functions to form a fitness function, performing several rounds of evolution, and ultimately screening out an optimized solution; performing energy management based on the vehicle's state, divided into braking and non-braking states, and selecting the number of fuel cell stacks to be deployed based on the difference between when the lithium battery is discharged and the fuel cell provides the remaining required power, and selecting the fuel cell stack to be deployed based on the degree of degradation. The present invention addresses the issues of fuel cell hybrid vehicles, such as the need to improve energy management efficiency and the difficulty in balancing energy consumption and service life.

Description

Technical Field

[0001] The present invention relates to the field of energy management technology, and in particular to a fuel cell hybrid vehicle energy management method and system, and a hybrid vehicle. Background Art

[0002] To address environmental concerns, new energy vehicles are gaining popularity worldwide. Fuel cells, with their high power density and zero emissions, have garnered significant attention. However, fuel cells suffer from drawbacks such as slow dynamic response and unstable output voltage. Therefore, they are often combined with batteries or supercapacitors. These hybrid systems, which mitigate the limitations of single fuel cells, have found widespread application in the automotive sector. However, fuel cell hybrid systems require appropriate energy management methods to address a range of technical challenges, including fuel cell durability and powertrain economics.

[0003] Existing fuel cell energy management methods are generally divided into two categories: rule-based and optimization-based. Rule-based strategies, such as a fuel cell bus brake energy management control method, are easy to understand and implement, but their drawback is that the selection of thresholds mainly depends on the experience of the developer, and it is often difficult to achieve the optimal performance effect. In contrast, optimization-based energy management methods can obtain more optimized solutions by applying algorithms such as dynamic programming. However, optimization-based strategies have high requirements for computing power, so there are certain limitations in practical applications; in addition, existing technologies also have optimization-based energy management methods that reduce computing requirements, such as a fuel cell hybrid bus energy management method that constructs a data table based on the Pontrigin minimum principle. This is an improvement on the long calculation time of the optimization algorithm, but it does not take into account the degradation of the fuel cell during use. Summary of the Invention

[0004] (1) Technical issues to be resolved

[0005] Based on the above problems, the present invention provides a fuel cell hybrid vehicle energy management method, system and hybrid vehicle to solve the problems that the energy management efficiency of fuel cell hybrid vehicles needs to be improved and it is difficult to balance energy consumption and service life.

[0006] (2) Technical solution

[0007] In response to the above technical problems, the present invention provides an energy management method for a fuel cell hybrid vehicle. The fuel cell hybrid vehicle is provided with a lithium battery and a fuel cell, wherein the fuel cell includes a plurality of fuel cell stacks. The energy management method comprises:

[0008] S1. Optimize the upper threshold SOC of lithium battery SOC based on genetic algorithm ub , the lower threshold value of lithium battery SOC lb, the output power of a single fuel cell stack P fc :Lithium battery SOC upper threshold SOC ub , the lower threshold value of lithium battery SOC lb , the output power of a single fuel cell stack P fc Encode it as a decision variable, combine the equivalent hydrogen consumption of the same mileage and the constraints introduced as a penalty function to form a fitness function, and perform several rounds of evolution to finally select the optimal solution;

[0009] S2, according to the optimized maximum power of the fuel cell and the upper threshold SOC of the lithium battery SOC ub And the lower threshold SOC of lithium battery SOC lb , and control the charging and discharging of lithium batteries and fuel cells and the launch and exit of fuel cell stacks; the maximum power of the fuel cell is determined according to the output power P of the single fuel cell stack. fc Multiply by the number of fuel cell stacks.

[0010] Furthermore, the step S1 includes:

[0011] S11. Genetically encode the decision variables, wherein the decision variables include the upper threshold SOC of the lithium battery SOC ub , the lower threshold value of lithium battery SOC lb , the output power of a single fuel cell stack P fc ;

[0012] S12, initialize the population, randomly generate a group of initial individuals, and set the population size as needed;

[0013] S13, calculating the fitness of each individual, the fitness function is the equivalent hydrogen consumption J, and the constraint condition is introduced into the fitness function as a penalty function;

[0014] S14. Generate a new population by selecting individuals with high fitness, performing crossover and mutation;

[0015] S15. Determine whether the maximum genetic generation is reached. If so, the optimization is completed. If not, return to step S13.

[0016] Furthermore, the equivalent hydrogen consumption J: J = C fc +C b , where C fc is the hydrogen consumption of the fuel cell, C b is the equivalent hydrogen consumption of lithium batteries.

[0017] Furthermore, the constraints are: Among them, SOC max The maximum SOC and SOC of the lithium batterymin is the minimum SOC of the lithium battery, P fcmax is the maximum output power of a single fuel cell stack, D fcmin is the minimum value of the given degradation coefficient, D fcn is the degradation coefficient of the nth fuel cell stack.

[0018] Furthermore, in step S2, the charging and discharging control of the lithium battery and the fuel cell includes:

[0019] S21, determine the state of the car, if the car is in the braking state, go to step S22, if the car is in the non-braking state, go to step S23;

[0020] S22, determine whether there is SOC b >SOC ub If yes, the lithium battery is not charged and the process returns to step S21; if no, the lithium battery is charged and braking energy is recovered and the process returns to step S21; SOC b is the lithium battery SOC;

[0021] S23, determine whether there is SOC b <SOC lb If yes, the lithium battery does not discharge, the fuel cell provides all the power, and the process goes to step S24; if no, the process goes to step S25;

[0022] S24, determine whether the maximum power of the fuel cell is greater than the power required by the vehicle P m If yes, the fuel cell charges the lithium battery and the process returns to step S21; otherwise, the process returns to step S21;

[0023] S25, determine whether the maximum power of the fuel cell is greater than the power required by the vehicle P m If so, the lithium battery discharges and provides all the power, and returns to step S21; otherwise, the lithium battery discharges, the fuel cell provides the remaining required power, and the fuel cell stack is put into and exited.

[0024] Furthermore, the determination of the vehicle state includes: obtaining the opening of the brake pedal, and determining whether the brake pedal is depressed; if so, the vehicle is in a braking state; if not, the vehicle is in a non-braking state.

[0025] Furthermore, in step S25, the fuel cell stack deployment and exit control includes:

[0026] S251, according to the power P required by the car m The difference between the discharge power of the lithium battery and the number of fuel cell stacks N required is obtained m ;

[0027] S252. Calculate the degradation degree of all fuel cell stacks, i.e., the degradation coefficient D fc , the degradation coefficient D for all fuel cell stacks fc Sort by

[0028] S253, the degradation coefficient D fc <D fcmin The fuel cell stack is retired, let N = degradation coefficient D fc ≥D fcmin The number of fuel cell stacks; D fcmin is the minimum value of the given degradation coefficient;

[0029] S254, determine whether N <N m If yes, the fuel cell stack with the largest degradation coefficient is put into use, N=N+1, and the process proceeds to step S256; otherwise, the process proceeds to step S255;

[0030] S255, determine whether N>N m , if so, the fuel cell stack with the smallest degradation coefficient is decommissioned, N=N-1, and the process proceeds to step S256; otherwise, the process proceeds to step S256;

[0031] S256, determine whether N=N m If yes, put the fuel cell stack corresponding to N into the fuel stack and end; otherwise, return to step S254.

[0032] The present invention also discloses a fuel cell hybrid vehicle energy management system, which runs the fuel cell hybrid vehicle energy management method.

[0033] The present invention also discloses a hybrid vehicle, comprising the fuel cell hybrid vehicle energy management system.

[0034] (3) Beneficial effects

[0035] The above technical solution of the present invention has the following advantages:

[0036] (1) The present invention combines rule-based and optimization-based strategies, uses the threshold value in the rule as the decision variable, takes the equivalent hydrogen consumption as the fitness function, and introduces the constraint condition as a penalty function into the fitness function. The threshold value is optimized through a genetic algorithm, thereby improving the energy management efficiency of the vehicle during operation. The energy management of each part can be implemented more quickly and effectively, is easy to operate, and has high practical value.

[0037] (2) The present invention is rule-based and rationally allocates the power of fuel cells and lithium batteries according to the different operating states of the vehicle. Compared with the existing technology that only considers economy and seeks to minimize fuel costs, the present invention considers multiple factors such as vehicle power demand, minimum fuel consumption, and average degradation degree, thereby taking into account both minimum energy consumption and extended system life. While ensuring the lowest energy consumption for vehicle operation, it also improves system durability.

[0038] (3) The fuel cell stack of the prior art of the present invention is usually regarded as a whole, and the output power is constantly changing. However, the system of the present invention adopts a modular structure, fixes the output power of the fuel cell, designs the fuel cell as a collection of several fuel cell stacks, and selects the fuel cell stacks for use based on the degree of degradation, thereby averaging the degradation of the fuel cell stack and improving the overall life of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The features and advantages of the present invention will be more clearly understood by referring to the accompanying drawings, which are schematic and should not be construed as limiting the present invention in any way. In the accompanying drawings:

[0040] Figure 1 This is a schematic structural diagram of a fuel cell hybrid vehicle according to an embodiment of the present invention;

[0041] Figure 2 This is a flow chart of a fuel cell hybrid vehicle energy management method according to an embodiment of the present invention;

[0042] Figure 3 Flowchart of a method for optimizing thresholds based on a genetic algorithm according to an embodiment of the present invention;

[0043] Figure 4 The flowchart is a method for selecting the number of fuel cell stacks to be put into operation according to the difference according to an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The following embodiments of the present invention are described in further detail with reference to the accompanying drawings and examples. The following examples are used to illustrate the present invention but are not intended to limit the scope of the present invention.

[0045] The fuel cell hybrid vehicle structure of the present invention is as follows Figure 1 As shown, fuel cells and lithium batteries are combined as two different power sources. The fuel cell, as the primary energy source, emits only water vapor, making it highly efficient and environmentally friendly. The battery provides additional power during starting, acceleration, and high-load driving, improving vehicle performance. Lithium batteries can also recover braking energy, further enhancing energy efficiency. In energy management, system lifespan is a key consideration, in addition to economic efficiency. Therefore, multiple fuel cell stacks are designed to increase system redundancy and maintain a fixed output power to extend the lifespan of the fuel cells.

[0046] The energy management method of the present invention is as follows Figure 2 As shown, the following steps are included:

[0047] S1. Optimize the upper threshold SOC of lithium battery SOC based on genetic algorithm ub , the lower threshold value of lithium battery SOC lb , the output power of a single fuel cell stack P fc ;

[0048] Genetic algorithm can be used to solve this optimization problem. The basic process is as follows: Figure 3 As shown in the figure, the previously defined decision variables (the upper and lower limits of the lithium battery SOC and the fixed output power of a single fuel cell stack) are first encoded. The equivalent hydrogen consumption for the same mileage and the constraints introduced as penalty functions together form the fitness function. After several rounds of evolution, the desired optimal solution is finally selected, including:

[0049] S11. Genetically encode the decision variables, wherein the decision variables include the upper threshold SOC of the lithium battery SOC ub , the lower threshold value of lithium battery SOC lb , the output power of a single fuel cell stack P fc ;

[0050] In rule-based energy management approaches, the lithium battery's SOC is considered a critical parameter threshold. Upper and lower limits are typically set to determine the power supply status of the fuel cell and lithium battery. This also limits the fuel cell's output power, so the choice of output power also needs to be considered. In existing technologies, thresholds rely on the designer's experience, which can result in suboptimal strategies in actual operation. Therefore, optimization algorithms are being considered to refine the threshold parameters.

[0051] The upper and lower limits of lithium battery SOC and the constant output power of fuel cell are selected as decision variables. The fuel cell module adopts the mode of n fuel cell stacks. During the operation of the vehicle, the vehicle power demand P can be adjusted according to the vehicle power demand P. m And lithium battery power P b The difference between the number of fuel cell stacks is used to select the number of fuel cell stacks to be put into use. In this way, the average use time of the fuel cell stack is reduced to extend the system life. Taking n = 3 as an example, the number of fuel cell stacks put into use can refer to the following formula, where the output power of a single fuel cell stack is P fc :

[0052]

[0053] The selection of decision variable x is shown in the following formula, where SOC ub Is the upper threshold of lithium battery SOC, SOClb is the lower threshold of lithium battery SOC, P fc is the output power of a single fuel cell stack.

[0054]

[0055] S12, initialize the population, randomly generate a group of initial individuals, and set the population size as needed;

[0056] S13, calculating the fitness of each individual, the fitness function is the equivalent hydrogen consumption J, and the constraint condition is introduced into the fitness function as a penalty function;

[0057] The energy management method formulated by the present invention takes the minimum economic cost as the objective function, that is, it is necessary to reduce energy consumption as much as possible. Energy consumption includes two aspects: fuel cell hydrogen consumption and lithium battery power consumption, which can be expressed as the following formula, where C fc is the hydrogen consumption of the fuel cell, C b is the equivalent hydrogen consumption of the lithium battery, and the equivalent hydrogen consumption J is obtained by adding:

[0058] J=C fc +C b (3)

[0059] According to operational requirements, the energy management method must also meet the following constraints:

[0060]

[0061] The main constraints include lithium battery SOC (SOC b ) is maintained within a certain range to ensure that the lithium battery operates in the best state and the fuel cell stack output power is less than the maximum output power P fcmax , while considering the degradation degree of the fuel cell, the degradation degree factor is expressed by the degradation coefficient D fc To measure, where D fc Defined as the ratio of the fuel cell stack voltage to the rated voltage, that is, the degradation coefficient D of all fuel cell stacks is constrained fc The minimum value of the degradation coefficient is not less than the given minimum value D fcmin In the actual operation process, the fuel cell stack selection is based on the degradation degree of each fuel cell stack, and the degradation degree of each stack is kept consistent as much as possible. max The maximum SOC and SOC of the lithium battery min is the minimum SOC of the lithium battery, D fcn is the degradation coefficient of the nth fuel cell stack.

[0062] S14. Generate a new population by selecting individuals with high fitness, performing crossover and mutation;

[0063] S15. Determine whether the maximum genetic generation is reached. If so, the optimization is completed. If not, return to step S13.

[0064] Based on the genetic algorithm, the upper threshold SOC of the lithium battery SOC is optimized. ub , the lower threshold value of lithium battery SOC lb And the output power P of a single fuel cell stack fc , and then according to the upper threshold SOC of the optimized lithium battery SOC ub , the lower threshold value of lithium battery SOC lb And the output power P of a single fuel cell stack fc Carry out subsequent energy management.

[0065] S2, according to the optimized maximum power of the fuel cell and the upper threshold SOC of the lithium battery SOC ub And the lower threshold SOC of lithium battery SOC lb , and control the charging and discharging of lithium batteries and fuel cells and the launch and exit of fuel cell stacks; the maximum power of the fuel cell is determined according to the output power P of the single fuel cell stack. fc Multiply by the number of fuel cell stacks. Specifically including:

[0066] S21, determine the state of the car, if the car is in the braking state, go to step S22, if the car is in the non-braking state, go to step S23;

[0067] Since the power requirements of a car vary greatly under different operating conditions, a classification discussion is adopted, that is, the car operation is divided into two categories: braking and non-braking. The car operating state can be judged from the brake pedal opening. Therefore, the brake pedal opening is obtained to determine whether the brake pedal is pressed. If so, the car is in a braking state and enters step S22. If not, the car is in a non-braking state and enters step S23.

[0068] S22, determine whether there is SOC b >SOC ub If yes, the lithium battery is not charged and the process returns to step S21; if no, the lithium battery is charged to recover braking energy and improve energy utilization, and the process returns to step S21;

[0069] At this time, the car is in braking state, if the lithium battery SOC does not exceed the upper threshold SOC ub , the SOC of the lithium battery is insufficient and needs to be charged to recover braking energy and improve energy utilization. Otherwise, the SOC of the lithium battery is sufficient and will not be charged.

[0070] S23, determine whether there is SOC b <SOC lbIf yes, the lithium battery does not discharge, the fuel cell provides all the power, and the process goes to step S24; if no, the process goes to step S25;

[0071] At this time, when the car is in a non-braking state, if the lithium battery SOC is less than the lower threshold SOC lb , the SOC of the lithium battery is too low, the lithium battery does not discharge, and all power is provided by the fuel cell.

[0072] S24, determine whether the maximum power of the fuel cell is greater than the power required by the vehicle P m If yes, the fuel cell charges the lithium battery and the process returns to step S21; otherwise, the process returns to step S21;

[0073] At this time, the car is in a non-braking state, and all the power is provided by the fuel cell. If the maximum power that the fuel cell can provide is greater than the power required by the car, P m , the fuel cell has excess energy to charge the lithium battery. The maximum power of the fuel cell is based on the output power P of a single fuel cell stack. fc get.

[0074] S25, determine whether the maximum power of the fuel cell is greater than the power required by the vehicle P m If yes, the lithium battery is discharged to provide all the power, and the process returns to step S21; otherwise, the lithium battery is discharged, the fuel cell provides the remaining required power, and the fuel cell stack is put into operation and exited to ensure that the use time of all fuel cell stacks is basically the same;

[0075] At this time, if the car is in a non-braking state and the lithium battery SOC is not less than the lower limit SOC lb , the lithium battery is discharged, and further judgment is made whether the maximum discharge power of the lithium battery is greater than the power required by the car P m , the lithium battery provides all the required power, otherwise the fuel cell provides the remaining required power;

[0076] In addition, the specific methods of fuel cell stack deployment and exit control are as follows: Figure 4 As shown, the following steps are included:

[0077] S251, according to the power P required by the car m The difference between the discharge power of the lithium battery and the number of fuel cell stacks N required is obtained m ;

[0078] S252. Calculate the degradation degree of all fuel cell stacks, i.e., the degradation coefficient D fc , the degradation coefficient D for all fuel cell stacks fc Sort by

[0079] S253, the degradation coefficient D fc <Dfcmin The fuel cell stack is retired, let N = degradation coefficient D fc ≥D fcmin the number of fuel cell stacks;

[0080] S254, determine whether N <N m If yes, the fuel cell stack with the largest degradation coefficient is put into use, N=N+1, and the process proceeds to step S256; otherwise, the process proceeds to step S255;

[0081] S255, determine whether N>N m , if so, the fuel cell stack with the smallest degradation coefficient is decommissioned, N=N-1, and the process proceeds to step S256; otherwise, the process proceeds to step S256;

[0082] S256, determine whether N=N m If yes, then put the fuel cell stack corresponding to N into the fuel stack and end; otherwise, return to step S254;

[0083] The degradation degree of each fuel cell stack is determined by referring to the degradation coefficient of each fuel cell stack. The degradation coefficient D fc Below the lower limit D fcmin That is, the fuel cell stack whose degradation degree reaches the maximum threshold will be withdrawn from use; and for the degradation coefficient D fc Not less than the lower limit D fcmin That is, for fuel cell stacks whose degradation degree has not reached the maximum threshold, each time a new fuel cell stack is put into use, the fuel cell stack with the largest degradation coefficient is selected to be put into use, and each time a fuel cell stack is withdrawn, the fuel cell stack with the smallest withdrawal degradation coefficient is selected to be withdrawn; in this way, the degradation degree of each fuel cell stack is ensured to be basically consistent, the system life is extended, and the durability of the fuel cell is improved.

[0084] In summary, the above-mentioned fuel cell hybrid vehicle energy management method, system, and hybrid vehicle have the following beneficial effects:

[0085] (1) The present invention combines rule-based and optimization-based strategies, uses the threshold value in the rule as the decision variable, takes the equivalent hydrogen consumption as the fitness function, and introduces the constraint condition as a penalty function into the fitness function. The threshold value is optimized through a genetic algorithm, thereby improving the energy management efficiency of the vehicle during operation. The energy management of each part can be implemented more quickly and effectively, is easy to operate, and has high practical value.

[0086] (2) The present invention is rule-based and rationally allocates the power of fuel cells and lithium batteries according to the different operating states of the vehicle. Compared with the existing technology that only considers economy and seeks to minimize fuel costs, the present invention considers multiple factors such as vehicle power demand, minimum fuel consumption, and average degradation degree, thereby taking into account both minimum energy consumption and extended system life. While ensuring the lowest energy consumption for vehicle operation, it also improves system durability.

[0087] (3) The fuel cell stack of the prior art of the present invention is usually regarded as a whole, and the output power is constantly changing. However, the system of the present invention adopts a modular structure, fixes the output power of the fuel cell, designs the fuel cell as a collection of several fuel cell stacks, and selects the fuel cell stacks for use based on the degree of degradation, thereby averaging the degradation of the fuel cell stack and improving the overall life of the system.

[0088] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations shall fall within the scope defined by the appended claims.

Claims

1. A fuel cell hybrid vehicle energy management method, characterized in that: The fuel cell hybrid vehicle is provided with a lithium battery and a fuel cell, wherein the fuel cell includes a plurality of fuel cell stacks, and the energy management method includes: S1. Optimize the upper threshold SOC of lithium battery SOC based on genetic algorithm ub , the lower threshold value of lithium battery SOC lb , the output power of a single fuel cell stack P fc :Lithium battery SOC upper threshold SOC ub , the lower threshold value of lithium battery SOC lb , the output power of a single fuel cell stack P fc Encode it as a decision variable, combine the equivalent hydrogen consumption of the same mileage and the constraints introduced as a penalty function to form a fitness function, and perform several rounds of evolution to finally select the optimal solution; The step S1 comprises: S11. Genetically encode the decision variables, wherein the decision variables include the upper threshold SOC of the lithium battery SOC ub , the lower threshold value of lithium battery SOC lb , the output power of a single fuel cell stack P fc ; S12, initialize the population, randomly generate a group of initial individuals, and set the population size as needed; S13, calculating the fitness of each individual, the fitness function is the equivalent hydrogen consumption J, and the constraint condition is introduced into the fitness function as a penalty function; The equivalent hydrogen consumption J: J = C fc +C b , where C fc is the hydrogen consumption of the fuel cell, C b is the equivalent hydrogen consumption of lithium batteries; The constraints are: Among them, SOC max The maximum SOC and SOC of the lithium battery min is the minimum SOC of the lithium battery, P fcmax is the maximum output power of a single fuel cell stack, D fcmin is the minimum value of the given degradation coefficient, D fcn is the degradation coefficient of the nth fuel cell stack, SOC b is the lithium battery SOC; S14. Generate a new population by selecting individuals with high fitness, performing crossover and mutation; S15, determine whether the maximum genetic generation is reached, if so, the optimization is completed, if not, return to step S13; S2, according to the optimized maximum power of the fuel cell and the upper threshold SOC of the lithium battery SOC ub And the lower threshold SOC of lithium battery SOC lb , and control the charging and discharging of lithium batteries and fuel cells and the launch and exit of fuel cell stacks; the maximum power of the fuel cell is determined according to the output power P of the single fuel cell stack. fc Multiply by the number of fuel cell stacks.

2. The fuel cell hybrid vehicle energy management method according to claim 1, characterized in that: In step S2, the charging and discharging control of the lithium battery and fuel cell includes: S21, determine the state of the car, if the car is in the braking state, go to step S22, if the car is in the non-braking state, go to step S23; S22, determine whether there is SOC b >SOC ub If yes, the lithium battery is not charged and the process returns to step S21; if no, the lithium battery is charged and braking energy is recovered and the process returns to step S21; SOC b is the lithium battery SOC; S23, determine whether there is SOC b <SOC lb If yes, the lithium battery does not discharge, the fuel cell provides all the power, and the process goes to step S24; if no, the process goes to step S25; S24, determine whether the maximum power of the fuel cell is greater than the power required by the vehicle P m If yes, the fuel cell charges the lithium battery and the process returns to step S21; otherwise, the process returns to step S21; S25, determine whether the maximum power of the fuel cell is greater than the power required by the vehicle P m If so, the lithium battery discharges and provides all the power, and returns to step S21; otherwise, the lithium battery discharges, the fuel cell provides the remaining required power, and the fuel cell stack is put into and exited.

3. The fuel cell hybrid vehicle energy management method according to claim 2, characterized in that: The vehicle state determination includes: obtaining the opening of the brake pedal, determining whether the brake pedal is stepped on, if so, the vehicle is in a braking state, and if not, the vehicle is in a non-braking state.

4. The fuel cell hybrid vehicle energy management method according to claim 2, characterized in that: In step S25, the fuel cell stack deployment and exit control includes: S251, according to the power P required by the car m The difference between the discharge power of the lithium battery and the number of fuel cell stacks N required is obtained m ; S252. Calculate the degradation degree of all fuel cell stacks, i.e., the degradation coefficient D fc , the degradation coefficient D for all fuel cell stacks fc Sort by S253, the degradation coefficient D fc <D fcmin The fuel cell stack is retired, let N = degradation coefficient D fc ≥D fcmin The number of fuel cell stacks; D fcmin is the minimum value of the given degradation coefficient; S254, determine whether N <N m If yes, the fuel cell stack with the largest degradation coefficient is put into use, N=N+1, and the process proceeds to step S256; otherwise, the process proceeds to step S255; S255, determine whether N>N m , if so, the fuel cell stack with the smallest degradation coefficient is decommissioned, N=N-1, and the process proceeds to step S256; otherwise, the process proceeds to step S256; S256, determine whether N=N m If yes, put the fuel cell stack corresponding to N into the fuel stack and end; otherwise, return to step S254.

5. A fuel cell hybrid vehicle energy management system, characterized in that: Run the fuel cell hybrid vehicle energy management method according to any one of claims 1 to 4.

6. A hybrid vehicle, characterized in that: Including the fuel cell hybrid vehicle energy management system according to claim 5.

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