Energy management control method and device for fuel cell system

Abstract

A method, apparatus, device and computer-readable storage medium for energy management control of a fuel cell system, comprising: predicting acquired vehicle information and road condition information based on a preset neural network model to obtain a vehicle-wide energy consumption value; and enabling the fuel cell system to generate corresponding output power to a motor based on the vehicle-wide energy consumption value and a preset duration. This solves the technical problem in related technologies of using the fuel cell system as a range extender, where the electricity generated by the fuel cell system first passes through a power battery and then is supplied to the motor, but this results in excessive power throughput by the power battery, thereby reducing the life of the power battery. This enables the electricity generated by the fuel cell system to be directly delivered to the motor, reducing the number of load changes in the fuel cell system and extending the life of the fuel cell system and the power battery.

Description

Technical Field

[0001] The present application relates to the field of fuel cell control, and specifically to an energy management control method, device, equipment, and computer-readable storage medium for a fuel cell system. Background Art

[0002] Fuel cell vehicles are environmentally friendly and efficient vehicles, offering the advantages of rapid energy replenishment and long driving range. Traditional fuel cell system energy management methods use the fuel cell system as a range extender. A range extender generally refers to an electric vehicle component that provides additional power, thereby increasing the vehicle's range. Traditionally, a range extender refers to a combination of an engine and a generator. The power generated by the fuel cell system first passes through the power battery and then to the motor. This can result in excessive power throughput in the power battery, shortening its lifespan. Summary of the Invention

[0003] The present application provides an energy management control method, device, equipment and computer-readable storage medium for a fuel cell system, which can solve the technical problem in the prior art of using the fuel cell system as a range extender. The electricity generated by the fuel cell system first passes through the power battery and then is supplied to the motor, but this will cause the power battery to have excessive power throughput, thereby reducing the life of the power battery.

[0004] In a first aspect, an embodiment of the present application provides an energy management control method for a fuel cell system, the energy management control method for the fuel cell system comprising:

[0005] Predict the acquired vehicle information and road condition information based on the preset neural network model to obtain the energy consumption value of the entire vehicle;

[0006] According to the energy consumption value of the entire vehicle and the preset duration, the fuel cell system generates corresponding output power to the motor.

[0007] In combination with the first aspect, in one embodiment, the step of causing the fuel cell system to generate corresponding output power to the motor based on the vehicle energy consumption value and the preset duration includes:

[0008] Calculate the average power required by the vehicle based on a preset formula, the vehicle energy consumption value, and the preset duration;

[0009] The fuel cell system generates corresponding output power to the motor based on the average power required by the vehicle.

[0010] In conjunction with the first aspect, in one embodiment, after calculating the average power required by the vehicle based on a preset formula, the vehicle energy consumption value, and the preset duration, the method further includes:

[0011] comparing the average power required by the vehicle with the preset power;

[0012] If the average power required by the vehicle is less than or equal to the preset power, the fuel cell system generates corresponding output power to the motor based on the average power required by the vehicle.

[0013] In combination with the first aspect, in one embodiment, after comparing the average power required by the vehicle with the preset power, the method further includes:

[0014] If the average power required by the vehicle is greater than the preset power, querying the charge ratio of the power battery;

[0015] If the charge ratio of the power battery is less than or equal to a first preset charge ratio, adjusting the preset power, and generating corresponding output power to the motor based on the average power required by the vehicle through the fuel cell system;

[0016] If the charge ratio of the power battery is greater than the first preset charge ratio and less than the second preset charge ratio, obtaining a first preset ratio;

[0017] The output powers of the fuel cell system and the power battery system are determined respectively according to the first preset ratio and the average power required by the vehicle, so that the fuel cell system and the power battery system generate corresponding output powers to the motor.

[0018] In conjunction with the first aspect, in one embodiment, predicting the acquired vehicle information and road condition information according to a preset neural network model to obtain a vehicle energy consumption value includes:

[0019] Obtaining vehicle information and road condition information, wherein the vehicle information includes average speed and vehicle load, and the road condition information includes distance, normal road conditions, and abnormal road conditions;

[0020] The obtained vehicle information and the road condition information are input into a preset network model to obtain a vehicle energy consumption value output by the preset network model.

[0021] In conjunction with the first aspect, in one embodiment, before inputting the acquired vehicle information and road condition information into a preset network model and obtaining the vehicle energy consumption value output by the preset network model, the method further includes:

[0022] Acquire data to be trained, wherein any set of data in the data to be trained includes historical vehicle information, historical road condition information, and energy consumption values, wherein the historical vehicle information includes historical average vehicle speed and historical vehicle load, and the historical road condition information includes historical distance, historical normal road conditions, and historical abnormal road conditions;

[0023] The preset neural network is trained based on the data to be trained to generate a corresponding neural network model, and the neural network model is used as the preset neural network model.

[0024] In conjunction with the first aspect, in one embodiment, before generating the corresponding neural network model and using the neural network model as the preset neural network model, the method further includes:

[0025] Obtaining a loss value or number of training times of the neural network after training;

[0026] If the number of training times reaches a preset number, it is determined that the neural network is in a convergence state, a corresponding neural network model is generated, and the neural network model is used as a preset neural network model;

[0027] Alternatively, if the loss value is less than or equal to a preset loss value, it is determined that the neural network is in a convergence state, a corresponding neural network model is generated, and the neural network model is used as a preset neural network model.

[0028] In a second aspect, an embodiment of the present application provides an energy management control device for a fuel cell system, the energy management control device for the fuel cell system comprising:

[0029] The prediction and acquisition module is used to predict the acquired vehicle information and road condition information based on the preset neural network model to obtain the energy consumption value of the entire vehicle;

[0030] The output module is used to enable the fuel cell system to generate corresponding output power to the motor according to the energy consumption value of the entire vehicle and the preset duration.

[0031] In a third aspect, an embodiment of the present application provides an energy management control device for a fuel cell system, wherein the energy management control device for the fuel cell system includes a processor, a memory, and an energy management control program for the fuel cell system stored on the memory and executable by the processor, wherein when the energy management control program for the fuel cell system is executed by the processor, the steps of the energy management control method for the fuel cell system as described above are implemented.

[0032] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which an energy management control program for a fuel cell system is stored. When the energy management control program for the fuel cell system is executed by a processor, the steps of the energy management control method for the fuel cell system described above are implemented. The technical solutions provided in the embodiments of the present application provide the following beneficial effects:

[0033] By predicting the acquired vehicle information and road condition information based on a preset neural network model, the energy consumption value of the entire vehicle is obtained; based on the energy consumption value of the entire vehicle and the preset duration, the fuel cell system generates corresponding output power to the motor, which solves the problem in related technologies that the fuel cell system is used as a range extender, and the electricity generated by the fuel cell system first passes through the power battery and then is supplied to the motor, but this will cause the power battery to have excessive power throughput, thereby reducing the life of the power battery. The power generated by the fuel cell system is directly delivered to the motor, reducing the number of load changes of the fuel cell system and extending the life of the fuel cell system and the power battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a flow chart of an embodiment of an energy management control method for a fuel cell system of the present application;

[0035] Figure 2 This is a flow chart of another embodiment of the energy management control method for a fuel cell system of the present application;

[0036] Figure 3 This is a functional module diagram of an embodiment of an energy management control device for a fuel cell system of the present application;

[0037] Figure 4 This is a schematic diagram of the hardware structure of the energy management control device of the fuel cell system involved in the embodiment of the present application. DETAILED DESCRIPTION

[0038] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0039] First, some technical terms in this application are explained to facilitate those skilled in the art to understand this application.

[0040] In order to make the objectives, technical solutions and advantages of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings.

[0041] In a first aspect, an embodiment of the present application provides an energy management control method for a fuel cell system.

[0042] In one embodiment, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the energy management control method for the fuel cell system of this application. Figure 1 As shown, the energy management control method of the fuel cell system includes:

[0043] Step S10: predicting the acquired vehicle information and road condition information according to a preset neural network model to obtain the energy consumption value of the entire vehicle;

[0044] Exemplarily, vehicle information and road condition information are obtained, including average speed and vehicle load, and road condition information including distance, normal road conditions, and abnormal road conditions. The vehicle information and road condition information are input into a preset neural network model, which then performs a prediction based on the input vehicle information and road condition information to obtain a corresponding vehicle energy consumption value. The preset neural network model is obtained by training the neural network using the training data.

[0045] Step S20: Based on the vehicle energy consumption value and the preset duration, the fuel cell system generates corresponding output power to the motor.

[0046] Exemplarily, a user-entered duration is obtained, or a duration is obtained based on the average speed in the vehicle information and the distance in the road condition information, and the obtained duration is used as the preset duration. The required output power of the vehicle is calculated based on the vehicle energy consumption value and the preset duration, so that the fuel cell generates the corresponding output power to the motor.

[0047] Specifically, the method of enabling the fuel cell system to generate corresponding output power to the motor based on the energy consumption value of the entire vehicle and the preset duration includes: calculating the average power required for the vehicle based on a preset formula, the energy consumption value of the entire vehicle and the preset duration; and generating corresponding output power to the motor based on the average power required for the vehicle through the fuel cell system.

[0048] For example, get the preset formula Among them, P is the output power, Q is the energy consumption value of the vehicle, and T is the preset time. The average power required by the vehicle is calculated using the vehicle's energy consumption value, Q, and a preset duration, T. After the average power required by the vehicle is calculated, a command is generated and sent to the fuel cell system. The fuel cell system then generates the corresponding output power to the motor based on the average power required by the vehicle.

[0049] Specifically, after calculating the average power required by the vehicle based on the preset formula, the energy consumption value of the whole vehicle and the preset duration, it also includes: comparing the average power required by the vehicle with the preset power; if the average power required by the vehicle is less than or equal to the preset power, then generating corresponding output power to the motor based on the average power required by the vehicle through the fuel cell system.

[0050] Exemplarily, the average power required by the vehicle is compared with the preset power of the fuel cell system. If the average power required by the vehicle is less than or equal to the preset power of the fuel cell system, the fuel cell system generates a corresponding output power to the motor based on the average power required by the vehicle. For example, if the average power required by the vehicle is 100 kW and the preset power of the fuel cell system is 100 kW, the fuel cell system generates a corresponding output power of 100 kW to the motor based on the average power required by the vehicle. If the average power required by the vehicle is 80 kW and the preset power of the fuel cell system is 100 kW, the fuel cell system generates a corresponding output power of 100 kW to the motor.

[0051] Specifically, after comparing the average power required by the vehicle with the preset power, it also includes: if the average power required by the vehicle is greater than the preset power, querying the charge of the power battery; if the charge ratio of the power battery is less than or equal to a first preset charge ratio, adjusting the preset power, and generating corresponding output power to the motor through the fuel cell system based on the average power required by the vehicle; if the charge ratio of the power battery is greater than the first preset charge ratio and less than the second preset charge ratio, obtaining a first preset ratio; and determining the output power of the fuel cell system and the power battery system respectively according to the first preset ratio and the average power required by the vehicle, so that the fuel cell system and the power battery system generate corresponding output power to the motor.

[0052] Exemplarily, if the average power required by the vehicle is greater than the preset power of the fuel cell system, the charge of the power battery is queried. For example, if the average power required by the vehicle is 120KW and the preset power of the fuel cell system is 100KW, the charge of the power battery is queried. If the charge ratio of the power battery is found to be less than or equal to the first preset charge ratio, the preset power is adjusted, and the fuel cell system generates a corresponding output power to the motor based on the average power required by the vehicle. For example, if the charge of the power battery is found to be less than or equal to 40% of the first preset charge ratio, the preset power of the fuel cell system is adjusted to 120KW, and the fuel cell system generates a corresponding output power to the motor based on the average power required by the vehicle.

[0053] If the power battery charge is found to be greater than 40% of the first preset charge ratio and less than 70% of the second preset charge ratio, the first preset ratio is obtained. Based on the first preset ratio and the average power required by the vehicle, the output power of the fuel cell system and the power battery system are determined, respectively, so that the fuel cell system and the power battery system generate corresponding output power to the motor. For example, the fuel cell system outputs 100 kW to the motor, and the power battery outputs 20 kW to the motor.

[0054] In this embodiment, the obtained vehicle information and road condition information are predicted according to a preset neural network model to obtain the energy consumption value of the entire vehicle; according to the energy consumption value of the entire vehicle and the preset duration, the fuel cell system is caused to generate corresponding output power to the motor, which solves the problem in the related art that the fuel cell system is used as a range extender, and the electricity generated by the fuel cell system first passes through the power battery and then is supplied to the motor, but this will cause the power battery to have excessive power throughput, thereby reducing the life of the power battery. The power generated by the fuel cell system is directly delivered to the motor, which reduces the number of load changes of the fuel cell system and extends the life of the fuel cell system and the power battery.

[0055] In one embodiment, referring to Figure 2 , Figure 2 This is a flow chart of another embodiment of the energy management control method of the fuel cell system of the present application. Figure 2 As shown, the energy management control method of the fuel cell system includes:

[0056] Step S30: Acquire data to be trained, wherein any set of data in the data to be trained includes historical vehicle information, historical road condition information, and energy consumption values, wherein the historical vehicle information includes historical average vehicle speed and historical vehicle load, and the historical road condition information includes historical distance, historical normal road condition, and historical abnormal road condition;

[0057] Exemplarily, data to be trained is obtained, and any set of data in the data to be trained includes historical vehicle information, historical road condition information and energy consumption value, historical vehicle information includes historical average vehicle speed and historical vehicle load, and historical road condition information includes historical distance, historical normal road condition and historical abnormal road condition.

[0058] Step S40: training a preset neural network based on the data to be trained to generate a corresponding neural network model, and using the neural network model as a preset neural network model;

[0059] Exemplarily, a preset neural network is trained on the training data, which may be a convolutional neural network or a recurrent neural network. A loss value or the number of training times of the neural network after training is obtained; if the number of training times reaches a preset number, the neural network is determined to be in a convergence state, a corresponding neural network model is generated, and the neural network model is used as the preset neural network model; or, if the loss value is less than or equal to the preset loss value, the neural network is determined to be in a convergence state, a corresponding neural network model is generated, and the neural network model is used as the preset neural network model.

[0060] Step S50: predicting the acquired vehicle information and road condition information according to a preset neural network model to obtain the energy consumption value of the entire vehicle;

[0061] Exemplarily, vehicle information and road condition information are obtained, including average vehicle speed and vehicle load, and road condition information including distance, normal road conditions, and abnormal road conditions. The vehicle information and road condition information are input into a preset neural network model, which then performs a prediction based on the input vehicle information and road condition information to obtain a corresponding vehicle energy consumption value.

[0062] Step S60: Based on the vehicle energy consumption value and the preset duration, the fuel cell system generates corresponding output power to the motor.

[0063] Exemplarily, a user-entered duration is obtained, or a duration is obtained based on the average speed in the vehicle information and the distance in the road condition information, and the obtained duration is used as the preset duration. The required output power of the vehicle is calculated based on the vehicle energy consumption value and the preset duration, so that the fuel cell generates the corresponding output power to the motor.

[0064] In this embodiment, a neural network is trained using historical vehicle information, historical road condition information, and energy consumption values ​​to generate a neural network model that predicts the energy demand of the entire vehicle throughout the entire driving mileage. Based on the energy consumption value of the entire vehicle and the preset duration, the fuel cell system is enabled to generate corresponding output power to the motor. This solves the problem in related technologies of using the fuel cell system as a range extender, where the electricity generated by the fuel cell system first passes through the power battery and then is supplied to the motor, but this causes the power battery to have excessive power throughput, thereby reducing the life of the power battery. This solves the problem of directly transmitting the electricity generated by the fuel cell system to the motor, reducing the number of load changes in the fuel cell system and extending the life of the fuel cell system and the power battery.

[0065] In a second aspect, an embodiment of the present application also provides an energy management control device for a fuel cell system.

[0066] In one embodiment, referring to Figure 3 , Figure 3 This is a functional module diagram of an embodiment of the energy management control device of the fuel cell system of the present application. Figure 3 As shown, the energy management control device of the fuel cell system includes:

[0067] The prediction and acquisition module 10 is used to predict the acquired vehicle information and road condition information according to a preset neural network model to obtain the energy consumption value of the entire vehicle;

[0068] The output module 20 is used to enable the fuel cell system to generate corresponding output power to the motor according to the vehicle energy consumption value and the preset duration.

[0069] Furthermore, in one embodiment, the output module 20 is used to:

[0070] Calculate the average power required by the vehicle based on a preset formula, the vehicle energy consumption value, and the preset duration;

[0071] The fuel cell system generates corresponding output power to the motor based on the average power required by the vehicle.

[0072] Furthermore, in one embodiment, the energy management control device of the fuel cell system further includes a new module for:

[0073] comparing the average power required by the vehicle with the preset power;

[0074] If the average power required by the vehicle is less than or equal to the preset power, the fuel cell system generates corresponding output power to the motor based on the average power required by the vehicle.

[0075] Furthermore, in one embodiment, the energy management control device of the fuel cell system further includes a new module for:

[0076] If the average power required by the vehicle is greater than the preset power, querying the charge of the power battery;

[0077] If the charge ratio of the power battery is less than or equal to a first preset charge ratio, adjusting the preset power, and generating corresponding output power to the motor based on the average power required by the vehicle through the fuel cell system;

[0078] If the charge ratio of the power battery is greater than the first preset charge ratio and less than the second preset charge ratio, obtaining a first preset ratio;

[0079] The output powers of the fuel cell system and the power battery system are determined respectively according to the first preset ratio and the average power required by the vehicle, so that the fuel cell system and the power battery system generate corresponding output powers to the motor.

[0080] Furthermore, in one embodiment, the prediction and acquisition module 10 is used to:

[0081] Obtaining vehicle information and road condition information, wherein the vehicle information includes average speed and vehicle load, and the road condition information includes distance, normal road conditions, and abnormal road conditions;

[0082] The obtained vehicle information and the road condition information are input into a preset network model to obtain a vehicle energy consumption value output by the preset network model.

[0083] Furthermore, in one embodiment, the energy management control device of the fuel cell system further includes a new module for:

[0084] Acquire data to be trained, wherein any set of data in the data to be trained includes historical vehicle information, historical road condition information, and energy consumption values, wherein the historical vehicle information includes historical average vehicle speed and historical vehicle load, and the historical road condition information includes historical distance, historical normal road conditions, and historical abnormal road conditions;

[0085] The preset neural network is trained based on the data to be trained to generate a corresponding neural network model, and the neural network model is used as the preset neural network model.

[0086] Furthermore, in one embodiment, the energy management control device of the fuel cell system further includes a new module for:

[0087] Obtaining a loss value or number of training times of the neural network after training;

[0088] If the number of training times reaches a preset number, it is determined that the neural network is in a convergence state, a corresponding neural network model is generated, and the neural network model is used as a preset neural network model;

[0089] Alternatively, if the loss value is less than or equal to a preset loss value, it is determined that the neural network is in a convergence state, a corresponding neural network model is generated, and the neural network model is used as a preset neural network model.

[0090] Among them, the functional implementation of each module in the energy management control device of the above-mentioned fuel cell system corresponds to the various steps in the embodiment of the energy management control method of the above-mentioned fuel cell system, and their functions and implementation processes are no longer repeated here.

[0091] In a third aspect, an embodiment of the present application provides an energy management and control device for a fuel cell system. The energy management and control device for the fuel cell system may be a personal computer (PC), a laptop computer, a server, or other device with data processing capabilities.

[0092] Reference Figure 4 , Figure 4 Schematic diagram of the hardware structure of the energy management control device of the fuel cell system involved in the embodiment of the present application. In the embodiment of the present application, the energy management control device of the fuel cell system may include a processor, a memory, a communication interface and a communication bus.

[0093] The communication bus may be of any type and is used to interconnect the processor, memory, and communication interface.

[0094] Communication interfaces include input / output (I / O) interfaces, physical interfaces, and logical interfaces, used to interconnect components within the fuel cell system's energy management and control equipment, as well as interfaces used to interconnect the fuel cell system's energy management and control equipment with other devices (such as other computing devices or user devices). Physical interfaces can include Ethernet, fiber optic, and ATM interfaces; user devices can include displays and keyboards.

[0095] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0096] The processor may be a general-purpose processor that can call the energy management control program of the fuel cell system stored in the memory and execute the energy management control method of the fuel cell system provided in the embodiment of the present application. For example, the general-purpose processor may be a central processing unit (CPU). The method executed when the energy management control program of the fuel cell system is called can refer to the various embodiments of the energy management control method of the fuel cell system of the present application, and will not be repeated here.

[0097] Those skilled in the art will understand that Figure 4 The hardware structure shown in the figure does not constitute a limitation to the present application and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0098] In a fourth aspect, an embodiment of the present application also provides a computer-readable storage medium.

[0099] The computer-readable storage medium of the present application stores an energy management control program for a fuel cell system, wherein when the energy management control program for the fuel cell system is executed by a processor, the steps of the energy management control method for the fuel cell system as described above are implemented.

[0100] Among them, the method implemented when the energy management control program of the fuel cell system is executed can refer to the various embodiments of the energy management control method of the fuel cell system of the present application, and will not be repeated here.

[0101] It should be noted that the serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0102] The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices. The terms "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit the "first", "second" and "third" to different types.

[0103] In the description of the embodiments of this application, the words "exemplary," "for example," or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary," "for example," or "for example" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "for example," or "for example" is intended to present the relevant concepts in a concrete manner.

[0104] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.

[0105] In some processes described in the embodiments of the present application, multiple operations or steps are included that appear in a specific order. However, it should be understood that these operations or steps may not be performed in the order in which they appear in the embodiments of the present application or may be performed in parallel. The sequence numbers of the operations are only used to distinguish between different operations, and the sequence numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations or steps may be performed in sequence or in parallel, and these operations or steps may be combined.

[0106] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for enabling a terminal device to execute the methods described in each embodiment of the present application.

[0107] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A fuel cell system energy management control method, characterized in that: The energy management control method of the fuel cell system includes: Predict the acquired vehicle information and road condition information based on the preset neural network model to obtain the energy consumption value of the entire vehicle; According to the energy consumption value of the entire vehicle and the preset duration, the fuel cell system generates corresponding output power to the motor; The method of causing the fuel cell system to generate corresponding output power to the motor according to the vehicle energy consumption value and the preset duration includes: Calculate the average power required by the vehicle based on a preset formula, the vehicle energy consumption value, and the preset duration; Based on the average power required by the vehicle, the fuel cell system generates a corresponding output power to the motor; After calculating the average power required by the vehicle based on the preset formula, the vehicle energy consumption value, and the preset duration, the method further includes: comparing the average power required by the vehicle with the preset power; If the average power required by the vehicle is less than or equal to the preset power, the fuel cell system generates a corresponding output power to the motor based on the average power required by the vehicle; After comparing the average power required by the vehicle with the preset power, the method further includes: If the average power required by the vehicle is greater than the preset power, querying the charge ratio of the power battery; If the charge ratio of the power battery is less than or equal to a first preset charge ratio, adjusting the preset power, and generating corresponding output power to the motor based on the average power required by the vehicle through the fuel cell system; If the charge ratio of the power battery is greater than the first preset charge ratio and less than the second preset charge ratio, obtaining a first preset ratio; The output powers of the fuel cell system and the power battery system are determined respectively according to the first preset ratio and the average power required by the vehicle, so that the fuel cell system and the power battery system generate corresponding output powers to the motor.

2. The energy management control method of the fuel cell system according to claim 1, wherein: The method of predicting the acquired vehicle information and road condition information according to the preset neural network model to obtain the energy consumption value of the entire vehicle includes: Obtaining vehicle information and road condition information, wherein the vehicle information includes average speed and vehicle load, and the road condition information includes distance, normal road conditions, and abnormal road conditions; The obtained vehicle information and the road condition information are input into a preset network model to obtain a vehicle energy consumption value output by the preset network model.

3. The energy management control method of the fuel cell system according to claim 2, wherein: Before inputting the acquired vehicle information and road condition information into a preset network model and obtaining the vehicle energy consumption value output by the preset network model, the method further includes: Acquire data to be trained, wherein any set of data in the data to be trained includes historical vehicle information, historical road condition information, and energy consumption values, wherein the historical vehicle information includes historical average vehicle speed and historical vehicle load, and the historical road condition information includes historical distance, historical normal road conditions, and historical abnormal road conditions; The preset neural network is trained based on the data to be trained to generate a corresponding neural network model, and the neural network model is used as the preset neural network model.

4. The energy management control method of the fuel cell system according to claim 3, wherein: Before generating the corresponding neural network model and using the neural network model as the preset neural network model, the method further includes: Obtaining a loss value or number of training times of the neural network after training; If the number of training times reaches a preset number, it is determined that the neural network is in a convergence state, a corresponding neural network model is generated, and the neural network model is used as a preset neural network model; Alternatively, if the loss value is less than or equal to a preset loss value, it is determined that the neural network is in a convergence state, a corresponding neural network model is generated, and the neural network model is used as a preset neural network model.

5. An energy management control device for a fuel cell system, characterized in that: The energy management control device of the fuel cell system includes: The prediction and acquisition module is used to predict the acquired vehicle information and road condition information based on the preset neural network model to obtain the energy consumption value of the entire vehicle; An output module, configured to enable the fuel cell system to generate corresponding output power to the motor according to the vehicle energy consumption value and the preset duration; The method of causing the fuel cell system to generate corresponding output power to the motor according to the vehicle energy consumption value and the preset duration includes: Calculate the average power required by the vehicle based on a preset formula, the vehicle energy consumption value, and the preset duration; Based on the average power required by the vehicle, the fuel cell system generates a corresponding output power to the motor; After calculating the average power required by the vehicle based on the preset formula, the vehicle energy consumption value, and the preset duration, the method further includes: comparing the average power required by the vehicle with the preset power; If the average power required by the vehicle is less than or equal to the preset power, the fuel cell system generates a corresponding output power to the motor based on the average power required by the vehicle; After comparing the average power required by the vehicle with the preset power, the method further includes: If the average power required by the vehicle is greater than the preset power, querying the charge ratio of the power battery; If the charge ratio of the power battery is less than or equal to a first preset charge ratio, adjusting the preset power, and generating corresponding output power to the motor based on the average power required by the vehicle through the fuel cell system; If the charge ratio of the power battery is greater than the first preset charge ratio and less than the second preset charge ratio, obtaining a first preset ratio; The output powers of the fuel cell system and the power battery system are determined respectively according to the first preset ratio and the average power required by the vehicle, so that the fuel cell system and the power battery system generate corresponding output powers to the motor.

6. An energy management control device for a fuel cell system, characterized in that: The energy management control device of the fuel cell system includes a processor, a memory, and an energy management control program of the fuel cell system stored on the memory and executable by the processor, wherein when the energy management control program of the fuel cell system is executed by the processor, the steps of the energy management control method of the fuel cell system as described in any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium stores an energy management control program for the fuel cell system, wherein when the energy management control program for the fuel cell system is executed by the processor, the steps of the energy management control method for the fuel cell system as described in any one of claims 1 to 4 are implemented.

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