Energy management control method and device of fuel cell system

Abstract

An energy management control method, apparatus and device for a fuel cell system, and a computer readable storage medium, the method comprising: predicting obtained vehicle information and road condition information according to a preset neural network model to obtain a vehicle energy consumption value; according to the whole vehicle energy consumption value and the preset duration, the fuel cell system generates corresponding output power to the motor, and the problems that in the related technology, the fuel cell system serves as a range extender, electricity generated by the fuel cell system passes through a power cell and then is supplied to the motor, however, the huff and puff electricity of the power cell is too large, and the service life of the motor is prolonged are solved. Therefore, the service life of the power battery is shortened, the electric quantity generated by the fuel cell system is directly transmitted to the motor, the load changing frequency of the fuel cell system is reduced, and the service life of the fuel cell system and the service life of the power battery are prolonged.

Description

Technical Field

[0001] The present application relates to the field of fuel cell control, and in particular 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 an environmentally friendly and efficient means of transportation with the advantages of rapid energy replenishment and long driving range. The traditional fuel cell system energy management method uses the fuel cell system as a range extender, where a range extender generally refers to an electric vehicle component that can provide additional electrical energy, thereby increasing the mileage of an electric vehicle. A range extender in the traditional sense refers to a combination of an engine and a generator. 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. 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 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.

[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 a fuel cell system comprising:

[0005] The obtained vehicle information and road condition information are predicted according to the preset neural network model to obtain the energy consumption value of the whole vehicle;

[0006] According to the energy consumption value of the whole 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 according to 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 combination with the first aspect, in one embodiment, 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:

[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 implementation, 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 the first preset charge ratio, the preset power is adjusted to generate a 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] According to the first preset ratio and the average power required by the vehicle, the output powers 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 powers to the motor.

[0018] In combination with the first aspect, in one implementation, 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] Acquire vehicle information and road condition information, wherein the vehicle information includes average vehicle speed and vehicle load, and the road condition information includes distance, normal road condition and abnormal road condition;

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

[0021] In combination with the first aspect, in one implementation, before inputting the acquired vehicle information and the road condition information into a preset network model and acquiring 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 value, 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;

[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 combination with the first aspect, in one implementation, the generating of the corresponding neural network model, before using the neural network model as the preset neural network model, 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 of times, 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 according to the preset neural network model to obtain the energy consumption value of the whole 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 whole vehicle and the preset duration.

[0031] In the third aspect, an embodiment of the present application provides an energy management control device of a fuel cell system, wherein the energy management control device of the fuel cell system comprises a processor, a memory, and an energy management control program of the fuel cell system stored in 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 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 of a fuel cell system is stored, wherein when the energy management control program of the fuel cell system is executed by a processor, the steps of the energy management control method of the fuel cell system as described above are implemented. The beneficial effects brought about by the technical solution provided in the embodiment of the present application include:

[0033] 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 whole vehicle; according to the energy consumption value of the whole vehicle and the preset duration, the fuel cell system generates corresponding output power to the motor, which solves the problem that the fuel cell system is used as a range extender in the related technology, 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 throughput too much electricity, thereby reducing the life of the power battery. The technical problem is that the electricity generated by the fuel cell system is directly transmitted 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. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0036] Figure 3 A schematic diagram of functional modules 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 solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, 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 the present application are explained to facilitate those skilled in the art to understand the present application.

[0040] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present 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 of the fuel cell system of the present 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 whole vehicle;

[0044] Exemplarily, vehicle information and road condition information are obtained, the vehicle information includes average vehicle speed and vehicle load, and the road condition information includes distance, normal road condition and abnormal road condition. The vehicle information and road condition information are input into a preset neural network model, and the preset neural network model is used to make 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 with the training data.

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

[0046] Exemplarily, the duration input by the user is obtained, or the duration is obtained according to 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 output power required by the vehicle is calculated based on the energy consumption value of the whole vehicle 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 whole 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 whole 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 whole vehicle, and T is the preset time. The energy consumption value Q of the whole vehicle and the preset duration T are used to calculate the average power required by the vehicle. 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 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 whole vehicle energy consumption value 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 a 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 100KW and the preset power of the fuel cell system is 100KW, the fuel cell system generates a corresponding output power of 100KW to the motor based on the average power required by the vehicle of 100KW. If the average power required by the vehicle is 80KW and the preset power of the fuel cell system is 100KW, the corresponding output power of 100KW is generated 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 the first preset charge ratio, adjusting the preset power, and generating a 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 powers 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 powers 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 less than or equal to the first preset charge ratio, the preset power is adjusted, and the corresponding output power is generated to the motor based on the average power required by the vehicle through the fuel cell system. For example, if the charge of the power battery is 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 corresponding output power is generated to the motor based on the average power required by the vehicle through the fuel cell system.

[0053] If the charge of the power battery is found to be greater than the first preset charge ratio of 40% and less than the second preset charge ratio of 70%, the first preset ratio is obtained. According to 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 output power of the fuel cell system is 100KW to the motor, and the output power of the power battery is 20KW 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 whole vehicle; according to the energy consumption value of the whole vehicle and the preset duration, the fuel cell system generates corresponding output power to the motor, which solves the problem that the fuel cell system is used as a range extender in the related technology, and the electricity generated by the fuel cell system first passes through the power battery and then is supplied to the motor, but it will cause the power battery to have excessive power throughput, thereby reducing the life of the power battery. The technical problem is that the electricity 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: obtaining 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 value, 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 values, 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.

[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, the preset neural network is trained with the data to be trained, and the preset neural network can be a convolutional neural network and a recurrent neural network. The loss value or the number of training times of the neural network after training is obtained; if the number of training times reaches the preset number of times, it is determined that the neural network is in a convergence state, and 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, it is determined that the neural network is in a convergence state, and 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 whole vehicle;

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

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

[0063] Exemplarily, the duration input by the user is obtained, or the duration is obtained according to 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 output power required by the vehicle is calculated based on the energy consumption value of the whole vehicle 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 through historical vehicle information, historical road condition information and energy consumption values ​​to generate a neural network model to predict the energy demand of the entire vehicle during the entire driving mileage. 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. This solves the problem in the related technology 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.

[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 whole 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 energy consumption value of the whole vehicle 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 the first preset charge ratio, the preset power is adjusted to generate a 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] According to the first preset ratio and the average power required by the vehicle, the output powers 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 powers to the motor.

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

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

[0082] The obtained vehicle information and the road condition information are input into a preset network model to obtain a whole 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 value, 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;

[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 of times, 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 its functions and implementation processes are no longer repeated here.

[0091] In a third 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 may be a personal computer (PC), a laptop computer, a server, or other device with data processing capabilities.

[0092] Reference Figure 4 , Figure 4 The 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 is shown in FIG. 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, the memory, and the communication interface.

[0094] The communication interface includes input / output (I / O) interface, physical interface and logical interface, etc., which are used to realize the interconnection of devices inside the energy management control device of the fuel cell system, and the interface used to realize the interconnection between the energy management control device of the fuel cell system and other devices (such as other computing devices or user devices). The physical interface can be an Ethernet interface, a fiber optic interface, an ATM interface, etc.; the user device can be a display, a keyboard, etc.

[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, which may 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 may refer to the various embodiments of the energy management control method of the fuel cell system of the present application, which 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 on the present application, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[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 only for description 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 "first", "second" and "third" to different types.

[0103] In the description of the embodiments of the present application, "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 the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "for example" or "for example" is intended to present related concepts in a specific way.

[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; the “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 that appear in a specific order are included, but it should be understood that these operations or steps may not be executed in the order in which they appear in the embodiments of the present application or in parallel, and the sequence number of the operation is only used to distinguish the different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed 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 a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application is essentially 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, disk, CD) as described above, and includes a number of instructions for 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 are not intended to 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 method for energy management control of a fuel cell system, characterized in that: The energy management control method of the fuel cell system comprises: The obtained vehicle information and road condition information are predicted according to the preset neural network model to obtain the energy consumption value of the whole vehicle; According to the energy consumption value of the whole vehicle and the preset duration, the fuel cell system generates corresponding output power to the motor.

2. The energy management control method of the fuel cell system according to claim 1, characterized in that: 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; The fuel cell system generates corresponding output power to the motor based on the average power required by the vehicle.

3. The energy management control method of the fuel cell system according to claim 2, characterized in that: 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 corresponding output power to the motor based on the average power required by the vehicle.

4. The energy management control method of the fuel cell system according to claim 3, characterized in that: 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 the first preset charge ratio, the preset power is adjusted to generate a 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; According to the first preset ratio and the average power required by the vehicle, the output powers 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 powers to the motor.

5. The energy management control method of the fuel cell system according to claim 1, characterized in that: 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 whole vehicle includes: Acquire vehicle information and road condition information, wherein the vehicle information includes average vehicle speed and vehicle load, and the road condition information includes distance, normal road condition and abnormal road condition; The obtained vehicle information and the road condition information are input into a preset network model to obtain a whole vehicle energy consumption value output by the preset network model.

6. The energy management control method of the fuel cell system according to claim 5, characterized in that: Before inputting the acquired vehicle information and road condition information into a preset network model and acquiring 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 value, 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; 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.

7. The energy management control method of the fuel cell system according to claim 6, characterized in that: The generating of the corresponding neural network model and before using the neural network model as the preset neural network model 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 of times, 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.

8. An energy management control device for a fuel cell system, characterized in that: The energy management control device of the fuel cell system comprises: The prediction and acquisition module is used to predict the acquired vehicle information and road condition information according to the preset neural network model to obtain the energy consumption value of the whole vehicle; 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 whole vehicle and the preset duration.

9. 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 in 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 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium 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 a fuel cell system as described in any one of claims 1 to 7 are implemented.

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