Method for obtaining control strategy of on-vehicle fuel cell system and related device

Abstract

The present application provides a method for obtaining a control strategy of an on-vehicle fuel cell system and related devices. The method is applied to an electric vehicle, which includes an on-vehicle fuel cell system. The method includes: obtaining current road condition detection data of the driving direction of the electric vehicle; obtaining current road condition information according to the current road condition detection data; predicting the predicted power consumption information of the electric vehicle according to the current road condition information; obtaining a control strategy according to the predicted power consumption information, where the control strategy is used to control the operation of the on-vehicle fuel cell system to increase the proportion of the duration in which the actual efficiency of the on-vehicle fuel cell system is in a preset efficiency range. On the one hand, for different road conditions, the corresponding control strategy can be obtained in advance before the actual power consumption situation changes. On the other hand, the corresponding control strategy can be used to compensate for the lag of the dynamic response of the on-vehicle fuel cell system, and the components of the on-vehicle fuel cell system are more durable.

Description

Technical Field

[0001] The present application relates to the technical field of fuel cells, and particularly to a method and device for obtaining a control strategy of an on-vehicle fuel cell system, an electronic device, an electric vehicle, and a computer-readable storage medium. Background Art

[0002] A fuel cell is a chemical device that directly converts the chemical energy of a fuel into electrical energy, also known as an electrochemical generator. Most fuel cells use hydrogen as fuel and oxygen as an oxidant to directly convert the chemical energy of the fuel into electrical energy. It is not restricted by the Carnot cycle and can operate continuously for a long time as long as there is sufficient fuel and oxygen. It also has characteristics such as high specific energy, low noise, pollution-free, zero emissions, and high energy conversion efficiency, and can be widely used in various fields such as small power stations, communication power supplies, robot power supplies, automobiles, power systems, and household life. Fuel cell technology is considered the preferred clean and efficient power generation technology in the 21st century. Fuel cells can be classified into alkaline fuel cells, phosphoric acid fuel cells, proton exchange membrane fuel cells, molten carbonate fuel cells, solid oxide fuel cells, etc. according to their different electrolytes.

[0003] An on-vehicle fuel cell generally uses oxygen as an oxidant to carry out an electrochemical reaction with hydrogen. The article "Analysis of the Efficiency Characteristics of the Start-up Process of a Fuel Cell Engine" was published in the Journal of Automotive Engineering in March 2013, which pointed out that: at the beginning of the start-up of the (on-vehicle fuel cell) engine, the hydrogen flow rate is relatively large, which is to sweep away the impurity gases remaining in the anode of the fuel cell. When the current steps up, the hydrogen flow rate also increases. Due to the lag of the electromagnetic valve with respect to the current change, the hydrogen flow rate does not directly reach the steady-state value but has a certain degree of lag and then gradually tends to the steady-state value. The system efficiency of the fuel cell engine increases sharply with the increase of the start-up time. After the system efficiency reaches the maximum value, it slowly decreases with the increase of the start-up time. The hydrogen utilization rate and the power of the auxiliary system have a significant impact on the system efficiency characteristics.

[0004] During the frequent start-up, stop, and speed change processes of an electric vehicle, the speed and acceleration fluctuations of the electric vehicle are very frequent, which requires parameters such as the fuel supply speed and the output power of the fuel cell to change rapidly to adapt to the load change. However, the dynamic response of the on-vehicle fuel cell system has a certain time lag, and its dynamic response process generally takes several seconds, while the electrochemical reaction process of hydrogen and oxygen is in the millisecond level, resulting in the on-vehicle fuel cell system being prone to operating in a non-optimal efficiency range, which will affect the service life of the core components of the on-vehicle fuel cell in the long run. Summary of the Invention

[0005] The purpose of this application is to provide a method, device, electronic device, electric vehicle, and computer-readable storage medium for obtaining a control strategy of an on-vehicle fuel cell system, which can utilize the corresponding control strategy to compensate for the lag of the dynamic response of the on-vehicle fuel cell system, so that the actual efficiency of the on-vehicle fuel cell system can be maintained within a preset efficiency range for a long time.

[0006] The purpose of this application is achieved by the following technical solutions:

[0007] In a first aspect, this application provides a method for obtaining a control strategy of an on-vehicle fuel cell system, which is applied to an electric vehicle. The electric vehicle includes an on-vehicle fuel cell system. The method includes: obtaining current road condition detection data in the driving direction of the electric vehicle; obtaining current road condition information according to the current road condition detection data; predicting the predicted power consumption information of the electric vehicle according to the current road condition information; obtaining a control strategy according to the predicted power consumption information, where the control strategy is used to control the operation of the on-vehicle fuel cell system to increase the proportion of the duration during which the actual efficiency of the on-vehicle fuel cell system is within a preset efficiency range. The beneficial effect of this technical solution is that the current road condition information can be obtained according to the current road condition detection data in the driving direction of the electric vehicle, the power consumption of the electric vehicle can be predicted according to the current road condition information to obtain the predicted power consumption information of the electric vehicle, and then the corresponding control strategy can be obtained according to the predicted power consumption information. On the one hand, for different road conditions, the corresponding control strategy can be obtained in advance before the actual power consumption changes, with a high level of intelligence and automation; on the other hand, the lag of the dynamic response of the on-vehicle fuel cell system can be compensated by using the corresponding control strategy, so that the actual efficiency of the on-vehicle fuel cell system can be maintained within a preset efficiency range for a long time, and the components of the on-vehicle fuel cell system are more durable.

[0008] In some alternative embodiments, the method further includes: controlling the operation of the on-vehicle fuel cell system according to the control strategy corresponding to the predicted power consumption information. The beneficial effect of this technical solution is that the corresponding control strategy can be obtained according to the predicted power consumption information, and the operation of the on-vehicle fuel cell system can be controlled according to the control strategy to compensate for the lag of the dynamic response of the on-vehicle fuel cell system, so that the actual efficiency of the on-vehicle fuel cell system can be in a relatively high range.

[0009] In some alternative embodiments, the current road condition detection data is obtained by a road condition detection device provided on the electric vehicle by real-time detecting the current road condition; or the current road condition detection data is sent by a cloud server. The beneficial effect of this technical solution is that the current road condition detection data can be obtained by real-time detecting by the detection device of the electric vehicle itself, and the current road condition detection data can also be sent by the cloud server. The data is not easily lost, safe and reliable, and has good stability and fast response speed.

[0010] In some alternative embodiments, the current road condition information includes at least one of the following: road surface type; average vehicle speed; average slope; road congestion level; whether a traffic accident has occurred on the current section; whether there are obstacles on the current section; the predicted power consumption information includes at least one of the following: predicted load power; predicted load current; predicted load voltage; the control strategy includes at least one of the following: output power of a single fuel cell; output power of a fuel cell stack; charge and discharge strategy of an energy storage battery. The beneficial effect of this technical solution is that for different road conditions, the power consumption of the electric vehicle can be predicted to obtain the predicted power consumption information, and then different control strategies can be adopted according to the predicted power consumption information, such as adjusting the output power of a single fuel cell, adjusting the output power of a fuel cell stack, charging or discharging the energy storage battery, etc.

[0011] In some alternative embodiments, predicting the predicted power consumption information of the electric vehicle according to the current road condition information includes: obtaining a plurality of sample road condition information and the power consumption information corresponding to each sample road condition information; training a power consumption prediction model by using a deep learning model according to the plurality of sample road condition information and the power consumption information corresponding to each sample road condition information; inputting the current road condition information into the power consumption prediction model to obtain the power consumption information corresponding to the current road condition information as the predicted power consumption information of the electric vehicle. The beneficial effect of this technical solution is that a power consumption prediction model can be trained by using a deep learning model according to a plurality of sample road condition information and the corresponding power consumption information. On the one hand, by inputting the current road condition information into the power consumption prediction model, the corresponding predicted power consumption information can be obtained; on the other hand, the power consumption prediction model can be trained by a large amount of sample data, can identify various road condition information, has a wide application range, and a high level of intelligence.

[0012] In some alternative embodiments, obtaining a control strategy according to the predicted power consumption information includes: obtaining a plurality of sample power consumption information and the control strategy corresponding to each sample power consumption information; training a control strategy model by using a deep learning model according to the plurality of sample power consumption information and the control strategy corresponding to each sample power consumption information; inputting the predicted power consumption information into the control strategy model to obtain the control strategy. The beneficial effect of this technical solution is that a control strategy model can be trained by using a deep learning model according to a plurality of sample power consumption information and the corresponding control strategy. On the one hand, by inputting the predicted power consumption information into the control strategy model, the corresponding control strategy can be obtained; on the other hand, the control strategy model can be trained by a large amount of sample data, can identify various predicted power consumption information, has a wide application range, and a high level of intelligence.

[0013] Second aspect, the present application provides a control strategy acquisition device for an on-vehicle fuel cell system, which is applied to an electric vehicle. The electric vehicle includes an on-vehicle fuel cell system. The device includes: a data acquisition module, configured to acquire current road condition detection data of the driving direction of the electric vehicle; an information acquisition module, configured to acquire current road condition information according to the current road condition detection data; a predicted power consumption module, configured to predict predicted power consumption information of the electric vehicle according to the current road condition information; a strategy acquisition module, configured to acquire a control strategy according to the predicted power consumption information, where the control strategy is used to control the operation of the on-vehicle fuel cell system to increase the proportion of the duration in which the actual efficiency of the on-vehicle fuel cell system is within a preset efficiency range.

[0014] In some optional embodiments, the device further includes: a system control module, configured to control the operation of the on-vehicle fuel cell system according to the control strategy corresponding to the predicted power consumption information.

[0015] In some optional embodiments, the current road condition detection data is obtained by a road condition detection device provided on the electric vehicle detecting the current road condition in real time; or, the current road condition detection data is sent by a cloud server.

[0016] In some optional embodiments, the current road condition information includes at least one of the following: road surface type; average vehicle speed; average slope; road surface congestion degree; whether a traffic accident occurs in the current section; whether there are obstacles in the current section; the predicted power consumption information includes at least one of the following: predicted load power; predicted load current; predicted load voltage; the control strategy includes at least one of the following: output power of a single fuel cell; output power of a fuel cell stack; charge and discharge strategy of an energy storage battery.

[0017] In some optional embodiments, the predicted power consumption module includes: a first sample unit, configured to acquire a plurality of sample road condition information and the power consumption information corresponding to each sample road condition information; a first training unit, configured to train a power consumption prediction model by using a deep learning model according to the plurality of sample road condition information and the power consumption information corresponding to each sample road condition information; a first input unit, configured to input the current road condition information into the power consumption prediction model to obtain the power consumption information corresponding to the current road condition information as the predicted power consumption information of the electric vehicle.

[0018] In some alternative embodiments, the policy acquisition module includes: a second sample unit configured to acquire a plurality of sample power consumption information and a control policy corresponding to each piece of the sample power consumption information; a second training unit configured to train a deep learning model based on the plurality of sample power consumption information and the control policy corresponding to each piece of the sample power consumption information to obtain a control policy model; and a second input unit configured to input the predicted power consumption information into the control policy model to obtain the control policy.

[0019] In a third aspect, the present application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of any one of the above methods are implemented.

[0020] In a fourth aspect, the present application provides an electric vehicle, which includes a housing, an on-vehicle fuel cell system, and the electronic device of any one of the above. The beneficial effect of this technical solution is that the electronic device may include a memory and a processor. Applying the electronic device to an electric vehicle can improve the automation level and intelligent level of the electric vehicle.

[0021] In some alternative embodiments, the electric vehicle further includes a road condition detection device disposed on the housing, and the road condition detection device includes at least one of the following: a front-view camera, a left rear-view camera, a right rear-view camera, a positioning device, a millimeter-wave radar, a left lidar, and a right lidar. The beneficial effect of this technical solution is that the electric vehicle can obtain current road condition detection data in real time according to the road condition detection device on the electric vehicle.

[0022] In a fifth aspect, the present application provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of any one of the above methods are implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The present application will be further described below with reference to the drawings and embodiments.

[0024] Figure 1 is a schematic flowchart of a method for acquiring a control policy of an on-vehicle fuel cell system provided by an embodiment of the present application;

[0025] Figure 2 is a schematic flowchart of a method for obtaining predicted power consumption information provided by an embodiment of the present application;

[0026] Figure 3 is a schematic diagram of changes in fuel cell efficiency and on-vehicle fuel cell system efficiency provided by an embodiment of the present application;

[0027] Figure 4 is a schematic flowchart of a method for acquiring a control policy provided by an embodiment of the present application;

[0028] Figure 5 It is a schematic flow chart of a method for obtaining a control strategy of an on-vehicle fuel cell system provided by an embodiment of the present application;

[0029] Figure 6 It is a schematic structural diagram of a device for obtaining a control strategy of an on-vehicle fuel cell system provided by an embodiment of the present application;

[0030] Figure 7 It is a schematic structural diagram of a predicted power consumption module provided by an embodiment of the present application;

[0031] Figure 8 It is a schematic structural diagram of a strategy acquisition module provided by an embodiment of the present application;

[0032] Figure 9 It is a schematic structural diagram of a device for obtaining a control strategy of an on-vehicle fuel cell system provided by an embodiment of the present application;

[0033] Figure 10 It is a schematic structural diagram of an electronic device provided by an embodiment of the present application;

[0034] Figure 11 It is a schematic partial structural diagram of an electric vehicle provided by an embodiment of the present application;

[0035] Figure 12 It is a schematic partial structural diagram of an electric vehicle provided by an embodiment of the present application;

[0036] Figure 13 It is a schematic partial structural diagram of an electric vehicle provided by an embodiment of the present application;

[0037] Figure 14 It is a schematic partial structural diagram of an electric vehicle provided by an embodiment of the present application;

[0038] Figure 15 It is a schematic partial structural diagram of an electric vehicle provided by an embodiment of the present application;

[0039] Figure 16 It is a schematic partial structural diagram of an electric vehicle provided by an embodiment of the present application;

[0040] Figure 17 It is a schematic partial structural diagram of an electric vehicle provided by an embodiment of the present application;

[0041] Figure 18 It is a schematic partial structural diagram of an electric vehicle provided by an embodiment of the present application;

[0042] Figure 19 It is a schematic structural diagram of a program product for implementing a method for obtaining a control strategy of an on-vehicle fuel cell system provided by an embodiment of the present application. Detailed implementation manners

[0043] Next, in combination with the accompanying drawings and specific implementation manners, the present application will be further described. It should be noted that, on the premise of no conflict, the following-described embodiments or technical features can be arbitrarily combined to form new embodiments with each other.

[0044] Refer to Figure 1 , an embodiment of the present application provides a method for obtaining a control strategy of an on-vehicle fuel cell system, which is applied to an electric vehicle. The electric vehicle can be an electric car, such as an electric sedan, an electric bus, etc. The electric vehicle includes an on-vehicle fuel cell system. The method includes steps S101 to S104.

[0045] Step S101: Obtain the current road condition detection data of the driving direction of the electric vehicle.

[0046] In a specific implementation manner, the current road condition detection data can be obtained by a road condition detection device provided on the electric vehicle detecting the current road condition in real time; or, the current road condition detection data can be sent by a cloud server. Among them, the cloud server is, for example, the background server of a vehicle-road collaborative system.

[0047] Thus, the current road condition detection data can be obtained by real-time detection by the detection device of the electric vehicle itself. The current road condition detection data can also be sent by the cloud server. The data is not easy to be lost, is safe and reliable, and has good stability and fast response speed.

[0048] Step S102: Obtain the current road condition information according to the current road condition detection data.

[0049] Step S103: Predict the predicted power consumption information of the electric vehicle according to the current road condition information.

[0050] Refer to Figure 2 , in a specific implementation manner, step S103 may include steps S201 to S203.

[0051] Step S201: Obtain a plurality of sample road condition information and the power consumption information corresponding to each sample road condition information.

[0052] Step S202: Train by using a deep learning model according to the plurality of sample road condition information and the power consumption information corresponding to each sample road condition information to obtain a power consumption prediction model.

[0053] Step S203: Input the current road condition information into the power consumption prediction model to obtain the power consumption information corresponding to the current road condition information as the predicted power consumption information of the electric vehicle.

[0054] Thus, based on multiple sample road condition information and corresponding power consumption information, a power consumption prediction model can be trained using a deep learning model. On the one hand, by inputting the current road condition information into the power consumption prediction model, the corresponding predicted power consumption information can be obtained; on the other hand, the power consumption prediction model can be trained by a large amount of sample data, can identify various road condition information, has a wide application range, and a high level of intelligence.

[0055] Step S104: Obtain a control strategy according to the predicted power consumption information. The control strategy is used to control the operation of the on-vehicle fuel cell system to increase the proportion of the duration during which the actual efficiency of the on-vehicle fuel cell system is within a preset efficiency range. Among them, the calculation method of the actual efficiency of the fuel cell system is recorded in "Fuel Cell System" published by Beihang University Press in September 2009, which will not be elaborated here. In addition, the preset efficiency range can be a pre-set efficiency range, such as the optimal efficiency range, and the optimal efficiency range is, for example, 60-80% or 70%-90%.

[0056] See Figure 3 , generally, on-vehicle fuel cells use oxygen as an oxidant to carry out an electrochemical reaction with hydrogen. During the frequent start, stop, and speed change processes of an electric vehicle, the speed and acceleration of the electric vehicle fluctuate very frequently, which requires parameters such as the fuel supply speed and the output power of the fuel cell to change rapidly to adapt to the load change. However, there is a certain time lag in the dynamic response of the on-vehicle fuel cell system, and its dynamic response process generally takes several seconds, while the electrochemical reaction process of hydrogen and oxygen is in the millisecond level, resulting in the on-vehicle fuel cell system being prone to operating in a non-optimal efficiency range. Over time, it will affect the service life of the core components of the on-vehicle fuel cell.

[0057] Thus, the current road condition information can be obtained according to the current road condition detection data in the driving direction of the electric vehicle, the power consumption of the electric vehicle can be predicted based on the current road condition information to obtain the predicted power consumption information of the electric vehicle, and then the corresponding control strategy can be obtained according to the predicted power consumption information. On the one hand, for different road conditions, the corresponding control strategy can be obtained in advance before the actual power consumption changes, with a relatively high level of intelligence and automation; on the other hand, the corresponding control strategy can be used to compensate for the lag of the dynamic response of the on-vehicle fuel cell system, so that the actual efficiency of the on-vehicle fuel cell system can be maintained within the preset efficiency range for a long time, and the components of the on-vehicle fuel cell system are more durable.

[0058] In a specific embodiment, the current road condition information may include at least one of the following: road surface type; average vehicle speed; average slope; road congestion level; whether there is a traffic accident on the current section; whether there are obstacles on the current section; the predicted power consumption information may include at least one of the following: predicted load power; predicted load current; predicted load voltage; the control strategy may include at least one of the following: output power of a single fuel cell; output power of a fuel cell stack; charge and discharge strategy of an energy storage battery. Specifically, the fuel cell stack is composed of a number of single fuel cells combined, and the energy storage battery is arranged on the electric vehicle, which may be a hybrid energy storage battery. Among them, the road surface type is, for example, flat or bumpy, the average slope is, for example, 15 degrees or 30 degrees, the road congestion level is, for example, not congested, slightly congested, moderately congested, severely congested, extremely congested, whether there is a traffic accident on the current section is, for example, "there is a traffic accident on the current section" or "there is no traffic accident on the current section", whether there are obstacles on the current section is, for example, "there are obstacles on the current section" or "there are no obstacles on the current section". The output power of a single fuel cell is, for example, 15W, the output power of the fuel cell stack is, for example, 500W, and the charge and discharge strategy of the energy storage battery is, for example, charging the energy storage battery or discharging the energy storage battery.

[0059] In a specific embodiment, when the current road condition information is that the road congestion level is severely congested, the corresponding change in the predicted power consumption information may be a decrease in the predicted load power, and the corresponding control strategy may be to reduce the output power of a single fuel cell and the output power of the fuel cell stack, and cooperate with adjusting the energy storage battery to charge, thereby enabling the actual efficiency of the on-vehicle fuel cell system to be in a relatively high range.

[0060] Thus, for different road conditions, the power consumption situation of the electric vehicle can be predicted to obtain the predicted power consumption information, and then different control strategies can be adopted according to the predicted power consumption information, such as adjusting the output power of a single fuel cell, adjusting the output power of the fuel cell stack, charging or discharging the energy storage battery, etc.

[0061] In a specific embodiment, the control strategy may further include at least one of the following: fuel supply quantity control strategy, where the fuel is, for example, hydrogen; processing system reaction condition control strategy; unreacted gas circulation control strategy; reaction temperature, pressure, air supply quantity control strategy; direct current and voltage control strategy, heat recovery system control strategy, voltage and alternating current frequency control strategy, current and voltage output control strategy.

[0062] See Figure 4 , in a specific embodiment, the method of obtaining the control strategy according to the predicted power consumption information in step S104 may include steps S301 to S303.

[0063] Step S301: Obtain multiple sample power consumption information and the control strategy corresponding to each of the sample power consumption information.

[0064] Step S302: According to the multiple sample power consumption information and the control strategy corresponding to each of the sample power consumption information, use a deep learning model for training to obtain a control strategy model.

[0065] Step S303: Input the predicted power consumption information into the control strategy model to obtain the control strategy.

[0066] Thus, according to multiple sample power consumption information and the corresponding control strategy, a deep learning model can be used for training to obtain a control strategy model. On the one hand, by inputting the predicted power consumption information into the control strategy model, the corresponding control strategy can be obtained; on the other hand, the control strategy model can be trained by a large amount of sample data, can identify various predicted power consumption information, has a wide application range, and a high level of intelligence.

[0067] See Figure 5 , in a specific embodiment, the method may further include step S105.

[0068] Step S105: Control the operation of the vehicle-mounted fuel cell system according to the control strategy corresponding to the predicted power consumption information.

[0069] Thus, the corresponding control strategy can be obtained according to the predicted power consumption information, and the operation of the vehicle-mounted fuel cell system can be controlled according to the control strategy to compensate for the lag in the dynamic response of the vehicle-mounted fuel cell system, so that the actual efficiency of the vehicle-mounted fuel cell system can be in a relatively high range.

[0070] See Figure 6 , an embodiment of the present application also provides a control strategy acquisition device for a vehicle-mounted fuel cell system. The specific implementation manner thereof is the same as the implementation manner and the achieved technical effects described in the embodiment of the control strategy acquisition method for the vehicle-mounted fuel cell system above, and some contents will not be repeated.

[0071] The device is applied to an electric vehicle, the electric vehicle includes a vehicle-mounted fuel cell system, and the device includes: a data acquisition module 101 for acquiring current road condition detection data in the driving direction of the electric vehicle; an information acquisition module 102 for acquiring current road condition information according to the current road condition detection data; a predicted power consumption module 103 for predicting the predicted power consumption information of the electric vehicle according to the current road condition information; and a strategy acquisition module 104 for acquiring a control strategy according to the predicted power consumption information, where the control strategy is used to control the operation of the vehicle-mounted fuel cell system to increase the proportion of the duration during which the actual efficiency of the vehicle-mounted fuel cell system is in a preset efficiency interval.

[0072] In a specific embodiment, the current road condition detection data may be obtained by a road condition detection device disposed on the electric vehicle in real time detecting the current road condition; alternatively, the current road condition detection data may be sent by a cloud server.

[0073] In a specific embodiment, the current road condition information may include at least one of the following: road surface type; average vehicle speed; average slope; road surface congestion degree; whether a traffic accident occurs in the current section; whether there are obstacles in the current section; the predicted power consumption information may include at least one of the following: predicted load power; predicted load current; predicted load voltage; the control strategy may include at least one of the following: output power of a single fuel cell; output power of a fuel cell stack; charge and discharge strategy of an energy storage battery.

[0074] See Figure 7 , in a specific embodiment, the predicted power consumption module 103 may include: a first sample unit 1031, which may be used to obtain a plurality of sample road condition information and the power consumption information corresponding to each sample road condition information; a first training unit 1032, which may be used to train by using a deep learning model according to the plurality of sample road condition information and the power consumption information corresponding to each sample road condition information to obtain a power consumption prediction model; a first input unit 1033, which may be used to input the current road condition information into the power consumption prediction model to obtain the power consumption information corresponding to the current road condition information as the predicted power consumption information of the electric vehicle.

[0075] See Figure 8 , in a specific embodiment, the policy acquisition module 104 may include: a second sample unit 1041, which may be used to obtain a plurality of sample power consumption information and the control strategy corresponding to each sample power consumption information; a second training unit 1042, which may be used to train by using a deep learning model according to the plurality of sample power consumption information and the control strategy corresponding to each sample power consumption information to obtain a control strategy model; a second input unit 1043, which may be used to input the predicted power consumption information into the control strategy model to obtain the control strategy.

[0076] See Figure 9 , in a specific embodiment, the device may further include: a system control module 105, which may be used to control the in-vehicle fuel cell system to work according to the control strategy corresponding to the predicted power consumption information.

[0077] See Figure 10 , an embodiment of the present application further provides an electronic device 200, and the electronic device 200 includes at least one memory 210, at least one processor 220, and a bus 230 connecting different platform systems.

[0078] The memory 210 may include a readable medium in the form of volatile memory, such as random access memory (RAM) 211 and / or cache memory 212, and may further include read-only memory (ROM) 213.

[0079] Among them, the memory 210 also stores a computer program, which can be executed by the processor 220, so that the processor 220 executes the steps of the control strategy acquisition method of the vehicle-mounted fuel cell system in the embodiments of the present application. The specific implementation manner is consistent with the implementation manner and the achieved technical effects described in the embodiments of the above-mentioned control strategy acquisition method of the vehicle-mounted fuel cell system, and some contents will not be elaborated here.

[0080] The memory 210 may further include a program / utilities 214 having a set (at least one) of program modules 215. Such program modules include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include the implementation of a network environment.

[0081] Correspondingly, the processor 220 may execute the above computer program and may also execute the program / utilities 214.

[0082] The bus 230 may represent one or more of several types of bus structures, including a memory bus or a memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus structures.

[0083] The electronic device 200 may also communicate with one or more external devices 240, such as a keyboard, a pointing device, a Bluetooth device, etc., and may also communicate with one or more devices capable of interacting with the electronic device 200, and / or communicate with any device (such as a router, a modem, etc.) that enables the electronic device 200 to communicate with one or more other computing devices. Such communication may be carried out through the input / output (I / O) interface 250. And the electronic device 200 may also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through the network adapter 260. The network adapter 260 may communicate with other modules of the electronic device 200 through the bus 230. It should be understood that although not shown in the figure, other hardware and / or software modules may be used in combination with the electronic device 200, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, etc.

[0084] See Figure 11, embodiments of the present application also provide an electric vehicle 20, and its specific implementation manner is consistent with the implementation manner and the achieved technical effects described in the embodiments of the above control method of the on-vehicle fuel cell system, and some contents will not be elaborated herein.

[0085] The electric vehicle 20 includes a housing 30, an on-vehicle fuel cell system (not shown in the figure), and any one of the above electronic devices 200.

[0086] Thus, the electronic device 200 may include a memory and a processor. Applying the electronic device 200 to the electric vehicle 20 can improve the automation level and intelligent level of the electric vehicle 20.

[0087] In some embodiments of the present application, the electric vehicle 20 includes a fuel cell 43, a storage battery 46, and a control system, and may further include a road condition detection device. The control system includes a vehicle controller 41, a fuel cell controller 42, and an efficiency controller 44, and may further include a storage battery controller 45 and an automatic transmission controller.

[0088] The vehicle controller 41 (Vehicle Control Unit, abbreviated as VCU) is the central control unit of the electric vehicle 20. The on-vehicle fuel cell system may include a fuel cell controller 42 and a fuel cell 43, and may further include a fuel subsystem, a thermal management subsystem, and a power conversion subsystem. The fuel cell 43 is the main power source of the electric vehicle 20, providing the energy for the vehicle to drive normally. The fuel cell 43 can also charge the storage battery 46. The fuel cell controller 42 (Fuel Cell Unit, abbreviated as FCU) can be used to control the operation of the fuel cell 43. Specifically, the vehicle controller 41 can be connected to the fuel cell controller 42 and send a signal of energy requirement to the fuel cell controller 42. After receiving the signal, the fuel cell controller 42 adjusts the working condition of the fuel cell 43, and then controls the working condition and output power of the fuel cell engine.

[0089] The storage battery 46 is an auxiliary power source of the electric vehicle 20. The surplus electric energy of the fuel cell 43 can be absorbed and stored by the storage battery 46. The storage battery 46 may include at least one of the following: lead-acid battery, nickel-metal hydride battery, and lithium-ion battery. The storage battery controller 45 is used to control the operation of the storage battery 46.

[0090] The efficiency controller 44 is, for example, an AI host, and the efficiency controller 44 is used to formulate control strategies for the fuel cell 43 and / or the storage battery 46.

[0091] In a specific embodiment, the road condition detection device acquires the current road condition detection data of the electric vehicle 20. The road condition detection device can accurately and real-time acquire the current road condition detection data. The efficiency controller 44 is connected to the road condition detection device to acquire the current road condition detection data, and the efficiency controller 44 can formulate the control strategies for the fuel cell 43 and / or the energy storage battery 46 according to the current road condition detection data.

[0092] In a specific embodiment, referring to Figure 12 , the road condition detection device includes at least one of the following: a front view camera 31, a left rear view camera 32, a right rear view camera 33, a positioning device 34, a millimeter wave radar 35, a left lidar 36, and a right lidar 37. Among them, the front view camera 31 is arranged on the front side of the electric vehicle 20, and / or the left rear view camera 32 and the right rear view camera 33 are respectively arranged on the left side and the right side of the electric vehicle 20, and / or the positioning device 34 is arranged on the electric vehicle 20, and / or the millimeter wave radar 35 is arranged on the front side of the electric vehicle 20, and / or the left lidar 36 and the right lidar 37 are respectively arranged on the left side and the right side of the electric vehicle 20.

[0093] In an alternative embodiment, the efficiency controller 44 is connected to a cloud server to acquire the current road condition detection data and / or the current road condition information. The cloud server is, for example, the background server of a vehicle-road collaborative system. The efficiency controller 44 can obtain the current road condition information of the driving direction of the electric vehicle 20 through the current road condition detection data, and / or directly obtain the current road condition information through the cloud server, and formulate the control strategies for the fuel cell 43 and / or the energy storage battery 46 according to the current road condition information.

[0094] When the control strategy is the control strategy of the fuel cell 43, referring to Figure 13 , the vehicle controller 41 is connected to the efficiency controller 44 to acquire the control strategy of the fuel cell 43. The vehicle controller 41 can be connected to the efficiency controller 44 through the CAN bus. The vehicle controller 41 is connected to the fuel cell controller 42 to send a signal to the fuel cell controller 42, and the fuel cell controller 42 controls the operation of the fuel cell 43 according to the signal; or, referring to Figure 14 , the efficiency controller 44 is connected to the fuel cell controller 42 to send a signal to the fuel cell controller 42, and the fuel cell controller 42 controls the operation of the fuel cell 43 according to the signal.

[0095] When the control strategy is the control strategy of the energy storage battery 46, referring to Figure 15, the control system further includes an energy storage battery controller 45. The vehicle controller 41 is connected to the efficiency controller 44 to obtain the control strategy of the energy storage battery 46. The vehicle controller 41 is connected to the energy storage battery controller 45 to send a signal to the energy storage battery controller 45, and the energy storage battery controller 45 controls the operation of the energy storage battery 46 according to the signal; or, refer to Figure 16 , the efficiency controller 44 is connected to the energy storage battery controller 45 to send a signal to the energy storage battery controller 45, and the energy storage battery controller 45 controls the operation of the energy storage battery 46 according to the signal.

[0096] When the control strategy is the control strategy of the fuel cell 43 and the energy storage battery 46, refer to Figure 17 , the control system further includes an energy storage battery controller 45. The vehicle controller 41 is connected to the efficiency controller 44 to obtain the control strategies of the fuel cell 43 and the energy storage battery 46. The vehicle controller 41 is respectively connected to the fuel cell controller 42 and the energy storage battery controller 45 to respectively send signals to the fuel cell controller 42 and the energy storage battery controller 45. The fuel cell controller 42 controls the operation of the fuel cell 43 according to the signal, and the energy storage battery controller 45 controls the operation of the energy storage battery 46 according to the signal; or, refer to Figure 18 , the efficiency controller 44 is respectively connected to the fuel cell controller 42 and the energy storage battery controller 45 to respectively send signals to the fuel cell controller 42 and the energy storage battery controller 45. The fuel cell controller 42 controls the operation of the fuel cell 43 according to the signal, and the energy storage battery controller 45 controls the operation of the energy storage battery 46 according to the signal.

[0097] Thus, the efficiency controller 44 can formulate the control strategies of the fuel cell 43 and / or the energy storage battery 46, and control the operation of the fuel cell 43 through the fuel cell controller 42, and / or control the operation of the energy storage battery 46 through the energy storage battery controller 45, thereby compensating for the lag of the dynamic response of the on-vehicle fuel cell system, so that the actual efficiency of the on-vehicle fuel cell system can be maintained within the preset efficiency range for a long time, and the components of the on-vehicle fuel cell system are more durable.

[0098] In a specific embodiment, the electric vehicle 20 further includes an automatic transmission controller (not shown) connected to the efficiency controller 44. The efficiency controller 44 sends a signal to the automatic transmission controller, and then the automatic transmission controller controls the operation of the automatic transmission.

[0099] An embodiment of the present application further provides a computer-readable storage medium for storing a computer program, which, when executed, implements the steps of the method for obtaining the control strategy of the on-vehicle fuel cell system in the embodiment of the present application. The specific implementation manner is consistent with the implementation manner and the achieved technical effects described in the embodiment of the method for obtaining the control strategy of the on-vehicle fuel cell system above, and some contents will not be elaborated here.

[0100] Figure 19 Fig. 4 shows a program product 300 provided in this embodiment for implementing the method for obtaining the control strategy of the on-vehicle fuel cell system above. It can be a portable compact disc read-only memory (CD-ROM) and includes program code, and can run on a terminal device, such as a personal computer. However, the program product 300 of the present invention is not limited to this. In this document, the readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, device, or component. The program product 300 can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0101] Computer-readable storage media may include data signals propagated in baseband or as part of a carrier wave, wherein readable program codes are carried. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium may also be any readable medium other than the readable storage medium, which may send, propagate, or transmit a program used by or in combination with an instruction execution system, an apparatus, or a device. The program code contained on the readable storage medium may be transmitted with any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above. The program code for performing the operation of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and also conventional procedural programming languages ​​such as "C" language or similar programming languages. The program code may be executed entirely on a user computing device, partially on a user device, as an independent software package, partially on a user computing device, partially on a remote computing device, or entirely on a remote computing device or server. Where a remote computing device is involved, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0102] This application is explained from the perspectives of purpose of use, effectiveness, progress and novelty. The practical progress it has is in line with the functional enhancement and usage requirements emphasized by the Patent Law. The above description and drawings of this application are only the preferred embodiments of this application, and are not intended to limit this application. Therefore, all structures, devices, features, etc. that are similar or identical to those of this application, that is, all equivalent replacements or modifications made in accordance with the scope of the patent application of this application, should fall within the scope of protection of the patent application of this application.

Claims

1. A method for obtaining a control strategy of a vehicle-mounted fuel cell system, characterized in that, Applied to an electric vehicle, the electric vehicle includes an on-vehicle fuel cell system, and the method includes: Obtain current road condition detection data in the driving direction of the electric vehicle; Obtain current road condition information according to the current road condition detection data; Predict the predicted power consumption information of the electric vehicle according to the current road condition information; According to the predicted power consumption information, before the actual power consumption situation changes, obtain a control strategy in advance, and the control strategy is used to control the operation of the on-vehicle fuel cell system to increase the proportion of the duration in which the actual efficiency of the on-vehicle fuel cell system is within a preset efficiency range; The predicting the predicted power consumption information of the electric vehicle according to the current road condition information includes: Obtain multiple sample road condition information and the power consumption information corresponding to each sample road condition information; Train using a deep learning model according to the multiple sample road condition information and the power consumption information corresponding to each sample road condition information to obtain a power consumption prediction model; Input the current road condition information into the power consumption prediction model to obtain the power consumption information corresponding to the current road condition information as the predicted power consumption information of the electric vehicle; The obtaining the control strategy in advance according to the predicted power consumption information before the actual power consumption situation changes includes: Obtain multiple sample power consumption information and the control strategy corresponding to each sample power consumption information; Train using a deep learning model according to the multiple sample power consumption information and the control strategy corresponding to each sample power consumption information to obtain a control strategy model; Input the predicted power consumption information into the control strategy model to obtain the control strategy.

2. The method for obtaining the control strategy of the vehicle-mounted fuel cell system according to claim 1, characterized in that, The method further includes: Control the operation of the on-vehicle fuel cell system according to the control strategy corresponding to the predicted power consumption information.

3. The method for obtaining the control strategy of the vehicle-mounted fuel cell system according to claim 1, wherein The current road condition detection data is obtained by a road condition detection device provided on the electric vehicle detecting the current road condition in real time; or The current road condition detection data is sent by a cloud server.

4. The method for obtaining the control strategy of the vehicle-mounted fuel cell system according to claim 1, wherein The current road condition information includes at least one of the following: Road surface type; Average vehicle speed; Average slope; Degree of road surface congestion; Whether there is a traffic accident on the current section; Whether there are obstacles on the current section; The predicted power consumption information includes at least one of the following: Predicted load power; Predicted load current; Predicted load voltage; The control strategy includes at least one of the following: Output power of a single fuel cell; Output power of a fuel cell stack; Charge and discharge strategy of an energy storage battery.

5. A control strategy acquisition device for a vehicle-mounted fuel cell system, characterized in that, Applied to an electric vehicle, the electric vehicle includes an on-vehicle fuel cell system, and the device includes: A data acquisition module for acquiring current road condition detection data in the driving direction of the electric vehicle; An information acquisition module for obtaining current road condition information according to the current road condition detection data; A predicted power consumption module for predicting the predicted power consumption information of the electric vehicle according to the current road condition information; A strategy acquisition module for obtaining a control strategy in advance according to the predicted power consumption information before the actual power consumption situation changes, and the control strategy is used to control the operation of the on-vehicle fuel cell system to increase the proportion of the duration in which the actual efficiency of the on-vehicle fuel cell system is within a preset efficiency range; The predicted power consumption module includes: A first sample unit for obtaining a plurality of sample road condition information and power consumption information corresponding to each of the sample road condition information; A first training unit for training a deep learning model based on the plurality of sample road condition information and the power consumption information corresponding to each of the sample road condition information to obtain a power consumption prediction model; A first input unit for inputting the current road condition information into the power consumption prediction model to obtain the power consumption information corresponding to the current road condition information as the predicted power consumption information of the electric vehicle; The policy acquisition module includes: A second sample unit for obtaining a plurality of sample power consumption information and control policies corresponding to each of the sample power consumption information; A second training unit for training a deep learning model based on the plurality of sample power consumption information and the control policies corresponding to each of the sample power consumption information to obtain a control policy model; A second input unit for inputting the predicted power consumption information into the control policy model to obtain the control policy.

6. The apparatus for obtaining the control strategy of the on-vehicle fuel cell system according to claim 5, wherein The device further includes: A system control module for controlling the operation of the on-vehicle fuel cell system according to the control policy corresponding to the predicted power consumption information.

7. The apparatus for obtaining the control strategy of the vehicle-mounted fuel cell system according to claim 5, wherein The current road condition detection data is obtained by a road condition detection device provided on the electric vehicle for real-time detection of the current road condition; or, The current road condition detection data is sent by a cloud server.

8. The device for obtaining the control strategy of the vehicle-mounted fuel cell system according to claim 5, characterized in that, The current road condition information includes at least one of the following: Road surface type; Average vehicle speed; Average slope; Degree of road surface congestion; Whether a traffic accident occurs on the current section; Whether there are obstacles on the current section; The predicted power consumption information includes at least one of the following: Predicted load power; Predicted load current; Predicted load voltage; The control policy includes at least one of the following: Output power of a single fuel cell; Output power of a fuel cell stack; Charge and discharge strategy of an energy storage battery.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, and the memory stores a computer program, and when the processor executes the computer program, the steps of the method according to any one of claims 1-4 are implemented.

10. An electric vehicle, characterized in that, The electric vehicle includes a housing, an on-vehicle fuel cell system, and the electronic device according to claim 9.

11. The electric vehicle according to claim 10, wherein, The electric vehicle further includes a road condition detection device provided on the housing, and the road condition detection device includes at least one of the following: a front-view camera, a left rear-view camera, a right rear-view camera, a positioning device, a millimeter-wave radar, a left lidar, and a right lidar.

12. A computer-readable storage medium, characterized in that, A computer program is stored, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1-4 are implemented.

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