Digital Twin-Based Energy Management Method, Cloud Server, and Storage Medium

Abstract

The present application provides an energy management method, a cloud server, and a storage medium based on digital twin, which relates to the technical field of vehicle management. Among them, the method includes: receiving a monitoring instruction, determining the driving path corresponding to the target twin model from the digital twin model of the target fuel cell vehicle as the current driving path according to the positioning information and the driving direction; determining a predicted section according to the positioning information, and determining the target driving scenario of the target driver from the target twin model according to the identification information of the target driver and the predicted section; sending an energy distribution control instruction corresponding to the target driving scenario to the vehicle controller, so that the vehicle controller distributes energy to the power battery and the fuel cell on the target fuel cell vehicle based on the energy distribution strategy corresponding to the energy distribution control instruction. The present application fully meets the energy requirements of the target driving scenario, and extends the driving range of the target fuel cell vehicle and the service life of the fuel cell.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle management. Specifically, it relates to an energy management method, a cloud server, and a storage medium based on digital twin. Background Art

[0002] With the rapid development of the logistics industry and the increasingly strict environmental protection policies, hydrogen energy commercial vehicles have gradually become the focus of market attention due to their advantages such as zero emissions and high efficiency. As the core power source of hydrogen energy commercial vehicles, the performance of the energy management system of hydrogen fuel cells directly affects the endurance, safety, and economy of the whole vehicle. Therefore, the energy management technology of hydrogen fuel cells for hydrogen energy commercial vehicles has become one of the key technologies to promote the green development of the commercial vehicle field.

[0003] Currently, the battery management system collects data such as the voltage, current, and temperature of the battery in real time, uses algorithms to estimate the state of charge, health state, etc. of the battery, and adopts preset protection strategies to prevent phenomena such as overcharging, over-discharging, and overheating of the battery. At the same time, by optimizing the energy distribution strategy, the charging and discharging process of the battery is balanced to extend the battery life and improve the overall performance of the vehicle.

[0004] However, in the existing methods, when the battery management system deals with complex working conditions such as high power and frequent start-stop of commercial vehicles, the energy management strategy has insufficient adaptability and cannot increase the battery life while ensuring the vehicle's power performance and extending the endurance mileage. Summary of the Invention

[0005] The purpose of the present application is to provide an energy management method, a cloud server, and a storage medium based on digital twin for the deficiencies in the above-mentioned existing technologies. The present application fully meets the energy requirements of the target driving scenario, and extends the endurance mileage of the target fuel cell vehicle while increasing the service life of the fuel cell.

[0006] To achieve the above purpose, the technical solutions adopted in the embodiments of the present application are as follows:

[0007] In a first aspect, an embodiment of the present application provides an energy management method based on digital twin, which is applied to a cloud server. The method includes:

[0008] Receiving a monitoring instruction uploaded by a vehicle controller of a target fuel cell vehicle, where the monitoring instruction includes: the positioning information, driving direction, and identification information of the target driver of the target fuel cell vehicle;

[0009] According to the positioning information and the driving direction, match a target twin model from multiple sub-twin models in the digital twin model of the target fuel cell vehicle, and determine the driving path corresponding to the target twin model as the current driving path; wherein, the multiple sub-twin models are respectively used to represent multiple driving paths of the multiple target fuel cell vehicles;

[0010] According to the positioning information, determine a predicted section on the current driving path;

[0011] According to the identification information of the target driver and the predicted section, determine the target driving scenario of the target driver from multiple driving scenarios of the target twin model; different driving scenarios correspond to different energy distribution strategies;

[0012] Send a energy distribution control instruction corresponding to the target driving scenario to the vehicle controller, and the energy distribution control instruction is used to enable the vehicle controller to perform energy distribution on the power battery and the fuel cell on the target fuel cell vehicle based on the energy distribution strategy corresponding to the energy distribution control instruction.

[0013] Optionally, the method further includes: before matching the target twin model from the multiple sub-twin models, obtaining multiple groups of historical driving data of the target fuel cell vehicle, and each group of historical driving data includes: road condition data and driving condition data during a driving process;

[0014] According to the identification information of the target fuel cell vehicle and the multiple groups of historical driving data of the target fuel cell vehicle, perform digital twin modeling respectively to obtain the multiple sub-twin models of the target fuel cell vehicle.

[0015] Optionally, the driving condition data includes: vehicle state data and driving condition data;

[0016] The step of performing digital twin modeling respectively according to the identification information of the target fuel cell vehicle and the multiple groups of historical driving data of the target fuel cell vehicle to obtain the multiple sub-twin models of the target fuel cell vehicle includes:

[0017] According to the vehicle state data, the driving condition data and the road condition data, determine driving road condition data, and the driving road condition data includes: driving road condition data of multiple specific sections on a driving path;

[0018] According to the driving condition data and the road condition data, determine steering driving condition data, and the steering driving condition data includes: driving condition data of multiple steering nodes on the driving path;

[0019] Based on the driving condition data of the multiple specific road sections, the driving condition data of the multiple steering nodes, and the road condition data, digital twin modeling is performed to obtain a sub-twin model of the target fuel cell vehicle.

[0020] Optionally, each set of historical driving data includes: energy consumption data and braking energy recovery data at multiple position points during the one driving process; the method further includes: before performing digital twin modeling, determining the energy consumption data and braking energy recovery data of the multiple specific road sections according to the energy consumption data and braking energy recovery data of the multiple position points;

[0021] The performing digital twin modeling according to the driving condition data of the multiple specific road sections, the driving condition data of the multiple steering nodes, and the road condition data to obtain a sub-twin model of the target fuel cell vehicle includes:

[0022] Performing digital twin modeling according to the driving condition data, energy consumption data, and braking energy recovery data of the multiple specific road sections, the driving condition data of the multiple steering nodes, and the road condition data to obtain a sub-twin model of the target fuel cell vehicle.

[0023] Optionally, the determining the target driving scenario of the target driver from multiple driving scenarios of the target twin model according to the identification information of the target driver and the predicted road section includes:

[0024] Obtaining the historical driving behavior data of the target driver according to the identification information of the target driver;

[0025] Determining the target driving scenario of the target driver from the multiple driving scenarios according to the historical driving behavior data and the predicted road section.

[0026] Optionally, the determining the target driving scenario of the target driver from the multiple driving scenarios according to the historical driving behavior data and the predicted road section includes:

[0027] If the predicted road section is an uphill road section, determining the target driving scenario from multiple driving scenarios of the uphill road section according to the uphill throttle stepping duration and throttle pedal opening in the historical driving behavior data;

[0028] Or, if the predicted road section is a downhill road section, determining the target driving scenario from multiple driving scenarios of the downhill road section according to the braking duration and brake pedal travel in the historical driving behavior data.

[0029] Optionally, determining the target driving scenario from multiple driving scenarios on the uphill section according to the uphill throttle application duration and throttle pedal opening in the historical driving behavior data includes:

[0030] If the uphill throttle application duration is greater than a first preset duration threshold and the throttle pedal opening is greater than a first preset opening threshold, determine that the target driving scenario is the first driving scenario;

[0031] Sending the energy distribution control instruction corresponding to the target driving scenario to the vehicle controller includes:

[0032] Sending a first control instruction to the vehicle controller, where the first control instruction is used to cause the vehicle controller to turn on the fuel cell in advance to jointly provide power energy using the fuel cell and the power battery.

[0033] Optionally, determining the target driving scenario from multiple driving scenarios on the uphill section according to the uphill throttle application duration and throttle pedal opening in the historical driving behavior data includes:

[0034] If the uphill throttle application duration is less than a second preset duration threshold and the throttle pedal opening is greater than a first preset opening threshold, determine that the target driving scenario is the second driving scenario;

[0035] Sending the energy distribution control instruction corresponding to the target driving scenario to the vehicle controller includes:

[0036] Sending a second control instruction and uphill energy consumption data to the vehicle controller, where the second control instruction is used to cause the vehicle controller not to turn on the fuel cell when the current state of charge parameter of the power battery can guarantee the uphill energy consumption data and the predicted state of charge parameter after completing the uphill using only the power battery is greater than a first preset state of charge threshold; and to turn on the fuel cell in advance when the current state of charge parameter cannot guarantee the energy consumption data and the predicted state of charge parameter after completing the uphill using only the power battery is less than a second preset state of charge threshold, so as to jointly provide uphill driving power energy using the fuel cell and the power battery.

[0037] Optionally, determining the target driving scenario from multiple driving scenarios on the downhill section according to the brake application duration and brake pedal stroke in the historical driving behavior data includes:

[0038] If the brake application duration is greater than a third preset duration threshold and the brake pedal stroke is greater than a preset brake stroke threshold, determine that the target driving scenario is the third driving scenario;

[0039] Issuing the energy distribution control instruction corresponding to the target driving scenario to the vehicle controller includes:

[0040] Issuing a third control instruction to the vehicle controller, where the third control instruction is used to cause the vehicle controller to turn on the braking energy recovery on the downhill section to recover the braking energy to the power battery and turn off the fuel cell to provide the downhill power energy only with the power battery.

[0041] Optionally, determining the target driving scenario from multiple driving scenarios on the downhill section according to the braking duration and the braking pedal travel in the historical driving behavior data includes:

[0042] If the braking duration is less than the fourth preset duration threshold and the braking pedal travel is greater than the preset braking travel threshold, determining that the target driving scenario is the fourth driving scenario;

[0043] Issuing the energy distribution control instruction corresponding to the target driving scenario to the vehicle controller includes:

[0044] Issuing a fourth control instruction and the downhill energy consumption data to the vehicle controller. The fourth control instruction is used to cause the vehicle controller to turn on the braking energy recovery on the downhill section to recover the braking energy to the power battery and use the fuel cell and the power battery to jointly provide the downhill power energy; only use the power battery to provide energy. When the current state of charge parameter of the power battery can guarantee the downhill energy consumption data and the predicted state of charge parameter after going downhill only with the power battery is greater than the third preset state of charge threshold, the fuel cell is not turned on; when the current state of charge parameter cannot guarantee the downhill energy consumption data and the predicted state of charge parameter after going downhill only with the power battery is less than the fourth preset state of charge threshold, the fuel cell is turned on in advance to use the fuel cell and the power battery to jointly provide the downhill power energy.

[0045] In a second aspect, another embodiment of the present application provides an energy management device based on digital twin, and the device includes:

[0046] A receiving module, configured to receive a monitoring instruction uploaded by a vehicle controller of a target fuel cell vehicle, where the monitoring instruction includes: the positioning information, the driving direction, and the identification information of the target driver of the target fuel cell vehicle;

[0047] The first determination module is configured to match a target twin model from multiple sub-twin models in the digital twin model of the target fuel cell vehicle according to the positioning information and the driving direction, and determine the driving path corresponding to the target twin model as the current driving path; wherein, the multiple sub-twin models are respectively used to characterize multiple driving paths of the multiple target fuel cell vehicles.

[0048] The second determination module is configured to determine a predicted section on the current driving path according to the positioning information.

[0049] The third determination module is configured to determine a target driving scenario of the target driver from multiple driving scenarios of the target twin model according to the identification information of the target driver and the predicted section; different driving scenarios correspond to different energy distribution strategies.

[0050] The distribution module is configured to send an energy distribution control instruction corresponding to the target driving scenario to the vehicle controller, and the energy distribution control instruction is used to enable the vehicle controller to perform energy distribution on the power battery and the fuel cell on the target fuel cell vehicle based on the energy distribution strategy corresponding to the energy distribution control instruction.

[0051] In a third aspect, another embodiment of the present application provides a cloud server, including a memory and a processor. A computer program that can run on the processor is stored in the memory. When the processor executes the computer program, it is configured to execute the steps of the digital twin-based energy management method according to any one of the above first aspects.

[0052] In a fourth aspect, another embodiment of the present application provides a storage medium, on which a computer program is stored. When the computer program is run by a processor, it is configured to execute the steps of the digital twin-based energy management method according to any one of the above first aspects.

[0053] In a fifth aspect, another embodiment of the present application provides an energy management system, which includes: the cloud server described in the above third aspect and a vehicle controller. The cloud server and the vehicle controller are communicatively connected, and the cloud server is configured to execute the steps of the digital twin-based energy management method according to any one of the first aspects.

[0054] The beneficial effects of the present application are:

[0055] The present application provides an energy management method, a cloud server, and a storage medium based on digital twins. By receiving a monitoring instruction uploaded by a vehicle controller of a target fuel cell vehicle, according to the positioning information and the driving direction, a target twin model is matched from multiple sub-twin models in the digital twin model of the target fuel cell vehicle, and the driving path corresponding to the target twin model is determined as the current driving path. According to the positioning information, a predicted section on the current driving path is determined. According to the identification information of the target driver and the predicted section, a target driving scenario of the target driver is determined from multiple driving scenarios of the target twin model, and an energy distribution control instruction corresponding to the target driving scenario is sent to the vehicle controller. The energy distribution control instruction is used to enable the vehicle controller to perform energy distribution on the power battery and the fuel cell on the target fuel cell vehicle based on the energy distribution strategy corresponding to the energy distribution control instruction. The present application determines the target twin model from the digital twin model of the target fuel cell vehicle according to the positioning information and the driving direction, determines the target driving scenario and the energy distribution control instruction according to the target twin model, and provides a personalized energy distribution strategy in a targeted manner to optimize the energy distribution strategy of the target fuel cell vehicle. By determining the corresponding energy distribution control instruction according to the energy distribution strategy, the vehicle controller performs energy distribution on the power battery and the fuel cell on the target fuel cell vehicle according to the corresponding energy distribution control instruction, ensuring the maximum utilization of the target fuel cell energy, fully meeting the energy requirements of the target driving scenario, increasing the service life of the fuel cell while extending the cruising range of the target fuel cell vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the accompanying drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present application and should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0057] Figure 1 It is a schematic flowchart of an energy management method based on digital twins provided by an embodiment of the present application;

[0058] Figure 2 It is a schematic flowchart of generating a twin model in the first energy management method based on digital twins provided by an embodiment of the present application;

[0059] Figure 3 It is a schematic flowchart of generating a twin model in the second energy management method based on digital twins provided by an embodiment of the present application;

[0060] Figure 4Schematic flow chart of generating a twin model in the third digital twin-based energy management method provided by an embodiment of the present application;

[0061] Figure 5 Schematic flow chart of determining a target driving scenario in a digital twin-based energy management method provided by an embodiment of the present application;

[0062] Figure 6 Schematic flow chart of determining an energy distribution control instruction in the first digital twin-based energy management method provided by an embodiment of the present application;

[0063] Figure 7 Schematic flow chart of determining an energy distribution control instruction in the second digital twin-based energy management method provided by an embodiment of the present application;

[0064] Figure 8 Schematic flow chart of determining an energy distribution control instruction in the third digital twin-based energy management method provided by an embodiment of the present application;

[0065] Figure 9 Schematic flow chart of determining an energy distribution control instruction in the fourth digital twin-based energy management method provided by an embodiment of the present application;

[0066] Figure 10 Schematic structural diagram of a digital twin-based energy management device provided by an embodiment of the present application;

[0067] Figure 11 Schematic structural diagram of a cloud server provided by an embodiment of the present application;

[0068] Figure 12 Schematic structural diagram of an energy management system provided by an embodiment of the present application. Detailed implementation manners

[0069] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. It should be understood that the accompanying drawings in the present application are only for the purposes of illustration and description, and are not used to limit the protection scope of the present application. In addition, it should be understood that the schematic drawings are not drawn to actual scale. The flowcharts used in the present application illustrate the operations implemented according to some embodiments of the present application. It should be understood that the operations in the flowchart may not be implemented in sequence, and steps without logical context relationships may be reversed or implemented simultaneously. In addition, those skilled in the art can add one or more other operations to the flowchart or remove one or more operations from the flowchart under the guidance of the content of the present application.

[0070] In addition, the described embodiments are only a part of the embodiments of the present application, rather than all embodiments. The components of the embodiments of the present application described and illustrated in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the present application claimed, but only represents the selected embodiments of the present application. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative efforts belong to the scope of protection of the present application.

[0071] It should be noted that the term "including" will be used in the embodiments of the present application to indicate the existence of the features stated thereafter, but does not exclude the addition of other features.

[0072] The digital twin-based energy management method provided by the present application is applied to a cloud server. Among them, the digital twin is used to create a digital twin model of the target fuel cell vehicle traveling through data technology. The cloud server includes: a user platform unit, a digital twin modeling unit, a model matching unit, and a scenario definition unit. Through the user platform unit, the vehicle number and the driver number can be set, and the position and related parameters of the vehicle in the current model can be viewed. The digital twin modeling unit models based on multiple sets of historical driving data of the target fuel cell vehicle and iteratively updates in real time according to dynamic data, and simulates and generates multiple sub-twin models. The model matching unit is used to match the target twin model from the multiple sub-twin models in the digital twin model of the target fuel cell vehicle according to the positioning information and the driving direction, and can predict the driving trajectory and working conditions. If the vehicle positioning deviates from the planned path, re-match in the digital twin model. If the section does not exist in the digital twin model, store and model the data of this section. The scenario definition unit is used to determine the target driving scenario of the target driver from multiple driving scenarios of the target twin model according to the identification information of the target driver and the predicted section.

[0073] In this application, the target fuel cell vehicle can be a hydrogen fuel cell vehicle, and the specific vehicle type can be a commercial hydrogen fuel cell vehicle. Since the driving route of a commercial hydrogen fuel cell vehicle is basically a fixed route within a fixed range, therefore, an energy management method based on digital twin can be used to determine the energy distribution control instruction of the target fuel cell vehicle, so as to ensure the power performance of the target fuel cell vehicle, while extending the cruising range of the target fuel cell vehicle and increasing the service life of the fuel cell. The target fuel cell vehicle includes a driving information acquisition module and an energy management module. The driving information acquisition module includes: a positioning module, an image acquisition module, and a driving condition acquisition module. The positioning module is used to determine the positioning information of the target fuel cell. The image acquisition module is used to obtain road condition data, such as intersections, traffic signs, obstacles, congestion, road environment, etc. The driving condition acquisition module is used to obtain driving condition data and vehicle state data. The driving condition data can be the current vehicle speed, throttle output, throttle depression duration, brake pedal travel, brake application duration, steering wheel angle, etc. The vehicle state data can be the states of the vehicle such as yaw, pitch, roll, acceleration, deceleration, and steering. The energy management module includes: an energy consumption calculation module, an energy monitoring module, and an energy distribution module. The energy consumption calculation module is used to calculate the energy consumption data of the target fuel cell and the regenerative braking energy data. The energy monitoring module is used to determine the current state of charge parameter of the power battery of the target fuel cell vehicle, the working state of the power battery, the remaining fuel amount of the fuel cell, and the working state of the fuel cell. The energy distribution module is used to perform energy distribution on the target fuel cell vehicle according to the energy distribution control.

[0074] The following continues to illustrate by example the energy management method based on digital twin executed in the cloud server in conjunction with the accompanying drawings. Figure 1 It is a schematic flowchart of an energy management method based on digital twin provided by an embodiment of this application. The method includes:

[0075] Step 101, receive the monitoring instruction uploaded by the vehicle controller of the target fuel cell vehicle.

[0076] The monitoring instruction includes: the positioning information of the target fuel cell vehicle, the driving direction, and the identification information of the target driver.

[0077] Among them, a positioning module and multiple different types of sensors are provided on the target fuel cell vehicle. The positioning information of the target fuel cell vehicle can be determined by the positioning module provided on the target fuel cell vehicle. The driving direction of the target fuel cell vehicle can be determined according to the steering angle sensor to determine the steering wheel rotation direction of the target fuel cell vehicle, and then the driving direction of the target fuel cell can be determined according to the steering wheel rotation direction. The identification information of the target driver is the information input to the vehicle controller by the driver before driving. For example, information such as work number and ID card number is not limited in the embodiments of the present application.

[0078] Optionally, receive the positioning information of the target fuel cell vehicle obtained by the vehicle controller of the target fuel cell vehicle according to the positioning module provided on the target fuel cell vehicle; receive the vehicle controller of the target fuel cell vehicle to obtain the data of the steering angle sensor according to multiple different types of sensors on the target fuel cell vehicle, so as to determine the steering wheel rotation direction of the target fuel cell vehicle; receive the identification information of the target driver input by the driver of the target fuel cell vehicle before driving by the vehicle controller of the target fuel cell vehicle.

[0079] Step 102: According to the positioning information and the driving direction, match the target twin model from multiple sub-twin models in the digital twin model of the target fuel cell vehicle, and determine the driving path corresponding to the target twin model as the current driving path.

[0080] Among them, the digital twin model of the target fuel cell vehicle is constructed according to the historical driving data of the target fuel cell. The digital twin model includes multiple sub-twin models, and the multiple sub-twin models are respectively used to represent multiple driving paths of multiple target fuel cell vehicles.

[0081] Optionally, since the driving path of a commercial vehicle is relatively fixed, when the positioning information and the driving direction of the target fuel cell vehicle are determined, the target twin model can be matched from multiple sub-twin models in the digital twin model of the target fuel cell vehicle, and the driving path corresponding to the target twin model is determined as the current driving path.

[0082] Step 103: Determine the predicted section on the current driving path according to the positioning information.

[0083] Among them, the predicted section can be a flat road section, an uphill section or a downhill section, etc.

[0084] Optionally, according to the positioning information, since the target vehicle is driving on the current driving path, it is not necessary to predict the sections that have been driven on the current driving path. Therefore, the sections that have not been driven in the positioning information are determined from the current driving path as the corresponding predicted sections.

[0085] Step 104: Based on the identification information of the target driver and the predicted road section, determine the target driving scenario of the target driver from multiple driving scenarios of the target twin model.

[0086] Among them, different driving scenarios correspond to different energy distribution strategies. The driving scenario can be determined according to the driving habits of the driver and the predicted road section.

[0087] Optionally, determine the driving habits of the target driver according to the identification information of the target driver, and based on the driving habits of the target driver and the predicted road section, determine the target driving scenario of the target driver from multiple driving scenarios of the target twin model.

[0088] Step 105: Send the energy distribution control instruction corresponding to the target driving scenario to the vehicle controller. The energy distribution control instruction is used to enable the vehicle controller to perform energy distribution on the power battery and fuel cell on the target fuel cell vehicle based on the energy distribution strategy corresponding to the energy distribution control instruction.

[0089] Optionally, the vehicle controller receives the energy distribution control instruction corresponding to the target driving scenario sent by the cloud server, and thus based on the energy distribution strategy corresponding to the energy distribution control instruction, controls the activation of the energy recovery of the power battery on the target fuel cell vehicle, and controls the start and stop of the fuel cell, so as to achieve energy distribution.

[0090] In the embodiment of the present application, receive the monitoring instruction uploaded by the vehicle controller of the target fuel cell vehicle, match the target twin model from multiple sub-twin models in the digital twin model of the target fuel cell vehicle according to the positioning information and driving direction, and determine the driving path corresponding to the target twin model as the current driving path. According to the positioning information, determine the predicted road section on the current driving path. Based on the identification information of the target driver and the predicted road section, determine the target driving scenario of the target driver from multiple driving scenarios of the target twin model, and send the energy distribution control instruction corresponding to the target driving scenario to the vehicle controller. The energy distribution control instruction is used to enable the vehicle controller to perform energy distribution on the power battery and fuel cell on the target fuel cell vehicle based on the energy distribution strategy corresponding to the energy distribution control instruction. The present application can determine the target twin model from the digital twin model of the target fuel cell vehicle according to the positioning information and driving direction, provide a personalized energy distribution strategy, and optimize the energy distribution strategy of the target fuel cell vehicle. By determining the corresponding energy distribution control instruction according to the energy distribution strategy, the vehicle controller performs energy distribution on the power battery and fuel cell on the target fuel cell vehicle according to the corresponding energy distribution control instruction, which can ensure the maximum utilization of the target fuel cell energy, fully meet the energy requirements of the target driving scenario, increase the service life of the fuel cell while extending the cruising range of the target fuel cell vehicle.

[0091] Based on the above embodiments, the present application further provides a process for generating a twin model in the first energy management method based on digital twin, Figure 2 which is a schematic flowchart of the process for generating a twin model in the first energy management method based on digital twin provided by the embodiments of the present application. As Figure 2 shown, before matching the target twin model from multiple sub-twin models in the above step 102, it further includes:

[0092] Step 201: Obtain multiple sets of historical driving data of the target fuel cell vehicle.

[0093] Each set of historical driving data includes: road condition data and driving condition data during a driving process.

[0094] Among them, an image acquisition module is provided on the target fuel cell vehicle. The image acquisition module can be an image sensor. During the driving process, intersections, traffic signs, obstacles, congestion, road environment, etc. are collected through the image acquisition module as road conditions. The vehicle state data and driving behavior data of the target fuel cell vehicle are determined through multiple sensors of the target fuel cell vehicle. The vehicle state data is used to indicate the state of the vehicle during driving, such as pitch, roll, or yaw, etc. The embodiments of the present application do not limit this. The driving behavior data can be: current vehicle speed, throttle output, throttle depression duration, brake pedal stroke, brake depression duration, steering wheel angle, etc. The embodiments of the present application do not limit this.

[0095] Step 202: Perform digital twin modeling respectively according to the identification information of the target fuel cell vehicle and the multiple sets of historical driving data of the target fuel cell vehicle to obtain multiple sub-twin models of the target fuel cell vehicle.

[0096] Optionally, according to the identification information of the target fuel cell vehicle, determine multiple sets of historical driving data corresponding to the target fuel cell vehicle, and perform digital twin modeling on the target fuel cell vehicle to obtain multiple sub-twin models of the target fuel cell vehicle. Each twin model corresponds to a different driving path.

[0097] In the embodiments of the present application, multiple sets of historical driving data of the target fuel cell vehicle are obtained, and digital twin modeling is performed respectively according to the multiple sets of historical driving data of the target fuel cell vehicle to obtain multiple digital twin models of the target fuel cell vehicle. The present application can more accurately determine the target twin model, reduce the deviation existing in a single model, improve the prediction accuracy, thereby optimizing the energy distribution strategy of the fuel cell and the power battery, and improving the energy utilization efficiency.

[0098] Based on the above embodiments, the driving condition data includes: vehicle state data and driving condition data. For this reason, the present application also provides a process for generating a twin model in the second digital twin-based energy management method, Figure 3 which is a schematic flowchart of the process for generating a twin model in the second digital twin-based energy management method provided by the embodiments of the present application. As Figure 3 shown, in the above step 202, digital twin modeling is respectively performed according to the identification information of the target fuel cell vehicle and multiple groups of historical driving data of the target fuel cell vehicle to obtain multiple sub-twin models of the target fuel cell vehicle, including:

[0099] Step 301: Determine the driving road condition data according to the vehicle state data, driving condition data, and road condition data.

[0100] The driving road condition data includes: the driving road condition data of multiple specific sections on a driving path. The driving road condition data can be downhill road condition data, flat road condition data, uphill road condition data, etc.

[0101] Optionally, determine the current state of the target fuel cell vehicle according to the pitch angle and / or roll angle in the vehicle state data, and determine the driving road condition data according to the current state of the target fuel cell vehicle, driving condition data, and road condition data. The driving road condition data also includes the specific positioning information of the driving road condition.

[0102] Exemplarily, when the pitch angle in the vehicle state data of the target fuel cell vehicle > 15° and lasts for more than three minutes, it is determined that the driving road condition data is uphill road condition data. When the pitch angle in the vehicle state data of the target fuel cell vehicle < -15° and lasts for more than three minutes, it is determined that the driving road condition data is uphill road condition data.

[0103] Step 302: Determine the steering driving condition data according to the driving condition data and road condition data.

[0104] Among them, the steering driving condition data includes: the driving condition data of multiple steering nodes on a driving path; the driving condition data can be: left-turn driving condition data, right-turn driving condition data, U-turn driving condition data, lane-changing driving condition data, and other data, which are not limited in the embodiments of the present application.

[0105] Optionally, determine the driving condition data of the target fuel cell vehicle at multiple steering nodes on a driving path according to the steering wheel angle in the driving condition data and the road condition data.

[0106] Step 303: Perform digital twin modeling according to the driving road condition data of multiple specific sections, the driving condition data of multiple steering nodes, and the road condition data to obtain a sub-twin model of the target fuel cell vehicle.

[0107] Among them, a sub-twin model is used to represent a driving route. A sub-twin model includes: driving condition data of multiple specific road sections on a driving route, driving condition data of multiple turning nodes, and road condition data.

[0108] Optionally, digital twin modeling is performed based on the driving condition data of multiple specific road sections, the driving condition data of multiple turning nodes, and the road condition data to obtain a sub-twin model of the target fuel cell vehicle. A digital twin model of the target fuel cell vehicle is constructed based on multiple sub-twin models.

[0109] Based on the embodiments of the present application, the driving condition data is determined according to the vehicle state data, the driving condition data, and the road condition data. The steering driving condition data is determined according to the driving condition data and the road condition data. Digital twin modeling is performed based on the driving condition data of multiple specific road sections, the driving condition data of multiple turning nodes, and the road condition data to obtain a sub-twin model of the target fuel cell vehicle. In the present application, the digital twin model can more accurately predict the driving scenarios of the vehicle, thereby identifying potential safety risks and improving the driving safety of the target fuel cell vehicle.

[0110] Based on the above embodiments, each set of historical driving data includes: energy consumption data and braking energy recovery data at multiple position points during a driving process. Therefore, the present application also provides a process for generating a twin model in the third digital twin-based energy management method. Figure 4 It is a schematic flow diagram of the process for generating a twin model in the third digital twin-based energy management method provided by the embodiments of the present application, as Figure 4 shown. Before obtaining a sub-twin model of the target fuel cell vehicle in step 303 above, the method further includes:

[0111] Step 401: Determine the energy consumption data and the braking energy recovery data of multiple specific road sections according to the energy consumption data and the braking energy recovery data at multiple position points.

[0112] Optionally, the energy consumption data and the braking energy recovery data of the specific road section between the starting point and the ending point are determined according to the energy consumption data and the braking energy recovery data corresponding to the starting point and the ending point of multiple specific road sections.

[0113] Exemplarily, when the energy consumption data or the braking energy recovery data of the starting point of the characteristic road section is a, and the energy consumption data or the braking energy recovery data of the ending point is b, then the energy consumption data of the characteristic road section is b - a.

[0114] In step 303 above, digital twin modeling is performed based on the driving condition data of multiple specific road sections, the driving condition data of multiple steering nodes, and the road condition data to obtain a sub-twin model of the target fuel cell vehicle, including:

[0115] Step 402: Perform digital twin modeling based on the driving condition data of multiple specific road sections, the energy consumption data, the braking energy recovery data, the driving condition data of multiple steering nodes, and the road condition data to obtain a sub-twin model of the target fuel cell vehicle.

[0116] Among them, the energy consumption data and the braking energy recovery data of multiple characteristic road sections are also included in the data twin model.

[0117] Optionally, digital twin modeling is performed based on the driving condition data of multiple specific road sections, the energy consumption data, the braking energy recovery data, the driving condition data of multiple steering nodes, and the road condition data to obtain a sub-twin model of the target fuel cell vehicle. Among them, based on a sub-twin model of the target fuel cell vehicle, the driving condition data, the energy consumption data, the braking energy recovery data, the driving condition data of multiple steering nodes, and the road condition data of multiple specific road sections of the target fuel cell vehicle can be determined.

[0118] In the embodiments of the present application, the energy consumption data and the braking energy recovery data of multiple specific road sections are determined based on the energy consumption data and the braking energy recovery data of multiple position points, so as to construct a sub-twin model of the target fuel cell vehicle. The present application can accurately predict the energy consumption data and the braking energy data during the driving process of the target fuel cell vehicle through the sub-twin model, thereby improving the accuracy of the energy distribution strategy.

[0119] On the basis of the above embodiments, the present application also provides a process for determining a target driving scenario in an energy management method based on digital twins, Figure 5 which is a schematic flow diagram of a process for determining a target driving scenario in an energy management method based on digital twins provided by the embodiments of the present application. As Figure 5 shown, in step 104 above, based on the identification information of the target driver and the predicted road section, the target driving scenario of the target driver is determined from multiple driving scenarios of the target twin model, including:

[0120] Step 501: Obtain the historical driving behavior data of the target driver according to the identification information of the target driver.

[0121] Among them, the historical driving behavior data may include: the duration of the driver stepping on the accelerator, the opening of the accelerator pedal, the travel of the brake pedal, the duration of stepping on the brake, etc., and the embodiments of the present application do not limit this. The identification information of the target driver may include: the name of the driver, the work number of the driver, the ID number of the driver, etc., and the embodiments of the present application do not limit this.

[0122] Optionally, the driving behavior data of the driver is determined by multiple sensors arranged in the target fuel cell vehicle, and the historical driving behavior data of the target driver is obtained according to the identification information of the target driver.

[0123] Step 502: Determine the target driving scenario of the target driver from multiple driving scenarios according to the historical driving behavior data and the predicted road section.

[0124] Optionally, according to the predicted road section, determine the driving scenario corresponding to the predicted road section from multiple driving scenarios, and determine the target driving scenario of the target driver from the driving scenarios corresponding to the predicted road section according to the historical driving behavior data.

[0125] In the present application, the historical driving behavior data is first determined, and according to the historical driving behavior data and the predicted road section, the target driving scenario of the target driver is determined from multiple driving scenarios. The present application can provide a more personalized driving experience for the driver, adapt to the driving habits and preferences of the driver, help predict potential driving risks, and thus take measures in advance to avoid accidents.

[0126] Based on the above embodiments, the present application also provides a process for determining the target driving scenario in another energy management method based on data twin. In step 502 above, according to the historical driving behavior data and the predicted road section, the target driving scenario of the target driver is determined from multiple driving scenarios, including:

[0127] If the predicted road section is an uphill section, then according to the uphill accelerator stepping duration and the accelerator pedal opening in the historical driving behavior data, determine the target driving scenario from multiple driving scenarios of the uphill section.

[0128] Among them, the accelerator stepping duration and the accelerator pedal opening can indicate the driving habits of the driver. When the driver has a long accelerator stepping duration and a large accelerator pedal opening, it means that the driver gives priority to ensuring the driving power performance. When the driver has a short accelerator stepping duration and a small accelerator pedal opening, it means that the driver gives priority to ensuring energy conservation. The present application only takes the above as an example for illustration, and the specific driving habits are determined according to the actual situation, and the embodiments of the present application do not limit this.

[0129] Optionally, if the predicted road section is an uphill road section, determine the driving habits of the driver according to the uphill throttle stepping duration and throttle pedal opening in the historical driving behavior data. Determine the target driving scenario from multiple driving scenarios of the uphill road section according to the driving habits of the driver.

[0130] Alternatively, if the predicted road section is a downhill road section, determine the target driving scenario from multiple driving scenarios of the downhill road section according to the braking duration and brake pedal travel in the historical driving behavior data.

[0131] Optionally, if the predicted road section is a downhill road section, determine the driving habits of the driver according to the braking duration and brake pedal travel in the historical driving behavior data. Determine the target driving scenario from multiple driving scenarios of the downhill road section according to the driving habits of the driver.

[0132] In the embodiment of the present application, determine the target driving scenario from multiple driving scenarios of different predicted road sections according to the predicted road section and the braking duration and brake pedal travel in the historical driving behavior data. The present application can more accurately predict the behavior of the driver on a specific road section, thereby improving the matching degree between the driver and the driving scenario, maximizing the energy recovered during braking, and improving battery efficiency.

[0133] Based on the above embodiments, the present application also provides a process for determining an energy distribution control instruction in the first digital-twin-based energy management method. Figure 6 It is a schematic flow chart for determining an energy distribution control instruction in the first digital-twin-based energy management method provided by the embodiment of the present application, as Figure 6 shown, in the above determining the target driving scenario from multiple driving scenarios of the uphill road section according to the uphill throttle stepping duration and throttle pedal opening in the historical driving behavior data, includes:

[0134] Step 601, if the uphill throttle stepping duration is greater than the first preset duration threshold and the throttle pedal opening is greater than the first preset opening threshold, determine that the target driving scenario is the first driving scenario.

[0135] Wherein, the first preset duration threshold is T1, and the first preset duration threshold can be determined according to the parameters of the power battery and the fuel cell, or the first preset duration threshold can be determined according to the road conditions of the current uphill road section, or other determination methods, which are not limited in the embodiment of the present application. The first preset opening threshold can be 30%. At this time, the degree to which the driver steps on the throttle pedal is 30% of the degree of fully stepping on the throttle pedal. The first driving scenario is: in the case of an uphill road section, the uphill throttle stepping duration is greater than the first preset duration threshold and the throttle pedal opening is greater than the first preset opening threshold.

[0136] Optionally, if the uphill throttle application duration is greater than the first preset duration threshold and the throttle pedal opening is greater than the first preset opening threshold, it indicates that the driver gives priority to ensuring driving power performance on the uphill section, and the target driving scenario is determined as the first driving scenario.

[0137] In step 105 above, sending an energy distribution control instruction corresponding to the target driving scenario to the vehicle controller includes:

[0138] Step 602, sending a first control instruction to the vehicle controller, and the first control instruction is used to cause the vehicle controller to turn on the fuel cell in advance to jointly provide power energy by using the fuel cell and the power battery.

[0139] Among them, the first control instruction is the "about to climb, turn on energy storage" instruction. The first control instruction not only includes the road condition of about to climb, but also indicates to turn on energy storage.

[0140] Optionally, when the target driving scenario is the first driving scenario, it indicates that the driver gives priority to ensuring driving power performance on the uphill section, and it is necessary to store energy in advance on the uphill section, turn on the fuel cell and the power battery to supply power simultaneously in advance. When the vehicle is at a preset distance from the uphill section, send the "about to climb, turn on energy storage" instruction to the vehicle controller to cause the vehicle controller to turn on the fuel cell in advance to jointly provide power energy by using the fuel cell and the power battery, so as to ensure the driving power performance of the target fuel cell vehicle. Among them, the preset distance can be 300 meters.

[0141] In the embodiment of the present application, the corresponding driving scenario can be determined according to the predicted section and throttle information, and the corresponding control instruction is determined according to the driving scenario, so that the vehicle controller performs energy distribution according to the corresponding control instruction, thereby providing a smoother driving experience, and at the same time, energy management can be effectively carried out to improve driving safety.

[0142] On the basis of the above embodiment, the present application also provides a process for determining an energy distribution control instruction in the second digital twin-based energy management method. Figure 7 It is a schematic flow chart of the process for determining an energy distribution control instruction in the second digital twin-based energy management method provided by the embodiment of the present application. As Figure 7 shown, in the above determination of the target driving scenario from multiple driving scenarios on the uphill section according to the uphill throttle application duration and throttle pedal opening in the historical driving behavior data, it includes:

[0143] Step 701, if the uphill throttle application duration is less than the second preset duration threshold and the throttle pedal opening is greater than the first preset opening threshold, determine the target driving scenario as the second driving scenario.

[0144] Among them, the second preset duration threshold is T2. The second preset duration threshold can be determined according to the parameters of the power battery and the fuel cell, or the second preset duration threshold can be determined according to the road conditions of the current uphill section, or other determination methods, which are not limited in the embodiments of the present application. The first preset opening threshold can be 30%. The second preset duration threshold T2 is less than the first preset duration threshold T1. The second driving scenario is: in the case of an uphill section, the duration of stepping on the accelerator during uphill is less than the second preset duration threshold and the opening of the accelerator pedal is greater than the first preset opening threshold.

[0145] Optionally, if the duration of stepping on the accelerator during uphill is less than the second preset duration threshold and the opening of the accelerator pedal is greater than the first preset opening threshold, it means that the driver gives priority to energy conservation on the uphill section, and the target driving scenario is determined to be the second driving scenario.

[0146] In step 105 above, sending an energy distribution control command corresponding to the target driving scenario to the vehicle controller includes:

[0147] Step 702: Send a second control command and uphill energy consumption data to the vehicle controller. The second control command is used to make the vehicle controller not turn on the fuel cell when the current state of charge parameter of the power battery can ensure the uphill energy consumption data and the predicted state of charge parameter after climbing only using the power battery is greater than the first preset state of charge threshold; when the current state of charge parameter cannot ensure the energy consumption data and the predicted state of charge parameter after climbing only using the power battery is less than the second preset state of charge threshold, turn on the fuel cell in advance to jointly provide the climbing power energy using the fuel cell and the power battery.

[0148] Among them, the uphill energy consumption data is Q1, and the uphill energy consumption data is the energy consumption data consumed during driving between the starting point and the ending point of the uphill section of the vehicle. The current state of charge parameter is Q0, the first preset state of charge threshold can be 45%, and the second preset state of charge threshold can be 85%. The second control command is an "about to climb" command, and the second control command only includes the road conditions about to climb and does not include the control of energy storage.

[0149] Optionally, when the target driving scenario is the second driving scenario, it indicates that the driver gives priority to energy conservation on the uphill section, and it is necessary to determine whether energy storage is required according to the state of charge parameter of the power battery in the target fuel cell vehicle. When the vehicle is at a preset distance from the uphill section, send the "about to climb" command to the vehicle controller, so that when the current state of charge parameter of the power battery can guarantee the uphill energy consumption data, and the predicted state of charge parameter after climbing using only the power battery is greater than the first preset state of charge threshold, the fuel cell is not turned on. Among them, the preset distance can be 300 meters. Specifically, when the difference between the current state of charge parameter Q0 of the power battery and the uphill energy consumption data Q1 is greater than the first preset state of charge threshold of 45%, the fuel cell is not turned on.

[0150] Optionally, when the current state of charge parameter cannot guarantee the energy consumption data, and after climbing using only the power battery, when the difference between the current state of charge parameter Q0 of the power battery and the uphill energy consumption data Q1 is less than the first preset state of charge threshold of 45%, then when the state of charge parameter of the power battery reaches 85%, the fuel cell is turned on in advance to use the fuel cell and the power battery to jointly provide the climbing power energy. When the state of charge parameter of the power battery reaches 95%, and the difference between the current state of charge parameter Q0 of the power battery and the uphill energy consumption data Q1 is greater than the first preset state of charge threshold of 45%, the fuel cell is turned off.

[0151] In the embodiments of the present application, the corresponding driving scenario can be determined according to the predicted road section and the throttle information, and the corresponding control command can be determined according to the driving scenario, so that the vehicle controller performs energy distribution according to the corresponding control command, helping the driver to make better choices under complex or uncertain driving conditions and improving the driver's driving experience.

[0152] On the basis of the above embodiments, the present application also provides a process for determining the energy distribution control command in the third digital twin-based energy management method. Figure 8 It is a schematic flowchart of the process for determining the energy distribution control command in the third digital twin-based energy management method provided by the embodiments of the present application, as Figure 8 shown, in the above-mentioned process of determining the target driving scenario from multiple driving scenarios on the downhill section according to the braking duration and the brake pedal stroke in the historical driving behavior data, it includes:

[0153] Step 801, if the braking duration is greater than the third preset duration threshold and the brake pedal stroke is greater than the preset brake stroke threshold, then determine that the target driving scenario is the third driving scenario.

[0154] Among them, the third preset duration threshold is T3. The third preset duration threshold can be determined according to the parameters of the power battery and the fuel cell, or the third preset duration threshold can be determined according to the road conditions of the current uphill section, or other determination methods, which are not limited in the embodiments of the present application. The preset braking stroke threshold can be 20%. The third driving scenario is: in the case of a downhill section, the braking duration is greater than the third preset duration threshold and the braking pedal stroke is greater than the preset braking stroke threshold.

[0155] Optionally, if the braking duration is greater than the third preset duration threshold and the braking pedal stroke is greater than the preset braking stroke threshold, it indicates that the driver gives priority to braking on the uphill section, and the target driving scenario is determined to be the third driving scenario.

[0156] In the above step 105, sending the energy distribution control instruction corresponding to the target driving scenario to the vehicle controller includes:

[0157] Step 802: Send a third control instruction to the vehicle controller. The third control instruction is used to make the vehicle controller turn on the braking energy recovery on the downhill section to recover the braking energy to the power battery, and turn off the fuel cell to only use the power battery to provide the downhill power energy.

[0158] Among them, the third control instruction is the instruction of "going downhill soon, please turn on the braking energy recovery". The third control instruction not only includes the road condition of going downhill soon, but also indicates to turn on the automatic energy recovery.

[0159] Optionally, when the target driving scenario is the third driving scenario, it indicates that the driver gives priority to braking on the downhill section. When the vehicle is at a preset distance from the downhill section, send the instruction of "going downhill soon, please turn on the braking energy recovery" to the vehicle controller, so that the vehicle controller turns on the automatic energy recovery in advance on the downhill section to recover the braking energy to the power battery, and turn off the fuel cell to only use the power battery to provide the downhill power energy. Among them, the preset distance can be 300 meters.

[0160] In the embodiments of the present application, the corresponding driving scenario can be determined according to the predicted section and the throttle information, and the corresponding control instruction can be determined according to the driving scenario, so that the vehicle controller performs energy distribution according to the corresponding control instruction, ensuring that the target fuel cell vehicle can prepare and distribute the necessary energy in advance, and improving the driving safety of the driver.

[0161] On the basis of the above embodiments, the present application also provides the process of determining the energy distribution control instruction in the fourth digital twin-based energy management method. Figure 9 It is a schematic diagram of the process of determining the energy distribution control instruction in the fourth digital twin-based energy management method provided by the embodiments of the present application, as Figure 9As shown, determining a target driving scenario from multiple driving scenarios on a downhill section according to the braking duration and brake pedal stroke in the historical driving behavior data includes:

[0162] Step 901: If the braking duration is less than the fourth preset duration threshold and the brake pedal stroke is greater than the preset brake stroke threshold, determine that the target driving scenario is the fourth driving scenario.

[0163] Among them, the fourth preset duration threshold is T4. The fourth preset duration threshold can be determined according to the parameters of the power battery and the fuel cell, or the fourth preset duration threshold can be determined according to the road conditions of the current uphill section, or other determination methods, which are not limited in the embodiments of the present application. The fourth preset duration threshold T4 is less than the third preset duration threshold T3. The fourth driving scenario is: in the case of a downhill section, the braking duration is less than the fourth preset duration threshold and the brake pedal stroke is greater than the preset brake stroke threshold.

[0164] Optionally, if the braking duration is less than the fourth preset duration threshold and the brake pedal stroke is greater than the preset brake stroke threshold, it means that the driver is driving normally on the downhill section, and it is determined that the target driving scenario is the fourth driving scenario.

[0165] In the above step 105, sending an energy distribution control instruction corresponding to the target driving scenario to the vehicle controller includes:

[0166] Step 902: Send the fourth control instruction and the downhill energy consumption data to the vehicle controller. The fourth control instruction is used to make the vehicle controller turn on the braking energy recovery on the downhill section to recover the braking energy to the power battery, and use the fuel cell and the power battery to jointly provide the downhill power energy; only use the power battery to provide energy. When the current state of charge parameter of the power battery can guarantee the downhill energy consumption data, and the predicted state of charge parameter after going downhill only by the power battery is greater than the third preset state of charge threshold, the fuel cell is not turned on; when the current state of charge parameter cannot guarantee the downhill energy consumption data, and the predicted state of charge parameter after going downhill only by the power battery is less than the fourth preset state of charge threshold, the fuel cell is turned on in advance to use the fuel cell and the power battery to jointly provide the downhill power energy.

[0167] Among them, the fourth control instruction is the "about to go downhill" instruction. That is to say, the fourth control instruction does not indicate whether to perform kinetic energy recovery on the power battery, and only includes the predicted road information. The downhill energy consumption data is Q2. The third preset state of charge threshold can be 90%, and the fourth preset state of charge threshold can be 80%.

[0168] Optionally, when the target driving scenario is the fourth driving scenario, it indicates that the vehicle and the driver are driving on a downhill section. When the vehicle is at a preset distance from the downhill section, the "about to go downhill" message and the downhill energy consumption data Q2 are sent to the vehicle controller, so that the vehicle controller can activate the braking energy recovery during the downhill section to recover the braking energy to the power battery, and use the fuel cell and the power battery to jointly provide the downhill driving energy.

[0169] Optionally, when the target driving scenario is the fourth driving scenario, it indicates that the vehicle and the driver are driving on a downhill section, and only the power battery provides the driving energy for the target fuel cell vehicle. When the vehicle is at a preset distance from the downhill section, the "about to go downhill" message and the downhill energy consumption data Q2 are sent to the vehicle controller. When the current state of charge parameter Q0 of the power battery can guarantee the downhill energy consumption data Q2, and the predicted state of charge parameter after going downhill using only the power battery is greater than the third preset state of charge threshold of 90%, the fuel cell is not activated; when the current state of charge parameter Q0 cannot guarantee the downhill energy consumption data Q2, and the predicted state of charge parameter after going downhill using only the power battery is less than the fourth preset state of charge threshold of 80%, the fuel cell is activated in advance to use the fuel cell and the power battery to jointly provide the downhill driving energy.

[0170] In the embodiments of the present application, the corresponding driving scenario can be determined according to the predicted road section and the throttle information, and the corresponding control instruction can be determined according to the driving scenario, so that the vehicle controller can perform energy distribution according to the corresponding control instruction, effectively manage the energy of the target fuel cell vehicle, increase the driving range of the target fuel cell vehicle, and reduce the fuel consumption of the fuel cell or the power consumption of the power battery, thereby reducing the operating cost of the target fuel cell vehicle.

[0171] Based on the same inventive concept, an energy management device based on digital twin corresponding to the energy management method based on digital twin is also provided in the embodiments of the present application. Since the principle of solving problems by the device in the embodiments of the present application is similar to that of the energy management method based on digital twin in the above embodiments of the present application, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0172] Figure 10 The following is a schematic structural diagram of an energy management device based on digital twin provided by the embodiments of the present application, as Figure 10 shown, the device includes:

[0173] A receiving module 1001, configured to receive a monitoring instruction uploaded by the vehicle controller of the target fuel cell vehicle, where the monitoring instruction includes: the positioning information, driving direction, and identification information of the target driver of the target fuel cell vehicle;

[0174] The first determination module 1002 is configured to match a target twin model from multiple sub-twin models in the digital twin model of the target fuel cell vehicle according to the positioning information and the driving direction, and determine the driving path corresponding to the target twin model as the current driving path; wherein, the multiple sub-twin models are respectively used to represent multiple driving paths of multiple target fuel cell vehicles;

[0175] The second determination module 1003 is configured to determine a predicted road section on the current driving path according to the positioning information;

[0176] The third determination module 1004 is configured to determine a target driving scenario of the target driver from multiple driving scenarios of the target twin model according to the identification information of the target driver and the predicted road section; different driving scenarios correspond to different energy distribution strategies;

[0177] The distribution module 1005 is configured to send an energy distribution control instruction corresponding to the target driving scenario to the vehicle controller, and the energy distribution control instruction is used to enable the vehicle controller to perform energy distribution on the power battery and the fuel cell on the target fuel cell vehicle based on the energy distribution strategy corresponding to the energy distribution control instruction.

[0178] Optionally, the first determination module 1002 is further configured to: obtain multiple groups of historical driving data of the target fuel cell vehicle, and each group of historical driving data includes: road condition data and driving condition data during one driving process;

[0179] Perform digital twin modeling respectively according to the identification information of the target fuel cell vehicle and the multiple groups of historical driving data of the target fuel cell vehicle to obtain multiple sub-twin models of the target fuel cell vehicle.

[0180] Optionally, the driving condition data includes: vehicle state data and driving condition data; the first determination module 1002 is specifically configured to: determine driving road condition data according to the vehicle state data, the driving condition data and the road condition data, and the driving road condition data includes: driving road condition data of multiple specific road sections on one driving path;

[0181] Optionally, the first determination module 1002 is specifically configured to: perform digital twin modeling according to the driving road condition data of multiple specific road sections, the driving condition data of multiple steering nodes and the road condition data to obtain a sub-twin model of the target fuel cell vehicle.

[0182] Optionally, each group of historical driving data includes: energy consumption data and braking energy recovery data of multiple position points during one driving process; the first determination module 1002 is further configured to: determine the energy consumption data and the braking energy recovery data of multiple specific road sections according to the energy consumption data of multiple position points and the braking energy recovery data;

[0183] Optionally, the first determination module 1002 is specifically configured to: perform digital twin modeling based on the driving condition data, energy consumption data, and braking energy recovery data of multiple specific road sections, the driving condition data of multiple steering nodes, and the road condition data, to obtain a sub-twin model of the target fuel cell vehicle.

[0184] Optionally, the third determination module 1004 is specifically configured to: obtain the historical driving behavior data of the target driver according to the identification information of the target driver;

[0185] Determine the target driving scenario of the target driver from multiple driving scenarios according to the historical driving behavior data and the predicted road section.

[0186] Optionally, the third determination module 1004 is specifically configured to: if the predicted road section is an uphill road section, determine the target driving scenario from multiple driving scenarios of the uphill road section according to the uphill throttle stepping duration and throttle pedal opening in the historical driving behavior data;

[0187] Or, if the predicted road section is a downhill road section, determine the target driving scenario from multiple driving scenarios of the downhill road section according to the braking duration and brake pedal travel in the historical driving behavior data.

[0188] Optionally, the third determination module 1004 is specifically configured to: if the uphill throttle stepping duration is greater than the first preset duration threshold and the throttle pedal opening is greater than the first preset opening threshold, determine the target driving scenario as the first driving scenario;

[0189] Optionally, the allocation module 1005 is specifically configured to: send a first control instruction to the vehicle controller, and the first control instruction is used to cause the vehicle controller to turn on the fuel cell in advance, so as to jointly provide power energy by using the fuel cell and the power battery.

[0190] Optionally, the third determination module 1004 is specifically configured to: if the uphill throttle stepping duration is less than the second preset duration threshold and the throttle pedal opening is greater than the first preset opening threshold, determine the target driving scenario as the second driving scenario;

[0191] Optionally, the allocation module 1005 is specifically configured to: send a second control instruction and uphill energy consumption data to the vehicle controller, and the second control instruction is used to cause the vehicle controller not to turn on the fuel cell when the current state of charge parameter of the power battery can guarantee the uphill energy consumption data and the predicted state of charge parameter after climbing only by using the power battery is greater than the first preset state of charge threshold; when the current state of charge parameter cannot guarantee the energy consumption data and the predicted state of charge parameter after climbing only by using the power battery is less than the second preset state of charge threshold, turn on the fuel cell in advance, so as to jointly provide climbing power energy by using the fuel cell and the power battery.

[0192] Optionally, the third determination module 1004 is specifically configured to: if the duration of stepping on the brake is greater than a third preset duration threshold and the stroke of the brake pedal is greater than a preset brake stroke threshold, determine that the target driving scenario is a third driving scenario;

[0193] Optionally, the allocation module 1005 is specifically configured to: send a third control instruction to the vehicle controller, where the third control instruction is used to cause the vehicle controller to turn on brake energy recovery on a downhill section to recover the brake energy to the power battery and turn off the fuel cell to only use the power battery to provide downhill power energy.

[0194] Optionally, the third determination module 1004 is specifically configured to: if the duration of stepping on the brake is less than a fourth preset duration threshold and the stroke of the brake pedal is greater than a preset brake stroke threshold, determine that the target driving scenario is a fourth driving scenario;

[0195] Optionally, the allocation module 1005 is specifically configured to: send a fourth control instruction and downhill energy consumption data to the vehicle controller. The fourth control instruction is used to cause the vehicle controller to turn on brake energy recovery on a downhill section to recover the brake energy to the power battery and use both the fuel cell and the power battery to provide downhill power energy; only use the power battery to provide energy when the current state of charge parameter of the power battery can ensure the downhill energy consumption data and the predicted state of charge parameter after downhill using only the power battery is greater than a third preset state of charge threshold, and do not turn on the fuel cell; when the current state of charge parameter cannot ensure the downhill energy consumption data and the predicted state of charge parameter after downhill using only the power battery is less than a fourth preset state of charge threshold, turn on the fuel cell in advance to use both the fuel cell and the power battery to provide downhill power energy.

[0196] For the processing flow of each module in the device and the interaction flow between the modules, reference can be made to the relevant descriptions in the above method embodiments, which will not be elaborated here.

[0197] An embodiment of the present application also provides a cloud server. Figure 11 As shown in the structure diagram of the cloud server provided by the embodiment of the present application, as Figure 11 shown, the cloud server 1100 includes: a processor 1101, a memory 1102, and optionally, a bus 1103 can also be included. The memory 1102 stores machine-readable instructions executable by the processor 1101. When the cloud server 1100 runs, the processor 1101 communicates with the memory 1102 through the bus 1103. When the machine-readable instructions are executed by the processor 1101, the steps of the above method for energy management based on digital twins are executed.

[0198] An embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, it executes the steps of the above-mentioned digital-twin-based energy management method.

[0199] An embodiment of the present application further provides an energy management system. Figure 12 As shown in the structural schematic diagram of an energy management system provided by an embodiment of the present application, Figure 12 the energy management system includes: a cloud server 1100 and a vehicle controller 1201. The cloud server 1100 and the vehicle controller 1201 are communicatively connected. The cloud server 1100 is used to execute the steps of the above-mentioned digital-twin-based energy management method.

[0200] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems and devices can refer to the corresponding processes in the method embodiments, which will not be repeated in this application. In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. The device embodiments described above are only illustrative. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation. For another example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection may be through some communication interfaces, and the indirect coupling or communication connection of the devices or modules may be in an electrical, mechanical, or other form.

[0201] In addition, in each embodiment of the present application, the functional units can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0202] The above are only specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application.

Claims

1. An energy management method based on digital twin, characterized in that Applied to a cloud server, the method includes: Receiving a monitoring instruction uploaded by a vehicle controller of a target fuel cell vehicle, where the monitoring instruction includes: positioning information of the target fuel cell vehicle, driving direction, and identification information of a target driver; According to the positioning information and the driving direction, matching a target twin model from multiple sub-twin models in the digital twin model of the target fuel cell vehicle, and determining the driving path corresponding to the target twin model as the current driving path; wherein the multiple sub-twin models are respectively used to represent multiple driving paths of multiple target fuel cell vehicles; Determining a predicted section on the current driving path according to the positioning information; Obtaining historical driving behavior data of the target driver according to the identification information of the target driver; If the predicted section is an uphill section, determining a target driving scenario from multiple driving scenarios of the uphill section according to the uphill throttle stepping duration and throttle pedal opening in the historical driving behavior data; Or, if the predicted section is a downhill section, determining the target driving scenario from multiple driving scenarios of the downhill section according to the braking duration and brake pedal stroke in the historical driving behavior data; different driving scenarios correspond to different energy distribution strategies; Sending a control instruction corresponding to the target driving scenario to the vehicle controller, where the energy distribution control instruction is used to enable the vehicle controller to perform energy distribution on a power battery and a fuel cell on the target fuel cell vehicle based on the energy distribution strategy corresponding to the energy distribution control instruction; Wherein, the determining the target driving scenario from multiple driving scenarios of the uphill section according to the uphill throttle stepping duration and throttle pedal opening in the historical driving behavior data includes: If the uphill throttle stepping duration is greater than a first preset duration threshold and the throttle pedal opening is greater than a first preset opening threshold, determining the target driving scenario as a first driving scenario; The sending the energy distribution control instruction corresponding to the target driving scenario to the vehicle controller includes: Sending a first control instruction to the vehicle controller, where the first control instruction is used to enable the vehicle controller to turn on the fuel cell in advance to use the fuel cell and the power battery to jointly provide power energy.

2. The energy management method based on digital twin according to claim 1, wherein The method further includes: Before matching the target twin model from the multiple sub-twin models, obtaining multiple sets of historical driving data of the target fuel cell vehicle, where each set of historical driving data includes: road condition data and driving condition data during a driving process; Performing digital twin modeling respectively according to the identification information of the target fuel cell vehicle and the multiple sets of historical driving data of the target fuel cell vehicle to obtain the multiple sub-twin models of the target fuel cell vehicle.

3. The energy management method based on digital twin according to claim 2, wherein The driving condition data includes: vehicle state data and driving condition data; Performing digital twin modeling respectively according to the identification information of the target fuel cell vehicle and the multiple sets of historical driving data of the target fuel cell vehicle to obtain the multiple sub-twin models of the target fuel cell vehicle, including: Determining driving road condition data according to the vehicle state data, the driving condition data and the road condition data, where the driving road condition data includes: driving road condition data of multiple specific road sections on a driving route; Determining steering driving condition data according to the driving condition data and the road condition data, where the steering driving condition data includes: driving condition data of multiple steering nodes on the driving route; Performing digital twin modeling according to the driving road condition data of the multiple specific road sections, the driving condition data of the multiple steering nodes and the road condition data to obtain a sub-twin model of the target fuel cell vehicle.

4. The energy management method based on digital twin according to claim 3, characterized in that, Each set of historical driving data includes: energy consumption data and braking energy recovery data of multiple position points during the one driving process; the method further includes: Before performing digital twin modeling, determining the energy consumption data and the braking energy recovery data of the multiple specific road sections according to the energy consumption data of the multiple position points and the braking energy recovery data; The performing digital twin modeling according to the driving road condition data of the multiple specific road sections, the driving condition data of the multiple steering nodes and the road condition data to obtain a sub-twin model of the target fuel cell vehicle includes: Performing digital twin modeling according to the driving road condition data, the energy consumption data and the braking energy recovery data of the multiple specific road sections, the driving condition data of the multiple steering nodes and the road condition data to obtain a sub-twin model of the target fuel cell vehicle.

5. The energy management method based on digital twin according to claim 1, wherein The determining the target driving scenario from the multiple driving scenarios of the uphill section according to the uphill throttle stepping duration and the throttle pedal opening in the historical driving behavior data includes: If the uphill throttle stepping duration is less than a second preset duration threshold and the throttle pedal opening is greater than a first preset opening threshold, determining that the target driving scenario is the second driving scenario; The sending the energy distribution control instruction corresponding to the target driving scenario to the vehicle controller includes: Sending a second control instruction and uphill energy consumption data to the vehicle controller, where the second control instruction is used to make the vehicle controller not turn on the fuel cell when the current state of charge parameter of the power battery can ensure the uphill energy consumption data and the predicted state of charge parameter after completing the uphill using only the power battery is greater than a first preset state of charge threshold; when the current state of charge parameter cannot ensure the energy consumption data and the predicted state of charge parameter after completing the uphill using only the power battery is less than a second preset state of charge threshold, turning on the fuel cell in advance to use the fuel cell and the power battery to jointly provide the climbing power energy.

6. The energy management method based on digital twin according to claim 1, wherein Determining the target driving scenario from multiple driving scenarios of the downhill section according to the braking duration and the braking pedal stroke in the historical driving behavior data includes: If the braking duration is greater than a third preset duration threshold and the braking pedal stroke is greater than a preset braking stroke threshold, determining that the target driving scenario is the third driving scenario; Sending the energy distribution control instruction corresponding to the target driving scenario to the vehicle controller includes: Sending a third control instruction to the vehicle controller, where the third control instruction is used to enable the vehicle controller to turn on regenerative braking energy recovery on the downhill section to recover the braking energy to the power battery and turn off the fuel cell to only use the power battery to provide downhill driving energy.

7. A cloud server, characterized in that, Including: A memory and a processor, where the memory stores a computer program that can run on the processor. When the processor executes the computer program, it performs the steps of the digital twin-based energy management method according to any one of claims 1-6 above.

8. A storage medium, characterized in that, A computer program is stored on the storage medium. When the computer program is run by the processor, it performs the steps of the digital twin-based energy management method according to any one of claims 1-6 above.

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