Vehicle energy consumption prediction detection method, device, vehicle and storage medium

Abstract

The present invention relates to the field of new energy vehicle testing technology, and in particular to a detection method, device, vehicle and storage medium for vehicle energy consumption prediction. The method comprises: inputting the target driving path information of the vehicle into an energy consumption prediction model to generate an initial energy consumption prediction result of the vehicle; continuously updating the energy consumption prediction value of the remaining mileage of the vehicle during the journey using the real-time position information of the vehicle obtained by a GPS simulator that meets the configuration conditions to obtain multiple new energy consumption prediction results, and analyzing all the energy consumption prediction results to determine the final energy consumption prediction value of the vehicle after the journey ends; and generating a vehicle energy consumption prediction detection result that meets the valid conditions when the actual energy consumption value is less than or equal to the final energy consumption prediction value. Thus, the problem that the relevant technology fails to fully consider multiple factors that change in real time, cannot accurately predict vehicle energy consumption under complex circumstances, and reduces the accuracy and reliability of vehicle energy consumption prediction is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy vehicle testing, and in particular to a detection method, device, vehicle and storage medium for vehicle energy consumption prediction. Background Art

[0002] With growing global awareness of environmental protection and the increasingly urgent need for energy transition, the new energy vehicle market is experiencing unprecedented rapid growth. As a crucial component of future transportation, new energy vehicles not only help reduce greenhouse gas emissions and improve urban air quality, but also promote the efficient use of renewable energy. Against this backdrop, improving vehicle energy efficiency has become a key goal for major automakers. Improved energy efficiency not only translates to longer driving range and lower operating costs, but also demonstrates a company's competitiveness and social responsibility. Consequently, manufacturers are continuously exploring innovations in battery technology, powertrain design, and vehicle lightweighting, striving to gain a competitive edge in the fiercely competitive market. Furthermore, supportive government policies are providing strong support for the advancement of new energy vehicle energy efficiency technologies, driving the entire industry towards a greener and more intelligent future.

[0003] In related technologies, the energy consumption of electric vehicles can be calculated based on historical trips, historical vehicle status information and the corresponding trip power consumption to obtain the energy consumption forecast for a fixed trip, or by integrating vehicle driving data at different times as input into the model to predict future vehicle energy consumption. That is, this method fully considers the impact of the vehicle's historical energy consumption data on future energy consumption.

[0004] However, the vehicle energy consumption prediction in related technologies fails to fully consider multiple factors that change in real time, cannot truly reflect the vehicle energy consumption during actual driving, and cannot accurately predict vehicle energy consumption under complex circumstances, which reduces the accuracy and reliability of vehicle energy consumption prediction and urgently needs to be solved. Summary of the Invention

[0005] The present invention provides a detection method, device, vehicle and storage medium for vehicle energy consumption prediction to solve the problems in related technologies such as the failure of vehicle energy consumption prediction to fully consider multiple factors that change in real time, the inability to truly reflect the vehicle energy consumption during actual driving, and the inability to accurately predict vehicle energy consumption under complex circumstances, thereby reducing the accuracy and reliability of vehicle energy consumption prediction.

[0006] A first aspect of the present invention provides a method for detecting vehicle energy consumption prediction, comprising the following steps: obtaining target driving path information of a target vehicle, and inputting the target driving path information into an energy consumption prediction model to generate an initial energy consumption prediction result of the target vehicle; obtaining real-time position information of the target vehicle using a target GPS (Global Positioning System) simulator that meets preset configuration conditions, and continuously updating the energy consumption prediction value of the target vehicle for the remaining mileage in the trip based on the real-time position information to obtain multiple new energy consumption prediction results, and analyzing the initial energy consumption prediction result and the multiple new energy consumption prediction results to determine the final energy consumption prediction value of the target vehicle after the trip; obtaining the actual energy consumption value of the target vehicle after the trip, and generating a vehicle energy consumption prediction detection result of the target vehicle that meets preset validity conditions when the actual energy consumption value is less than or equal to the final energy consumption prediction value.

[0007] Optionally, in one embodiment of the present invention, obtaining the target driving path information of the target vehicle includes: obtaining the target travel information of the target vehicle; inputting the target travel information of the target vehicle into a preset online map path planning model to generate the target driving path information of the target vehicle.

[0008] Optionally, in one embodiment of the present invention, the target driving path information is input into the energy consumption prediction model to generate the initial energy consumption prediction result of the target vehicle, including: inputting the target driving path information into the energy consumption prediction model to determine the target driving condition of the target vehicle; generating the initial energy consumption prediction result of the target vehicle based on the target driving condition, vehicle dynamics model and target energy efficiency characteristics of the target vehicle.

[0009] Optionally, in one embodiment of the present invention, the use of a target GPS simulator that meets preset configuration conditions to obtain the real-time location information of the target vehicle includes: determining the target geographical environment, target driving trajectory and target signal interference intensity of the target vehicle based on the target driving path information, and setting the dynamic driving parameters of the target vehicle; configuring the initial GPS simulator using the target geographical environment, the target driving trajectory, the target signal interference intensity and the dynamic driving parameters to generate the target GPS simulator that meets the preset configuration conditions; and obtaining the real-time location information of the target vehicle using the target GPS simulator that meets the preset configuration conditions.

[0010] Optionally, in one embodiment of the present invention, after generating the vehicle energy consumption prediction test result of the target vehicle that meets the preset validity conditions, it also includes: generating a test report of the vehicle energy consumption prediction based on the initial energy consumption prediction result, the final energy consumption prediction value and the actual energy consumption value; sending the test report to a preset terminal to display the vehicle information, test route, energy consumption prediction data and VCU control strategy evaluation results in the test report on the preset terminal.

[0011] The second aspect of the present invention provides a detection device for vehicle energy consumption prediction, including: an acquisition module for acquiring target driving path information of a target vehicle, and inputting the target driving path information into an energy consumption prediction model to generate an initial energy consumption prediction result of the target vehicle; a determination module for acquiring real-time position information of the target vehicle using a target GPS simulator that meets preset configuration conditions, and continuously updating the energy consumption prediction value of the remaining mileage of the target vehicle in the trip based on the real-time position information to obtain multiple new energy consumption prediction results, and analyzing the initial energy consumption prediction result and the multiple new energy consumption prediction results to determine the final energy consumption prediction value of the target vehicle after the trip; a detection module for acquiring the actual energy consumption value of the target vehicle after the trip, and generating a vehicle energy consumption prediction detection result of the target vehicle that meets preset validity conditions when the actual energy consumption value is less than or equal to the final energy consumption prediction value.

[0012] Optionally, in one embodiment of the present invention, the acquisition module includes: a first acquisition unit, used to obtain the target travel information of the target vehicle; and a first generation unit, used to input the target travel information of the target vehicle into a preset online map path planning model to generate the target driving path information of the target vehicle.

[0013] Optionally, in one embodiment of the present invention, the acquisition module includes: a first determination unit, used to input the target driving path information into the energy consumption prediction model to determine the target driving condition of the target vehicle; and a second generation unit, used to generate the initial energy consumption prediction result of the target vehicle based on the target driving condition, vehicle dynamics model and target energy efficiency characteristics of the target vehicle.

[0014] Optionally, in one embodiment of the present invention, the determination module includes: a second determination unit, used to determine the target geographical environment, target driving trajectory and target signal interference intensity of the target vehicle based on the target driving path information, and set the dynamic driving parameters of the target vehicle; a third generation unit, used to configure the initial GPS simulator using the target geographical environment, the target driving trajectory, the target signal interference intensity and the dynamic driving parameters to generate the target GPS simulator that meets the preset configuration conditions; a second acquisition unit, used to obtain the real-time position information of the target vehicle using the target GPS simulator that meets the preset configuration conditions.

[0015] Optionally, in one embodiment of the present invention, the device of the embodiment of the present invention further includes: a generation module for generating a vehicle energy consumption prediction test result of the target vehicle that meets the preset validity conditions, and then generating a vehicle energy consumption prediction test report based on the initial energy consumption prediction result, the final energy consumption prediction value and the actual energy consumption value; a sending module for sending the test report to a preset terminal after generating the vehicle energy consumption prediction test result of the target vehicle that meets the preset validity conditions, so as to display the vehicle information, test route, energy consumption prediction data and VCU control strategy evaluation results in the test report on the preset terminal.

[0016] A third aspect of the present invention provides a vehicle, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle energy consumption prediction detection method as described in the above embodiment.

[0017] A fourth aspect of the present invention provides a computer-readable storage medium, which stores a computer program. When the program is executed by a processor, it implements the above-mentioned vehicle energy consumption prediction detection method.

[0018] A fifth aspect of the present invention provides a computer program product, comprising a computer program, which, when executed, is used to implement the above-mentioned detection method for predicting vehicle energy consumption.

[0019] The embodiment of the present invention can input the target driving path information of the target vehicle into the energy consumption prediction model to generate the initial energy consumption prediction result of the target vehicle, and then continuously update the energy consumption prediction value of the remaining mileage of the target vehicle in the trip using the real-time position information of the target vehicle obtained by the target GPS simulator that meets the preset configuration conditions to obtain multiple new energy consumption prediction results, and analyze the initial energy consumption prediction result and the multiple new energy consumption prediction results to determine the final energy consumption prediction value of the target vehicle after the trip ends, and generate the vehicle energy consumption prediction detection result of the target vehicle that meets the preset validity conditions when the actual energy consumption value is less than or equal to the final energy consumption prediction value, thereby effectively improving the accuracy and reliability of the vehicle energy consumption prediction. Thus, the problems that the vehicle energy consumption prediction in the related art fails to fully consider the various factors that change in real time, cannot truly reflect the energy consumption of the vehicle during actual driving, and cannot accurately predict the vehicle energy consumption under complex conditions, thereby reducing the accuracy and reliability of the vehicle energy consumption prediction, etc.

[0020] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0022] Figure 1 A schematic diagram of a vehicle energy consumption prediction detection system provided according to an embodiment of the present invention;

[0023] Figure 2 A flowchart of a detection method for vehicle energy consumption prediction according to an embodiment of the present invention;

[0024] Figure 3 This is a logic diagram of online map path planning according to a specific embodiment of the present invention;

[0025] Figure 4 A vehicle energy consumption prediction logic diagram according to a specific embodiment of the present invention;

[0026] Figure 5 A schematic diagram of a GPS simulator configuration process according to a specific embodiment of the present invention;

[0027] Figure 6 A schematic diagram of a driver and vehicle model constructed in a HIL test bench according to a specific embodiment of the present invention;

[0028] Figure 7 A schematic diagram of a detection implementation process of vehicle energy consumption prediction according to a specific embodiment of the present invention;

[0029] Figure 8 A schematic structural diagram of a detection device for predicting vehicle energy consumption according to an embodiment of the present invention;

[0030] Figure 9 A schematic structural diagram of a vehicle provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The following describes embodiments of the present invention in detail, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and are not to be construed as limiting the present invention.

[0032] The following describes a detection method, device, vehicle and storage medium for vehicle energy consumption prediction according to an embodiment of the present invention with reference to the accompanying drawings. In view of the problem that the vehicle energy consumption prediction in the related art mentioned in the above background technology fails to fully consider multiple factors that change in real time, cannot accurately predict vehicle energy consumption under complex circumstances, and reduces the accuracy and reliability of vehicle energy consumption prediction, the present invention provides a detection method for vehicle energy consumption prediction, in which the target driving path information of the target vehicle can be input into the energy consumption prediction model to generate an initial energy consumption prediction result of the target vehicle, then, the real-time position information of the target vehicle obtained by the target GPS simulator that meets the preset configuration conditions is used to continuously update the energy consumption prediction value of the target vehicle for the remaining mileage in the trip to obtain multiple new energy consumption prediction results, and analyze the initial energy consumption prediction result and the multiple new energy consumption prediction results to determine the final energy consumption prediction value of the target vehicle after the trip, and when the actual energy consumption value is less than or equal to the final energy consumption prediction value, generate a vehicle energy consumption prediction detection result of the target vehicle that meets the preset validity conditions, effectively improving the accuracy and reliability of the vehicle energy consumption prediction. This solves the problem that the vehicle energy consumption prediction in the related technology fails to fully consider multiple factors that change in real time, cannot accurately predict vehicle energy consumption under complex circumstances, and reduces the accuracy and reliability of vehicle energy consumption prediction.

[0033] like Figure 1 As shown, the present invention establishes a vehicle energy consumption prediction detection system, which includes: a GPS simulator, a HIL (Hardware-in-the-Loop Test Bench) test bench, a host computer, an industrial computer, and a VCU (Vehicle Control Unit). These components work together to simulate a real driving environment and verify the performance of the VCU control strategy.

[0034] Among them, the GPS simulator is used to simulate the position changes of the vehicle during actual operation; the online map path planning model and energy consumption prediction model are preset in the industrial computer to plan the optimal path and energy consumption prediction; the driver model and vehicle model are preset in the HIL test bench to simulate the driver's behavior pattern and vehicle dynamic response; the host computer is mainly responsible for recording and storing relevant data; the VCU converts the received information into specific control commands to drive the vehicle movement.

[0035] During the hardware-in-the-loop test, the system combines online maps and GPS simulators to predict the energy consumption of new energy vehicles. By comparing the actual energy consumption calculated with the vehicle model, the rationality and effectiveness of the VCU control strategy are verified, which can effectively avoid the problem of experimental resource limitations and save development cycles.

[0036] Specifically, Figure 2 A flow chart of a method for detecting vehicle energy consumption prediction provided by an embodiment of the present invention.

[0037] like Figure 2 As shown, the detection method for vehicle energy consumption prediction includes the following steps:

[0038] In step S201 , target driving path information of a target vehicle is obtained, and the target driving path information is input into an energy consumption prediction model to generate an initial energy consumption prediction result of the target vehicle.

[0039] In the embodiment of the present invention, the target vehicle is a vehicle for simulation testing; and the target driving path information is optimal driving path information.

[0040] It can be understood that the embodiment of the present invention can obtain the target driving path information of the target vehicle. For example, the optimal driving path information is determined through the online map path planning model in the following steps, and the optimal driving path information is input into the energy consumption prediction model to generate the initial energy consumption prediction result of the target vehicle, so that the energy consumption of the vehicle can be preliminarily predicted, which effectively improves the feasibility of the energy consumption prediction of the detection vehicle.

[0041] Among them, in one embodiment of the present invention, obtaining the target driving path information of the target vehicle includes: obtaining the target travel information of the target vehicle; inputting the target travel information of the target vehicle into a preset online map path planning model to generate the target driving path information of the target vehicle.

[0042] During the actual implementation process, the embodiment of the present invention can establish an online map path planning model through the industrial computer of the above-mentioned system, which can include a data management module, a path calculation module, a user interface module, a real-time update module, a performance optimization module, a security and privacy protection module, and a testing and maintenance module. By obtaining information such as the route starting point, destination, waypoints, and driving selection strategy input by the user, multiple driving routes are automatically generated, and the optimal driving route is determined to ensure the provision of efficient, accurate and safe path planning services.

[0043] In an embodiment of the present invention, the target trip information may be a vehicle's route starting point, destination, waypoints, and target driving strategy.

[0044] For example, Figure 3 The figure below shows the logic diagram for online map route planning. The online map route planning model integrates advanced GIS (Geographic Information System) and internet technologies to provide the optimal route from origin to destination. First, enter your departure point, destination, and waypoints, and select an appropriate driving strategy, such as speed priority, distance priority, or cost priority. Then, based on the online map and taking into account factors such as distance, time, and real-time traffic conditions, the system calculates multiple recommended routes and provides detailed navigation instructions in a graphical interface. Throughout the journey, the system dynamically adjusts the route based on real-time location and the latest traffic data provided by the GPS simulator, ensuring that users reach their destination efficiently and smoothly.

[0045] Among them, in one embodiment of the present invention, the target driving path information is input into the energy consumption prediction model to generate the initial energy consumption prediction result of the target vehicle, including: inputting the target driving path information into the energy consumption prediction model to determine the target driving condition of the target vehicle; generating the initial energy consumption prediction result of the target vehicle based on the target driving condition of the target vehicle, the vehicle dynamics model and the target energy efficiency characteristics.

[0046] In an embodiment of the present invention, an energy consumption prediction model can be built through the industrial computer of the above-mentioned system, including a signal receiving module, a driving condition generation module, an energy consumption prediction module and an energy consumption correction module. By receiving online map information, the driving condition is generated, and the required energy consumption is predicted based on the driving condition combined with the vehicle signal.

[0047] For example, Figure 4The following figure shows the vehicle energy consumption prediction logic diagram. After receiving route planning information from an online map, the system first generates the vehicle's driving conditions for that route based on the specific conditions of the journey, such as distance, estimated travel time, road type, traffic flow, and weather. Subsequently, using this driving condition data, combined with the vehicle's dynamic model and energy efficiency characteristics, the system predicts the vehicle's energy consumption during the journey, effectively improving the accuracy of vehicle energy consumption predictions.

[0048] It's important to note that this embodiment of the present invention takes into account various uncertainties that may arise during actual driving (such as unexpected traffic jams or weather changes). The system also dynamically adjusts and corrects the initial energy consumption forecast to ensure that the forecast is as close to actual conditions as possible. Finally, the energy consumption forecast, after this series of sophisticated processing, is output to a display interface, providing a more intuitive and real-time view of the forecast results.

[0049] In step S202, the real-time location information of the target vehicle is obtained using a target GPS simulator that meets the preset configuration conditions, and the energy consumption prediction value of the target vehicle for the remaining mileage in the trip is continuously updated based on the real-time location information to obtain multiple new energy consumption prediction results, and the initial energy consumption prediction result and the multiple new energy consumption prediction results are analyzed to determine the final energy consumption prediction value of the target vehicle after the trip.

[0050] In the embodiment of the present invention, the preset configuration condition is a condition for performing corresponding configuration of the GPS simulator according to the planned driving path information.

[0051] It can be understood that the embodiment of the present invention can use a target GPS simulator that meets the configuration conditions to obtain the real-time location information of the target vehicle, so as to obtain the latest road information in real time, and continuously update the energy consumption prediction value of the remaining mileage based on the real-time location information, continuously obtain new energy consumption prediction results, and after the vehicle is completed, all energy consumption prediction results are summarized and analyzed to determine the final energy consumption prediction value of the target vehicle after the trip, that is, when the target vehicle arrives at the destination, the final predicted energy consumption value of the entire journey is generated and displayed, which effectively improves the real-time and accuracy of the vehicle energy consumption prediction.

[0052] Among them, in one embodiment of the present invention, the real-time position information of the target vehicle is obtained by using a target GPS simulator that meets the preset configuration conditions, including: determining the target geographical environment, target driving trajectory and target signal interference intensity of the target vehicle based on the target driving path information, and setting the dynamic driving parameters of the target vehicle; configuring the initial GPS simulator using the target geographical environment, target driving trajectory, target signal interference intensity and dynamic driving parameters to generate a target GPS simulator that meets the preset configuration conditions; and obtaining the real-time position information of the target vehicle using the target GPS simulator that meets the preset configuration conditions.

[0053] In an embodiment of the present invention, the GPS simulator of the above-mentioned system can be configured to configure relevant scenarios and generate trajectories based on the generated driving path information; at the same time, the dynamic parameters of the vehicle, such as speed, acceleration, direction, etc., can be set; finally, factors such as signal strength interference can be adjusted to simulate signal strength changes in different environments.

[0054] For example, if Figure 5 Figure 2 shows the GPS simulator configuration process. When configuring the GPS simulator, you first need to set up a scenario. This involves introducing route planning information to create a simulated geographical environment, including city streets, highways, or mountain roads. Weather conditions, such as sunny, rainy, and foggy, are also included. These settings ensure that the test environment is as close to real-world scenarios as possible, thereby improving test effectiveness.

[0055] Next, we set up the trajectory. By importing the planned road data, we create a driving trajectory, including the starting point, destination, and waypoints, which is completely consistent with the planned route, thus simulating complex and realistic driving routes. These routes can be used to simulate vehicle driving under different road conditions, helping to test the system's performance under various driving conditions. Through detailed trajectory settings, we can ensure that the test covers all possible driving scenarios.

[0056] Next, configure dynamic parameters. This involves defining the vehicle's speed profile, including acceleration, deceleration, constant speed, and variable speed driving, to simulate different driving behaviors. Furthermore, set the vehicle's driving direction, including turns and lane changes, to simulate complex driving maneuvers. Furthermore, set the simulation start time and duration to ensure smooth execution of the test plan. By setting these dynamic parameters, the GPS simulator generates realistic driving data, enabling comprehensive testing of the VCU under test.

[0057] Finally, adjusting signal conditions involves adjusting signal strength to simulate signal reception in different environments, such as urban areas with tall buildings versus open rural areas. Setting multipath parameters to simulate the effects of signal reflection and refraction is particularly important for testing system performance in complex environments. Furthermore, interference from other radio signals can be introduced to test the system's interference resistance. By adjusting these signal conditions, test results more closely resemble real-world conditions, thereby improving test reliability and effectiveness.

[0058] In step S203, the actual energy consumption value of the target vehicle after the trip is obtained, and when the actual energy consumption value is less than or equal to the final energy consumption prediction value, a vehicle energy consumption prediction detection result of the target vehicle that meets the preset validity conditions is generated.

[0059] In the embodiment of the present invention, the preset validity condition is a condition that the VCU control strategy is reasonable and effective.

[0060] It can be understood that the embodiment of the present invention can obtain the actual energy consumption value of the target vehicle after the end of the trip. For example, the actual energy consumption value of the target vehicle after the end of the trip is calculated through the relevant parameters of the vehicle model built on the HIL test bench, and when the actual energy consumption value is less than or equal to the final energy consumption prediction value, a vehicle energy consumption prediction detection result of the target vehicle that meets the preset validity conditions is generated, that is, the VCU control strategy is reasonable and effective and does not need to be adjusted; for example, the embodiment of the present invention can evaluate and verify the rationality and effectiveness of the VCU control strategy based on the comparison results of the actual energy consumption value and the final energy consumption prediction value. The smaller the actual energy consumption value is than the final energy consumption prediction value, the more reasonable and effective the VCU control strategy is.

[0061] In this embodiment of the present invention, a driver model and a vehicle model can be built using the HIL test bench of the aforementioned system. The driver model is designed to mimic the behavior of a real driver, including reaction time and acceleration / deceleration pedal opening. The vehicle model includes a dynamics model, an electric drive system model, a battery management system model, a braking system model, a tire model, an aerodynamics model, and a thermal management model. Data signals are exchanged based on the energy transmission relationship between the models, and the vehicle model is used to simulate the various characteristics of a real vehicle.

[0062] For example, Figure 6Figure 2 shows a schematic diagram of the driver model and vehicle model built in the HIL test bench. The driver model can be considered a technical means of simulating driving behavior, imitating how a real driver adjusts operations such as steering, acceleration, or deceleration based on the vehicle's current operating conditions. Specifically, the driver model receives information about the vehicle's current actual state (such as speed and direction) and compares it with the desired target state to identify the difference between the two. This error is then processed through a PI (Proportional-Integral) control mechanism, converted into specific control commands (such as adjusting the throttle opening or changing the steering wheel angle). These commands are then sent back to the vehicle model for execution, achieving precise control of the vehicle's state. The entire process forms a continuous feedback loop that ensures the vehicle drives according to the predetermined target state.

[0063] The vehicle model also includes a dynamics model covering longitudinal, lateral, and vertical dynamics; an electric drive system model encompassing the motor, inverter, and controller; a battery management system model covering battery characteristics, thermal management, and charging; a braking system model covering regenerative braking, hydraulic braking, and brake fusion; a tire model covering rolling resistance, cornering force, and grip; an aerodynamic model covering drag coefficient and lift coefficient; and a thermal management model covering motor and inverter heat dissipation, as well as battery pack heat dissipation. The combined performance of these subsystems directly impacts the overall vehicle performance. The control strategy implemented by the vehicle's VCU, combined with input from external operating conditions, effectively enables vehicle energy consumption testing.

[0064] Optionally, in one embodiment of the present invention, after generating a vehicle energy consumption prediction test result of the target vehicle that meets preset validity conditions, it also includes: generating a vehicle energy consumption prediction test report based on the initial energy consumption prediction result, the final energy consumption prediction value and the actual energy consumption value; sending the test report to a preset terminal to display the vehicle information, test route, energy consumption prediction data and VCU control strategy evaluation results in the test report on the preset terminal.

[0065] In some embodiments, the embodiments of the present invention can generate a vehicle energy consumption prediction test report based on the initial energy consumption prediction result at the time of vehicle departure, the updated final energy consumption prediction value, and the actual energy consumption value after the vehicle actually drives. The report content may include vehicle information (such as model, battery status, etc.), test route details, changes in energy consumption prediction, and evaluation results of the vehicle VCU control strategy, etc., and the test report is displayed on the technician's computer or mobile phone, so that the technician can clearly see the difference between the prediction and the actual situation, and can understand the accuracy of the vehicle energy consumption prediction.

[0066] For example, if Figure 7The figure below is a schematic diagram of the vehicle energy consumption prediction testing and implementation process. First, a cloud-based online map is used to plan and generate a route. The GPS simulator is then configured based on the planned route information, including the scenario, trajectory, dynamic parameters, and related signals. Energy consumption is then predicted using the route planning information combined with vehicle-related parameters. Simultaneously, the driver and vehicle models test the VCU's energy management control strategy based on these planned routes. During operation, the route is dynamically adjusted based on the real-time location and latest traffic data provided by the GPS simulator, achieving dynamic updates. At the end of the run, the overall energy consumption prediction is displayed. By comparing it with the actual energy consumption calculated based on the vehicle model's related parameters, the rationality and effectiveness of the VCU control strategy are evaluated and verified. The closer the calculated value is to or smaller than the predicted value, the more rational and effective the VCU control strategy is, and vice versa.

[0067] The embodiment of the present invention introduces a GPS simulator to simulate geographic location, speed, time and other navigation parameters, which can simulate various driving conditions and environmental changes, and can complete the prediction and calculation of vehicle energy consumption without actual road tests, thereby greatly improving the test efficiency. In addition, the driving conditions generated after online map path planning are also introduced. The online map path planning can generate test conditions according to the specific driving route and traffic conditions, making the test closer to the real driving environment, and can generate a variety of driving scenarios, including urban roads, highways, rural roads, etc., to comprehensively evaluate the accuracy of the energy consumption prediction model and the vehicle's energy consumption performance. The embodiment of the present invention can verify the rationality and effectiveness of the VCU control strategy, and can replace the actual vehicle verification and calibration of the vehicle energy management system performance, so as to better optimize the energy management system and control strategy.

[0068] According to the detection method for vehicle energy consumption prediction proposed in an embodiment of the present invention, the target driving path information of the target vehicle can be input into the energy consumption prediction model to generate the initial energy consumption prediction result of the target vehicle. Then, the real-time position information of the target vehicle obtained by the target GPS simulator that meets the preset configuration conditions is used to continuously update the energy consumption prediction value of the target vehicle for the remaining mileage in the trip to obtain multiple new energy consumption prediction results, and the initial energy consumption prediction result and the multiple new energy consumption prediction results are analyzed to determine the final energy consumption prediction value of the target vehicle after the trip ends. When the actual energy consumption value is less than or equal to the final energy consumption prediction value, a vehicle energy consumption prediction detection result that meets the preset effective conditions is generated for the target vehicle, thereby effectively improving the accuracy and reliability of the vehicle energy consumption prediction. Thus, the problem that the vehicle energy consumption prediction in the related art fails to fully consider the various factors that change in real time, cannot accurately predict the vehicle energy consumption under complex circumstances, and reduces the accuracy and reliability of the vehicle energy consumption prediction is solved.

[0069] Next, the detection device for predicting vehicle energy consumption according to an embodiment of the present invention will be described with reference to the accompanying drawings.

[0070] Figure 8 It is a block diagram of a detection device for predicting vehicle energy consumption according to an embodiment of the present invention.

[0071] like Figure 8 As shown, the vehicle energy consumption prediction detection device 10 includes: an acquisition module 100, a determination module 200 and a detection module 300.

[0072] Specifically, the acquisition module 100 is used to acquire target driving path information of the target vehicle and input the target driving path information into the energy consumption prediction model to generate an initial energy consumption prediction result of the target vehicle.

[0073] The determination module 200 is used to obtain the real-time location information of the target vehicle using a target GPS simulator that meets preset configuration conditions, and continuously update the energy consumption prediction value of the target vehicle for the remaining mileage during the trip based on the real-time location information to obtain multiple new energy consumption prediction results, and analyze the initial energy consumption prediction result and the multiple new energy consumption prediction results to determine the final energy consumption prediction value of the target vehicle after the trip.

[0074] The detection module 300 is used to obtain the actual energy consumption value of the target vehicle after the trip, and when the actual energy consumption value is less than or equal to the final energy consumption prediction value, generate a vehicle energy consumption prediction detection result of the target vehicle that meets the preset validity conditions.

[0075] Optionally, in one embodiment of the present invention, the acquisition module 100 includes: a first acquisition unit and a first generation unit.

[0076] The first acquisition unit is used to acquire target travel information of the target vehicle.

[0077] The first generating unit is used to input the target travel information of the target vehicle into a preset online map path planning model to generate the target driving path information of the target vehicle.

[0078] Optionally, in one embodiment of the present invention, the acquisition module 100 includes: a first determination unit and a second generation unit.

[0079] The first determination unit is configured to input the target driving path information into the energy consumption prediction model to determine the target driving condition of the target vehicle.

[0080] The second generating unit is used to generate an initial energy consumption prediction result of the target vehicle according to the target driving condition, vehicle dynamics model and target energy efficiency characteristics of the target vehicle.

[0081] Optionally, in one embodiment of the present invention, the determination module 200 includes: a second determination unit, a third generation unit and a second acquisition unit.

[0082] Among them, the second determination unit is used to determine the target geographical environment, target driving trajectory and target signal interference intensity of the target vehicle according to the target driving path information, and set the dynamic driving parameters of the target vehicle.

[0083] The third generating unit is used to configure the initial GPS simulator using the target geographical environment, the target driving trajectory, the target signal interference intensity and the dynamic driving parameters to generate a target GPS simulator that meets the preset configuration conditions.

[0084] The second acquisition unit is used to acquire the real-time position information of the target vehicle by using a target GPS simulator that meets preset configuration conditions.

[0085] Optionally, in one embodiment of the present invention, the apparatus 10 of the embodiment of the present invention further includes: a generating module and a sending module.

[0086] Among them, the generation module is used to generate a vehicle energy consumption prediction test report based on the initial energy consumption prediction result, the final energy consumption prediction value and the actual energy consumption value after generating the vehicle energy consumption prediction test result of the target vehicle that meets the preset validity conditions.

[0087] The sending module is used to send the test report to the preset terminal after generating the vehicle energy consumption prediction test results of the target vehicle that meet the preset validity conditions, so as to display the vehicle information, test route, energy consumption prediction data and vehicle control unit control strategy evaluation results in the test report on the preset terminal.

[0088] It should be noted that the above explanation of the embodiment of the detection method for vehicle energy consumption prediction is also applicable to the detection device for vehicle energy consumption prediction of this embodiment, and will not be repeated here.

[0089] According to the detection device for predicting vehicle energy consumption proposed in an embodiment of the present invention, the target driving path information of the target vehicle can be input into the energy consumption prediction model to generate the initial energy consumption prediction result of the target vehicle. Then, the real-time position information of the target vehicle obtained by the target GPS simulator that meets the preset configuration conditions is used to continuously update the energy consumption prediction value of the target vehicle for the remaining mileage in the trip to obtain multiple new energy consumption prediction results, and the initial energy consumption prediction result and the multiple new energy consumption prediction results are analyzed to determine the final energy consumption prediction value of the target vehicle after the trip ends. When the actual energy consumption value is less than or equal to the final energy consumption prediction value, a vehicle energy consumption prediction detection result that meets the preset effective conditions is generated for the target vehicle, thereby effectively improving the accuracy and reliability of the vehicle energy consumption prediction. Thus, the problem that the vehicle energy consumption prediction in the related art fails to fully consider the various factors that change in real time, cannot accurately predict the vehicle energy consumption under complex circumstances, and reduces the accuracy and reliability of the vehicle energy consumption prediction is solved.

[0090] Figure 9 A schematic diagram of the structure of a vehicle provided in an embodiment of the present invention. The vehicle may include:

[0091] A memory 901 , a processor 902 , and a computer program stored in the memory 901 and executable on the processor 902 .

[0092] When the processor 902 executes the program, the detection method for vehicle energy consumption prediction provided in the above embodiment is implemented.

[0093] Furthermore, the vehicle further comprises:

[0094] The communication interface 903 is used for communication between the memory 901 and the processor 902 .

[0095] The memory 901 is used to store computer programs that can be run on the processor 902 .

[0096] The memory 901 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0097] If the memory 901, processor 902, and communication interface 903 are implemented independently, the communication interface 903, memory 901, and processor 902 can be connected to each other via a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 9 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0098] Optionally, in a specific implementation, if the memory 901, the processor 902 and the communication interface 903 are integrated on a chip, the memory 901, the processor 902 and the communication interface 903 can communicate with each other through an internal interface.

[0099] The processor 902 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0100] This embodiment also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned vehicle energy consumption prediction detection method.

[0101] This embodiment also provides a computer program product, including a computer program. When the computer program is executed, it is used to implement the above-mentioned vehicle energy consumption prediction detection method.

[0102] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples without contradiction.

[0103] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "N" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0104] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or N executable instructions for implementing a custom logical function or step of a process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0105] The logic and / or steps represented in a flowchart or otherwise described herein, for example, can be considered a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" is any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (not exhaustive) of computer-readable media include: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program can be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing it in other suitable ways as necessary, and then storing it in a computer memory.

[0106] It should be understood that various components of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiment, the N steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, it can be implemented using any one or a combination of the following technologies known in the art: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having suitable combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0107] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0108] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing module, or each unit may exist physically separately, or two or more units may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or in the form of software functional modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium.

[0109] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and are not to be construed as limiting the present invention. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A detection method for predicting vehicle energy consumption, characterized in that: The following steps are involved: Obtaining target driving path information of a target vehicle, and inputting the target driving path information into an energy consumption prediction model to generate an initial energy consumption prediction result of the target vehicle, wherein inputting the target driving path information into the energy consumption prediction model to generate the initial energy consumption prediction result of the target vehicle comprises: inputting the target driving path information into the energy consumption prediction model to determine a target driving condition of the target vehicle; generating the initial energy consumption prediction result of the target vehicle based on the target driving condition, a vehicle dynamics model, and a target energy efficiency characteristic of the target vehicle; Utilize a target global positioning system simulator that meets preset configuration conditions to obtain the real-time location information of the target vehicle, and continuously update the energy consumption prediction value of the remaining mileage of the target vehicle in the trip based on the real-time location information to obtain multiple new energy consumption prediction results, and analyze the initial energy consumption prediction result and the multiple new energy consumption prediction results to determine the final energy consumption prediction value of the target vehicle after the trip ends, wherein the use of the target global positioning system simulator that meets the preset configuration conditions to obtain the real-time location information of the target vehicle includes: determining the target geographical environment, target driving trajectory and target signal interference intensity of the target vehicle based on the target driving path information, and setting the dynamic driving parameters of the target vehicle; configuring an initial global positioning system simulator using the target geographical environment, the target driving trajectory, the target signal interference intensity and the dynamic driving parameters to generate the target global positioning system simulator that meets the preset configuration conditions; and obtaining the real-time location information of the target vehicle using the target global positioning system simulator that meets the preset configuration conditions; Obtaining an actual energy consumption value of the target vehicle after the trip ends, and generating a vehicle energy consumption prediction detection result of the target vehicle that meets preset validity conditions when the actual energy consumption value is less than or equal to the final energy consumption prediction value; The step of obtaining target driving path information of the target vehicle includes: Obtaining target trip information of the target vehicle, wherein the target trip information includes a vehicle route starting point, a destination, waypoints, and a target driving strategy; Inputting the target travel information of the target vehicle into a preset online map path planning model to generate the target driving path information of the target vehicle; After generating the vehicle energy consumption prediction test result of the target vehicle that meets the preset validity conditions, the method further includes: Generating a detection report of the vehicle energy consumption prediction based on the initial energy consumption prediction result, the final energy consumption prediction value and the actual energy consumption value; The test report is sent to a preset terminal to display the vehicle information, test route, energy consumption prediction data and vehicle control unit control strategy evaluation results in the test report on the preset terminal.

2. A detection device for predicting vehicle energy consumption, characterized in that: include: an acquisition module, configured to acquire target driving path information of a target vehicle and input the target driving path information into an energy consumption prediction model to generate an initial energy consumption prediction result of the target vehicle, wherein inputting the target driving path information into the energy consumption prediction model to generate the initial energy consumption prediction result of the target vehicle comprises: inputting the target driving path information into the energy consumption prediction model to determine a target driving condition of the target vehicle; and generating the initial energy consumption prediction result of the target vehicle based on the target driving condition, a vehicle dynamics model, and a target energy efficiency characteristic of the target vehicle; A determination module is used to obtain the real-time location information of the target vehicle using a target global positioning system simulator that meets preset configuration conditions, and continuously update the energy consumption prediction value of the remaining mileage of the target vehicle in the trip based on the real-time location information to obtain multiple new energy consumption prediction results, and analyze the initial energy consumption prediction result and the multiple new energy consumption prediction results to determine the final energy consumption prediction value of the target vehicle after the trip ends, wherein the use of the target global positioning system simulator that meets the preset configuration conditions to obtain the real-time location information of the target vehicle includes: determining the target geographical environment, target driving trajectory and target signal interference intensity of the target vehicle based on the target driving path information, and setting the dynamic driving parameters of the target vehicle; configuring an initial global positioning system simulator using the target geographical environment, the target driving trajectory, the target signal interference intensity and the dynamic driving parameters to generate the target global positioning system simulator that meets the preset configuration conditions; and obtaining the real-time location information of the target vehicle using the target global positioning system simulator that meets the preset configuration conditions; a detection module, configured to obtain an actual energy consumption value of the target vehicle after the trip ends, and, if the actual energy consumption value is less than or equal to the final energy consumption prediction value, generate a vehicle energy consumption prediction detection result of the target vehicle that meets a preset validity condition; The acquisition module includes: an acquiring unit, configured to acquire target trip information of the target vehicle, wherein the target trip information includes a vehicle route starting point, a destination, waypoints, and a target driving strategy; a generating unit, configured to input the target travel information of the target vehicle into a preset online map path planning model to generate the target driving path information of the target vehicle; After generating the vehicle energy consumption prediction test result of the target vehicle that meets the preset validity conditions, the method further includes: Generating a detection report of the vehicle energy consumption prediction based on the initial energy consumption prediction result, the final energy consumption prediction value and the actual energy consumption value; The test report is sent to a preset terminal to display the vehicle information, test route, energy consumption prediction data and vehicle control unit control strategy evaluation results in the test report on the preset terminal.

3. A vehicle, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle energy consumption prediction detection method according to claim 1.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the vehicle energy consumption prediction detection method as claimed in claim 1.

5. A computer program product comprising a computer program, characterized in that The computer program is executed by a processor to implement the vehicle energy consumption prediction detection method as claimed in claim 1.

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