Detection method and device for vehicle energy consumption prediction, vehicle and storage medium

Abstract

The invention relates to the technical field of new energy automobile testing, in particular to a vehicle energy consumption prediction detection method and device, a vehicle and a storage medium, and the method comprises the steps: 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 predicted value of the remaining mileage of the vehicle in the journey by using the real-time position information of the vehicle obtained by the GPS simulator meeting the configuration condition so as to obtain a plurality of new energy consumption predicted results, and analyzing all the energy consumption predicted results so as to determine the final energy consumption predicted value of the vehicle after the end of the journey; and under the condition that the actual energy consumption value is smaller than or equal to the final energy consumption prediction value, generating a vehicle energy consumption prediction detection result, meeting effective conditions, of the vehicle. Therefore, the problems that in the prior art, multiple factors changing in real time are not fully considered, accurate prediction of the vehicle energy consumption cannot be conducted under the complex condition, and the accuracy and reliability of vehicle energy consumption prediction are reduced are 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 predicting vehicle energy consumption. Background Art

[0002] With the increasing global awareness of environmental protection and the increasingly urgent need for energy structure transformation, the new energy vehicle market is experiencing unprecedented rapid development. As an important part of future transportation, new energy vehicles not only help reduce greenhouse gas emissions and improve urban air quality, but also promote the effective use of renewable energy. In this context, improving vehicle energy efficiency has become one of the key goals pursued by major automakers. The improvement of energy efficiency not only means longer driving range and lower operating costs, but also a concrete manifestation of corporate competitiveness and social responsibility. Therefore, from battery technology, power system design to vehicle lightweighting and other aspects, manufacturers are constantly exploring innovations, striving to occupy a favorable position in the fierce market competition. At the same time, the support of government policies also provides a strong guarantee for the advancement of new energy vehicle energy efficiency technology, and promotes the entire industry to develop in a greener and smarter direction.

[0003] In related technologies, the energy consumption of electric vehicles can be predicted 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 the related technology fails to fully consider the various factors that change in real time, cannot truly reflect the vehicle energy consumption during actual driving, and cannot accurately predict the vehicle energy consumption under complex circumstances, which reduces the accuracy and reliability of vehicle energy consumption prediction and needs to be solved urgently. Summary of the invention

[0005] The present invention provides a detection method, device, vehicle and storage medium for predicting vehicle energy consumption, so as to solve the problems that the vehicle energy consumption prediction in the related technology 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 the vehicle energy consumption under complex circumstances, thereby reducing the accuracy and reliability of the vehicle energy consumption prediction.

[0006] An embodiment of the first aspect of the present invention provides a detection method for vehicle energy consumption prediction, including 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; using a target GPS (Global Positioning System) emulator that meets preset configuration conditions to obtain real-time position information of the target vehicle, and continuously updating the energy consumption prediction value of the remaining mileage of the target vehicle during the journey according to 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 a final energy consumption prediction value of the target vehicle after the journey ends; obtaining the actual energy consumption value of the target vehicle after the journey ends, and generating a vehicle energy consumption prediction detection result that meets preset effective conditions for the target vehicle when the actual energy consumption value is less than or equal to the final energy consumption prediction value.

[0007] Optionally, in an embodiment of the present invention, the obtaining target driving path information of the target vehicle includes: obtaining target journey information of the target vehicle; inputting the target journey 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 an embodiment of the present invention, the inputting the target driving path information into an energy consumption prediction model to generate an initial energy consumption prediction result of the target vehicle includes: 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 according to the target driving condition of the target vehicle, a vehicle dynamics model, and target energy efficiency characteristics.

[0009] Optionally, in an embodiment of the present invention, the using a target GPS emulator that meets preset configuration conditions to obtain real-time position information of the target vehicle includes: determining a target geographical environment, a target driving trajectory, and a target signal interference intensity of the target vehicle according to the target driving path information, and setting dynamic driving parameters of the target vehicle; configuring the initial GPS emulator with the target geographical environment, the target driving trajectory, the target signal interference intensity, and the dynamic driving parameters to generate the target GPS emulator that meets the preset configuration conditions; using the target GPS emulator that meets the preset configuration conditions to obtain the real-time position information of the target vehicle.

[0010] Optionally, in an embodiment of the present invention, after generating the vehicle energy consumption prediction detection result of the target vehicle that meets the preset valid conditions, the method further includes: generating a detection report of the vehicle energy consumption prediction according to the initial energy consumption prediction result, the final energy consumption prediction value, and the actual energy consumption value; and sending the detection report to a preset terminal to display vehicle information, a test route, energy consumption prediction data, and a VCU control strategy evaluation result in the detection report on the preset terminal.

[0011] An embodiment of the second aspect of the present invention provides a detection device for vehicle energy consumption prediction, including: 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; a determination module, configured to use a target GPS simulator that meets preset configuration conditions to acquire real-time position information of the target vehicle, continuously update an energy consumption prediction value of the remaining mileage of the target vehicle during the journey according to the real-time position information to obtain a plurality of new energy consumption prediction results, and analyze the initial energy consumption prediction result and the plurality of new energy consumption prediction results to determine a final energy consumption prediction value of the target vehicle after the journey ends; a detection module, configured to acquire an actual energy consumption value of the target vehicle after the journey ends, and generate a vehicle energy consumption prediction detection result of the target vehicle that meets preset valid conditions when the actual energy consumption value is less than or equal to the final energy consumption prediction value.

[0012] Optionally, in an embodiment of the present invention, the acquisition module includes: a first acquisition unit, configured to acquire target journey information of the target vehicle; a first generation unit, configured to input the target journey 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 an embodiment of the present invention, the acquisition module includes: a first determination unit, configured to input the target driving path information into the energy consumption prediction model to determine a target driving condition of the target vehicle; a second generation unit, configured to generate the initial energy consumption prediction result of the target vehicle according to the target driving condition of the target vehicle, a vehicle dynamics model, and target energy efficiency characteristics.

[0014] Optionally, in an embodiment of the present invention, the determining module includes: a second determining unit, configured to determine a target geographical environment, a target driving trajectory, and a target signal interference intensity for the target vehicle to travel according to the target driving path information, and set dynamic driving parameters of the target vehicle; a third generating unit, configured to configure the initial GPS simulator by 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 obtaining unit, configured to obtain the real-time position information of the target vehicle by using the target GPS simulator that meets the preset configuration conditions.

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

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

[0017] An embodiment of the fourth aspect of the present invention provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the program is executed by a processor, the detection method for vehicle energy consumption prediction as described above is implemented.

[0018] An embodiment of the fifth aspect of the present invention provides a computer program product, including a computer program, and when the computer program is executed, it is used to implement the detection method for vehicle energy consumption prediction as described above.

[0019] In an embodiment of the present invention, the target driving path information of a target vehicle can be input into an 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 a target GPS simulator that meets the preset configuration conditions is used to continuously update the energy consumption prediction value of the remaining mileage of the target vehicle during the journey, so as 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 journey 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 valid conditions of the target vehicle is generated, effectively improving the accuracy and reliability of vehicle energy consumption prediction. Thus, it solves the problems in the related art that vehicle energy consumption prediction fails to fully consider various factors that change in real time, cannot truly reflect the vehicle energy consumption situation during actual driving, and cannot accurately predict vehicle energy consumption in complex situations, reducing the accuracy and reliability of vehicle energy consumption prediction, etc.

[0020] Additional aspects and advantages of the present invention will be given in part in the following description, become apparent in part from the following description, or be understood through the 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 be readily understood from the following description of the embodiments in conjunction with the drawings, where: Figure 1 is a schematic diagram of a detection system for vehicle energy consumption prediction according to an embodiment of the present invention; Figure 2 is a flowchart of a detection method for vehicle energy consumption prediction according to an embodiment of the present invention; Figure 3 is an online map path planning logic diagram of a specific embodiment of the present invention; Figure 4 is a vehicle energy consumption prediction logic diagram of a specific embodiment of the present invention; Figure 5 is a schematic diagram of the GPS simulator configuration process of a specific embodiment of the present invention; Figure 6 is a schematic diagram of a driver and vehicle model built in a HIL bench of a specific embodiment of the present invention; Figure 7 is a schematic diagram of the detection implementation process of vehicle energy consumption prediction of a specific embodiment of the present invention; Figure 8 is a schematic structural diagram of a detection device for vehicle energy consumption prediction according to an embodiment of the present invention; Figure 9 is a schematic diagram of the structure of a vehicle according to an embodiment of the present invention. Detailed Implementation Modes

[0022] The embodiments of the present invention will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present invention, but should not be construed as limiting the present invention.

[0023] The detection method, device, vehicle and storage medium for vehicle energy consumption prediction according to the embodiments of the present invention will be described below with reference to the accompanying drawings. Aiming at the problems in the related art mentioned in the above background art that the vehicle energy consumption prediction fails to fully consider various factors that change in real time, cannot accurately predict the vehicle energy consumption in complex situations, and reduces the accuracy and reliability of vehicle energy consumption prediction, the present invention provides a detection method for vehicle energy consumption prediction. In this method, 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 remaining mileage of the target vehicle during the journey 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 journey ends, 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 that meets the preset effective conditions of the target vehicle is generated, effectively improving the accuracy and reliability of vehicle energy consumption prediction. Thus, the problems in the related art that the vehicle energy consumption prediction fails to fully consider various factors that change in real time, cannot accurately predict the vehicle energy consumption in complex situations, and reduces the accuracy and reliability of vehicle energy consumption prediction are solved.

[0024] As Figure 1 shown, the present invention establishes a detection system for vehicle energy consumption prediction, 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 cooperate with each other to simulate a real driving environment and verify the performance of the VCU control strategy.

[0025] Among them, the GPS simulator is used to simulate the position change of the vehicle during actual operation; the online map path planning model and the energy consumption prediction model are preset in the industrial computer for planning the best path and energy consumption prediction; the driver model and the vehicle model are preset in the HIL test bench for simulating the behavior mode of the driver and the dynamic response of the vehicle; 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 to move.

[0026] During the hardware-in-the-loop test, the system combines an online map with a GPS emulator to predict the energy consumption of new energy vehicles. By comparing with the actual energy consumption calculated by the vehicle model, it verifies the rationality and effectiveness of the VCU control strategy and can effectively avoid the problem of test resource limitations and save the development cycle.

[0027] Specifically, Figure 2 It is a schematic flowchart of a detection method for predicting vehicle energy consumption provided by an embodiment of the present invention.

[0028] As Figure 2 shown, the detection method for predicting vehicle energy consumption includes the following steps: In step S201, obtain the 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.

[0029] In an embodiment of the present invention, the target vehicle is a vehicle for simulation testing; the target driving path information is the optimal driving path information.

[0030] It can be understood that the embodiment of the present invention can obtain the target driving path information of the target vehicle. For example, determine the optimal driving path information through the online map path planning model in the following steps and input the optimal driving path information into the energy consumption prediction model to generate an initial energy consumption prediction result of the target vehicle, thereby preliminarily predicting the energy consumption of the vehicle and effectively improving the executability of detecting vehicle energy consumption prediction.

[0031] Among them, in an embodiment of the present invention, obtaining the target driving path information of the target vehicle includes: obtaining the target itinerary information of the target vehicle; inputting the target itinerary information of the target vehicle into a preset online map path planning model to generate the target driving path information of the target vehicle.

[0032] In the actual execution process, the embodiment of the present invention can establish an online map path planning model through the industrial control computer of the above 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 test and maintenance module. Automatically generate multiple driving routes by obtaining information such as the starting point, destination, waypoints, and driving selection strategy input by the user, and determine the optimal driving route to ensure the provision of efficient, accurate, and safe path planning services.

[0033] In an embodiment of the present invention, the target itinerary information can be the starting point, destination, waypoints, and target driving strategy of the vehicle.

[0034] For example, as Figure 3As shown in the figure, it is a logic diagram of online map route planning. The online map route planning model integrates advanced GIS (Geographic Information System) and Internet technologies to provide the optimal route plan from the starting point to the end point. First, input the starting point, destination, and waypoints, and select a suitable driving strategy, such as speed priority, distance priority, cost priority, etc. Subsequently, based on the online map and considering factors such as distance, time, and real-time traffic conditions, multiple recommended routes are calculated and detailed navigation guidance is provided in the form of a graphical interface. During the entire journey, the system will also dynamically adjust the route according to the real-time position provided by the GPS emulator and the latest traffic data to ensure that users can reach the destination efficiently and smoothly.

[0035] Among them, in an 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 according to the target driving condition of the target vehicle, the vehicle dynamics model, and the target energy efficiency characteristics.

[0036] In the embodiment of the present invention, the energy consumption prediction model can be built by the industrial control computer of the above 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 according to the driving condition in combination with vehicle signals.

[0037] For example, as Figure 4 shown, it is a logic diagram of vehicle energy consumption prediction. When the system receives the driving route planning information provided by the online map, it will first generate the driving condition of the vehicle on this section according to the specific situation of this journey, such as driving distance, estimated driving time, road type, traffic flow, and weather conditions. Subsequently, using these condition data and combining the vehicle dynamics model and energy efficiency characteristics, the system can predict the energy consumption of the vehicle during the journey, effectively improving the accuracy of vehicle energy consumption prediction.

[0038] It should be noted that in the embodiment of the present invention, considering various uncertainties that may occur during actual driving (such as sudden traffic congestion or weather changes), the system also needs to dynamically adjust and correct the initial energy consumption prediction result to ensure that the predicted value is as close as possible to the actual situation. Finally, the energy consumption prediction value after this series of fine processing will be output to the display interface to more intuitively obtain the real-time prediction result.

[0039] In step S202, a target GPS emulator that meets the preset configuration conditions is used to obtain the real-time position information of the target vehicle, and the energy consumption prediction value of the remaining mileage of the target vehicle during the journey is continuously updated according to the real-time position information, so as 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 journey ends.

[0040] In the embodiment of the present invention, the preset configuration condition is the condition for configuring the GPS emulator according to the planned driving path information.

[0041] It can be understood that the embodiment of the present invention can use a target GPS emulator that meets the configuration conditions to obtain the real-time position information of the target vehicle, so as to obtain the latest road information in real time, continuously update the energy consumption prediction value of the remaining mileage according to the real-time position information, continuously obtain new energy consumption prediction results, and after the vehicle finishes driving, summarize and analyze all the energy consumption prediction results to determine the final energy consumption prediction value of the target vehicle after the journey ends, that is, when the target vehicle reaches the destination, generate and display the predicted energy consumption value for the entire journey, effectively improving the real-time performance and accuracy of vehicle energy consumption prediction.

[0042] Among them, in an embodiment of the present invention, using a target GPS emulator that meets the preset configuration conditions to obtain the real-time position information of the target vehicle includes: determining the target geographical environment, target driving trajectory, and target signal interference intensity of the target vehicle according to the target driving path information, and setting the dynamic driving parameters of the target vehicle; configuring the initial GPS emulator with the target geographical environment, target driving trajectory, target signal interference intensity, and dynamic driving parameters to generate a target GPS emulator that meets the preset configuration conditions; using the target GPS emulator that meets the preset configuration conditions to obtain the real-time position information of the target vehicle.

[0043] In the embodiment of the present invention, the GPS emulator of the above system can be configured. According to the generated driving path information, relevant scenarios are configured and trajectories are generated; at the same time, the dynamic parameters of the vehicle are set, such as speed, acceleration, direction, etc.; finally, factors such as signal strength interference are adjusted to simulate the signal strength changes in different environments.

[0044] For example, as Figure 5 shown, it is a process diagram for configuring the GPS emulator. When configuring the GPS emulator, first, scene settings need to be carried out. By introducing relevant information of path planning, a simulated geographical environment is created, including urban streets, highways, or mountain roads, etc. In addition, weather conditions are also included, such as sunny days, rainy days, foggy days, etc. Through these settings, it can be ensured that the test environment is as close as possible to the real usage scenario, thereby improving the effectiveness of the test.

[0045] Next, perform trajectory setting. Create a driving trajectory by importing the road data of path planning, including the starting point, destination, and waypoints, which is exactly the same as the planned path, thereby simulating a complex and realistic driving route. These routes can be used to simulate the driving conditions of the vehicle under different road conditions to help test the performance of the system under various driving conditions. Through detailed trajectory setting, it can be ensured that the test covers all possible driving scenarios.

[0046] Secondly, configure dynamic parameters. It is necessary to define the speed curve of the vehicle, including acceleration, deceleration, constant-speed driving, and variable-speed driving, to simulate different driving behaviors. At the same time, set the driving direction of the vehicle, including turning and lane-changing, to simulate complex driving actions. In addition, it is also necessary to set the start time and duration of the simulation to ensure the smooth execution of the test plan. Through the setting of these dynamic parameters, it can be ensured that the GPS simulator can generate realistic driving data, thereby comprehensively testing the performance of the VCU under test.

[0047] Finally, adjust the signal conditions. It is necessary to adjust the signal strength to simulate the signal reception in different environments. For example, in urban areas with high-rise buildings and open rural areas. Set the multipath effect parameters to simulate the influence of signal reflection and refraction, which is particularly important for testing the performance of the system in complex environments. In addition, interference from other radio signals can also be introduced to test the anti-interference ability of the system. Through the adjustment of these signal conditions, it can be ensured that the test results are closer to the real-world situation, thereby improving the reliability and effectiveness of the test.

[0048] In step S203, obtain the actual energy consumption value of the target vehicle after the journey ends, and generate a vehicle energy consumption prediction detection result that meets the preset effective conditions for the target vehicle when the actual energy consumption value is less than or equal to the final energy consumption prediction value.

[0049] In the embodiment of the present invention, the preset effective condition is the condition that the VCU control strategy is reasonable and effective.

[0050] It can be understood that the embodiment of the present invention can obtain the actual energy consumption value of the target vehicle after the journey ends. For example, calculate the actual energy consumption value of the target vehicle after the journey ends through the relevant parameters of the vehicle model built on the HIL bench, and generate a vehicle energy consumption prediction detection result that meets the preset effective conditions for the target vehicle when the actual energy consumption value is less than or equal to the final energy consumption prediction value, 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 according to the comparison result of the actual energy consumption value and the final energy consumption prediction value. The smaller the actual energy consumption value is compared with the final energy consumption prediction value, the more reasonable and effective the VCU control strategy is.

[0051] In the embodiments of the present invention, a driver model and a vehicle model can be built on the HIL bench of the above system. The driver model aims to imitate the behavior of a real driver, including reaction time, accelerator and brake pedal opening, etc. 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, a thermal management model, etc., and data signals are interacted according to the energy transfer relationship between the models. The vehicle model is used to simulate various characteristics of a real vehicle.

[0052] For example, as Figure 6 shown, it is a schematic diagram of the driver model and the vehicle model built on the HIL bench. The driver model can be regarded as a technical means to simulate driving behavior, imitating how a real driver adjusts operations according to the current running conditions of the vehicle, such as steering, accelerating or decelerating, etc. Specifically, the driver model receives the actual state information of the vehicle (such as speed, direction, etc.) and compares it with the expected target state to find the difference between the two. Then, through the mechanism of PI (Proportional-Integral) control, this error value is processed and converted into specific control instructions (such as adjusting the throttle opening, changing the steering wheel angle, etc.). These instructions are then sent back to the vehicle model for execution to achieve precise control of the vehicle state. The whole process forms a continuous feedback system to ensure that the vehicle can travel according to the predetermined target state.

[0053] In addition, the vehicle model includes a dynamics model covering longitudinal, lateral and vertical dynamics; an electric drive system model covering motors, inverters and controllers; a battery management system model covering battery characteristics, thermal management and charging models; a braking system model covering regenerative braking, hydraulic braking and braking integration; a tire model covering rolling resistance, side force and grip; an aerodynamics model covering drag coefficient and lift coefficient; and a thermal management model covering motor and inverter heat dissipation, battery pack heat dissipation. The comprehensive performance of these subsystems directly affects the overall performance of the vehicle. Through the control strategy implemented by the vehicle VCU and combined with the input of external working conditions, the test of vehicle energy consumption can be effectively completed.

[0054] Optionally, in an embodiment of the present invention, after generating a vehicle energy consumption prediction detection result that meets the preset effective conditions for the target vehicle, it further includes: generating a detection report for vehicle energy consumption prediction according to the initial energy consumption prediction result, the final energy consumption prediction value and the actual energy consumption value; sending the detection report to a preset terminal to display vehicle information, test route, energy consumption prediction data and VCU control strategy evaluation results in the detection report on the preset terminal.

[0055] In some embodiments, the embodiments of the present invention can generate a detection report for vehicle energy consumption prediction 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 travels. The report content may include vehicle information (such as model, battery status, etc.), test route details, changes in energy consumption prediction, and the evaluation result of the vehicle VCU control strategy, etc., and display the detection report 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 understand the prediction accuracy of vehicle energy consumption.

[0056] For example, as Figure 7 shown, it is a schematic diagram of the detection implementation process of vehicle energy consumption prediction. First, use the cloud online map for route planning and generate a route, then configure the corresponding GPS simulator according to the planned route information, including the configuration of scenarios, trajectories, dynamic parameters, and relevant signals. Then, use the route planning information combined with vehicle-related parameters for energy consumption prediction. At the same time, the driver and the vehicle model will test the energy management control strategy of the VCU based on these planned routes, and will also dynamically adjust the route according to the real-time position and the latest traffic data provided by the GPS simulator during the operation to achieve dynamic update. After the operation ends, the overall energy consumption prediction value will be displayed. By comparing it with the actual energy consumption calculated from the vehicle model-related parameters, the rationality and effectiveness of the VCU control strategy can be evaluated and verified. The closer and smaller the calculated value is to the prediction value, the more reasonable and effective the VCU control strategy is, and vice versa.

[0057] The embodiments of the present invention introduce a GPS simulator to simulate geographical 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, greatly improving the test efficiency; in addition, the driving conditions generated after introducing the online map route planning are also introduced. The online map route planning can generate test conditions according to the specific driving route and traffic conditions, making the test closer to the real driving environment and being able to generate various driving scenarios, including urban roads, highways, rural roads, etc., to comprehensively evaluate the accuracy of the energy consumption prediction model and the vehicle energy consumption performance; the embodiments of the present invention can verify the rationality and effectiveness of the VCU control strategy, can replace the in-vehicle verification and calibration of the vehicle energy management system performance, and better optimize the energy management system and control strategy.

[0058] According to the detection method for vehicle energy consumption prediction proposed by 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 an initial energy consumption prediction result of the target vehicle. Then, the energy consumption prediction value of the remaining mileage of the target vehicle during the journey is continuously updated by 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 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 journey ends. 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 that meets the preset effective conditions of the target vehicle is generated, effectively improving the accuracy and reliability of vehicle energy consumption prediction. Thus, the problems in the related art that the vehicle energy consumption prediction fails to fully consider various factors that change in real time, cannot accurately predict the vehicle energy consumption in complex situations, and reduce the accuracy and reliability of vehicle energy consumption prediction are solved.

[0059] Next, a detection device for vehicle energy consumption prediction proposed according to an embodiment of the present invention will be described with reference to the accompanying drawings.

[0060] Figure 8 It is a block diagram of a detection device for vehicle energy consumption prediction according to an embodiment of the present invention.

[0061] As Figure 8 shown, the detection device 10 for vehicle energy consumption prediction includes: an acquisition module 100, a determination module 200, and a detection module 300.

[0062] Specifically, the acquisition module 100 is configured to acquire the 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.

[0063] The determination module 200 is configured to use the target GPS simulator that meets the preset configuration conditions to acquire the real-time position information of the target vehicle, and continuously update the energy consumption prediction value of the remaining mileage of the target vehicle during the journey according to the real-time position 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 journey ends.

[0064] The detection module 300 is configured to acquire the actual energy consumption value of the target vehicle after the journey ends, and generate a vehicle energy consumption prediction detection result that meets the preset effective conditions of the target vehicle when the actual energy consumption value is less than or equal to the final energy consumption prediction value.

[0065] Optionally, in an embodiment of the present invention, the acquisition module 100 includes: a first acquisition unit and a first generation unit.

[0066] Among them, the first acquisition unit is used to acquire the target travel information of the target vehicle.

[0067] The first generation 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.

[0068] Optionally, in an embodiment of the present invention, the acquisition module 100 includes: a first determination unit and a second generation unit.

[0069] Among them, the first determination unit is used to input the target driving path information into an energy consumption prediction model to determine the target driving condition of the target vehicle.

[0070] The second generation unit is used to generate an initial energy consumption prediction result of the target vehicle according to the target driving condition of the target vehicle, the vehicle dynamics model, and the target energy efficiency characteristics.

[0071] Optionally, in an embodiment of the present invention, the determination module 200 includes: a second determination unit, a third generation unit, and a second acquisition unit.

[0072] Among them, the second determination unit is used to determine the target geographical environment, the target driving trajectory, and the 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.

[0073] The third generation unit is used to configure an initial GPS simulator by 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.

[0074] The second acquisition unit is used to acquire the real-time position information of the target vehicle by using the target GPS simulator that meets the preset configuration conditions.

[0075] Optionally, in an embodiment of the present invention, the device 10 of the present invention embodiment further includes: a generation module and a sending module.

[0076] Among them, the generation module is used to generate a detection report of vehicle energy consumption prediction according to the initial energy consumption prediction result, the final energy consumption prediction value, and the actual energy consumption value after generating a vehicle energy consumption prediction detection result of the target vehicle that meets the preset effective conditions.

[0077] The sending module is used to send the detection report to a preset terminal after generating a vehicle energy consumption prediction detection result of the target vehicle that meets the preset effective conditions, so as to display the vehicle information, the test route, the energy consumption prediction data, and the evaluation result of the vehicle control unit control strategy in the detection report on the preset terminal.

[0078] It should be noted that the foregoing explanation of the embodiments of the detection method for vehicle energy consumption prediction also applies to the detection device for vehicle energy consumption prediction in this embodiment, and will not be elaborated here.

[0079] According to the detection device for vehicle energy consumption prediction provided by the embodiments of the present invention, 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 energy consumption prediction value of the remaining mileage of the target vehicle during the journey is continuously updated by using the real-time position information of the target vehicle obtained by the target GPS simulator that meets the preset configuration conditions, so as 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 journey ends. 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 that meets the preset effective conditions of the target vehicle is generated, effectively improving the accuracy and reliability of vehicle energy consumption prediction. Thus, the problems in the related art that the vehicle energy consumption prediction fails to fully consider various factors that change in real time, cannot accurately predict the vehicle energy consumption in complex situations, and reduce the accuracy and reliability of vehicle energy consumption prediction are solved.

[0080] Figure 9 The structural schematic diagram of the vehicle provided by the embodiments of the present invention. The vehicle may include: A memory 901, a processor 902, and a computer program stored on the memory 901 and executable on the processor 902.

[0081] When the processor 902 executes the program, it implements the detection method for vehicle energy consumption prediction provided in the foregoing embodiments.

[0082] Furthermore, the vehicle further includes: A communication interface 903 for communication between the memory 901 and the processor 902.

[0083] The memory 901 is used to store a computer program executable on the processor 902.

[0084] The memory 901 may include a high-speed RAM memory, and may also include non-volatile memory, such as at least one disk memory.

[0085] If the memory 901, the processor 902, and the communication interface 903 are implemented independently, the communication interface 903, the memory 901, and the processor 902 can be interconnected via a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 9 only a thick line is used in Figure 9 , but it does not mean that there is only one bus or one type of bus.

[0086] Optionally, in a specific implementation, if the memory 901, the processor 902, and the communication interface 903 are integrated on a single chip, the memory 901, the processor 902, and the communication interface 903 can communicate with each other through an internal interface.

[0087] The processor 902 may be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0088] This embodiment also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the detection method for vehicle energy consumption prediction as described above is implemented.

[0089] This embodiment also provides a computer program product, including a computer program, and when the computer program is executed, it is used to implement the detection method for vehicle energy consumption prediction as described above.

[0090] In the description of this specification, the descriptions with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0091] In addition, the terms "first" and "second" are used only for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "N" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0092] Any process or method description shown in the flowchart or described in other ways herein can be understood to represent a module, segment, or portion of code including one or N executable instructions for implementing a customized logic function or process, and the scope of the preferred embodiments of the present invention includes additional implementations, where the functions may be executed in a substantially simultaneous manner or in the reverse order according to the functions involved, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0093] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definable sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in conjunction with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be 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 (non-exhaustive list) of computer-readable media include the following: an electrical connection part (electronic device) having one or N wirings, a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then stored in a computer memory.

[0094] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above-described embodiments, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: 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.

[0095] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the methods of the above-described embodiments can be completed by a program instructing relevant hardware, 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 embodiments.

[0096] In addition, in each embodiment of the present invention, each functional unit can be integrated into a processing module, or each unit can exist physically alone, or two or more units can be integrated into one module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0097] The above-mentioned storage medium can be a read-only memory, a magnetic disk, an optical disc, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can 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; Using a target global positioning system simulator that meets preset configuration conditions to obtain real-time location information of the target vehicle, and continuously updating the energy consumption prediction value of the remaining mileage of the target vehicle in the trip according to the real-time location 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 ends; The actual energy consumption value of the target vehicle after the trip is completed 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.

2. The vehicle energy consumption prediction detection method according to claim 1, characterized in that: The step of obtaining target driving path information of the target vehicle includes: Obtaining target travel information of the target vehicle; The target travel information of the target vehicle is input into a preset online map path planning model to generate the target driving path information of the target vehicle.

3. The vehicle energy consumption prediction detection method according to claim 1, characterized in that: The step of inputting the target driving path information into an energy consumption prediction model to generate an initial energy consumption prediction result of the target vehicle includes: Inputting the target driving path information into the energy consumption prediction model to determine the target driving condition of the target vehicle; The initial energy consumption prediction result of the target vehicle is generated according to the target driving condition, vehicle dynamics model and target energy efficiency characteristics of the target vehicle.

4. The detection method for vehicle energy consumption prediction according to claim 1, characterized in that: The method of obtaining the real-time position information of the target vehicle by using a target global positioning system simulator that meets preset configuration conditions includes: 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; 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; The real-time position information of the target vehicle is acquired by using the target global positioning system simulator that meets the preset configuration conditions.

5. The vehicle energy consumption prediction detection method according to claim 1, characterized in that: After generating the vehicle energy consumption prediction test result of the target vehicle that meets the preset validity conditions, the method further includes: Generate a detection report of the vehicle energy consumption prediction according to 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.

6. A detection device for predicting vehicle energy consumption, characterized in that: include: An acquisition module, used 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, configured to obtain the real-time location information of the target vehicle by 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 according to 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; The detection module is used to obtain the actual energy consumption value of the target vehicle after the trip ends, 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.

7. The detection device for predicting vehicle energy consumption according to claim 6, characterized in that: The acquisition module comprises: An acquisition unit, used for acquiring target travel information of the target vehicle; A 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.

8. 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 as described in any one of claims 1 to 5.

9. 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 described in any one of claims 1-5.

10. 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 described in any one of claims 1-5.

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