Real-time working condition prediction method based on map information

Abstract

The invention belongs to the technical field of simulation, and particularly relates to a real-time working condition prediction method based on map information, which comprises the following steps of: extracting specific information through an Atde navigation information interface; n data fragments are screened out through an original driving database, and a working condition fragment library is generated; selecting data fragments from the working condition fragment library and splicing the data fragments by taking the average speed of Atde navigation as a target, and respectively calculating the data fragments after the data fragments are repeated for multiple times, so as to select optimal data from the data fragments; and based on MATLAB programming, inputting a navigation address, outputting a real-time working condition, and determining a prediction method if the error between the real-time working condition simulation average vehicle speed and the average vehicle speed of Atde navigation is within a set range. The real-time working condition prediction method based on the map information can effectively guide the early-stage energy management strategy development of the new energy vehicle model, and greatly shortens the project development period. The method is combined with a whole vehicle power consumption simulation model, a destination is input based on a map, and the remaining mileage after the destination is reached can be accurately output.

Description

Technical Field

[0001] This invention belongs to the field of simulation technology, specifically relating to a real-time working condition prediction method based on map information. Background Technology

[0002] With the development of pure electric vehicles, pure electric vehicle users are paying more attention to the accuracy of the displayed range, mainly because there is a large gap between the actual driving mileage and the displayed range reduction mileage, which can easily lead to range anxiety.

[0003] Current technology involves testing with real vehicles to collect time and speed signals during the process, which are then processed to form actual road conditions, but these are not real-time conditions. Real-vehicle road tests have long development cycles, high costs, limitations in terms of time and road conditions, and lack of real-time accuracy. Summary of the Invention

[0004] The purpose of this invention is to provide a real-time operating condition prediction method based on map information to solve the problems existing in the background art.

[0005] To achieve the above objectives, this application employs the following technical solution:

[0006] A real-time operating condition prediction method based on map information, comprising the following steps:

[0007] S1. Extract specific information through the Gaode navigation information interface;

[0008] S2. Filter out N data segments from the original driving database to generate a working condition segment library;

[0009] S3. Using the average vehicle speed from Gaode Navigation as the target, select data segments from the working condition segment library and splice them together. Repeat this process multiple times and calculate the results separately to select the optimal data.

[0010] S4. Based on MATLAB programming, input the navigation address and output the real-time operating conditions. If the error between the average vehicle speed in the real-time operating condition simulation and the average vehicle speed in Gaode navigation is within the set range, then the prediction method is determined.

[0011] Furthermore, in step S1, specific information is extracted through the Gaode navigation information interface, including the following steps:

[0012] S11. Use the Gaode Navigation Web Service API;

[0013] S12, Gaode Navigation Route Planning;

[0014] S13. Configure the vehicle-mounted interface;

[0015] S14. The vehicle-side interface interacts with Gaode Navigation for data exchange.

[0016] Furthermore, in step S11, using the Gaode Navigation Web Service API includes: providing road conditions for each step of the path based on the given starting point and the distance, duration, and altitude coordinates of each step; weather query; geocoding / reverse geocoding; static map and coordinate transformation.

[0017] Furthermore, in step S11, the specific information extracted includes: data on driving origin planning, a list of driving route planning information, driving transfer schemes, driving distance, estimated driving time, navigation strategy, number of traffic lights and navigation segments.

[0018] Furthermore, in step S2, the data fragment construction process is as follows:

[0019] S21. Import the raw data and convert its format;

[0020] S21. After preprocessing the raw data, divide it into segments;

[0021] S23. Calculate and statistically analyze fragment information, and perform feature calculations and statistics on fragment information to obtain data fragments.

[0022] Furthermore, step S3 includes multiple detailed optimization models for various operating conditions to improve calculation accuracy.

[0023] Furthermore, it also includes joint simulation prediction of operating conditions and power consumption, including: building a bridge from operating conditions to power consumption simulation by writing initial files, inputting navigation information, driving characteristics and initial SOC; and outputting operating conditions and power consumption.

[0024] The beneficial effects of this invention are:

[0025] This invention presents a real-time operating condition prediction method based on map information, which can effectively guide the early-stage energy management strategy development of new energy vehicle models and significantly shorten the project development cycle. Combined with a vehicle energy consumption simulation model, and based on a map-input destination, it can accurately output the remaining driving range after reaching the destination. Attached Figure Description

[0026] Figure 1 This is a table showing the interaction information between Gaode Navigation and the vehicle's infotainment system.

[0027] Figure 2 This is a schematic diagram of the working condition segment constructed in this invention.

[0028] Figure 3 This is a flowchart of the construction process segment of the present invention.

[0029] Figure 4 A table for calculating feature information of the fragment library.

[0030] Figure 5This is a schematic diagram of the overall software architecture.

[0031] Figure 6 The diagram illustrates the preprocessing of the raw data. Figure a shows the segment selection method, figure b shows the traffic light parking model, figure c shows the intersection deceleration model, figure d shows the driving characteristic processing during deceleration, figure e shows the correction during idling, and figure f shows the forced correction during constant speed simulation.

[0032] Figure 7 This is a logic diagram of the calculation process in step S3 of the present invention, where a is the software calculation flow, b is the calculation speed segment condition, c is the design step condition, and d is the design intersection connection segment.

[0033] Figure 8 Output diagrams for real-time operating condition simulation software.

[0034] Figure 9 This is a flowchart of the joint simulation of operating conditions and power consumption.

[0035] Figure 10 Output graphs for operating conditions and power consumption in the joint simulation of operating conditions and power consumption. Detailed Implementation

[0036] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings. The following embodiments are merely exemplary and can only be used to explain and illustrate the technical solution of the present invention, and should not be construed as limiting the technical solution of the present invention.

[0037] like Figures 1 to 10 As shown, this application provides a real-time operating condition prediction method based on map information. The computer can use the real-time operating conditions to accurately predict the real-time power consumption of a certain vehicle model, guiding the development of energy management for the actual operating conditions of the vehicle model. In addition, if the vehicle end integrates a model of vehicle simulation power consumption, it can calculate the future power consumption in real time. Through power conversion, the remaining driving range when reaching the next destination can be obtained more accurately.

[0038] A real-time operating condition prediction method based on map information, comprising the following steps:

[0039] S1. Extract specific information through the Gaode navigation information interface, including the following steps:

[0040] S11. Use the Gaode Navigation Web Service API, as shown in Table 1:

[0041] Table 1

[0042]

[0043] Continued from Table 1

[0044]

[0045] S12, Key information extracted from the Gaode route planning web interface.

[0046] S13. Configure the vehicle-mounted interface:

[0047] Long getSegmentCount()

[0048] Get the number of navigation segments

[0049] Segmentinfo getSegmentinfo(long index)

[0050] Obtain the navigation segment data access object.

[0051] S14. The vehicle-side interface interacts with Gaode Navigation for data exchange.

[0052] Specifically, using the Amap Navigation Web Service API includes: providing road conditions for each step of the path based on a given starting point and the distance, duration, and altitude coordinates of each step; weather queries; geocoding / reverse geocoding; static maps and coordinate transformations.

[0053] Extracting specific information includes: data on driving origin planning, a list of driving route planning information, driving transfer options, driving distance, estimated driving time, navigation strategy, number of traffic lights and navigation segments.

[0054] S2. Through the original driving database, N data segments are selected to generate a working condition segment library. In this embodiment, based on the China working condition project, the original driving data referenced includes 12.5GB of data from four cities: Hefei, Qingdao, Tianjin, and Changchun. Finally, 3731 data segments with a total size of 70MB (balancing rate and accuracy) are selected and used. The developed software supports one-click import of driving data to generate the segment library.

[0055] The data fragment construction process is as follows:

[0056] S21. Import the raw data and convert its format;

[0057] S21. After preprocessing the raw data, divide it into segments;

[0058] S23. Calculate and statistically analyze fragment information, and perform feature calculations and statistics on fragment information to obtain data fragments.

[0059] S3. Using the average vehicle speed from Gaode Navigation as the target, select data segments from the working condition segment library, splice them together, repeat the process multiple times, calculate separately, and select the optimal data.

[0060] Basic principle: Using the average vehicle speed of Gaode Map as the target, select and splice from the working condition segment library, calculate multiple times and select the optimal solution.

[0061] Multiple operating condition detail optimization modules, such as traffic light model (congestion, severe congestion), deceleration segment model (deceleration driving characteristic processing: gentle, normal, aggressive), idle segment correction, and uniform speed simulation forced correction, to improve calculation accuracy.

[0062] S4. Based on MATLAB programming, input the navigation address and output real-time driving conditions. If the error between the average vehicle speed in the real-time driving condition simulation and the average vehicle speed in the Gaode navigation is within a set range, then the prediction method is determined. In this embodiment, the average vehicle speed error in the road simulation is set to be within 3%, as shown in Table 2.

[0063] Table 2

[0064]

[0065] This application also includes joint simulation prediction of operating conditions and energy consumption, including: building a bridge from operating conditions to energy consumption simulation by writing an initial file, inputting navigation information, driving characteristics and initial SOC (vehicle information is modified through the initial file); outputting operating conditions and energy consumption (real-time energy consumption, real-time SOC, energy consumption per 100 kilometers).

[0066] This technical solution, combined with the vehicle energy consumption simulation model, can accurately output the remaining driving mileage after reaching the destination based on the map input destination, as shown in Table 3.

[0067] Table 3

[0068]

[0069] The above are preferred embodiments of the present invention. The basic principles and advantages of the present invention have been shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are only illustrative of the principles of the present invention. Various changes and modifications can be made to the present invention without departing from the spirit and scope of the present invention. All such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A real-time operating condition prediction method based on map information, characterized in that, The following steps are adopted: S1. Extract specific information through the Gaode navigation information interface; S2. Filter out N data segments from the original driving database to generate a working condition segment library; S3. Using the average vehicle speed from Gaode Navigation as the target, select data segments from the working condition segment library and splice them together. Repeat this process multiple times and calculate the results separately to select the optimal data. S4. Based on MATLAB programming, input the navigation address and output the real-time operating conditions. If the error between the average vehicle speed in the real-time operating condition simulation and the average vehicle speed in Gaode navigation is within the set range, then the prediction method is determined.

2. The real-time operating condition prediction method based on map information according to claim 1, characterized in that, In step S1, specific information is extracted through the Gaode navigation information interface, including the following steps: S11. Use the Gaode Navigation Web Service API; S12, Gaode Navigation Route Planning; S13. Configure the vehicle-mounted interface; S14. The vehicle-side interface interacts with Gaode Navigation for data exchange.

3. The real-time operating condition prediction method based on map information according to claim 2, characterized in that, In step S11, using the Gaode Navigation Web Service API includes: providing road conditions for each step of the path based on the given starting point and the distance, duration, and altitude coordinates of each step; weather query; geocoding / reverse geocoding; static map and coordinate transformation.

4. The real-time operating condition prediction method based on map information according to claim 2, characterized in that, In step S11, the specific information extracted includes: data on driving origin planning, a list of driving route planning information, driving transfer options, driving distance, estimated driving time, navigation strategy, number of traffic lights and navigation segments.

5. The real-time operating condition prediction method based on map information according to claim 1, characterized in that, In step S2, the data fragment construction process is as follows: S21. Import the raw data and convert its format; S21. After preprocessing the raw data, divide it into segments; S23. Calculate and statistically analyze fragment information, and perform feature calculations and statistics on fragment information to obtain data fragments.

6. The real-time operating condition prediction method based on map information according to claim 1, characterized in that, Step S3 includes multiple detailed optimization models for different operating conditions to improve calculation accuracy.

7. The real-time operating condition prediction method based on map information according to claim 1, characterized in that, It also includes joint simulation prediction of operating conditions and power consumption, including: building a bridge from operating conditions to power consumption simulation by writing initial files, inputting navigation information, driving characteristics and initial SOC; and outputting operating conditions and power consumption.

Images