CN114925155A - Time-sharing energy consumption map extraction method and system based on electric vehicle driving data - Google Patents

Time-sharing energy consumption map extraction method and system based on electric vehicle driving data Download PDF

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CN114925155A
CN114925155A CN202210616025.9A CN202210616025A CN114925155A CN 114925155 A CN114925155 A CN 114925155A CN 202210616025 A CN202210616025 A CN 202210616025A CN 114925155 A CN114925155 A CN 114925155A
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time
data
road section
travel
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严俊
赵成
王磊
万龙
张宇
曹东
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South Sagittarius Integration Co Ltd
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Abstract

The invention discloses a time-sharing energy consumption map extraction method and system based on electric vehicle driving data, and relates to the technical field of time-sharing energy consumption map extraction, wherein the method comprises the steps of obtaining vehicle-mounted signal data in the vehicle driving process, obtaining the vehicle stroke, and dividing the vehicle stroke into stroke sections according to preset rules; converting the trajectory of each run segment into an ordered set containing only fork codes; converting the travel section into road sections based on the intersection codes and the setting rules in the travel section corresponding ordered sets, and calculating to obtain the power consumption of the vehicle on each road section; obtaining a vehicle starting state label of the road section based on the relevant temperature information, and obtaining a time label of the road section based on the acquisition time of the vehicle-mounted signal data; and writing the power consumption of the road section, the vehicle starting state label and the time label into a distributed database. The method has the advantages of simple logic, easy implementation, strong practicability, good effect and good real-time performance, and can ensure the support of the energy consumption correlation analysis application scene of the electric automobile.

Description

Time-sharing energy consumption map extraction method and system based on electric vehicle driving data
Technical Field
The invention relates to the technical field of time-sharing energy consumption map extraction, in particular to a time-sharing energy consumption map extraction method and system based on electric vehicle driving data.
Background
The new energy automobile, especially the pure electric automobile, has obvious advantages in the aspects of energy conservation, emission reduction and stronger power compared with the traditional fuel oil vehicle. The leading areas of the automobile industry and main automobile enterprises achieve high consensus around the future automobile electromotion development, and issue electromotion strategic targets at a time to accelerate the automobile electromotion transformation. From the enterprise level, almost all key automotive enterprises issue new electric targets and product plans. New energy vehicles, particularly pure electric vehicles, can be greatly promoted to gradually replace traditional fuel vehicles based on the double-carbon strategy.
However, the currently mentioned electric vehicle is concerned about mileage anxiety as a closed topic, so compared with a fuel vehicle, the owner of the electric vehicle pays more attention to the energy consumption and endurance of the vehicle, and the factors influencing the energy consumption and endurance of the electric vehicle mainly include: the energy density of the battery, the appearance design/wind resistance coefficient, the vehicle body load, the ambient temperature, the driving road condition, the driving habit, the charging habit and the like, wherein the first three items are related to the vehicle design, and the last four items can reasonably avoid high energy consumption in the vehicle using stage.
The traditional navigation function mainly carries out path planning based on driving time and driving mileage, and a path planning scheme based on energy consumption prediction is lacked. Meanwhile, research and development of related technologies of electric automobiles are in an urgent breakthrough stage, acquisition and analysis of related test data are very important, how to acquire road sections with different energy consumption levels in different time periods and time periods is of great significance to automobile road tests, and research and development personnel can pertinently select different real driving road conditions and driving environments to test and collect related performance data to guide optimization of vehicle energy consumption.
No matter the route planning based on energy consumption prediction or the vehicle energy consumption optimization of vehicle road test is guided, data support is needed, and the current new energy automobile is provided with a vehicle-mounted driving signal acquisition device, so that abundant internet-of-vehicles data can be acquired for big data analysis and mining. Based on data such as a vehicle-mounted GPS (Global Positioning System), a motor signal, a battery signal and the like, an energy consumption map of different vehicle types can be extracted, the energy consumption map can reflect energy consumption levels of different road sections in different seasons and different time periods, and application scenes such as low-energy-consumption driving route recommendation, road test road section selection guidance and the like can be supported. It can be seen that the energy consumption map is the basis and core of the applications, and how to accurately extract the energy consumption map directly determines the effects of the applications.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide the time-sharing energy consumption map extraction method and system based on the electric automobile driving data, which have the advantages of simple logic, easy realization, strong practicability, good effect and good real-time performance, and can ensure the support of the electric automobile energy consumption related analysis application scene.
In order to achieve the above purpose, the invention provides a time-sharing energy consumption map extraction method based on electric vehicle driving data, which specifically comprises the following steps:
acquiring vehicle-mounted signal data in the vehicle running process, obtaining the travel of the vehicle, and dividing the travel of the vehicle into travel sections according to a preset rule;
based on the intersection data after the geohash coding, converting the track of each travel section into an ordered set only containing the intersection codes;
converting the travel sections into road sections based on the intersection codes and the setting rules in the travel section corresponding ordered sets, and calculating to obtain the power consumption of the vehicle on each road section;
obtaining a vehicle starting state label of the road section based on the relevant temperature information, and obtaining a time label of the road section based on the acquisition time of the vehicle-mounted signal data;
the power consumption of the road segment, the vehicle start status tag, and the time tag are written to the distributed database in the form of fields.
On the basis of the technical scheme, the vehicle-mounted signal data comprise GPS data, motor temperature, battery temperature, output voltage and current of a power battery and data acquisition time.
On the basis of the technical scheme, the method for acquiring the vehicle-mounted signal data in the vehicle driving process comprises the following specific steps:
acquiring vehicle-mounted signal data in the vehicle driving process in real time, analyzing the vehicle-mounted signal data according to a data protocol, and pushing the vehicle-mounted signal data to kafka;
pulling the vehicle-mounted signal data through the flink, screening the vehicle-mounted signal data, and simultaneously performing data cleaning conversion operation;
the data fields after screening operation comprise longitude, latitude, motor temperature, battery output voltage, battery output current and data acquisition time stamps; and the data cleaning conversion operation is to remove the boundary-crossing data and convert and restore the data of the original data with scaling or offset.
On the basis of the technical scheme, the method comprises the following steps of dividing the travel of the vehicle into travel sections according to preset rules:
grouping the vehicle-mounted signal data according to the frame number, and dividing the travel of the vehicle into travel sections according to preset rules according to the vehicle-mounted signal data of each vehicle;
wherein, the division rule of each stroke section is as follows: and after the vehicle is started and runs for a preset time, the vehicle is stopped, the change of the geographic position of the vehicle exceeds a threshold value, and the running mileage of the vehicle after the vehicle is started is not less than a preset kilometer.
On the basis of the technical scheme, the intersection data after the geohash coding is used for converting the track of each travel segment into an ordered set only containing the fork codes, and the method specifically comprises the following steps:
and on the basis of the intersection data after the geohash coding, sequentially comparing the track points of each travel section of the vehicle with the intersection data, replacing the current track points with the geohash coding if the current track points fall into the area represented by the geohash of the intersection, and discarding the current track points if the current track points do not fall into the area represented by the geohash of the intersection, thereby obtaining the ordered set of the fork codes of each travel section.
On the basis of the technical scheme, the travel section is converted into the road section based on the intersection codes and the setting rules in the travel section corresponding ordered sets, wherein the setting rules are as follows: in the ordered set corresponding to the travel section, two adjacent fork codes represent a road section.
On the basis of the above technical solution, the calculating to obtain the power consumption of the vehicle in each road segment specifically includes: calculating the power consumption of each road section based on the output voltage and current time sequence data of the power battery of each road section, wherein the calculation mode is as follows:
Figure BDA0003673352070000041
wherein, V i Represents the voltage reported the ith time in the current road section, A i And the current reported at the ith time in the current road section is represented, T represents a reporting time interval, and n represents the reporting times of the voltage and the current of the current road section.
On the basis of the technical proposal, the device comprises a shell,
the relevant temperature information comprises a motor temperature and a battery temperature;
the vehicle starting state label comprises a cold starting initial stage and a non-cold starting initial stage;
the time labels comprise driving time, seasons, working days, non-working days, holidays, small time periods and hour period grading;
the division rule of the cold start initial stage is as follows: determining the temperature ranges of the motor temperature and the battery temperature of the vehicle during normal running based on the ideal running motor temperature range and the battery temperature range of the vehicle and the statistical result of the actual Internet of vehicles data, wherein the time period when the motor temperature or the battery temperature is lower than the minimum value of the range at the initial running stage of the vehicle is the initial cold start stage;
if the ratio of the time of the vehicle in the initial cold start period in the current road section to the whole time of the road section is greater than the preset value, the vehicle start state label of the road section is the initial cold start period, otherwise, the vehicle start state label is the initial non-cold start period.
On the basis of the technical scheme, the power consumption of the road section, the vehicle starting state label and the time label are written into the distributed database in a field form, wherein the field of the road section comprises a travel id, a road section code, a vehicle type, the power consumption of the road section, whether the field belongs to the initial cold start stage, the road section running time, the running season, the running week, whether the field is a working day, whether the field is a holiday, a small running period and a small running hour quantile.
The invention provides a time-sharing energy consumption map extraction system based on electric vehicle driving data, which comprises:
the system comprises a dividing unit, a processing unit and a processing unit, wherein the dividing unit is used for acquiring vehicle-mounted signal data in the vehicle running process, obtaining the travel of the vehicle and dividing the travel of the vehicle into travel sections according to a preset rule;
the conversion unit is used for converting the track of each travel segment into an ordered set only containing fork codes based on the intersection data subjected to the geohash coding;
the calculation unit is used for converting the travel section into the road section based on the fork codes and the setting rules in the ordered set corresponding to the travel section, and calculating the power consumption of the vehicle in each road section;
the execution unit is used for obtaining a vehicle starting state label of the road section based on the relevant temperature information and obtaining a time label of the road section based on the acquisition time of the vehicle-mounted signal data;
and the writing unit is used for writing the power consumption of the road section, the vehicle starting state label and the time label into the distributed database in a field form.
Compared with the prior art, the invention has the advantages that: the time-sharing energy consumption map extraction of the electric automobile driving data is realized by acquiring the power consumption of the road section, the vehicle starting state label and the time label, the method is simple in logic, easy to realize, strong in practicability, good in effect and real-time performance, and the electric automobile energy consumption related analysis application scene can be ensured to be supported.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the description below are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a flowchart of a time-sharing energy consumption map extraction method based on electric vehicle driving data in an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments.
In the actual application process, the energy consumption levels of the same electric vehicle type at different time periods and different road sections in different seasons have differences, the difference degree cannot be easily quantized, and aiming at accurately quantizing the energy consumption levels under different conditions, the invention provides an energy consumption map extraction method based on massive electric vehicle driving data, which can obtain the driving energy consumption of each road section with different combinations of seasons, working days, non-working days, holidays, time periods and the like, and the work result is the core data support of application scenes related to the energy consumption levels, such as low-energy-consumption driving route recommendation, road test road section selection guidance and the like, so that the method has important significance for upper-layer related application.
Referring to fig. 1, an embodiment of the present invention provides a time-sharing energy consumption map extraction method based on electric vehicle driving data, where data collected by an internet of vehicles is large, the method is based on a hadoop cluster, collects data in real time, processes the data through a flink (a distributed processing engine and a framework for performing state calculation on bounded and unbounded data streams), performs aggregation with a trip as a unit, pushes the finished trip-related data to Kafka (an open source stream processing platform), and then performs quasi-real-time processing in batches at a certain frequency by using spark streaming (a streaming processing framework). The time-sharing energy consumption map extraction method specifically comprises the following steps:
s1: acquiring vehicle-mounted signal data in the vehicle running process, obtaining the travel of the vehicle, and dividing the travel of the vehicle into travel sections according to a preset rule; the vehicle-mounted signal data comprises GPS data, motor temperature, battery temperature, output voltage and current of the power battery and data acquisition time.
In the embodiment of the invention, vehicle-mounted signal data in the driving process of a vehicle are acquired, and the specific steps comprise:
s101: acquiring vehicle-mounted signal data in the vehicle running process in real time, analyzing the vehicle-mounted signal data according to a data protocol, and pushing the vehicle-mounted signal data to kafka;
s102: pulling the vehicle-mounted signal data through the flink, screening the vehicle-mounted signal data, and simultaneously performing data cleaning conversion operation; and pulling the vehicle-mounted signal data through the flink so as to perform the next data processing on the vehicle-mounted signal data, thereby screening data fields and only reserving the fields required by the subsequent calculation.
The data fields after screening operation comprise longitude, latitude, motor temperature, battery output voltage, battery output current and data acquisition time stamps; and the data cleaning conversion operation is to remove the boundary-crossing data and convert and restore the data of the original data with scaling or offset.
In the embodiment of the invention, the travel of the vehicle is divided into travel sections according to a preset rule, and the method specifically comprises the following steps: grouping the vehicle-mounted signal data according to the frame number, and dividing the travel of the vehicle into travel sections according to preset rules according to the vehicle-mounted signal data of each vehicle; wherein, the division rule of each stroke section is as follows: and after the vehicle is started and runs for a preset time, the vehicle is stopped, the change of the geographic position of the vehicle exceeds a threshold value, and the running mileage of the vehicle after the vehicle is started is not less than a preset kilometer. The preset kilometer can be 3 kilometers, namely, the journey running less than 3 kilometers after starting is eliminated.
The method is characterized in that original vehicle-mounted signal data are aggregated into a stroke, the original collected data of each vehicle are divided into a section of stroke according to rules, no signal data are reported in the vehicle stopping process, and therefore the rule is divided into no data when the time exceeds a threshold value.
S2: converting the track of each travel segment into an ordered set only containing a fork code based on the intersection data coded by the geohash (an address code); the method comprises the following steps: and on the basis of the intersection data after the geohash coding, sequentially comparing the track points of each travel section of the vehicle with the intersection data, replacing the current track points with the geohash coding if the current track points fall into the area represented by the geohash of the intersection, and discarding the current track points if the current track points do not fall into the area represented by the geohash of the intersection, thereby obtaining the ordered set of the fork codes of each travel section.
It should be noted that, when the intersections are coded, the different intersections have differences in coverage, so that a reasonable geohash coding length needs to be set according to actual conditions, the intersection is covered with the smallest rectangular range, and when continuous track points fall into the area represented by the geohash of the same intersection, only one code is reserved, that is, the continuous same codes cannot appear in the final ordered track codes.
S3: converting the travel section into road sections based on the intersection codes and the setting rules in the travel section corresponding ordered sets, and calculating to obtain the power consumption of the vehicle on each road section; the rule is set as follows: in the ordered set corresponding to the travel section, two adjacent fork codes represent a road section.
In the embodiment of the present invention, the power consumption of the vehicle in each road section is calculated, specifically: calculating the power consumption of each road section based on the output voltage and current time sequence data of the power battery of each road section, wherein the calculation mode is as follows:
Figure BDA0003673352070000081
wherein, V i Represents the voltage reported the ith time in the current road section, A i The current reported in the ith time in the current road section is represented, T represents the reporting time interval which can be 10s, n represents the voltage and current reporting times of the current road section, and the voltage and the current are reported simultaneously when the voltage and the current are reported.
S4: obtaining a vehicle starting state label of the road section based on the relevant temperature information, and obtaining a time label of the road section based on the acquisition time of the vehicle-mounted signal data;
in the embodiment of the invention, the related temperature information comprises the motor temperature and the battery temperature; the vehicle starting state label comprises a cold starting initial stage and a non-cold starting initial stage; the time labels comprise travel time, seasons, working days, non-working days, holidays, hours and hour period positions (one interval every 10 minutes);
the division rule of the cold start initial stage is as follows: determining the temperature ranges of the motor temperature and the battery temperature of the vehicle during normal running based on the ideal running motor temperature range and the battery temperature range of the vehicle and the statistical result of the actual Internet of vehicles data, wherein the time period when the motor temperature or the battery temperature is lower than the minimum value of the range at the initial running stage of the vehicle is the initial cold start stage;
if the ratio of the time of the vehicle in the initial cold start period in the current road section to the whole time of the road section is greater than the preset value, the vehicle start state label of the road section is the initial cold start period, otherwise, the vehicle start state label is the initial non-cold start period.
When a label of 'whether the time belongs to the initial cold start' is marked on each road section, a part of time of a certain road section belongs to the initial cold start, and a part of time does not belong to the initial cold start.
It should be noted that the time labels of the road segments may be obtained by collecting time and performing conversion or calculation, in individual cases, there may be a plurality of time labels converted from a certain driving road segment, and at this time, the label with the most number of converted labels in the road segment, such as the road segments a-b, needs to be selected, and 30% of the time belongs to the sub-period 7: 00-8:00, leaving 70% of the time in the range of 8: 00-9: 00, the corresponding hour segment of the road segment is 8: 00-9: 00.
s5: and writing the power consumption of the road section, the vehicle starting state label and the time label into the distributed database in a field form.
In the embodiment of the invention, the power consumption of the road section, the vehicle starting state label and the time label are written into the distributed database in a field form, wherein the field of the road section comprises a travel id (Identity document), a road section code, a vehicle type, the power consumption of the road section at this time, whether the road section belongs to a cold start initial stage, the road section running time, a running season, a running week, whether a working day, whether a holiday, a small running period and a small running hour quantile.
And further, processing the newly added vehicle-mounted signal data according to the step logic, and writing the obtained road section result into a distributed database.
The energy consumption map can be generated based on the output road section correlation result data, in a low-energy-consumption route recommendation application scene, the general energy consumption of each optional route can be further obtained by calculating the current corresponding time label of each road section appointed vehicle type in the optional route (small-granularity time labels of the road sections behind the route accumulate the average running time of the front road sections to sequentially calculate the approximate time of running to different road sections, so as to further determine the time labels, and meanwhile, the general energy consumption of each optional route can be further obtained according to the average power consumption values of the initial cold start time of different time sections in different seasons and the power consumption of the corresponding labels of the corresponding road sections, and the general energy consumption is used as the basis for sequencing to carry out low-energy-consumption route recommendation. In addition, different drivers can drive the same vehicle model and have different driving energy consumption levels, the total power consumption of each optional line can be calculated by adopting the power consumption corresponding to the quantiles for each road section according to the quantile of the individual driving energy consumption level of the drivers in the same vehicle model, the average power consumption is not uniformly used, and the calculated total power consumption of the line is more personalized.
For road test research and development personnel, preliminary multidimensional statistical analysis can be carried out based on output road section related result data, points that the energy consumption level is different from design or the research and development personnel consider unreasonable are found, then environments (roads, time and the like) needing real vehicle test are selected through an energy consumption map according to the conditions, and vehicle test signal data are collected to carry out next sub-deep analysis.
In the practical application process of the road section correlation result data, the time weight of the data can be considered, and the longer the time is, the smaller the corresponding weight is.
The time-sharing energy consumption map extraction system based on the electric automobile driving data comprises a dividing unit, a conversion unit, a calculation unit, an execution unit and a writing unit.
The dividing unit is used for acquiring vehicle-mounted signal data in the vehicle running process, obtaining the travel of the vehicle and dividing the travel of the vehicle into travel sections according to a preset rule; the conversion unit is used for converting the track of each travel segment into an ordered set only containing a fork code based on the intersection data after the geohash code; the calculation unit is used for converting the travel section into the road section based on the fork codes and the setting rules in the travel section corresponding ordered set, and calculating the power consumption of the vehicle in each road section; the execution unit is used for obtaining a vehicle starting state label of the road section based on the relevant temperature information and obtaining a time label of the road section based on the acquisition time of the vehicle-mounted signal data; the writing unit is used for writing the power consumption of the road section, the vehicle starting state label and the time label into the distributed database in a field form.
The above description is merely exemplary of the present application and is presented to enable those skilled in the art to understand and practice the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

Claims (10)

1. A time-sharing energy consumption map extraction method based on electric vehicle driving data is characterized by comprising the following steps:
acquiring vehicle-mounted signal data in the vehicle running process, obtaining the travel of the vehicle, and dividing the travel of the vehicle into travel sections according to a preset rule;
based on the intersection data after the geohash coding, converting the track of each travel section into an ordered set only containing the intersection codes;
converting the travel sections into road sections based on the intersection codes and the setting rules in the travel section corresponding ordered sets, and calculating to obtain the power consumption of the vehicle on each road section;
obtaining a vehicle starting state label of the road section based on the relevant temperature information, and obtaining a time label of the road section based on the acquisition time of the vehicle-mounted signal data;
and writing the power consumption of the road section, the vehicle starting state label and the time label into the distributed database in a field form.
2. The time-sharing energy consumption map extraction method based on the electric vehicle driving data as claimed in claim 1, characterized in that: the vehicle-mounted signal data comprises GPS data, motor temperature, battery temperature, output voltage and current of the power battery and data acquisition time.
3. The time-sharing energy consumption map extraction method based on electric vehicle driving data as claimed in claim 2, wherein the step of obtaining vehicle-mounted signal data in the vehicle driving process comprises the following specific steps:
acquiring vehicle-mounted signal data in the vehicle driving process in real time, analyzing the vehicle-mounted signal data according to a data protocol, and pushing the vehicle-mounted signal data to kafka;
pulling the vehicle-mounted signal data through the flink, screening the vehicle-mounted signal data, and simultaneously performing data cleaning conversion operation;
the data fields after screening operation comprise longitude, latitude, motor temperature, battery output voltage, battery output current and data acquisition time stamps; and the data cleaning and converting operation is to remove the boundary-crossing data and convert and restore the original data with scaling or offset.
4. The time-sharing energy consumption map extraction method based on electric vehicle driving data as claimed in claim 1, wherein the step of dividing the vehicle into the travel segments according to the preset rule is specifically as follows:
grouping the vehicle-mounted signal data according to the frame number, and dividing the travel of the vehicle into travel sections according to preset rules according to the vehicle-mounted signal data of each vehicle;
wherein, the division rule of each stroke section is as follows: and after the vehicle is started and runs for a preset time, the vehicle is stopped, the change of the geographic position of the vehicle exceeds a threshold value, and the running mileage of the vehicle after the vehicle is started is not less than a preset kilometer.
5. The time-sharing energy consumption map extraction method based on electric vehicle driving data as claimed in claim 1, wherein the intersection data based on the geohash coding is used for converting the track of each travel segment into an ordered set only containing intersection codes, and the specific steps include:
and on the basis of the intersection data after the geohash code, sequentially comparing the track points of each travel section of the vehicle with the intersection data, if the current track point falls into the area represented by the geohash of the intersection, replacing the current track point with the geohash code, and if the current track point does not fall into the area represented by the geohash of the intersection, discarding the current track point, thereby obtaining the ordered set of the fork codes of each travel section.
6. The time-sharing energy consumption map extraction method based on electric vehicle driving data as claimed in claim 1, wherein the travel segments are converted into the road segments based on the fork codes and the setting rules in the travel segment corresponding ordered set, wherein the setting rules are as follows: in the ordered set corresponding to the travel segment, two adjacent fork codes represent a road segment.
7. The time-sharing energy consumption map extraction method based on electric vehicle driving data as claimed in claim 1, wherein the calculating obtains the electric power consumption of the vehicle in each road section, specifically: calculating the power consumption of each road section based on the output voltage and current time sequence data of the power battery of each road section, wherein the calculation mode is as follows:
Figure FDA0003673352060000031
wherein, V i Represents the voltage reported the ith time in the current road section, A i And the current reported at the ith time in the current road section is represented, T represents a reporting time interval, and n represents the reporting times of the voltage and the current of the current road section.
8. The time-sharing energy consumption map extraction method based on the electric vehicle driving data as claimed in claim 1, characterized in that:
the relevant temperature information comprises a motor temperature and a battery temperature;
the vehicle starting state label comprises a cold starting initial stage and a non-cold starting initial stage;
the time labels comprise driving time, seasons, working days, non-working days, holidays, small time periods and hour period positions;
the division rule of the initial cold start is as follows: determining the temperature ranges of the motor temperature and the battery temperature of the vehicle during normal running based on the ideal running motor temperature range and the battery temperature range of the vehicle and the statistical result of the actual Internet of vehicles data, wherein the time period when the motor temperature or the battery temperature is lower than the minimum value of the range at the initial running stage of the vehicle is the initial cold start stage;
if the ratio of the time of the vehicle in the initial cold start period in the current road section to the whole time of the road section is greater than the preset value, the vehicle start state label of the road section is the initial cold start period, otherwise, the vehicle start state label is the initial non-cold start period.
9. The time-sharing energy consumption map extraction method based on electric vehicle driving data as claimed in claim 1, wherein the power consumption of the road section, the vehicle start state tag and the time tag are written into the distributed database in the form of fields, wherein the fields of the road section comprise a travel id, a road section code, a vehicle type, the power consumption of the current road section, whether the current road section belongs to a cold start initial stage, a road section driving time, a driving season, a driving week, a working day, a holiday, a driving hour period and a driving hour slot.
10. A time-sharing energy consumption map extraction system based on electric vehicle driving data is characterized by comprising:
the system comprises a dividing unit, a processing unit and a processing unit, wherein the dividing unit is used for acquiring vehicle-mounted signal data in the vehicle running process, obtaining the travel of the vehicle and dividing the travel of the vehicle into travel sections according to a preset rule;
the conversion unit is used for converting the track of each travel segment into an ordered set only containing fork codes based on the intersection data subjected to the geohash coding;
the calculation unit is used for converting the travel section into the road section based on the fork codes and the setting rules in the ordered set corresponding to the travel section, and calculating the power consumption of the vehicle in each road section;
the execution unit is used for obtaining a vehicle starting state label of the road section based on the related temperature information and obtaining a time label of the road section based on the acquisition time of the vehicle-mounted signal data;
and a writing unit for writing the power consumption amount of the road section, the vehicle start state tag and the time tag in the distributed database in a field form.
CN202210616025.9A 2022-05-31 2022-05-31 Time-sharing energy consumption map extraction method and system based on electric vehicle driving data Pending CN114925155A (en)

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