CN115952932A - Vehicle power consumption and hydrogen consumption prediction method based on driving route - Google Patents
Vehicle power consumption and hydrogen consumption prediction method based on driving route Download PDFInfo
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Abstract
The invention provides a vehicle power consumption and hydrogen consumption prediction method based on a driving route, which comprises the following steps: s1, planning a road path according to travel requirements; s2, calculating the characteristics of the working conditions of the route according to the road path planned in the step S1; s3, working conditions of a secondary branch, a main trunk and a express way are constructed, and working condition characteristics, correction factors and standard energy consumption are calculated; s4, carrying out working condition matching on the driving route by using the data obtained in the steps S2 and S3; s5, calculating power consumption and hydrogen consumption by using the data obtained in the steps S3 and S4; and S6, respectively calculating the power consumption and the hydrogen consumption of the vehicles on each route in the step S1, and outputting the route with the lowest energy consumption. The invention has the beneficial effects that: the method for predicting the power consumption and the hydrogen consumption of the vehicle based on the driving route is established by utilizing the hub energy consumption test data and comprehensively considering various influence factors such as the user traveling route, the environment temperature, the cold start and the like, the lowest energy consumption route is provided for the user, the prediction precision is high, the universality is good, and the mileage anxiety of the user can be effectively relieved.
Description
Technical Field
The invention belongs to the field of transportation, and particularly relates to a vehicle power consumption and hydrogen consumption prediction method based on a driving route.
Background
The electric energy and the hydrogen energy are used as substitutes of the traditional energy of the automobile, and have the advantages of small pollution, low cost and the like. However, due to the problems of short driving range and less hydrogen stations, drivers often worry about mental anxiety caused by insufficient driving range. The mileage anxiety forces the user to consider whether the current remaining endurance mileage meets the travel requirement in advance when the user goes out, greatly influences the user experience, and influences the popularization and development of the electric automobile and the hydrogen energy automobile to a certain extent.
The user's demand for vehicle range can be summarized in two aspects: increase the vehicle energy supply and predict the energy consumption as accurately as possible. Due to the material limitation of the power battery and the influence of a hydrogen energy storage mode, the problem of short endurance mileage of the vehicle inevitably exists for a long time. Therefore, the energy consumption factors such as external environment, road conditions, cold start and the like are comprehensively considered, the vehicle energy consumption is accurately predicted, and the user cruising anxiety can be relieved to a certain extent, so that the user experience is optimized, and the popularization and development of the electric automobile and the hydrogen energy automobile are promoted.
Disclosure of Invention
In view of this, the invention aims to provide a method for predicting power consumption and hydrogen consumption of a vehicle based on a driving route, which is established by utilizing the hub energy consumption test data and comprehensively considering various influence factors such as a user travel route, an environment temperature, cold start and the like.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
the vehicle power consumption and hydrogen consumption prediction method based on the driving route comprises the following steps:
s1, planning a road path according to travel requirements;
s2, calculating the characteristics of the working conditions of the route according to the road path planned in the step S1;
s3, working conditions of a secondary branch, a main trunk and an express way are constructed, and working condition characteristics, correction factors and standard energy consumption are calculated;
s4, carrying out working condition matching on the driving route by using the data obtained in the steps S2 and S3;
and S5, calculating the power consumption and/or the hydrogen consumption by using the data obtained in the steps S3 and S4.
And S6, calculating the power consumption and the hydrogen consumption of the vehicles in each route in the S1, and outputting the route with the lowest energy consumption.
Further, in step S1, according to the keywords of the starting and ending points and the travel demand of the user, the driving route planning result between the starting and ending points under different travel strategies (shortest route, shortest time, great roads, etc.) is obtained from the map platform, and each route includes detailed information such as road name, length, direction, etc.
Further, in step S2, the specific steps are as follows:
a1, predicting the average speed of each road at a corresponding moment by using a long-short term memory network model according to the historical average speed of the road;
a2, obtaining the average running speed and the corresponding duration of each road by using the vehicle route planning result obtained in the step S1 and the average speed of each road predicted in the step A1;
a3, according to the result obtained in the step A2, counting the ratio of the average speed distribution duration of the trip road, and calculating the average speed of the trip;
a4, collecting actual vehicle running information, segmenting actual running data into segment segments, calculating maximum speed characteristic parameters of the segment, matching with the multi-vehicle average speed of the geographic information system road at the corresponding moment, and obtaining a relation between the multi-vehicle average speed of the geographic information system road and the maximum speed of a single-vehicle segment by utilizing linear regression;
and A5, converting the average speed distribution of the travel road into the maximum speed distribution by using the average speed-maximum speed relation obtained in the step A4, dividing the path into three working condition intervals of a secondary branch, a main trunk and an express way, and calculating the proportion of the three speed intervals.
Further, in step S3, the specific steps are as follows:
b1, constructing working conditions of a secondary branch, a main trunk and an express way: cutting a large amount of actual vehicle road running speed data in a short segment mode, then removing abnormal short segments, dividing the short segments into a secondary branch road, a main trunk road and an express way short segment library according to the maximum speed of 50 km/h and 70km/h, respectively calculating the working condition characteristics of each segment library, and using the working condition characteristics as a reference, and selecting an optimal short segment combination as the working conditions of the secondary branch road, the main trunk road and the express way by using gray relevance analysis;
b2, utilizing a pure electric vehicle type or a hydrogen energy vehicle type, after a heat engine is completed on a rotating hub of a laboratory at the environmental temperature of 20 ℃, sequentially performing three working condition energy consumption tests of a secondary branch, a main trunk and a fast path, and repeating for 3 times; calculating average hundred kilometer energy consumption E of three working conditions of vehicle under 20℃ environment 1 、E 2 、E 3 ；
B3, performing energy consumption tests on three working conditions at the temperature of-20-40 ℃ at intervals of 10 ℃ after the heat engine is finished, and calculating the environmental temperature correction factors k of the three working conditions at different temperatures temp,1 ，k temp,2 ，k temp,3 Then, performing curve spline interpolation on the data to obtain correction factor curves of different environmental temperatures under various working conditions;
b4, performing energy consumption experiments under three working conditions at the ambient temperature of-7 ℃, calculating the average hundred kilometers of energy consumption data of the working conditions in the beginning 5 minutes and the 5 minutes after the starting, and taking the average value of the cold start correction factors of the three working conditions as the cold start correction factor k of the vehicle cold 。
Further, in step S4, the specific steps are as follows:
c1, normalizing the characteristics of the working conditions of the secondary branch road, the main road and the express way and the working condition of the trip by adopting a dispersion standardization method, wherein the characteristics of the working conditions comprise an average speed and three speed interval proportions obtained in the step C1 and calculated;
c2, searching k by using genetic algorithm 1 、k 2 、k 3 Optimal solution of weight coefficient, where k 1 +k 2 +k 3 And =1, weighting three working condition characteristics of a secondary branch, a trunk and an express way according to weight coefficients, minimizing the sum of absolute errors of the three working condition combination characteristics and travel working condition characteristics, and setting different weights according to the importance degrees of the four characteristics to carry out secondary weighting.
Further, in step S5, the specific steps are as follows:
obtaining a temperature correction factor k according to the current ambient temperature temp,1 ，k temp,2 ，k temp,3 (ii) a When the ambient temperature is more than 30 ℃, k cold =1；
Then the calculation formula of the power consumption and the hydrogen consumption of the vehicle on the current trip route is as follows:
wherein the content of the first and second substances,
e is energy consumption of the driving route;
E i hundred kilometers of energy consumption under the ith working condition;
k i the weight of the ith working condition;
k temp,i the correction factor of the environmental temperature under the ith working condition;
k cold is a cold start correction factor;
t is travel time in min;
and L is the mileage of the driving route.
Further, in step S6, the specific steps are as follows:
and respectively calculating the power consumption and the hydrogen consumption of vehicles on different routes in the road path planning in the step S1, and outputting the route with the lowest energy consumption to a user.
Further, the electronic device comprises a processor and a memory, wherein the memory is connected with the processor in a communication mode and is used for storing executable instructions of the processor, and the processor is used for executing the vehicle electricity consumption and hydrogen consumption prediction method based on the driving route.
Further, the present disclosure discloses a server comprising at least one processor, and a memory communicatively coupled to the processor, the memory storing instructions executable by the at least one processor, the instructions being executable by the processor to cause the at least one processor to perform a method for predicting vehicle electricity and hydrogen consumption based on a route traveled.
Further, the present disclosure discloses a computer-readable storage medium storing a computer program which, when executed by a processor, implements a method for predicting power consumption and hydrogen consumption of a vehicle based on a travel route.
Compared with the prior art, the method for predicting the power consumption and the hydrogen consumption of the vehicle based on the driving route has the following beneficial effects:
the vehicle power consumption and hydrogen consumption prediction method based on the driving route establishes a vehicle power consumption and hydrogen consumption prediction method based on the driving route by utilizing the hub energy consumption test data and comprehensively considering various influence factors such as a user trip route, the environmental temperature, cold start and the like, and has the advantages of high prediction precision, good universality and the like, and the mileage anxiety of a user can be effectively relieved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an embodiment of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic diagram of a method for predicting electricity and hydrogen consumption of a vehicle based on a driving route according to an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating the relationship between the average speed and the maximum speed according to an embodiment of the present invention;
fig. 3 is a schematic view of an ambient temperature correction factor curve according to an embodiment of the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
The method of the present invention will be described in further detail with reference to the accompanying drawings, and fig. 1 is an overall flow chart of a method for predicting power consumption and hydrogen consumption of a vehicle based on a driving route. Taking a certain pure electric vehicle type energy consumption as an example, the prediction method is as follows.
1. Road path planning
According to keywords and travel requirements of a user starting and ending point, driving route planning results between the starting and ending points under different travel strategies (shortest route, shortest time, multiple roads and the like) are obtained from a map platform, and each route comprises detailed information such as road name, length, direction and the like.
2. Driving route condition characteristic calculation
(1) Prediction of road average speed distribution: taking 5 minutes as a time interval, taking the road GIS traffic data of the road in the past year as a data training set. In order to avoid the influence of dimension, the data is normalized by adopting a dispersion normalization method, and the result is mapped between [0,1 ]. And predicting the road speed of the approach road at the future time by adopting a Long short-term memory (LSTM) model, and predicting the output at the t + delta t time by taking the data of t times as input. The LSTM model time step is 5, the initial learning rate is 0.001, the loss function is the mean square error, and an Adam optimizer is used.
(2) Vehicle speed-schedule statistics: and obtaining the average running speed and the corresponding duration of each road by using the obtained vehicle route planning result and the average speed of each road. Taking the example that the vehicle starts from the second route of Tianjin city to the airport of Tianjin city at 8.
TABLE 1 vehicle speed-time schedule
(3) And (3) counting the speed distribution of the driving route: and counting the average speed distribution duration ratio of the trip road, and calculating the average speed of the trip.
(4) Finding the relation between the average speed and the maximum speed: the method comprises the steps of collecting actual vehicle running information, segmenting actual running data into segments, calculating maximum speed characteristic parameters of the segments, matching the maximum speed characteristic parameters with the multi-vehicle average speed of the geographic information system road at the corresponding moment, and obtaining a relational expression of the multi-vehicle average speed of the geographic information system road and the maximum speed of a single-vehicle segment by utilizing linear regression, wherein the relation is shown in figure 2.
(5) And (3) calculating the speed interval proportion: converting the average speed distribution of the travel road into maximum speed distribution according to an average speed-maximum speed relational expression, dividing the section into three working condition sections of a secondary branch, a main trunk and an express way by taking 50/70km/h as a speed section threshold value, and calculating the proportion of the three speed sections.
3. Constructing working conditions of a secondary branch, a main trunk and an express way, and calculating working condition characteristics, correction factors and standard energy consumption
(1) And (3) working condition construction of a secondary branch, a main trunk and an express way: cutting a large amount of vehicle actual road running speed data in a short segment mode, and then removing abnormal short segments. And dividing the short segment into a secondary branch, a main trunk and a fast short segment library according to the maximum speed of 50 km/h and 70 km/h. And respectively calculating the working condition characteristics of each segment library, and selecting the optimal short segment combination as the working conditions of a secondary branch, a main trunk and an express way by using the gray relevance analysis on the basis of the working condition characteristics.
(2) Calculating standard electricity consumption and hydrogen consumption: performing energy consumption experiments under three working conditions of a secondary branch, a main trunk and an express way on a rotating hub of a laboratory at the environmental temperature of 20 ℃, and repeating the experiments for three times; calculating average hundred kilometers energy consumption E of three working conditions of vehicle under 20℃ environment 1 、E 2 、E 3 。
(3) Calculating an environment temperature correction factor: performing energy consumption tests of three working conditions at the temperature of between 20 ℃ below zero and 40 ℃ at intervals of 10 ℃, and calculating an energy consumption correction factor k of the air conditioner at different temperatures temp,1 ，k temp,2 ，k temp,3 Then, the data are processed with curve spline interpolation to obtain the environmental temperature correction factor curve under each working condition, and the environmental temperature correction factor curve is as the figure3, respectively.
(4) Cold start correction factor calculation: performing energy consumption experiments under three working conditions at the ambient temperature of-7 ℃, calculating the average hundred kilometers of energy consumption data of 5 minutes after the starting and 5 minutes after the starting of the working conditions, and taking the average value of the cold start correction factors of the three working conditions as the cold start correction factor k of the vehicle cold 。
4. Driving route condition matching
(1) Normalization of characteristic parameters: and carrying out deviation standardization processing on three working conditions of suburb high speed of the city and the average speed and speed interval proportion of the trip.
(2) Optimal combination parameters: finding k using genetic algorithms 1 、k 2 、k 3 (where k is 1 +k 2 +k 3 = 1) optimal solution of the weight coefficient, weighting the characteristics of the secondary branch, the main trunk and the express way according to the weight coefficient, so that the sum of the absolute error of the combined characteristics of the three working conditions and the absolute error of the characteristics of the travel working conditions is minimum, and in the process, different weights can be set according to the importance degrees of the four characteristics to carry out secondary weighting. In the present example, the four motion feature weight factors are set to be equal in value. The combined operating characteristics are calculated as follows. The characteristic average speed of the travel working condition is 50.74km/h, and the weights of the three speed intervals are 10.97%, 18.93% and 70.10% respectively.
In the genetic algorithm parameter optimizing process, the parameter k is used 1 、k 2 、k 3 Coding is carried out to convert the gene into individuals consisting of genes, 0 and 1 are used as upper and lower limits of a range, the sum of absolute values of errors of combination characteristics of the three working conditions and characteristic errors of travel working conditions is taken as a fitness function, the population number of individuals is 50, the maximum iteration is 100, the variation probability is 0.01, and the cross probability is 0.7. And (3) continuously performing genetic operations such as selection, crossing, mutation and the like by using randomly generated individuals to obtain excellent offspring. The algorithm terminates until the optimal individual reaches a given threshold or a set number of iterations. At this time, k 1 、k 2 、k 3 Is the optimal parameter.
5. Calculation of Power and Hydrogen consumption
(1) And (3) calculating a correction factor: obtaining a temperature correction factor k according to the current ambient temperature temp,1 ，k temp,2 ，k temp,3 (ii) a When the ambient temperature is more than 30 ℃, k cold =1。
(2) The calculation formula of the vehicle power consumption and the hydrogen consumption is as follows:
wherein E is the electricity consumption of the trip, and the unit is kw x h, E i The power consumption is hundreds kilometers under the ith working condition, and the unit is kw h/100km; k is a radical of i Is the weight of the ith operating condition, k temp,i For the i-th working condition ambient temperature correction factor, k cold A cold start correction factor; t is the time required by the trip, and the unit is min; and L is the trip mileage of this time in km.
The user starts from the second route of Tianjin City to the airport of Tianjin City at 8 ℃ and the ambient temperature is 26 ℃, and the power consumption of the vehicle is 2.73kw.
6. Route with lowest output energy consumption
And (3) respectively calculating the power consumption and the hydrogen consumption of vehicles on different routes in the road path planning in the step (1), and outputting the route with the lowest energy consumption to a user.
Those of ordinary skill in the art will appreciate that the elements and method steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of clearly illustrating the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the several embodiments provided in the present application, it should be understood that the disclosed method and system may be implemented in other ways. For example, the above described division of elements is merely a logical division, and other divisions may be realized, for example, multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not executed. The units may or may not be physically separate, and components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment of the present invention.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (8)
1. The method for predicting the electricity consumption and the hydrogen consumption of the vehicle based on the driving route is characterized by comprising the following steps of:
s1, planning a road path according to travel requirements to obtain at least one road path;
s2, calculating the working condition characteristics of the road path planned in the step S1;
s3, constructing working conditions of a secondary branch, a main trunk and an express way, wherein the working conditions of the express way comprise the working conditions of the express way, and calculating the characteristics, correction factors and standard energy consumption of each working condition;
s4, carrying out working condition matching on the road path planned in the step S1 by using the data obtained in the steps S2 and S3;
s5, calculating power consumption and hydrogen consumption by using the data obtained in the steps S3 and S4;
s6, respectively calculating the power consumption and the hydrogen consumption of the vehicles in each route in the step S1, and outputting a route with the lowest energy consumption;
in step S2, the specific steps are as follows:
a1, predicting the average speed of each road at a corresponding moment by using a long-short term memory network model according to the historical average speed of the road;
a2, obtaining the average running speed and the corresponding duration of each road by using the vehicle path planning result obtained in the step S1 and the average speed of each road predicted in the step A1;
a3, according to the result obtained in the step A2, calculating the average speed distribution duration ratio of the trip road, and calculating the average speed of the trip;
a4, collecting actual vehicle running information, segmenting actual running data into short segments, calculating maximum speed characteristic parameters of the segments, matching the maximum speed characteristic parameters with the average speed of multiple vehicles on the road of the geographic information system at the corresponding moment, and obtaining a relational expression of the average speed of multiple vehicles on the road of the geographic information system and the maximum speed of a single vehicle short segment by utilizing linear regression;
a5, converting the average speed distribution of the travel road into the maximum speed distribution by using the relation between the average speed and the maximum speed obtained in the step A4, dividing the path into three working condition intervals, namely a secondary branch, a main trunk and an express way, and calculating the proportion of the three speed intervals;
in step S4, the following steps are included:
c1, normalizing the characteristics of the working conditions of the secondary branch road, the main road and the express way and the working condition of the trip by adopting a deviation standardization method, wherein the characteristics of the working conditions comprise an average speed and three speed interval proportions obtained in the step B1 and calculated;
c2, searching k by using genetic algorithm 1 、k 2 、k 3 Optimal solution of weight coefficient, where k 1 +k 2 +k 3 And =1, weighting three working condition characteristics of a secondary branch, a trunk and a express way according to weight coefficients, and minimizing the sum of absolute errors of the three working condition combination characteristics and travel working condition characteristics.
2. The travel route-based vehicle electricity and hydrogen consumption prediction method according to claim 1, characterized in that: in step S1, according to the keywords and the travel strategy of the user start and end points, the driving route planning result between the start and end points, including the detailed information of the road name, length, and direction of each route segment, is obtained from the map navigation system.
3. The method for predicting electricity and hydrogen consumption of a vehicle based on a driving route according to claim 1, wherein in step S3, the detailed steps are as follows:
b1, constructing working conditions of a secondary branch, a main trunk and an express way: cutting a large amount of actual road driving speed data of vehicles in a short segment mode, then eliminating abnormal short segments, dividing the short segments into a secondary branch road, a main trunk road and an express way short segment library according to the maximum speed of 50 km/h and 70km/h, respectively calculating the working condition characteristics of each short segment library, and selecting an optimal short segment combination as the working conditions of the secondary branch road, the main trunk road and the express way by using gray relevance analysis with the working condition characteristics of each short segment library as a reference;
b2, utilizing a pure electric vehicle type or a hydrogen energy vehicle type, after a heat engine is completed on a rotating hub of a laboratory at the environmental temperature of 20 ℃, sequentially performing three working condition energy consumption tests of a secondary branch, a main trunk and a fast path, and repeating for 3 times; calculating average hundred kilometer energy consumption E of the vehicle under three working conditions under 20℃ environment 1 、E 2 、E 3 ；
B3, performing energy consumption tests on three working conditions at the temperature of-20-40 ℃ at intervals of 10 ℃ after the heat engine is finished, and calculating the environmental temperature correction factors k of the three working conditions at different temperatures temp,1 ，k temp,2 ，k temp,3 Then, performing curve spline interpolation on the data to obtain correction factor curves of different environmental temperatures under various working conditions;
b4, at the ambient temperature of-7 DEG CEnergy consumption experiments under three working conditions, calculating average hundred kilometer energy consumption data of 5 minutes after starting and 5 minutes after starting under the working conditions, and taking the average value of cold start correction factors under the three working conditions as a cold start correction factor k of a vehicle cold 。
4. The method for predicting the electricity and hydrogen consumption of the vehicle based on the traveling route according to claim 1, wherein in the step S5, the specific steps are as follows:
obtaining a temperature correction factor k according to the current ambient temperature temp,1 ，k temp,2 ，k temp,3 (ii) a When the ambient temperature is more than 30 ℃, k cold =1；
Then the calculation formula of the power consumption and the hydrogen consumption of the vehicle on the current trip route is as follows:
wherein, the first and the second end of the pipe are connected with each other,
e is energy consumption of the driving route;
E i hundred kilometers of energy consumption under the ith working condition;
k i the weight of the ith working condition;
k temp,i correcting factors for the ambient temperature under the ith working condition;
k cold is a cold start correction factor;
t is travel time in min;
and L is the mileage of the driving route.
5. The method for predicting the electricity and hydrogen consumption of the vehicle based on the traveling route according to claim 1, wherein in step S6, the concrete steps are as follows:
and respectively calculating the power consumption and the hydrogen consumption of vehicles on different routes in the road path planning in the step S1, and outputting the route with the lowest energy consumption to a user.
6. An electronic device comprising a processor and a memory communicatively coupled to the processor and configured to store processor-executable instructions, wherein: the processor is configured to execute the method for predicting electricity and hydrogen consumption of a vehicle based on a driving route according to any one of claims 1 to 5.
7. A server, characterized by: comprising at least one processor, and a memory communicatively coupled to the processor, the memory storing instructions executable by the at least one processor to cause the at least one processor to perform the travel route based vehicle electricity and hydrogen consumption prediction method of any of claims 1-5.
8. A computer-readable storage medium storing a computer program, characterized in that: the computer program, when executed by a processor, implements the method for predicting electricity and hydrogen consumption of a vehicle based on a travel route according to any one of claims 1 to 5.
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| CN114001989A (en) * | 2021-10-22 | 2022-02-01 | 中汽研汽车检验中心（天津）有限公司 | Method and device for predicting energy consumption of single-vehicle air conditioner based on working condition identification |
| CN114435138A (en) * | 2022-01-25 | 2022-05-06 | 中国第一汽车股份有限公司 | Vehicle energy consumption prediction method and device, vehicle and storage medium |
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| CN115952932B (en) | 2023-05-23 |
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