CN115952932B - Method for predicting power consumption and hydrogen consumption of vehicle based on driving route - Google Patents
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Abstract
The invention provides a method for predicting power consumption and hydrogen consumption of a vehicle based on a driving route, which comprises the following steps: s1, planning a road path according to travel requirements; s2, calculating route working condition characteristics aiming at the road route planned in the step S1; s3, constructing a secondary branch, a main road and a expressway working condition, and calculating working condition characteristics, correction factors and standard energy consumption; s4, carrying out working condition matching on the driving route by utilizing the data obtained in the steps S2 and S3; s5, calculating electricity 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 the lowest energy consumption route. The invention has the beneficial effects that: the vehicle electricity consumption and hydrogen consumption prediction method based on the driving route is established by utilizing the hub energy consumption test data and comprehensively considering various influencing factors such as the user travel route, the ambient temperature and the cold start, the minimum energy consumption route is provided for the user, the prediction precision is high, the universality is good, and the user mileage anxiety can be effectively relieved.
Description
Technical Field
The invention belongs to the field of transportation, and particularly relates to a method for predicting vehicle electricity consumption and hydrogen consumption based on a driving route.
Background
The electric energy and the hydrogen energy are used as substitutes of the traditional energy sources of the automobiles, and have the advantages of small pollution, low cost and the like. However, due to the problems of short driving range and less hydrogen addition stations, drivers often worry about anxiety caused by insufficient driving range. The mileage anxiety forces the user to consider in advance whether the current remaining endurance mileage meets the travel requirement when going out, greatly influences the user experience, and also 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 ways: increasing the energy supply to the vehicle and as accurate an energy consumption prediction as possible. Due to the influence of the material limitation of the power battery and the hydrogen energy storage mode, the problem of short endurance mileage of the vehicle is inevitably long-term. Therefore, the influence energy consumption factors such as the external environment, road conditions and cold start are comprehensively considered, the energy consumption of the vehicle can be accurately predicted, and the endurance anxiety of the user 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 the above, the invention aims to provide a vehicle electricity consumption and hydrogen consumption prediction method based on a driving route, which is established by utilizing the energy consumption test data of a rotating hub and comprehensively considering various influencing factors such as a user driving route, ambient temperature, cold start and the like.
In order to achieve the above purpose, the technical scheme of the invention is realized as follows:
the method for predicting the power consumption and the hydrogen consumption of the vehicle based on the driving route comprises the following steps:
s1, planning a road path according to travel requirements;
s2, calculating route working condition characteristics aiming at the road route planned in the step S1;
s3, constructing a secondary branch, a main road and a expressway working condition, and calculating working condition characteristics, correction factors and standard energy consumption;
s4, carrying out working condition matching on the driving route by utilizing the data obtained in the steps S2 and S3;
s5, calculating the electricity consumption and/or the hydrogen consumption by using the data obtained in the steps S3 and S4.
S6, calculating the electricity consumption and the hydrogen consumption of the vehicles in each route in the S1, and outputting the lowest energy consumption route.
Further, in step S1, driving route planning results between starting and ending points under different travel strategies (shortest route, shortest time, large number of routes, etc.) are obtained from the map platform according to the keywords of the starting and ending points and travel requirements of the user, and each route includes detailed information of road names, lengths, directions, etc.
Further, in step S2, the specific steps are as follows:
a1, predicting the average speed of each road at the corresponding moment by using a long-short-period 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, counting the average speed distribution duration ratio of the travel road according to the result obtained in the step A2, and calculating the average travel speed;
a4, acquiring actual running information of the vehicle, segmenting the actual running data into segments, calculating maximum speed characteristic parameters of the segments, matching the maximum speed characteristic parameters with the average speed of the multiple vehicles of the geographic information system road at the corresponding moment, and obtaining a relationship formula of the average speed of the multiple vehicles of the geographic information system road and the maximum speed of the single vehicle segment by utilizing linear regression;
and A5, converting the average speed distribution of the travel road into maximum speed distribution by using the average speed-maximum speed relation obtained in the step A4, dividing the path into three working condition sections of a secondary branch, a main road and a expressway, and calculating the proportions of the three speed sections.
Further, in step S3, the specific steps are as follows:
b1, secondary branch, main road and expressway working condition construction: cutting a large number of vehicle actual road running speed data in the form of short segments, removing abnormal short segments, dividing the short segments into sub-branches, main roads and expressway short segment libraries according to maximum speeds of 50 km/h and 70km/h, respectively calculating working condition characteristics of each segment library, and using gray correlation analysis to select optimal short segment combinations as working conditions of the sub-branches, the main roads and the expressways by taking the working condition characteristics as a benchmark;
b2, performing energy consumption tests on three working conditions of a secondary branch, a main road and a expressway in sequence after finishing heat engine on a rotating hub of a laboratory at the environmental temperature of 20 ℃ by using a pure electric vehicle type or a hydrogen energy vehicle type, and repeating for 3 times; calculating the average hundred kilometer energy consumption E of the vehicle under the environment of 20℃ under three working conditions 1 、E 2 、E 3 ；
B3, performing three working condition energy consumption tests after the heat engine is completed at intervals of 10 ℃ below zero to 40 ℃ below zero, 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 different environment temperature correction factor curves under each working condition;
b4, performing three working condition energy consumption experiments at the ambient temperature of minus 7 ℃, calculating average hundred kilometers of energy consumption data of 5 minutes after the working condition starts and 5 minutes after the working condition starts, and taking the average value of the cold start correction factors of the three working conditions as a cold start correction factor k of the vehicle cold 。
Further, in step S4, the specific steps are as follows:
carrying out normalization processing on three working conditions of a secondary branch, a main road and a expressway and working condition characteristics of the trip by adopting a deviation normalization method, wherein the working condition characteristics comprise average speed and three speed interval proportions obtained in the step C1;
c2, searching k by genetic algorithm 1 、k 2 、k 3 A weight coefficient optimal solution, where k 1 +k 2 +k 3 And (1) weighting three working condition characteristics of the secondary branch, the main road and the expressway according to weight coefficients, wherein the sum of the absolute value of the error of the combined characteristic of the three working condition characteristics and the travel working condition characteristic is minimum, and in the process, different weights can be set according to the importance degree 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 The method comprises the steps of carrying out a first treatment on the surface of the When the ambient temperature is greater than 30℃, k cold =1；
The calculation formulas of the current travel route vehicle electricity consumption and the hydrogen consumption are as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
e is the energy consumption of the driving route;
E i hundred kilometers of energy consumption for the ith working condition;
k i the weight of the ith working condition;
k temp,i the temperature correction factor is the i-th working condition environment temperature correction factor;
k cold a correction factor for cold start;
t is travel time, and the unit is min;
l is the driving route mileage.
Further, in step S6, the specific steps are as follows:
in the step S1, the power consumption and the hydrogen consumption of the vehicles in different routes in the road path planning are calculated respectively, and the lowest energy consumption route is output to the user.
Further, the scheme discloses electronic equipment, which comprises a processor and a memory, wherein the memory is in communication connection with the processor and is used for storing executable instructions of the processor, and the processor is used for executing a vehicle electricity consumption and hydrogen consumption prediction method based on a driving route.
Further, the present solution discloses a server comprising at least one processor and a memory communicatively connected 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 driving route based vehicle electricity consumption and hydrogen consumption prediction method.
Further, the present solution 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 driving route.
Compared with the prior art, the prediction method for the electricity consumption and the hydrogen consumption of the vehicle based on the driving route has the following beneficial effects:
the vehicle electricity consumption and hydrogen consumption prediction method based on the driving route is established by utilizing the hub energy consumption test data and comprehensively considering various influencing factors such as the user driving route, the environment temperature, the cold start and the like, and has the advantages of high prediction precision, good universality and the like, and can effectively relieve the anxiety of mileage of the user.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention. In the drawings:
FIG. 1 is a schematic diagram of a method for predicting power consumption and hydrogen consumption of a vehicle based on a driving route according to an embodiment of the present invention;
FIG. 2 is a graph showing the relationship between average velocity and maximum velocity according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an environmental temperature correction factor according to an embodiment of the invention.
Detailed Description
It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other.
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 of a method for predicting power consumption and hydrogen consumption of a vehicle based on a driving route. Taking energy consumption of a certain pure electric vehicle as an example, the prediction method is as follows.
1. Road path planning
And obtaining driving route planning results among starting and ending points under different travel strategies (shortest route, shortest time, large number of roads and the like) from the map platform according to keywords of the starting and ending points of the user and travel requirements, wherein each section of route comprises detailed information such as road names, lengths, directions and the like.
2. Travel route condition feature calculation
(1) Predicting average speed distribution of a road: the road GIS traffic data of the past year is taken as a data training set with 5 minutes as a time interval. 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 at the future time of the road by adopting a Long short-term memory (LSTM) model, and predicting the output at the time t+delta t by taking data at t times as input. The LSTM model time step was 5, the initial learning rate was 0.001, the loss function was the mean square error, and an Adam optimizer was 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 Tianjin city two-way road to Tianjin city airport at 8:40, the calculation results are shown in table 1.
Table 1 vehicle speed-time table
(3) And (3) carrying out travel route speed distribution statistics: and counting the average speed distribution duration ratio of the travel road, and calculating the average speed of the travel.
(4) Finding the relation between the average speed and the highest speed: the method comprises the steps of collecting actual running information of a vehicle, segmenting the actual running data into segments, calculating maximum speed characteristic parameters of the segments, matching the maximum speed characteristic parameters with the average speed of the multiple vehicles on the road of the geographic information system at corresponding moments, and obtaining a relationship between the average speed of the multiple vehicles on the road of the geographic information system and the maximum speed of the segment of a single vehicle by using linear regression, wherein the relationship is shown in figure 2.
(5) Calculating the speed interval proportion: according to the average speed-maximum speed relation, the average speed distribution of the travel road is converted into maximum speed distribution, and the interval is divided into three working condition intervals of a secondary branch, a main road and a expressway by taking 50/70km/h as a speed interval threshold value, so that the proportion of the three speed intervals is calculated.
3. Working conditions of a secondary branch, a main road and a expressway are constructed, and working condition characteristics, correction factors and standard energy consumption are calculated
(1) Secondary branch, main road and expressway working conditions are constructed: cutting a large amount of vehicle actual road running speed data in the form of short segments, and then eliminating abnormal short segments. Dividing the short segments into secondary branches, main branches and a quick-way short segment library according to the maximum speed of 50 km/h and 70 km/h. And respectively calculating working condition characteristics of the segment libraries of each segment, and using gray correlation analysis to select the optimal short segment combination as working conditions of a secondary branch, a main road and a expressway by taking the working condition characteristics as a benchmark.
(2) Standard electricity consumption and hydrogen consumption calculation: carrying out three working condition energy consumption experiments of a secondary branch, a main road and a expressway on a rotating hub of a laboratory at the environmental temperature of 20 ℃ respectively, and repeating the experiments for three times; calculating the average hundred kilometers energy consumption E of the vehicle under the three working conditions of 20 DEG C 1 、E 2 、E 3 。
(3) Calculating an ambient temperature correction factor: performing three working condition energy consumption tests at intervals of 10 ℃ below zero to 40 ℃ below zero, and calculating air conditioner energy consumption correction factors k at different temperatures temp,1 ，k temp,2 ，k temp,3 And then, performing curve spline interpolation on the data to obtain an environment temperature correction factor curve under each working condition, wherein the environment temperature correction factor curve is shown in figure 3.
(4) Cold start correction factor calculation: at the ambient temperature of minus 7 ℃, three working condition energy consumption experiments are carried out, average hundred kilometers of energy consumption data of 5 minutes from the beginning of working condition and 5 minutes after starting are calculated, and the average value of the cold start correction factors of the three working conditions is used as a cold start correction factor k of the vehicle cold 。
4. Travel route condition matching
(1) Normalization of characteristic parameters: and carrying out deviation standardization processing on three working conditions of suburb high speed and the average speed and speed interval proportion of the trip.
(2) Optimal combination parameters: finding k using genetic algorithm 1 、k 2 、k 3 (wherein k 1 +k 2 +k 3 Weight of =1)And the coefficient optimal solution weights the three working condition characteristics of the secondary branch, the main road and the expressway according to the weight coefficient, so that the sum of the absolute value of the error of the combined characteristic of the three working conditions and the characteristic of the travel working condition is minimum, and in the process, different weights can be set according to the importance degree of the four characteristics to carry out secondary weighting. In this example, the four motion feature weighting factors are set to equal values. The combined operating mode feature calculation is shown below. The average speed of the travel working condition characteristics 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 calculated 1 、k 2 、k 3 The coding is converted into individuals consisting of genes, 0 and 1 are used as the upper limit and the lower limit of a range, the sum of the absolute values of the combination characteristics of the three working conditions and the characteristic errors of the travel working conditions is used as a fitness function, the number of individuals in the population is 50, the maximum iteration is 100, the variation probability is 0.01, and the cross probability is 0.7. And (5) continuously performing genetic operations such as selection, crossover, mutation and the like by utilizing randomly generated individuals, and obtaining excellent offspring. Until the optimal individual reaches a given threshold or a set number of iterations is reached, the algorithm aborts. At this time, k 1 、k 2 、k 3 Is the optimal parameter.
5. Electricity consumption and hydrogen consumption calculation
(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 The method comprises the steps of carrying out a first treatment on the surface of the When the ambient temperature is greater than 30℃, k cold =1。
(2) The calculation formulas of the vehicle electricity consumption and the hydrogen consumption are as follows:
wherein E is the current trip electricity consumption, and the unit is kw h and E i The unit is kw, h/100km, which is the electricity consumption of hundred kilometers under the ith working condition; k (k) i Is the weight of the ith working condition, k temp,i Is the i-th working condition environment temperature correction factor, k cold A correction factor for cold start; t is the time required by the trip, and the unit is min; l is the trip mileage of this time, and the unit is km.
The user starts from the Tianjin city two-way road at 8:40, and reaches Tianjin city airport, the environment temperature is 26 ℃, and the vehicle power consumption is 2.73 kw.
6. Route with lowest output energy consumption
And (3) respectively calculating the power consumption and the hydrogen consumption of the vehicles in different routes in the road path planning in the step (1), and outputting the lowest energy consumption route to a user.
Those of ordinary skill in the art will appreciate that the elements and method steps of each example described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the elements and steps of each example have been described generally in terms of functionality in the foregoing description to clearly illustrate this 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 solution. 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 this application, it should be understood that the disclosed methods and systems may be implemented in other ways. For example, the above-described division of units is merely a logical function division, and there may be another division manner when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted or not performed. The units may or may not be physically separate, and components shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiment of the present invention.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention, and are intended to be included within the scope of the appended claims and description.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (7)
1. The method for predicting the power 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 a secondary branch, a main road and a highway working condition, wherein the highway working condition comprises a highway working condition, 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 electricity 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 vehicle in each route in the step S1, and outputting the route with the lowest energy consumption;
in step S2, the specific steps are as follows:
a1, predicting the average speed of each road at the corresponding moment by using a long-short-period 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, counting the average speed distribution duration ratio of the travel road according to the result obtained in the step A2, and calculating the average travel speed;
a4, acquiring actual running information of the vehicle, segmenting the 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 the multiple vehicles of the geographic information system road at corresponding moments, and obtaining a relationship between the average speed of the multiple vehicles of the geographic information system road and the maximum speed of the short segments of the single vehicle by utilizing linear regression;
a5, converting the average speed distribution of the travel road into maximum speed distribution by using the average speed-maximum speed relation obtained in the step A4, dividing the path into three working condition sections of a secondary branch, a main road and a expressway, and calculating the proportion of the three speed sections;
in step S4, the following steps are included:
carrying out normalization processing on three working conditions of a secondary branch, a main road and a expressway and working condition characteristics of the trip by adopting a deviation normalization method, wherein the working condition characteristics comprise average speed and three speed interval proportions obtained in the step B1;
c2, searching k by genetic algorithm 1 、k 2 、k 3 A weight coefficient optimal solution, where k 1 +k 2 +k 3 The method comprises the following steps of (1) weighting three working condition characteristics of a secondary branch, a main road and a expressway according to weight coefficients, wherein the sum of the absolute value of the combined characteristic of the three working conditions and the error of the characteristic of the travel working condition is minimum;
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 The method comprises the steps of carrying out a first treatment on the surface of the When the ambient temperature is greater than 30℃, k cold ＝1；
The calculation formulas of the current travel route vehicle electricity consumption and the hydrogen consumption are as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
e is the energy consumption of the driving route;
E i hundred kilometers of energy consumption for the ith working condition;
k i the weight of the ith working condition;
k temp,i the temperature correction factor is the i-th working condition environment temperature correction factor;
k cold a correction factor for cold start;
t is travel time, and the unit is min;
l is the driving route mileage.
2. The travel route-based vehicle electricity consumption and hydrogen consumption prediction method according to claim 1, characterized in that: in step S1, driving route planning results between starting and ending points are obtained from the map navigation system according to the keywords of the starting and ending points and the trip strategy of the user, and the driving route planning results comprise detailed information of road names, lengths and directions of each section of route.
3. The travel route-based vehicle electricity consumption and hydrogen consumption prediction method according to claim 1, characterized in that in step S3, the specific steps are as follows:
b1, secondary branch, main road and expressway working condition construction: cutting a large number of vehicle actual road running speed data in the form of short segments, removing abnormal short segments, dividing the short segments into sub-branches, main roads and quick-way short segment libraries according to maximum speed of 50 km/h and 70km/h, respectively calculating working condition characteristics of each short segment library, and using gray correlation analysis to select optimal short segment combinations as working conditions of the sub-branches, the main roads and the quick-way by taking the working condition characteristics of each short segment library as a reference;
b2, performing energy consumption tests on three working conditions of a secondary branch, a main road and a expressway in sequence after finishing heat engine on a rotating hub of a laboratory at the environmental temperature of 20 ℃ by using a pure electric vehicle type or a hydrogen energy vehicle type, and repeating for 3 times; calculating the average hundred kilometer energy consumption E of the vehicle under the environment of 20℃ under three working conditions 1 、E 2 、E 3 ；
B3, performing three working condition energy consumption tests after the heat engine is completed at intervals of 10 ℃ below zero to 40 ℃ below zero, 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 different environment temperature correction factor curves under each working condition;
b4, performing three working condition energy consumption experiments at the ambient temperature of minus 7 ℃, calculating average hundred kilometers of energy consumption data of 5 minutes after the working condition starts and 5 minutes after the working condition starts, and taking the average value of the cold start correction factors of the three working conditions as a cold start correction factor k of the vehicle cold 。
4. The travel route-based vehicle electricity consumption and hydrogen consumption prediction method according to claim 1, characterized in that in step S6, the specific steps are as follows:
and (3) respectively calculating the power consumption and the hydrogen consumption of the vehicles of different routes in the road path planning in the step S1, and outputting the lowest energy consumption route to a user.
5. An electronic device comprising a processor and a memory communicatively coupled to the processor for storing processor-executable instructions, characterized in that: the processor is configured to execute the travel route-based vehicle electricity consumption and hydrogen consumption prediction method according to any one of claims 1 to 4.
6. A server, characterized by: comprising at least one processor and a memory communicatively connected to the processor, the memory storing instructions executable by the at least one processor to cause the at least one processor to perform the route-based vehicle electricity and hydrogen consumption prediction method of any one of claims 1-4.
7. A computer-readable storage medium storing a computer program, characterized in that: the computer program, when executed by a processor, implements the travel route-based vehicle electricity consumption and hydrogen consumption prediction method of any one of claims 1 to 4.
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