CN113753053A - Energy consumption optimal driving route selection method considering meteorological factors - Google Patents

Energy consumption optimal driving route selection method considering meteorological factors Download PDF

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CN113753053A
CN113753053A CN202111049775.4A CN202111049775A CN113753053A CN 113753053 A CN113753053 A CN 113753053A CN 202111049775 A CN202111049775 A CN 202111049775A CN 113753053 A CN113753053 A CN 113753053A
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energy consumption
vehicle
road section
navigation
wind
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芦克龙
陈明
张高爽
张辉香
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Zhejiang Geely Holding Group Co Ltd
Geely Automobile Research Institute Ningbo Co Ltd
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Zhejiang Geely Holding Group Co Ltd
Geely Automobile Research Institute Ningbo Co Ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W50/08—Interaction between the driver and the control system
    • B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/80—Technologies aiming to reduce greenhouse gasses emissions common to all road transportation technologies
    • Y02T10/84—Data processing systems or methods, management, administration

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  • Automation & Control Theory (AREA)
  • Transportation (AREA)
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Abstract

The invention relates to an energy consumption optimal driving route selection method considering meteorological factors, which comprises the following steps: 1) based on the information of the departure place and the destination, a plurality of vehicle navigation routes are obtained through a vehicle-mounted navigation system, and each navigation route contains each road section information; 2) acquiring meteorological environment data of each road section of each navigation route based on a weather forecast system, and collecting the meteorological environment information predicted by each road section to a vehicle ECU; 3) based on the influence of wind resistance on the vehicle speed and the vehicle type, evaluating the wind resistance corresponding to each road section of the vehicle in each navigation route, and further evaluating the energy consumption level of each road section; 4) acquiring the total energy consumption level of each navigation route based on the energy consumption level of each road section; 5) and the automobile ECU compares the overall energy consumption levels of all navigation sections, and selects the route with the lowest energy consumption to the driver. Compared with the prior art, the method has the advantages of realizing more accurate driving route optimization, reducing driving energy consumption and the like.

Description

Energy consumption optimal driving route selection method considering meteorological factors
Technical Field
The invention relates to the technical field of new energy automobiles, in particular to an energy consumption optimal driving route selection method considering meteorological factors.
Background
New energy vehicles, especially pure electric vehicles, are developing rapidly. However, under the conditions that the energy density of the battery and the quick charging technology of the battery are not breakthrough and the layout of the charging infrastructure is not perfect, the driving range becomes a bottleneck problem and the long-distance driving of the electric automobile is also restricted.
In recent years, vehicle navigation software has become more prevalent and is also popular with vehicle drivers. For drivers, navigation software is indispensable in the driving process, particularly in the long-distance driving process. The navigation software provides various route choices, and the driving pressure is greatly reduced. With the popularity of electric vehicles, it is becoming a trend to drive electric vehicles for long distances. The endurance mileage is a problem that needs to be considered by the driver of the electric automobile. In the process of high-speed driving, the wind resistance has a large influence on the endurance mileage of the electric automobile, the wind in the nature has uncertainty and instantaneity, and the wind speed and the wind direction change in real time. The aerodynamic drag accounts for 70% of the overall vehicle drag, and is even higher. The wind resistance of the whole vehicle under real road conditions is not only restricted by the factors of the vehicle (modeling and aerodynamic external member), but also influenced by meteorological environment (wind speed and wind direction) at any time. An economic driving route is selected, so that energy consumption can be reduced, and mileage anxiety can be solved, and the method has great significance for drivers of electric vehicles. However, the navigation route provided by the current navigation software can only estimate the vehicle passing time, but cannot estimate the wind resistance and the total energy consumption level of the vehicle on each road section.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide an energy consumption optimal driving route selection method considering meteorological factors.
The purpose of the invention can be realized by the following technical scheme:
an energy consumption optimal driving route selection method considering meteorological factors comprises the following steps:
s1: and acquiring a plurality of vehicle navigation routes through a vehicle-mounted navigation system of the electric vehicle based on the information of the departure place and the destination, and acquiring each piece of road section information contained in each navigation route.
S2: acquiring meteorological environment data of each road section of each navigation route based on a weather forecast system, and collecting the meteorological environment information predicted by each road section to a vehicle ECU; the meteorological environment data comprise the predicted wind speed and wind direction of the electric automobile passing through each road section.
S3: and based on the influence of the wind resistance on the vehicle speed and the vehicle type of the electric vehicle, evaluating the wind resistance corresponding to each road section of the electric vehicle in each navigation route, and further evaluating the energy consumption level of each road section.
S4: and acquiring the total energy consumption level of each navigation route based on the energy consumption level of each road section.
S5: and the automobile ECU compares the overall energy consumption levels of all navigation sections, and selects the route with the lowest energy consumption to the driver.
In S3, the wind resistance corresponding to each segment is obtained by using the following formula:
Figure BDA0003252486590000021
in the formula, F is the wind resistance of the whole vehicle corresponding to each road section, rho is the air density, and v is the wind speed vWind powerAnd the relative velocity v between the electric vehicle and the groundVehicle with wheelsA is the orthographic projection area of the whole vehicle, Cd is the wind resistance coefficient of the whole vehicle, and the coefficient is the wind speed vWind powerAnd the relative velocity v between the electric vehicle and the groundVehicle with wheelsThe yaw angle beta formed therebetween. And the influence relation between the yaw angle beta and the finished automobile wind resistance coefficient Cd is obtained through a wind resistance database.
And acquiring air resistance power according to the calculated wind resistance corresponding to each road section, further acquiring driving power, and acquiring the energy consumption level of each road section through the driving power. The expression of the driving power of each road section is:
P＝P1+P2+P3+P4
where P is driving power, P1 is air resistance power, P2 is slope resistance power, P3 is rolling resistance power, and P4 is acceleration resistance power, where the expression for air resistance power P1 is:
Figure BDA0003252486590000022
the navigation route is different, but the departure place and the destination are fixed, that is, the gradient resistance power P2 can be considered to be the same. For the high-speed driving condition, the constant-speed driving condition is dominant, and the acceleration resistance power P4 can be temporarily not considered. And the rolling resistance power P3 is the same for the same vehicle. The driving power is only affected by the air resistance power P1.
The driving power P directly determines the energy consumption level E of the whole vehicle, and the smaller the driving power is, the lower the corresponding energy consumption level is, the longer the endurance is.
Compared with the prior art, the method has the advantages that instantaneous weather factors of the vehicle passing through each navigation route section are considered in real time, the wind resistance of the vehicle in each section is evaluated according to the weather factors of the vehicle passing through each navigation section, so that the total energy consumption level is obtained, more accurate driving route optimization can be realized, driving energy consumption can be reduced, and mileage anxiety of new energy vehicle owners can be relieved; relevant researches show that under the constant-speed driving condition of 75km/h, the wind resistance is reduced by 10%, the endurance mileage can be increased by 9.5%, the driving mileage is increased by reducing the wind resistance, and great economic benefits are achieved.
Drawings
FIG. 1 is a schematic diagram of an energy consumption optimal driving route selection method considering meteorological factors in an embodiment;
FIG. 2 is a schematic diagram of a navigation route provided by navigation software in an embodiment;
FIG. 3 is a schematic diagram showing the effect of wind on the automobile in the embodiment;
FIG. 4 is a schematic diagram illustrating a relationship between a yaw angle and a wind resistance coefficient in the embodiment.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, shall fall within the scope of protection of the present invention.
Examples
The invention relates to an energy consumption optimal driving route selection method considering meteorological factors, which has the principle as shown in figure 1, and navigation software provides a plurality of navigation routes after a driver of an electric automobile inputs a destination. And by means of weather forecast, accurately predicting the meteorological environment of the vehicle passing through each road section of each navigation route. And then, according to a rich wind resistance database in the research and development stage, estimating the wind resistance of the whole vehicle corresponding to each navigation route, and predicting the energy consumption corresponding to each route according to the wind resistance. The method specifically comprises the following steps:
step 1: navigation route
Based on a plurality of navigation routes provided by the navigation software, after a driver inputs a departure place and a destination by the navigation software, the navigation software automatically provides a plurality of navigation routes: A. b, …. .
Correspondingly, the navigation route a can be divided into a plurality of sections, and is recommended to be divided into one section every 1 km. The route consists of a1+ a2+ A3+ … + An.
Correspondingly, the navigation route B consists of a route B1+ B2+ B3+ … + Bn.
And so on.
Step 2: meteorological environment monitoring
By means of an advanced weather forecasting system, the meteorological environment data of the automobile passing each road section are accurately predicted. The meteorological environment data mainly comprises the predicted wind speed and wind direction when the electric automobile passes through the road section.
At the present stage, the weather forecasting technology is mature, and the forecasting precision is high. The common weather forecast APP can be used for forecasting meteorological environment data of each navigation section. The weather forecast APP can be accessed by means of well-established car networking technology.
Correspondingly, the meteorological environments corresponding to the links in the navigation route a are Aw1, Aw2, Aw3 and ….
Correspondingly, the meteorological environments corresponding to the links of the navigation route B are Bw1, Bw2, Bw3 and ….
And so on.
Navigation information/weather forecast information is collected to the vehicle ECU by means of communication technology between the in-vehicle devices. The weather environment information of each road section is collected to the vehicle ECU.
And step 3: weather environment influence judgment on wind resistance
Under the windless working condition, the formula of the aerodynamic resistance of the whole vehicle is as follows:
Figure BDA0003252486590000041
wherein F is the wind resistance of the whole vehicle, rho is the air density, V is the relative speed of the vehicle and the ground, A is the orthographic projection area of the whole vehicle, and Cd is the wind resistance coefficient of the whole vehicle.
However, wind generally exists at all times, and the wind speed and direction also change at all times. The effect of wind on the car is shown in figure 3. Wind speed vWind powerAnd the relative speed (opposite number) v of the vehicle to the groundVehicle with wheelsIs v, and acts on the vehicle at a certain yaw angle beta.
Generally, Cd increases with increasing β. In the vehicle model development stage, an aerodynamic engineer can establish a wind resistance database by means of a wind tunnel test, namely a relationship curve of the influence of beta on Cd, which is shown in an attached figure 4. Meanwhile, the database may be stored in the car ECU as the vehicle information parameter. Different influence relation curves of different beta on Cd can be obtained from wind resistance databases in vehicle model research and development stages.
Considering the influence of wind, the wind resistance of the whole vehicle is as follows:
Figure BDA0003252486590000042
and evaluating the wind resistance of the vehicle type on each navigation route based on the corresponding instantaneous meteorological environment of each road section, and further evaluating the energy consumption level.
According to the power balance relation of the running of the electric vehicle, the driving power and the resistance power are balanced, and the following results are obtained:
P＝P1+p2+p3+p4
where P is driving power, P1 is air resistance power, P2 is gradient resistance power, P3 is rolling resistance power, and P4 is acceleration resistance power. The smaller the wind resistance, the smaller the air resistance power, and correspondingly, the lower the energy consumption level. Wherein:
Figure BDA0003252486590000051
the navigation route is different, but the departure place and the destination are fixed, i.e. the gradient resistance power P2 can be considered to be the same. The high-speed running condition and the constant-speed running condition occupy the dominant position, and the acceleration resistance power P4 can be temporarily not considered. And the rolling resistance power P3 is the same for the same vehicle. The driving power is only affected by the air resistance power P1.
The driving power P directly determines the energy consumption level E of the whole vehicle, and the smaller the driving power is, the lower the corresponding energy consumption level is, the longer the endurance is.
The wind resistance corresponding to the vehicle on each road section of the navigation route A is FA1, FA2, FA3 and … respectively, the energy consumption levels of each road section are EA1, EA2, EA3 and …, and the total energy consumption is predicted to be EA.
And B, predicting total energy consumption as EB.
And so on.
Relevant researches show that under the constant-speed driving condition of 75km/h, the wind resistance is reduced by 10%, the endurance mileage can be increased by 9.5%, the driving mileage is increased by reducing the wind resistance, and great economic benefits are achieved.
And 4, step 4: driving route judgment
And the automobile ECU compares the energy consumption prediction levels of all navigation road sections, and selects the route with the lowest energy consumption to recommend to the driver. The route may be provided to the driver directly by onboard voice or by navigation software.
According to the method, instantaneous weather factors of the vehicle passing through each navigation route section are considered in real time, and the wind resistance of the vehicle in each section is evaluated according to the weather factors of the vehicle passing through each navigation section, so that the total energy consumption level is obtained, more accurate driving route optimization can be realized, driving energy consumption can be reduced, and mileage anxiety of new energy vehicle owners can be relieved.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and those skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (6)

1. An energy consumption optimal driving route selection method considering meteorological factors is characterized by comprising the following steps:
1) based on the information of the departure place and the destination, a plurality of vehicle navigation routes are obtained through a vehicle-mounted navigation system of the electric vehicle, and each navigation route contains each road section information;
2) acquiring meteorological environment data of each road section of each navigation route based on a weather forecast system, and collecting the meteorological environment information predicted by each road section to a vehicle ECU;
3) based on the influence of wind resistance on the vehicle speed and the vehicle type of the electric vehicle, evaluating the wind resistance corresponding to each road section of the electric vehicle in each navigation route, and further evaluating the energy consumption level of each road section;
4) acquiring the total energy consumption level of each navigation route based on the energy consumption level of each road section;
5) and the automobile ECU compares the overall energy consumption levels of all navigation sections, and selects the route with the lowest energy consumption to the driver.
2. The method of claim 1, wherein the meteorological parameters are used to predict wind speed and direction for the electric vehicle to travel through various road segments.
3. The method for selecting the optimal energy consumption driving route according to the claim 2, wherein in the step 3), the wind resistance corresponding to each road section is obtained by the following formula:
Figure FDA0003252486580000011
in the formula, F is the wind resistance of the whole vehicle corresponding to each road section, rho is the air density, and v is the wind speed vWind powerAnd the relative velocity v between the electric vehicle and the groundVehicle with wheelsA is the orthographic projection area of the whole vehicle, Cd is the wind resistance coefficient of the whole vehicle, and the coefficient is the wind speed vWind powerAnd the relative velocity v between the electric vehicle and the groundVehicle with wheelsThe yaw angle beta formed therebetween.
4. The method for selecting the energy consumption optimal driving route considering the meteorological factors as claimed in claim 3, wherein the influence relationship between the yaw angle β and the wind resistance coefficient Cd of the whole vehicle is obtained through a wind resistance database.
5. The method for selecting the optimal energy consumption driving route according to claim 3, wherein the driving power is obtained by obtaining the air resistance power according to the calculated wind resistance corresponding to each road section, and the energy consumption level of each road section is obtained by the driving power.
6. The method for selecting the optimal energy consumption driving route according to claim 5, wherein the expression of the driving power of each road section is as follows:
P＝P1+P2+P3+P4
where P is driving power, P1 is air resistance power, P2 is slope resistance power, P3 is rolling resistance power, and P4 is acceleration resistance power, where the expression for air resistance power P1 is:
Figure FDA0003252486580000021
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CN103605885A (en) * 2013-11-11 2014-02-26 清华大学 Traffic network information based residual mileage estimation method for electric vehicle
JP2018095047A (en) * 2016-12-12 2018-06-21 いすゞ自動車株式会社 Air resistance reduction device
CN110126841A (en) * 2019-05-09 2019-08-16 吉林大学 EV Energy Consumption model prediction method based on road information and driving style
CN111516552A (en) * 2020-04-21 2020-08-11 东风汽车集团有限公司 Method for optimizing driving path according to energy consumption of pure electric vehicle

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130325335A1 (en) * 2012-06-05 2013-12-05 Kia Motors Corporation Method for identifying an eco-route using a state of charge consumption ratio
CN103471605A (en) * 2012-06-05 2013-12-25 现代自动车株式会社 Method for identifying an eco-route using a state of charge consumption ratio
JP2013257204A (en) * 2012-06-12 2013-12-26 Clarion Co Ltd Information terminal and program
CN103605885A (en) * 2013-11-11 2014-02-26 清华大学 Traffic network information based residual mileage estimation method for electric vehicle
JP2018095047A (en) * 2016-12-12 2018-06-21 いすゞ自動車株式会社 Air resistance reduction device
CN110126841A (en) * 2019-05-09 2019-08-16 吉林大学 EV Energy Consumption model prediction method based on road information and driving style
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