CN110281812A - A kind of course continuation mileage estimating system based on operating mode's switch - Google Patents
A kind of course continuation mileage estimating system based on operating mode's switch Download PDFInfo
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- CN110281812A CN110281812A CN201910568659.XA CN201910568659A CN110281812A CN 110281812 A CN110281812 A CN 110281812A CN 201910568659 A CN201910568659 A CN 201910568659A CN 110281812 A CN110281812 A CN 110281812A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L3/00—Electric devices on electrically-propelled vehicles for safety purposes; Monitoring operating variables, e.g. speed, deceleration or energy consumption
- B60L3/12—Recording operating variables ; Monitoring of operating variables
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
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- 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
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
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Abstract
The present invention relates to a kind of course continuation mileage estimating system based on operating mode's switch, which includes operating mode's switch module, course continuation mileage estimation block and information display module；Operating mode's switch module is used to determine the unit mileage energy consumption of sample vehicle future operating condition；Course continuation mileage estimation block is for calculating course continuation mileage estimated value；Information display module is for showing that sample vehicle has consumed energy and course continuation mileage estimated value.The present invention is based on vehicle driving cycle sample big data, the following operating condition unit mileage energy consumption is estimated, while considering sample vehicle, same brand vehicle and different brands same model energy consumption data improve estimate accuracy.
Description
Technical field
The invention belongs to new-energy automobile control technology fields more particularly to a kind of course continuation mileage based on operating mode's switch to estimate
Calculation system.
Background technique
In face of global environmental pollution aggravation and energy shortage two greatly challenge society development, vehicle electric driveization at
For major country in the world and the important development of Automobile Enterprises strategy.The energy of power system of electric automobile is entirely from electricity
Pond, the mileage that can be exercised that once charges are the important indicators for measuring electric car performance.But since the energy content of battery is limited, electricity
Pond charging needs long time again, and when for the trip of driving electric vehicle, especially commercial vehicle is gone on a journey, accurate course continuation mileage is estimated
It calculates very necessary.
The course continuation mileage of electric car is affected by many factors.It is SOC first, battery dump energy substantially determines whole
The general direction of vehicle course continuation mileage；Driver's driving style also will affect course continuation mileage, and radical type driving style is driven with conservative
Sailing lattice are different to the consumption of electricity；Driving operating condition equally will affect course continuation mileage, for vehicle, hill path and urban road
Demand to energy is entirely different.
The course continuation mileage assessment of electric car at present, mainly influence of the consideration remaining capacity to course continuation mileage: according to list
The difference estimation of the state-of-charge and unit time internally-powered battery charge state of operating range and battery in the time of position
The remaining course continuation mileage of vehicle；Influence of the driving cycles to course continuation mileage mainly considers specified path, driving path, this
For needing to be undoubtedly a kind of limitation for the personnel of alternative routing at any time.
Summary of the invention
The technical problem to be solved in the present invention is to provide a kind of the course continuation mileage estimating system based on operating mode's switch, the system
The influence of remaining capacity and the following road conditions to course continuation mileage can be comprehensively considered, the estimation precision of course continuation mileage is improved based on GPS.
In order to solve the above-mentioned technical problem, the course continuation mileage estimating system of the invention based on operating mode's switch includes operating condition
Identification module, course continuation mileage estimation block and information display module；
Operating mode's switch module: being obtained vehicle driving cycle sample big data, obtained road ahead operating condition in real time using GPS,
And the unit mileage energy consumption E of sample vehicle future operating condition is determined according to the consumption information in road ahead operating condition and sample big data1；
Course continuation mileage estimation block: sample vehicle mileage travelled L is calculated in real time0ENERGY E is consumed0, calculate and travelled
The unit mileage energy consumption E of mileage2=E0/L0；Calculate mileage deviation factor k=(E1-E2)/E1, k value range is [- 1,1]；Root
Course continuation mileage correction factor F is searched according to table 1a；If there is no corresponding mileage deviation factor k in table 1, according to mileage deviation factor
K obtains corresponding course continuation mileage correction factor F as interpolation calculationa；
Table 1
Calculate course continuation mileage estimated value L=E/ (E1×Fa)
E is sample vehicle residue gross energy, and L is course continuation mileage estimated value；
Information display module: real-time display sample vehicle has consumed ENERGY E0With course continuation mileage estimated value.
The information display module also shows sample vehicle information of charging pile nearby.
The vehicle driving cycle sample big data includes following information: sample vehicle is in the unit of road ahead operating condition
Journey energy consumption E21, unit mileage energy consumption E of the same brand vehicle in road ahead operating condition22, different brands same model is in front road
The unit mileage energy consumption E of road operating condition23；
The unit mileage energy consumption E of the following operating condition1It is calculated according to formula 1:
E1=E21×F21+E22×F22+E23×F23 (1)
Wherein F21, F22, F23For the energy consumption factor, F21Take 0.6~0.7, F22Take 0.2~0.3, F23Take 0.1, and F21+
F22+F23=1.
The vehicle driving cycle sample big data includes following information: sample Che Yu road ahead operating condition is mutually gone the same way
The unit mileage energy consumption E of section31, same brand vehicle and unit mileage energy consumption E in road ahead operating condition same road segment32, different
Brand same model is in the unit mileage energy consumption E with road ahead operating condition same road segment33。
The unit mileage energy consumption E of the following operating condition1It is calculated according to formula 2:
E1=E31×F21+E22×F22+E23×F23 (2)
Wherein F21, F22, F23For the energy consumption factor, F21Take 0.4~0.5, F22Take 0.4~0.5, F23Take 0.1, and F21+
F22+F23=1.
The unit mileage energy consumption E of the following operating condition1It is calculated according to formula 3:
E1=E21×F21+E32×F22+E23×F23 (3)
Wherein F21, F22, F23For the energy consumption factor, F21Take 0.4~0.5, F22Take 0.4~0.5, F23Take 0.1, and F21+
F22+F23=1.
The unit mileage energy consumption E of the following operating condition1It is calculated according to formula 4:
E1=E21×F21+E22×F22+E33×F23 (4)
Wherein F21, F22, F23For the energy consumption factor, F21Take 0.4~0.5, F22Take 0.4~0.5, F23Take 0.1, and F21+
F22+F23=1.
The unit mileage value is 1 kilometer.
Vehicle driving cycle sample big data is stored according to GPS physical location, when sample vehicle is travelled to current location, root
The energy expenditure information of different vehicle in the position is obtained according to GPS positioning.
Invention effect
The present invention improves course continuation mileage estimate accuracy using following methods:
1. being based on vehicle driving cycle sample big data, the following operating condition unit mileage energy consumption is estimated, is considered simultaneously
Sample vehicle, same brand vehicle and different brands same model energy consumption data, improve estimate accuracy；
2. for prevent that instrument from show can continual mileage frequently change, introduce the unit mileage average energy consumption that has travelled to continuing
Boat mileage is modified；Further improve estimate accuracy；
3. information display module real-time display vehicle energy consumption, course continuation mileage and neighbouring information of charging pile are convenient for driver
Route is adjusted flexibly in accurate whole vehicle information of grasping, and eliminates mileage anxiety.
Detailed description of the invention
Fig. 1 is the composition block diagram of the course continuation mileage estimating system of the invention based on operating mode's switch.
Specific embodiment
The present invention is explained in detail with reference to the accompanying drawing:
As shown in Figure 1, the course continuation mileage estimating system of the invention based on operating mode's switch includes following operating mode's switch module,
Course continuation mileage estimation block and information display module；
Operating mode's switch module: it is offline to obtain vehicle driving cycle sample big data, obtain road ahead work in real time using GPS
Condition determines the following operating condition unit mileage energy consumption E according to road condition and sample data consumption information1。
Unit mileage energy consumption E1Calculation method is as follows:
E1=Etotal/Ltotal
Wherein,t1And t2For sample data period, EtotalFor period (t2-t1) in
Total energy consumption, U (t) are battery power feeds voltage, and I (t) is battery power feeds electric current, LtotalIn period (t2-t1) in mileage travelled.
Other unit mileage Calculation Method of Energy Consumption is identical.
Vehicle driving cycle sample big data includes following information: unit mileage energy consumption of the sample vehicle in road ahead operating condition
E21, unit mileage energy consumption E of the same brand vehicle in road ahead operating condition22, different brands same model is in road ahead operating condition
Unit mileage energy consumption E23；The unit mileage energy consumption E of sample vehicle future operating condition1It is calculated by following formula:
E1=E21×F21+E22×F22+E23×F23,
F21, F22, F23For the energy consumption factor, F21Take 0.6~0.7, F22Take 0.2~0.3, F23Take 0.1, F21+F22+F23
=1.
The vehicle driving cycle sample big data also includes following information: sample Che Yu road ahead operating condition is identical
The unit mileage energy consumption E in section31, same brand vehicle is in the unit mileage energy consumption E with road ahead operating condition same road segment32, no
With brand same model in the unit mileage energy consumption E with road ahead operating condition same road segment33；
If road ahead operating condition does not have sample vehicle running data, the unit mileage of the following operating condition is calculated according to formula 2
Energy consumption
E1；E1=E31×F21+E22×F22+E23×F23 (2)
If the section does not have same brand vehicle running data, the unit mileage energy consumption E of the following operating condition1According to formula
3 calculate:
E1=E21×F21+E32×F22+E23×F23 (3)
If the section does not have different brands same model running data, the unit mileage energy consumption E of the following operating condition1According to
Formula 4 calculates:
E1=E21×F21+E22×F22+E33×F23 (4)
Wherein F21, F22, F23For the energy consumption factor, F21Take 0.4~0.5, F22Take 0.4~0.5, F23Take 0.1, and F21+
F22+F23=1.
Unit mileage value is 1 kilometer；
Vehicle driving cycle sample big data is stored according to GPS physical location, can when sample vehicle is travelled to current location
To obtain the energy expenditure information of different vehicle in the position according to GPS positioning；Different vehicle includes sample vehicle, with sample vehicle phase
With three kinds of data informations of brand vehicle and different brands same model；Sample vehicle is the current sample car of driver-operated；Vehicle
Driving cycle sample big data stores different data, the average energy consumption including sample vehicle under different road conditions according to traffic information simultaneously
E31, with sample vehicle same brand vehicle average energy consumption E32, different brands same model average energy consumption E33, road conditions are divided into expressway,
Hill path, rural area dirt road, city road, rural road.
Course continuation mileage estimation block calculates vehicle mileage travelled L in real time0ENERGY E is consumed0, calculate in having travelled
The unit mileage energy consumption E of journey2=E0/L0；Calculate mileage deviation factor k=(E1-E2)/E1, k value range is [- 1,1]；According to
Table 1 searches course continuation mileage correction factor Fa；If there is no corresponding mileage deviation factor k in table 1, according to mileage deviation factor k
Corresponding course continuation mileage correction factor F is obtained as interpolation calculationa(course continuation mileage correction factor F in table 1aIt can be micro- in a small range
It adjusts, adjusting range ± 0.5).
1 course continuation mileage correction chart of table
Calculate course continuation mileage estimated value L=E/ (E1×Fa)
E is sample vehicle residue gross energy, and L is course continuation mileage estimated value.
Mileage travelled average energy consumption, future trajectory estimate energy consumption, course continuation mileage to information display module real-time display vehicle
Estimated value and neighbouring information of charging pile, renewal time are to update once for every 5 minutes, accurately grasp whole vehicle information convenient for driver
Route is adjusted flexibly, eliminates mileage anxiety.
Sample vehicle running data is imported into vehicle driving cycle sample big data.
Herein, vehicle driving cycle sample big data not only includes vehicle energy consumption data, can be according to sample
Vehicle functional requirement and data collection ability are enriched constantly, and later Function Extension is convenient for.
Claims (8)
1. a kind of course continuation mileage estimating system based on operating mode's switch, it is characterised in that include operating mode's switch module, course continuation mileage
Estimation block and information display module；
Operating mode's switch module: vehicle driving cycle sample big data is obtained, obtains road ahead operating condition, and root in real time using GPS
The unit mileage energy consumption E of sample vehicle future operating condition is determined according to the consumption information in road ahead operating condition and sample big data1；
Course continuation mileage estimation block: sample vehicle mileage travelled L is calculated in real time0ENERGY E is consumed0, calculate mileage travelled
Unit mileage energy consumption E2=E0/L0；Calculate mileage deviation factor k=(E1-E2)/E1, k value range is [- 1,1]；According to table 1
Search course continuation mileage correction factor Fa；If there is no corresponding mileage deviation factor k in table 1, inserted according to mileage deviation factor k
Corresponding course continuation mileage correction factor F is calculated in valuea；
Table 1
Calculate course continuation mileage estimated value L=E/ (E1×Fa)
E is sample vehicle residue gross energy, and L is course continuation mileage estimated value；
Information display module: real-time display sample vehicle has consumed ENERGY E0With course continuation mileage estimated value.
2. the course continuation mileage estimating system according to claim 1 based on operating mode's switch, it is characterised in that the information
Display module also shows sample vehicle information of charging pile nearby.
3. the course continuation mileage estimating system according to claim 1 based on operating mode's switch, it is characterised in that the vehicle
Driving cycle sample big data includes following information: unit mileage energy consumption E of the sample vehicle in road ahead operating condition21, same brand
Unit mileage energy consumption E of the vehicle in road ahead operating condition22, unit mileage energy of the different brands same model in road ahead operating condition
Consume E23。
4. the course continuation mileage estimating system according to claim 3 based on operating mode's switch, it is characterised in that the following operating condition
Unit mileage energy consumption E1It is calculated according to formula 1:
E1=E21×F21+E22×F22+E23×F23 (1)
Wherein F21, F22, F23For the energy consumption factor, F21Take 0.6~0.7, F22Take 0.2~0.3, F23Take 0.1, and F21+F22+
F23=1.
5. the course continuation mileage estimating system according to claim 3 based on operating mode's switch, it is characterised in that the vehicle
Driving cycle sample big data also includes following information: the unit mileage energy consumption of sample Che Yu road ahead operating condition same road segment
E31, same brand vehicle and unit mileage energy consumption E in road ahead operating condition same road segment32, different brands same model with
The unit mileage energy consumption E of road ahead operating condition same road segment33。
6. the course continuation mileage estimating system according to claim 5 based on operating mode's switch, it is characterised in that the following operating condition
Unit mileage energy consumption E1It is calculated according to formula 2:
E1=E31×F21+E22×F22+E23×F23 (2)
Wherein F21, F22, F23For the energy consumption factor, F21Take 0.4~0.5, F22Take 0.4~0.5, F23Take 0.1, and F21+F22+
F23=1.
7. the course continuation mileage estimating system according to claim 5 based on operating mode's switch, it is characterised in that the following operating condition
Unit mileage energy consumption E1It is calculated according to formula 3:
E1=E21×F21+E32×F22+E23×F23 (3)
Wherein F21, F22, F23For the energy consumption factor, F21Take 0.4~0.5, F22Take 0.4~0.5, F23Take 0.1, and F21+F22+
F23=1.
8. the course continuation mileage estimating system according to claim 5 based on operating mode's switch, it is characterised in that the following operating condition
Unit mileage energy consumption E1It is calculated according to formula 4:
E1=E21×F21+E22×F22+E33×F23 (4)
Wherein F21, F22, F23For the energy consumption factor, F21Take 0.4~0.5, F22Take 0.4~0.5, F23Take 0.1, and F21+F22+
F23=1.
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| CN111516500B (en) * | 2020-04-19 | 2022-02-11 | 神龙汽车有限公司 | Hybrid electric vehicle endurance mileage prediction method |
| CN113733918A (en) * | 2021-08-20 | 2021-12-03 | 合众新能源汽车有限公司 | Method and device for calculating remaining mileage of electric vehicle |
| CN114572055A (en) * | 2022-03-17 | 2022-06-03 | 一汽解放汽车有限公司 | Endurance mileage estimation method, endurance mileage estimation device, computer equipment and storage medium |
| CN114572055B (en) * | 2022-03-17 | 2023-08-18 | 一汽解放汽车有限公司 | Method, device, computer equipment and storage medium for estimating endurance mileage |
| CN116572799A (en) * | 2023-07-13 | 2023-08-11 | 四川轻化工大学 | Power battery charge duration prediction method, system and terminal based on deep learning |
| CN116572799B (en) * | 2023-07-13 | 2023-09-05 | 四川轻化工大学 | Power battery charge duration prediction method, system and terminal based on deep learning |
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