CN108146265A - Battery based on big data recommends method, apparatus, storage medium and terminal - Google Patents
Battery based on big data recommends method, apparatus, storage medium and terminal Download PDFInfo
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- CN108146265A CN108146265A CN201711279521.5A CN201711279521A CN108146265A CN 108146265 A CN108146265 A CN 108146265A CN 201711279521 A CN201711279521 A CN 201711279521A CN 108146265 A CN108146265 A CN 108146265A
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- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000004364 calculation method Methods 0.000 claims abstract description 17
- 238000013507 mapping Methods 0.000 claims description 13
- 238000004590 computer program Methods 0.000 claims description 9
- 238000007599 discharging Methods 0.000 claims description 3
- 230000005611 electricity Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000005266 casting Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
Classifications
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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
- B60L50/00—Electric propulsion with power supplied within the vehicle
- B60L50/50—Electric propulsion with power supplied within the vehicle using propulsion power supplied by batteries or fuel cells
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/023—Services making use of location information using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
-
- 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
- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/70—Interactions with external data bases, e.g. traffic centres
-
- 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
- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/70—Interactions with external data bases, e.g. traffic centres
- B60L2240/72—Charging station selection relying on external data
-
- 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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- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Life Sciences & Earth Sciences (AREA)
- Sustainable Development (AREA)
- Sustainable Energy (AREA)
- Power Engineering (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Navigation (AREA)
- Electric Propulsion And Braking For Vehicles (AREA)
Abstract
The embodiment of the present invention provides a kind of battery based on big data and recommends method, apparatus, storage medium and terminal.This method includes the following steps：The history driving information that user drives electric vehicle is obtained, the history driving information includes the travel route in preset time period；Obtain the location information of each battery altering point in the travel route pre-determined distance；According to the positional information calculation along the first traveling distance value between two farthest battery altering points of the operating range on the direction of the travel route；Target battery capability value is calculated according to the described first traveling distance value；Recommend the battery program of corresponding model to user according to the target battery capability value.
Description
Technical field
The present invention relates to field of communication technology, more particularly to a kind of battery based on big data recommends method, apparatus, storage
Medium and terminal.
Background technology
With the enhancing of the environmental consciousness of people, battery-driven vehicle is also more and more, such as battery-operated motor cycle, electronic
Automobile etc..But the cruising ability of battery is not as good as gasoline always, often will appear in driving way dead battery leads to electricity
Motor-car casts anchor.
There is rentable battery business at present, the bigger battery of battery capacity is more expensive, and user is difficult to know how selection one
The suitable battery of money is used as the driving battery of electric vehicle.
Invention content
The embodiment of the present invention provides a kind of battery based on big data and recommends method, apparatus, storage medium and terminal, can be with
Recommend the battery of suitable capacity to user to avoid casting anchor in driving way.
The embodiment of the present invention provides a kind of battery based on big data and recommends method, which is characterized in that the method includes
Following steps：
The history driving information that user drives electric vehicle is obtained, the history driving information includes the row in preset time period
Sail route；
Obtain the location information of each battery altering point in the travel route pre-determined distance；
According to the positional information calculation along farthest two of the operating range on the direction of the travel route
The first traveling distance value between battery altering point；
Target battery capability value is calculated according to the described first traveling distance value；
Recommend the battery of corresponding model to user according to the target battery capability value.
In the battery recommendation method of the present invention based on big data, it is described according to the positional information calculation along
First between the two battery altering points of operating range farthest on the direction of the travel route travels distance value
Step includes：
According to the positional information calculation along two batteries of arbitrary neighborhood on the direction of the travel route more
Each second operating range value between changing a little；
Selected from each second operating range value the second maximum operating range value as operating range farthest two
The first traveling distance value between a battery altering point.
It is described according to the described first traveling distance value meter in the battery recommendation method of the present invention based on big data
The step of calculating target battery capability value includes：
Obtain the mapping relations between operating range value and the power consumption in preset time period；
First power consumption is calculated according to the described first traveling distance value and the mapping relations；
Target battery capability value is calculated according to first power consumption.
In the battery recommendation method of the present invention based on big data, the history driving information includes default
The daily power consumption in travel route and preset time period in period；
Described the step of calculating target battery capability value according to first power consumption, includes：
The target battery capability value is calculated according to the daily power consumption and first power consumption.
It is described according to the daily power consumption and institute in the battery recommendation method of the present invention based on big data
The step of the first power consumption calculates the target battery capability value is stated to include：
The target battery is calculated according to preset formula, the daily power consumption Q1 and the first power consumption Q2 to hold
Magnitude Q3, wherein Q1/a+Q2=bQ3, wherein, a is daily charging times, and b is the discharging efficiency of battery.
A kind of battery recommendation apparatus based on big data, described device include：
First acquisition module, for obtaining the history driving information that user drives electric vehicle, the history driving information packet
Include the travel route in preset time period；
Second acquisition module, for obtaining the position of each battery altering point in the travel route pre-determined distance
Information；
First computing module, for according to the positional information calculation along the traveling on the direction of the travel route away from
From the first traveling distance value between two farthest battery altering points；
Second computing module, for calculating target battery capability value according to the described first traveling distance value；
Recommend the battery of corresponding model to user according to the target battery capability value.
In the battery recommendation apparatus of the present invention based on big data, first computing module includes：
According to the positional information calculation along two batteries of arbitrary neighborhood on the direction of the travel route more
Each second operating range value between changing a little；
Selected from each second operating range value the second maximum operating range value as operating range farthest two
The first traveling distance value between a battery altering point.
In the battery recommendation apparatus of the present invention based on big data, second computing module includes：
First acquisition unit, for obtaining the mapping relations between operating range value and power consumption in preset time period；
First computing unit, for calculating the first power consumption according to the described first traveling distance value and the mapping relations
Amount；
Second computing unit, for calculating target battery capability value according to first power consumption.The embodiment of the present invention
A kind of storage medium is also provided, computer program is stored in the storage medium, when the computer program on computers
During operation so that the computer performs the above method.
The embodiment of the present invention also provides a kind of terminal, and including processor and memory, calculating is stored in the memory
Machine program, the processor is by calling the computer program stored in the memory, for performing the above method.
From the foregoing, it will be observed that the embodiment of the present invention drives the history driving information of electric vehicle, the history row by obtaining user
It sails information and includes the travel route in preset time period；Obtain each battery altering in the travel route pre-determined distance
The location information of point；According to two of the positional information calculation along the operating range on the direction of the travel route farthest
The first traveling distance value between the battery altering point；Target battery capacity is calculated according to the described first traveling distance value
Value；Recommend the battery of corresponding model to user according to the target battery capability value；So as to fulfill the recommendation of battery, have and avoid
The advantageous effect that electric vehicle casts anchor due to not enough power supply in the process of moving.
Description of the drawings
To describe the technical solutions in the embodiments of the present invention more clearly, make required in being described below to embodiment
Attached drawing is briefly described.It should be evident that the accompanying drawings in the following description is only some embodiments of the present invention, for
For those skilled in the art, without creative efforts, it can also be obtained according to these attached drawings other attached
Figure.
Fig. 1 is the flow diagram that the battery provided in an embodiment of the present invention based on big data recommends method.
Fig. 2 is the structure diagram of the battery recommendation apparatus provided in an embodiment of the present invention based on big data.
Specific embodiment
Below in conjunction with the attached drawing in the embodiment of the present invention, the technical solution in the embodiment of the present invention is carried out clear, complete
Site preparation describes.Obviously, described embodiment is only part of the embodiment of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, the every other implementation that those skilled in the art are obtained under the premise of not making the creative labor
Example, belongs to protection scope of the present invention.
Term " first ", " second ", " third " in description and claims of this specification and above-mentioned attached drawing etc.
(if present) is the object for distinguishing similar, and specific sequence or precedence are described without being used for.It should be appreciated that this
The object of sample description can be interchanged in the appropriate case.In addition, term " comprising " and " having " and their any deformation, meaning
Figure is to cover non-exclusive include.For example, it contains the process of series of steps, method or contains a series of modules or list
The device of member, terminal, system are not necessarily limited to those steps clearly listed or module or unit, can also include unclear
The step of ground is listed or module or unit can also be included for intrinsic its of these processes, method, apparatus, terminal or system
Its step or module or unit.
With reference to figure 1, Fig. 1 is the flow chart that a kind of battery based on big data recommends method.The method includes following steps
Suddenly：
S101, the history driving information that user drives electric vehicle is obtained, the history driving information includes preset time period
Interior travel route.
In this step, which can include the travel route in preset time period, the preset time period
It can be nearest Monday month etc..The history driving information can be obtained from the alignment system and battery management system of battery
It takes, alignment system can be in real time sent on the location information to terminal of itself.
In some embodiments, which further includes daily electric quantity consumption value.The battery management system
The information about power of itself can be sent in real time and is sent to terminal, and terminal is so as to calculate daily electric quantity consumption.
The location information of each battery altering point of S102, acquisition in the travel route pre-determined distance.
In this step, which could be provided as 200 meters, 500 meters etc., such as be set as 200 meters, and the battery is more
It changes a little apart from the minimum range of the travel route within 200 meters.
S103, according to the positional information calculation along farthest two of the operating range on the direction of the travel route
The first traveling distance value between the battery altering point.
In some embodiments, step S103 includes：
S1031, according to the positional information calculation along the arbitrary neighborhood two on the direction of the travel route
Each second operating range value between battery altering point.
S1032, the second maximum operating range value is selected from each second operating range value as operating range most
The first traveling distance value between two remote battery altering points.
In this step, such as on travel route there are five battery altering points of A, B, C, D, E in pre-determined distance successively.Example
Such as, along travel route, it be the operating range of x2, C and D is x3, D and E that the operating range of A and B, which is the operating range of x1, B and C,
Operating range be x4.Wherein, x3 is maximum operating range, therefore using C and D as along operating range on the travel route
Two farthest battery altering points.
S104, target battery capability value is calculated according to the described first traveling distance value.
In this step, can the target battery capacity be calculated according to the first traveling distance value so that the target battery
Capacity can at least support electric vehicle to travel the first traveling distance value.
In some embodiments, step S104 includes：
S1041, the operating range value in acquisition preset time period and the mapping relations between power consumption.
Since the power consumption efficiency of the electric vehicle of different brands is different, that is to say identical electric drive electric vehicle traveling
Apart from different.Even if the electric vehicle of same brand, since its newness degree is different, power consumption efficiency is also different.It is old
Electric vehicle power consumption efficiency it is low.Therefore, in order to improve the accuracy of calculating, need to obtain traveling in nearest preset time period away from
From the mapping relations between value and power consumption.
S1042, the first power consumption is calculated according to the described first traveling distance value and the mapping relations.
In this step, which is to have travelled the required minimum power consumption of first distance value.
S1043, target battery capability value is calculated according to first power consumption.
In this step, it is known that first power consumption can calculate corresponding target battery capacity so that the battery
The electric vehicle can at least be supported to travel first distance value.
Advanced optimize ground, in some embodiments, history driving information include preset time period in travel route with
And the daily power consumption in preset time period.Step S1043 includes：Target battery is calculated according to first power consumption to hold
The step of magnitude, includes：The target battery capability value is calculated according to daily power consumption and the first power consumption.Wherein it is possible to
The target battery capability value Q3 is calculated according to preset formula, the daily power consumption Q1 and the first power consumption Q2,
Wherein Q1/a+Q2=bQ3, wherein, a is daily charging times, and b is the discharging efficiency of battery.
The a can be the daily average charge number of the user obtained according to historical information or input by user
The charging times that can be received daily.Since electricity cannot be supplied to electric vehicle to be driven by battery completely, battery must be given
The management system power supply of itself, and after battery capacity is less than certain threshold value, electric vehicle can not be driven to travel again, therefore,
The ratio of battery total capacity is that the electricity of b can be supplied to electric vehicle to be travelled.
S105, the battery for recommending corresponding model to user according to the target battery capability value.
The price of the target battery capability value and then reference battery is calculated, recommendation is a can to meet user power utilization
The demand and relatively cheap battery of price is to user.
From the foregoing, it will be observed that the embodiment of the present invention drives the history driving information of electric vehicle, the history row by obtaining user
It sails information and includes the travel route in preset time period；Obtain each battery altering in the travel route pre-determined distance
The location information of point；According to two of the positional information calculation along the operating range on the direction of the travel route farthest
The first traveling distance value between the battery altering point；Target battery capacity is calculated according to the described first traveling distance value
Value；Recommend the battery of corresponding model to user according to the target battery capability value；So as to fulfill the recommendation of battery, have and avoid
The advantageous effect that electric vehicle casts anchor due to not enough power supply in the process of moving.
Fig. 2 is please referred to, Fig. 2 is the structure chart of the battery recommendation apparatus based on big data in one embodiment of the invention.It should
Battery recommendation apparatus based on big data includes：
First acquisition module 201, for obtaining the history driving information that user drives electric vehicle, the history driving information
Including the travel route in preset time period.
Second acquisition module 202, for obtaining each battery altering point in the travel route pre-determined distance
Location information.
First computing module 203, for according to the positional information calculation along the row on the direction of the travel route
Sail the first traveling distance value between two farthest battery altering points of distance.
Second computing module 204, for calculating target battery capability value according to the described first traveling distance value.
Recommending module 205, for recommending the battery of corresponding model to user according to the target battery capability value.
In some embodiments, the first computing module 203 includes：
Third computing unit, for according to the positional information calculation along the arbitrary phase on the direction of the travel route
Each second operating range value between adjacent two battery altering points.
Selecting unit, for selecting the second maximum operating range value from each second operating range value as traveling
The first traveling distance value between two farthest battery altering points of distance.
In some embodiments, the second computing module 204 includes：
First acquisition unit, for obtaining the mapping relations between operating range value and power consumption in preset time period.
First computing unit, for calculating the first power consumption according to the described first traveling distance value and the mapping relations
Amount.
Second computing unit, for calculating target battery capability value according to first power consumption.
From the foregoing, it will be observed that the embodiment of the present invention drives the history driving information of electric vehicle, the history row by obtaining user
It sails information and includes the travel route in preset time period；Obtain each battery altering in the travel route pre-determined distance
The location information of point；According to two of the positional information calculation along the operating range on the direction of the travel route farthest
The first traveling distance value between the battery altering point；Target battery capacity is calculated according to the described first traveling distance value
Value；Recommend the battery of corresponding model to user according to the target battery capability value；So as to fulfill the recommendation of battery.
The embodiment of the present invention also provides a kind of storage medium, and computer program is stored in the storage medium, when the calculating
When machine program is run on computers, which performs the battery recommendation side based on big data described in any of the above-described embodiment
Method.
The embodiment of the present invention also provides a kind of terminal, and including processor and memory, calculating is stored in the memory
Machine program, the processor are above-mentioned based on big for performing by calling the computer program stored in the memory
The battery of data recommends method.
It should be noted that one of ordinary skill in the art will appreciate that whole in the various methods of above-described embodiment or
Part steps are relevant hardware can be instructed to complete by program, which can be stored in computer-readable storage medium
In matter, which can include but is not limited to：Read-only memory (ROM, Read Only Memory), random access memory
Device (RAM, Random Access Memory), disk or CD etc..
The battery based on big data provided above the embodiment of the present invention recommends method, apparatus, storage medium and end
End is described in detail, and specific case used herein is expounded the principle of the present invention and embodiment, more than
The explanation of embodiment is merely used to help understand the method and its core concept of the present invention；Meanwhile for those skilled in the art
Member, thought according to the present invention, there will be changes in specific embodiments and applications, in conclusion this explanation
Book content should not be construed as limiting the invention.
Claims (10)
1. a kind of battery based on big data recommends method, which is characterized in that the described method comprises the following steps：
The history driving information that user drives electric vehicle is obtained, the history driving information includes the traveling road in preset time period
Line；
Obtain the location information of each battery altering point in the travel route pre-determined distance；
According to the positional information calculation along two farthest batteries of the operating range on the direction of the travel route
Replace the first traveling distance value between point；
Target battery capability value is calculated according to the described first traveling distance value；
Recommend the battery of corresponding model to user according to the target battery capability value.
2. the battery according to claim 1 based on big data recommends method, which is characterized in that described according to the position
Information is calculated along first between two farthest battery altering points of the operating range on the direction of the travel route
The step of operating range value, includes：
According to the positional information calculation along the battery altering point of arbitrary neighborhood two on the direction of the travel route
Between each second operating range value；
The second maximum operating range value two institutes farthest as operating range are selected from each second operating range value
State the first traveling distance value between battery altering point.
3. the battery according to claim 1 based on big data recommends method, which is characterized in that described according to described first
Operating range value calculates the step of target battery capability value and includes：
Obtain the mapping relations between operating range value and the power consumption in preset time period；
First power consumption is calculated according to the described first traveling distance value and the mapping relations；
Target battery capability value is calculated according to first power consumption.
4. the battery according to claim 3 based on big data recommends method, which is characterized in that the history traveling
Information includes the travel route in preset time period and the daily power consumption in preset time period；
Described the step of calculating target battery capability value according to first power consumption, includes：
The target battery capability value is calculated according to the daily power consumption and first power consumption.
5. the battery according to claim 4 based on big data recommends method, which is characterized in that described according to described daily
The step of power consumption and first power consumption calculate the target battery capability value includes：
The target battery capability value is calculated according to preset formula, the daily power consumption Q1 and the first power consumption Q2
Q3, wherein Q1/a+Q2=bQ3, wherein, a is daily charging times, and b is the discharging efficiency of battery.
6. a kind of battery recommendation apparatus based on big data, which is characterized in that described device includes：
First acquisition module, for obtaining the history driving information that user drives electric vehicle, the history driving information includes pre-
If the travel route in the period；
Second acquisition module, for obtaining the position of each battery altering point in travel route pre-determined distance letter
Breath；
First computing module, for according to the positional information calculation along the operating range on the direction of the travel route most
The first traveling distance value between two remote battery altering points；
Second computing module, for calculating target battery capability value according to the described first traveling distance value；
Recommending module, for recommending the battery of corresponding model to user according to the target battery capability value.
7. the battery recommendation apparatus according to claim 6 based on big data, which is characterized in that first computing module
Including：
Third computing unit, for according to the positional information calculation along the arbitrary neighborhood two on the direction of the travel route
Each second operating range value between a battery altering point；
Selecting unit, for selecting the second maximum operating range value from each second operating range value as operating range
The first traveling distance value between two farthest battery altering points.
8. the battery recommendation apparatus according to claim 6 based on big data, which is characterized in that second computing module
Including：
First acquisition unit, for obtaining the mapping relations between operating range value and power consumption in preset time period；
First computing unit, for calculating the first power consumption according to the described first traveling distance value and the mapping relations；
Second computing unit, for calculating target battery capability value according to first power consumption.
9. a kind of storage medium, which is characterized in that computer program is stored in the storage medium, when the computer program
When running on computers so that the computer perform claim requires 1 to 5 any one of them method.
10. a kind of terminal, which is characterized in that including processor and memory, computer program, institute are stored in the memory
Processor is stated by calling the computer program stored in the memory, for any one of perform claim requirement 1 to 5 institute
The method stated.
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| CN111806291A (en) * | 2020-07-14 | 2020-10-23 | 武汉蔚来能源有限公司 | Vehicle and battery matching method, device and system and readable storage medium |
| CN112748680A (en) * | 2019-10-29 | 2021-05-04 | 丰田自动车株式会社 | Information processing apparatus, information processing method, and non-transitory computer-readable storage medium |
| CN114683911A (en) * | 2020-12-31 | 2022-07-01 | 奥动新能源汽车科技有限公司 | Battery swapping processing method and system, computer device and storage medium |
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