CN108146265B - Big data-based battery recommendation method and device, storage medium and terminal - Google Patents
Big data-based battery recommendation method and device, storage medium and terminal Download PDFInfo
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- CN108146265B CN108146265B CN201711279521.5A CN201711279521A CN108146265B CN 108146265 B CN108146265 B CN 108146265B CN 201711279521 A CN201711279521 A CN 201711279521A CN 108146265 B CN108146265 B CN 108146265B
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- 238000000034 method Methods 0.000 title claims abstract description 34
- 238000004364 calculation method Methods 0.000 claims description 14
- 238000013507 mapping Methods 0.000 claims description 13
- 238000004590 computer program Methods 0.000 claims description 11
- 230000015556 catabolic process Effects 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000005611 electricity Effects 0.000 description 2
- 238000007599 discharging Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
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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
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- 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
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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
- 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
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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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- Life Sciences & Earth Sciences (AREA)
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- Mechanical Engineering (AREA)
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Abstract
The embodiment of the invention provides a battery recommendation method and device based on big data, a storage medium and a terminal. The method comprises the following steps: acquiring historical driving information of a user driving an electric vehicle, wherein the historical driving information comprises a driving route in a preset time period; acquiring position information of each battery replacement point within a preset distance from the driving route; calculating a first travel distance value between two of the battery exchange points, which are farthest in travel distance in a direction along the travel route, according to the position information; calculating a target battery capacity value according to the first travel distance value; and recommending the battery with the corresponding model to the user according to the target battery capacity value.
Description
Technical Field
The invention relates to the technical field of communication, in particular to a battery recommendation method and device based on big data, a storage medium and a terminal.
Background
With the increasing awareness of environmental protection, more and more vehicles driven by batteries, such as electric motorcycles, electric automobiles, etc., are used. However, the cruising ability of the battery is always inferior to that of gasoline, and the electric vehicle is usually broken down due to the fact that the battery is dead in the driving process.
At present, battery leasing business appears, the larger the battery capacity is, the more expensive the battery is, and a user can hardly know how to select a proper battery to be used as a driving battery of an electric vehicle.
Disclosure of Invention
The embodiment of the invention provides a battery recommendation method, device, storage medium and terminal based on big data, which can recommend a battery with proper capacity to a user to avoid driving breakdown.
The embodiment of the invention provides a battery recommendation method based on big data, which is characterized by comprising the following steps:
acquiring historical driving information of a user driving an electric vehicle, wherein the historical driving information comprises a driving route in a preset time period;
acquiring position information of each battery replacement point within a preset distance from the driving route;
calculating a first travel distance value between two of the battery exchange points, which are farthest in travel distance in a direction along the travel route, according to the position information;
calculating a target battery capacity value according to the first travel distance value;
and recommending the battery with the corresponding model to the user according to the target battery capacity value.
In the big-data-based battery recommendation method according to the present invention, the calculating a first travel distance value between two battery exchange points that are farthest in travel distance in a direction along the travel route from the location information includes:
calculating each second driving distance value between any two adjacent battery replacement points in the direction along the driving route according to the position information;
the largest second driving distance value is selected from the second driving distance values as the first driving distance value between the two battery exchange points with the largest driving distance.
In the battery recommendation method based on big data according to the present invention, the calculating a target battery capacity value from the first travel distance value includes:
acquiring a mapping relation between a driving distance value and power consumption in a preset time period;
calculating first power consumption according to the first travel distance value and the mapping relation;
and calculating a target battery capacity value according to the first power consumption.
In the big data-based battery recommendation method, the historical driving information comprises a driving route in a preset time period and daily power consumption in the preset time period;
the step of calculating a target battery capacity value based on the first power consumption amount includes:
and calculating the target battery capacity value according to the daily power consumption and the first power consumption.
In the big data-based battery recommendation method according to the present invention, the calculating the target battery capacity value based on the daily power consumption amount and the first power consumption amount includes:
the target battery capacity value Q3 is calculated according to a preset formula, the daily power consumption amount Q1 and the first power consumption amount Q2, where Q1/a + Q2 ═ bQ3, where a is the number of charges per day and b is the discharge efficiency of the battery.
A big-data based battery recommendation apparatus, the apparatus comprising:
the system comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is used for acquiring historical driving information of an electric vehicle driven by a user, and the historical driving information comprises a driving route in a preset time period;
the second acquisition module is used for acquiring the position information of each battery replacement point within a preset distance from the driving route;
a first calculation module for calculating a first travel distance value between two of the battery exchange points that are the farthest travel distances in a direction along the travel route, according to the position information;
the second calculation module is used for calculating a target battery capacity value according to the first travel distance value;
and recommending the battery with the corresponding model to the user according to the target battery capacity value.
In the big data-based battery recommendation apparatus according to the present invention, the first calculation module includes:
calculating each second driving distance value between any two adjacent battery replacement points in the direction along the driving route according to the position information;
the largest second driving distance value is selected from the second driving distance values as the first driving distance value between the two battery exchange points with the largest driving distance.
In the big data-based battery recommendation apparatus according to the present invention, the second calculation module includes:
the first acquisition unit is used for acquiring a mapping relation between a driving distance value and power consumption in a preset time period;
the first calculation unit is used for calculating first power consumption according to the first travel distance value and the mapping relation;
and the second calculating unit is used for calculating a target battery capacity value according to the first power consumption. An embodiment of the present invention further provides a storage medium, where a computer program is stored in the storage medium, and when the computer program runs on a computer, the computer is caused to execute the above method.
The embodiment of the invention also provides a terminal, which comprises a processor and a memory, wherein the memory is stored with a computer program, and the processor is used for executing the method by calling the computer program stored in the memory.
As can be seen from the above, in the embodiment of the present invention, historical driving information of the user driving the electric vehicle is obtained, where the historical driving information includes a driving route within a preset time period; acquiring position information of each battery replacement point within a preset distance from the driving route; calculating a first travel distance value between two of the battery exchange points, which are farthest in travel distance in a direction along the travel route, according to the position information; calculating a target battery capacity value according to the first travel distance value; recommending batteries with corresponding models to a user according to the target battery capacity value; therefore, the recommendation of the battery is realized, and the beneficial effect of avoiding the breakdown of the electric vehicle due to insufficient electric quantity in the driving process is achieved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
Fig. 1 is a schematic flowchart of a big data-based battery recommendation method according to an embodiment of the present invention.
Fig. 2 is a schematic structural diagram of a big data-based battery recommendation device according to an embodiment of the present invention.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. 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, belong to the scope of protection of the present invention.
The terms "first," "second," "third," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the objects so described are interchangeable under appropriate circumstances. Furthermore, the terms "comprising" and "having," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, or apparatus, terminal, system comprising a list of steps is not necessarily limited to those steps or modules or elements expressly listed, and may include other steps or modules or elements not expressly listed, or inherent to such process, method, apparatus, terminal, or system.
Referring to fig. 1, fig. 1 is a flowchart of a big data-based battery recommendation method. The method comprises the following steps:
s101, obtaining historical driving information of the electric vehicle driven by the user, wherein the historical driving information comprises a driving route in a preset time period.
In this step, the historical travel information may include a travel route within a preset time period, which may be the last monday month, or the like. The historical driving information can be obtained from a battery positioning system and a battery management system, and the positioning system can send own position information to the terminal in real time.
In some embodiments, the historical travel information further includes a daily power consumption value. The battery management system also sends the self electric quantity information to the terminal in real time, and the terminal calculates the daily electric quantity consumption.
And S102, acquiring the position information of each battery replacement point within a preset distance from the driving route.
In this step, the preset distance may be set to 200 meters, 500 meters, etc., for example, to 200 meters, and the minimum distance of the battery exchange point from the driving route is within 200 meters.
S103, calculating a first travel distance value between two battery replacement points which are farthest in travel distance in the direction along the travel route according to the position information.
In some embodiments, this step S103 includes:
and S1031, calculating each second driving distance value between any two adjacent battery replacement points in the direction along the driving route according to the position information.
S1032 selects a maximum second distance traveled value from the respective second distance traveled values as a first distance traveled value between two of the battery replacement points having the farthest distance traveled.
In this step, for example, A, B, C, D, E battery replacement points are sequentially located within a preset distance on the driving route. For example, along the driving route, the driving distance between A and B is x1, the driving distance between B and C is x2, the driving distance between C and D is x3, and the driving distance between D and E is x 4. Where x3 is the maximum travel distance, C and D are set as the two battery replacement points that are the farthest travel distances along the travel route.
And S104, calculating a target battery capacity value according to the first travel distance value.
In this step, the target battery capacity may be calculated according to the first travel distance value such that the target battery capacity may support at least the electric vehicle traveling by the first travel distance value.
In some embodiments, this step S104 includes:
and S1041, acquiring a mapping relation between the driving distance value and the power consumption in a preset time period.
Because the electric vehicles of different brands have different electricity utilization efficiency, namely, the same electrically-driven electric vehicle has different running distance. Even if the electric vehicles of the same brand are different in old and new degrees, the electricity utilization efficiency is different. The power utilization efficiency of the old electric vehicle is low. Therefore, in order to improve the accuracy of the calculation, it is necessary to acquire the mapping relationship between the travel distance value and the power consumption amount in the latest preset time period.
And S1042, calculating a first power consumption according to the first travel distance value and the mapping relation.
In this step, the first power consumption amount is a minimum power consumption amount required to travel the first distance value.
And S1043, calculating a target battery capacity value according to the first power consumption.
In this step, knowing the first power consumption, the corresponding target battery capacity can be calculated, so that the battery can at least support the electric vehicle to run for the first distance value.
Further optimally, in some embodiments, the historical travel information includes a travel route for a preset time period and a daily power consumption amount for the preset time period. The step S1043 includes: the step of calculating a target battery capacity value based on the first power consumption amount includes: and calculating the target battery capacity value according to the daily power consumption and the first power consumption. The target battery capacity value Q3 may be calculated according to a preset formula, the daily power consumption amount Q1 and the first power consumption amount Q2, where Q1/a + Q2 is bQ3, where a is the number of charging times per day and b is the discharging efficiency of the battery.
The a may be an average charging number per day of the user obtained from the history information, or may be a charging number that the user can receive per day. The battery can not completely supply the electric quantity to the electric vehicle for driving, the battery can also supply power to a management system of the battery, and the electric vehicle can not be driven to run after the electric quantity of the battery is lower than a certain threshold value, so that the electric quantity with the proportion of the total capacity of the battery being b can be supplied to the electric vehicle for running.
And S105, recommending the battery with the corresponding model to the user according to the target battery capacity value.
After the target battery capacity value is calculated, the price of the battery is referred to, and a battery which can meet the power consumption requirement of the user and is relatively low in price is recommended to the user.
As can be seen from the above, in the embodiment of the present invention, historical driving information of the user driving the electric vehicle is obtained, where the historical driving information includes a driving route within a preset time period; acquiring position information of each battery replacement point within a preset distance from the driving route; calculating a first travel distance value between two of the battery exchange points, which are farthest in travel distance in a direction along the travel route, according to the position information; calculating a target battery capacity value according to the first travel distance value; recommending batteries with corresponding models to a user according to the target battery capacity value; therefore, the recommendation of the battery is realized, and the beneficial effect of avoiding the breakdown of the electric vehicle due to insufficient electric quantity in the driving process is achieved.
Referring to fig. 2, fig. 2 is a structural diagram of a big data based battery recommendation apparatus according to an embodiment of the present invention. The big data-based battery recommendation device comprises:
the system comprises a first obtaining module 201, configured to obtain historical driving information of a user driving an electric vehicle, where the historical driving information includes a driving route within a preset time period.
The second obtaining module 202 is configured to obtain location information of each battery replacement point within a preset distance from the driving route.
A first calculating module 203, configured to calculate a first travel distance value between two battery exchange points that are farthest in travel distance in a direction along the travel route according to the position information.
A second calculating module 204, configured to calculate a target battery capacity value according to the first travel distance value.
And the recommending module 205 is configured to recommend a battery of a corresponding model to the user according to the target battery capacity value.
In some embodiments, the first calculation module 203 comprises:
a third calculation unit configured to calculate, from the position information, respective second travel distance values between any two adjacent battery replacement points in the direction along the travel route.
A selection unit configured to select a largest second travel distance value from the respective second travel distance values as a first travel distance value between two of the battery exchange points whose travel distances are the farthest.
In some embodiments, the second calculation module 204 includes:
the first obtaining unit is used for obtaining the mapping relation between the driving distance value and the power consumption in the preset time period.
And the first calculating unit is used for calculating first power consumption according to the first travel distance value and the mapping relation.
And the second calculating unit is used for calculating a target battery capacity value according to the first power consumption.
As can be seen from the above, in the embodiment of the present invention, historical driving information of the user driving the electric vehicle is obtained, where the historical driving information includes a driving route within a preset time period; acquiring position information of each battery replacement point within a preset distance from the driving route; calculating a first travel distance value between two of the battery exchange points, which are farthest in travel distance in a direction along the travel route, according to the position information; calculating a target battery capacity value according to the first travel distance value; recommending batteries with corresponding models to a user according to the target battery capacity value; thereby realizing the recommendation of the battery.
The embodiment of the present invention further provides a storage medium, where a computer program is stored in the storage medium, and when the computer program runs on a computer, the computer executes the big data based battery recommendation method according to any of the above embodiments.
The embodiment of the invention also provides a terminal, which comprises a processor and a memory, wherein the memory is stored with a computer program, and the processor is used for executing the battery recommendation method based on the big data by calling the computer program stored in the memory.
It should be noted that, those skilled in the art can understand that all or part of the steps in the methods of the above embodiments can be implemented by hardware related to instructions of a program, and the program can be stored in a computer readable storage medium, which can include but is not limited to: read Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disks, and the like.
The method, the device, the storage medium and the terminal for recommending the battery based on the big data provided by the embodiment of the invention are described in detail, a specific example is applied in the description to explain the principle and the implementation of the invention, and the description of the embodiment is only used for helping to understand the method and the core idea of the invention; meanwhile, for those skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (8)
1. A big data-based battery recommendation method is characterized by comprising the following steps:
acquiring historical driving information of a user driving an electric vehicle, wherein the historical driving information comprises a driving route in a preset time period;
acquiring position information of each battery replacement point within a preset distance from the driving route;
calculating a first travel distance value between two of the battery exchange points, which are farthest in travel distance in a direction along the travel route, according to the position information;
calculating a target battery capacity value according to the first travel distance value;
recommending batteries with corresponding models to a user according to the target battery capacity value;
the step of calculating a first travel distance value between two of the battery exchange points, which are farthest in travel distance in a direction along the travel route, from the position information includes:
calculating each second driving distance value between any two adjacent battery replacement points in the direction along the driving route according to the position information;
the largest second driving distance value is selected from the second driving distance values as the first driving distance value between the two battery exchange points with the largest driving distance.
2. The big-data-based battery recommendation method according to claim 1, wherein the step of calculating a target battery capacity value from the first travel distance value comprises:
acquiring a mapping relation between a driving distance value and power consumption in a preset time period;
calculating first power consumption according to the first travel distance value and the mapping relation;
and calculating a target battery capacity value according to the first power consumption.
3. The big data-based battery recommendation method according to claim 2, wherein the historical travel information comprises a travel route for a preset time period and a daily power consumption amount for a preset time period;
the step of calculating a target battery capacity value based on the first power consumption amount includes:
and calculating the target battery capacity value according to the daily power consumption and the first power consumption.
4. The big data-based battery recommendation method according to claim 3, wherein said calculating the target battery capacity value according to the daily power consumption amount and the first power consumption amount comprises:
the target battery capacity value Q3 is calculated according to a preset formula, the daily power consumption amount Q1 and the first power consumption amount Q2, where Q1/a + Q2 ═ bQ3, where a is the number of charges per day and b is the discharge efficiency of the battery.
5. A big data based battery recommendation apparatus, the apparatus comprising:
the system comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is used for acquiring historical driving information of an electric vehicle driven by a user, and the historical driving information comprises a driving route in a preset time period;
the second acquisition module is used for acquiring the position information of each battery replacement point within a preset distance from the driving route;
a first calculation module for calculating a first travel distance value between two of the battery exchange points that are the farthest travel distances in a direction along the travel route, according to the position information; the first computing module includes: a third calculation unit configured to calculate, from the position information, respective second travel distance values between any two adjacent battery replacement points in the direction along the travel route; a selection unit configured to select a largest second travel distance value from the respective second travel distance values as a first travel distance value between two of the battery exchange points whose travel distances are the farthest;
the second calculation module is used for calculating a target battery capacity value according to the first travel distance value;
and the recommending module is used for recommending the battery with the corresponding model to the user according to the target battery capacity value.
6. The big-data-based battery recommendation device according to claim 5, wherein the second calculation module comprises:
the first acquisition unit is used for acquiring a mapping relation between a driving distance value and power consumption in a preset time period;
the first calculation unit is used for calculating first power consumption according to the first travel distance value and the mapping relation;
and the second calculating unit is used for calculating a target battery capacity value according to the first power consumption.
7. A storage medium having stored thereon a computer program which, when run on a computer, causes the computer to perform the method of any one of claims 1 to 4.
8. A terminal, characterized in that it comprises a processor and a memory, in which a computer program is stored, the processor being adapted to carry out the method of any one of claims 1 to 4 by calling the computer program stored in the memory.
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| JP7172948B2 (en) * | 2019-10-29 | 2022-11-16 | トヨタ自動車株式会社 | Information processing device, information processing method, and information processing program |
| CN111806291B (en) * | 2020-07-14 | 2021-11-12 | 武汉蔚来能源有限公司 | Vehicle and battery matching method, device and system and 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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