CN115848219A - New energy automobile charging prediction reminding system based on big data - Google Patents

New energy automobile charging prediction reminding system based on big data Download PDF

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CN115848219A
CN115848219A CN202211327190.9A CN202211327190A CN115848219A CN 115848219 A CN115848219 A CN 115848219A CN 202211327190 A CN202211327190 A CN 202211327190A CN 115848219 A CN115848219 A CN 115848219A
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廖志玻
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00001—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
    • 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
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • 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
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B60L53/66—Data transfer between charging stations and vehicles
    • B60L53/665—Methods related to measuring, billing or payment
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00006—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment
    • H02J13/00022—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment using wireless data transmission
    • H02J13/00026—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment using wireless data transmission involving a local wireless network, e.g. Wi-Fi, ZigBee or Bluetooth
    • 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
    • H04W4/024—Guidance services
    • 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
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    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02B90/20—Smart grids as enabling technology in buildings sector
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    • 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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    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
    • 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
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    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
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    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
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    • Y04S30/00—Systems supporting specific end-user applications in the sector of transportation
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Abstract

The invention discloses a new energy automobile charging prediction reminding system based on big data, which comprises: the novel energy automobile charging device comprises a single chip microcomputer, a pressure detection module, a GPS module, an electric quantity detection module, a controller, a big data server, an indicator lamp, a WIFI module and a mobile phone terminal, the situation that the electric quantity is insufficient but not known can occur in the driving process of a new energy automobile, a charging prediction reminding system is required to remind a driver of charging, the identity of the driver is judged through comparison of data detected by the pressure detection module and data stored in the big data server, the electric quantity detection module detects the residual electric quantity, when the electric quantity is smaller than a preset SOC threshold value, the indicator lamp is controlled, the data are transmitted to the mobile phone terminal through the WIFI module to remind the driver of charging, the position of a charging pile is located through the GPS module, and the driver is helped to plan a driving route.

Description

New energy automobile charging prediction reminding system based on big data
Technical Field
The invention relates to the technical field of intelligent assistance of new energy automobiles, in particular to a new energy automobile charging prediction reminding system based on big data.
Background
With the increasingly prominent energy and environmental problems, new energy vehicles are rapidly developed as important components of new energy strategies, for new energy vehicles driven by electric energy, the premise of normal driving is that the vehicles can be timely and effectively charged under the condition of insufficient electric quantity, batteries can be damaged due to serious insufficient electric quantity, however, drivers often do not know when to charge to protect the batteries in the driving process, in addition, when the vehicles cannot be charged due to insufficient electric quantity in the high-speed driving process, the drivers need to find the charging piles at a high speed for charging, and the drivers are difficult to select proper charging positions due to more than one charging piles, and need to plan an optimal route.
Therefore, a new energy automobile charging prediction reminding system based on big data is needed to solve the problems.
Disclosure of Invention
The invention aims to provide a new energy automobile charging prediction reminding system based on big data, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: the utility model provides a new energy automobile charging prediction warning system based on big data, includes: the intelligent electric vehicle comprises a single chip microcomputer, a pressure detection module, an electric quantity detection module, a GPS module, a controller, a big data server, a WIFI module, a mobile phone terminal and an indicator lamp, wherein the output end of the pressure detection module, the output end of the GPS module and the output end of the electric quantity detection module are connected with the input end of the single chip microcomputer, the output end of the single chip microcomputer is connected with the input end of the controller, the output end of the controller is connected with the input end of the WIFI module, the indicator lamp is connected with the input end of the big data server, the output end of the WIFI module is connected with the mobile phone terminal, the output end of the big data server is connected with the input end of the controller, the big data server is in two-way communication with the controller, the two-way communication with the controller is used for comparing detected data with normal data stored in the big data server in advance, and planning an optimal route for a driver is facilitated.
Furthermore, the big data server stores driving data of different drivers, positions of charging piles on a common driving route, driving time in a full power state and a preset SOC threshold, the driving data of the different drivers comprises average driving speed, average driving power consumption, positions of holding a steering wheel during driving and grip strength, the preset SOC threshold refers to the percentage of the lowest residual power of the new energy automobile to the total power, the preset SOC threshold is 10%, and the storage of the preset SOC threshold in the big data server provides a basis for the controller to compare the power and control on and off of the indicator lights.
Further, the data that the pressure detection module detected and the residual capacity that the electric quantity detection module detected transmit to the singlechip through the IO mouth, the singlechip will detected data and residual capacity transmit to the controller, the controller will detected data and residual capacity transmit to big data server, compare the data that store in the big data server with data in the controller, the result of comparison transmit to the controller, the controller passes through the result that the WIFI module will be compared transmit to cell phone terminal, the controller is according to the bright or the off of the result control pilot lamp of comparison, data and the detected data of storing in the big data server are compared and are favorable to the driver to judge more accurately whether the residual capacity of car has been less than predetermined SOC threshold value, if be less than predetermined SOC threshold value, need in time charge the car.
Further, the result of comparing passes through big data server transmits to behind the controller, the residual capacity is less than during the predetermined SOC threshold value, controller control the pilot lamp is bright red light and is reminded the driver to charge, and in the charging process, when the abnormal conditions appeared in the charged state, controller control the pilot lamp is bright yellow light and is reminded driver charged state unusual pause charging, has restoreed and has changed after the battery the controller control the pilot lamp is bright green light, continue to charge after the pilot lamp is bright green light, the pilot lamp goes out to remind the driver to charge and has accomplished, and whether the residual capacity that has reminded the driver car more simply obviously through the bright state of going out of pilot lamp and the state of car when charging has unusual.
Further, the method for planning the optimal route comprises the following steps:
s1: a pressure sensor is arranged on the periphery of the steering wheel, and is used for detecting the data of a driver and judging the identity of the driver;
s2: calling driving data which are collected in advance and correspond to a driver from the big data server;
s3: calculating the power consumption percentage of the driver in the driving of the remaining distance according to the distance to the destination;
s4: comparing the database to judge whether the driver can reach the destination;
s5: positioning the position of a nearby charging pile through the GPS module;
s6: an optimal route is planned for the driver.
Further, in step S1: the pressure sensor is disposed at a periphery of the steering wheel, and the detected data of the driver includes: the driver holds the position of the steering wheel and the strength of the steering wheel, the data are compared with the driving data of different drivers stored in the big data server, a group of data which is most matched with the detection data is found out, the identity of the corresponding driver of the group of data is confirmed, the identity of the driver is accurately found out through the habit of driving to hold the steering wheel, and the corresponding driving data are convenient to call from the big data server.
Further, in step S2: the driving data of the driver collected by the big data server in advance comprises the following steps: corresponding to a driving speed set and a power consumption percentage set per hundred kilometers in the daily vehicle opening process of a driver, setting the average driving speed of the corresponding driver as V, setting the average power consumption percentage of the corresponding driver per hundred kilometers as A, and if the collected driving speed set of the corresponding driver is V{v 1 ，v 2 ，v 3 ，...v i Calculating the average driving speed of the corresponding driver
Figure DEST_PATH_IMAGE002
(i is more than 0 and i is an integer), if the collected power consumption percentage set of the corresponding driver driving per hundred kilometers is { a } 1 ，a 2 ，a 3 ，...a j Calculating the average power consumption percentage of the driver driving every hundred kilometers
Figure DEST_PATH_IMAGE004
(j is more than 0 and j is an integer), the calculated average power consumption percentage A corresponding to the driving person driving per hundred kilometers is favorable for planning an optimal route in the subsequent steps.
Further, in step S3: setting the distance from the driver to the destination in the middle of high speed as D, setting the percentage of the residual electric quantity in the middle of high speed as b, setting the percentage of the power consumption of the corresponding driver in the running of the residual distance as a, and calculating the percentage of the power consumption of the corresponding driver in the running of the residual distance according to the calculated average percentage of the power consumption A of the corresponding driver in each hundred kilometers in the running of the corresponding driver
Figure DEST_PATH_IMAGE006
If the remaining capacity percentage b during high-speed driving is greater than the power consumption percentage a during remaining distance driving, it is determined that the corresponding driver can reach the destination without charging, and if the remaining capacity percentage b during high-speed driving is less than the power consumption percentage a during remaining distance driving, it is necessary to perform GPS positioning of the position of the nearby charging pile in step S5.
Further, in step S5: because no charging pile is available for charging at a high speed, a driver needs to position the charging pile at a high speed according to the GPS module, and the positions of two different charging piles are respectively set as a point E (x) according to different driving routes of the charging pile according to different positions 3 ，y 3 ) And point F (x) 4 ，y 4 ) The lower high speed position is set as point C (x) 1 ，y 1 ) The location of the destination is set to point G (x) 2 ，y 2 ) Setting two different routes, namely a route CEG and a route CFG, and calculating the distance from a point C to a point E
Figure DEST_PATH_IMAGE008
Distance from point E to point G
Figure DEST_PATH_IMAGE010
The distance from point C to point F is calculated>
Figure DEST_PATH_IMAGE012
The distance from point F to point G->
Figure DEST_PATH_IMAGE014
The driver can accurately find the position of the charging pile by the aid of GPS module positioning, and time is saved.
Further, in step S6: setting the percentage of the residual electric quantity of the position point C with the lower high speed to be N, setting the percentage of the power consumption from the point C to the point E to be N1, setting the percentage of the power consumption from the point E to the point G to be N2, setting the percentage of the power consumption from the point C to the point F to be N3, setting the percentage of the power consumption from the point F to the point G to be N4, and according to a formula
Figure DEST_PATH_IMAGE016
Calculating the power consumption percentage N1 from the point C to the point E, calculating the residual power percentage of the point E as N-N1, and calculating the residual power percentage of the point E according to a formula
Figure DEST_PATH_IMAGE018
Calculating the percentage of power consumption N2 from the point E to the point G according to a formula
Figure DEST_PATH_IMAGE020
Calculating the power consumption percentage N3 from the point C to the point F, calculating the residual electric quantity percentage of the point F as N-N3, and based on a formula->
Figure DEST_PATH_IMAGE022
Calculating the percentage of power consumed from the point F to the point GCompared with N4, when D1+ D2= D3+ D4 (D1 + D2 > D and D3+ D4 > D), the total power consumption percentage of the route CEG and the route CFG is the same, and the judgment needs to be carried out according to the charged quantity, if N-N1 > N1-N3 (N-N1 is more than or equal to 10% and N-N3 is more than or equal to 10%), the route CEG is preferentially selected, and otherwise, the route CFG is selected; when D1+ D2 ≠ D3+ D4 (D1 + D2 > D and D3+ D4 > D), the route CFG is selected if N1+ N2 > N3+ N4, whereas the route CEG is selected otherwise.
The steps S1 to S6 comprehensively consider the power consumption, the charging amount and the charge during driving, plan an optimal route which can be driven to the destination when the driver needs to drive the vehicle at high speed midway and cannot charge, help the driver to reach the destination as soon as possible, and are beneficial to reducing the power consumption of the new energy vehicle and saving the charge cost.
Compared with the prior art, the invention has the following beneficial effects:
1. the data detected by the electric quantity detection module is compared with the existing data stored in the big data server, the controller controls the indicator lamp to give a red light alarm when the residual electric quantity is lower than the SOC threshold value preset in the big data server, so that a driver is reminded of charging the automobile in time, and in the charging process, when an abnormal condition occurs, the controller controls the indicator lamp to give a yellow light to remind a user, so that the user can be helped to detect and suspend charging in time;
2. because different people drive at different speeds and different amounts of electricity consumption of automobiles during driving, a pressure detection module is required to detect the position and the grip strength of a steering wheel held by a driver through a pressure sensor, compare and match the position and the grip strength with data stored in a big data server to judge the identity of the driver, and call driving data of the corresponding driver so as to calculate the average driving speed and the average electricity consumption percentage of the corresponding driver, and establish an optimal route for a user according to actual conditions;
3. on the way of high speed, when the vehicle can not reach the destination without charging, the driver needs to charge at high speed, and the position of the charging pile is positioned through the GPS module.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a flow chart of a new energy vehicle charging prediction reminding system based on big data according to the invention;
FIG. 2 is a flow chart of the controller controlling the indicator lights of the present invention;
FIG. 3 is a flow chart of a route planning method of the present invention;
FIG. 4 is a view of the pressure sensor mounting location of the present invention;
in the figure: 1. a steering wheel; 2. a pressure sensor.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
Referring to fig. 1-4, the present invention provides the following technical solutions: the utility model provides a new energy automobile charging prediction warning system based on big data, includes: the singlechip, the pressure detection module, the electric quantity detection module, the GPS module, a controller, big data server, the WIFI module, cell-phone terminal and pilot lamp, the output of pressure detection module, the output of GPS module and the output of electric quantity detection module all are connected with the input of singlechip, the output of singlechip is connected with the input of controller, the output and the input of WIFI module of controller, pilot lamp and big data server's input are connected, the output and the cell-phone terminal of WIFI module are connected, big data server's output and the input of controller are connected, big data server and controller carry out both-way communication, the both-way communication of big data server and controller is used for comparing the normal data of storing in advance in the data that detect and the big data server, plan optimal route for the driver.
The big data server stores driving data of different drivers, charging pile positions on common driving routes, driving time in a full power state and a preset SOC threshold, the driving data of the different drivers comprises average driving speed, average driving power consumption, positions of a hand-held steering wheel during driving and grip strength, the preset SOC threshold refers to the percentage of the lowest residual power of the new energy automobile to the total power, the preset SOC threshold is 10%, and the preset SOC threshold is stored in the big data server and used for providing a basis for a controller to compare the power and control on and off of indicator lights.
Data that pressure detection module detected and the residual capacity that electric quantity detection module detected transmit to through the IO mouth the singlechip, the singlechip will detect data and residual capacity transmission to controller, the controller will detect data and residual capacity transmission to big data server, compare the data of storage in the big data server with the data in the controller, the result of comparison is transmitted to the controller, the controller passes through the WIFI module and transmits the result of comparison to cell-phone terminal, the controller is according to the bright of the result control pilot lamp that compares, the data of storage is convenient for in the big data server with the comparison of detected data can judge more accurately whether the residual capacity of car has been less than predetermined SOC threshold value for the driver, if be less than predetermined SOC threshold value, in time charge for the car.
After the comparison result is transmitted to the controller through the big data server, when the residual electric quantity is lower than a preset SOC threshold value, the controller controls the indicator lamp to light the red light to remind a driver of charging, in the charging process, when the charging state is abnormal, the controller controls the indicator lamp to light the yellow light to remind the driver of stopping charging abnormally in the charging state, after the battery is repaired and replaced, the controller controls the indicator lamp to light the green light, the indicator lamp continues charging after lighting the green light, the indicator lamp goes out to remind the driver of completing charging, and the indicator lamp is turned on or off to remind the driver of whether the residual electric quantity of the automobile and the charging state of the automobile are abnormal.
The method for planning the optimal route comprises the following steps:
s1: a pressure sensor is arranged on the periphery of the steering wheel, and is used for detecting the data of a driver and judging the identity of the driver;
s2: calling driving data which are collected in advance and correspond to a driver from a big data server;
s3: calculating the power consumption percentage of the driver in the driving of the remaining distance according to the distance to the destination;
s4: comparing the database to judge whether the driver can reach the destination;
s5: positioning the position of a nearby charging pile through a GPS module;
s6: an optimal route is planned for the driver.
In step S1: the pressure sensor 2 is disposed on the periphery of the steering wheel 1, and the detected data of the driver includes: the data is compared with driving data of different drivers stored in a big data server to find a group of data which is most matched with the detection data, and the matched data is used for confirming the identity of the corresponding driver.
In step S2: the driving data of the driver collected by the big data server in advance comprises the following steps: corresponding to a driving speed set and a power consumption percentage set per hundred kilometers in the daily vehicle opening process of a driver, setting the average driving speed of the corresponding driver as V, setting the average power consumption percentage of the corresponding driver per hundred kilometers as A, and if the collected driving speed set of the corresponding driver is { V } 1 ，v 2 ，v 3 ，...v i Calculating the average driving speed of the corresponding driver
Figure 261257DEST_PATH_IMAGE002
(i is more than 0 and i is an integer), if the collected power consumption percentage set of the corresponding driver driving per hundred kilometers is { a } 1 ，a 2 ，a 3 ，...a j Calculating the average power consumption percentage of each hundred kilometers of the corresponding driver
Figure 178398DEST_PATH_IMAGE004
(j is more than 0 and j is an integer), and the calculated average power consumption percentage A of each hundred kilometers of the corresponding driver is used for providing data support for planning the optimal route in the subsequent steps.
In step S3: setting the distance from the driver to the destination in the middle of high speed as D, setting the percentage of the residual electric quantity in the middle of high speed as b, setting the percentage of the power consumption of the corresponding driver in the running of the residual distance as a, and calculating the percentage of the power consumption of the corresponding driver in the running of the residual distance according to the calculated average percentage of the power consumption A of the corresponding driver in each hundred kilometers in the running of the corresponding driver
Figure 99080DEST_PATH_IMAGE006
If the remaining capacity percentage b during high-speed driving is greater than the power consumption percentage a during remaining distance driving, it is determined that the corresponding driver can reach the destination without charging, and if the remaining capacity percentage b during high-speed driving is less than the power consumption percentage a during remaining distance driving, it is necessary to perform GPS positioning of the position of the nearby charging pile in step S5.
In step S5: because no charging pile is available for charging at high speed, the driver needs to position the charging pile at high speed according to the GPS module, the driving route is different according to the position of the charging pile, and the positions of two different charging piles are set to be points E (x) respectively 3 ，y 3 ) And point F (x) 4 ，y 4 ) The lower high speed position is set as point C (x) 1 ，y 1 ) The location of the destination is set to point G (x) 2 ，y 2 ) Setting two different routes as a route CEG and a route CFG, and calculating the distance from a point C to a point E
Figure 494290DEST_PATH_IMAGE008
The distance from point E to point G->
Figure 574241DEST_PATH_IMAGE010
Calculate the point C to the pointDistance F>
Figure 662283DEST_PATH_IMAGE012
Distance from point F to point G
Figure 929316DEST_PATH_IMAGE014
And the driver can be helped to quickly and accurately find the position of the charging pile by positioning through the GPS module.
In step S6: setting the percentage of the remaining electricity of a position point C at a low high speed as N, setting the percentage of electricity consumption from the point C to the point E as N1, setting the percentage of electricity consumption from the point E to the point G as N2, setting the percentage of electricity consumption from the point C to the point F as N3, setting the percentage of electricity consumption from the point F to the point G as N4 according to a formula
Figure 128216DEST_PATH_IMAGE016
Calculating the power consumption percentage N1 from the point C to the point E, calculating the residual electric quantity percentage of the point E as N-N1, and based on the formula->
Figure 62674DEST_PATH_IMAGE018
The percentage of power consumption N2 is calculated from point E to point G, based on the formula->
Figure 196983DEST_PATH_IMAGE020
Calculating the power consumption percentage N3 from the point C to the point F, calculating the residual electric quantity percentage of the point F as N-N3, and based on the formula->
Figure 951313DEST_PATH_IMAGE022
Calculating the power consumption percentage N4 from the point F to the point G, when D1+ D2= D3+ D4 (D1 + D2 > D and D3+ D4 > D), the total power consumption percentage of the route CEG and the route CFG is the same, and the judgment needs to be carried out according to the quantity of charged quantity, if N-N1 > N1-N3 (N-N1 is more than or equal to 10% and N-N3 is more than or equal to 10%), preferentially selecting the route CEG, otherwise, selecting the route CFG; when D1+ D2 ≠ D3+ D4 (D1 + D2 > D and D3+ D4 > D), the route CFG is selected if N1+ N2 > N3+ N4, whereas the route CEG is selected otherwise.
The steps S1 to S6 comprehensively consider the amount of power consumption, the amount of charge, and the cost during driving, and plan an optimal route that can be traveled to the destination for the driver when the vehicle needs to be charged during traveling at a high speed.
The first embodiment is as follows: the distance D =500km from the middle of the high speed to the destination, the average power consumption per hundred kilometers A = 10%/hundred kilometers, the remaining energy percentage N =40% at the point C of the low speed, the distance D1=200km from the point C to the point E, the distance D2=400km from the point E to the point G, the distance D3=300km from the point C to the point F, and the distance D4=280km from the point F to the point G are set, because D1+ D2= D3+ D4, according to the formula
Figure 953904DEST_PATH_IMAGE006
Calculating the power consumption percentage a =50% > N of the driver in the driving process of the remaining distance, the driver cannot arrive at the destination without charging, the driver needs to arrive at a point E or a point F to charge the electric pile, and the route CEG: according to the formula>
Figure 477289DEST_PATH_IMAGE016
Calculating the power consumption percentage N1=20% from the point C to the point E, calculating the remaining power percentage N-N1=20% > 10% from the point E, and based on the formula->
Figure 172713DEST_PATH_IMAGE018
Calculating the power consumption percentage from point E to point G, N2=40%, N1+ N2=60%; route CFG: according to the formula>
Figure 414338DEST_PATH_IMAGE020
Calculating the power consumption percentage N3=30% from the point C to the point F, calculating the residual power percentage N-N3=10% from the point F, and after the point F is fully charged, calculating the power consumption percentage according to the formula
Figure 955041DEST_PATH_IMAGE022
Calculating the power consumption percentage from point F to point G, N4=28%, N3+ N4=58% < N1+ N2, the route CFG reaching the destination with less power consumption than the route CEG, and selecting the route CFG.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described above, or equivalents may be substituted for elements thereof. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (6)

1. The utility model provides a new energy automobile charging prediction warning system based on big data, includes: the output ends of the pressure detection module, the GPS module and the electric quantity detection module are connected with the input end of the single chip microcomputer, the output end of the single chip microcomputer is connected with the input end of the controller, the output end of the controller is connected with the input end of the WIFI module, the indicator light and the input end of the big data server, the output end of the WIFI module is connected with the mobile phone terminal, the output end of the big data server is connected with the input end of the controller, the big data server is in two-way communication with the controller, and the two-way communication between the big data server and the controller is used for comparing detected data with data stored in the big data server in advance and planning an optimal route for a driver;
the big data server stores driving data of different drivers, charging pile positions on common driving routes, driving time in a full-power state and a preset SOC threshold, wherein the driving data of the different drivers comprise driving average speed, driving average power consumption, positions of holding a steering wheel during driving and grip strength, and the preset SOC threshold refers to the percentage of the remaining minimum electric quantity of the new energy automobile to the total electric quantity;
the planning of the optimal route comprises the following steps:
s1: a pressure sensor is arranged on the periphery of the steering wheel, and is used for detecting the data of a driver and judging the identity of the driver;
s2: calling driving data which are collected in advance and correspond to a driver from the big data server;
s3: calculating the power consumption percentage of the driver in the driving of the remaining distance according to the distance to the destination;
s4: comparing the database to judge whether the driver can reach the destination;
s5: positioning the position of a nearby charging pile through the GPS module;
s6: planning an optimal route for a driver;
the data detected by the pressure detection module and the residual electric quantity detected by the electric quantity detection module are transmitted to the single chip microcomputer through an IO port, the single chip microcomputer transmits the detected data and the residual electric quantity to a controller, the controller transmits the detected data and the residual electric quantity to the big data server, the data stored in the big data server and the data in the controller are compared, the compared result is transmitted to the controller, the controller transmits the compared result to the mobile phone terminal through the WIFI module, and the controller controls the on and off of an indicator light according to the compared result;
in step S1: the pressure sensor (2) is arranged at the periphery of the steering wheel (1), and the detected data of the driver comprises: the data is compared with the driving data of different drivers stored in the big data server, a group of data which is most matched with the detection data is found out, and the identity of the corresponding driver of the group of data is confirmed.
2. The big-data-based charging prediction reminding system for the new energy automobile, according to claim 1, characterized in that: the result of comparison passes through big data server transmits to behind the controller, the residual capacity is less than during the predetermined SOC threshold value, controller control the pilot lamp is bright red light and is reminded the driver to charge, and in the charging process, when the abnormal conditions appeared in the charged state, controller control the pilot lamp is bright yellow light and is reminded the driver charged state unusual pause charging, has restoreed to change after the battery the controller control the pilot lamp is bright green light, continue to charge after the pilot lamp is bright green light, the pilot lamp goes out to remind the driver to charge and has accomplished.
3. The big-data-based charging prediction reminding system for the new energy automobile, according to claim 1, characterized in that: in step S2: the driving data of the driver collected by the big data server in advance comprises the following steps: corresponding to a driving speed set and a power consumption percentage set of every hundred kilometers in the daily vehicle opening process of a driver, setting the average driving speed of the corresponding driver as V, setting the average power consumption percentage of every hundred kilometers of the corresponding driver as A, and if the collected driving speed set of the corresponding driver is { V [ V ] m 1 ，v 2 ，v 3 ，...v i Calculating the average driving speed of the corresponding driver
Figure QLYQS_1
I is more than 0 and i is an integer, if the collected power consumption percentage set of the corresponding driver driving per hundred kilometers is { a 1 ，a 2 ，a 3 ，...a j Calculating the average power consumption percentage of the driver driving every hundred kilometers
Figure QLYQS_2
J > 0 and j is an integer.
4. The big-data-based charging prediction reminding system for the new energy automobile is characterized in that: in step S3: setting the distance from the driver to the destination at high speed midway as D, the percentage of the residual electricity quantity when the driver drives at high speed midway as b, the percentage of the power consumption of the corresponding driver in the driving of the residual distance as a, and calculating the percentage of the power consumption of the corresponding driver in the driving of the residual distance according to the calculated average percentage of the power consumption A of the corresponding driver per hundred kilometers in the driving of the corresponding driver
Figure QLYQS_3
If the percentage b of the remaining capacity when the vehicle is traveling at the high speed midway is larger than the percentage a of the power consumption when the vehicle is traveling on the remaining route, the determination is made as to whether the vehicle is traveling at the high speed midwayIf the remaining power percentage b during high speed driving is less than the power consumption percentage a during remaining distance driving, the driver needs to GPS the position of the nearby charging pile in step S5.
5. The big-data-based charging prediction reminding system for the new energy automobile, according to claim 1, characterized in that: in step S5: because no charging pile is available for charging at a high speed, a driver needs to position the charging pile at a high speed according to the GPS module, and the positions of two different charging piles are respectively set as a point E (x) according to different driving routes of the charging pile according to different positions 3 ，y 3 ) And point F (x) 4 ，y 4 ) The lower high speed position is set as point C (x) 1 ，y 1 ) The location of the destination is set to point G (x) 2 ，y 2 ) Setting two different routes, namely a route CEG and a route CFG, and calculating the distance from a point C to a point E
Figure QLYQS_4
The distance from point E to point G->
Figure QLYQS_5
The distance from point C to point F is calculated>
Figure QLYQS_6
The distance from point F to point G->
Figure QLYQS_7
。
6. The big-data-based charging prediction reminding system for the new energy automobile is characterized in that: in step S6: setting the percentage of the remaining electric quantity of the position point C of the lower high speed to be N, setting the percentage of the electric consumption from the point C to the point E to be N1, setting the percentage of the electric consumption from the point E to the point G to be N2, setting the percentage of the electric consumption from the point C to the point F to be N3, and setting the pointThe percentage of power consumed from F to point G is N4, according to the formula
Figure QLYQS_8
Calculating the power consumption percentage N1 from the point C to the point E, calculating the residual electric quantity percentage of the point E as N-N1, and based on a formula->
Figure QLYQS_9
Calculating the power consumption percentage N2 from the point E to the point G according to the formula->
Figure QLYQS_10
Calculating the power consumption percentage N3 from the point C to the point F, calculating the remaining electric quantity percentage of the point F as N-N3, and based on a formula->
Figure QLYQS_11
Calculating the power consumption percentage N4 from the point F to the point G, when D1+ D2= D3+ D4, D1+ D2 > D and D3+ D4 > D, the total power consumption percentages of the route CEG and the route CFG are the same, and the judgment needs to be carried out according to the quantity of charged quantity, if N-N1 > N1-N3, N-N1 is more than or equal to 10% and N-N3 is more than or equal to 10%, the route CEG is preferentially selected, otherwise, the route CFG is selected; when D1+ D2 ≠ D3+ D4, D1+ D2 > D and D3+ D4 > D, the route CFG is selected if N1+ N2 > N3+ N4, whereas the route CEG is selected otherwise. />
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