CN108773279B - Method and device for planning charging path of electric vehicle - Google Patents

Method and device for planning charging path of electric vehicle Download PDF

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CN108773279B
CN108773279B CN201810391319.XA CN201810391319A CN108773279B CN 108773279 B CN108773279 B CN 108773279B CN 201810391319 A CN201810391319 A CN 201810391319A CN 108773279 B CN108773279 B CN 108773279B
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time
electric automobile
destination
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CN108773279A (en
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苏粟
杨恬恬
胡勇
张仁尊
李玉璟
张晓晴
李家浩
光鸿伟
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Beijing Jiaotong University
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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
    • B60L2240/40—Drive Train control parameters
    • B60L2240/54—Drive Train control parameters related to batteries
    • 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
    • B60L2260/00—Operating Modes
    • B60L2260/40—Control modes
    • B60L2260/44—Control modes by parameter estimation
    • 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
    • B60L2260/00—Operating Modes
    • B60L2260/40—Control modes
    • B60L2260/50—Control modes by future state prediction
    • B60L2260/52—Control modes by future state prediction drive range estimation, e.g. of estimation of available travel distance
    • 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
    • B60L2260/00—Operating Modes
    • B60L2260/40—Control modes
    • B60L2260/50—Control modes by future state prediction
    • B60L2260/54—Energy consumption estimation
    • 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
    • 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
    • Y02T10/7072—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
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/12—Electric charging stations
    • 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
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
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Abstract

The application provides a method and a device for planning a charging path of an electric vehicle, aiming at the problem that a user of the electric vehicle needs to be charged emergently in the traveling process. The method considers real-time dynamic energy consumption in the driving process and mainly comprises the following steps: firstly, calculating an average energy consumption value of the electric automobile under various driving conditions according to historical driving condition data of the electric automobile; secondly, dynamically calculating the dynamic residual electric quantity of the electric automobile after passing through each road section according to the real-time driving condition of each road section in the driving process; calculating the travel distance, travel time and charging cost of a user according to real-time traffic information published by the internet and information published by a charging station operation monitoring system; and planning the charging path of the electric automobile by adopting an ant colony algorithm and taking the remaining battery capacity as constraint and the optimal sum of the travel distance, the travel time and the charging cost as a target.

Description

Method and device for planning charging path of electric vehicle
Technical Field
The invention relates to the field of electric vehicle charging path planning, in particular to a method and a device for planning an electric vehicle charging path.
Background
With the rapid development of economy in China, the demand of automobiles is continuously increased, and the problems of energy shortage and environmental pollution caused by the demand are more urgent. In order to meet the urban energy conservation and emission reduction requirements and improve the environmental awareness of people, the new energy automobile industry needs to be rapidly developed. Wherein, perfect charging facility is the important guarantee of developing new energy automobile industry. However, the growth scale of the new energy automobile is greatly higher than that of the number of the charging piles, gaps between the piles are still continuously enlarged, the rapid charging on the road is still a main factor for restraining the development of the charging automobile, and in order to solve the problems of insufficient remaining driving range, long charging time and the like in the traveling process of the new energy automobile, it is necessary to actively construct a new energy automobile charging intelligent service platform under real-time information, promote the bidirectional interaction of energy and information between the electric automobile and a charging facility, and provide services such as path planning, charging guidance and the like for electric automobile users.
At present, the research on the charging guidance strategy of the electric automobile at home and abroad usually only considers the static road network environment and ignores the influence of the time-varying property of the traffic environment on the power consumption of the electric automobile under the actual condition, so that how to provide the real-time power consumption condition in the driving process for the owner of the electric automobile through a method with simple, clear and effective thought is one of the important problems in the charging industry of the electric automobile at present.
In view of this, a method and an apparatus for planning a charging path of an electric vehicle are provided.
Disclosure of Invention
The invention provides an electric vehicle charging path planning method considering dynamic energy consumption in a driving process, which utilizes dynamic traffic information published by the Internet to calculate the dynamic energy consumption of an electric vehicle in the driving process, calculates the travel distance, the travel time and the charging cost of a user by combining the dynamic traffic information of a road section from a starting point to a destination, considers the residual electric quantity constraint in the path driving process, adopts an ant colony algorithm to solve the electric vehicle charging path, and reduces the travel anxiety of the user.
In order to achieve the purpose, the invention adopts the following technical scheme:
an electric vehicle charging path planning method comprises the following steps:
s1, acquiring various driving conditions of the electric automobile, and calculating an average energy consumption value of the electric automobile under the various driving conditions;
s2, acquiring a driving route, and calculating the dynamic residual electric quantity of the electric automobile passing through each road section during driving;
s3, calculating the travel distance, the travel time and the charging cost of the user according to the travel route;
and S4, determining the charging path of the electric automobile according to the calculation results of the steps S1-S3.
An electric vehicle charging path planning apparatus, the apparatus comprising:
the first calculation module is used for acquiring various driving working conditions of the electric automobile and calculating the average energy consumption value of the electric automobile under the various driving working conditions;
the second calculation module is used for acquiring a driving route and calculating the dynamic residual electric quantity of the electric automobile passing through each road section during driving;
the third calculation module is used for calculating the travel distance, the travel time and the charging cost of the user according to the travel route;
and the path planning module determines the charging path of the electric automobile according to the calculation results of the first, second and third calculation modules.
Drawings
The following detailed description of embodiments of the invention is provided in conjunction with the appended drawings:
FIG. 1 is a schematic diagram of a method for planning a charging path of an electric vehicle;
FIG. 2 is a diagram illustrating an embodiment of the processing step S1;
FIG. 3 is a diagram illustrating an embodiment of the processing step of step S2;
FIG. 4 is a diagram illustrating an embodiment of the processing step of step S3;
FIG. 5 is a schematic diagram of the steps of solving an optimal solution for an ant colony algorithm flowchart;
fig. 6 is a schematic diagram of an electric vehicle charging path planning apparatus.
Detailed Description
In order to more clearly illustrate the present application, the present application is further described below in conjunction with the preferred embodiments and the accompanying drawings. Similar components in the figures are denoted by the same reference numerals. It is to be understood by persons skilled in the art that the following detailed description is illustrative and not restrictive, and is not intended to limit the scope of the present application.
The present application provides a method and an apparatus for planning a charging path of an electric vehicle, which can be described in further detail with reference to the following drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and do not limit the present application.
According to an aspect of the present application, the present embodiment provides an electric vehicle charging path planning method, as shown in fig. 1, the method including:
s1, calculating an average energy consumption value of the electric automobile under various driving conditions;
s2, calculating the dynamic residual electric quantity of the electric automobile passing through each road section during running;
s3, calculating the travel distance, the travel time and the charging cost of the user;
and S4, determining the charging path of the electric automobile according to the calculation results of the steps S1-S3.
Further, referring to fig. 2, the S1 includes the following steps:
s101, selecting a driving condition characteristic parameter capable of reflecting the opening and closing of an air conditioner of the electric automobile and the driving characteristics of a driver;
preferably, the characteristic parameters cited in the prior art are selected, and include: eight characteristic parameters, namely maximum speed, average acceleration, maximum acceleration, acceleration proportion, deceleration proportion, uniform speed proportion and idle speed proportion, are used for classifying the driving conditions, in the embodiment, each characteristic is obtained into a plurality of groups for specific calculation, and the parameters are shown in reference to tables 1 to 3 after being filtered. Wherein, table 1 is the related data of maximum speed, average acceleration and maximum acceleration, table 2 is the related data of acceleration ratio, deceleration ratio, uniform speed ratio and idle ratio, and table 3 is the related data of ambient temperature, air conditioner running power, standard difference of acceleration and standard difference of rate of change of acceleration; further, the subsequent calculations in this embodiment all use the data provided by the above tables.
Secondly, the ambient temperature can influence the running of the vehicle, and two characteristic parameters of the ambient temperature and the running power of the air conditioner are selected for the classification of the running conditions.
And finally, selecting the acceleration standard deviation and the acceleration change rate standard deviation as characteristic parameters reflecting different driving characteristics in the driving working condition.
TABLE 1
Figure BDA0001643514220000031
TABLE 2
Figure BDA0001643514220000032
Figure BDA0001643514220000041
TABLE 3
Figure BDA0001643514220000042
S102, analyzing the correlation between the selected driving condition characteristic parameters and the power consumption and the strong and weak correlation between the characteristic parameters;
preferably, a driving process from after the current charging to before the next charging is called a cycle, in each cycle, the electric vehicle is divided into driving segments every 1km, the divided driving segments are analyzed, and the 12 characteristic parameters and the power consumption amount under each segment are calculated. The correlation between the 12 characteristic parameters and the energy consumption of the electric automobile is calculated by adopting the spearman rank correlation coefficient, the calculation formula is shown as the formula (1),
Figure BDA0001643514220000043
and deleting the characteristic parameter variable with the minimum correlation with the power consumption according to the calculation result.
S103, performing dimensionality reduction analysis on the characteristic parameters of the driving condition by adopting a principal component analysis method;
the method comprises the steps of analyzing fragments containing 11 driving condition characteristic parameters by adopting a principal component analysis method to obtain characteristic values and contribution rates of the principal components, selecting n principal components with the accumulated contribution rate of more than 80 percent according to the principle of principal component analysis, wherein the n principal components can be considered to sufficiently reflect information contained in original variables, and selecting m driving condition characteristic parameters which are mainly reflected by the n principal components and have weak correlation for cluster analysis.
S104, classifying historical driving condition data of the electric automobile by adopting a fuzzy C-mean clustering algorithm; and calculating the average energy consumption value of the electric automobile under various driving conditions.
Performing cluster analysis on sample data containing m driving condition characteristic parameters, determining the category of each historical driving condition segment of the electric automobile according to the clustering center of each working condition and the minimum distance principle, wherein the distance calculation formula is
dq＝||x-cq|| q＝1，2...，12 (2)
Wherein x is [ x ]1，x2，...，x8]Is a characteristic parameter of a segment, cq＝[cq1，cq2，...，cq12]Is the cluster center parameter of cluster q. And taking the average value of the energy consumption of each fragment in the same type of driving working condition as the energy consumption of the electric automobile under the working condition.
Further, referring to fig. 3, the S2 includes the following steps:
s201, acquiring real-time driving conditions of all road sections by using the Internet;
s202, calculating real-time energy consumption of the electric automobile passing through each road section during running; the remaining amount of electricity after passing through the section is calculated.
Preferably, the vehicle travel section RijA distance of rijCalculating the clustering center of the road section by using the real-time driving road condition of the road section published by the internet, and judging the real-time driving condition class of the road section to be gqEnergy consumption per kilometer of corresponding road section is EqThen the power consumption through the road section is
Eij＝Eq×rij(3)
Further, referring to fig. 4, the S3 includes the following steps:
s301, calculating the travel distance cost of each road section from the departure point to the destination;
preferably, the total distance of the user in the process of going to the destination includes a total distance of travel of the electric vehicle from the starting point o to the destination D through the charging station S. The trip distance cost is then:
Figure BDA0001643514220000051
in the formula, xijA variable is determined for the 0-1 path if the electric automobile passes through a road section r between nodes i and j of the road networkijThen xij1, otherwise xij＝0。LaSet of road network nodes representing electric vehicles passing by, SaRepresents a set of charging station nodes through which the electric vehicle passes.
S302, calculating the travel time cost required from the starting point to the destination;
preferably, the S302 includes:
s3021, calculating the required travel time from the starting point to the destination;
road section r published according to internetijSpeed of
Figure BDA0001643514220000052
Estimating a user's passage of a road section rijTime T of route travelijComprises the following steps:
Figure BDA0001643514220000061
the travel time of all the road sections passed by the user from the starting point to the destination is as follows:
Figure BDA0001643514220000062
s3022, acquiring information such as the number of electric vehicles by using a charging station operation monitoring system;
real-time monitoring charging station of electric vehicle charging station monitoring management systemThe total number of chargers, the number of chargers in use, the number of electric vehicles entering a queuing sequence and other basic information are respectively expressed as the number of EV stations arriving at the charging station at the acquisition moments t and t-1
Figure BDA0001643514220000063
Number of EVs receiving charging service at time t and t-1
Figure BDA0001643514220000064
Calculating the average user arrival rate and the average service rate of the charging equipment at charging station j at time t, i.e.
Figure BDA0001643514220000065
Figure BDA0001643514220000066
The average waiting time of the vehicle is:
Figure BDA0001643514220000067
in the formula SjThe number of charging piles configured for charging station j,
the queuing waiting time of all charging stations for the electric vehicle user to travel from the initial point to the destination access is as follows:
Figure BDA0001643514220000068
calculating the charging waiting time and the charging time of each charging station from the departure point to the destination;
preferably, the charging time is an estimated value of the time taken for the electric vehicle to reach charging station j and then to be charged from the remaining charge to 80% of the state of charge.
Figure BDA0001643514220000069
Figure BDA00016435142200000610
In the formula, E0The rated capacity (kW.h) of the electric automobile,
Figure BDA00016435142200000611
in order to reach charging station j, the electric vehicle has the remaining capacity,
Figure BDA00016435142200000612
for the charging capacity at charging station j, PeAnd charging power (kW) of a quick charging machine for the charging station.
The total charging time of the electric vehicle from the starting point to the destination is as follows:
Figure BDA00016435142200000613
s303, calculating the charging cost required from the departure point to the destination;
preferably, the S303 calculates a charge price cost of the electric vehicle, including:
s3031, acquiring real-time charging electricity price information of each charging station from a departure point to a destination by using a charging station operation monitoring system;
and S3032, calculating the charging cost of each charging station from the departure point to the destination.
The charge price cost includes a charge electricity price and a charge service fee. And (4) according to the real-time charging price (t) issued by the charging station operation system.
The quick charging cost of the electric vehicle at the charging station j is
Figure BDA0001643514220000071
Wherein price (t) is a function (Yuan/degree) of the charging price and the charging service price of the electric vehicle changing with time. t is tjThe time (h) when the electric vehicle arrives at the charging station j.
The total fast charge cost of the electric vehicle from the starting point to the destination is:
Figure BDA0001643514220000072
the S4 includes the steps of: and planning the charging path of the electric automobile by adopting an ant colony algorithm and taking the remaining battery capacity as constraint and the optimal sum of the travel distance, the travel time and the charging cost as a target.
According to the remaining capacity of the electric vehicle after passing through each road section during the driving process calculated in the step S2. When the electric automobile is at the starting point, the residual electric quantity is EstartThen, the remaining capacity E is reached to the node jjThe calculation is as follows:
Figure BDA0001643514220000073
Ejsatisfies the following conditions: ej≥0,j∈La∪SaThe electric vehicle remaining capacity of the road network node and the charging station node which are randomly accessed is not less than zero.
To reach node j for remaining capacity EjFor constraint, the total cost in step S3 is a target, and the ant colony algorithm flowchart shown in fig. 5 is referred to solve the problem, so that the optimal solution is the planned charging path.
According to another aspect of the present application, the present embodiment provides an electric vehicle charging path planning apparatus, as shown in fig. 6, the apparatus including:
the first calculation module is used for acquiring various driving working conditions of the electric automobile and calculating the average energy consumption value of the electric automobile under the various driving working conditions;
the second calculation module is used for acquiring a driving route and calculating the dynamic residual electric quantity of the electric automobile passing through each road section during driving;
the third calculation module is used for calculating the travel distance, the travel time and the charging cost of the user according to the travel route;
and the path planning module determines the charging path of the electric automobile according to the calculation results of the first, second and third calculation modules.
Further, the first calculation module includes:
the parameter acquisition unit is used for selecting driving condition characteristic parameters capable of reflecting the opening and closing of the air conditioner of the electric automobile and the driving characteristics of a driver;
preferably, the characteristic parameters cited in the prior art are selected, and include: eight characteristic parameters of maximum speed, average acceleration, maximum acceleration, acceleration proportion, deceleration proportion, uniform speed proportion and idle speed proportion are used for classifying the running conditions. In this embodiment, each feature acquisition is specifically calculated for a plurality of groups, and the parameters are shown in tables 1 to 3 after being filtered. Wherein, table 1 is the related data of maximum speed, average acceleration and maximum acceleration, table 2 is the related data of acceleration ratio, deceleration ratio, uniform speed ratio and idle ratio, and table 3 is the related data of ambient temperature, air conditioner running power, standard difference of acceleration and standard difference of rate of change of acceleration; further, the subsequent calculations in this embodiment all use the data provided by the above tables.
Secondly, the ambient temperature can influence the running of the vehicle, and two characteristic parameters of the ambient temperature and the running power of the air conditioner are selected for the classification of the running conditions.
And finally, selecting the acceleration standard deviation and the acceleration change rate standard deviation as indexes for reflecting different driving characteristics in the driving working condition.
TABLE 1
Figure BDA0001643514220000081
TABLE 2
Figure BDA0001643514220000082
Figure BDA0001643514220000091
TABLE 3
Figure BDA0001643514220000092
The first analysis unit is used for analyzing the correlation between the driving condition characteristic parameters selected by the parameter acquisition unit and the power consumption and the correlation strength between the characteristic parameters;
preferably, a driving process from after the current charging to before the next charging is called a cycle, in each cycle, the electric vehicle is divided into driving segments every 1km, the divided driving segments are analyzed, and the 12 characteristic parameters and the power consumption amount under each segment are calculated. The correlation between the 12 characteristic parameters and the energy consumption of the electric automobile is calculated by adopting the spearman rank correlation coefficient, the calculation formula is shown as the formula (1),
Figure BDA0001643514220000093
and deleting the characteristic parameter variable with the minimum correlation with the power consumption according to the calculation result.
The second analysis unit is used for performing dimensionality reduction analysis on the characteristic parameters of the running condition by adopting a principal component analysis method according to the analysis result of the first analysis unit;
the method comprises the steps of analyzing fragments containing 11 driving condition characteristic parameters by adopting a principal component analysis method to obtain characteristic values and contribution rates of the principal components, selecting n principal components with the accumulated contribution rate of more than 80 percent according to the principle of principal component analysis, wherein the n principal components can be considered to sufficiently reflect information contained in original variables, and selecting m driving condition characteristic parameters which are mainly reflected by the n principal components and have weak correlation for cluster analysis.
The energy consumption calculation unit is used for classifying the historical driving condition data of the electric automobile by adopting a fuzzy C-mean clustering algorithm; and calculating the average energy consumption value of the electric automobile under various driving conditions.
Performing cluster analysis on sample data containing m driving condition characteristic parameters, determining the category of each historical driving condition segment of the electric automobile according to the clustering center of each working condition and the minimum distance principle, wherein the distance calculation formula is
dq＝||x-cq|| q＝1，2，...，12 (2)
Wherein x is [ x ]1，x2，...，x8]Is a characteristic parameter of a segment, cq＝[cq1，cq2，...，cq12]Is the cluster center parameter of cluster q. And taking the average value of the energy consumption of each fragment in the same type of driving working condition as the energy consumption of the electric automobile under the working condition.
Further, the second calculation module includes:
the real-time driving condition acquisition unit acquires the real-time driving conditions of each road section by using the Internet;
the energy consumption calculation unit is used for calculating the real-time energy consumption of the electric automobile passing through each road section during the driving process;
and the residual electric quantity calculating unit is used for calculating the residual electric quantity after the road section passes through according to the real-time energy consumption calculated by the energy consumption calculating unit.
Preferably, the vehicle travel section RijA distance of rijCalculating the clustering center of the road section by using the real-time driving road condition of the road section published by the internet, and judging the real-time driving condition class of the road section to be gqEnergy consumption per kilometer of corresponding road section is EqThen the power consumption through the road section is
Eij＝Eq×rij(3)
Further, the third computing module comprises:
a first calculation unit which calculates travel distance costs of each section from a departure point to a destination;
preferably, the total distance of the user in the process of going to the destination includes a total distance of travel of the electric vehicle from the starting point O to the destination D through the charging station S. The trip distance cost is then:
Figure BDA0001643514220000101
in the formula, xijDeciding variables for 0-1 pathIf the electric automobile passes through the road section r between the nodes i and j of the road networkijThen xij1, otherwise xij＝0。LaSet of road network nodes representing electric vehicles passing by, SaRepresents a set of charging station nodes through which the electric vehicle passes.
A second calculation unit that calculates a travel time cost required from the departure point to the destination;
preferably, the travel time cost required from the departure point to the destination includes:
s3021, calculating the required travel time from the starting point to the destination;
road section r published according to internetijSpeed of
Figure BDA0001643514220000111
Estimating a user's passage of a road section rijTime T of route travelijComprises the following steps:
Figure BDA0001643514220000112
the travel time of all the road sections passed by the user from the starting point to the destination is as follows:
Figure BDA0001643514220000113
s3022, acquiring information such as the number of electric vehicles by using a charging station operation monitoring system, and calculating charging waiting time and charging time of each charging station from a departure point to a destination path;
the monitoring and management system for the electric vehicle charging station monitors basic information such as the total number of charging machines of the charging station, the number of charging machines in use, the number of electric vehicles entering a queuing sequence and the like in real time, and the number of the EV stations arriving at the charging station at the moment t and the moment t-1 are respectively represented as
Figure BDA0001643514220000114
Number of EVs receiving charging service at time t and t-1
Figure BDA0001643514220000115
Calculating the average user arrival rate and the average service rate of the charging equipment at charging station j at time t, i.e.
Figure BDA0001643514220000116
Figure BDA0001643514220000117
The average waiting time of the vehicle is:
Figure BDA0001643514220000118
in the formula SjThe number of charging piles configured for charging station j,
the queuing waiting time of all charging stations for the electric vehicle user to travel from the initial point to the destination access is as follows:
Figure BDA0001643514220000119
preferably, the charging time is an estimated value of the time taken for the electric vehicle to reach charging station j and then to be charged from the remaining charge to 80% of the state of charge.
Figure BDA0001643514220000121
Figure BDA0001643514220000122
In the formula, E0The rated capacity (kW.h) of the electric automobile,
Figure BDA0001643514220000123
in order to reach charging station j, the electric vehicle has the remaining capacity,
Figure BDA0001643514220000124
for the charging capacity at charging station j, PeAnd charging power (kW) of a quick charging machine for the charging station.
The total charging time of the electric vehicle from the starting point to the destination is as follows:
Figure BDA0001643514220000125
a third calculation unit that calculates a charging cost required from the departure point to the destination;
preferably, the calculating of the required charging cost from the departure point to the destination includes:
s3031, acquiring real-time charging price information of each charging station from a departure point to a destination by using a charging station operation monitoring system;
and S3032, calculating the charging cost of each charging station from the departure point to the destination.
The charge price cost includes a charge electricity price and a charge service fee. And (4) according to the real-time charging price (t) issued by the charging station operation system.
The quick charging cost of the electric vehicle at the charging station j is
Figure BDA0001643514220000126
Wherein price (t) is a function (Yuan/degree) of the charging price and the charging service price of the electric vehicle changing with time. t is tjThe time (h) when the electric vehicle arrives at the charging station j.
The total fast charge cost of the electric vehicle from the starting point to the destination is:
Figure BDA0001643514220000127
the route planning module acquires the calculation results of the first calculation module, the second calculation module and the third calculation module, and plans the electric vehicle charging route by adopting an ant colony algorithm and taking the residual battery capacity as a constraint and the optimal sum of the travel distance, the travel time and the charging cost as a target.
And planning the charging path of the electric automobile by adopting an ant colony algorithm and taking the remaining battery capacity as constraint and the optimal sum of the travel distance, the travel time and the charging cost as a target.
According to the remaining capacity of the electric vehicle after passing through each road section during the driving process calculated in the step S2. When the electric automobile is at the starting point, the residual electric quantity is EstartThen, the remaining capacity E is reached to the node jjThe calculation is as follows:
Figure BDA0001643514220000131
Ejsatisfies the following conditions: ej≥0,j∈La∪SaThe electric vehicle remaining capacity of the road network node and the charging station node which are randomly accessed is not less than zero.
To reach node j for remaining capacity EjFor constraint, the total cost in step S3 is a target, and the ant colony algorithm flowchart shown in fig. 6 is referred to solve the problem, so that the optimal solution is the planned charging path.
It should be understood that the above-mentioned examples are only for the purpose of clearly illustrating the invention and are not to be construed as limiting the embodiments of the present application, and that various other modifications and variations which are obvious to those skilled in the art may be made on the basis of the above description.

Claims (10)

1. An electric vehicle charging path planning method is characterized by comprising the following steps:
s1, acquiring various driving conditions of the electric automobile, and calculating an average energy consumption value of the electric automobile under the various driving conditions; the S1 includes the steps of:
s101, selecting a driving condition characteristic parameter capable of reflecting the opening and closing of an air conditioner of the electric automobile and the driving characteristics of a driver;
s102, analyzing the correlation between the selected driving condition characteristic parameters and the power consumption and the correlation strength between the characteristic parameters;
s103, performing dimensionality reduction analysis on the characteristic parameters of the driving condition by adopting a principal component analysis method;
s104, classifying historical driving condition data of the electric automobile by adopting a fuzzy C-mean clustering algorithm, and calculating an average energy consumption value of the electric automobile under various driving conditions;
s2, acquiring a driving route, and calculating the dynamic residual electric quantity of the electric automobile passing through each road section during driving;
the S2 includes the steps of:
s201, acquiring real-time driving conditions of all road sections by using the Internet;
s202, calculating real-time energy consumption of the electric automobile passing through each road section during driving, and calculating the residual electric quantity after passing through the road section;
s3, calculating the travel distance, the travel time and the charging cost of the user according to the travel route;
and S4, determining the charging path of the electric automobile according to the calculation results of the steps S1-S3.
2. The method for planning the charging path of the electric vehicle according to claim 1, wherein the step S3 includes the steps of:
s301, calculating travel distance cost of each road section from the departure point to the destination;
s302, calculating the travel time cost required from the starting point to the destination;
and S303, calculating the charging cost required from the departure point to the destination.
3. The method for planning the charging path of the electric vehicle according to claim 2, wherein the step S302 includes:
s3021, calculating the required travel time from the starting point to the destination;
and S3022, acquiring the quantity information of the electric vehicles by using the charging station operation monitoring system, and calculating the charging waiting time and the charging time of each charging station from the departure point to the destination.
4. The method for planning the charging path of the electric vehicle according to claim 2, wherein the step S303 includes:
s3031, acquiring real-time charging electricity price information of each charging station from a departure point to a destination by using a charging station operation monitoring system;
and S3032, calculating the charging cost of each charging station from the departure point to the destination.
5. The method for planning the charging path of the electric vehicle according to any one of claims 1 to 4, wherein the step S4 comprises the steps of: and planning the charging path of the electric automobile by adopting an ant colony algorithm and taking the remaining battery capacity as constraint and the optimal sum of the travel distance, the travel time and the charging cost as a target.
6. An electric vehicle charging path planning apparatus, the apparatus comprising:
the first calculation module is used for acquiring various driving working conditions of the electric automobile and calculating the average energy consumption value of the electric automobile under the various driving working conditions;
the second calculation module is used for acquiring a driving route and calculating the dynamic residual electric quantity of the electric automobile passing through each road section during driving;
the third calculation module is used for calculating the travel distance, the travel time and the charging cost of the user according to the travel route;
the path planning module is used for determining a charging path of the electric automobile according to the calculation results of the first, second and third calculation modules;
the first computing module includes:
the parameter acquisition unit is used for selecting driving condition characteristic parameters capable of reflecting the opening and closing of the air conditioner of the electric automobile and the driving characteristics of a driver;
the first analysis unit is used for analyzing the correlation between the driving condition characteristic parameters selected by the parameter acquisition unit and the power consumption and the correlation strength between the characteristic parameters;
the second analysis unit is used for performing dimensionality reduction analysis on the characteristic parameters of the running condition by adopting a principal component analysis method according to the analysis result of the first analysis unit;
the energy consumption calculation unit is used for classifying the historical driving condition data of the electric automobile by adopting a fuzzy C-mean clustering algorithm; calculating the average energy consumption value of the electric automobile under various driving conditions;
the second calculation module includes:
the real-time driving condition acquisition unit acquires the real-time driving conditions of each road section by using the Internet;
the energy consumption calculation unit is used for calculating the real-time energy consumption of the electric automobile passing through each road section during the driving process;
and the residual electric quantity calculating unit is used for calculating the residual electric quantity after the road section passes through according to the real-time energy consumption calculated by the energy consumption calculating unit.
7. The electric vehicle charging path planning apparatus according to claim 6, wherein the third calculation module comprises:
a first calculation unit which calculates travel distance costs of each section from a departure point to a destination;
a second calculation unit that calculates a travel time cost required from the departure point to the destination;
and a third calculation unit calculating a required charging cost from the departure point to the destination.
8. The electric vehicle charging path planning apparatus of claim 7, wherein the cost of travel time required from the departure point to the destination comprises:
s3021, calculating the required travel time from the starting point to the destination;
and S3022, acquiring the quantity information of the electric vehicles by using the charging station operation monitoring system, and calculating the charging waiting time and the charging time of each charging station from the departure point to the destination.
9. The electric vehicle charging path planning apparatus of claim 7, wherein the calculating the charging cost required from the departure point to the destination comprises:
s3031, acquiring real-time charging electricity price information of each charging station from a departure point to a destination by using a charging station operation monitoring system;
and S3032, calculating the charging cost of each charging station from the departure point to the destination.
10. The electric vehicle charging path planning device according to any one of claims 6 to 9, wherein the path planning module obtains the calculation results of the first, second and third calculation modules, and plans the electric vehicle charging path with an ant colony algorithm with the remaining battery capacity as a constraint and the optimal sum of the travel distance, the travel time and the charging cost as a target.
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