CN112550050B - Electric vehicle charging method and system - Google Patents

Electric vehicle charging method and system Download PDF

Info

Publication number
CN112550050B
CN112550050B CN202011468928.4A CN202011468928A CN112550050B CN 112550050 B CN112550050 B CN 112550050B CN 202011468928 A CN202011468928 A CN 202011468928A CN 112550050 B CN112550050 B CN 112550050B
Authority
CN
China
Prior art keywords
charging
node
electric automobile
electric
determining
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202011468928.4A
Other languages
Chinese (zh)
Other versions
CN112550050A (en
Inventor
刘雪飞
李俊
邵良友
许伯阳
曹阳
李扬
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shenzhen Power Supply Co ltd
Original Assignee
Shenzhen Power Supply Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shenzhen Power Supply Co ltd filed Critical Shenzhen Power Supply Co ltd
Priority to CN202011468928.4A priority Critical patent/CN112550050B/en
Publication of CN112550050A publication Critical patent/CN112550050A/en
Application granted granted Critical
Publication of CN112550050B publication Critical patent/CN112550050B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • B60L53/63—Monitoring or controlling charging stations in response to network capacity
    • 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
    • B60L53/10—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 characterised by the energy transfer between the charging station and the vehicle
    • B60L53/14—Conductive energy transfer
    • 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
    • B60L53/62—Monitoring or controlling charging stations in response to charging parameters, e.g. current, voltage or electrical charge
    • 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
    • Y02T90/14—Plug-in electric vehicles

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)

Abstract

The invention provides a method and a system for charging an electric automobile, wherein the method comprises the following steps: acquiring node position information of an electric automobile in a current trip chain and first charge state information at the current moment; determining possible decision-making behaviors of each other node of the electric automobile in the trip chain according to the first charge state information and the current node position information; acquiring second charge state information of the charging pile at the current moment, and determining the charge state transition probability of the charging pile according to the second charge state information and possible decision behaviors; and determining the optimal decision-making behavior of the electric automobile based on the minimum peak-valley difference of the total load curve of the power distribution network according to the charge state transition probability of the charging pile, and controlling the charging of the electric automobile according to the optimal decision-making behavior. The method can optimize the load curve of the power distribution network.

Description

Electric vehicle charging method and system
Technical Field
The invention relates to the technical field of charging, in particular to a charging method and a charging system for an electric vehicle.
Background
An Electric Vehicle (EV) is a green vehicle with zero or low emission, and its large-scale popularization can effectively alleviate the increasingly serious social problems of energy crisis, environmental pollution, etc. nowadays, so it has received wide attention from all circles of society. The electric automobile and Grid interaction (V2G) means that the charging and discharging behaviors of the electric automobile are optimally managed through a reasonable strategy and an advanced communication means.
In the prior art, research on charging and discharging behaviors of electric vehicles is mostly carried out on the basis of assuming a charging time period and a charging mode, the randomness of the charging state of the electric vehicle in space and time is ignored, the influence of electric vehicle users and node charging piles is less considered, the randomness of the charging state of the electric vehicle in space and time is ignored, and the mutual influence of the electric vehicle users and the node charging piles is less considered.
Disclosure of Invention
The invention aims to solve the technical problem of providing a charging method and a charging system for an electric vehicle, so as to overcome the defect that the influence of electric vehicle users and node charging piles is not considered in the charging and discharging research of the electric vehicle in the prior art.
In order to solve the above technical problem, an aspect of the present invention provides a charging method for an electric vehicle, including the following steps:
acquiring node position information of an electric automobile in a current trip chain and first charge state information at the current moment;
determining possible decision-making behaviors of each other node of the electric automobile in a trip chain according to the first charge state information and the current node position information;
acquiring second charge state information of the charging pile at the current moment, and determining the charge state transition probability of the charging pile according to the second charge state information and the possible decision-making behaviors;
and determining the optimal decision-making behavior of the electric automobile based on the minimum peak-valley difference of the total load curve of the power distribution network according to the charge state transition probability of the charging pile, and controlling the electric automobile to be charged according to the optimal decision-making behavior.
In a specific embodiment, the method further comprises:
acquiring a probability density function and a charging time probability density function of the charging electric quantity of the electric automobile;
obtaining the planned charging electric quantity required by the electric automobile through Monte Carlo simulation according to the probability density function and the charging time probability density function of the charging electric quantity;
and determining first charge state information of the electric automobile at the current moment according to the battery capacity of the electric automobile and the planned charging electric quantity.
In a specific embodiment, the determining, according to the first state of charge information and the current node location information, possible decision-making behaviors of the electric vehicle at other nodes in the trip chain specifically includes:
respectively calculating the distance between the current node position and each other node position of the electric automobile, and calculating the electric quantity loss value and the driving time of the electric automobile from the current node position to each other node according to the distance;
and judging whether the electric automobile can be charged at each other node or not according to the first charge state information, the electric quantity loss value corresponding to each other node and the planned driving time, wherein if yes, the decision behavior of the electric automobile at each other node comprises a charging behavior, otherwise, the decision behavior of the electric automobile at each other node does not comprise the charging behavior.
In a specific embodiment, the determining whether the electric vehicle can be charged at each of the other nodes according to the first state of charge information, the power loss value corresponding to each of the other nodes, and the driving time specifically includes:
determining whether the electric automobile meets the requirement of the electric quantity charged at each other node or not according to the first charge state information and the electric quantity loss value corresponding to each other node;
calculating the stay time of the electric automobile at each other node according to the running time, and judging whether the electric automobile meets the requirement of charging time according to the stay time and the planned charging time; for each other node, if the electric automobile meets the requirements of the charging electric quantity and the charging time, determining that the electric automobile can be charged at the node, otherwise, determining that the electric automobile cannot be charged.
In a specific embodiment, the determining whether the electric vehicle meets the requirement of the electric quantity charged at each other node according to the first state of charge information and the electric quantity loss value corresponding to each other node specifically includes:
and calculating a difference value between the first charge state information and the lowest electric quantity threshold value of the battery, and judging whether the difference value is larger than a corresponding electric quantity loss value or not, wherein if yes, the electric automobile meets the requirement of the charging electric quantity, and otherwise, the electric automobile does not meet the requirement of the charging electric quantity.
In a specific embodiment, the determining whether the electric vehicle meets the charging time requirement according to the staying time and the scheduled charging time specifically includes:
and judging whether the residence time is greater than the planned charging time, if so, enabling the electric automobile to meet the requirement of the charging time, otherwise, enabling the electric automobile not to meet the requirement of the charging time.
In a specific embodiment, the determining the state of charge transition probability of the charging pile according to the second state of charge information and the possible decision-making behavior specifically includes:
determining third charge state information of the charging pile at the next moment according to the second charge state information and the possible decision-making behaviors;
and calculating the charge state transition probability of the charging pile according to the second charge state information and the third charge state information.
In a particular embodiment of the method of the present invention,
the step of determining the optimal decision-making behavior of the electric automobile based on the minimum peak-valley difference of the total load curve of the power distribution network according to the charge state transition probability of the charging pile specifically comprises the steps of controlling the charging of the electric automobile according to the optimal decision-making behavior,
solving an objective function by adopting a genetic algorithm to obtain the state transition probability of the charging pile, wherein the objective function is as follows:
Figure BDA0002835542600000031
minLdif＝min{maxLnew,t-minLnew,t}
wherein L isdifThe peak-valley difference of the load curve of the power distribution network for calculating the charging load of the electric automobile; l istThe original load of the power distribution network at the moment t; ri,i+1The charge state transition probability of the charging pile; qi+1Is third state of charge information; qiIs the second state of charge information; n is the total number of the charging pile nodes; t is tcCharging time for the electric vehicle; u shapekIs the battery of the kth electric automobileCapacity;
and determining the decision-making behavior of the node charging pile according to the state transition probability of the charging pile, and determining the decision-making behavior of the electric automobile at the node according to the decision-making behavior of the node charging pile.
The invention also provides an electric vehicle charging system, comprising:
the first obtaining unit is used for obtaining the position information of a node where the electric automobile is located in a current trip chain and the first charge state information at the current moment;
the possible decision-making behavior determining unit is used for determining possible decision-making behaviors of each other node of the electric automobile in the trip chain according to the first charge state information and the current node position information;
the charging pile control device comprises a charging pile control unit, a charging state transition probability determining unit and a charging pile control unit, wherein the charging pile control unit is used for acquiring second charging state information of a charging pile at the current moment and determining the charging state transition probability of the charging pile according to the second charging state information and the possible decision behaviors;
and the optimal decision-making behavior determining unit is used for determining the optimal decision-making behavior of the electric automobile based on the minimum peak-valley difference of the total load curve of the power distribution network according to the charge state transition probability of the charging pile and controlling the charging of the electric automobile according to the optimal decision-making behavior.
In a specific embodiment, the possible decision behavior determining unit is specifically configured to:
respectively calculating the distance between the current node position and each other node position of the electric automobile, and calculating the electric quantity loss value and the driving time of the electric automobile from the current node position to each other node according to the distance;
and judging whether the electric automobile can be charged at each other node or not according to the first charge state information, the electric quantity loss value corresponding to each other node and the planned driving time, wherein if yes, the decision behavior of the electric automobile at each other node comprises a charging behavior, otherwise, the decision behavior of the electric automobile at each other node does not comprise the charging behavior.
The embodiment of the invention has the beneficial effects that: according to the charging method, the possible decision-making behaviors of the electric automobile at each other node are determined according to the first charge state information by acquiring the first charge state information of the electric automobile and the node position information of the electric automobile in a trip chain, the second charge state information of the charging pile is acquired, the charge state transition probability of the charging pile is determined according to the second charge state information and the possible decision-making behaviors of the electric automobile, the optimal decision-making behavior of the electric automobile is determined according to the charge state transition probability of the charging pile based on the minimum peak-valley difference of the total load curve of the power distribution network, and the electric automobile is controlled to be charged according to the optimal decision-making behavior. According to the method, the influence of charging and discharging of the electric automobile and the influence of the node charging pile are considered, so that the load curve of the power distribution network can be optimized through the charging and discharging of the electric automobile.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a charging method for an electric vehicle according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments refers to the accompanying drawings, which are included to illustrate specific embodiments in which the invention may be practiced.
Referring to fig. 1 below, an embodiment of the present invention provides a charging method based on an electric vehicle, including the following steps:
s1, obtaining the position information of the node where the electric automobile is located in the current trip chain and the first state of charge information at the current moment.
According to analysis, the charging capacity of the electric automobile conforms to normal distribution, and the probability density function of the charging capacity conforms to the following probability density function:
Figure BDA0002835542600000051
wherein Q is the charging capacity of the electric automobile; mu.sQThe expected value of the charging electric quantity is; sigmaQIs its standard deviation.
According to analysis, the charging time of the electric automobile conforms to the lognormal distribution, and the probability density function of the charging time is as follows:
Figure BDA0002835542600000052
wherein T is the charging time of the electric automobile; mu.sTThe expected value of the natural logarithm of the charging time is obtained; sigmaTIs its standard deviation.
And obtaining the planned charging electric quantity and the planned charging time required by the electric automobile through Monte Carlo simulation based on the characteristic quantity probability density function obtained by analyzing the running characteristics of the electric automobile. Specifically, the normal distribution-fit planned charging amount and planned charging time of the electric vehicle can be generated in matlab by using the norm function. And subtracting the planned charging electric quantity from the battery capacity of the electric automobile to calculate first state of charge information of the electric automobile.
Specifically, the scheduled charging time is that the user chooses to start charging just when the destination is reached, until full, then the scheduled charging time at i is:
Figure BDA0002835542600000061
wherein η is the charging efficiency; pC,iFor charging power at i, EhAs battery capacity, RSOC,iIs first state of charge information, i is a node of the current position of the electric automobile, Rsoc,0The state of charge information is the state of charge information of the electric automobile when the electric automobile is fully charged.
And S2, determining possible decision behaviors of the electric automobile at other nodes in the trip chain according to the first state of charge information and the current node position information.
Specifically, whether the electric vehicle can be charged at a certain node can be determined by determining whether the electric vehicle satisfies the charging electric quantity requirement and the charging time requirement at the same time at the certain node.
In a specific embodiment, the determining whether the electric vehicle meets the requirement of the charging capacity at a certain node specifically includes: and calculating a difference value between the first charge information and the lowest electric quantity threshold value of the battery, and judging whether the difference value is larger than the corresponding electric quantity loss value or not, wherein if yes, the electric automobile meets the requirement of the charging electric quantity, and otherwise, the electric automobile does not meet the requirement of the charging electric quantity.
For the electric automobile, if the last position point in the current driving mileage can meet the requirement of the charging electric quantity, the position points before the last position point can meet the requirement of the charging electric quantity, and therefore, the condition of whether the requirement of the charging electric quantity is met is judged in a mode of going forward from the last position point in the current driving mileage. Assuming that the node where the electric vehicle is located at present is the ith node in the current driving range, the last node in the current driving range is n, and for the last node n, if the first charge state information meets the following formula, it is indicated that the electric vehicle meets the requirement of the charging point quantity at the last node.
Figure BDA0002835542600000062
In the formula: rSOC,iThe first state of charge information is represented by i, a node where the electric automobile is located at present and xi is a lowest electric quantity threshold value; ehIs the battery capacity; omegai,i+1Power consumption per unit distance from i to i + 1; li,i+1The mileage is from i to i + 1. If the first charge state information does not satisfy the following formula, whether a node before the last node in the current trip mileage in the trip chain meets the requirement of the charging electric quantity is judged until the judgment of all nodes in the current trip mileage is completed or the fact that the electric automobile must be charged at a certain node is determined.
Initial charging timetstart,iIs composed of
tstart,i＝ti-1+Δti,i-1 i＝1,2,…,n
In the formula: t is tiRandomly extracting the starting time from the position i to the next destination; Δ ti,i-1Is the time from i-1 to i.
Dwell time Δ tiIs composed of
Δti＝ti-tstart,i＝ti-(ti-1+Δti,i-1) i＝1,2,…,n
Meanwhile, the stay time constraint must be considered, that is, the stay time of the user must not be less than the planned charging time, otherwise, the user reselects the stay point satisfying the constraint for charging, and the stay time constraint is as follows:
TC,i≤Δti
for any node, if the node meets the requirements of the charging electric quantity and the charging time, the decision-making behavior of the electric vehicle at the node comprises charging, otherwise, the decision-making behavior of the electric vehicle at the node does not comprise charging.
It should be noted that, at each node, the decision-making behavior of the electric vehicle is divided into charging (including fast charging and slow charging), driving, and neither charging nor driving. Electric vehicle charge state transition probability EijThe state of charge of the current moment and the decision-making action a taken by the electric vehicle in the time period from the current moment to the next momentiIt is related. Such as ai-1 represents electric vehicle driving; a isi1+ + means that it is charged quickly; a isi1+ means that it is slow-charged; a isi0 means that it neither charges nor travels.
Knowing the first state of charge (SOC) of the electric automobile at the current moment and the decision-making behavior taken within a period of time from the current moment, the SOC value of the electric automobile at the next moment can be calculated, and the SOC value is as follows:
when ai1+ or aiWhen 1+ + is satisfied, the electric vehicle is charged, then
Figure BDA0002835542600000071
When aiWhen 0 is satisfied, the electric vehicle is neither charged nor discharged, and S is set toi+1＝Si；
③ when aiWhen the vehicle runs under-1, the electric vehicle runs under
Figure BDA0002835542600000081
Therein, SOCiIs the state of charge (SOC) information of the electric vehicle at the current momenti+1The charge state information of the electric vehicle at the next moment, Pc is the charging power of the electric vehicle, tcFor charging electric vehicle, UkIs the battery capacity, W, of the kth electric vehicledFor the power consumption of the electric vehicle per unit distance traveled, |dThe distance traveled by the electric vehicle from the current moment to the next moment is shown.
And S3, obtaining second state of charge information of the charging pile at the current moment, and determining the state of charge transition probability of the charging pile according to the second state of charge information and the possible decision behaviors.
Assuming that each charging pile can only be connected to 1 electric vehicle at most, according to the decision-making behaviors of the electric vehicles, the behaviors of the nodes are mainly divided into 4 types: bi1+ represents that the node has slow charging of the electric vehicle; bi1+ + represents that the node has electric vehicle fast charge; bi0 means that the node is in the presence of an electric vehicle but is not charging; bi-1 indicates that the node is absent of an electric vehicle.
It should be noted that the decision-making behavior of the charging pile is a passive decision-making behavior, that is, the decision-making behavior of the charging pile is determined by the decision-making behavior of the electric vehicle, and when the node charging pile has electric vehicle charging, that is, the decision-making behavior of the electric vehicle at the node charging pile is charging, the corresponding decision-making behavior of the charging pile is bi1+ or bi1+ +, when the decision-making behavior of the electric vehicle at the node charging pile is no charging, the decision-making behavior corresponding to the charging pile is bi1 or bi＝0。
Knowing second state of charge information of the charging pile at the current moment and behaviors of the charging pile from the current moment to the next moment in time, calculating the state of charge information of the next moment as follows:
when bi1+ or biWhen 1+ +, the node fills the electric pile and releases the electric quantity, then
Figure BDA0002835542600000082
When bi1 or biWhen 0, the node fills electric pile and does not release electric quantity, then Qi+1＝Qi；
In the formula: pcCharging power for the charging pile; t is tcCharging it for a length of time; u shapekIs the battery capacity, Q, of the kth electric vehicleiIs the second state of charge information; qi+1Is the third state of charge information. In summary, there are
Figure BDA0002835542600000083
The charge state transition probability of the node charging pile is the conditional probability of the occurrence of the charge state transition event at the adjacent moment,
namely Ri,i+1＝P(Qi→Qi+1)＝P(Qi|Qi+1)
In the formula, Ri,i+1The charge state transition probability of the charging pile; qiSecond state of charge information for its current time; qi+1And is the state of charge information at its next time, i.e., the third state of charge information.
S4, determining the optimal decision-making behavior of the electric automobile based on the minimum peak-valley difference of the total load curve of the power distribution network according to the charge state transition probability of the charging pile, and controlling the charging of the electric automobile according to the optimal decision-making behavior.
The charge state transition probability R of the node charging pile is obtainedi,i+1And then, the peak-valley difference is minimized to be adjusted, so that the load curve of the power distribution network on which the weighted sum of the charging pile charge state transition probability and the charging quantity is superposed is more gentle. Is provided withThe adjustment quantity of the charging pile charge state transition probability, namely the decision variable is delta Ri,i+1Then the objective function for optimizing the peak-to-valley difference is:
Figure BDA0002835542600000091
minLdif＝min{maxLnew,t-minLnew,t}
wherein L isdifThe peak-valley difference of the load curve of the power distribution network for calculating the charging load of the electric automobile; l istThe original load of the power distribution network at the moment t; ri,i+1The charge state transition probability of the charging pile; qi+1Is third state of charge information; qiIs the second state of charge information; n is the total number of the charging pile nodes; t is tcCharging time for the electric vehicle; u shapekThe battery capacity of the kth electric vehicle.
Optimized solution is carried out through a genetic algorithm, and the charging pile state of charge transition probability (R) after adjustment can be obtainedi,i+1+ΔRi,i+1) And an optimized load curve. According to the new charging pile charge state transition probability, the decision-making behavior b of the charging pile under each probability can be solvedi. Because the decision-making behavior of the charging pile is determined depending on the decision-making behavior of the electric vehicle, the decision-making behavior a of the electric vehicle at the moment can be obtained in turni。
According to the electric vehicle charging method, the possible decision-making behaviors of the electric vehicle at each other node are determined according to the first charge state information by obtaining the first charge state information of the electric vehicle and the node position information of the electric vehicle in a trip chain, the second charge state information of the charging pile is obtained, the charge state transition probability of the charging pile is determined according to the second charge state information and the possible decision-making behaviors of the electric vehicle, the optimal decision-making behavior of the electric vehicle is determined according to the charge state transition probability of the charging pile based on the minimum peak-valley difference of the total load curve of the power distribution network, and the electric vehicle is controlled to be charged according to the optimal decision-making behavior. According to the method, the influence of charging and discharging of the electric automobile and the influence of the node charging pile are considered, so that the load curve of the power distribution network can be optimized through the charging and discharging of the electric automobile.
Based on the first embodiment of the present invention, the second embodiment of the present invention provides an electric vehicle charging system, which includes a first obtaining unit, configured to obtain position information of a node where an electric vehicle is located in a current trip chain and first state of charge information at a current time; the possible decision-making behavior determining unit is used for determining possible decision-making behaviors of each other node of the electric automobile in the trip chain according to the first charge state information and the current node position information; the charging pile control device comprises a charging pile control unit, a charging state transition probability determining unit and a charging pile control unit, wherein the charging pile control unit is used for acquiring second charging state information of a charging pile at the current moment and determining the charging state transition probability of the charging pile according to the second charging state information and the possible decision behaviors; and the optimal decision-making behavior determining unit is used for determining the optimal decision-making behavior of the electric automobile based on the minimum peak-valley difference of the total load curve of the power distribution network according to the charge state transition probability of the charging pile and controlling the charging of the electric automobile according to the optimal decision-making behavior.
Specifically, the possible decision-making behavior determining unit is specifically configured to calculate a distance between the current node position and each other node position of the electric vehicle, calculate an electric quantity loss value and a travel time of the electric vehicle traveling from the current node position to each other node according to the distance, and determine whether the electric vehicle can be charged at each other node according to the first state of charge information, the electric quantity loss value corresponding to each other node, and a planned travel time, where if yes, the decision-making behavior of the electric vehicle at each other node includes a charging behavior, and otherwise, the decision-making behavior of the electric vehicle at each other node does not include a charging behavior.
For the working principle and the advantageous effects thereof, please refer to the description of the first embodiment of the present invention, which will not be described herein again.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention, and it is therefore to be understood that the invention is not limited by the scope of the appended claims.

Claims (7)

1. An electric vehicle charging method, comprising:
acquiring node position information of an electric automobile in a current trip chain and first charge state information at the current moment;
determining possible decision-making behaviors of each other node of the electric automobile in a trip chain according to the first charge state information and the current node position information; the method specifically comprises the following steps: respectively calculating the distance between the current node position and each other node position of the electric automobile, and calculating the electric quantity loss value and the driving time of the electric automobile from the current node position to each other node according to the distance; judging whether the electric automobile can be charged at each other node or not according to the first charge state information, the electric quantity loss value corresponding to each other node and the planned driving time, wherein if yes, the decision-making behavior of the electric automobile at each other node comprises a charging behavior, otherwise, the decision-making behavior of the electric automobile at each other node does not comprise the charging behavior; the step of judging whether the electric vehicle can be charged at each other node according to the first state of charge information, the electric quantity loss value and the running time corresponding to each other node specifically comprises the following steps: determining whether the electric automobile meets the requirement of the electric quantity charged at each other node or not according to the first charge state information and the electric quantity loss value corresponding to each other node; calculating the stay time of the electric automobile at each other node according to the running time, and judging whether the electric automobile meets the requirement of charging time according to the stay time and the planned charging time; for each other node, if the electric automobile meets the requirements of the charging electric quantity and the charging time, determining that the electric automobile can be charged at the node, otherwise, determining that the electric automobile cannot be charged;
acquiring second charge state information of the charging pile at the current moment, and determining the charge state transition probability of the charging pile according to the second charge state information and the possible decision-making behaviors;
and determining the optimal decision-making behavior of the electric automobile based on the minimum peak-valley difference of the total load curve of the power distribution network according to the charge state transition probability of the charging pile, and controlling the electric automobile to be charged according to the optimal decision-making behavior.
2. The method of claim 1, further comprising:
acquiring a probability density function of electric quantity charged by the electric automobile and a probability density function of charging time;
obtaining the planned charging electric quantity required by the electric automobile through Monte Carlo simulation according to the probability density function of the charging electric quantity and the probability density function of the charging time;
and determining first charge state information of the electric automobile at the current moment according to the battery capacity of the electric automobile and the planned charging electric quantity.
3. The method of claim 1, wherein the determining whether the electric vehicle meets the power requirement for charging at each other node according to the first state of charge information and the power loss value corresponding to each other node specifically comprises:
and calculating a difference value between the first charge state information and the lowest electric quantity threshold value of the battery, and judging whether the difference value is larger than a corresponding electric quantity loss value or not, wherein if yes, the electric automobile meets the requirement of the charging electric quantity, and otherwise, the electric automobile does not meet the requirement of the charging electric quantity.
4. The method according to claim 1, wherein the determining whether the electric vehicle meets the charging time requirement according to the stay time and the scheduled charging time specifically comprises:
and judging whether the residence time is greater than the planned charging time, if so, enabling the electric automobile to meet the requirement of the charging time, otherwise, enabling the electric automobile not to meet the requirement of the charging time.
5. The method of any of claims 1-4, wherein the determining the state-of-charge transition probability of the charging pole based on the second state-of-charge information and the possible decision-making behavior specifically comprises:
determining third charge state information of the charging pile at the next moment according to the second charge state information and the possible decision-making behaviors;
and calculating the charge state transition probability of the charging pile according to the second charge state information and the third charge state information.
6. The method of claim 5, wherein the determining an optimal decision-making behavior of the electric vehicle based on the minimum peak-to-valley difference of the total load curve of the power distribution network according to the state-of-charge transition probability of the charging pile specifically comprises,
solving an objective function by adopting a genetic algorithm to obtain the state transition probability of the charging pile, wherein the objective function is as follows:
Figure FDA0003495506520000021
minLdif＝min{maxLnew,t-minLnew,t}
wherein L isdifThe peak-valley difference of the load curve of the power distribution network for calculating the charging load of the electric automobile; l istThe original load of the power distribution network at the moment t; ri,i+1The charge state transition probability of the charging pile; qi+1Is third state of charge information; qiIs the second state of charge information; n is the total number of the charging pile nodes; t is tcCharging time for the electric vehicle; u shapekThe battery capacity of the kth electric automobile;
and determining the decision-making behavior of the node charging pile according to the state transition probability of the charging pile, and determining the decision-making behavior of the electric automobile at the node according to the decision-making behavior of the node charging pile.
7. An electric vehicle charging system, comprising:
the first obtaining unit is used for obtaining the position information of a node where the electric automobile is located in a current trip chain and the first charge state information at the current moment;
the possible decision-making behavior determining unit is used for determining possible decision-making behaviors of each other node of the electric automobile in the trip chain according to the first charge state information and the current node position information;
the charging pile control device comprises a charging pile control unit, a charging state transition probability determining unit and a charging pile control unit, wherein the charging pile control unit is used for acquiring second charging state information of a charging pile at the current moment and determining the charging state transition probability of the charging pile according to the second charging state information and the possible decision behaviors;
the optimal decision-making behavior determining unit is used for determining the optimal decision-making behavior of the electric automobile according to the charge state transition probability of the charging pile based on the minimum peak-valley difference of the total load curve of the power distribution network, and controlling the electric automobile to be charged according to the optimal decision-making behavior;
the possible decision behavior determination unit is specifically configured to:
respectively calculating the distance between the current node position and each other node position of the electric automobile, and calculating the electric quantity loss value and the driving time of the electric automobile from the current node position to each other node according to the distance;
judging whether the electric automobile can be charged at each other node or not according to the first charge state information, the electric quantity loss value corresponding to each other node and the planned driving time, wherein if yes, the decision-making behavior of the electric automobile at each other node comprises a charging behavior, otherwise, the decision-making behavior of the electric automobile at each other node does not comprise the charging behavior;
the step of judging whether the electric vehicle can be charged at each other node according to the first state of charge information, the electric quantity loss value and the running time corresponding to each other node specifically comprises the following steps: determining whether the electric automobile meets the requirement of the electric quantity charged at each other node or not according to the first charge state information and the electric quantity loss value corresponding to each other node; calculating the stay time of the electric automobile at each other node according to the running time, and judging whether the electric automobile meets the requirement of charging time according to the stay time and the planned charging time; for each other node, if the electric automobile meets the requirements of the charging electric quantity and the charging time, determining that the electric automobile can be charged at the node, otherwise, determining that the electric automobile cannot be charged.
CN202011468928.4A 2020-12-15 2020-12-15 Electric vehicle charging method and system Active CN112550050B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011468928.4A CN112550050B (en) 2020-12-15 2020-12-15 Electric vehicle charging method and system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011468928.4A CN112550050B (en) 2020-12-15 2020-12-15 Electric vehicle charging method and system

Publications (2)

Publication Number Publication Date
CN112550050A CN112550050A (en) 2021-03-26
CN112550050B true CN112550050B (en) 2022-04-26

Family

ID=75064607

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011468928.4A Active CN112550050B (en) 2020-12-15 2020-12-15 Electric vehicle charging method and system

Country Status (1)

Country Link
CN (1) CN112550050B (en)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114211978B (en) * 2021-12-14 2023-07-14 华人运通(江苏)技术有限公司 Charging power distribution method, system, equipment and storage medium of electric automobile
CN116345477B (en) * 2023-05-19 2023-10-17 国网信息通信产业集团有限公司 Electric automobile load time sequence allocation method under electric quantity type demand response

Family Cites Families (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103413180A (en) * 2013-07-22 2013-11-27 上海电力实业有限公司 Electric car charging load forecasting system and method based on Monte Carlo simulation method
CN104953652A (en) * 2015-06-11 2015-09-30 国网山东省电力公司电力科学研究院 Control method for ordered charging of electromobile
CN105262167A (en) * 2015-10-22 2016-01-20 江苏省电力公司南京供电公司 Intra-regional electric vehicle ordered charging control method
US11061424B2 (en) * 2017-01-12 2021-07-13 Johnson Controls Technology Company Building energy storage system with peak load contribution and stochastic cost optimization
CN107745650B (en) * 2017-10-26 2020-04-14 电子科技大学 Electric vehicle ordered charging control method based on peak-valley time-of-use electricity price
DE102018114593A1 (en) * 2018-06-18 2019-12-19 TOP KA-Projekt GmbH Charging system for dynamic charging of electric vehicles
CN108944531A (en) * 2018-07-24 2018-12-07 河海大学常州校区 A kind of orderly charge control method of electric car
CN111199320B (en) * 2020-01-07 2023-07-28 国家电网有限公司 Electric vehicle charging load space-time distribution prediction method based on travel probability matrix

Also Published As

Publication number Publication date
CN112550050A (en) 2021-03-26

Similar Documents

Publication Publication Date Title
Chen et al. Particle swarm optimization-based optimal power management of plug-in hybrid electric vehicles considering uncertain driving conditions
Liu et al. A bi-level control for energy efficiency improvement of a hybrid tracked vehicle
Chen et al. An on-line predictive energy management strategy for plug-in hybrid electric vehicles to counter the uncertain prediction of the driving cycle
Montazeri-Gh et al. Optimized predictive energy management of plug-in hybrid electric vehicle based on traffic condition
Zhao et al. A deep reinforcement learning framework for optimizing fuel economy of hybrid electric vehicles
Du et al. Trip-oriented stochastic optimal energy management strategy for plug-in hybrid electric bus
Feng et al. Optimal energy management with balanced fuel economy and battery life for large hybrid electric mining truck
Gong et al. Optimal power management of plug-in HEV with intelligent transportation system
Taherzadeh et al. A comprehensive study of the parameters impacting the fuel economy of plug-in hybrid electric vehicles
Hao et al. Energy management strategy on a parallel mild hybrid electric vehicle based on breadth first search algorithm
CN113511082A (en) Hybrid electric vehicle energy management method based on rule and double-depth Q network
CN108596667B (en) Electric automobile real-time charging electricity price calculation method based on Internet of vehicles
CN112550050B (en) Electric vehicle charging method and system
CN103499792B (en) The Forecasting Methodology of available capacity of EV power battery cluster
CN112215434A (en) LSTM model generation method, charging duration prediction method and medium
CN107919675B (en) Charging station load scheduling model comprehensively considering benefits of vehicle owners and operators
CN113553769B (en) Electric vehicle charging facility optimal configuration method considering charging efficiency
Fries et al. Operational strategy of hybrid heavy-duty trucks by utilizing a genetic algorithm to optimize the fuel economy multiobjective criteria
CN115071505A (en) Fuel cell automobile layered planning method, system, device and storage medium
CN105262167A (en) Intra-regional electric vehicle ordered charging control method
CN115239032A (en) Highway service area microgrid planning method and system considering energy self-consistency rate
CN116054316A (en) Ordered charging and discharging method for electric automobile based on chaotic sparrow optimization algorithm
CN114122466A (en) Fuel cell vehicle battery optimization management method, system, device and medium
Azim Mohseni et al. Energy management strategies of hybrid electric vehicles: A comparative review
Zhang et al. HESS and its application in series hybrid electric vehicles

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant