CN114781141B - Electric automobile travel guiding method based on dynamic traffic flow and user psychology - Google Patents

Electric automobile travel guiding method based on dynamic traffic flow and user psychology Download PDF

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CN114781141B
CN114781141B CN202210374131.0A CN202210374131A CN114781141B CN 114781141 B CN114781141 B CN 114781141B CN 202210374131 A CN202210374131 A CN 202210374131A CN 114781141 B CN114781141 B CN 114781141B
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electric automobile
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CN114781141A (en
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杨军
柯松
陈磊
吴赋章
范培潇
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Wuhan University WHU
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H02J3/322—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/06—Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]

Abstract

The invention provides an electric automobile travel guiding method based on dynamic traffic flow and user psychology. An improved accumulated prospect theory method is provided, and a travel psychological expected utility model of the estimated user based on multiple reference points is established so as to quantify the value of travel decisions of the user; the method comprises the steps of establishing a dynamic traffic flow model, evaluating a travel decision through a traffic flow state and EV user travel demands based on a user psychological expected utility model, establishing a travel guiding strategy considering the traffic flow state and the user psychological expected, and constructing an EV travel guiding platform based on the dynamic traffic flow and the user psychological expected so as to reduce the congestion probability and the power consumption of EV user travel, improve the travel experience of the user, simultaneously slow down the communication congestion condition, reduce the charging load of the EV and simultaneously realize the optimization of a power grid and a road network.

Description

Electric automobile travel guiding method based on dynamic traffic flow and user psychology
Technical Field
The invention belongs to the technical field of traffic, and particularly relates to an electric automobile travel guiding method based on dynamic traffic flow and user psychology.
Background
An Electric Vehicle (EV) is used as an important tie for coupling a power grid and a traffic network, and as the EV is used on a large scale, the EV has profound effects on the power grid and the traffic network in the charging and traveling processes, and the power grid and the traffic network form a complex coupling system under the action of the large-scale Electric Vehicle. The existing electric automobile travel guiding and charging control technology generally divides the EV clusters from the perspective of power grid operation, considers EV electrical characteristics, is inclined to power grid requirements in charging control, weakens the difference of charging behaviors and requirements among EV users, does not consider the influence of traffic flow, also can neglect subjectivity of the EV users in regulation and control, and does not relate to user psychology. At present, the traveling behavior guiding and controlling technology of the traveling demands and psychological expectation of users is fully considered under the coupling of the power grid and the traffic network, so that the further development of the electric automobile is limited, and the influence on the power grid and the traffic network is aggravated.
The premise that the EV users participate in the power grid operation regulation is that travel and time psychological expectation of the users are met, and the EV is guaranteed to have sufficient State of Charge (SOC) and flexible travel time. The travel behavior of EVs can be divided into the following two categories: the method is characterized by commuter behavior with strong regularity and relatively fixed time and place, and for the commuter behavior, individual preference can be subdivided through historical data related to user trip, so that heterogeneity is represented. Aiming at the dynamic and microscopic psychological preferences of the resident commuting decision process, the commuting behavior is subdivided into 4 types of business demand type, pursuing efficiency type, economic and environment-friendly type and conservative type, and the travel schemes among the various types are obviously different, but the decisions of various behaviors are relatively fixed and difficult to guide and regulate. The other group is activity trip behaviors with strong randomness, high flexibility and randomness of time and places, such as entertainment, visiting friends and the like. In the random trip behaviors of the user, when the trip chain takes a family as a starting point, the duty behavior accounts for 44%, the behaviors of going to school, park and shopping account for 26%, 6% and 24%, respectively, and in the non-working time such as weekends, evenings and the like, the entertainment trip behavior of the user accounts for more than 50%. Aiming at the active travel with strong randomness, under the condition that traffic jam perception is caused by different individual drivers and travel characteristic factors, a significant difference is generated in path adjustment decision. In addition, the change of the actual working condition of the EV caused by traffic jam, such as time consumption increase, and the use of air conditioning refrigeration/heating, illumination, sound and the like of the EV can influence energy consumption and endurance. Users may have varying expectations for time and duration of random trips when considering these factors.
In order to improve the wish of users to participate in regulation and control, and the regulation and control capability of the power grid and the transportation capability of the traffic network under the coupling of the power grid and the traffic network, the connection between the travel and charging behavior characteristics of the users and the psychology of the users needs to be described, and the randomness of the travel behavior and the charging behavior of the EV users is analyzed. How to stand at the angle of travelers, the charging behavior and the travel decision of the user are actively guided under the traffic flow state characteristics so as to meet the requirements of the user. The electric automobile travel guiding platform can actively change and optimize traffic flow and charging control, reduce congestion probability of user travel, reduce charging load of EV and simultaneously realize optimization of a power grid and a road network.
Disclosure of Invention
In order to solve the problems, the invention analyzes the psychological expectation of the user trip, proposes an improved accumulated prospect theoretical method, and establishes an estimated user trip psychological expectation utility model based on multiple reference points so as to quantify the value of the user trip decision; establishing a dynamic traffic flow model, evaluating a travel decision based on a psychological expected utility model of a user through a traffic flow state and an EV user travel demand, thereby establishing a travel guiding strategy considering the traffic flow state and the user psychological expected, guiding the user to adjust the travel decision according to the psychological expected utility value, namely adjusting the departure time and optimizing the travel path, and performing coupling and interaction through the travel guiding strategy based on the user psychological expected utility and the flow residual of the path passing duration, thereby forming a dynamic traffic flow closely related to the foreground function form and the random travel utility of the user; then, the randomness of the charge state and the travel time of the user is fully considered, a distribution robust optimization (Distributed Robust Optimization, DRO) model of the charging power control is established, EV charging power is regulated and controlled on the premise that the user demand is met, and peak-valley difference of the load of the power distribution network area can be reduced. Based on the technology, an electric automobile travel guiding platform based on dynamic traffic flow and user psychology is built. The invention can reduce the congestion probability and the power consumption of the travel of the electric automobile user, simultaneously alleviate the alternating current congestion condition, reduce the charging load of the EV and simultaneously realize the optimization of the power grid and the road network. And finally, through the operation test of the electric automobile travel guide platform, the validity of the platform is verified.
The invention combines engineering reality, and is applicable to guiding travel time, path selection decision and charging control of electric vehicles in various scales under the coupling of a power grid and a traffic network.
The technical scheme of the invention is as follows:
an electric automobile travel guiding method based on dynamic traffic flow and user psychology is characterized by comprising the following steps:
step 1, introducing rated charging power of a charging pile, travel decision-related parameters of electric car users and traffic distribution network related parameters of each electric car user;
the trip decision-related parameters of the electric automobile user in the step 1 include: the destination of each electric vehicle user, the arrival reference time of each electric vehicle user, the preference reference time of each electric vehicle user, the arrival reference time of each electric vehicle user, the initial SOC of each electric vehicle user, the upper SOC limit expected at the arrival time of each electric vehicle user, the lower SOC limit expected at the arrival time of each electric vehicle user, the upper SOC limit expected at the departure time of each electric vehicle user, the lower SOC limit expected at the departure time of each electric vehicle user, the expected departure charging station time of each electric vehicle user, and the SOC/time weight coefficient of each electric vehicle user;
The traffic distribution network related parameters of each electric automobile user in step 1 include:
topology and inter-node relation of a power distribution network, topology and inter-node relation of a traffic network, daily load data of a power distribution network area, current capacity of traffic road sections, free passing time of the traffic road sections, and state of traffic flow at the current moment in a period t;
step 2, according to the early reaching reference time of each electric automobile user, the preference reference time of each electric automobile user, the late reaching reference time of each electric automobile user, the initial SOC of each electric automobile user, the expected SOC upper limit of the arrival time of each electric automobile user, the expected SOC lower limit of the arrival time of each electric automobile user, the expected SOC upper limit of the departure time of each electric automobile user, the expected SOC lower limit of the departure time of each electric automobile user, the expected departure charging station time of each electric automobile user and the SOC/time weight coefficient of each electric automobile user, the psychological expected utility model of the electric automobile user is constructed.
The psychological expected utility model of the electric automobile user in the step 2 is composed of an electric automobile user time cost function model and an electric automobile user SOC cost function model, and G is G, and G is the psychological expected utility model of all electric automobile users in the guiding period, and is specifically as follows:
Value g ＝V t g +w g V soc g
Wherein Value is g Representing the psychological expected utility value, V, of the g-th electric automobile user t g Representing a g-th electric automobile user time cost function model, V soc g Representing the SOC value function model of the g-th electric automobile user, w g SOC/time weight coefficient for the g-th electric automobile user;
the time cost function model of the electric automobile user, the time cost function of the g-th electric automobile user is specifically defined as follows:
wherein: v (V) t g Time cost function value of g-th electric automobile user, gamma 1 To face the risk sensitivity coefficient in case of loss of time value, gamma 2 To face risk sensitivity coefficient in time value benefit, t i,j A g For the time, t, from charging station i to destination j for the g-th electric vehicle user R min g For the g-th electric automobile user to arrive at the reference moment, t R max g The user of the g-th electric automobile arrives at the reference moment t R P g Preferential arrival time for g-th electric automobile user, and t R min g <t R Pg <t R max g ；
The SOC cost function model of the electric automobile user, and the SOC cost function of the g-th electric automobile user is specifically defined as follows:
in the method, in the process of the invention,mu for the rounding down operation 1 To face risk sensitivity coefficient in case of loss of SOC value, mu 2 To face risk sensitivity coefficient when SOC value is obtained soc L g SOC cost function for the departure time of the g-th electric automobile user, V soc A g SOC value function of arrival time of g-th electric automobile user, SOC max L g The expected SOC upper limit for the departure time of the g-th electric automobile user and SOC min L g SOC lower limit expected at departure time of g-th electric automobile user and SOC max A g SOC upper limit expected for arrival time of g-th electric automobile user, SOC min A g SOC lower limit expected for arrival time of g-th electric automobile user, SOC L g The actual SOC and the SOC of the electric automobile when the user leaves the station A g The actual SOC when the electric automobile user arrives at the destination. SOC (State of Charge) L g The charging station time can be calculated by the initial SOC of the g-th electric vehicle user, the expected leaving charging station time of the g-th electric vehicle user,And calculating the rated charging power of the charging pile.
The psychological expected utility model of the electric automobile user in the step 2, the psychological expected utility function of the g-th electric automobile user is specifically as follows:
CPV g ＝CPV t g +ω g CPV SOC g
wherein: CPV (chlorinated polyvinyl chloride) g Is the psychological expected utility value of the g-th electric automobile user, n is the number of travel alternatives which are uncertain for the g-th electric automobile user and pi i g The probability weight function value which can occur for the ith travel alternative scheme of the g-th electric automobile user;
Step 3, constructing a traffic balance flow distribution model based on the destination of the electric automobile user in the travel decision related parameters of each electric automobile user in step 1, the topology and the inter-node relation of the traffic network in step 1, the current capacity of the traffic road section and the free traffic time of the traffic road section, and the state of the traffic flow at the current time t period;
the traffic balance flow distribution model in the step 3 comprises the following steps:
the method comprises the steps of a path selection model, a road section and path flow rate association matrix, an electric vehicle road section running time, a travel path cost and travel path selection conditions of an electric vehicle user traveling O-D pair;
according to g t Electric automobile user (g) t ∈G t ,G t For guiding all electric car users in the period t), the topology and the node relation of the traffic network can obtain all electric car users G in the period t t Path selection model for travel O-D pairs:
assuming that the estimated traffic cost and the travel demand of the traveler of each road section of the traffic flow are approximately unchanged in the time interval tau, when the O-D demand distribution is completed in the time interval t, all the selected paths between a certain O-D pair satisfy the following relation:
wherein f k,t rs Is the flow on the kth path between O-D pair rs in the state of the traffic flow at the current time t period, K is E K rs ，K rs For the set of active paths existing between O-D pairs rs, q t rs For all traffic demands from r to s in t time period, all electric automobile users G in time t t The composition, namely the O-D quantity, further ensures the travel requirement q corresponding to each O-D pair (r, s) in the moment t rs Can be satisfied.
Obtaining a road section and path flow association matrix according to the topology of the traffic network and the relationship between nodes in the step 1:
road section flow x at time t a,t And path flow f k,t rs Can pass through the incidence matrix delta a,k rs The connection is as follows:
δ a,k rs representing the association coefficient between road segment and path, road segment a on the kth path connecting O-D pair rs, then delta a,k rs =1, otherwise δ a,k rs ＝0；
The electric automobile user section travel time:
the running time t of the electric vehicle at the moment t in the road section a can be determined according to the current capacity of the traffic road section, the free running time of the traffic road section and the state of the traffic flow at the moment t a (x a,t ) The calculation formula is as follows:
wherein t is a 0 Is the free transit time of road section a, x a,t For the flow on the section a at time t, C a Is the current-carrying capacity of road segment a.
The travel path cost:
travel path cost estimation value c at time t k,t rs The following is shown:
the travel path selection condition:
thus, the filling condition for connecting path k between O-D pair rs at time t is selected by the user as follows:
Wherein c m rs And estimating the travel path cost of the O-D for any other path m of rs.
The travel path selection principle based on user balance is as follows:
wherein mu rs Representing the minimum travel cost between the O-D pair rs and f under the balanced state of the traffic network k rs 、c k rs Representing the flow and travel cost on the kth path between the O-D pair rs.
the traffic flow state under the traffic balance flow distribution model at the time t can be described as the following mathematical programming model:
wherein q is t rs For all traffic demands from r to s in the time t, all electric automobile users G in the time t t Composition, S t rs [c t rs ]For the expected estimated cost between the O-D pair rs at time t, x a,t For the flow on the section a at time T, T a,t (x a,t ) For the time t of the electric automobile running on the road section a, t a (w) is the road section running time, and the road section flow x along with the time t a,t In a variation of the method, the device,E[]indicating the desire.
According to the state of the traffic flow at the current time t in the step 1 and the travel path selection of each electric automobile user, solving can be carried out through a mathematical programming optimization algorithm, and the traffic flow state under the traffic balance flow distribution model at the time t is obtained.
Step 4, calculating a user psychological expected utility value and a user psychological expected utility threshold value;
according to the current traffic flow state input in the step 1 and travel decision related parameters of the g-th electric automobile user, calculating a psychological expected utility value of the g-th electric automobile user under the current travel decision through the user psychological expected utility model in the step 2 and the traffic balance flow distribution model in the step 3; according to the user psychological expectation utility model in the step 2 and the traffic flow state under the current traffic balance flow distribution model obtained in the step 3, the comprehensive value function value of the G-th electric automobile user in the step 2 is obtained, namely the user psychological expectation utility threshold value, G epsilon G t ,G t All electric automobile users in the current moment t moment;
step 5, according to the state of the traffic flow at the current moment t and the travel related parameters of the G-th electric automobile user input in the step 1, G epsilon G t ,G t For all electric automobile users in the moment t of the current moment, the difference value between the user psychological expected utility value and the user psychological expected utility threshold value obtained in the step 4 is obtained, if the difference value exceeds the allowable minimum range, the travel decision of the corresponding g electric automobile user is guided, and the travel decision after guidance is obtained: care-versus-expected utility value CPV g Greater than the psychographic utility threshold CPV set g When in use, the travel block can be preset according to the userThe driver can go out directly without receiving guidance; when CPV g Less than a psychographic utility threshold CPV set g When the travel guidance strategy is formulated, a corresponding travel guidance strategy is formulated: according to CPV g The result guides the travel decision:
when SOC accumulates the foreground value CPV soc g < SOC cumulative prospect value threshold CPV soc_set g Time cumulative foreground value CPV t g <Time cumulative foreground value threshold CPV t_set g At the time, the charging power is set to the maximum charging power P max The method comprises the steps of carrying out a first treatment on the surface of the When the weight coefficient omega g When more than 1, the characterization user attaches more importance to the SOC and delays the departure time to the SOC L g Satisfy CPV soc g ＞CPV soc_set g The method comprises the steps of carrying out a first treatment on the surface of the When the weight coefficient omega g When the time is less than 1, characterizing the user to attach more importance to the time, and advancing the departure time to the current time;
When CPV soc g ＜CPV soc_set g ，CPV t g ＞CPV t_set g When the charging power is set to the charging pile, the maximum charging power P can be provided max And delay the trip time appropriately;
when CPV soc g ＞CPV soc_set g ，CPV t g ＜CPV t_set g According to the peak-valley condition of the daily load data of the power distribution network area in the step 1, the charging power can be properly reduced, the travel time is advanced to the current time, according to the redetermined travel time and the corresponding traffic flow state, the estimated value of travel path cost is calculated according to the traffic flow state under the traffic balance flow distribution model at the time t calculated in the step 3, and the travel path is redeployed with the minimum estimated value of travel path cost as a target.
The g-th electric automobile user can be guided to adjust the travel time, and the charging power between the current time and the travel time of the corresponding electric automobile user is regulated and controlled, specifically as follows:
at the current moment, the SOC real-time state of the g-th electric automobile user is smaller than the expected SOC upper limit SOC at the arrival moment L min g ThenIn the time interval of the current time and the departure time, the maximum power P is used max Charging is performed.
At the current moment, the SOC real-time state of the g-th electric automobile user is larger than the expected SOC lower limit SOC at the arrival moment L min g An upper SOC limit less than the expected SOC at the departure time L max g Then 0.5P is used in the time interval of the current time and the departure time max Charging is performed.
At the current moment, the SOC real-time state of the g-th electric automobile user is larger than the expected SOC upper limit SOC at the arrival moment L max g Indicating that the SOC real-time state reaches the expected SOC upper limit SOC at the arrival time L max g At this time, the charging power thereof may be regarded as 0.
Step 6, according to the guided travel decision obtained in step 5, adjusting all electric automobile users G in the moment t at the current moment t Through the user psychological expected utility model in the step 2 and the traffic balance flow distribution model in the step 3, the psychological expected utility value of the user under the guided travel decision is estimated again, a dynamic traffic balance flow distribution model under the new travel decision of the user is constructed, and the recurrence relation of traffic flows at all times can be obtained according to the traffic flow state under the current traffic balance flow distribution model in the step 3 as follows:
the residual flow r corresponding to the O-D pair (r, s) at the t moment is firstly defined t rs ，f k,t rs Is at presentFlow on kth path between O-D pair rs in state of traffic flow at time t period, K is K rs ，K rs Is an effective path set existing between the O-D pair rs; secondly taking into account the traffic demand q of the remaining flow t moment O-D pair (r, s) t rs Correction of q t rs For all traffic demands from r to s in the time t, all electric automobile users G in the time t t The composition, namely the O-D quantity, further ensures the travel requirement q corresponding to each O-D pair (r, s) in the moment t rs Can be satisfied, q t rs,mod R for corrected traffic demand t-1 rs For the residual flow corresponding to O-D pair (r, s) at t-1 moment, t k,t rs For the path transit time, τ is the period length.
Therefore, the invention has the following advantages:
the platform provided by the invention can be applied to operation software and map navigation software of a charging station, and by guiding the decision of the travel of the user of the electric vehicle, on the premise of meeting the travel requirement of the user, the traffic jam probability and the travel power consumption of the electric vehicle can be reduced, the travel experience of the user can be improved, the peak-valley difference of a power grid can be reduced, and the cooperative operation of the power grid and a traffic network can be realized.
Drawings
Fig. 1: is a time cost function that considers multiple reference points.
Fig. 2: is a schematic diagram of the relationship between the SOC of the EV and the travel time.
Fig. 3: is a trip guidance strategy that takes into account psychological expectations of the user.
Fig. 4: the method is a solving flow chart of a dynamic traffic balance flow distribution model considering travel guidance of a user.
Fig. 5: is a schematic diagram of a power grid-traffic network coupling network applied by the platform.
Fig. 6: the utility value comparison is conducted by the platform to guide the psychological expectation of the user before and after the user.
Fig. 7: the system is used for comparing the SOC, power consumption and time consumption of travel decisions of the user before and after the user is guided by the platform.
Fig. 8: the load curves of the front and rear distribution network areas are guided by the platform to be compared.
Fig. 9: is a flow chart of the method of the invention.
Detailed Description
The technical scheme of the invention is specifically described below through examples and with reference to fig. 1-9.
First, a first embodiment of the present invention will be described:
an electric automobile traveling guide platform based on dynamic traffic flow and user psychology is characterized by comprising the following steps:
step 1, introducing rated charging power of a charging pile, travel decision-related parameters of electric car users and traffic distribution network related parameters of each electric car user;
the trip decision-related parameters of the electric automobile user in the step 1 include: the destination of each electric vehicle user, the arrival reference time of each electric vehicle user, the preference reference time of each electric vehicle user, the arrival reference time of each electric vehicle user, the initial SOC of each electric vehicle user, the upper SOC limit expected at the arrival time of each electric vehicle user, the lower SOC limit expected at the arrival time of each electric vehicle user, the upper SOC limit expected at the departure time of each electric vehicle user, the lower SOC limit expected at the departure time of each electric vehicle user, the expected departure charging station time of each electric vehicle user, and the SOC/time weight coefficient of each electric vehicle user;
The traffic distribution network related parameters of each electric automobile user in step 1 include:
topology and inter-node relation of a power distribution network, topology and inter-node relation of a traffic network, daily load data of a power distribution network area, current capacity of traffic road sections, free passing time of the traffic road sections, and state of traffic flow at the current moment in a period t;
step 2, according to the early reaching reference time of each electric automobile user, the preference reference time of each electric automobile user, the late reaching reference time of each electric automobile user, the initial SOC of each electric automobile user, the expected SOC upper limit of the arrival time of each electric automobile user, the expected SOC lower limit of the arrival time of each electric automobile user, the expected SOC upper limit of the departure time of each electric automobile user, the expected SOC lower limit of the departure time of each electric automobile user, the expected departure charging station time of each electric automobile user and the SOC/time weight coefficient of each electric automobile user, the psychological expected utility model of the electric automobile user is constructed.
The psychological expected utility model of the electric automobile user in the step 2 is composed of an electric automobile user time cost function model and an electric automobile user SOC cost function model, and G is G, and G is the psychological expected utility model of all electric automobile users in the guiding period, and is specifically as follows:
Value g ＝V t g +w g V soc g
Wherein Value is g Representing the psychological expected utility value, V, of the g-th electric automobile user t g Representing a g-th electric automobile user time cost function model, V soc g Representing the SOC value function model of the g-th electric automobile user, w g SOC/time weight coefficient for the g-th electric automobile user;
the time cost function model of the electric automobile user, the time cost function of the g-th electric automobile user is specifically defined as follows:
wherein: v (V) t g Time cost function value of g-th electric automobile user, gamma 1 To face the risk sensitivity coefficient in case of loss of time value, gamma 2 To face risk sensitivity coefficient in time value benefit, t i,j A g For the time, t, from charging station i to destination j for the g-th electric vehicle user R min g For the g-th electric automobile user to arrive at the reference moment, t R max g The user of the g-th electric automobile arrives at the reference moment t R P g Preferential arrival time for g-th electric automobile user, and t R min g <t R Pg <t R max g ；
The SOC cost function model of the electric automobile user, and the SOC cost function of the g-th electric automobile user is specifically defined as follows:
in the method, in the process of the invention,mu for the rounding down operation 1 To face risk sensitivity coefficient in case of loss of SOC value, mu 2 To face risk sensitivity coefficient when SOC value is obtained soc L g SOC cost function for the departure time of the g-th electric automobile user, V soc A g SOC value function of arrival time of g-th electric automobile user, SOC max L g The expected SOC upper limit for the departure time of the g-th electric automobile user and SOC min L g SOC lower limit expected at departure time of g-th electric automobile user and SOC max A g SOC upper limit expected for arrival time of g-th electric automobile user, SOC min A g SOC lower limit expected for arrival time of g-th electric automobile user, SOC L g The actual SOC and the SOC of the electric automobile when the user leaves the station A g The actual SOC when the electric automobile user arrives at the destination. SOC (State of Charge) L g The initial SOC of the g-th electric vehicle user, the expected leaving charging station time of the g-th electric vehicle user and the rated charging power of the charging pile can be calculated.
The psychological expected utility model of the electric automobile user in the step 2, the psychological expected utility function of the g-th electric automobile user is specifically as follows:
CPV g ＝CPV t g +ω g CPV SOC g
wherein: CPV (chlorinated polyvinyl chloride) g Is the psychological expected utility value of the g-th electric automobile user, n is the number of travel alternatives which are uncertain for the g-th electric automobile user and pi i g The probability weight function value which can occur for the ith travel alternative scheme of the g-th electric automobile user;
Step 3, constructing a traffic balance flow distribution model based on the destination of the electric automobile user in the travel decision related parameters of each electric automobile user in step 1, the topology and the inter-node relation of the traffic network in step 1, the current capacity of the traffic road section and the free traffic time of the traffic road section, and the state of the traffic flow at the current time t period;
the traffic balance flow distribution model in the step 3 comprises the following steps:
the method comprises the steps of a path selection model, a road section and path flow rate association matrix, an electric vehicle road section running time, a travel path cost and travel path selection conditions of an electric vehicle user traveling O-D pair;
according to g t Electric automobile user (g) t ∈G t ,G t For guiding all electric car users in the period t), the topology and the node relation of the traffic network can obtain all electric car users G in the period t t Path selection model for travel O-D pairs:
assuming that the estimated traffic cost and the travel demand of the traveler of each road section of the traffic flow are approximately unchanged in the time interval tau, when the O-D demand distribution is completed in the time interval t, all the selected paths between a certain O-D pair satisfy the following relation:
wherein f k,t rs Is the flow on the kth path between O-D pair rs in the state of the traffic flow at the current time t period, K is E K rs ，K rs For the set of active paths existing between O-D pairs rs, q t rs For all traffic demands from r to s in t time period, all electric automobile users G in time t t The composition, namely the O-D quantity, further ensures the travel requirement q corresponding to each O-D pair (r, s) in the moment t rs Can be satisfied.
Obtaining a road section and path flow association matrix according to the topology of the traffic network and the relationship between nodes in the step 1:
road section flow x at time t a,t And path flow f k,t rs Can pass through the incidence matrix delta a,k rs The connection is as follows:
δ a,k rs representing the association coefficient between road segment and path, road segment a on the kth path connecting O-D pair rs, then delta a,k rs =1, otherwise δ a,k rs ＝0；
The electric automobile user section travel time:
the running time t of the electric vehicle at the moment t in the road section a can be determined according to the current capacity of the traffic road section, the free running time of the traffic road section and the state of the traffic flow at the moment t a (x a,t ) The calculation formula is as follows:
wherein t is a 0 Is the free transit time of road section a, x a,t For a flow on section a at time tAmount of C a Is the current-carrying capacity of road segment a.
The travel path cost:
travel path cost estimation value c at time t k,t rs The following is shown:
the travel path selection condition:
thus, the filling condition for connecting path k between O-D pair rs at time t is selected by the user as follows:
Wherein c m rs And estimating the travel path cost of the O-D for any other path m of rs.
The travel path selection principle based on user balance is as follows:
wherein mu rs Representing the minimum travel cost between the O-D pair rs and f under the balanced state of the traffic network k rs 、c k rs Representing the flow and travel cost on the kth path between the O-D pair rs.
the traffic flow state under the traffic balance flow distribution model at the time t can be described as the following mathematical programming model:
wherein q is t rs For all traffic demands from r to s in the time t, all electric automobile users G in the time t t Composition, S t rs [c t rs ]For the expected estimated cost between the O-D pair rs at time t,x a,t for the flow on the section a at time T, T a,t (x a,t ) For the time t of the electric automobile running on the road section a, t a (w) is the road section running time, and the road section flow x along with the time t a,t In a variation of the method, the device,E[]indicating the desire.
According to the state of the traffic flow at the current time t in the step 1 and the travel path selection of each electric automobile user, solving can be carried out through a mathematical programming optimization algorithm, and the traffic flow state under the traffic balance flow distribution model at the time t is obtained.
Step 4, calculating a user psychological expected utility value and a user psychological expected utility threshold value;
according to the current traffic flow state input in the step 1 and travel decision related parameters of the g-th electric automobile user, calculating a psychological expected utility value of the g-th electric automobile user under the current travel decision through the user psychological expected utility model in the step 2 and the traffic balance flow distribution model in the step 3; according to the user psychological expectation utility model in the step 2 and the traffic flow state under the current traffic balance flow distribution model obtained in the step 3, the comprehensive value function value of the G-th electric automobile user in the step 2 is obtained, namely the user psychological expectation utility threshold value, G epsilon G t ,G t All electric automobile users in the current moment t moment;
step 5, according to the state of the traffic flow at the current moment t and the travel related parameters of the G-th electric automobile user input in the step 1, G epsilon G t ,G t For all electric automobile users in the moment t of the current moment, the difference value between the user psychological expected utility value and the user psychological expected utility threshold value obtained in the step 4 is obtained, if the difference value exceeds the allowable minimum range, the travel decision of the corresponding g electric automobile user is guided, and the travel decision after guidance is obtained: care-versus-expected utility value CPV g Greater than the psychographic utility threshold CPV set g When the user is going on, the user can go on directly according to a preset travel decision, and guidance is not required to be accepted; when CPV g Less than psychological expectation of utilityThreshold CPV set g When the travel guidance strategy is formulated, a corresponding travel guidance strategy is formulated: according to CPV g The result guides the travel decision:
when SOC accumulates the foreground value CPV soc g < SOC cumulative prospect value threshold CPV soc_set g Time cumulative foreground value CPV t g <Time cumulative foreground value threshold CPV t_set g At the time, the charging power is set to the maximum charging power P max The method comprises the steps of carrying out a first treatment on the surface of the When the weight coefficient omega g When more than 1, the characterization user attaches more importance to the SOC and delays the departure time to the SOC L g Satisfy CPV soc g ＞CPV soc_set g The method comprises the steps of carrying out a first treatment on the surface of the When the weight coefficient omega g When the time is less than 1, characterizing the user to attach more importance to the time, and advancing the departure time to the current time;
When CPV soc g ＜CPV soc_set g ，CPV t g ＞CPV t_set g When the charging power is set to the charging pile, the maximum charging power P can be provided max And delay the trip time appropriately;
when CPV soc g ＞CPV soc_set g ，CPV t g ＜CPV t_set g According to the peak-valley condition of the daily load data of the power distribution network area in the step 1, the charging power can be properly reduced, the travel time is advanced to the current time, according to the redetermined travel time and the corresponding traffic flow state, the estimated value of travel path cost is calculated according to the traffic flow state under the traffic balance flow distribution model at the time t calculated in the step 3, and the travel path is redeployed with the minimum estimated value of travel path cost as a target.
The g-th electric automobile user can be guided to adjust the travel time, and the charging power between the current time and the travel time of the corresponding electric automobile user is regulated and controlled, specifically as follows:
at the current moment, the SOC real-time state of the g-th electric automobile user is smaller than the expected SOC upper limit SOC at the arrival moment L min g In the time interval of the current time and the departure time, the maximum power P is used max Proceeding withAnd (5) charging.
At the current moment, the SOC real-time state of the g-th electric automobile user is larger than the expected SOC lower limit SOC at the arrival moment L min g An upper SOC limit less than the expected SOC at the departure time L max g Then 0.5P is used in the time interval of the current time and the departure time max Charging is performed.
At the current moment, the SOC real-time state of the g-th electric automobile user is larger than the expected SOC upper limit SOC at the arrival moment L max g Indicating that the SOC real-time state reaches the expected SOC upper limit SOC at the arrival time L max g At this time, the charging power thereof may be regarded as 0.
Step 6, according to the guided travel decision obtained in step 5, adjusting all electric automobile users G in the moment t at the current moment t Through the user psychological expected utility model in the step 2 and the traffic balance flow distribution model in the step 3, the psychological expected utility value of the user under the guided travel decision is estimated again, a dynamic traffic balance flow distribution model under the new travel decision of the user is constructed, and the recurrence relation of traffic flows at all times can be obtained according to the traffic flow state under the current traffic balance flow distribution model in the step 3 as follows:
the residual flow r corresponding to the O-D pair (r, s) at the t moment is firstly defined t rs ，f k,t rs Is the flow on the kth path between O-D pair rs in the state of the traffic flow at the current time t period, K is E K rs ，K rs Is an effective path set existing between the O-D pair rs; secondly taking into account the traffic demand q of the remaining flow t moment O-D pair (r, s) t rs Correction of q t rs For all traffic demands from r to s in the time t, all electric automobile users G in the time t t The composition, namely the O-D quantity, further ensures the travel requirement q corresponding to each O-D pair (r, s) in the moment t rs Can be satisfied, q t rs,mod R for corrected traffic demand t-1 rs For the residual flow corresponding to O-D pair (r, s) at t-1 moment, t k,t rs For the path transit time, τ is the period length.
Further introduced is a second embodiment of the present invention, an electric vehicle trip guidance platform based on dynamic traffic flow and user psychology.
The method specifically comprises the following steps:
step 1, introducing travel decision-related parameters of each electric automobile user, and traffic network and distribution network related parameters;
the trip decision-related parameters of each electric automobile user in step 1 include: the method comprises the steps of enabling each electric vehicle user to arrive at a reference moment early, enabling each electric vehicle user to have a preference reference moment, enabling each electric vehicle user to arrive at the reference moment late, enabling each electric vehicle user to have an initial SOC, enabling each electric vehicle user to arrive at an expected SOC upper limit at the moment of arrival of each electric vehicle user, enabling each electric vehicle user to arrive at an expected SOC lower limit at the moment of departure of each electric vehicle user, enabling each electric vehicle user to arrive at a destination according to the expected SOC lower limit at the moment of departure of each electric vehicle user, enabling each electric vehicle user to arrive at a charging station (departure) according to the expected time, and enabling each electric vehicle user to arrive at a destination according to the expected SOC weight coefficient w; the related parameters of the traffic network and the power distribution network in the step 1 include: topology of the traffic network, relationships among nodes, daily load data of a power distribution network area, current capacity of traffic road sections, free transit time of the traffic road sections and traffic flow state at the current moment.
And 2, constructing a user psychological expected utility model according to the state of the traffic flow at the current moment in the step 1 and the travel decision-related parameters of each electric automobile user in the step 1.
The psychological expected utility model in the step 2 comprises a cost function, a probability weight function, a cumulative decision weight function, a psychological expected utility mapping function, a cumulative prospect value and a comprehensive cost function value. The method comprises the following steps:
in the activity trip behavior, because the user has uncertainty in the SOC before trip, the arrival time of trip demands is more flexible than the commute behavior, and in addition, the departure place and the destination charging cost can be different, so that the user has different psychological expectations on the SOC and the time of trip. Decision of the travel of the electric automobile user is related to various factors and is mainly divided into a departure time, an arrival time and an SOC corresponding to the EV. The congestion condition of the traffic network can influence the selection of the departure time and the actual arrival time of the user, and also influence the SOC of the EV when the EV leaves and arrives. Therefore, the user selects travel according to different travel modes, departure time and travel path prospects in the travel process, the user usually pursues satisfaction rather than optimality in selection, and the psychological expected utility of the user can be modeled by using the accumulated prospect theory.
Cost function v (x):
the reference point is an important parameter of the foreground theory and is used for measuring the psychological expectation of a decision maker. The cost function reflects the functional relationship between the "benefit" or "loss" result and the decision maker's subjective satisfaction size, defined as follows:
wherein x is 0 Is the reference point of the decision maker; alpha, beta E (0, 1)]As risk sensitivity coefficient, when α, β=1, it indicates that the decision maker maintains a risk neutral attitude to the decision event, λ > 1 measures the loss avoidance degree, and the larger it means that the peer loss is more sensitive, the greater the loss avoidance degree, the parameter is calibrated by Tversky and Kahneman, α=β=0.88, λ=2.25.
Probability weight function w (p):
in the foreground theory, the probability weight function which strictly monotonically increases is specifically as follows:
when faced with "benefits":
when faced with "loss":
wherein w is + (0)＝w ﹣ (0)＝0，w + (1)＝w ﹣ (1) =1. Gamma, delta reflects the curvature of the probability weight function curve, p is the probability of occurrence of the result, w + (p),w ﹣ (p) subjective perceptual probabilities in the face of "benefit" and "loss".
Accumulating a decision weight function:
the dummy device selection scheme consists of a series of combinations (x i ,p i ) Is composed of m is less than or equal to i is less than or equal to n. Cumulative decision weight function faced with "revenue" or "loss And->Can be expressed as:
wherein p is i Represents the probability of occurrence of the ith situation, p n A situation corresponding to the nth "benefit" representing a probability value; p is p m A probability value corresponding to the mth "loss" case is represented. From Tversky and Kahneman calibration, γ=0.61, δ=0.69.
Psychological expectation utility mapping function:
the reference point, the cost function v (x) and the probability weight function w (p) are determined, and then the decision weight function pi (·) is determined according to the alternative scheme, so as to calculate the CPV. Assuming Z is the set of mental states of the decision maker,representing a certain mental state, R represents the set of results, i.e. the set of all possible occurrences of the decision made by the decision maker, i.e. the results of possible "profits" or "losses". Defining a function h, where Z.fwdarw.R represents a mapping from the mental state to the possible occurrence of the result. Then, based on the accumulated foreground theory, the following calculation formula holds:
accumulating foreground values:
the cumulative foreground value CPV may be expressed as:
CPV(h)＝CPV(h + )+CPV(h - ) (8)
wherein CPV (h) represents the overall cumulative prospect value; CPV (h) + ) A cumulative prospect value representing a "benefit" portion; CPV (h) - ) Representing the cumulative foreground value of the "lost" portion.
Comprehensive value of the value:
the comprehensive Value function Value of the psychological expected utility model is divided into a time Value function V t And SOC cost function V soc Two parts, and value=v t +wV soc Wherein w is a comprehensive value weight coefficient, and is determined according to the psychological expected specific gravity of the user, and the decision specific gravity of the user between the SOC and the time is weighed. Part is a time cost function V t According to the traffic distribution and travel distribution model, the travel user can be given the expected departure time t of travel demands from the departure point i to the destination j ij L The SOC interval value when the departure is expected and the departure time can be calculated according to the expected travel time and the current time, and the state of charge at the expected departure time is recorded as the SOC L Then according to the traffic flow distribution, determining a travel path, and calculating the expected arrival time t ij A And the power consumption ΔSOC, and further determining the SOC at the time of arrival, and recording as the SOC A There is Δsoc=soc L ﹣SOC A 。
For a time cost function, as shown in FIG. 1, a user typically has three time reference points: acceptable earliest time of arrival t R min Acceptable latest time of arrival t R max Nearer t R max Is (are) preferred arrival times t R P And t R min <t R P <t R max The method comprises the steps of carrying out a first treatment on the surface of the Corresponding to the SOC value function, in order to avoid the adverse effect on the service life of the battery caused by the overcharge and overdischarge of the battery during the charging and traveling processes of the user, and consider the charge service cost difference, the SOC value function V when the user expects to be separated into the stations soc L And SOC cost function V at the arrival at the destination soc A The reference point of the SOC is divided into the maximum value and the minimum value SOC when the station is separated L max 、SOC L min Maximum and minimum value SOC at arrival A max 、SOC A min . The integrated cost function for the multiple reference points is considered as follows:
Value＝V t +ωV soc (11)
wherein Value is the Value of the comprehensive Value, V t As a function of time value, V soc For the SOC cost function, ω is a weight coefficient, characterizing the user's trade-off between time and SOC.
Time cost function V t The following formula is shown:
wherein: gamma ray 1 ＝0.69，γ 2 ＝0.61。
SOC cost function V SOC The following formula is shown:
wherein:
in the middle ofMu for the rounding down operation 1 ＝γ 1 ，μ 2 ＝γ 2 。
Based on formulas (1) - (13), the psycho-expected utility model for user travel is as follows:
CPV(EV)＝CPV t +ωCPV SOC (14)
wherein: CPV is the user's psycho-expected utility value (cumulative prospect value), where n is the number of travel alternatives indeterminate by the user, pi i The probability weight function value may occur for each travel alternative.
Therefore, when the user makes a trip decision, the SOC is given according to the trip requirement L ，SOC A ，t ij L And t ij A Further determining a travel path from the traffic flow, evaluating a travel decision by considering a user psychological expectation utility model of multiple reference points, determining whether the travel decision satisfies the user's expectation according to equations (2) - (14), if the expected value of the accumulated prospect value is greater than a set threshold CPV set The travel can be made according to the original decision; otherwise, the travel guiding strategy is accepted, and the travel decision is adjusted.
Step 3, constructing a dynamic traffic balance flow distribution model based on travel decision related parameters of each electric automobile user in the step 1 and the state of traffic flow at the current moment in the step 1;
the dynamic traffic balance flow distribution model in the step 3 comprises a path selection model of a user traveling O-D pair, a road section and path flow correlation matrix, electric vehicle road section traveling time, traveling path cost and traveling path selection conditions, and the dynamic traffic balance flow distribution model specifically comprises the following steps:
in the actual travel process, the traveler cannot fully understand the traffic network condition, and therefore, the path cost is regarded as a random variable. In the travel implementation process, the traveler always selects the path with the minimum estimated cost for travel. The variation in personal behavior results in a variation in the distribution of traffic flows over the road network, i.e., a dynamic distribution model. When the traffic flow reaches equilibrium, it is impossible for any traveler to reduce his estimated cost by changing the travel path unilaterally.
Path selection model for user travel O-D pairs:
assuming that the estimated traffic cost and the travel demand of the traveler of each road section of the traffic flow are approximately unchanged in the time interval tau, all the selected paths between a certain O-D pair satisfy the following relationship when the O-D demand distribution is completed in the time interval t:
Wherein f k,t rs Is the flow on the kth path between the t period O-D pair rs, K ε K rs ，K rs For the set of active paths existing between O-D pairs rs, q t rs To ensure the travel requirement q corresponding to each O-D pair (r, s) by the total traffic requirement from r to s, namely the O-D quantity rs Can be satisfied.
Road segment and path traffic correlation matrix:
road section flow x at time t a,t And path flow f k,t rs Can pass through the incidence matrix delta a,k rs The connection is as follows:
δ a,k rs representing the association coefficient between road segment and path, road segment a on the kth path connecting O-D pair rs, then delta a,k rs =1, otherwise δ a,k rs ＝0；
Electric automobile road section travel time:
determining the time t of the electric vehicle running on the road section a in the t period a (x a,t ) The calculation formula is as follows:
wherein t is a 0 Is the free transit time of road section a, x a,t For the flow on the section a at time t, C a Is the traffic capacity of road segment a.
Travel path cost:
in the characterization of travel path costs, only travel time of the travel path is considered. travel path cost C at t period k,t rs Estimate of (2)c k,t rs The following is shown:
travel path selection conditions:
thus, the filling condition for the path k between the connection O-D pair rs at time t is selected by the user as follows:
wherein c m rs And estimating the travel path cost of the O-D for any other path m of rs.
The travel path selection principle based on 'user balance' is as follows:
wherein mu rs Representing the minimum travel cost between the O-D pair rs and f under the balanced state of the traffic network k rs 、c k rs Representing the flow and travel cost on the kth path between the O-D pair rs.
And (3) selecting and obtaining the traffic flow state under the current traffic balance flow distribution model according to the state of the traffic flow at the current moment in the step (1) and the travel path of each electric automobile user.
And 4, calculating a user psychological expected utility value and a user psychological expected utility threshold value.
Determining a current travel decision of a user according to the current traffic flow state input in the step 1 and travel related parameters of each electric automobile user, and calculating a psychological expected utility value of the user under the current travel decision through the user psychological expected utility model in the step 2 and the dynamic traffic balance flow distribution model in the step 3; according to the lower limit of the SOC expected at the arrival time of each electric vehicle user in the step 1, the lower limit of the SOC expected at the departure time, the expected departure charging station time and the expected arrival destination time reference point, the comprehensive cost function value in the step 2 obtained by obtaining the traffic flow state under the current traffic balance flow distribution model in the step 3 is obtained based on the user psychological expected utility model in the step 2, namely the user psychological expected utility threshold.
The psychological expected utility of the user is calculated mainly from the travel time and the SOC. And carrying out EV plan trip decision through the user self-set parameters and road network traffic flow states, and further calculating the user psychological expectation utility CPV.
And 5, guiding the corresponding user travel decision according to the current traffic flow state and the travel related parameters of the electric automobile user, which are input in the step 1, and the difference between the user psychological expected utility value and the user psychological expected utility threshold value, which are obtained in the step 4, if the difference exceeds the allowable minimum range, and obtaining the guided travel decision.
When the time accumulation prospect value does not meet the threshold value, the travel time cost increase caused by traffic flow deterioration can be properly avoided by advancing or delaying the travel time; when the SOC accumulated prospect value does not meet the threshold value, the starting time can be delayed or advanced, and the charging power can be controlled to optimize the SOC, so that the psychological expectation of a user is improved. Therefore, the SOC of the EV has a relationship with the travel time as shown in fig. 2: and adjusting the charging power or the departure time before the departure of the user according to the psychological expected utility value of the user from the two aspects of the trip time and the SOC of the user. (1) When the user normally goes out, the charging power P is used before going out 1 Charging to reach the expected SOC L And normally travel according to the expected travel time of the user, at this time P 1 ≤P max The method comprises the steps of carrying out a first treatment on the surface of the (2) When the travel estimation time consumption of the user is too large, the travel time is advanced, the charging time from the current time to the departure time is reduced, and the charging power P is increased x So that SOC L’ Reach the originally expected SOC L The method comprises the steps of carrying out a first treatment on the surface of the (3) When the estimated power consumption of the user is too large, the time of the user at the time of traveling is estimated, the charging time from the current time to the departure time is increased, and the charging power P is increased 1 Keep unchanged, and increase the charging time length to enable the SOC to be L” ＞SOC L . Therefore, when the departure time is advanced or pushed, the SOC can be maintained and raised by increasing the charging power and extending the charging time L 。
The specific guiding mode is as follows: when the expected utility value CPV is more than the threshold CPV set When the user is going on, the user can go on directly according to a preset travel decision, and guidance is not required to be accepted; when CPV is less than the threshold CPV set When the travel guidance strategy is formulated, a corresponding travel guidance strategy is formulated: guiding the travel decision according to the CPV result: (1) When SOC accumulates the foreground value CPV soc < SOC cumulative prospect value threshold CPV soc_set Time cumulative foreground value CPV t <Time cumulative foreground value threshold CPV t_set At the time, the charging power is set to the maximum charging power P max The method comprises the steps of carrying out a first treatment on the surface of the When the weight coefficient omega is more than 1, the user is characterized to pay more attention to the SOC, and the departure time is delayed to the SOC L Satisfy CPV soc ＞CPV soc_set The method comprises the steps of carrying out a first treatment on the surface of the When the weight coefficient omega is less than 1, characterizing that the user attaches more importance to time, and advancing the departure time to the current moment; (2) When CPV soc ＜CPV soc_set ，CPV t ＞CPV t_set At the time, the charging power is set to P max And delay the trip time appropriately; (3) When CPV soc ＞CPV soc_set ，CPV t ＜CPV t_set The charging power can be regulated and controlled, and the travel time is advanced to the current time. And (3) calculating an estimated value of the travel path cost according to the redetermined travel time and the corresponding traffic flow state and the traffic balance flow distribution model in the step (3), and re-planning the travel path with the minimum estimated value of the travel path cost as a target.
Therefore, the corresponding electric automobile user can be guided to adjust the travel time, and the charging power between the current time and the travel time of the corresponding electric automobile user can be regulated and controlled:
(1) At the current moment, the SOC real-time state is smaller than the expected SOC lower limit SOC at the departure moment L min In the time interval of the current time and the departure time, the maximum power P is used max Charging is performed.
(2) At the current moment, the SOC real-time state is larger than the expected SOC lower limit SOC at the departure moment L min Less than the separationStation time desired SOC upper limit SOC L max Then 0.5P is used in the time interval of the current time and the departure time max Charging is performed.
(3) At the current moment, the SOC real-time state is larger than the expected SOC upper limit SOC at the departure moment L max
The SOC real-time state has reached the user's expected SOC L max At this time, the charging power thereof may be regarded as 0.
The departure time is different, and corresponding to different traffic flow states, the time consumption and the power consumption of traveling are possibly reduced both in advance and at the time of traveling after pushing, and the traveling experience of a user is improved. The trip guidance strategy considering psychological expectations of the user is shown in fig. 3.
And 6, adjusting travel related parameters of the electric automobile user according to the guided travel decision obtained in the step 5, re-evaluating the psychological expected utility value of the user under the guided travel decision through the user psychological expected utility model in the step 2 and the dynamic traffic balance flow distribution model in the step 3, constructing a dynamic traffic balance flow distribution model under the new travel decision of the user, solving the relationship of the traffic flow states at different moments according to the traffic flow state under the current traffic balance flow distribution model in the step 3 and the following formula, and obtaining a new traffic flow by utilizing a mathematical programming optimization algorithm.
As can be seen from (18) - (20), the travel path cost C k,t rs The route selection of the travel user under the determination of the O-D pair is influenced, namely the travel decision of the user is determined, so that the route selection is influenced, and the route flow f k rs Will also change with it, and the path flow will affect the travel path cost C k,t rs And further, the traffic distribution balance considering the random travel demands of the users is achieved, so that the cost of the optimized target travel path and the cost of the users are the lowest. The traffic balance flow model can be described as a mathematical programming model as follows:
wherein S is t rs [c t rs (x)]For the expected estimated cost between t-period O-D pair rs,
the propagation dynamics of traffic flow among time periods, namely the afterflow phenomenon, is considered, and each time period is still approximately a static traffic balance flow distribution model. The recurrence relation of traffic flow between the time periods is as follows:
equation (22) defines the residual flow r corresponding to the t-period O-D pair (r, s) t rs Which represents the travel traffic that has not reached the destination at the end of the t period, which needs to be considered in the (t+1) period; residual flow r t rs From the path flow f k,t rs Path transit time t k,t rs And a period length τ; equation (23) is the traffic demand q for (r, s) in consideration of the residual flow t period O-D t rs Correction of q t rs,mod For the corrected traffic demand, equation (24) is similar to equation (15), ensuring travel demand q corresponding to each O-D pair (r, s) rs Can be satisfied. A flow chart for solving the dynamic traffic balance flow distribution model considering the travel guidance of the user is shown in fig. 4.
Taking a power grid-traffic network coupling network schematic diagram shown in fig. 5 as an example, the effectiveness of the travel guiding platform for the electric automobile provided by the invention is illustrated.
The EV-related parameter settings and EV-user parameter sampling probability distributions are shown in tables 1 and 2. The relevant parameters of the traffic network and the power grid and the basic load values of the power distribution network are shown in tables 3 and 4.
Table 1 simulation parameter settings
Table 2 EV user parameter sampling probability distribution
TABLE 3 related parameters of traffic and electric networks
Table 4 distribution network base load values
Fig. 6-9 are graphs of the SOC/power consumption/time consumption comparisons of user's trip decisions, road network traffic flow status comparisons, and load curves of distribution network areas, before and after platform guidance, with a 5.4% decrease in road network average flow over the studied period, road segment maximum flow 21: the number of the 12 th road sections is reduced from 67 to 56, the traffic flow distribution of the road network is more balanced compared with that before guiding, the traffic flow distribution of each road section is more gentle, and the average load of the study period is 4752kW. The guiding strategy is divided into adjusting travel time and optimizing charging power, wherein the travel adjustment refers to adjusting EV travel time according to a user psychological expected utility value, and the optimization refers to controlling charging power. The EV travel is not regulated, and the EV charging load when the charging power is not optimized is 462kW; the EV travel is regulated, and the EV charging load is 407kW when the charging power is not optimized; the EV travel is not regulated, and the EV charging load is 425.9kW when the charging power is optimized; EV travel was adjusted and the EV charge duty at optimum charge power was 333.5kW. Through the guiding strategy, the charging load of 128.5kW can be reduced, the charging load of 27.8% can be reduced on the premise of meeting the travel requirement of a user, and the peak clipping rate reaches 2.46%. According to the method, when the travel decision of the user is guided, the psychological expected utility value of the user can be improved, the travel time of the user is adjusted, the EV charging power is optimized, the peak-valley difference can be effectively reduced, and the validity of the platform constructed by the method is proved, so that the travel congestion probability of the EV user can be reduced, and the charging load of a power grid can be reduced.
It should be understood that parts of the specification not specifically set forth herein are all prior art.
It should be understood that the foregoing description of the preferred embodiments is not intended to limit the scope of the invention, but rather to limit the scope of the claims, and that those skilled in the art can make substitutions or modifications without departing from the scope of the invention as set forth in the appended claims.

Claims (1)

1. An electric automobile travel guiding method based on dynamic traffic flow and user psychology is characterized by comprising the following steps:
step 1, introducing rated charging power of a charging pile, travel decision-related parameters of electric car users and traffic distribution network related parameters of each electric car user;
the trip decision-related parameters of the electric automobile user in the step 1 include: the destination of each electric vehicle user, the arrival reference time of each electric vehicle user, the preference reference time of each electric vehicle user, the arrival reference time of each electric vehicle user, the initial SOC of each electric vehicle user, the upper SOC limit expected at the arrival time of each electric vehicle user, the lower SOC limit expected at the arrival time of each electric vehicle user, the upper SOC limit expected at the departure time of each electric vehicle user, the lower SOC limit expected at the departure time of each electric vehicle user, the expected departure charging station time of each electric vehicle user, and the SOC/time weight coefficient of each electric vehicle user;
The traffic distribution network related parameters of each electric automobile user in step 1 include:
topology and inter-node relation of a power distribution network, topology and inter-node relation of a traffic network, daily load data of a power distribution network area, current capacity of traffic road sections, free passing time of the traffic road sections, and state of traffic flow at the current moment in a period t;
step 2, according to the early reaching reference time of each electric automobile user, the preference reference time of each electric automobile user, the late reaching reference time of each electric automobile user, the initial SOC of each electric automobile user, the expected SOC upper limit of the arrival time of each electric automobile user, the expected SOC lower limit of the arrival time of each electric automobile user, the expected SOC upper limit of the departure time of each electric automobile user, the expected SOC lower limit of the departure time of each electric automobile user, the expected departure charging station time of each electric automobile user and the SOC/time weight coefficient of each electric automobile user, constructing an electric automobile user psychological expected utility model;
the psychological expected utility model of the electric automobile user in the step 2 is composed of an electric automobile user time cost function model and an electric automobile user SOC cost function model, and G is G, and G is the psychological expected utility model of all electric automobile users in the guiding period, and is specifically as follows:
Value g ＝V t g +w g V soc g
Wherein Value is g Representing the psychological expected utility value, V, of the g-th electric automobile user t g Representing a g-th electric automobile user time cost function model, V soc g Representing the SOC value function model of the g-th electric automobile user, w g SOC/time weight coefficient for the g-th electric automobile user;
the time cost function model of the electric automobile user, the time cost function of the g-th electric automobile user is specifically defined as follows:
wherein: v (V) t g Time cost function value of g-th electric automobile user, gamma 1 To face the risk sensitivity coefficient in case of loss of time value, gamma 2 To face risk sensitivity coefficient in time value benefit, t i,j Ag For the time, t, from charging station i to destination j for the g-th electric vehicle user R ming For the g-th electric automobile user to arrive at the reference moment, t R maxg The user of the g-th electric automobile arrives at the reference moment t R Pg Preferential arrival time for g-th electric automobile user, and t R ming <t R Pg <t R maxg ；
The SOC cost function model of the electric automobile user, and the SOC cost function of the g-th electric automobile user is specifically defined as follows:
in the method, in the process of the invention,mu for the rounding down operation 1 To face loss of SOC valueRisk susceptibility coefficient, mu 2 To face risk sensitivity coefficient when SOC value is obtained soc Lg SOC cost function for the departure time of the g-th electric automobile user, V soc Ag SOC value function of arrival time of g-th electric automobile user, SOC max Lg The expected SOC upper limit for the departure time of the g-th electric automobile user and SOC min Lg SOC lower limit expected at departure time of g-th electric automobile user and SOC max Ag SOC upper limit expected for arrival time of g-th electric automobile user, SOC min Ag SOC lower limit expected for arrival time of g-th electric automobile user, SOC Lg The actual SOC and the SOC of the electric automobile when the user leaves the station Ag The actual SOC of the electric automobile user when arriving at the destination; SOC (State of Charge) Lg The initial SOC of the g-th electric vehicle user, the expected leaving charging station time of the g-th electric vehicle user and the rated charging power of the charging pile can be calculated;
the psychological expected utility model of the electric automobile user in the step 2, the psychological expected utility function of the g-th electric automobile user is specifically as follows:
CPV g ＝CPV t g +ω g CPV SOC g
wherein: CPV (chlorinated polyvinyl chloride) g Is the psychological expected utility value of the g-th electric automobile user, n is the number of travel alternatives which are uncertain for the g-th electric automobile user and pi i g The probability weight function value which can occur for the ith travel alternative scheme of the g-th electric automobile user;
Step 3, constructing a traffic balance flow distribution model based on the destination of the electric automobile user in the travel decision related parameters of each electric automobile user in step 1, the topology and the inter-node relation of the traffic network in step 1, the current capacity of the traffic road section and the free traffic time of the traffic road section, and the state of the traffic flow at the current time t period;
the traffic balance flow distribution model in the step 3 comprises the following steps:
the method comprises the steps of a path selection model, a road section and path flow rate association matrix, an electric vehicle road section running time, a travel path cost and travel path selection conditions of an electric vehicle user traveling O-D pair;
according to g t The destination of each electric automobile user, the topology of the traffic network and the relationship between nodes can obtain all electric automobile users G in the guiding period t t Path selection model of travel O-D pair g t ∈G t ,G t All electric automobile users in the guiding period t;
assuming that the estimated traffic cost and the travel demand of the traveler of each road section of the traffic flow are approximately unchanged in the time interval tau, when the O-D demand distribution is completed in the time interval t, all the selected paths between a certain O-D pair satisfy the following relation:
wherein f k,t rs Is the flow on the kth path between O-D pair rs in the state of the traffic flow at the current time t period, K is E K rs ，K rs For the set of active paths existing between O-D pairs rs, q t rs For all traffic demands from r to s in t time period, all electric automobile users G in time t t The composition, namely the O-D quantity, further ensures the travel requirement q corresponding to each O-D pair (r, s) in the moment t rs Can be satisfied;
obtaining a road section and path flow association matrix according to the topology of the traffic network and the relationship between nodes in the step 1:
road section flow x at time t a,t And path flow f k,t rs Can pass through the incidence matrix delta a,k rs The connection is as follows:
δ a,k rs representing the association coefficient between road segment and path, road segment a on the kth path connecting O-D pair rs, then delta a,k rs =1, otherwise δ a,k rs ＝0；
The electric automobile user section travel time:
the running time t of the electric vehicle at the moment t in the road section a can be determined according to the current capacity of the traffic road section, the free running time of the traffic road section and the state of the traffic flow at the moment t a (x a,t ) The calculation formula is as follows:
wherein t is a 0 Is the free transit time of road section a, x a,t For the flow on the section a at time t, C a Is the current-carrying capacity of road section a;
the travel path cost:
travel path cost estimation value c at time t k,t rs The following is shown:
the travel path selection condition:
thus, the filling condition for connecting path k between O-D pair rs at time t is selected by the user as follows:
Wherein c m rs Estimating the travel path cost of the O-D for any other path m of rs;
the travel path selection principle based on user balance is as follows:
wherein mu rs Representing the minimum travel cost between the O-D pair rs and f under the balanced state of the traffic network k rs 、c k rs Representing the flow and travel expense on the kth path between the O-D pairs rs;
the traffic flow state under the traffic balance flow distribution model at the time t can be described as the following mathematical programming model:
wherein q is t rs For all traffic demands from r to s in the time t, all electric automobile users G in the time t t Composition, S t rs [c t rs ]For the expected estimated cost between the O-D pair rs at time t, x a,t For the flow on the section a at time T, T a,t (x a,t ) For the time t of the electric automobile running on the road section a, t a (w) is the road section running time, and the road section flow x along with the time t a,t In a variation of the method, the device,E[]representing the desire;
according to the state of the traffic flow at the current time t in the step 1 and the travel path selection of each electric automobile user, solving through a mathematical programming optimization algorithm to obtain the traffic flow state under the traffic balance flow distribution model at the time t;
step 4, calculating a user psychological expected utility value and a user psychological expected utility threshold value;
according to the current traffic flow state input in the step 1 and the travel decision-related parameters of the g-th electric automobile user, through the user psychological expected utility model described in the step 2 and the traffic balance flow distribution model described in the step 3, Calculating a psychological expected utility value of the g-th electric automobile user under the current trip decision; according to the user psychological expectation utility model in the step 2 and the traffic flow state under the current traffic balance flow distribution model obtained in the step 3, the comprehensive value function value of the G-th electric automobile user in the step 2 is obtained, namely the user psychological expectation utility threshold value, G epsilon G t ,G t All electric automobile users in the current moment t moment;
step 5, according to the state of the traffic flow at the current moment t and the travel related parameters of the G-th electric automobile user input in the step 1, G epsilon G t ,G t For all electric automobile users in the moment t of the current moment, the difference value between the user psychological expected utility value and the user psychological expected utility threshold value obtained in the step 4 is obtained, if the difference value exceeds the allowable minimum range, the travel decision of the corresponding g electric automobile user is guided, and the travel decision after guidance is obtained: care-versus-expected utility value CPV g Greater than the psychographic utility threshold CPV set g When the user is going on, the user can go on directly according to a preset travel decision, and guidance is not required to be accepted; when CPV g Less than a psychographic utility threshold CPV set g When the travel guidance strategy is formulated, a corresponding travel guidance strategy is formulated: according to CPV g The result guides the travel decision:
When SOC accumulates the foreground value CPV soc g < SOC cumulative prospect value threshold CPV soc_set g Time cumulative foreground value CPV t g <Time cumulative foreground value threshold CPV t_set g At the time, the charging power is set to the maximum charging power P max The method comprises the steps of carrying out a first treatment on the surface of the When the weight coefficient omega g When more than 1, the characterization user attaches more importance to the SOC and delays the departure time to the SOC Lg Satisfy CPV soc g ＞CPV soc_set g The method comprises the steps of carrying out a first treatment on the surface of the When the weight coefficient omega g When the time is less than 1, characterizing the user to attach more importance to the time, and advancing the departure time to the current time;
when CPV soc g ＜CPV soc_set g ，CPV t g ＞CPV t_set g When the charging power is set to the charging pile, the maximum charging power can be providedP max And delay the trip time appropriately;
when CPV soc g ＞CPV soc_set g ，CPV t g ＜CPV t_set g According to the peak-valley condition of the daily load data of the power distribution network area in the step 1, the charging power can be properly reduced, the travel time is advanced to the current time, according to the redetermined travel time and the corresponding traffic flow state, the estimated value of travel path cost is calculated according to the traffic flow state under the traffic balance flow distribution model at the time t calculated in the step 3, and the travel path is redeployed with the minimum estimated value of travel path cost as a target;
the g-th electric automobile user can be guided to adjust the travel time, and the charging power between the current time and the travel time of the corresponding electric automobile user is regulated and controlled, specifically as follows:
at the current moment, the SOC real-time state of the g-th electric automobile user is smaller than the expected SOC upper limit at the arrival moment Then the maximum power P is used in the time interval of the current time and the departure time max Charging;
at the current moment, the SOC real-time state of the g-th electric automobile user is larger than the expected SOC lower limit of the arrival momentAn upper SOC limit less than that expected at the departure time +.>Then 0.5P is used in the time interval of the current time and the departure time max Charging;
at the current moment, the SOC real-time state of the g-th electric automobile user is larger than the expected SOC upper limit of the arrival momentIndicating that the SOC real-time state has reachedMoment desired SOC upper limit->The charging power thereof can be regarded as 0 at this time;
step 6, according to the guided travel decision obtained in step 5, adjusting all electric automobile users G in the moment t at the current moment t Through the user psychological expected utility model in the step 2 and the traffic balance flow distribution model in the step 3, the psychological expected utility value of the user under the guided travel decision is estimated again, a dynamic traffic balance flow distribution model under the new travel decision of the user is constructed, and the recurrence relation of traffic flows at all times can be obtained according to the traffic flow state under the current traffic balance flow distribution model in the step 3 as follows:
the residual flow r corresponding to the O-D pair (r, s) at the t moment is firstly defined t rs ，f k,t rs Is the flow on the kth path between O-D pair rs in the state of the traffic flow at the current time t period, K is E K rs ，K rs Is an effective path set existing between the O-D pair rs; secondly taking into account the traffic demand q of the remaining flow t moment O-D pair (r, s) t rs Correction of q t rs For all traffic demands from r to s in the time t, all electric automobile users G in the time t t The composition, namely the O-D quantity, further ensures the travel requirement q corresponding to each O-D pair (r, s) in the moment t rs Can be satisfied, q t rs,mod R for corrected traffic demand t-1 rs For the residual flow corresponding to O-D pair (r, s) at t-1 moment, t kt rs For the path transit time, τ is the period length.
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