CN117172629B - Charging scheme screening method based on electric operation vehicle charging decision model - Google Patents
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Abstract
The invention belongs to the technical field of electric vehicle charging decision making, and discloses a charging scheme screening method based on an electric operation vehicle charging decision making model, which comprises the following steps: constructing an electric operation vehicle charging decision model based on time perception difference, which consists of a psychological perception model based on time expansion and contraction and a irrational decision model based on a foreground theory; determining psychological safety electric quantity of the heterogeneous vehicle owners under the time perception difference by utilizing the psychological perception model based on the time expansion and contraction, and psychological expectation benefits; and determining a cost function by using the irrational decision model based on the foreground theory, calculating the accumulated foreground value of each scheme, and selecting the optimal charging scheme. The charging scheme screening method based on the electric operation vehicle charging decision model can more accurately reflect the irrational decision behaviors of the vehicle owners. The invention establishes a psychological perception model based on Doppler time expansion and contraction, and carries out charging decision of heterogeneous vehicle owners under time perception difference.
Description
Technical Field
The invention belongs to the technical field of electric vehicle charging decision making, and particularly relates to a charging scheme screening method based on an electric operation vehicle charging decision making model.
Background
Currently, an Electric Vehicle (EV) is used as a green travel tool, and the number of the Electric vehicles has a large-scale trend in recent years, and an alternative fuel Vehicle has become a trend of time development in the future. However, the travel and charging of large-scale electric vehicles bring great challenges to the coordinated development of urban traffic and a power grid, the influence of electric vehicle charging on the power grid load is not only related to the scale of the electric vehicles, but also directly related to the charging behavior of the electric vehicles, and the local power grid load can be greatly influenced by the tiny disturbance of the charging randomness of the electric vehicles, namely, the change of the starting time and the charging capacity of the charging. Therefore, research on the charging behavior of the electric vehicle has positive significance for solving the problem of overload of the power grid load.
Different electric automobile user groups show different charging behavior characteristics, and electric operation car owners need to pay attention to electric automobile electric quantity states when making a decision due to the working properties of the electric operation car owners so as to avoid running in a low electric quantity state and mileage anxiety, and meanwhile, need to pay attention to charging cost and income of a guest bill, so that higher profit is ensured as much as possible. In the existing research on the charging behavior of the owner of the electric operation vehicle, objective factors such as arrival time and residual electric quantity are paid attention to, and in the modeling, the objective factors and the residual electric quantity are mostly based on an expected utility theory, and a decision maker is assumed to be completely rational, but in fact, the owner of the electric operation vehicle is not completely rational, and the decision behavior of the owner of the electric operation vehicle is jointly determined by the objective factors and subjective perception.
Through the above analysis, the problems and defects existing in the prior art are as follows: the prior art cannot accurately reflect irrational decision behaviors of the owners of the operation vehicles, the prediction of the charging decisions is inaccurate, and the screening result of the charging scheme is inaccurate.
Disclosure of Invention
Aiming at the problems existing in the prior art, the invention provides a charging scheme screening method based on an electric operation vehicle charging decision model.
The invention is realized in such a way, and the charging scheme screening method based on the charging decision model of the electric operation vehicle comprises the following steps:
constructing an electric operation vehicle charging decision model based on time perception difference, which consists of a psychological perception model based on time expansion and contraction and a irrational decision model based on a foreground theory;
determining psychological safety electric quantity of the heterogeneous vehicle owners under the time perception difference by utilizing the psychological perception model based on the time expansion and contraction, and psychological expectation benefits;
and determining a cost function by using the irrational decision model based on the foreground theory, calculating the accumulated foreground value of each scheme, and selecting the optimal charging scheme.
Further, the charging scheme screening method based on the electric operation vehicle charging decision model comprises the following steps:
Firstly, constructing a psychological perception model based on time expansion and contraction, respectively determining expected benefits of a heterogeneous vehicle owner after Doppler time contraction and expected electric quantity consumption of the heterogeneous vehicle owner after Doppler time expansion or contraction, and calculating psychological benefits of the heterogeneous vehicle owner;
modeling the decision behavior of the vehicle owner based on the accumulated prospect theory; and evaluating each charging scheme by calculating an improved accumulated prospect value of each charging scheme to obtain an optimal charging scheme.
Further, define v' as the heterogeneous vehicle owner at t 0 The speed of obtaining the benefit before the moment, v is the speed of a typical vehicle owner at t 0 Acquiring the speed of the profit before the moment, wherein the typical car owners are car owners with the speed of acquiring the profit and the power consumption conforming to the average value of the car owners; the determination of the presence of a heterogeneous vehicle ownerThe expected benefits after doppler time contraction include:
(1) Determining the heterogeneous vehicle owner at t by using 0 Speed of revenue acquisition before time:
v'＝C'/t 0 ；
wherein C' represents t 0 The heterogeneous vehicle owners have benefits before decision-making before the moment;
(2) Determining a typical vehicle owner at t using 0 Speed of revenue acquisition before time:
v＝C 0 /t 0 ；
wherein C represents t 0 Typical car owners have benefits before the moment;
(3) The speed of obtaining the benefits of the heterogeneous vehicle owners relative to the typical vehicle owners is determined as follows:
v s ＝v′-v；
(4) Judging whether the income of the heterogeneous vehicle owners is higher than that of typical vehicle owners, and determining the expected income of the heterogeneous vehicle owners after Doppler time expansion or contraction:
when the income of the heterogeneous vehicle owners at the same moment is higher than that of a typical vehicle owner, the expected income of the heterogeneous vehicle owners after Doppler time expansion is determined by the following formula:
wherein v is max The limiting speed of obtaining benefits of the vehicle owner is represented;
when the income of the heterogeneous vehicle owners at the same moment is lower than that of a typical vehicle owner, the expected income of the heterogeneous vehicle owners after Doppler time contraction is determined by using the following formula:
further, determining the expected power consumption of the heterogeneous vehicle owner after doppler time expansion or contraction comprises:
1) Determining the heterogeneous vehicle owner at t by using 0 Consumption of electricity at a momentSpeed of:
w'＝Q'/t 0 ；
wherein Q' represents up to t 0 Accumulating consumed electric quantity by the heterogeneous vehicle owners until the moment; d represents the accumulated driving distance and,the unit mileage electricity consumption is represented;
2) Determining a typical vehicle owner at t using 0 Speed of time consuming power:
w＝Q/t 0 ；
wherein Q represents up to t 0 The typical car owner accumulates the electricity consumption at the moment;
3) The running speed of the heterogeneous vehicle owner relative to a typical vehicle owner is calculated as follows:
w s ＝w′-w；
4) Judging whether the self-consumption electric quantity of the heterogeneous vehicle owner is higher than that of a typical vehicle owner, and determining the expected electric quantity consumption of the heterogeneous vehicle owner after Doppler time expansion or contraction:
When the consumption of the heterogeneous vehicle owner per se is higher than that of a typical vehicle owner at the same moment, the expected electricity consumption of the heterogeneous vehicle owner after Doppler time contraction is determined by the following formula:
wherein w is max A limit speed indicating the amount of consumed electricity;
when the self consumption of the heterogeneous vehicle owner is lower than that of a typical vehicle owner at the same moment, the expected power consumption of the heterogeneous vehicle owner after Doppler time expansion is determined by the following formula:
further, the calculating the psychological benefit of the heterogeneous vehicle owner includes:
firstly, calculating psychological safety electric quantity of a heterogeneous vehicle owner by using the following formula:
secondly, determining psychological perception benefits of the heterogeneous vehicle owners under the time perception model by using the following formula:
wherein T' j The time of any time interval j after Doppler time expansion or contraction is represented;n represents the number of alternative charging schemes, m represents the interval reached by the vehicle owner, and j represents the interval for selecting charging; t'. char Representing the charge time under time perception, T' char_j And T' char_j+1 Respectively representing charging time in j and j+1 time periods under time perception; c (C) 0 Indicating that there is a benefit before decision making; t'. k Representing the duration of k intervals under time perception; t is t 0 Indicating the moment at which the decision is made; v (V) j Representing the speed of obtaining the profit of the interval j running list S j Denoted as charging electricity price for interval j; s is S j+1 The charging electricity rate in the interval j+1 is shown.
Further, modeling the decision behavior of the vehicle owner based on the accumulated prospect theory includes:
modeling the psychological perception time of the heterogeneous vehicle owners by taking the Doppler effect as a reference, calculating subjective psychological perception benefits of the vehicle owners, and determining a cost function and a weight function of the model by taking benefits of typical vehicle owners as psychological reference points;
modeling the decision behavior of the vehicle owner based on the accumulated prospect theory comprises the following steps:
(1) Determining a cost function:
I 0 ＝U(x)＝∑(Y ij ,p ij )；
wherein,the subjective value of an ith random scene of a vehicle owner under a charging scheme j is represented; y is Y ij Representing psychological perception benefits of heterogeneous vehicle owners in a scene i under a charging scheme j; p is p ij Representing probability corresponding to ith random scene under charging scheme j, I 0 The expected utility value representing the benefit is used for accumulating the foreground theory reference points; alpha and beta respectively represent the sensitivity of the vehicle owner to risks; λ represents a loss avoidance coefficient;
(2) Determining a weight function:
when the owner faces the benefit, the weighting function is as follows:
when the owner faces the loss, the weight function is as follows:
wherein p is ij Represents the probability, ω, of occurrence of the ith scene under the charging scenario j + (p ij ) Representing a forward subjective probability weight; omega － (p ij ) Indicating a negative subjective probability weight;
further, the evaluating each charging scheme by calculating the improved accumulated prospect value of each charging scheme, and obtaining the optimal charging scheme comprises the following steps:
the improved cumulative prospect value for each charging regime is calculated using the following formula:
wherein,representing forward cumulative foreground values under scheme j; />Representing negative cumulative foreground values under scheme j; />Representing the actual integrated cumulative prospect value;
π ij + representing a forward cumulative decision weight function,π ij － cumulative decision weight function representing negative direction, +.>n represents the positive possible result perceived by the vehicle owner under each charging scheme; m represents the negative possible result perceived by the vehicle owner under each charging scheme; omega + And omega － Is a strict increasing function and satisfies: omega + (0)＝ω - (0)＝0；ω + (1)＝ω - (1)＝1。
Another object of the present invention is to provide a charging scheme screening system based on an electric vehicle charging decision model of a charging scheme screening method based on an electric vehicle charging decision model, the charging scheme screening system based on the electric vehicle charging decision model comprising:
the psychological perception model building module is used for building a psychological perception model based on time expansion and contraction;
The irrational decision model construction module is used for modeling the decision behaviors of the vehicle owners based on the accumulated prospect theory to obtain irrational decision models based on the prospect theory;
the psychological benefit calculating module is used for respectively determining expected benefits of the heterogeneous vehicle owners after Doppler time contraction and expected electric quantity consumption of the heterogeneous vehicle owners after Doppler time expansion or contraction by using a psychological perception model based on time expansion and contraction, and calculating psychological benefits of the heterogeneous vehicle owners;
and the charging scheme evaluation module is used for evaluating each charging scheme by calculating an improved accumulated prospect value of each charging scheme by using a non-rational decision model based on a prospect theory to obtain an optimal charging scheme.
It is a further object of the present invention to provide a computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the steps of the charging scheme screening method based on an electric operator charging decision model.
The invention further aims to provide an information data processing terminal which is used for realizing the charging scheme screening method based on the electric operation vehicle charging decision model.
In combination with the technical scheme and the technical problems to be solved, the technical scheme to be protected has the following advantages and positive effects:
firstly, the invention provides an electric operation vehicle charging decision model which is composed of a psychological perception model based on time expansion and contraction and a non-rational decision model based on a foreground theory and takes time perception difference into consideration, and simulation verification is carried out through electric operation vehicle charging related data in certain city, so as to obtain the following conclusion: based on the difference of the profit situation or the electricity consumption situation of the car owners, when the car owners feel anxiety about time, the aggressive scheme with high risk but higher profit is more prone to be selected, and the aggressive scheme is represented as a charging time push in the case; when the owner feels comfortable with time, a conservative solution with lower returns, but also low risk, will be more favored, in the case where the charging time is advanced. The case result shows that the modeling type can more accurately describe the irrational decision behavior of the vehicle owner from the time perception angle, and a certain thought is provided for researching the charging behavior of the large-scale electric vehicle.
Secondly, the invention establishes a psychological perception model based on Doppler time expansion and contraction, and carries out charging decision of heterogeneous vehicle owners under time perception difference. Through analysis and research on the charging behavior of an electric operation vehicle in a certain market, the method can more accurately reflect the irrational decision behavior of the owner of the operation vehicle, and provides a certain thought for optimizing the charging behavior of a large-scale electric vehicle.
The invention verifies that when the car owner feels anxiety with time, the car owner can be more prone to select an aggressive scheme with high risk but higher benefit to charge, and the charging time is shown as a post-charging time in the case; when the owner feels comfortable about time, the owner may prefer to charge a conservative solution with lower benefits but lower risk, in the case where charging time is advanced. The case result provided by the invention shows that the time perception of the car owner of the operation car influences the charging decision result.
Thirdly, the charging behavior of the electric vehicle has strong randomness and disorder, the problem of peak-to-peak load can be caused, the safety and stability of the power grid are affected, and when an electric vehicle owner makes a charging decision, the charging behavior is a key factor with randomness and is interfered by strong irrational factors. According to the invention, the irrational charging decision model of the electric operation vehicle is established from the angles of time perception and risk attitude, so that reference significance can be provided for coordinating the stable operation of the power grid, peak clipping and valley filling can be realized, and the optimization cost of the power grid can be reduced.
Fourth, the technical scheme of the invention has the following positive effects:
1) In the current research on the charging demand of the owner of the electric operation vehicle, objective factors such as decision time, residual electric quantity, electricity price and the like are paid attention to, the personal judgment of the owner of the electric operation vehicle on the situation of acquiring income and consuming electric quantity is taken into consideration, and a charging demand model combining the objective factors with subjective perception of the owner is established.
2) According to the invention, a psychological time perception model of the vehicle owner is constructed based on time expansion and contraction under the Doppler effect, subjective time perception of the vehicle owner is accurately reflected, and further expected benefits of the heterogeneous vehicle owner after Doppler time expansion or contraction and expected electric quantity consumption of the heterogeneous vehicle owner after Doppler time expansion or contraction are respectively determined.
3) The prior study on irrational decision of the vehicle owner is on one side, the psychological perception of the vehicle owner on time is not considered, the subjective perception of the vehicle owner on time is considered, and when the conditions of obtaining benefits and consuming electricity of the vehicle owner are different from the average conditions of all vehicle owners, the vehicle owner generates anxiety or a graceful emotion, and under the anxiety emotion, the perception of the vehicle owner on time is changed, so that the vehicle owner shows different judgments in decision making.
Drawings
FIG. 1 is a decision model research framework provided by an embodiment of the present invention;
fig. 2 is a flowchart of a charging scheme screening method based on an electric vehicle charging decision model according to an embodiment of the present invention;
fig. 3 is a charge state division diagram provided by an embodiment of the present invention;
FIG. 4 is a diagram of psychological perception benefits of a vehicle owner under time perception provided by an embodiment of the present invention;
FIG. 5 is an electrocardiogram of psychological perception of a vehicle owner under time perception provided by an embodiment of the present invention;
FIG. 6 is a state vehicle owner desired utility charge decision graph provided by an embodiment of the present invention;
FIG. 7 is a theoretical charge decision chart of a state-vehicle owner accumulated prospect provided by the embodiment of the invention;
FIG. 8 is a state two-master desired utility charge decision graph provided by an embodiment of the present invention;
FIG. 9 is a theoretical charge decision chart of accumulated prospects of state-two vehicle owners provided by the embodiment of the invention;
FIG. 10 is a diagram of a revenue efficient time-aware charging decision graph provided by an embodiment of the present invention;
FIG. 11 is a diagram illustrating a charge decision under sense of time for a revenue balancing method according to an embodiment of the present invention;
FIG. 12 is a diagram of a charge decision under the perception of vehicle owner time with low benefit provided by an embodiment of the present invention;
FIG. 13 is a charge decision diagram under the sense of time of a vehicle owner of an electric quantity sensitive type provided by an embodiment of the invention;
fig. 14 is a charge decision diagram under the perception of the vehicle owner time with balanced electric quantity provided by the embodiment of the invention;
fig. 15 is a diagram of charge decision diagrams under the perception of time of a vehicle owner with a low electric quantity according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the following examples in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
As shown in fig. 1, the charging scheme screening method based on the electric operation vehicle charging decision model provided by the embodiment of the invention comprises the following steps:
s101, constructing a psychological perception model based on time expansion and contraction, respectively determining expected benefits of a heterogeneous vehicle owner after Doppler time contraction and expected electric quantity consumption of the heterogeneous vehicle owner after Doppler time expansion or contraction, and calculating psychological benefits of the heterogeneous vehicle owner;
s102, modeling decision behaviors of a vehicle owner based on an accumulated prospect theory; and evaluating each charging scheme by calculating an improved accumulated prospect value of each charging scheme to obtain an optimal charging scheme.
As shown in fig. 2, the charging scheme screening method based on the electric operation vehicle charging decision model provided by the embodiment of the invention specifically includes:
1. electric operation car charge state model
Because of the limitation of battery capacity, the electric operation vehicle can be charged for a plurality of times every day, and the charging behavior at night is basically fixed.
1.1 State of charge division for Carrier vehicles
The electric operation vehicle can select any time period in one day to charge, and the charging behavior of the vehicle owner can be influenced by different factors such as the residual electric quantity, the arrival time, the psychological safety electric quantity and the like, and the peak flat electricity price and the guest bill income are also important factors, so that each time period in the whole day can be divided into n charging decision-making intervals according to the peak flat electricity price and the guest bill income.
Charging end time t of owner of electric operation vehicle end Determines the charging state, the cut-off time and the decision time t 0 And charging time T char The relationship of (2) is as follows:
t end ＝T char +t 0 (2)
wherein: b is the rated capacity of the battery of the electric automobile; e is the residual electric quantity; e (E) max The state of charge of the battery when the charging is completed; p (P) EV Charging power of the electric automobile; θ char The charging efficiency of the electric automobile is improved; t (T) char Is the charging time.
The invention takes the charging states in the range of a time interval j and a interval j+1 as an example to study, wherein t j And t j+1 Represents the start time of interval j and interval j+1, T max Fig. 3 is a charge state analysis chart of the interval j and the interval j+1, describing the relationship among the arrival time, the remaining capacity, the charge cutoff time and the charge state.
Let the main charge state be in region a 0 b 0 d 0 e 0 The charging state is in region d 0 f 0 e 0 The inner is the second state, and the charging state is in the region f 0 e 0 i 0 h 0 The interior is the third state.
The vehicle owner in the first state selects to start charging in the interval j and the charging time is smaller than the remaining time of the interval j, the vehicle owner in the second state selects to start charging in the interval j and the charging time is larger than the remaining time of the interval j, and the vehicle owner in the third state selects to start charging in the interval j+1 and the charging time is smaller than the remaining time of the interval j+1. The relation between the residual electric quantity E of the vehicle owner starting to charge at any time t and the charge deadline is as follows:
The state of charge is in region a 0 b 0 d 0 e 0 And region f 0 e 0 i 0 h 0 The relation between the charging time and the residual electric quantity of the car owner is as follows:
the charge state is in region d 0 f 0 e 0 The relation between the charging time and the residual electric quantity of the car owner is as follows:
T char,j ＝t j+1 -t (5)
T char,j +T char,j+1 ＝T char (7)
wherein: t (T) char,j Indicating the charging time of the vehicle owner in the interval j, T char,j+1 Indicating the charging time of the vehicle owner in interval j+1.
1.2 operating vehicle revenue function
The owner of the operation car has a psychological threshold value for the residual electric quantity, and the psychological threshold value is set as psychological safety electric quantity, E is used saf And (3) representing. The residual electricity when reached is higher than E saf When the vehicle owner chooses not to charge; below E saf When the vehicle owner chooses to charge. I.e. the residual electric quantity of the car owner decision to select charging meets E<E saf 。
According to the invention, expected benefits of the vehicle owners are defined as the difference between total ticket benefits, charging time ticket losses and charging cost, and based on a charging demand model and psychological safety electric quantity of the vehicle owners, aiming at n alternative charging schemes, when the vehicle owners arrive at a section m and are selected to charge at a section j, the benefits of heterogeneous operation vehicles are as follows:
wherein: y is Y ij To select the actual benefit of the ith random scenario when charging interval j, C 0 Indicating the benefit of prior to decision, T k Represents the duration of the k interval, t 0 Representing the decision making time, vj is the speed of obtaining the benefit through running the form in the interval j, S j Denoted as charging electricity price for interval j; s is S j+1 The electricity price is charged for the interval j+1.
And 2, taking the time perception difference into consideration to obtain a charging decision model of the electric operation vehicle.
2.1 decision model research framework
The invention provides an electric operation vehicle charging decision model which is composed of a psychological perception model based on time expansion and contraction and a non-rational decision model based on a foreground theory and takes into consideration time perception difference.
2.2 time-aware model with time expansion and contraction taken into account
When a heterogeneous vehicle owner generates a charging demand, if the speed of acquiring benefits and the speed of consuming electricity are different from those of a typical vehicle, the vehicle owner generates time anxiety or is a graceful emotion, the time perceived by an individual as being applicable to a certain activity is regarded as a continuous coordinate axis from a time distribution angle, and the time anxiety or the time anxiety is the opposite ends of the continuous coordinate axis; human brain processes time in a manner similar to the Einstein narrow relativity theory, which allows a meaningful analogy between the narrow relativity theory and the judgment of time; the invention describes the time available for order receiving and the time still needing to run and consume electricity under subjective perception of the vehicle owners by referring to the time expansion and contraction effect based on the Doppler formula, and discusses the psychological safety electric quantity and psychological expectation gain of heterogeneous vehicle owners under the difference of time perception.
2.2.1 time dilation and Doppler Effect
The doppler effect is mainly the variation of the wavelength of the object radiation due to the relative motion of the source and the observer. In front of the moving wave source, the wave is compressed, the wavelength becomes shorter and the frequency becomes higher; the opposite effect occurs after the moving wave source. The wavelength becomes longer and the frequency becomes lower; the higher the velocity of the wave source, the greater the effect produced.
The doppler formula is not only used for the conversion of the wave source frequency between different frames of reference, it is given a broader meaning and can be applied to the general measurement of the time interval relative movement of a moving object. The time interval on the moving object is converted into a space interval in a static reference system, the optical signal passes through the space distance with limited propagation speed to cause signal travel difference, the travel difference effect is overlapped on the time expansion effect to form Doppler effect, when the moving object approaches to the observer, the time interval on the moving object observed by the observer is Doppler prolonged, and when the moving object moves away from the observer, the time interval on the moving object observed by the observer is contracted.
Two objects A and B are arranged, the object B moves at a speed v relative to the object A and approaches the object A, and two events occur on the object A successively at the moment t 1 、t 2 The two events have undergone a time interval of Δt=t 2 -t 1 Then the time interval between two events measured on object a is Δt '=t' 2 -t 1 Relative to DeltatΔt will expand:
if object B moves away from object a at a velocity of-v relative to a, then Δt and Δt' are related:
wherein: gamma denotes the lorentz factor and c denotes the speed of light.
2.2.2 psychological perception model based on time expansion and contraction
And defining the vehicle owners with the obtained benefits and the electricity consumption rate which meet the average value of the vehicle owners as typical vehicle owners, so as to be used for reference when the heterogeneous vehicle owners make decisions. And defining a coordinate system of a typical vehicle owner as a k system, and defining a coordinate system of a heterogeneous vehicle owner as a k' system. The difference between the speed of obtaining the benefit or the power consumption of the heterogeneous vehicle owner and the speed of the typical vehicle owner is analogized to the speed of the object B relative to the object A in the Doppler effect, and the limit speed of obtaining the benefit of the vehicle owner is set as v max The limit speed of the consumed electric quantity is set as w max Corresponding to the speed of light in the doppler formula.
The invention discusses two situations of paying attention to the income of the guest bill and paying attention to the electricity consumption condition from the car owner:
(1) For heterogeneous owners, the speed at which the owner obtains the benefits can have an impact on the owner's decisions.
If it is up to t 0 Before the moment, the available benefit of the heterogeneous vehicle owner is C', and the heterogeneous vehicle owner at t 0 The speed of obtaining the benefit before the moment is as follows:
v'＝C'/t 0 (11)
let t be 0 The typical car owner has the benefit of C at the moment, and the typical car owner is at t 0 The speed of obtaining the benefit before the moment is as follows:
v＝C 0 /t 0 (12)
the speed of gain of heterogeneous car owners relative to typical car owners is:
v s ＝v′-v (13)
when the income of the heterogeneous car owners at the same moment is higher than that of a typical car owner, namely the average income level of the same day is exceeded, the risk of not achieving expected income is low, and the car owner can generate a time graceful emotion; he subjectively experiences more time available for running the ticket than in the coordinate system k in which the typical car owner resides. The reference doppler equation modeling can be obtained:
when the income of the heterogeneous vehicle owners at the same moment is lower than that of a typical vehicle owner, namely lower than the average income level on the same day, the risk of not achieving expected income is higher, and the vehicle owners generate anxiety emotion in time; he subjectively experiences less time available for running a ride than in the coordinate system k in which a typical car owner resides. The reference doppler equation modeling can be obtained:
the expected benefit map of the heterogeneous vehicle owner after doppler time expansion or contraction is shown in figure 4.
(2) For heterogeneous vehicle owners, the power consumption speed of the vehicle owner battery can influence the decision of the vehicle owner.
The accumulated electricity consumption of the vehicle depends on the accumulated driving mileage, and the relation can be approximately satisfied:
Wherein: q is the accumulated electricity consumption, D is the accumulated driving mileage,the unit mileage electricity consumption.
If it is up to t 0 By the time of the day, differentThe accumulated electricity consumption of the heterogeneous vehicle owner is Q', and the heterogeneous vehicle owner is at t 0 The speed of the electricity consumption at the moment is as follows:
w'＝Q'/t 0 (17)
let t be 0 The accumulated electricity consumption of a typical vehicle owner at moment is Q, and the typical vehicle owner at t 0 The speed of obtaining the benefit before the moment is as follows:
w＝Q/t 0 (18)
the heterogeneous vehicle owner has the following driving speeds relative to a typical vehicle owner:
w s ＝w′-w (19)
when the consumption of the heterogeneous vehicle owners is higher than that of a typical vehicle owner at the same moment, namely, the average power consumption level of the heterogeneous vehicle owners is higher than that of the current day, the risk of power consumption is higher, and the vehicle owners generate anxiety emotion in time; he subjectively experiences a higher time to continue driving with the consumed power than in the coordinate system k of a typical vehicle owner. The reference doppler equation modeling can be obtained:
when the self consumption of the heterogeneous vehicle owners at the same moment is lower than that of a typical vehicle owner, namely lower than the average power consumption level of the same day, the risk of power consumption is lower, and the vehicle owners can generate time graceful emotion; he subjectively experiences a lower time to continue driving with the consumed power than in the coordinate system k of a typical vehicle owner. The reference doppler equation modeling can be obtained:
the expected power consumption of the heterogeneous vehicle owner after doppler time expansion or contraction is shown in fig. 5.
2.2.3 heterogeneous vehicle owner psychological benefit calculation
For any vehicle owner, his mental safety power depends on the power he expects to consume after completing the charging behavior, the more power he expects to consume, the higher the mental safety power.
The psychological safety electric quantity of the heterogeneous vehicle owner is as follows:
the time after Doppler time expansion or contraction of any time interval j is as follows:
based on the charging demand model and the psychological safety electric quantity of the heterogeneous vehicle owners, aiming at n alternative charging schemes, when the vehicle owners arrive at a section m and charge at a section j, the psychological perception benefits of the heterogeneous vehicle under the time perception model are considered as follows:
wherein: t ', T' char Representing the charge time under consideration of time perception, T' char_j And T' char_j+1 Representing the charge time in j and j+1 periods, respectively, taking into account time perception, C 0 Indicating the benefit of prior to decision, T k Represents the duration of k interval under time perception, t 0 Representing the decision making time, vj is the speed of obtaining the profit for the running form of the interval j, S j Denoted as charging electricity price for interval j; s is S j+1 The electricity price is charged for the interval j+1.
2.3 irrational decision model based on cumulative prospect theory
According to the time perception model, an owner of the vehicle can generate irrational perception on the risk, and the accumulated prospect theory describes the risk attitude of irrational decision makers from the angles of 'income' and 'loss', so that the owner of the vehicle can show risk avoidance attitude when facing income, and risk pursuit attitude when facing loss. Aiming at the risk that expected benefits and battery power consumption cannot be achieved, the method models decision behaviors of the vehicle owners by using an accumulated prospect theory, models psychological perception time of heterogeneous vehicle owners by taking Doppler effect as a reference in an editing stage of the accumulated prospect theory, calculates subjective psychological perception benefits of the vehicle owners, and determines a cost function and a weight function of the model by taking benefits of typical vehicle owners as psychological reference points; in the evaluation phase, an improved cumulative prospect value for each charging regime is calculated and the best charging regime is selected.
(1) Establishment of a cost function
I 0 ＝U(x)＝∑(Y ij ,p ij ) (26)
Wherein:the subjective value of the ith random scene of the vehicle owner under the charging scheme j is given; y is Y ij The psychological perception benefits of heterogeneous vehicle owners in the ith scene under the charging scheme j are obtained; p is p ij For the probability corresponding to the ith random scene under charging scheme j, I 0 The expected utility value for benefit is used as the cumulative prospect theoretical reference point. Alpha and beta are the sensitivity of the vehicle owner to risks; λ is a loss avoidance coefficient. Through a large amount of experimental data analysis, α=β=0.88, λ=2.25 is suitable.
(2) Weight function establishment
The invention adopts the weight functions proposed by Yversky and Kahneman.
When the owner faces the benefit:
when the owner of the vehicle is facing the loss:
wherein:p ij represents the probability, ω, of occurrence of the ith scene under the charging scenario j + (p ij ) Is a forward subjective probability weight; omega － (p ij ) Subjective probability weight for negative direction; wherein, gamma ﹦ is 0.61 and delta ﹦ is 0.69, which are consistent with the empirical data.
Decision weight function pi of CPT ij + And pi ij － The following can be defined:
wherein: pi ij + Is a forward cumulative decision weight function; pi ij － A cumulative decision weight function that is negative; n is the positive possible result perceived by the vehicle owner under each charging scheme; m is the negative possible result perceived by the vehicle owner under each charging scheme. Omega + And omega － Is a strict increasing function and satisfies:
ω + (0)＝ω - (0)＝0 (31)
ω + (1)＝ω - (1)＝1 (32)
(3) Cumulative foreground value calculation
Wherein:accumulating foreground values for the forward direction under the scheme j; />Accumulating foreground values for the negative direction under the scheme j; />The foreground values are accumulated for the actual synthesis.
It should be noted that the embodiments of the present invention can be realized in hardware, software, or a combination of software and hardware. The hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or special purpose design hardware. Those of ordinary skill in the art will appreciate that the apparatus and methods described above may be implemented using computer executable instructions and/or embodied in processor control code, such as provided on a carrier medium such as a magnetic disk, CD or DVD-ROM, a programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The device of the present invention and its modules may be implemented by hardware circuitry, such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., as well as software executed by various types of processors, or by a combination of the above hardware circuitry and software, such as firmware.
According to the invention, the charging behavior of the electric operation vehicle in certain city is analyzed and researched, and the effectiveness and accuracy of the model are verified through the following three cases:
1, comparing charging decisions of heterogeneous vehicle owners under the conditions that the peak level electricity price is 3 times, the residual electric quantity E-N (0.5, 0.25) and the decision time is 0-8 time period in two models of an expected utility theory and an accumulated prospect theory considering subjective perception of human on risk.
2, introducing a time perception model, and when the owner pays attention to the benefits, using the ownerThe accumulated ticket benefits are used as reference standards for time perception, owners are divided into a benefit efficient type, a benefit balanced type and a benefit low-efficiency type, and the accumulated benefit conditions of three owner decision moments are set as C' =13C 0 ，C’＝C 0 ，C’＝0.7C 0 The other parameter settings are the same as 1. And comparing charging decision behaviors of the heterogeneous vehicle owners under three different accumulated income conditions under a psychological perception model considering time expansion and contraction.
3, introducing a time perception model, when the vehicle owner pays attention to the electric quantity, taking the accumulated electric quantity consumption of the vehicle owner as a reference standard of time perception, dividing the vehicle owner into an electric quantity sensitive type, an electric quantity balanced type and an electric quantity dullness type, and setting the accumulated power consumption condition of three vehicle owner decision moments: q' =1.3q 0 ，Q’＝Q 0 ，Q’＝0.7Q 0 The other parameter settings are the same as 1. And comparing charging decision behaviors of the heterogeneous vehicle owners under three different accumulated electricity consumption conditions under a psychological perception model considering time expansion and contraction.
The invention refers to a certain city peak-valley electricity price to divide time periods, the research time is set to 9:00-17:00 for 8 hours, 9:00-14:00 is peak time period, 14:00-17:00 is normal time period, and the optional decision scheme is divided into the following steps:
scheme one: at 9:00 to 14: charging in a 00 time period;
scheme II: at 14:00 to 17: charging in a 00 time period;
the parameter settings of different time periods in the invention are shown in the following table 1, the guest single flow setting obeys poisson distribution, and the average guest single flow is taken as the average value; the time-sharing electricity price and the average price of the guest bill are referred to a certain city, and the residual electric quantity is set to be compliant with normal distribution, namely E-N (0.5, 0.25) and 0.1<E<E saf When the electric quantity reaches 0.9, the charging is completed; the average running speed is researched and selected to have a peak time interval of 30km/h and a normal time interval of 40km/h; the model of a common operation car in certain market is BYDE6 type electric car, and the related parameter settings are shown in table 2.
Table 1 parameter settings for different time periods
Table 2 charge related parameter settings
/>
For ease of calculation and visual presentation, the present invention case discussed as 9:00-17:00 is equivalent to 0-8 time, and the T is calculated according to the charge state dividing model and the parameters max ＝1.083，t j+1 -T max ＝3.917。
The effectiveness of the model built in the invention is verified by analyzing the charging decisions of the vehicle owners under different profit conditions or different power consumption scenes in time perception by design consideration and comparing the charging decisions of the vehicle owners under the accumulated prospect theory. According to the invention, an excel built-in function and a VBA macro program are utilized to build a model, a cost function of a vehicle owner decision is calculated, a cost function value is normalized for facilitating analysis, and finally a charging decision diagram of the vehicle owner is drawn through matlab.
(1) Charging decision analysis under two different models of expected utility theory and accumulated prospect theory
Modeling analysis is carried out on charging decision preference of heterogeneous vehicle owners in the residual electric quantity E-N (0.5, 0.25) in two models of an expected utility theory and an accumulated prospect theory considering subjective perception of human on risk, and the decision time is 0-8 time period, so that charging decisions of the vehicle owners of the operating vehicles in two different models are obtained.
When the decision time is 5-8, namely the vehicle owner is in a third state, only a second scheme exists in the available decision scheme; when the decision time is in the period of 0-5 and the vehicle owner belongs to one state, the decision of the vehicle owner in the expected utility theory and accumulated prospect theory model is shown in fig. 6-7.
As can be seen from fig. 6 to fig. 7, when the vehicle owner status belongs to the status-time decision scheme, the scheme two is selected, which is due to the large difference of expected utility values of benefits, and the subjective risk attitude of the vehicle owner does not affect the decision of the vehicle owner.
When the vehicle owner belongs to the second state, the decision of the vehicle owner in the expected utility theory and accumulated prospect theory model is shown in fig. 8-9.
8-9, in the period of 3.917-4.5, the expected utility theory is the same as the decision of the vehicle owner in the accumulated prospect theory model, and a scheme II is selected; in the 4.5-5.0 time period, the vehicle owner selects a scheme II with higher expected benefits and higher risk under the expected utility theory, and selects a scheme I with lower risk although the expected benefits are low under the accumulated prospect theory.
The decision result shows that the expected utility theory defaults to the complete rationality of the vehicle owner, and the influence of subjective psychological perception of the vehicle owner is ignored from an objective angle. The accumulated prospect theory considers the subjective risk avoidance psychology of the person facing the benefits, and the subjective experience and risk attitude are included and analyzed, so that the irrational decision-making behavior of the person can be reflected better.
(2) Introducing a time perception model, when a vehicle owner pays attention to benefits, taking accumulated passenger list benefits of the vehicle owner as a reference standard of time perception, dividing the vehicle owner into a benefit efficient type, a benefit balanced type and a benefit low-efficiency type, and setting the accumulated benefit conditions of three vehicle owner decision moments as C' =1.3C 0 ，C’＝C 0 ，C’＝0.7C 0 . When the vehicle owner is in a second state, modeling analysis is carried out on charging decision behaviors of the heterogeneous vehicle owner in three different accumulated income conditions under a psychological perception model considering time expansion and contraction.
As can be seen from fig. 10 to 12: in the 3.917-4.4 time period, the decision of the vehicle owners is the same, and a scheme II is selected; when the vehicle owner is in the 4.4-5.0 time period, the decision of the vehicle owner with balanced income is not influenced by time perception, and the charging is carried out by starting to select the scheme I at the moment of 4.5; the charging decision of the profit high-efficiency vehicle owner starts to change at the moment 4.4, and the charging is carried out by selecting a scheme I; and the profit inefficiency type car owner starts to select a scheme I to charge at the moment of 4.8.
The decision result shows that when the accumulated passenger bill income of the vehicle owner is higher than that of a typical vehicle owner, the vehicle owner generates a time-free emotion, overestimation can occur in the income evaluation of the rest period, the overall risk in decision is reduced, and the vehicle owner is more prone to select a conservative scheme with lower expected income but lower risk; when the accumulated ticket benefits of the vehicle owners are lower than that of typical vehicle owners, underestimation can occur in the benefit evaluation of the rest period, the overall risk in decision making is increased, and the vehicle owners tend to select aggressive schemes with high risk but higher benefits.
(3) Introducing a time perception model, when a vehicle owner pays attention to electric quantity, taking accumulated electric quantity consumption of the vehicle owner as a reference standard of time perception, dividing the vehicle owner into an electric quantity sensitive type, an electric quantity balanced type and an electric quantity dullness type, and setting accumulated power consumption conditions of three vehicle owner decision moments: q' =1.3q 0 ，Q’＝Q 0 ，Q’＝0.7Q 0 . When the vehicle owner is in a second state, modeling analysis is carried out on charging decision behaviors of the heterogeneous vehicle owner in three different accumulated electricity consumption conditions under a psychological perception model considering time expansion and contraction.
As can be seen from fig. 13 to 15: in the 3.917-4.2 time period, the decision of the vehicle owners is the same, and a scheme II is selected; when the vehicle owner is in the 4.2-5.0 time period, the decision of the electric quantity balance type vehicle owner is not influenced by time perception, and the first scheme is selected to charge at the moment 4.5; the charging decision of the electric quantity sensitive vehicle owner starts to change at the moment 4.2, and a scheme I is selected for charging; and the vehicle owner with low electric quantity starts to select a scheme I for charging at the moment of 4.8.
Decision results show that when the accumulated power consumption of the vehicle owners is higher than that of typical vehicle owners, the vehicle owners generate anxiety emotion on time, overestimation on the power consumption of the rest time can occur, and the vehicle owners tend to be more a stable scheme with lower income and lower risk; when the accumulated power consumption of the vehicle owners is lower than that of a typical vehicle owner, underestimation of the power consumption of the rest period occurs, and the vehicle owners tend to select aggressive schemes with high expected selection risks but higher benefits.
The foregoing is merely illustrative of specific embodiments of the present invention, and the scope of the invention is not limited thereto, but any modifications, equivalents, improvements and alternatives falling within the spirit and principles of the present invention will be apparent to those skilled in the art within the scope of the present invention.
Claims (6)
1. The charging scheme screening method based on the electric operation vehicle charging decision model is characterized by comprising the following steps of:
constructing an electric operation vehicle charging decision model based on time perception difference, which consists of a psychological perception model based on time expansion and contraction and a irrational decision model based on a foreground theory;
determining psychological safety electric quantity of the heterogeneous vehicle owners under the time perception difference by utilizing the psychological perception model based on the time expansion and contraction, and psychological expectation benefits;
determining a cost function by using the irrational decision model based on the foreground theory, calculating the accumulated foreground value of each scheme, and selecting the optimal charging scheme;
the charging scheme screening method based on the electric operation vehicle charging decision model comprises the following steps of:
firstly, constructing a psychological perception model based on time expansion and contraction, respectively determining expected benefits of a heterogeneous vehicle owner after Doppler time contraction and expected electric quantity consumption of the heterogeneous vehicle owner after Doppler time expansion or contraction, and calculating psychological benefits of the heterogeneous vehicle owner;
Modeling the decision behavior of the vehicle owner based on the accumulated prospect theory; evaluating each charging scheme by calculating an improved accumulated prospect value of each charging scheme to obtain an optimal charging scheme;
the determining the expected benefit of the heterogeneous vehicle owner after Doppler time contraction comprises:
(1) Determining the heterogeneous vehicle owner at t by using 0 Speed of revenue acquisition before time:
v'＝C'/t 0 ；
wherein C' represents t 0 The heterogeneous vehicle owners have benefits before decision-making before the moment;
(2) Determining a typical vehicle owner at t using 0 Speed of revenue acquisition before time:
v＝C 0 /t 0 ；
wherein C isRepresenting t 0 Typical car owners have benefits before the moment; the typical vehicle owners are vehicle owners with the obtained benefits and the electricity consumption rate conforming to the average value of the vehicle owners;
(3) The speed of obtaining the benefits of the heterogeneous vehicle owners relative to the typical vehicle owners is determined as follows:
v s ＝v′-v；
(4) Judging whether the income of the heterogeneous vehicle owners is higher than that of typical vehicle owners, and determining the expected income of the heterogeneous vehicle owners after Doppler time expansion or contraction:
when the income of the heterogeneous vehicle owners at the same moment is higher than that of a typical vehicle owner, the expected income of the heterogeneous vehicle owners after Doppler time expansion is determined by the following formula:
wherein v is max The limiting speed of obtaining benefits of the vehicle owner is represented; two events successively occurring on object A at time t 1 、t 2 The two events have undergone a time interval of Δt=t 2 -t 1 ；
When the income of the heterogeneous vehicle owners at the same moment is lower than that of a typical vehicle owner, the expected income of the heterogeneous vehicle owners after Doppler time contraction is determined by using the following formula:
the determining the expected power consumption of the heterogeneous vehicle owner after Doppler time expansion or contraction comprises:
1) Determining the heterogeneous vehicle owner at t by using 0 Speed of time consuming power:
w'＝Q'/t 0 ；
wherein Q' is a tableShow up to t 0 Accumulating consumed electric quantity by the heterogeneous vehicle owners until the moment; d represents the accumulated driving distance and,the unit mileage electricity consumption is represented;
2) Determining a typical vehicle owner at t using 0 Speed of time consuming power:
w＝Q/t 0 ；
wherein Q represents up to t 0 The typical car owner accumulates the electricity consumption at the moment;
3) The running speed of the heterogeneous vehicle owner relative to a typical vehicle owner is calculated as follows:
w s ＝w′-w；
4) Judging whether the self-consumption electric quantity of the heterogeneous vehicle owner is higher than that of a typical vehicle owner, and determining the expected electric quantity consumption of the heterogeneous vehicle owner after Doppler time expansion or contraction:
when the consumption of the heterogeneous vehicle owner per se is higher than that of a typical vehicle owner at the same moment, the expected electricity consumption of the heterogeneous vehicle owner after Doppler time contraction is determined by the following formula:
wherein w is max A limit speed indicating the amount of consumed electricity;
when the self consumption of the heterogeneous vehicle owner is lower than that of a typical vehicle owner at the same moment, the expected power consumption of the heterogeneous vehicle owner after Doppler time expansion is determined by the following formula:
The calculating of the psychological benefit of the heterogeneous vehicle owner comprises:
firstly, calculating psychological safety electric quantity of a heterogeneous vehicle owner by using the following formula:
secondly, determining psychological perception benefits of the heterogeneous vehicle owners under the time perception model by using the following formula:
wherein T' j The time of any time interval j after Doppler time expansion or contraction is represented;n represents the number of alternative charging schemes, m represents the interval reached by the vehicle owner, and j represents the interval for selecting charging; t'. char Representing the charge time under time perception, T' char_j And T' char_j+1 Respectively representing charging time in j and j+1 time periods under time perception; c (C) 0 Indicating that there is a benefit before decision making; t'. k Representing the duration of k intervals under time perception; t is t 0 Indicating the moment at which the decision is made; v (V) j Representing the speed of obtaining the profit of the interval j running list S j Denoted as charging electricity price for interval j; s is S j+1 Representing the charging electricity price of the interval j+1; t is t j+1 Represents the start time of interval j+1, E max The state of charge of the battery when the charging is completed; p (P) EV Charging power of the electric automobile; θ char The charging efficiency of the electric automobile is improved; w' represents that the heterogeneous vehicle owner is at t 0 The speed of consuming electricity at the moment; w (w) s Indicating that the heterogeneous vehicle owner is traveling at a speed relative to a typical vehicle owner.
2. The charging scheme screening method based on the electric operator vehicle charging decision model according to claim 1, wherein modeling the decision behavior of the vehicle owner based on the accumulated prospect theory comprises:
Modeling the psychological perception time of the heterogeneous vehicle owners by taking the Doppler effect as a reference, calculating subjective psychological perception benefits of the vehicle owners, and determining a cost function and a weight function of the model by taking benefits of typical vehicle owners as psychological reference points;
modeling the decision behavior of the vehicle owner based on the accumulated prospect theory comprises the following steps:
(1) Determining a cost function:
I 0 ＝U(x)＝∑(Y ij ,p ij )；
wherein,the subjective value of an ith random scene of a vehicle owner under a charging scheme j is represented; y is Y ij Representing psychological perception benefits of heterogeneous vehicle owners in a scene i under a charging scheme j; p is p ij Representing probability corresponding to ith random scene under charging scheme j, I 0 The expected utility value representing the benefit is used for accumulating the foreground theory reference points; alpha and beta respectively represent the sensitivity of the vehicle owner to risks; λ represents a loss avoidance coefficient;
(2) Determining a weight function:
when the owner faces the benefit, the weighting function is as follows:
when the owner faces the loss, the weight function is as follows:
wherein p is ij Represents the probability, ω, of occurrence of the ith scene under the charging scenario j + (p ij ) Representing a forward subjective probability weight; omega － (p ij ) Indicating a negative subjective probability weight.
3. The method for screening charging schemes based on the electric vehicle charging decision model according to claim 1, wherein the evaluating each charging scheme by calculating the improved cumulative prospect value of each charging scheme comprises:
The improved cumulative prospect value for each charging regime is calculated using the following formula:
wherein,representing forward cumulative foreground values under scheme j; />Representing negative cumulative foreground values under scheme j; />Representing the actual integrated cumulative prospect value;
π ij + representing a forward cumulative decision weight function,π ij － cumulative decision weight function representing negative direction, +.>n represents the positive possible result perceived by the vehicle owner under each charging scheme; m represents the negative possible result perceived by the vehicle owner under each charging scheme; omega + And omega － Is a strict increasing function and satisfies: omega + (0)＝ω - (0)＝0；ω + (1)＝ω - (1)＝1。
4. A charging scheme screening system based on an electric vehicle charging decision model for implementing the charging scheme screening method based on an electric vehicle charging decision model according to any one of claims 1 to 3, characterized in that the charging scheme screening system based on an electric vehicle charging decision model comprises:
the psychological perception model building module is used for building a psychological perception model based on time expansion and contraction;
the irrational decision model construction module is used for modeling the decision behaviors of the vehicle owners based on the accumulated prospect theory to obtain irrational decision models based on the prospect theory;
the psychological benefit calculating module is used for respectively determining expected benefits of the heterogeneous vehicle owners after Doppler time contraction and expected electric quantity consumption of the heterogeneous vehicle owners after Doppler time expansion or contraction by using a psychological perception model based on time expansion and contraction, and calculating psychological benefits of the heterogeneous vehicle owners;
And the charging scheme evaluation module is used for evaluating each charging scheme by calculating an improved accumulated prospect value of each charging scheme by using a non-rational decision model based on a prospect theory to obtain an optimal charging scheme.
5. A computer device, characterized in that it comprises a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the steps of the charging scheme screening method based on the electric operator charging decision model according to any one of claims 1-3.
6. An information data processing terminal, characterized in that the information data processing terminal is used for realizing the charging scheme screening method based on the electric operation vehicle charging decision model according to any one of claims 1-3.
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