CN108133329B

CN108133329B - An analysis method of electric vehicle travel and charging demand considering charging feedback effect

Info

Publication number: CN108133329B
Application number: CN201711498767.1A
Authority: CN
Inventors: 刘洪�; 张旭; 葛少云
Original assignee: Tianjin University
Current assignee: Tianjin University
Priority date: 2017-12-29
Filing date: 2017-12-29
Publication date: 2021-06-08
Anticipated expiration: 2037-12-29
Also published as: CN108133329A

Abstract

An electric vehicle travel and charging demand analysis method considering the charging feedback effect: modeling the travel demand of electric vehicle users in destination mode; time series interaction analysis of travel and charging demand, including establishing a travel demand-based power consumption when the user is in a driving state Model, a charging demand generation model based on fuzzy theory is established when the user is in the driving state, a power replenishment model considering the adequacy of the charging facilities is established when the user is in the parking state, and user differential decision-making based on the charging feedback effect when the user is in the driving state ; Time-series interaction simulation of user travel and charging demand. The invention can realize the time sequence interaction between the travel demand and the charging demand of the electric vehicle user, and analyze the change trend of the charging demand of the user and the delay and recovery of the travel demand.

Description

Electric automobile travel and charging demand analysis method considering charging feedback effect

Technical Field

The invention relates to a method for predicting and analyzing charging load of an electric automobile. In particular to an electric vehicle travel and charging demand analysis method considering charging feedback effect and suitable for charging demand of private electric vehicle users in a destination mode.

Background

In the face of the increasingly serious problems of resource shortage and environmental pollution, the research of the fuel automobile sale prohibition schedule is started in China. The electric automobile becomes a future development direction of new energy automobiles and is vigorously developed due to the great advantages of the electric automobile in energy consumption and environmental protection performance. The private car is a main object of popularization of the electric automobile in the future, and tends to be in a destination charging mode due to the characteristics of long stay state time and short running state time, namely charging is carried out in the process of long-time parking at the destination such as at home and at work places. The popularization of electric private cars can cause a large amount of charging demands, and the accurate analysis of the outgoing and charging demands of users of the electric private cars in a destination mode is the basis of researches on planning layout of charging facilities, analysis of influence of charging loads on a power grid, planning operation of the power distribution network considering electric car access and the like.

At present, many scholars research the problem of the charging requirement of the electric automobile. Due to the mobility of the electric automobile, the charging demand analysis needs to be based on user travel rule simulation. The user travel rule simulation method mainly focuses on a method based on random variable fitting, a travel chain and space dimension uncertainty. However, in the existing research, when charging demand analysis is performed, it is considered that charging facilities can completely meet any charging demand, and the influence of charging facility distribution on the charging demand of the electric vehicle is not considered, and in fact, the space-time distribution of the charging demand of the electric vehicle and the travel demand of a user change due to different charging facility distributions.

When the charging requirement analysis of the electric automobile is carried out, whether the charging facility is sufficient or not directly influences whether the user generates the charging requirement or not and whether the charging service can be obtained in time or not, and further influences the charge state of the electric automobile, and the charge state is correlated with the travel requirement of the user.

Disclosure of Invention

The invention aims to solve the technical problem of providing an electric vehicle travel and charging demand analysis method which can accurately analyze the influence of charging facility distribution on the travel demand and the charging demand of an electric vehicle user and considers the charging feedback effect.

The technical scheme adopted by the invention is as follows: an electric vehicle travel and charging demand analysis method considering a charging feedback effect comprises the following steps:

1) modeling of travel demand of electric vehicle user in destination mode

CX＝[L_p,A_p,X_d,L_T,X_t,A_T,T_t]

In the formula: l is_pIs a starting place; a. the_pIs a parking place; x_dIs the driving mileage; l is_TIs the departure time; x_tIs the driving time length; a. the_TIs the time of parking; t is_tIs the parking duration;

2) travel and charging demand time sequence interactive analysis

The whole analysis process comprises four states, namely, establishing an electric quantity consumption model based on travel requirements when a user is in a driving state, establishing a charging requirement generation model based on a fuzzy theory when the user is in a driving state, establishing an electric quantity supplement model considering the abundance of charging facilities when the user is in a staying state, and making a user difference decision based on a charging feedback effect when the user is in a driving state;

3) time sequence interactive simulation of user traveling and charging requirements

Firstly, setting, and carrying out time sequence interactive simulation by taking dt as a time interval, wherein the trip state of a user in [ T-dt, T ] is unchanged; the time sequence interactive simulation of the user traveling and the charging requirement is carried out by taking the equal step length as a clock advancing mode, and the time sequence interactive simulation comprises the following steps:

(1) initializing T to be 0;

(2) initializing a user k as 1;

(3) judging the travel state of the user in [ T-dt, T ];

(4) when the user travel state is driving, updating the charge state according to the electric quantity consumption model; when the user is in a trip state, judging generation and abandonment of a charging demand according to a charging demand generation model; when the user is in a stop state, updating the charge state and the charging demand according to the electric quantity supplement model; when the user is in a driving-away state, determining a charging demand and a traveling demand according to the charge state support degree;

(5) when the user k is k +1, repeating the step (3) and the step (4) until all users are traversed, and storing the charging demand and the traveling demand of each user in [ T-dt, T ];

(6) and (4) repeating the steps (2) to (5) until the simulation period is ended.

The electric quantity consumption model based on the travel demand in the step 2) is established as follows when the user travels in the travel state [ T-dt, T ]:

in the formula: dt is a time sequence interactive simulation time interval; SOC_TRepresenting the state of charge of the electric vehicle at the T moment; SOC_T-dtRepresenting the state of charge of the electric automobile at the T-dt moment; v is the average speed of the electric vehicle; x_DThe driving mileage of the electric automobile is obtained.

The charging demand generation model based on the fuzzy theory in the step 2) is established when the user is in the trip state of [ T-dt, T ] for entering, and comprises the following steps:

wherein:

A_SOC≥m₂at time, the state of charge is fully sufficient for the next trip, with no charging demand, M (A)_SOC) Taking the value of 0;

m₁≤A_SOC<m₂when, A_SOCThe closer to m₂，M(A_SOC) The closer to 0, A_SOCThe closer to m₁，M(A_SOC) The closer to 1

In the formula: f (A)_SOC,P_T) Representing that the user drives into the parking place P and the state of charge sufficiency is A_SOCThere is a probability of charging demand; p_TThe number of the remaining charging piles of the parking place P at the moment T is represented; a. the_SOCIndicating state of charge sufficiency; m (A)_SOC) Is represented by A_SOCThe fuzzy set has the membership degree of the charging requirement; m is₁Is the elastic coefficient; SOC_TRepresenting the state of charge of the electric vehicle at the T moment; c_EVIs the battery capacity; w represents the unit mileage power consumption; c is a constant reflecting weather and road conditions; x_{d_i+1}A mileage indicating a next trip of the user; m is₂Is a blurring coefficient.

The electric quantity supplement model considering the abundance of the charging facility in the step 2) is established when the user stays in the travel state of [ T-dt, T ], and comprises the following steps:

in the formula: SOC_TRepresenting the state of charge of the electric vehicle at the T moment; SOC_T-dtRepresenting the state of charge of the electric automobile at the T-dt moment; q is the electric vehicle battery charging power; dt is a time sequence interactive simulation time interval; c_EVIs the battery capacity; p_T-dtIndicating the number of remaining charging piles of the parking spot P at the time of T-dt.

If the charge state of the electric vehicle reaches 100% in the process of staying, the charging requirement of the user disappears, and the charge state is unchanged before the electric vehicle is driven away; otherwise, the user keeps the charging requirement and continues to update the state of charge of the user according to the electric quantity supplement model.

The user difference decision based on the charging feedback effect in the step 2) comprises the following steps:

(1) and (3) calculating the charge state support degree:

in the formula, B_SOCRepresenting the state of charge support; SOC_TRepresenting the state of charge of the electric vehicle at the T moment; c_EVIs the battery capacity; c is a constant reflecting weather and road conditions; w represents the unit mileage power consumption; x_{d_i}The driving mileage of the current trip of the user is represented;

(2) setting n₁Determining a charging feedback result according to the state of charge support degree for supporting the coefficient

If B is_SOC≥n₁The charge state supports the current journey, and the feedback result is as follows: the user can drive away, and the travel demand is unchanged when the user makes a decision of driving away according to the original plan;

if B is_SOC<n₁The charge state cannot support the current trip, and the feedback result is as follows: the user can not drive away temporarily, namely the user can not actually drive away from the parking place actually, and the driving away at the moment is understood as the original travel demand of the user;

when the feedback result is that the user can not drive away temporarily, the user has different decision criteria because the travel activity types of the current journey are different:

when the travel category is working, the factor influencing the user decision is the arrival time, and the user decision criterion is A_{T_0}+dt＞A_{T_max}；

When the travel category is leisure, the factor influencing the user decision is leisure time, and the user decision criterion is T_{t_0}-dt＜T_{t_min}；

When the travel category is that the travel is finished and the user returns home, the factor influencing the user decision is the arrival time, and the user decision criterion is A_{T_0}+dt>A_{T_max}；

When the travel category is short-time home returning, judging is not carried out;

wherein A is_{T_0}For the originally planned stopping time of the user, A_{T_max}For the latest tolerated moment of parking of the user, T_{t_0}Originally planned dwell time for the user, T_{t_min}The shortest tolerated duration for the user to stay;

(3) the user makes user difference decision according to the travel category and the corresponding criterion

(3.1) when the travel activity is work or goes home after finishing traveling, if the travel activity meets the criterion A_{T_0}+dt>A_{T_max}If the current journey does not meet the charging requirement, the electric automobile is judged to be in a waiting state;

(3.2) whenIf the current journey activity is leisure, the criterion T is met_{t_0}-dt<T_{t_min}If not, making a decision of keeping the charging requirement for a period of time;

and (3.3) when the travel activity returns home in a short time, judging is not carried out, and a decision of giving up the travel and continuing the next travel or ending the travel is made.

The abandonment of the current trip in (3.3) of the step (3) and the continuation of the next trip, if the trip of the day is not finished, the state of charge support degree B 'needs to be recalculated at this time'_SOCIf B'_SOC≥n₁And the user continues to make a decision of giving up the current trip and continuing the next trip or ending the trip, otherwise, the user makes a decision of keeping the charging requirement for a period of time again.

If the user does not coincide with the electric vehicle, the user does not arrive at the place where the electric vehicle is abandoned and cannot take the electric vehicle; if the positions are coincident, and B'_SOC≥n₁The user successfully takes the electric automobile back and drives the electric automobile to leave, otherwise, the electric automobile leaves again in other transportation modes.

According to the method for analyzing the travel and charging requirements of the electric automobile considering the charging feedback effect, the variation trend of the charging requirements of the user and the delay and recovery phenomena of the travel requirements after the charging facility abundance is considered are analyzed on the basis of the charging feedback model. Starting from basic research of modeling and simulation, close connection and variation trend of user traveling and charging requirements after considering the abundance of charging facilities are more truly disclosed, and further, basic support is provided for planning operation research of a power distribution network considering electric automobile access, analysis of influence of charging loads of the electric automobile on the power grid, and especially planning layout of the charging facilities of the electric automobile. The method and the device can realize the time sequence interaction of the travel demand and the charging demand of the electric vehicle user, and analyze the variation trend of the charging demand of the user and the delay and recovery of the travel demand.

Drawings

FIG. 1 is a flow chart of the present invention for user differentiation decisions based on charging feedback effects;

in the figure: decision 1: driving away according to the original plan; decision 2: keeping the charging demand waiting for a period of time; decision 3: giving up the current journey and continuing the next journey or ending the trip; decision 4: giving up the electric automobile to adopt other traffic modes to continue the journey

FIG. 2a shows the number of electric vehicles with charging demand at parking node H1;

FIG. 2b is the number of electric vehicles with charging demands at the parking nodes R3-R4;

FIG. 2c is the number of electric vehicles with charging demands at the parking nodes W5-W8;

FIG. 3 is the number of electric vehicles parked at the parking node H1;

fig. 4 is a time distribution of the number of electric vehicle users who drive out of the parking node H1.

Detailed Description

The following describes in detail an electric vehicle travel and charging demand analysis method considering a charging feedback effect according to the present invention with reference to the following embodiments and the accompanying drawings.

The invention discloses an electric automobile travel and charging demand analysis method considering a charging feedback effect, which comprises the following steps of:

1) modeling of travel demand of electric vehicle user in destination mode

The charging demand of the user is closely related to the traveling demand, the user travels by taking the urban transportation network as a foundation and a carrier, and the reasonable description of the traveling demand of the user in the urban transportation network is the basis for analyzing the charging demand. For the electric private car, a user goes back and forth among a plurality of nodes of the urban traffic network, and the daily travel structure is basically fixed, such as 'working-going home', 'working-leisure-going home', and 'working-short-time-going home-leisure-going home'.

A travel chain refers to the form of a connection of individuals for different travel purposes over a time sequence in order to complete one or several activities. For the above-mentioned spatio-temporal distribution in which the user starts from home and returns home after several trips, the travel chain can be well described. For various travel structures, the travel demands of users can be represented by the number of daily travels, the travel starting and stopping points and distances of each travel, the travel starting and stopping time and duration and other indexes. The travel demand modeling of the electric vehicle user in the destination mode is as follows:

CX＝[L_p,A_p,X_d,L_T,X_t,A_T,T_t]

2) travel and charging demand time sequence interactive analysis

(1) the electric quantity consumption of the electric automobile is generated along with the driving of the user, and the electric quantity consumption model based on the travel demand is established when the travel state of the user is driving within [ T-dt, T ] as follows:

(2) The charging demand of the user is closely related to whether the state of charge of the electric vehicle is sufficient for the next trip demand. The charging demand generation model based on the fuzzy theory is established when a user enters a travel state [ T-dt, T ] and comprises the following steps:

wherein:

A_SOC≥m₂at time, the state of charge is fully sufficient for the next trip, with no charging demand, M (A)_SOC) Taking the value of 0; m is₁≤A_SOC<m₂When, A_SOCThe closer to m₂，M(A_SOC) The closer to 0, A_SOCThe closer to m₁，M(A_SOC) The closer to 1

According to the state of charge sufficiency of the user at the time T, the user can be divided into a non-elastic user and an elastic user. If A_SOC<m₁The state of charge cannot satisfy the next trip, defined as a non-elastic user; if A_SOC≥m₁The state of charge may satisfy the next trip, defined as the resilient user. Inelasticity of the user is inevitableCharging requirements are generated without the need for obfuscation, while flexible user charging requirements are obfuscated.

It is also necessary for the flexible user to consider his/her tolerance to the charging services provided by the charging service network. When the parking place where the elastic user is located has no residual charging pile at the time T, the elastic user can actively give up the charging requirement. And the generation problem of the charging requirement of the user is converted into a probability problem by combining a fuzzy theory. The probability of a non-elastic user generating a charging demand is 1, and the probability of an elastic user generating a charging demand is M (A)_SOC) And is tolerant to the charging service network.

(3) After the charging demand generation model is used for determining the charging demand of the user, the charging demand generation model is combined with the abundance of charging facilities in the charging service network to judge whether the charging demand of the user can be met. If the charging power is sufficient, the charging service is obtained, the charge state of the user is increased according to the charging power, and otherwise, the charge state is not changed. The electric quantity supplementing model considering the abundance of the charging facility is established when the user stays in the travel state [ T-dt, T ] as follows:

(4) And when the user travel state is driving away in [ T-dt, T ], simulating the user to make a difference decision so as to reflect the feedback effect of the satisfied charging demand on the user travel demand. On the basis of continuously updating the charge state and the charging demand of the user according to the electric quantity supplement model, whether the charge state can support the current journey determines the feedback result of the situation that the charging demand of the user is met to the travel demand. As shown in fig. 1, the user differential decision based on the charging feedback effect includes:

(4.1) calculating the state of charge support:

(4.2) setting n₁Determining a charging feedback result according to the state of charge support degree for supporting the coefficient

when the travel category is working, the factor influencing the user decision is the arrival time, and the user decision criterion is A_{T_0}+dt>A_{T_max}；

When the travel category is leisure, the factor influencing the user decision is leisure time, and the user decision criterion is T_{t_0}-dt<T_{t_min}；

the main factors and criteria affecting the user decision are shown in table 1.

TABLE 1 Main factors and criteria affecting user decision

(4.3) making user difference decision by the user according to the travel category and the corresponding criterion

(4.3.1) when the travel activity is work or comes home after the trip is finished, if the travel activity meets the criterion A_{T_0}+dt>A_{T_max}If the current journey does not meet the charging requirement, the electric automobile is judged to be in a waiting state; the abandoned electric automobile continues the journey in other traffic ways, the possibility that the user takes the electric automobile back is considered, and if the position of the user is not coincident with that of the electric automobile, the user does not arrive at the abandoned electric automobile and cannot take the electric automobile back; if the positions are coincident, and B'_SOC≥n₁The user successfully takes the electric automobile back and drives the electric automobile to leave, otherwise, the electric automobile leaves again in other transportation modes.

(4.3.2) when the current journey activity is leisure, if the current journey activity meets the criterion T_{t_0}-dt＜T_{t_min}If not, making a decision of keeping the charging requirement for a period of time;

(4.3.3) when the travel activity returns home in a short time, judging is not carried out, and a decision of giving up the travel and continuing the next travel or ending the travel is made.

Giving up the current trip and continuing the next trip, if the trip of the day is not finished, the trip needs to be finished at the momentTo recalculate the State of Charge support B'_SOCIf B'_SOC≥n₁And the user continues to make a decision of giving up the current trip and continuing the next trip or ending the trip, otherwise, the user makes a decision of keeping the charging requirement for a period of time again.

(1) initializing T to be 0;

(2) initializing a user k as 1;

(3) judging the travel state of the user in [ T-dt, T ];

Specific examples are given below:

the simulation is performed in the destination mode, so that the urban traffic network is reasonably simplified and the relevant information of the main parking lot (parking node) where the user parks in the simulation area is given, and the specific information is shown in table 2. The simulation area has 4000 electric vehicles. Referring to the technical parameters of the Leaf of the daily product, the lithium battery capacity C of the electric automobile_EV48 kW.h, 100km power consumption E₁₀₀15kWh, mileage X_D160km, 6kW for charging power q, and 40km/h for average running speed v. Suppose an electric automobile starterThe initial state of charge follows a normal distribution of N (0.51, 0.18). The constant c reflecting the factors such as weather and road conditions is (1, 1.5)]The random number of (2). Coefficient of elasticity m₁1.2, coefficient of blur m ₂2, support coefficient n₁The time-series interactive simulation interval dt is 1.05 min.

TABLE 2 parking node information

The simulation distinguishes working days and resting days for continuous simulation. The invention assumes that the proportion of three typical travel structures of a user working day is 52.8%, 24.1% and 23.1% respectively. Assuming that the out proportion of the rest day is 70%, wherein 35% of the travel time follows normal distribution of N (8.92, 3.24), and 35% of the travel time follows normal distribution of N (16.47, 3.41); the remaining 30% of the users do not go on the rest day. And the leisure time of the working day of the user is assumed to meet the uniform distribution of [1, 2] h, and the rest day meets the uniform distribution of [1, 5] h. The research object of the invention is the electric private car, so that all electric cars are parked in the residential area during initialization, and the electric private car returns to the residential area at night after the user finishes traveling. The parking place of each journey is randomly extracted according to the type of the journey, the parking place of the same user is only extracted once, the leisure place is randomly extracted once, the first trip time and the next trip time are only extracted once, and the leisure time is randomly extracted once.

To reduce the effect of initialization, the week 1 simulation results were not used in the results analysis. Because the simulation time is longer, the simulation results of 2 to 9 weeks are taken for analysis without loss of generality. Taking the charging pile installation quantity of the parking node H1 (residential area) in the urban transportation network as an example, the influence of charging facility distribution in the charging service network on the space-time distribution of the charging demand of the electric automobile and the variation of the user travel demand caused by the interaction of the charging demand and the travel demand of the user are analyzed. The distribution of the charging piles of each parking node is shown in table 3. Wherein, the charging pile installation number of the parking node H1 is changed, and the setting is changed from 350 to 0 by taking 25 as a step in the calculation example. The number of the charging piles installed on the rest parking nodes is shown in table 3.

Table 3 distribution of charging piles for each parking node

When the distribution of the charging piles of the parking nodes changes as shown in table 3, the change of the charging demands of the parking nodes is shown in fig. 2a, 2b and 2 c.

It can be seen from fig. 2a that as the number of installed charging piles at the parking node H1 decreases, the curve of the number of electric vehicles with charging demands at the parking node H1 changes in trend and magnitude. In the aspect of trend, when the charging pile is sufficient, the charging demand of a working day is concentrated in the time period from returning home to leaving home in the evening to the next morning and can be reduced to zero, the charging demand of a rest day is distributed uniformly, and the curve is in a wave shape; along with the reduction of the number of charging piles, the charging demand distribution in working days is more dispersed, a platform cannot be reduced to zero in the reduction process, the charging demand in rest days is reduced very slowly, and the curve is in a step shape; fill electric pile quantity and further reduce, the weekday demand of charging all very slowly that rises, and the weekday demand of charging is nearly unchangeable, and there is not obvious difference weekday and weekend, and the curve is "gentle type". In terms of amplitude, the change law of first slow increase and then sharp increase can be obviously seen. This is because when filling electric pile very insufficient, a large amount of electric automobile can not supply the electric quantity for a long time, and the user can't drive electric automobile trip and select other modes of transportation even to make the continuous accumulation of demand for charging lead to.

As can be seen from fig. 2b and 2c, as the number of installed charging piles at the parking node H1 decreases, the charging demand curves of the parking nodes R3-R4 and W5-W8 increase in amplitude and decrease in amplitude, and the trend is always "peaked". Since the parking node H1 is a residential area, users whose charging demands are not satisfied will shift the demands to business and work area parking nodes, the charging demands at R3-R4 and W5-W8 will increase. The number of charging piles is reduced to a certain degree, the charging demand of users is met to a lower degree, and a considerable part of electric automobiles are abandoned by the users at the parking node H1, so that the number of electric automobiles reaching the parking nodes of the business district and the working district is greatly reduced, and the charging demand at R3-R4 and W5-W8 is reduced. The reason for this can be verified by the change of the number of electric vehicle stops at the parking node H1, as shown in fig. 3.

The user makes different decisions according to the feedback of the satisfied condition of the charging demand, so that the travel demand of the user is changed regularly. Taking week 9 as an example, the variation of the user's travel demand is shown in fig. 4.

In fig. 4, the heights of the different color bars represent the number of the electric vehicle users in the corresponding time interval at the moment of driving away from the parking node H1, and the trend line represents the accumulated number of the driving away users to the upper limit of the corresponding time interval. As can be seen from the analysis of fig. 4, as the number of the charging piles of the parking node H1 decreases, the total trend of the number of the driving-away users accumulated to the upper limit of each time interval increases after decreasing. The number of the driving-away users accumulated to the upper limit of each time interval is reduced, that is, the time when the user drives away from the parking node H1 is delayed, which indicates that the travel demand of the user is delayed, and this is caused by that the waiting time of the user is prolonged due to the insufficient number of the charging piles. The number of the users who leave the electric vehicle is increased when the charging pile is insufficient, and the user's trip demand is recovered, which is caused by the fact that the user gives up the electric vehicle to take other transportation modes to trip when the charging pile is insufficient.

Wherein the reduction and increase in the number of drive-off users accumulated to 5:00, 5:30, 6:00, 6:30 and 7:00 is small because the charging demand has not accumulated to a large extent; along with the passage of time, the number of the users who drive away to 7:30, 8:00, 8:30 and 9:00 is obviously reduced, increased and changed, because 7:30-9:00 are in the peak period of work, the influence of insufficient charging pile on the trip demand of the users is the largest; the smaller ranges of change cumulatively to 9:30 and 10:00, and cumulatively to 11:00 and beyond, are due to the smaller total number of drive-off requests in these time intervals.

The method and the system model the travel demand of the electric private car user in the destination mode, and complete the time sequence interactive closed-loop simulation of the travel and charging demand of the user. According to the method, the interaction between the travel demand and the charging demand of the electric automobile user is fully considered, so that the charging demand analysis result is more in line with the practical life, and a research foundation can be provided for analyzing the influence of the electric automobile connected to a power grid and building an electric automobile charging facility.

Claims

1. an electric vehicle travel and charging demand analysis method considering charging feedback effect, is characterized in that, comprises the steps:

1) Modeling of travel demand of EV users in destination mode

CX=[L _p ,A _p ,X _d ,L _T ,X _t ,A _T ,T _t ]

where L _p is the starting point; _Ap is the parking place; X _d is the mileage; L _T is the departure time; X _t is the driving time; A _T is the parking time; T _t is the parking time;

2) Time sequence interaction analysis of travel and charging demand

The whole analysis process includes four states, one is to establish a power consumption model based on travel demand when the user is in a driving state, the other is to establish a charging demand generation model based on fuzzy theory when the user is in a driving state, and the third is that the user is in a stop state. The fourth is the user differential decision based on the charging feedback effect when the user is in the driving state; among them,

The power consumption model based on travel demand is that when the user travel state is driving within [T-dt, T], the established power consumption model is as follows:

In the formula: dt is the time interval of time series interactive simulation; SOC _T represents the state of charge of the electric vehicle at the time of T; SOC _T-dt represents the state of charge of the electric vehicle at the time of T-dt; v is the average speed of the electric vehicle; X _D is the cruising range of electric vehicles;

The described charging demand generation model based on fuzzy theory is that when the user's travel state is driving in within [T-dt, T], the established charging demand generation model is as follows:

in:

When A _SOC ≥m ₂ , the state of charge is completely sufficient for the next trip, and there is no charging demand, and the value of M(A _SOC ) is 0; when m ₁ ≤A _SOC <m ₂ , the closer A _SOC is to m ₂ , the M (A _SOC ) is closer to 0, A _SOC is closer to m ₁ , and M(A _SOC ) is closer to 1

In the formula: F(A _SOC , P _T ) represents the probability that the user has a charging demand when the user enters the parking place P and the adequacy of the state of charge is A _SOC ; P _T represents the number of remaining charging piles at the parking place P at time T; A _SOC represents the adequacy of the state of charge; M(A _SOC ) represents the membership degree of A _SOC to the fuzzy set, that is, the charging demand; m ₁ is the elasticity coefficient; SOC _T represents the state of charge of the electric vehicle at time T; C _EV is the battery capacity; w represents the power consumption per unit mileage; c is a constant reflecting weather and road conditions; X _{d_i+1} represents the mileage of the user's next trip; m ₂ is a fuzzy coefficient;

The power replenishment model considering the adequacy of charging facilities is when the user's travel state is staying within [T-dt,T], the established power replenishment model is as follows:

In the formula: SOC _T represents the state of charge of the electric vehicle at time T; SOC _T-dt represents the state of charge of the electric vehicle at time T-dt; q is the charging power of the battery of the electric vehicle; dt is the time interval of time series interactive simulation; C _EV is Battery capacity; P _T-dt represents the number of remaining charging piles at the parking location P at the time of T-dt;

The user differential decision-making based on the charging feedback effect includes:

(1) Calculate the state of charge support:

In the formula, B _SOC represents the state of charge support; SOC _T represents the state of charge of the electric vehicle at time T; C _EV is the battery capacity; c is the constant reflecting weather and road conditions; w represents the power consumption per unit mileage; X _{d_i} represents the user The mileage of the trip;

(2) Set n ₁ as the support coefficient, and determine the charging feedback result according to the state of charge support degree

If B _SOC ≥n ₁ , the state of charge supports this trip, and the feedback result is: it is possible to leave, the user makes a decision to leave according to the original plan, and the travel demand remains unchanged;

If B _SOC <n ₁ , the state of charge cannot support this trip, and the feedback result is: temporarily unable to drive away, that is, the user cannot actually drive away from the parking place, and the departure at this time is understood as the user's original travel demand;

When the feedback result is that it is temporarily impossible to leave, the user has different decision criteria due to the different types of travel activities in this trip:

When the itinerary category is work, the factor influencing the user's decision-making is the arrival time, and the user's decision-making criterion is A _{T_0} +dt>A _{T_max} ;

When the itinerary category is leisure, the factor that affects the user's decision-making is the leisure time, and the user's decision-making criterion is T _{t_0} -dt<T _{t_min} ;

When the itinerary category is to end the trip and go home, the factor affecting the user's decision-making is the arrival time, and the user's decision-making criterion is A _{T_0} +dt>A _{T_max} ;

When the itinerary category is short-term homecoming, no judgment will be made;

Among them, A _{T_0} is the parking time originally planned by the user, A _{T_max} is the latest tolerated time for the user to park, T _{t_0} is the original planned stay time of the user, and T _{t_min} is the shortest tolerated time of the user stay;

(3) Users make differentiated decisions based on itinerary categories and corresponding criteria

(3.1) When the trip activity is work or going home after the trip, if the criterion A _{T_0} +dt>A _{T_max} is met, the decision to abandon the electric vehicle and adopt other modes of transportation to continue the trip will be made, otherwise the decision will be made. The decision to keep the charging demand waiting for a period of time;

(3.2) When this trip is leisure, if it meets the criterion T _{t_0} -dt<T _{t_min} , then make a decision to give up this trip and continue the next trip or end the trip, otherwise make a decision to keep the charging demand and wait for a period of time decision;

(3.3) When this trip is a short-term homecoming, no judgment is made, and a decision to give up the trip and continue the next trip or end the trip is made;

3) Time-series interaction simulation of user travel and charging demand

First, set the time series interactive simulation with dt as the time interval, and the user travel status in [T-dt, T] remains unchanged; the time series interactive simulation of user travel and charging needs is carried out with equal steps as the clock advancement method, including:

(1) Initialize T=0;

(2) Initialize user k=1;

(3) Determine the user's travel status within [T-dt, T];

(4) When the travel state of the user is driving, the state of charge is updated according to the power consumption model; when the travel state of the user is driving, the generation and abandonment of the charging demand are judged according to the charging demand generation model; when the travel state of the user is stop When the state of charge and charging demand are updated according to the power replenishment model; when the travel state of the user is driving away, the charging demand and travel demand are determined according to the state of charge support degree;

(5) When user k=k+1, repeat steps (3) and (4) until all users are traversed, and save the charging requirements and travel requirements of each user in [T-dt, T];

(6) T=T+dt, repeat steps (2) to (5) until the simulation period ends.

2. The method for analyzing electric vehicle travel and charging demand considering charging feedback effect according to claim 1, characterized in that, if the state of charge of the electric vehicle reaches 100% during the user's stay, the user's charging demand disappears, and The state of charge does not change until driving away; otherwise the user maintains the charging demand and continues to update its state of charge according to the charge replenishment model.

3. The electric vehicle travel and charging demand analysis method considering charging feedback effect according to claim 1, is characterized in that, in step 2) the step (3.3) of step (3) described in abandoning this trip and continuing to the next time During the trip, if the daily trip has not ended, the state of charge support B' _SOC needs to be recalculated at this time. If B' _SOC ≥n ₁ , the user will continue to make a decision to abandon the trip and continue the next trip or end the trip, otherwise Again, make the decision to keep charging demand waiting for a while.

4. The electric vehicle travel and charging demand analysis method considering the charging feedback effect according to claim 1, wherein the step 2) in (3.1) of the (3) step is to abandon the electric vehicle and adopt other modes of transportation Continuing this trip, it is necessary to consider the possibility of the user retrieving the electric vehicle. If the positions of the user and the electric vehicle do not overlap, the user has not reached the point where the electric vehicle was abandoned and cannot be retrieved; if the positions overlap, and B' _SOC ≥n ₁ , then the user successfully retrieves the electric car and drives the electric car away, otherwise he takes another mode of transportation to leave.