CN110543967A - electric vehicle waiting time distribution short-time prediction method in network connection charging station environment - Google Patents
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Abstract
A method for predicting the distribution of electric vehicle waiting time in a short time under the environment of a network connection charging station comprises the following specific steps: step 1, calculating a charging requirement; and 2, predicting the distribution of the waiting time. The method comprehensively considers the influence factors of the user selecting the charging stations, and calculates the charging requirements of the charging stations; and solving a charging waiting time distribution function of the electric automobile by adopting an M/G/n queuing model and utilizing real-time information of the network connection charging station, so as to realize short-time prediction of the charging waiting time distribution of the electric automobile.
Description
Technical Field
the invention relates to a method for predicting the distribution of electric vehicle waiting time in a network charging station environment in a short time, and belongs to the field of intelligent transportation.
Background
The current world faces double examinations of energy shortage and environmental pollution, and the electric automobile is rapidly developed due to the advantages of energy conservation, zero emission and the like. However, due to the battery technology and the charging facility technology, the electric automobile has the problems of short endurance mileage, long charging time, low charging efficiency of the rapid charging pile and the like. In order to relieve the mileage anxiety of users, charging facilities are vigorously built nationwide, and many scholars also strive to optimize the layout of the electric vehicle charging station from the planning point of view. However, due to factors such as land, infrastructure, environment and the like, the charging station is difficult to realize in reasonable layout, so that the charging station has the phenomena of 'no pile in a vehicle' and 'no pile in a vehicle'. Meanwhile, the randomness and the blindness of the user for selecting the charging stations aggravate the phenomenon of unbalanced distribution of the service levels of the charging stations, so that part of the charging stations are too long in queue, the space-time resources are wasted, and the travel cost of the user is increased. Because the number of vehicles arriving at the charging station and the charging time of the electric vehicle are randomly changed, and different vehicles have different tolerance degrees on the reliability of the waiting time, the average waiting time is difficult to be suitable for different time periods, different charging stations and different vehicles, the statistical law of the charging waiting time can be completely described through the distribution of the waiting time, and a basis is provided for formulating a more detailed and reasonable traffic demand distribution and guidance scheme. Under the environment of the charging station connected with the internet, the service condition of the charging pile, the number of the electric vehicles needing to be charged on the road network and the position information can be obtained in real time, the information can effectively process and analyze the selection difference of users, and the calculation precision of the charging requirement is improved. Therefore, the method for predicting the short-time distribution of the waiting time of the electric vehicle in the network charging station environment is significant.
Disclosure of Invention
Aiming at the problems, the invention provides a method for predicting the distribution of the charging waiting time of the electric vehicle in the environment of the network connection charging station. Comprehensively considering influence factors of a user for selecting the charging stations, and calculating the charging requirements of the charging stations; and solving a charging waiting time distribution function of the electric automobile by adopting an M/G/n queuing model and utilizing real-time information of the network connection charging station, so as to realize short-time prediction of the charging waiting time distribution of the electric automobile.
A method for predicting the distribution of electric vehicle waiting time in a short time under the environment of a network connection charging station comprises the following specific steps:
1. Calculating a charging requirement;
The total number Nnc of vehicles needing to be charged on the road network in the time period can be obtained under the condition of the network connection charging station. The number Ns of vehicles reaching the kth charging station in each time interval is equal to the product of the total number Nnc of vehicles needing to be charged in the road network in the time interval and the probability Pk of selecting the kth charging station
N＝N·P (1)
The probability Pk of the k-th charging station being selected can be determined from the probability of the electric vehicle user i selecting the k-th charging station
In the formula, Pi, k is the electric vehicle user i selection kth charging station probability, and Am is the selectable charging station set.
There are multiple factors influencing the user to select the charging station, each influencing factor has a quantifiable utility value, and the user selects the charging station to charge according to the utility maximization principle. Thus, the probability that the user i of the electric vehicle selects the k-th charging station is
In the formula, Vi, k is a utility value of the electric vehicle user i selecting the kth charging station.
the utility function of selecting the charging station by the user is constructed by utilizing the charging cost, the distance from the charging station, the charging time period, the nearby buildings and the occupation condition of the charging pile, and the utility function of selecting the k-th charging station by the user i of the electric vehicle is constructed due to the independence of all factors
V＝θC+θD+θT+θZ+θB (4)
In the formula: ci and k represent a charging cost index of the k charging station for the electric vehicle user i; di, k represents a distance index of the electric vehicle user i from the charging station k; ti, k represents a charging time period influence index of the kth charging station on the electric vehicle user i; bi and k represent the influence indexes of buildings near the charging station k on the electric vehicle user i; zi, k represents an influence index of the charging pile occupation condition of the charging station k on the electric vehicle user i; and theta 1, theta 2, theta 3, theta 4 and theta 5 are respectively influence factor parameter coefficients to be estimated.
and solving the coefficient of the influencing factor parameter by adopting maximum likelihood estimation. For the Multi-logit model, before solving, it is necessary to introduce an indicative function IA, expressed in the form
In the formula: ω i is the charging station actually selected by user i; a (k) is the kth element of the selectable charging station set Am. Using maximum likelihood estimation
In the formula: l is a maximum likelihood function; θ is the system vector.
taking logarithm on two sides
Then respectively carrying out derivation on theta 1, theta 2, theta 3, theta 4 and theta 5, and simultaneously enabling the derivative to be equal to 0 to obtain an equation set
And substituting the influencing factor parameters into an equation to obtain the values of the coefficients theta 1, theta 2, theta 3, theta 4 and theta 5, namely the optimal estimated value.
2. predicting a latency distribution;
The arrival of the vehicles at the charging station is in a poisson process, the charging service time length is in gamma distribution, and a queuing system of the electric vehicles at the charging station belongs to an M/G/n queuing model. At present, operation research has no clear mathematical expression on system operation indexes of an M/G/n queuing model, and in order to obtain an explicit theoretical solution of waiting time distribution, the method combines a diffusion approximation model and a substitute quantity to solve a charging waiting time distribution function of the electric automobile.
in the electric vehicle M/G/n queuing model, n is the total number of the quick charging piles of the charging station; lambda is the average arrival rate of the electric vehicle in the poisson process and represents the average number of vehicles arriving at a charging station in unit time; mu is the service rate of a single charging pile, and represents that mu vehicles finish service and leave the charging station in unit time on the single charging pile; na is the number of arriving vehicles; ρ is the total service intensity of the charging station, is a dimensionless quantity, and represents the service time required to be provided by the system in unit time, the expression is CT which is the variation coefficient of the service time T and is used for measuring the volatility of the service time T, the expression is where σ (T) is the standard deviation of the service time, and e (T) is the mean value of the service time. The expression of the proposed stable probability distribution of the queue length is
In the formula, the sum of infinitesimal variances is an average value, and the expressions of the substitution quantity substitution amounts are respectively
From equation (9), it can be seen that the stationary probability distribution is mainly affected by the variables λ and CT. Now, after considering the statistical balance (i.e. under the condition service intensity rho < 1), the related differential equation is obtained, and for solving the equation, na is more than or equal to n-1, and sigma is an undetermined coefficient.
Therefore, the charging waiting time distribution of the electric automobile after statistical balance is
And acquiring the predicted average arrival rate lambda nw of the charging demand by using the charging station data in the network connection charging station, and establishing a prediction algorithm of the charging waiting time distribution of the electric vehicle in the network connection charging station environment by combining the real-time acquired charged time length of the charged electric vehicle occupying the quick charging piles.
After the predicted average arrival rate lambda nw is obtained, the total service intensity rho nw of the charging station under the environment of the networking charging station can be obtained
The charging time length of each charged electric vehicle occupying the quick charging pile can be acquired in real time in the environment of the network connection charging station, and when the charging station has a queuing condition, the queuing waiting time of the system can be reduced due to the charging time length. Since the standard deviation σ (T) of the charged time period has a small influence on the charged time period, the variation coefficient CT is changed mainly by the average charged time period e (T), so that the waiting time distribution function is changed. The total charging time of the electric vehicle in the charging station is recorded as Tsum, and the average charging time Enw (T) of the electric vehicle in the charging station environment is recorded as
In the formula: e (t) is an average charging duration of the electric vehicle in the non-networked charging station environment, and is denoted by enw (t) ═ E (t) — Δ E. The influence of the variance of the charging duration is small, and it can be regarded that σ (T) ═ cte (T), and the coefficient of variation in the environment of the network charging station is
Under the environment of a network connection charging station, infinitesimal variance and mean expression are respectively corrected middle substitution quantity in a probability density function and are respectively steady probability distribution expression
Therefore, the charging waiting time of the electric vehicle under the environment of the network connection charging station is distributed as
The invention has the advantages that: according to the method and the device, the use condition of the charging pile, the number and the position of the electric vehicles needing to be charged on the road network and other information which are obtained in real time in the environment of the network connection charging station are utilized, the selection difference of the user is effectively processed and analyzed, and the calculation precision of the charging requirement is improved. Meanwhile, the obtained waiting time distribution tends to be consistent with the actual distribution, and a reference basis can be provided for a user to select a charging station in advance.
drawings
FIG. 1 is a flow chart of the charging requirement of the present invention.
FIG. 2 is a flow chart of the method of the present invention.
Fig. 3 is a graph comparing the latency distribution with the actual distribution at different arrival rates.
examples of the invention
After the distribution of the waiting time under the charging station data in the network-connected charging stations is solved, the actual distribution of the waiting time of the charging stations 1 in a certain area of a certain city in a period of 5, 14 days in 2018 is selected to be compared with the predicted distribution, the arrival rate λ of the charging stations 1 in 6:00-6:30 is 6, the arrival rate λ of 7:30-8:00 is 9, the arrival rate λ of 8:30-9:00 is 11 are obtained through the charging demand prediction, and the comparison of the distribution of the waiting time under each arrival rate of the charging stations 1 with the actual distribution is shown in fig. 3.
As can be seen from fig. 3, since the average arrival rate per period changes with the period, the distribution of the waiting time per period of the charging station changes, and the distribution of the waiting time in the networked charging station environment can describe the statistical regularity of the actual overall waiting time and tends to be consistent with the actual distribution. Therefore, the prediction of the short-time waiting time distribution in the network charging station environment can provide reference for the user to select the charging station in advance.
The embodiments described in this specification are merely illustrative of implementations of the inventive concept and the scope of the present invention should not be considered limited to the specific forms set forth in the embodiments but rather by the equivalents thereof as may occur to those skilled in the art upon consideration of the present inventive concept.
Claims (1)
1. A method for predicting the distribution of electric vehicle waiting time in a short time under the environment of a network connection charging station comprises the following specific steps:
Step 1, calculating a charging requirement;
The total number Nnc of the vehicles needing to be charged on the road network in the time period can be obtained under the condition of the network connection charging station; the number Ns of vehicles reaching the kth charging station in each time interval is equal to the product of the total number Nnc of vehicles needing to be charged in the road network in the time interval and the probability Pk of selecting the kth charging station
N＝N·P (1)
The probability Pk of the k-th charging station being selected can be determined from the probability of the electric vehicle user i selecting the k-th charging station
In the formula, Pi and k are probabilities that the electric vehicle user i selects the kth charging station, and Am is a selectable charging station set;
The charging station selection of the user is influenced by various factors, each influencing factor has a quantifiable utility value, and the user selects the charging station to charge according to the utility maximization principle; thus, the probability that the user i of the electric vehicle selects the k-th charging station is
In the formula, Vi, k is a utility value of the electric vehicle user i for selecting the kth charging station;
the utility function of selecting the charging station by the user is constructed by utilizing the charging cost, the distance from the charging station, the charging time period, the nearby buildings and the occupation condition of the charging pile, and the utility function of selecting the k-th charging station by the user i of the electric vehicle is constructed due to the independence of all factors
V＝θC+θD+θT+θZ+θB (4)
in the formula: ci and k represent a charging cost index of the k charging station for the electric vehicle user i; di, k represents a distance index of the electric vehicle user i from the charging station k; ti, k represents a charging time period influence index of the kth charging station on the electric vehicle user i; bi and k represent the influence indexes of buildings near the charging station k on the electric vehicle user i; zi, k represents an influence index of the charging pile occupation condition of the charging station k on the electric vehicle user i; theta 1, theta 2, theta 3, theta 4 and theta 5 are respectively influence factor parameter coefficients to be estimated;
Solving the parameter coefficient of the influencing factor by adopting maximum likelihood estimation; for the Multi-logit model, before solving, it is necessary to introduce an indicative function IA, expressed in the form
In the formula: ω i is the charging station actually selected by user i; a (k) is the kth element of the selectable charging station set Am; using maximum likelihood estimation
In the formula: l is a maximum likelihood function; theta is a system vector;
Taking logarithm on two sides
Then respectively carrying out derivation on theta 1, theta 2, theta 3, theta 4 and theta 5, and simultaneously enabling the derivative to be equal to 0 to obtain an equation set
Substituting the influencing factor parameters into an equation to obtain the values of coefficients theta 1, theta 2, theta 3, theta 4 and theta 5, namely the optimal estimated value;
Step 2, predicting the distribution of waiting time;
The vehicle arrival of the charging station is in a poisson process, the charging service time length is in gamma distribution, and a queuing system of the electric vehicle at the charging station belongs to an M/G/n queuing model; at present, operation research has no clear mathematical expression on system operation indexes of an M/G/n queuing model, and in order to obtain an explicit theoretical solution of waiting time distribution, the method combines a diffusion approximation model and a substitute quantity to solve a charging waiting time distribution function of the electric automobile;
In the electric vehicle M/G/n queuing model, n is the total number of the quick charging piles of the charging station; lambda is the average arrival rate of the electric vehicle in the poisson process and represents the average number of vehicles arriving at a charging station in unit time; mu is the service rate of a single charging pile, and represents that mu vehicles finish service and leave the charging station in unit time on the single charging pile; na is the number of arriving vehicles; rho is the total service intensity of the charging station, is dimensionless quantity, represents the service time required to be provided by the system in unit time, has an expression CT as a variation coefficient of the service time T and is used for measuring the fluctuation of the service time T, and has an expression where sigma (T) is the standard deviation of the service time and E (T) is the mean value of the service time; the expression of the proposed stable probability distribution of the queue length is
In the formula, the sum of infinitesimal variances is an average value, and the expressions of the substitution quantity substitution amounts are respectively
From equation (9), it can be seen that the stationary probability distribution is mainly affected by the variables λ and CT; considering the statistical balance (namely under the condition that the service intensity rho is less than 1), obtaining a related differential equation, and in order to solve the equation, making na be more than or equal to n-1, and sigma be an undetermined coefficient;
Therefore, the charging waiting time distribution of the electric automobile after statistical balance is
Acquiring the predicted average arrival rate lambda nw of the charging demand by using the charging station data in the network connection charging station, and establishing a prediction algorithm of the charging waiting time distribution of the electric vehicle in the network connection charging station environment by combining the real-time acquired charged time length of the charged electric vehicle occupying each quick charging pile;
after the predicted average arrival rate lambda nw is obtained, the total service intensity rho nw of the charging station under the environment of the networking charging station can be obtained
the method can acquire the charged time length of each charged electric vehicle occupying the quick charging pile in real time in the environment of the network charging station, and when the charging station has a queuing condition, the charged time length can reduce the queuing waiting time of the system; since the standard deviation σ (T) of the charged time length has a small influence, the variation coefficient CT is changed mainly by the average charged time length e (T), so that the waiting time distribution function is changed; the total charging time of the electric vehicle in the charging station is recorded as Tsum, and the average charging time Enw (T) of the electric vehicle in the charging station environment is recorded as
In the formula: e (t) is an average charging duration of the electric vehicle in the non-networking charging station environment, and is denoted by enw (t) ═ E (t) — Δ E; the influence of the variance of the charging duration is small, and it can be regarded that σ (T) ═ cte (T), and the coefficient of variation in the environment of the network charging station is
under the environment of a network connection charging station, infinitesimal variance and mean expression are respectively corrected middle substitution quantity in a probability density function and are respectively steady probability distribution expression
Therefore, the charging waiting time of the electric vehicle under the environment of the network connection charging station is distributed as
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