An Electric Vehicle Guidance Method and System Suitable for Urban Fast Charging Stations

Abstract

The present invention discloses an electric vehicle guiding method and system applicable to urban rapid charging stations, belonging to the field of urban electric vehicle charging guidance. Based on establishing a queue model of rapid charging stations in the area to describe the spatial distribution of electric vehicles, the present invention evaluates the stability degree of the charging station queue through the Lyapunov function; then, based on the Lyapunov optimization theory, the user dissatisfaction degree is introduced as a penalty function into the optimization process, and by constructing the form of a drift-penalty term function, an electric vehicle guiding decision model is established. The proposed model fully considers the compliance willingness of electric vehicle owners. By guiding the electric vehicles, the charging congestion situation of the rapid charging stations is effectively alleviated, the number of electric vehicles waiting inside the rapid charging stations is reduced, the time-consuming of the electric vehicle charging process is reduced, and the charging satisfaction of the vehicle owners is improved.

Description

Technical Field

[0001] The present invention relates to a method and system for guiding electric vehicles applicable to urban fast charging stations, belonging to the field of guiding electric vehicle charging in urban areas. Background Art

[0002] Electric vehicles have developed rapidly due to their advantages in low carbon emissions. Since the travel of electric vehicles shows strong regularity, the disorderly access of electric vehicles places higher requirements on the service capabilities of some fast charging stations. Severe charging blockage phenomena will greatly increase the charging time of electric vehicles and reduce the charging experience of vehicle owners. Given that the selection of electric vehicles inside fast charging stations usually has strict requirements on the charging time, it is necessary to effectively control the access process of electric vehicles to minimize the time consumed during the charging process of electric vehicles and improve user charging satisfaction. Summary of the Invention

[0003] The present invention provides a method and system for guiding electric vehicles applicable to urban fast charging stations to obtain a guiding decision result for electric vehicles.

[0004] The technical solution of the present invention is: A method for guiding electric vehicles applicable to urban fast charging stations, comprising:

[0005] Obtaining the spatial position distribution of each fast charging station participating in the scheduling process;

[0006] Obtaining the spatial position distribution of electric vehicles with charging requirements, as well as the corresponding remaining power and expected power values;

[0007] Based on the spatial position distribution of fast charging stations, the spatial position distribution of electric vehicles, and the remaining power information, determining whether the electric vehicles meet the guiding feasibility conditions and determining the range of fast charging stations that the electric vehicles can select; if two types of constraints are selected for the guiding feasibility conditions, retain the fast charging stations that meet the two types of constraints, otherwise delete them;

[0008] Evaluating the stability of the established fast charging station queue model;

[0009] Determining the evaluation result of the owner dissatisfaction degree index based on the owner dissatisfaction degree index;

[0010] Establishing an objective function of an electric vehicle guiding decision model based on the stability of the fast charging station queue model and the evaluation result of the owner dissatisfaction degree index; Solving the objective function according to the constraint conditions to determine the electric vehicle guiding decision result.

[0011] The guiding feasibility conditions specifically include the following two types:

[0012] Remaining power constraint:

[0013]

[0014] In the formula, E r (i) represents the remaining power uploaded by the owner of the i-th electric vehicle, and E min represents the lower limit of the battery power of the electric vehicle set, and L 100 represents the power consumption per 100 kilometers of the electric vehicle; d p (i), d o (i) respectively represent the distances of the i-th electric vehicle to the target fast charging station and the nearest fast charging station;

[0015] Travel time increment constraint:

[0016] t p (i) - t o (i) ≤ Δt max

[0017] In the formula, t p (i), t o (i) respectively represent the travel times of the i-th electric vehicle to the target fast charging station and the nearest fast charging station, and Δt max represents the upper limit of the travel time increment of the electric vehicle acceptable to the owner.

[0018] The fast charging station queue model is obtained based on the summary of the queue length at the corresponding time, and the specific structure is as follows:

[0019] π(t) = {M1(t), Q1(t), …, M s (t), Q s (t), …}

[0020] In the formula, π(t) refers to the fast charging station queue model, and M s (t), Q s (t) respectively represent the number of electric vehicles going to and in the queue at the fast charging station s at time t.

[0021] The fast charging station queue model is divided into a going queue M s and a queue in the station Q s in two categories according to different charging stages of electric vehicles, and the corresponding queue lengths are represented by the number of electric vehicles in the queue at each moment; among them, M s queue is used to refer to all electric vehicles that have accessed the s-th fast charging station but have not yet reached the fast charging station; Q s queue is used to refer to all electric vehicles that have arrived at the fast charging station s but have not completed charging; M s , Q s The queue lengths can be updated respectively based on the following formulas:

[0022] M s (t + 1)= M s (t)+ n s (t)- A s (t)

[0023] Q s (t + 1)= max{Q s (t)- D s (t), 0}+ A s (t)

[0024] Wherein, n s (t) represents the number of electric vehicles allocated to the fast charging station s at time t; A s (t) represents the number of electric vehicles arriving at the fast charging station s at time t, D s (t) represents the number of electric vehicles that have completed charging in the fast charging station s at time t; max{} represents selecting the maximum value of each parameter in the brackets;

[0025] On this basis, the number of electric vehicles allocated to the fast charging station at each moment should not be greater than the upper limit of the charging request quantity that it can respond to, specifically as follows:

[0026] n s (t)≤ n max

[0027] Wherein, n max represents the upper limit of the charging request that can be responded to by the set fast charging station.

[0028] The stability of the established fast charging station queue model is evaluated using the Lyapunov function, specifically as follows:

[0029]

[0030] Wherein, V(π(t)) represents the value of the Lyapunov function of the fast charging station queue model at time t. The smaller the value of the Lyapunov function of the fast charging station queue model, the higher the stability of the corresponding queue model; N s represents the number of fast charging stations in the area, are the weight coefficients of the going queue and the in-station queue respectively, M s (t), Q s (t) represent the number of electric vehicles in the going queue and the in-station queue at the fast charging station s at time t respectively.

[0031] The dissatisfaction of the vehicle owners is mainly caused by two aspects: the increase in travel time and the queue waiting time, specifically as follows:

[0032] Dissatisfaction caused by the increase in travel time:

[0033]

[0034] In the formula, F dis (i) represents the dissatisfaction degree caused by the incremental travel time of the i-th electric vehicle, t p (i), t o (i) respectively represent the travel time of the i-th electric vehicle to the target fast charging station and the travel time corresponding to the nearest fast charging station;

[0035] Dissatisfaction degree caused by the queue waiting time:

[0036]

[0037] In the formula, F con (i) represents the dissatisfaction degree caused by the queue waiting time of the i-th electric vehicle, t dri (i), t chi (i) respectively represent the duration of the i-th electric vehicle going to and during the charging stage, Δt represents the time step of a single decision-making period; ceil(.) is the ceiling function;

[0038] Based on the above analysis, the evaluation formula of the owner's dissatisfaction degree index is as follows:

[0039]

[0040] In the formula, y(t) represents the evaluation result of the owner's dissatisfaction degree corresponding to all the electric vehicles to be allocated at time t, N(t) represents the total number of electric vehicles to be allocated at time t; α1 and α2 respectively represent the weight coefficients corresponding to the dissatisfaction degrees caused by the incremental travel time and the queue waiting time.

[0041] The guiding decision model is based on the Lyapunov optimization theory and conducts optimization modeling by constructing a "drift-penalty term function"; among them, the Lyapunov drift is defined as the change in the value of the Lyapunov function within a single period, which can be used to characterize the stability change of the fast charging station queue model, specifically as follows:

[0042]

[0043]

[0044] In the formula, ΔV(t) represents the value of the Lyapunov drift function of the fast charging station queue model at time t, represents the upper bound of the value of the Lyapunov drift function of the fast charging station queue model at time t; V(π(t)) represents the value of the Lyapunov function of the fast charging station queue model at time t; M s (t), Q s (t) respectively represent the number of electric vehicles going to and in the queue at the fast charging station s at time t, are the weight coefficients of the departure queue and the in-station queue respectively; n s (t) represents the number of electric vehicles allocated to the fast charging station s at time t, D s (t) represents the number of electric vehicles that have completed charging at the fast charging station s at time t, A s (t) represents the number of electric vehicles arriving at the fast charging station s at time t, N s represents the number of fast charging stations in the area; E{A s (t)}, E{n s (t)}, E{D s (t)} represent A s (t), n s (t), D s (t) respectively; the expected values of

[0045] On this basis, guiding the decision-making model to minimize the upper bound of the queue Lyapunov drift as the goal, after introducing the owner's dissatisfaction degree as a penalty term into the decision-making process, the objective function of the electric vehicle guiding decision-making model is obtained as follows:

[0046]

[0047] In the formula, DPP(t) represents the drift-penalty term function of the guiding decision-making model at time t, represents the upper bound of the value of the Lyapunov drift function of the fast charging station queue model at time t; y(t) represents the evaluation result of the owner's dissatisfaction degree corresponding to all the electric vehicles to be allocated at time t, and β represents the weight coefficient of the owner's dissatisfaction function;

[0048] Since the number of electric vehicles allocated to different fast charging stations at each time can be obtained by summing the corresponding column vectors of the guiding matrix, that is:

[0049]

[0050] In the formula, n s (t) represents the number of electric vehicles allocated to the fast charging station s at time t, and N(t) represents the total number of electric vehicles to be allocated at time t; r es is the binary variable corresponding to the electric vehicle guiding decision at time t. When the value is 1, it means guiding the e-th electric vehicle to be allocated to the fast charging station s, otherwise it means not guiding the e-th electric vehicle to the fast charging station s;

[0051] And at each time, A s (t), D s (t) parameter values can be determined based on the electric vehicle dispatch decision in the previous period, so A s (t), D s(t) After the parameter affects, the objective function of the electric vehicle guidance decision model is modified as follows:

[0052] Objective function:

[0053]

[0054] In the formula, N s represents the number of fast charging stations in the area, and N(t) represents the total number of electric vehicles to be allocated at time t; M s (t) respectively represent the number of electric vehicles in the queue going to the fast charging station s at time t, is the weight coefficient of the queue going; F es represents the calculation result of the penalty term function when the electric vehicle e goes to the fast charging station s. For the fast charging stations that do not meet the guidance feasibility conditions, the penalty term value of the model is changed to infinity; r es is the binary variable corresponding to the electric vehicle guidance decision. When the value is 1, it means guiding the e-th electric vehicle to be allocated to the fast charging station s, otherwise it means not guiding the e-th electric vehicle to the fast charging station s;

[0055] Constraint conditions:

[0056]

[0057] In the formula, N s represents the number of fast charging stations in the area, and N(t) represents the total number of electric vehicles to be allocated at time t; r es is the binary variable corresponding to the electric vehicle guidance decision. When the value is 1, it means guiding the e-th electric vehicle to be allocated to the fast charging station s, otherwise it means not guiding the e-th electric vehicle to the fast charging station s.

[0058] The guidance decision constructs a guidance decision matrix. The dimension of the guidance decision matrix depends on the number of vehicle owners uploading information during the current dispatch period and the number of fast charging stations in the area. The specific structure is as follows:

[0059]

[0060] In the formula, r(t) represents the electric vehicle guidance decision matrix corresponding to time t.

[0061] According to another aspect of the present invention, there is also provided an electric vehicle guidance system applicable to urban fast charging stations, including:

[0062] The first acquisition module is used to acquire the spatial position distribution of each fast charging station participating in the scheduling process;

[0063] The second acquisition module is used to acquire the spatial position distribution of electric vehicles with charging requirements, as well as the corresponding remaining power and expected power values;

[0064] A first determination module, configured to determine whether an electric vehicle meets the guiding feasibility condition based on the spatial location distribution of fast charging stations, the spatial location distribution of electric vehicles, and the remaining power information, and determine the range of fast charging stations that the electric vehicle can select; if two types of constraints are selected for the guiding feasibility condition, retain the fast charging stations that meet the two types of constraints, otherwise delete them;

[0065] An evaluation module, configured to evaluate the stability of the established fast charging station queue model;

[0066] A second determination module, configured to determine the evaluation result of the owner dissatisfaction degree index based on the owner dissatisfaction degree index;

[0067] A third determination module, configured to establish an objective function of an electric vehicle guiding decision model based on the stability of the fast charging station queue model and the evaluation result of the owner dissatisfaction degree index; solve the objective function according to the constraint conditions, and determine the electric vehicle guiding decision result.

[0068] It further includes:

[0069] A first feedback module, configured to feedback the fast charging station number to be accessed, the arrival time at the fast charging station, and the pick-up time to the electric vehicle owner who uploads information according to the electric vehicle guiding decision result;

[0070] A second feedback module: configured to feedback the arrival quantity of electric vehicles at each moment, as well as the access and removal plans of electric vehicles to the fast charging station operator according to the electric vehicle guiding decision result.

[0071] The beneficial effects of the present invention are: based on establishing a fast charging station queue model in the region to describe the spatial distribution of electric vehicles, the present invention evaluates the stability of the charging station queue through the Lyapunov function; then, based on the Lyapunov optimization theory, the user dissatisfaction degree is introduced as a penalty function into the optimization process, and an electric vehicle guiding decision model is established by constructing the form of a drift-penalty term function. The proposed model fully considers the compliance willingness of electric vehicle owners, and effectively alleviates the charging congestion situation of fast charging stations by guiding electric vehicles, reduces the number of electric vehicles waiting inside the fast charging stations, reduces the time-consuming of the electric vehicle charging process, and improves the charging satisfaction of the owners. Description of the Drawings

[0072] Figure 1 is a flowchart of the method of the present invention;

[0073] Figure 2 is the structure of the traffic road network connection diagram of the embodiment of the present invention;

[0074] Figure 3For the number of electric vehicles belonging to each fast charging station before and after the guidance of the present invention;

[0075] Figure 4 For the queue length of model Q before and after the guidance of the present invention;

[0076] Figure 5 For the facility utilization rate of the No. 3 fast charging station before and after the guidance of the present invention;

[0077] Figure 6 For the facility utilization rate of the No. 4 fast charging station before and after the guidance of the present invention. Detailed implementation manners

[0078] The following further describes the invention in conjunction with the drawings and embodiments, but the content of the present invention is not limited to the described scope.

[0079] Embodiment 1: As Figure 1-6 shown, an electric vehicle guidance method applicable to urban fast charging stations includes:

[0080] Obtain the spatial position distribution of each fast charging station participating in the scheduling process;

[0081] Obtain the spatial position distribution of electric vehicles with charging requirements, as well as the corresponding remaining battery levels and expected battery level values;

[0082] Based on the spatial position distribution of fast charging stations, the spatial position distribution of electric vehicles, and the remaining battery level information, determine whether the electric vehicle meets the guidance feasibility conditions, and determine the range of fast charging stations that the electric vehicle can select; if two types of constraints are selected for the guidance feasibility conditions, retain the fast charging stations that meet the two types of constraints, otherwise delete them;

[0083] Evaluate the stability of the established fast charging station queue model;

[0084] Based on the owner dissatisfaction degree index, determine the evaluation result of the owner dissatisfaction degree index;

[0085] Based on the stability of the fast charging station queue model and the evaluation result of the owner dissatisfaction degree index, establish the objective function of the electric vehicle guidance decision model; according to the constraint conditions, solve the objective function to determine the electric vehicle guidance decision result.

[0086] Optionally, the guidance feasibility conditions specifically include the following two types:

[0087] Remaining battery level constraint, the remaining battery level constraint aims to ensure that the battery level of the electric vehicle is sufficient to support it to travel to the fast charging station, and the corresponding specific mathematical expression is as follows:

[0088]

[0089] In the formula, Er (i) represents the remaining power uploaded by the owner of the i-th electric vehicle, E min represents the lower limit of the battery power of the electric vehicle set, L 100 represents the power consumption per 100 kilometers of the electric vehicle; d p (i), d o (i) respectively represent the distance of the i-th electric vehicle to the target fast charging station and the distance to the nearest fast charging station;

[0090] Travel time increment constraint. The travel time increment constraint reflects the maximum acceptable degree of the vehicle owner for the guidance instruction. If the travel time of the electric vehicle increases sharply after the guidance, the vehicle owner will refuse to respond to the guidance instruction. The mathematical expression of the travel time increment constraint is as follows:

[0091] t p (i) - t o (i) ≤ Δt max

[0092] In the formula, t p (i), t o (i) respectively represent the travel time of the i-th electric vehicle to the target fast charging station and the travel time corresponding to the nearest fast charging station, and Δt max represents the upper limit of the acceptable travel time increment of the electric vehicle for the vehicle owner.

[0093] Optionally, the fast charging station queue model is obtained based on the summary of the queue length at the corresponding moment (that is, the fast charging station queue model mainly determines the queue length based on the electric vehicle dispatch results at the previous moment), and the specific structure is as follows:

[0094] π(t) = {M1(t), Q1(t), …, M s (t), Q s (t), …}

[0095] In the formula, π(t) refers to the fast charging station queue model, and M s (t), Q s (t) respectively represent the number of electric vehicles going to and in the queue at the fast charging station s at time t.

[0096] Optionally, the fast charging station queue model is divided into a going queue M s and an in-station queue Q s in two categories according to different charging stages of the electric vehicle, and the corresponding queue lengths are represented by the number of electric vehicles in the queue at each moment; among them, M s queue is used to refer to all electric vehicles that have accessed the s-th fast charging station but have not yet reached the fast charging station; Q sThe queue is used to refer to all the electric vehicles that have arrived at the fast charging station s but have not completed charging; M s , Q s The queue lengths can be updated respectively based on the following formulas:

[0097] M s M(t + 1) = M s (t) + n s (t) - A s (t)

[0098] Q s Q(t + 1) = max{Q s (t) - D s (t), 0} + A s (t)

[0099] In the formulas, M s (t), Q s (t) respectively represent the number of electric vehicles going to and in the queue at the fast charging station s at time t; n s (t) represents the number of electric vehicles allocated to the fast charging station s at time t; A s (t) represents the number of electric vehicles arriving at the fast charging station s at time t, D s (t) represents the number of electric vehicles that have completed charging at the fast charging station s at time t; max{} represents selecting the maximum value of each parameter in the brackets;

[0100] On this basis, the number of electric vehicles allocated to the fast charging station at each moment of the model should not be greater than the upper limit of the charging request quantity that it can respond to, specifically as follows:

[0101] n s (t) ≤ n max

[0102] In the formula, n max represents the upper limit of the charging requests that the set fast charging station can respond to.

[0103] Optionally, the stability of the established fast charging station queue model is evaluated using the Lyapunov function, specifically as follows:

[0104]

[0105] In the formula, V(π(t)) represents the Lyapunov function value of the fast charging station queue model at time t. The smaller the value of the Lyapunov function of the fast charging station queue model, the higher the stability of the corresponding queue model; N s represents the number of fast charging stations in the area, are the weight coefficients of the going queue and the in-station queue respectively, M s (t), Q s(t) represents the number of electric vehicles going to and in the queue at the fast charging station s at time t.

[0106] Optionally, the dissatisfaction degree of the vehicle owner is mainly caused by two aspects: the increment of travel time and the queue waiting time, which are specifically as follows:

[0107] The dissatisfaction caused by the increment of travel time. The dissatisfaction degree of users brought by the increment of travel time mainly depends on the increased driving time during the process of the electric vehicle going to the fast charging station after the guidance occurs, and is evaluated using an exponential function, specifically as follows:

[0108]

[0109] In the formula, F dis (i) represents the dissatisfaction degree caused by the increment of travel time of the i-th electric vehicle, t p (i), t o (i) respectively represent the travel time of the i-th electric vehicle going to the target fast charging station and the travel time corresponding to the nearest fast charging station;

[0110] The dissatisfaction caused by the queue waiting time. The dissatisfaction caused by the queue waiting time is mainly reflected in the total duration of the entire charging process after the electric vehicle receives the guidance instruction, which is specifically as follows:

[0111]

[0112] In the formula, F con (i) represents the dissatisfaction degree caused by the queue waiting time of the i-th electric vehicle, t dri (i), t chi (i) respectively represent the duration of the i-th electric vehicle going and charging stages, Δt represents the time step of a single decision period; ceil(.) is the ceiling function, and its return result is the integer closest to the value in the parentheses and not less than this parameter;

[0113] Based on the above analysis, the evaluation formula for the dissatisfaction degree index of the vehicle owner is as follows:

[0114]

[0115] In the formula, y(t) represents the evaluation result of the dissatisfaction degree of all vehicle owners to be allocated at time t, N(t) represents the total number of electric vehicles to be allocated at time t; α1 and α2 respectively represent the weight coefficients corresponding to the dissatisfaction degrees caused by the increment of travel time and the queue waiting time.

[0116] Optionally, the guided decision model is based on Lyapunov optimization theory and performs optimization modeling by constructing a "drift-penalty term function"; wherein Lyapunov drift is defined as the change in the value of the Lyapunov function within a single time period, which can be used to characterize the stability change of the fast charging station queue model, as follows:

[0117]

[0118]

[0119] Where ΔV(t) represents the Lyapunov drift function value of the fast charging station queue model at time t, represents the upper bound of the Lyapunov drift function of the fast charging station queue model at time t; V(π(t)) represents the value of the Lyapunov function of the fast charging station queue model at time t; M s (t), Q s (t) represents the number of electric vehicles in the queue heading to and at the fast charging station s at time t, are the weight coefficients of the going queue and the station queue respectively; n s (t) represents the number of electric vehicles assigned to the fast charging station s at time t, D s (t) represents the number of electric vehicles that have completed charging in the fast charging station s at time t, A s (t) represents the number of electric vehicles arriving at the fast charging station s at time t, N s Indicates the number of fast charging stations in the area; E{A s (t)}, E{n s (t)}, E{D s (t)} respectively represent A s (t), n s (t), D s (t) expected value;

[0120] On this basis, the guidance decision model aims to minimize the upper bound of the queue Lyapunov drift. After introducing the owner's dissatisfaction as a penalty term into the decision process, the objective function of the electric vehicle guidance decision model is as follows:

[0121]

[0122] Where DPP(t) represents the drift-penalty function of the guided decision model at time t, represents the upper bound of the Lyapunov drift function of the fast charging station queue model at time t; y(t) represents the dissatisfaction evaluation results of all the car owners to be assigned at time t, and β represents the weight coefficient of the car owner dissatisfaction function;

[0123] Since the number of electric vehicles allocated to different fast charging stations at each moment of the model can be obtained by summing the corresponding column vectors of the guiding matrix, that is:

[0124]

[0125] In the formula, n s (t) represents the number of electric vehicles allocated to fast charging station s at moment t, and N(t) represents the total number of electric vehicles to be allocated at moment t; r es is the binary variable corresponding to the electric vehicle guiding decision at moment t. When the value is 1, it means guiding the e-th electric vehicle to be allocated to fast charging station s, and vice versa, it means not guiding the e-th electric vehicle to fast charging station s;

[0126] And the parameter values of A s (t) and D s (t) at each moment of the model can be determined based on the electric vehicle dispatching decisions in the previous period. Therefore, after ignoring the influence of the parameters of A s (t) and D s (t), the objective function of the electric vehicle guiding decision model is modified as follows:

[0127] Objective function:

[0128]

[0129] In the formula, N s represents the number of fast charging stations in the region, and N(t) represents the total number of electric vehicles to be allocated at moment t; M s (t) respectively represents the number of electric vehicles in the queue going to fast charging station s at moment t, is the weight coefficient of the queue going; F es represents the calculation result of the penalty term function when electric vehicle e goes to fast charging station s. To avoid the fast charging stations that do not meet the guiding conditions being selected by the model, when solving the proposed model, for the fast charging stations that do not meet the guiding feasibility conditions, the penalty term value of the model is changed to infinity; r es is the binary variable corresponding to the electric vehicle guiding decision. When the value is 1, it means guiding the e-th electric vehicle to be allocated to fast charging station s, and vice versa, it means not guiding the e-th electric vehicle to fast charging station s;

[0130] Constraint conditions:

[0131]

[0132] In the formula, N s represents the number of fast charging stations in the region, and N(t) represents the total number of electric vehicles to be allocated at moment t; r esIt is a binary variable corresponding to the guiding decision for electric vehicles. When the value is 1, it means guiding the e-th electric vehicle to be allocated to the fast charging station s, and vice versa, it means not guiding the e-th electric vehicle to the fast charging station s.

[0133] Optionally, the guiding decision constructs a guiding decision matrix. The dimension of the guiding decision matrix depends on the number of vehicle owners who upload information during the current dispatching period and the number of fast charging stations in the area. The specific structure is as follows:

[0134]

[0135] In the formula, r(t) represents the electric vehicle guiding decision matrix corresponding to the t-th moment.

[0136] According to another aspect of the present invention, there is also provided an electric vehicle guiding system applicable to urban fast charging stations, including:

[0137] A first acquisition module, used to acquire the spatial position distribution of each fast charging station participating in the scheduling process;

[0138] A second acquisition module, used to acquire the spatial position distribution of electric vehicles with charging requirements, as well as the corresponding remaining battery power and expected battery power values;

[0139] A first determination module, used to determine whether the electric vehicle meets the guiding feasibility conditions based on the spatial position distribution of the fast charging station, the spatial position distribution of the electric vehicle, and the remaining battery power information, and determine the range of fast charging stations that the electric vehicle can choose; if two types of constraints are selected for the guiding feasibility conditions, retain the fast charging stations that meet the two types of constraints, otherwise delete them;

[0140] An evaluation module, used to evaluate the stability of the established fast charging station queue model;

[0141] A second determination module, used to determine the evaluation result of the owner dissatisfaction degree index based on the owner dissatisfaction degree index;

[0142] A third determination module, used to establish the objective function of the electric vehicle guiding decision model based on the stability of the fast charging station queue model and the evaluation result of the owner dissatisfaction degree index; solve the objective function according to the constraint conditions, and determine the electric vehicle guiding decision result.

[0143] It also includes:

[0144] A first feedback module, used to feedback the fast charging station number to be accessed, the arrival time at the fast charging station, and the pick-up time to the electric vehicle owner who uploads information according to the electric vehicle guiding decision result;

[0145] Second feedback module: used to feed back the arrival volume of electric vehicles at each moment, as well as the access and removal plans of electric vehicles, to the fast charging station operator according to the electric vehicle guidance decision result.

[0146] The present invention evaluates the stability of the fast charging station queue model based on the Lyapunov function, and based on the Lyapunov optimization theory, carries out optimization modeling considering the dissatisfaction degree of vehicle owners during the guidance process. The proposed model fully considers the compliance willingness of electric vehicle owners by designing the guidance feasibility conditions, can effectively alleviate the charging congestion phenomenon of electric vehicles at fast charging stations, reduce the time-consuming of the electric vehicle charging process, and improve the charging experience of electric vehicle owners.

[0147] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein.

[0148] Furthermore, the specific steps of the above optional embodiment method are as follows:

[0149] Step S1, fast charging station information acquisition: Before the start of each scheduling cycle, collect the spatial position distribution of each fast charging station participating in the scheduling process;

[0150] Step S2, electric vehicle status information input: Collect the spatial position distribution of electric vehicles with charging requirements and the corresponding parameters;

[0151] Step S3, according to the spatial position distribution of the fast charging station, the spatial position distribution of the electric vehicle, and the parameter information, determine whether the electric vehicle meets the guidance feasibility conditions, and determine the range of fast charging stations that the electric vehicle can choose; If two types of constraints are selected for the guidance feasibility conditions, retain the fast charging stations that meet the two types of constraints, otherwise delete them;

[0152] Step S4, evaluation of the stability of the charging station queue: Evaluate the stability of the established fast charging station queue model;

[0153] Step S5, according to the dissatisfaction degree index of the vehicle owner, obtain the evaluation result of the dissatisfaction degree index of the vehicle owner; Based on the stability of the fast charging station queue model and the evaluation result of the dissatisfaction degree index of the vehicle owner, establish the objective function of the electric vehicle guidance decision model; Solve the objective function, obtain the electric vehicle guidance decision result, and feedback it to the vehicle owner and the fast charging station operator. Based on the electric vehicle guidance decision result, dynamically update the status information of the fast charging station queue model.

[0154] To demonstrate the implementation effect of the present invention, a simulation analysis is now carried out based on the actual road network structure in a certain place in the southwest. Steps S1 - S6 are sequentially executed for each time period. The detailed parameter settings for the simulation analysis are as follows:

[0155] (1) The starting time is set to 7:00 am. The interval of the reservation time period for electric vehicles, that is, the step size, is set to 5 minutes, and the number of time periods is 288.

[0156] (2) The traffic network connectivity graph is as shown in the appendix Figure 2 As shown, the road network system contains a total of 6 fast charging stations. The location distribution of each fast charging station is as shown in the figure. The number of charging piles installed in a single fast charging station is set to 15. The total number of electric vehicles in the region is set to 1000, and their spatial positions are evenly distributed among different nodes. The individual parameter information of electric vehicles is shown in Table 1.

[0157] Table 1 Simulation parameters of electric vehicles

[0158]

[0159] (3) In the queue models of each fast charging station, the weight coefficients of the going - to and in - station queues are both set to 0.5. The weight coefficients α1 and α2 corresponding to the dissatisfaction caused by the incremental travel time and the queue stay duration are also set to 0.5, and the weight coefficient β of the dissatisfaction function of the vehicle owner in the guiding decision - making model is set to 5.

[0160] Based on the road network structure as described in the appendix Figure 2 As described, the queue model at time t contains a total of 6 going - to queues and 6 in - station queues, specifically as follows:

[0161] π(t) = {M1(t), Q1(t), M2(t), Q2(t), M3(t), Q3(t), M4(t), Q4(t), M5(t), Q5(t), M6(t), Q6(t)}

[0162] Taking the 104th electric vehicle dispatching time period as an example, the number of electric vehicles to be allocated at this time is 9, and the dimension of the corresponding electric vehicle dispatching matrix is 9×6.

[0163] Taking the 6th electric vehicle to be allocated in the 104th electric vehicle dispatching time period as an example, the calculation results of the penalty item coefficients corresponding to each fast charging station in the region are shown in the following table:

[0164] Table 2 Calculation results of the penalty item coefficients of the 6th electric vehicle to be allocated in the 104th electric vehicle dispatching time period

[0165]

[0166] It can be seen that since the electric vehicle does not have the feasible conditions to go to the No. 3 fast charging station, the model will set the corresponding penalty term coefficient to infinity at this time to avoid the charging station being selected by the model. On this basis, based on the calculation result of the penalty term coefficient, the model will guide the electric vehicle to the No. 3 fast charging station for charging.

[0167] As shown in the appendix Figures 3-6 After the guidance occurs, as shown, the number of electric vehicles belonging to the No. 3 fast charging station, where there were originally a large number of electric vehicles, has decreased significantly. On the contrary, the number of electric vehicles belonging to the No. 4 fast charging station, where there were originally fewer electric vehicles, has increased significantly compared to before.

[0168] Based on the above analysis, it can be seen that the reduced number of electric vehicles belonging to the charging station helps to alleviate the electric vehicle charging congestion phenomenon at the No. 3 fast charging station, and thus reduces the number of electric vehicles waiting at the station. As shown in the appendix Figure 4 After the intervention of the electric vehicle guidance model, as shown, the number of electric vehicles included in the station queue Q of the model has decreased significantly compared to before, and the charging congestion phenomenon has been significantly improved.

[0169] It should be noted that the proposed guidance model aims to improve the charging experience of vehicle owners by making full use of the charging service capabilities of fast charging stations in the region. However, the results listed in the appendix Figure 5 and Figure 6 show that the intervention of the proposed guidance model will not significantly reduce the facility utilization rate of fast charging stations with a large number of originally belonging electric vehicles. In other words, the economic effects of the operators of these fast charging stations will not be damaged due to the guidance.

[0170] The specific embodiments of the present invention have been described in detail above in conjunction with the accompanying drawings. However, the present invention is not limited to the above embodiments, and various changes can be made without departing from the spirit of the present invention within the scope of knowledge possessed by those of ordinary skill in the art.

Claims

1. An electric vehicle guiding method applicable to rapid charging stations in urban areas, characterized in that: include: Obtain the spatial location distribution of each fast charging station participating in the scheduling process; Obtain the spatial location distribution of electric vehicles that need to be charged, as well as the corresponding remaining power and expected power values; According to the spatial distribution of fast charging stations, the spatial distribution of electric vehicles and the remaining power information, it is determined whether the electric vehicle meets the guidance feasibility conditions and the range of fast charging stations that the electric vehicle can choose is determined; if the guidance feasibility conditions use two types of constraints, the fast charging stations that meet the two types of constraints are retained, otherwise they are deleted; Evaluate the stability of the established fast charging station fleet model; Determine the evaluation result of the car owner dissatisfaction index according to the car owner dissatisfaction index; The objective function of the electric vehicle guidance decision model is established based on the stability of the fast charging station queue model and the evaluation results of the car owner dissatisfaction index; according to the constraints, the objective function is solved to determine the electric vehicle guidance decision result; The dissatisfaction of the vehicle owners is caused by the increase in travel time and the length of time spent in the queue, as follows: Dissatisfaction caused by the increase in travel time: where F dis (i) represents the dissatisfaction caused by the increased travel time of the i-th electric vehicle, and t p (i), t o (i) respectively represent the travel time of the i-th electric vehicle to the target fast charging station and the travel time corresponding to the nearest fast charging station; Dissatisfaction caused by the length of time spent in queue: where, F con (i) represents the dissatisfaction of the i-th electric vehicle caused by the queue residence time, t dri (i), t chi (i) respectively represent the durations of the i-th electric vehicle during the going and charging stages, Δt represents the time step of a single decision period; ceil(.) is the ceiling function; Based on the above analysis of dissatisfaction caused by the increase in travel time and the dissatisfaction caused by the length of time spent in the queue, the evaluation formula for the driver dissatisfaction index is as follows: Where y(t) represents the dissatisfaction evaluation result of all the car owners to be allocated at time t, N(t) represents the total number of electric vehicles to be allocated at time t; α1 and α2 represent the weight coefficients corresponding to the dissatisfaction caused by the incremental travel time and the queue stay time, respectively.

2. The electric vehicle guiding method applicable to urban fast charging stations according to claim 1, characterized in that: The guiding feasibility conditions specifically include the following two categories: Remaining power limit: Where E r (i) represents the remaining power uploaded by the owner of the i-th electric vehicle, and E min represents the lower limit of the battery power of the electric vehicle set, and L 100 represents the power consumption per 100 kilometers of the electric vehicle; d p (i), d o (i) respectively represent the distance of the i-th vehicle to the target fast charging station and the distance to the nearest fast charging station; Travel time increment constraint: t p (i)-t o (i)≤Δt max where t p (i) and t o (i) respectively represent the travel time of the i-th electric vehicle to the target fast charging station and the travel time corresponding to the nearest fast charging station, and Δt max represents the upper limit of the allowable increment of the travel time of the electric vehicle that the vehicle owner can accept.

3. The electric vehicle guiding method applicable to urban fast charging stations according to claim 1, characterized in that: The fast charging station queue model is obtained based on summarizing the queue lengths at corresponding moments, and the specific structure is as follows: π(t) = {M1(t), Q1(t), …, M s (t), Q s (t), …} Where, π(t) refers to the fast charging station queue model, and M s (t), Q s (t) represent the number of electric vehicles going to and in the queue at the fast charging station s at time t, respectively.

4. The electric vehicle guiding method applicable to urban fast charging stations according to claim 3, characterized in that: The fast charging station queue model is divided into a going queue M s and a queue in the station Q s in two categories, and the corresponding queue lengths are represented by the number of electric vehicles in the queue at each moment; among them, M s queue is used to refer to all electric vehicles that have accessed the s-th fast charging station but have not yet reached the fast charging station; Q s queue is used to refer to all electric vehicles that have arrived at the fast charging station s but have not completed charging; M s , Q s The queue lengths are updated based on the following formulas respectively: M s (t + 1)=M s (t)+n s (t)-A s (t) Q s (t + 1) = max{Q s (t) - D s (t), 0} + A s (t) where n s (t) represents the number of electric vehicles allocated to the fast charging station s at time t; A s (t) represents the number of electric vehicles arriving at the fast charging station s at time t, D s (t) represents the number of electric vehicles that have completed charging in the fast charging station s at time t; max{} represents selecting the maximum value of each parameter within the brackets; On this basis, the number of electric vehicles allocated to a fast charging station at each moment should not be greater than the upper limit of the number of charging requests it can respond to, as follows: n s (t) ≤ n max where n max represents the upper limit of the number of charging requests that the set fast charging stations can respond to.

5. The electric vehicle guiding method applicable to urban fast charging stations according to claim 1, wherein: The stability of the established fast charging station queue model is evaluated using the Lyapunov function, as follows: Where \(V(\pi(t))\) represents the value of the Lyapunov function of the fast charging station queue model at time \(t\). The smaller the value of the Lyapunov function of the fast charging station queue model, the higher the stability of the corresponding queue model; \(N\) s represents the number of fast charging stations in the area, are the weight coefficients of the arrival queue and the in-station queue respectively, \(M\) s (t), \(Q\) s (t) represent the number of electric vehicles in the arrival queue and the in-station queue at the fast charging station \(s\) at time \(t\) respectively.

6. The electric vehicle guiding method applicable to an urban rapid charging station according to claim 1, characterized in that: The guided decision model is based on the Lyapunov optimization theory and is optimized by constructing a "drift-penalty term function". The Lyapunov drift is defined as the change in the value of the Lyapunov function within a single period, which can be used to characterize the stability change of the fast charging station queue model, as follows: Where, ΔV(t) represents the value of the Lyapunov drift function of the fast charging station queue model at time t, represents the upper bound of the value of the Lyapunov drift function of the fast charging station queue model at time t; V(π(t)) represents the value of the Lyapunov function of the fast charging station queue model at time t; M s (t), Q s (t) represent the number of electric vehicles going to and in the queue at the fast charging station s at time t, are the weight coefficients of the going queue and the in-station queue respectively; n s (t) represents the number of electric vehicles allocated to the fast charging station s at time t, D s (t) represents the number of electric vehicles that have completed charging at the fast charging station s at time t, A s (t) represents the number of electric vehicles arriving at the fast charging station s at time t, N s represents the number of fast charging stations in the region; E{A s (t)}, E{n s (t)}, E{D s (t)} represent the expected values of A s (t), n s (t), D s (t) respectively; On this basis, the guidance decision model aims to minimize the upper bound of the queue Lyapunov drift. After introducing the owner's dissatisfaction as a penalty term into the decision process, the objective function of the electric vehicle guidance decision model is as follows: where DPP(t) represents the drift-penalty term function of the guiding decision-making model at time t, represents the upper bound of the value of the Lyapunov drift function of the fast charging station queue model at time t; y(t) represents the dissatisfaction evaluation result of all owners corresponding to the allocation to be made at time t, and β represents the weight coefficient of the owner dissatisfaction function; Since the number of electric vehicles allocated to different fast charging stations at each moment can be obtained by summing the corresponding column vectors of the steering matrix, that is, where n s (t) represents the number of electric vehicles allocated to the fast charging station s at time t, and N(t) represents the total number of electric vehicles to be allocated at time t; r es is the binary variable corresponding to the electric vehicle guidance decision at time t. When the value is 1, it means that the e-th electric vehicle to be allocated is guided to the fast charging station s, otherwise it means that the e-th electric vehicle is not guided to the fast charging station s; And at each moment A s (t), D s (t) parameter values are determined based on the electric vehicle dispatching decisions in previous periods. Therefore, after ignoring the influence of A s (t), D s (t) parameters, the objective function of the electric vehicle guidance decision model is modified as follows: Objective function: Where N s represents the number of fast charging stations in the area, and N(t) represents the total number of electric vehicles to be allocated at time t; M s (t) respectively represent the number of electric vehicles in the queue going to fast charging station s at time t, is the weight coefficient of the queue going; F es represents the calculation result of the penalty term function when electric vehicle e goes to fast charging station s. For fast charging stations that do not meet the guiding feasibility conditions, the penalty term value of the model is changed to infinity; r es is the binary variable corresponding to the electric vehicle guiding decision. When the value is 1, it means guiding the e-th electric vehicle to be allocated to fast charging station s, otherwise it means not guiding the e-th electric vehicle to fast charging station s; Constraints: where N s represents the number of fast charging stations in the area, and N(t) represents the total number of electric vehicles to be allocated at time t; r es is a binary variable corresponding to the electric vehicle guidance decision. When the value is 1, it means that the e-th electric vehicle to be allocated is guided to the fast charging station s, otherwise it means that the e-th electric vehicle is not guided to the fast charging station s.

7. The electric vehicle guiding method applicable to urban fast charging stations according to claim 6, characterized in that: The guiding decision corresponds to the binary variable to construct a guiding decision matrix. The dimension of the guiding decision matrix depends on the number of car owners who upload information during the current dispatch period and the number of fast charging stations in the area. The specific structure is as follows: Where r(t) represents the electric vehicle guidance decision matrix corresponding to time t.

8. An electric vehicle guidance system applicable to a rapid charging station in an urban area for performing the method according to claim 1, characterized in that: include: The first acquisition module is used to acquire the spatial location distribution of each fast charging station participating in the scheduling process; The second acquisition module is used to acquire the spatial location distribution of electric vehicles with charging requirements, as well as the corresponding remaining battery power and expected battery power values; The first determination module is used to determine whether an electric vehicle meets the guidance feasibility condition based on the spatial location distribution of fast charging stations, the spatial location distribution of electric vehicles, and the remaining battery power information, and determine the range of fast charging stations that the electric vehicle can choose; if two types of constraints are selected for the guidance feasibility condition, the fast charging stations that meet the two types of constraints are retained, otherwise they are deleted; The evaluation module is used to evaluate the stability of the established fast charging station queue model; The second determination module is used to determine the evaluation result of the owner dissatisfaction degree index based on the owner dissatisfaction degree index; The third determination module is used to establish the objective function of the electric vehicle guidance decision-making model based on the stability of the fast charging station queue model and the evaluation result of the owner dissatisfaction degree index; solve the objective function according to the constraint conditions to determine the electric vehicle guidance decision-making result.

9. The electric vehicle guiding system applicable to urban rapid charging stations according to claim 8, characterized in that: It further includes: The first feedback module is used to feedback the fast charging station number to be accessed, the arrival time at the fast charging station, and the pick-up time to the electric vehicle owner who uploads information according to the electric vehicle guidance decision-making result; The second feedback module: is used to feedback the arrival quantity of electric vehicles at each moment, as well as the access and removal plans of electric vehicles to the fast charging station operator according to the electric vehicle guidance decision-making result.

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