Optimal scheduling method and system for electric vehicle charging network design

Abstract

The application discloses an optimal scheduling method and system for an electric vehicle charging network design, and relates to the technical field of electric vehicle fleet routing. The application comprises the following steps: defining a charging function and a wear cost function according to an actual battery charging process and a battery wear condition; constructing a mathematical model of the wear cost function according to a monotonic relationship between the wear cost and the charging state, and setting a constraint condition; and solving the mathematical model of the wear cost function by using a three-stage efficient heuristic algorithm of CWIGALNS to obtain an optimal scheduling scheme. The application fully considers the influence of a charging process on electric vehicle scheduling, can systematically analyze and solve a collaborative optimization problem of electric vehicle distribution service and charging scheduling, and reduces logistics operation cost.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle fleet routing, and in particular to an optimal scheduling method and system designed for electric vehicle charging networks. Background Art

[0002] Compared with traditional logistics vehicles, electric vehicles incur additional charging costs, such as charging time and battery wear costs. It is crucial to reasonably arrange the routes and charging plans of electric vehicles based on power distribution service requirements and battery characteristics, delay battery degradation, and extend battery cycle life.

[0003] Most existing methods determine route scheduling or facility site selection plans under limited battery range. This approach, which adds mileage restrictions to traditional research methods, usually ignores the impact of the battery charging process on electric vehicle scheduling and fails to incorporate charging strategies into the location decisions and routing issues of charging facilities. To this end, the present invention proposes an optimal scheduling method and system for electric vehicle charging network design. Summary of the Invention

[0004] The purpose of the present invention is to provide an optimal scheduling method and system for electric vehicle charging network design, which can systematically analyze and solve the problem of coordinated optimization of electric vehicle delivery services and charging scheduling, and reduce logistics operation costs.

[0005] According to a first aspect of the present invention, to achieve the above-mentioned purpose, the present invention provides the following technical solution: an optimal scheduling method for electric vehicle charging network design, comprising the following steps:

[0006] According to the actual charging process and battery wear of the battery, the charging function and the wear cost function are defined;

[0007] According to the monotonically increasing or decreasing relationship between wear cost and charging state, a mathematical model of the corresponding wear cost function is constructed and constraints are set;

[0008] A mixed integer linear programming formula is proposed to minimize the running cost as follows:

[0009]

[0010]

[0011] (6)

[0012] Where, is the distance from node g to node h; is the unit charging station construction cost of node j; is a binary variable that takes the value 1 if the site is located at node j and 0 otherwise; is a binary variable, which takes the value 1 if vehicle k goes from node i to node j, and 0 otherwise; is the total wear cost of vehicle k that passes through nodes g, j, and h consecutively at charging station j; The wear cost per kilowatt-hour of charging and discharging in interval d; is the charging time per unit of electricity in interval d; is the charging time cost of vehicle k passing through nodes g, j, and h at charging station j; The interval for vehicle k to charge in the parking lot quantity;

[0013] The three-stage efficient heuristic algorithm of CWIGALNS is used to solve the mathematical model of the wear cost function and obtain the optimal scheduling solution;

[0014] The three-stage efficient heuristic algorithm of CWIGALNS includes Clarke and Wright preservation algorithm CW, iterative greedy algorithm IG and adaptive large neighborhood search algorithm ALNS.

[0015] Furthermore, according to the actual battery charging process and battery wear, the charging function and wear cost function are defined as follows:

[0016] (21) The changes in current and voltage during the actual battery charging process are nonlinear, so the charging function including the constant current and constant voltage stages is nonlinear;

[0017] (22) Assume that each charger has a specific constant current-constant voltage charging function, and the charging function has breakpoints, and fitted the real constant current-constant pressure concave function, assuming that the breakpoints The relevant charging status is ,in ;

[0018] Assuming that the charging current between consecutive breakpoints is constant, the charging function is:

[0019] (1)

[0020] Where, is the total charging time, The battery charging state is in the range Unit charging time within The battery charging state is in the range The amount of electricity inside;

[0021] set up is the set of charging state time intervals, let For the length of each time interval:

[0022] (2)

[0023] (3)

[0024] Where, They are the upper and lower limits of the charging state respectively;

[0025] Battery Price ( ) can be expressed as:

[0026] (4)

[0027] (5)

[0028] Where, For The cost of charging and discharging per kilowatt-hour, It is the energy value of each state of charge interval, DOD is the depth of discharge, and ACC is the battery cycle count.

[0029] Furthermore, based on the monotonically increasing relationship between wear cost and state of charge, a mathematical model of the corresponding wear cost function is constructed, and constraints are set as follows:

[0030] The constraints are as follows:

[0031] Each customer is served only once:

[0032] (7)

[0033] The vehicle is charged at a location with charging facilities:

[0034] (8)

[0035] Traffic flow balance at each node:

[0036] (9)

[0037] Vehicle departure and return from the depot:

[0038] (10)

[0039] Each electric vehicle serves at most one route:

[0040] (11)

[0041] The amount of remaining goods in the electric vehicle before and after passing through a node:

[0042]

[0043] (12)

[0044] The range of the remaining goods in the electric vehicle:

[0045] (13)

[0046] The amount of remaining energy in the electric vehicle before and after passing through each node on the business route:

[0047]

[0048] (14)

[0049] The amount of remaining energy in the electric vehicle when it leaves the distribution center:

[0050] (15)

[0051] The amount of battery power at the customer node does not change:

[0052] (16)

[0053] The amount of power in the vehicle before and after leaving the charging station:

[0054] (17)

[0055] The upper and lower limits of the amount of charging:

[0056] (18)

[0057] Ensure that the vehicle has enough power to complete the distribution task:

[0058] (19)

[0059] Ensure that the total amount of energy charged by charging station j in all charging state intervals is equal to the total amount of charging by charging station j for vehicle k:

[0060] (20)

[0061] Formulas (21) - (23) ensure that there is enough power in each charging state interval:

[0062] (21)

[0063] (22)​

[0064] (23)

[0065] Energy consumption cost of vehicle k charging at charging station:

[0066] (24)

[0067] Charging time cost of energy charged to vehicle k at charging station:

[0068] (25)

[0069] Equations (26) - (27) ensure that within a charging state interval, the variables and must be equal to a charging state interval whose upper limit is higher than the amount of electricity before vehicle k arrives at charging station j or leaves the charging station:

[0070] (26)

[0071] (27)

[0072] Binary constraints:

[0073] (28)

[0074] where, is the set of candidate stations; is the set of customers; is the set of vehicles; is the set of charging state intervals; is a single warehouse; is another warehouse of the same; is the set of vertices; is the battery wear cost coefficient; is the depot charging time cost coefficient, is the path cost per unit distance, is the driving distance of the vehicle, the remaining electricity must be maintained between a certain minimum and maximum and ; is the distance from node g to node h; is the unit charging station construction cost of node j; is a binary variable, taking the value 1 if the site is located at node j, otherwise taking 0; is a binary variable, taking the value 1 if vehicle k goes from node i to node j, otherwise taking 0; is a binary variable that takes the value 1 if vehicle k is charged in the vehicle segment using the time interval d, and 0 otherwise; is a binary variable that takes the value 1 if vehicle k is charged at charging station j using interval d and passes through nodes g, j, and h consecutively, and 0 otherwise; The total amount charged at charging station j for vehicle k that passes through nodes g, j, and h consecutively; is the residual load when vehicle k leaves node g and arrives at node h; The number of battery charges for vehicle k that is at charging station j and passes through nodes g, j, and h consecutively; is the total wear cost of vehicle k that passes through nodes g, j, and h consecutively at charging station j; The interval for vehicle k to charge in the parking lot quantity; is the charging time cost of vehicle k passing through nodes g, j, and h at charging station j; The maximum distance allowed by the remaining power when vehicle k leaves node g to h; is the maximum distance allowed by the remaining power when vehicle k reaches node h from g.

[0075] Furthermore, based on the monotonically decreasing relationship between wear cost and charge state, a mathematical model of the corresponding wear cost function is constructed, and constraints are set as follows:

[0076] (41) Define variables Charging status interval When the vehicle In the function The amount of electricity released; variable For vehicles In the function Total wear and tear costs on

[0077] If the function The battery of vehicle k is discharged, then the binary variable is equal to 1; Indicates that vehicle k is in the function ), the specific formula is as follows:

[0078]

[0079]

[0080] (29)

[0081] (42) The above function (29) is subject to the constraints of equations (7) - (25) and (28) as well as the following constraints:

[0082] Equations (31) - (32) constrain the maximum acceptable gap by calculating the difference between its lower bound and the current SOC when vehicle k leaves station j or depot o:

[0083]

[0084] (30)

[0085] (31)

[0086] Ensure that the maximum discharge energy for each SOC gap is not exceeded by L if available:

[0087] (32)

[0088] Ensure that for each vehicle k, the total discharge over all SOC gaps at charging station j is equal to the total discharge at charging station j:

[0089] (33)

[0090] The total emissions for vehicle k along the route from node g to node h:

[0091] (34)

[0092] Constraint (35) is the same as constraint (30) but only for discharges:

[0093] (35)

[0094] The loss cost for the total amount of vehicle k discharged on the function :

[0095] (36).

[0096] Further, the Clarke and Wright saving algorithm is as follows:

[0097] (51) Create a route for each customer ;

[0098] (52) Identify edge nodes that are directly connected to the warehouse ;

[0099] (53) Select the route and​ Two edge nodes in and As a save pair, calculate the save value and find the total demand for the new route;

[0100] (54) Select the saved pairs in SPL in the order of saved values , identify the edge nodes and Route and routes ;

[0101] (55)Connecting routes and ,If the total demand of the new route exceeds the loading capacity of the vehicle, the merging operation is abandoned, otherwise, the merging operation is ended;

[0102] (56) Delete the saved pair from the program , repeat the process from step (54) to step (56) until the program is empty.

[0103] Furthermore, at the beginning of the iterative greedy algorithm, all charging stations will be deleted and reselected based on the placement cost. The specific parameters are as follows:

[0104] (61) Breakpoint: Under the mileage limit, if the electric vehicle does not charge at a charging station during driving, it will not be able to reach certain nodes; breakpoint Has the following properties: , define the route The first breakpoint on ;

[0105] (62) Node accessibility: To compare the impact of different charging stations on customer points in the route, define the parameter Point accessibility for customers, ,parameter It is used to indicate whether the electric vehicle will run out of power on the way, and the minimum value of the path customer point accessibility is defined as ;

[0106] (63) Charging strategy: If charging time and battery wear increase with the state of charge, electric vehicles need to recharge their batteries at the depot or charging station until they reach the next stop or return to the depot, and the minimum power is not less than ;

[0107] (64) Placement cost: on the path Node on Add a charging station , recorded as , the placement cost is recorded as :

[0108] (37)

[0109] (38)

[0110] (39)

[0111] (40)

[0112] (41)

[0113] Where, is the increase in accessibility, is the additional routing cost, is the wear cost, is the additional billable time cost, is the penalty value; denote the accessibility of v before and after insertion into site j, respectively; and They represent the minimum value of the current path reachability before and after inserting j.

[0114] Furthermore, in the ALNS algorithm, the delete operator is used to delete the current route. Customer nodes and repeatedly use the insertion operator to reinsert them into the route, Set as A random value in :

[0115] (71) Assign to the current solution and optimal solution ;

[0116] (72) If the number of iterations is less than the given number, the insertion and removal operators are selected and Assign to neighboring solutions , otherwise, the search ends;

[0117] (73) Use the removal and insertion operators to handle ;

[0118] (74) If the acceptance criteria are met at this time, the value endowment ;

[0119] (75) If , then endowment ;

[0120] (76) Update the score and weight of each operator and return to step (72);

[0121] The adaptive search mechanism used by the ALNS algorithm is as follows:

[0122] At the beginning of each stage, the operator weights are adjusted and the operators are selected according to the preset rules to achieve adaptive update of the search process, as follows:

[0123] Set up the first Phase Operator The weight is , then the probability of the operator being selected is , where H is the operator set; The stage requires that the operator weights be updated according to the following rules after each search stage:

[0124] Define the number of times operator I is adopted in stage j, and the scores are and ; If the operator is used in stage j , then its weight in the next stage is , otherwise the weight remains unchanged, where is the reaction coefficient, set to 0.3;

[0125] If the neighborhood solution Better than current solutions , then the neighborhood solution is retained; otherwise, the probability that the neighborhood solution is retained is , where the initial temperature .

[0126] According to a second aspect of the present invention, the present invention provides an optimal scheduling system for electric vehicle charging network design, which is used to implement the above-mentioned optimal scheduling method for electric vehicle charging network design, including:

[0127] Function definition module, used to define charging function and wear cost function according to the actual charging process of the battery and the battery wear;

[0128] A construction module is used to construct a mathematical model of the corresponding wear cost function according to the monotonically increasing or decreasing relationship between the wear cost and the charging state, and set constraints;

[0129] The solution output module is used to solve the mathematical model of the wear cost function using the three-stage efficient heuristic algorithm of CWIGALNS to obtain the optimal scheduling solution.

[0130] The present invention has at least the following beneficial effects:

[0131] 1. The present invention fully considers the impact of the charging process on electric vehicle scheduling, can systematically analyze the collaborative optimization problem of electric vehicle delivery service and charging scheduling, establish an overall service decision-making plan based on nonlinear charging time function and battery wear characteristics, and reduce logistics operation costs.

[0132] 2. The present invention proposes a nonlinear charging function, so that the battery charging rate is no longer a constant value, but

[0133] is a function of the current state of the battery.

[0134] 3. The present invention uses a three-stage efficient heuristic algorithm that is more efficient and more stable than the traditional CPLEX algorithm.

[0135] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0136] Figure 1 The figure is a flowchart of the scheduling method described in an embodiment of the present invention. DETAILED DESCRIPTION

[0137] The following will be combined with the accompanying drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the embodiments described are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present disclosure.

[0138] See also Figure 1 The present invention provides a technical solution: an optimal scheduling method for electric vehicle charging network design, comprising the following steps:

[0139] S1. Based on the actual battery charging process and battery wear, define the charging function and wear cost function as follows:

[0140] S11. During actual battery charging, the changes in current and voltage are nonlinear. Therefore, the charging function consisting of the constant current (CC) and constant voltage (CV) stages is also nonlinear.

[0141] As time goes by, the state of charge (SOC) is approximated piecewise linearly, assuming that each charger has a specific constant current-constant voltage charging function with breakpoints, and fitted the real constant current-constant pressure concave function, assuming that the breakpoints The relevant charging status is ,in ;

[0142] Assuming that the charging current between consecutive breakpoints is constant, the charging function is:

[0143] (1)

[0144] Where, is the total charging time, The battery charging state is in the range Unit charging time within The battery charging state is in the range The amount of electricity inside;

[0145] S12. In actual applications, batteries undergo multiple cycles during their lifecycle. The cycle life, depth of discharge (DOD), and initial achievable cycle count (ACC) of each cycle are different, which will cause different degrees of wear on the battery.

[0146] set up is the set of charging state time intervals, let For the length of each time interval:

[0147] (2)

[0148] (3)

[0149] Where, They are the upper and lower limits of the charging state respectively;

[0150] Battery Price ( ) can be expressed as:

[0151] (4)

[0152] (5)

[0153] Where, For The cost of charging and discharging per kilowatt-hour, is the energy value for each state of charge interval, DOD is the depth of discharge, and ACC is the battery cycle count;

[0154] S2. Based on the monotonically increasing or decreasing relationship between wear cost and state of charge, construct a mathematical model of the corresponding wear cost function and set constraints;

[0155] S21. Based on the monotonically increasing relationship between wear cost and state of charge, a mathematical model of the corresponding wear cost function is constructed, and constraints are set as follows:

[0156] (S21.1) The solution derived from the model always automatically fills the lower SOC interval. The lowest acceptable interval is no longer bounded by the length of the interval, L, but by the difference between its upper limit and the SOC of the vehicle arriving at the current station (or destination);

[0157] In a non-decreasing wear function with respect to SOC, all the charging energy will subsequently be released at any optimal solution of the Electric Vehicle Routing Problem with Nonlinear Charging and Battery Wear (EVRP-NCBW), and any initial charge in the vehicle battery will be fully discharged.

[0158] Based on this, a mixed integer linear programming formula is proposed to minimize the operating costs (mainly including: construction cost, route cost, battery wear cost and charging time cost), as follows:

[0159]

[0160]

[0161] (6)

[0162] Where, is the distance from node g to node h; is the unit charging station construction cost of node j; is a binary variable that takes the value 1 if the site is located at node j and 0 otherwise; is a binary variable, which takes the value 1 if vehicle k goes from node i to node j, and 0 otherwise; is the total wear cost of vehicle k that passes through nodes g, j, and h consecutively at charging station j; The wear cost per kilowatt-hour of charging and discharging in interval d; is the charging time per unit of electricity in interval d; is the charging time cost of vehicle k passing through nodes g, j, and h at charging station j; The interval for vehicle k to charge in the parking lot quantity.

[0163] (S21.2) The constraints are as follows:

[0164] Each customer is served only once:

[0165] (7)

[0166] The vehicle is charged at a location with charging facilities:

[0167] (8)

[0168] Traffic balance for each node:

[0169] (9)

[0170] Vehicle departure and return from the depot:

[0171] (10)

[0172] Each electric vehicle (EV) only serves one route:

[0173] (11)

[0174] Residual cargo quantity of electric vehicles before and after passing through a node:

[0175]

[0176] (12)

[0177] Range of residual cargo in electric vehicles:

[0178] (13)

[0179] Residual energy of electric vehicles passing through each node on the business route:

[0180]

[0181] (14)

[0182] Residual battery capacity of electric vehicles when leaving the distribution center:

[0183] (15)

[0184] Battery capacity remains unchanged at customer nodes:

[0185] (16)

[0186] Battery capacity before and after the vehicle leaves the charging station:

[0187] (17)

[0188] Charging capacity Upper and lower limits:

[0189] (18)

[0190] Ensure that vehicles have sufficient battery capacity to complete the distribution task:

[0191] (19)

[0192] Ensure that the total energy charged by charging station j in all charging state intervals is equal to the total charge amount of charging station j to vehicle k:

[0193] (20)

[0194] Formula (21)-Formula (23) ensures that sufficient power is charged in each charging state interval:

[0195] (twenty one)

[0196] (twenty two)

[0197] (twenty three)

[0198] The energy consumption cost of vehicle k charging at the charging station is:

[0199] (twenty four)

[0200] The charging time cost of the energy charged to vehicle k at the charging station:

[0201] (25)

[0202] Formula (26)-Formula (27) ensures that within the charging state interval, the variable and must be equivalent to a state-of-charge interval whose upper limit is higher than the charge level of vehicle k before it arrives at charging station j or leaves the charging station:

[0203]

[0204] (26)

[0205] (27)

[0206] Binary constraints:

[0207] (28)

[0208] Where, is a collection of candidate stations; Collection for customers; For the collection of vehicles; A collection of state-of-charge intervals; For a single warehouse; For the same another warehouse; is the vertex set; a battery wear cost coefficient; a depot charging time cost coefficient, a path cost per unit distance, a driving range of the vehicle, the remaining power must be maintained at a certain minimum and maximum value and ; a distance from node g to node h; a unit charging station construction cost of node j; a binary variable, taking the value 1 if the site is located at node j, otherwise taking 0; a binary variable, taking the value 1 if vehicle k passes from node i to node j, otherwise taking 0; a binary variable, taking the value 1 if vehicle k is charged at charging station j using time interval d, otherwise taking 0; a binary variable, taking the value 1 if vehicle k is charged at charging station j using interval d, continuously passing through nodes g, j and h, otherwise taking 0; a total amount of battery charging of vehicle k at charging station j, continuously passing through nodes g, j and h; a remaining load of vehicle k when leaving node g to node h; a number of battery charges of vehicle k at charging station j and continuously passing through nodes g, j and h; a total wear cost of vehicle k at charging station j, continuously passing through nodes g, j and h; a number of intervals of vehicle k charging at the depot; a charging time cost of vehicle k at charging station j, continuously passing through nodes g, j and h; a maximum distance allowed by the remaining power of vehicle k when leaving node g to node h; a maximum distance allowed by the remaining power of vehicle k when arriving at node h from g.

[0209] S22. According to the monotonicity decreasing relationship between the wear cost and the charging state, a mathematical model corresponding to the wear cost function is constructed, and the constraint conditions are set, as follows:

[0210] (S22.1) The wear cost function decreases monotonically with respect to SOC, indicating that battery degradation is more severe when cycling at lower SOC. In this case, in order to achieve (or maintain) a more ideal SOC interval, part of the energy will be charged in the optimization scheme of the electric freight vehicle distribution network model with nonlinear charging and battery wear model (ELRP-NCBW). This means that some energy may be charged without ever having the opportunity to discharge. Therefore, the cycle degradation of charge and discharge must be calculated separately, so that the charge and discharge energy of each SOC interval in the degradation model can be monitored separately.

[0211] Defining variables Charging status interval When the vehicle In the function The amount of electricity released; variable For vehicles In the function Total wear and tear costs on

[0212] If the function The battery of vehicle k is discharged, then the binary variable is equal to 1; Indicates that vehicle k is in the function ), the specific formula is as follows:

[0213]

[0214]

[0215] (29)

[0216] (S22.2) The above function (29) is subject to the constraints of formulas (7)-(25) and (28), and is also subject to the following constraints:

[0217] Formula (31)-Formula (32) constrains the maximum acceptable interval when vehicle k leaves station j or depot o by calculating the difference between its lower limit and the current SOC:

[0218]

[0219] (30)

[0220] (31)

[0221] Ensure that the maximum discharge energy per SOC interval does not exceed L under applicable conditions:

[0222] (32)

[0223] Ensure that for each vehicle k, the total discharge amount in all intervals at charging station j is equal to the total discharge amount at charging station j:

[0224] (33)

[0225] The total emissions of vehicle k along the route from node g to node h:

[0226] (34)

[0227] Constraint (35) is the same as constraint (30), but only for discharging:

[0228] (35)

[0229] The loss cost of the total amount of vehicle k discharging on the function :

[0230] (36)

[0231] wherein, is the set of candidate stations; is the set of customers; is the set of vehicles; is the set of charging state intervals; is a single warehouse; is another warehouse of the same kind; is the set of vertices; is the battery wear cost coefficient; is the depot charging time cost coefficient, is the path cost per unit distance, is the driving distance of the vehicle, the remaining amount of electricity must be maintained between a certain minimum and maximum and ; is the distance from node g to node h; is the unit charging station construction cost of node j; is a binary variable, taking the value 1 if the station is located at node j, and 0 otherwise; is a binary variable, taking the value 1 if vehicle k travels from node i to node j, and 0 otherwise; is a binary variable, taking the value 1 if vehicle k is charged at charging station j using time interval d, and 0 otherwise; is a binary variable, taking the value 1 if vehicle k is charged at charging station j using interval d, continuously passing through nodes g, j and h, and 0 otherwise; total amount of money for vehicle k charging at charging station j and continuously passing through nodes g, j and h; remaining load for vehicle k leaving node g to node h; battery charging number for vehicle k charging at charging station j and continuously passing through nodes g, j and h; total wear cost for vehicle k continuously passing through nodes g, j and h at charging station j; interval for vehicle k charging at the parking lot number; charging time cost for vehicle k continuously passing through nodes g, j, h at charging station j; maximum distance allowed by the remaining amount of electricity for vehicle k leaving node g to node h; maximum distance allowed by the remaining amount of electricity for vehicle k from node g to node h;

[0232] S3. A three-stage efficient heuristic algorithm using CWIGALNS is used to solve the mathematical model of the wear cost function to obtain an optimal scheduling scheme;

[0233] The three-stage efficient heuristic algorithm of CWIGALNS is Clarke and Wright Saving Algorithm (CW), Iterative Greedy Algorithm (IG) and Adaptive Large Neighborhood Search (ALNS), which are as follows:

[0234] S31. Clarke and Wright Saving Algorithm is as follows:

[0235] (S31.1) Create a route for each customer;

[0236] (S31.2) Identify the edge nodes directly connected to the warehouse;

[0237] (S31.3) Select two edge nodes and in the route and as a saving pair, calculate the saving value and find the total demand of the new route;

[0238] (S31.4) Select the saving pair in the SPL in the order of the saving value, identify the route and the route containing the edge nodes and respectively;

[0239] (S31.5) Connect the routes and, if the total demand of the new route exceeds the loading capacity of the vehicle, abandon this merging operation, otherwise, end this merging operation;

[0240] (S31.6) Delete the saving pair from the program, repeat the process of steps (S31.4) to (S31.6) until the program is empty;

[0241] S32. At the beginning of the iterative greedy algorithm, all charging stations are removed and charging stations are reselected according to the placement cost, which is described as follows:

[0242] (S32.1) Breakpoint: If the electric vehicle does not charge at a charging station during the journey, it will not be able to reach some nodes under the limit of driving range; the breakpoint has the following properties: , define the route The first breakpoint on the route is ;

[0243] (S32.2) Node accessibility: In order to compare the impact of different charging stations on customer points in the route, define the parameter as the accessibility of customer points, Parameter is used to indicate whether the electric vehicle will run out of power on the way, and the minimum value of the accessibility of the path customer point is defined as ;

[0244] (S32.3) Charging strategy: If the charging time and battery wear increase with the charging state, the electric vehicle needs to charge the battery at the factory or charging station until it reaches the next station or returns to the factory, and the minimum power is not less than ; For scenarios with monotonically decreasing or general wear cost functions, the JAVA language can be used to call the MIP solver Cplex or Gurobi to directly calculate the charging amount of the electric vehicle;

[0245] (S32.4) Placement cost: Add a charging station at node on the path , denoted as , and the placement cost is denoted as :

[0246] (37)

[0247] (38)

[0248] (39)

[0249] (40)

[0250] (41)

[0251] In the formula, is the accessibility increase, is the additional routing cost, is the wear cost, is the additional billable time cost, is the penalty value; denote the accessibility of v before and after insertion into site j, respectively; and Respectively represent the minimum value of the current path reachability before and after inserting j

[0252] In the S33.ALNS algorithm, use the delete operator to delete from the current route. Customer nodes and repeatedly use the insertion operator to reinsert them into the route, Set as A random value in :

[0253] (S33.1) Assign to the current solution and optimal solution ;

[0254] (S33.2) If the number of iterations is less than the given number, select the insertion and removal operators and Assign to neighboring solutions , otherwise, the search ends;

[0255] (S33.3) Use the removal and insertion operators to handle ;

[0256] (S33.4) If the acceptance criteria are met at this point, the value endowment ;

[0257] (S33.5) If , then endowment ;

[0258] (S33.6) Update the score and weight of each operator and return to step (S33.2);

[0259] The adaptive search mechanism used by the ALNS algorithm is as follows:

[0260] At the beginning of each stage, the operator weights are adjusted and the operators are selected according to the preset rules to achieve adaptive update of the search process, as follows:

[0261] Set up the first Phase Operator The weight is , then the probability of the operator being selected is , where H is the operator set; The stage requires that the operator weights be updated according to the following rules after each search stage:

[0262] define the number of times the operator I is adopted at stage j, the fraction is and ; if the operator I is used at stage j , its weight at the next stage is , otherwise the weight remains unchanged, where is the reaction coefficient, set to 0.3;

[0263] If the neighborhood solution is better than the current solution , the neighborhood solution is retained; otherwise, the probability that the neighborhood solution is retained is , where the initial temperature .

[0264] To sum up, the application firstly describes the actual charging process and battery wear condition; then, according to the monotonicity of wear cost and state of charge (SOC) in different cases, a corresponding comprehensive mathematical model is constructed; finally, a three-stage efficient heuristic algorithm named CWIGALNS is proposed to solve the model, which can quickly analyze the collaborative optimization problem of electric vehicle distribution service and charging scheduling, establish an overall service decision scheme based on the nonlinear charging time function and battery degradation characteristics, and reduce the logistics operating cost.

[0265] Embodiment Two:

[0266] The embodiment provides an optimal scheduling system for electric vehicle charging network design, which is used for implementing the optimal scheduling method for electric vehicle charging network design described in embodiment one, and comprises the following steps:

[0267] A function definition module is configured to define a charging function and a wear cost function according to the actual charging process of the battery and the battery wear condition.

[0268] A construction module is configured to construct a mathematical model of the wear cost function according to the monotonicity of the wear cost and the state of charge, and set a constraint condition.

[0269] A solution output module is configured to solve the mathematical model of the wear cost function by using a three-stage efficient heuristic algorithm of CWIGALNS, and obtain an optimal scheduling scheme.

[0270] Specifically, the function definition module, the construction module and the solution output module can be embedded into a computer processing system, and the computer can complete the task of collaborative optimization of electric vehicle distribution service and charging scheduling by calling the modules according to the optimal scheduling method for electric vehicle charging network design provided above; the function definition module, the construction module and the solution output module can execute operations according to the specific steps given in the optimal scheduling method for electric vehicle charging network design.

[0271] It should be understood that the division of the various modules of the above system is only a logical division of functions, and in actual implementation, all or part of the modules can be integrated into one physical entity, or can be physically separated, and the modules can all be implemented in the form of software called by a processing element; or all can be implemented in the form of hardware; or some modules can be implemented in the form of software called by a processing element, and some modules can be implemented in the form of hardware. For example, the definition module can be a separate processing element, or can be integrated into a chip of the above device, in addition, it can also be stored in the form of program code in the memory of the above device, and the function of the above signal processing module is called and executed by a processing element of the above device, and the implementation of other modules is similar. In addition, all or part of the modules can be integrated together, or can be implemented independently. The processing element described herein can be an integrated circuit having signal processing capability, and in the implementation process, each step of the above method or each module can be completed by the integrated logic circuit of the hardware in the processing element or the instructions in the form of software.

[0272] For example, the above modules can be one or more integrated circuits configured to implement the above method, such as one or more application specific integrated circuits (ASICs), or one or more digital signal processors (DSPs), or one or more field programmable gate arrays (FPGAs), etc. For another example, when a certain module above is implemented in the form of program code called by a processing element, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call program code. For another example, the modules can be integrated together to implement in the form of system on a chip (SOC).

[0273] It should be noted that in this paper, relationship terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between the entities or operations. Moreover, the terms "include", "contain" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device.

[0274] For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances. When an element is referred to as being "assembled on", "installed on", "fixed on" or "set on" another element, it can be directly on the other element or there can be a central element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there can be a central element at the same time. The terms "vertical", "horizontal", "up", "down", "left", "right" and similar expressions used herein are for illustrative purposes only and are not intended to be the only embodiment.

[0275] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

[0276] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

Claims

1. An optimal scheduling method for electric vehicle charging network design, characterized by: The following steps are involved: According to the actual charging process and battery wear of the battery, the charging function and the wear cost function are defined; According to the monotonically increasing or decreasing relationship between wear cost and charging state, a mathematical model of the corresponding wear cost function is constructed and constraints are set; A mixed integer linear programming formula is proposed to minimize the running cost as follows: （6） Where, is the distance from node g to node h; is the unit charging station construction cost of node j; is a binary variable that takes the value 1 if the site is located at node j and 0 otherwise; is a binary variable, which takes the value 1 if vehicle k goes from node i to node j, and 0 otherwise; is the total wear cost of vehicle k that passes through nodes g, j, and h consecutively at charging station j; The wear cost per kilowatt-hour of charging and discharging in interval d; is the charging time per unit of electricity in interval d; is the charging time cost of vehicle k passing through nodes g, j, and h at charging station j; The interval for vehicle k to charge in the parking lot quantity; The three-stage efficient heuristic algorithm of CWIGALNS is used to solve the mathematical model of the wear cost function and obtain the optimal scheduling solution; The three-stage efficient heuristic algorithm of CWIGALNS includes Clarke and Wright preservation algorithm CW, iterative greedy algorithm IG and adaptive large neighborhood search algorithm ALNS.

2. The optimal scheduling method for electric vehicle charging network design according to claim 1 is characterized in that: According to the actual battery charging process and battery wear, the charging function and wear cost function are defined as follows: (21) The changes in current and voltage during the actual battery charging process are nonlinear, so the charging function including the constant current and constant voltage stages is nonlinear; (22) Assume that each charger has a specific constant current-constant voltage charging function, and the charging function has breakpoints, and fitted the real constant current-constant pressure concave function, assuming that the breakpoints The relevant charging status is ,in ; Assuming that the charging current between consecutive breakpoints is constant, the charging function is: (1) Where, is the total charging time, The battery charging state is in the range Unit charging time within The battery charging state is in the range The amount of electricity inside; set up is the set of charging state time intervals, let For the length of each time interval: (2) (3) Where, They are the upper and lower limits of the charging state respectively; Battery Price ( ) can be expressed as: (4) (5) Where, For The cost of charging and discharging per kilowatt-hour, It is the energy value of each state of charge interval, DOD is the depth of discharge, and ACC is the battery cycle count.

3. The optimal scheduling method for electric vehicle charging network design according to claim 2 is characterized in that: According to the monotonically increasing relationship between wear cost and state of charge, a mathematical model of the corresponding wear cost function is constructed, and constraints are set as follows: The constraints are as follows: Each customer is served only once: （7） The vehicle is charged at a location with charging facilities: （8） Traffic flow balance at each node: （9） Vehicle departure and return from the depot: (10) Each electric vehicle serves at most one route: (11) The remaining cargo quantity before and after the electric vehicle passes a certain node: (12) Range of remaining cargo in electric vehicle: (13) The remaining energy of the electric vehicle at each node on the service route: (14) The remaining charge of the electric vehicle when it leaves the distribution center: (15) At the client node, the battery level remains unchanged: (16) The vehicle's charge level before and after leaving the charging station: (17) Charge capacity The upper and lower limits of: (18) Make sure the vehicle has enough charge to complete the assigned task: (19) Ensure that the total energy charged by charging station j in all charging state intervals is equal to the total charge amount of charging station j to vehicle k: (20) Formula (21)-Formula (23) ensures that sufficient power is charged in each charging state interval: (21) (22) (23) The energy consumption cost of vehicle k charging at the charging station is: (24) The charging time cost of the energy charged to vehicle k at the charging station: (25) Formula (26)-Formula (27) ensures that within the charging state interval, the variable and must be equivalent to a state-of-charge interval whose upper limit is higher than the charge of vehicle k before it arrives at charging station j or leaves the charging station: (26) (27) Binary constraints: (28) Where, is a collection of candidate stations; Collection for customers; For the collection of vehicles; A collection of state-of-charge intervals; For a single warehouse; For the same another warehouse; is the vertex set; is the battery wear cost coefficient; Depot charging time cost coefficient, Path cost per unit distance, To ensure the vehicle's range, the remaining charge must be maintained at certain minimum and maximum values. and between; is the distance from node g to node h; is the unit charging station construction cost of node j; is a binary variable that takes the value 1 if the site is located at node j and 0 otherwise; is a binary variable, which takes the value 1 if vehicle k goes from node i to node j, and 0 otherwise; is a binary variable that takes the value 1 if vehicle k is charged in the vehicle segment using the time interval d, and 0 otherwise; is a binary variable that takes the value 1 if vehicle k is charged at charging station j using interval d and passes through nodes g, j, and h consecutively, and 0 otherwise; The total amount charged at charging station j for vehicle k that passes through nodes g, j, and h consecutively; is the residual load when vehicle k leaves node g and arrives at node h; The number of battery charges for vehicle k that is at charging station j and passes through nodes g, j, and h consecutively; is the total wear cost of vehicle k that passes through nodes g, j, and h consecutively at charging station j; The interval for vehicle k to charge in the parking lot quantity; is the charging time cost of vehicle k passing through nodes g, j, and h at charging station j; The maximum distance allowed by the remaining power when vehicle k leaves node g to h; is the maximum distance allowed by the remaining power when vehicle k reaches node h from g.

4. The optimal scheduling method for electric vehicle charging network design according to claim 3 is characterized in that: According to the monotonically decreasing relationship between wear cost and charge state, a mathematical model of the corresponding wear cost function is constructed, and constraints are set as follows: (41) Define variables Charging status interval When the vehicle In the function The amount of electricity released; variable For vehicles In the function Total wear and tear costs on If the function The battery of vehicle k is discharged, then the binary variable is equal to 1; Indicates that vehicle k is in the function ), the specific formula is as follows: （29） (42) The above function (29) is subject to the constraints of formulas (7)-(25) and (28), and is also subject to the following constraints: Formula (31)-Formula (32) constrains the maximum acceptable interval when vehicle k leaves station j or depot o by calculating the difference between its lower limit and the current SOC: (30) (31) Ensure that the maximum discharge energy per SOC interval does not exceed L under applicable conditions: (32) Ensure that for each vehicle k, the total discharge amount in all SOC intervals at charging station j is equal to the total discharge amount at charging station j: (33) The total emissions of vehicle k along the route from node g to node h: (34) Constraint (35) is the same as constraint (30), but only applies to discharge: (35) Vehicle k in the function The total loss cost of discharge is: (36)。 5. The optimal scheduling method for electric vehicle charging network design according to claim 4 is characterized in that: The Clarke and Wright preservation algorithm is as follows: (51) For each customer Create a route ; (52) Identify direct connections to the warehouse edge nodes; (53) Select a route and Two edge nodes in and As a save pair, calculate the save value and find the total demand for the new route; (54) Select the saved pairs in SPL in the order of saved values , identify the edge nodes and Route and routes ; (55)Connecting routes and ,If the total demand of the new route exceeds the loading capacity of the vehicle, the merging operation is abandoned, otherwise, the merging operation is ended; (56) Delete the saved pair from the program , repeat the process from step (54) to step (56) until the program is empty.

6. The optimal scheduling method for electric vehicle charging network design according to claim 5 is characterized in that: At the beginning of the iterative greedy algorithm, all charging stations are deleted and reselected based on the placement cost. The specific parameters are described as follows: (61) Breakpoint: Under the mileage limit, if the electric vehicle does not charge at a charging station during driving, it will not be able to reach certain nodes; breakpoint Has the following properties: , define the route The first breakpoint on ; (62) Node accessibility: To compare the impact of different charging stations on customer points in the route, define the parameter Point accessibility for customers, ,parameter It is used to indicate whether the electric vehicle will run out of power on the way, and the minimum value of the path customer point accessibility is defined as ; (63) Charging strategy: If charging time and battery wear increase with the state of charge, electric vehicles need to recharge their batteries at the depot or charging station until they reach the next stop or return to the depot, and the minimum power is not less than ; (64) Placement cost: on the path Node on Add a charging station , recorded as , the placement cost is recorded as : (37) (38) (39) (40) (41) Where, is the increase in accessibility, is the additional routing cost, is the wear cost, is the additional billable time cost, is the penalty value; denote the accessibility of v before and after insertion into site j, respectively; and They represent the minimum value of the current path reachability before and after inserting j.

7. The optimal scheduling method for electric vehicle charging network design according to claim 6 is characterized in that: In the ALNS algorithm, the delete operator is used to delete from the current route. Customer nodes and repeatedly use the insertion operator to reinsert them into the route, Set as A random value in : (71) Assign to the current solution and optimal solution ; (72) If the number of iterations is less than the given number, the insertion and removal operators are selected and Assign to neighboring solutions , otherwise, the search ends; (73) Use the removal and insertion operators to handle ; (74) If the acceptance criteria are met at this time, the value endowment ; (75) If , then endowment ; (76) Update the score and weight of each operator and return to step (72); The adaptive search mechanism used by the ALNS algorithm is as follows: At the beginning of each stage, the operator weights are adjusted and the operators are selected according to the preset rules to achieve adaptive update of the search process, as follows: Set up the first Phase Operator The weight is , then the probability of the operator being selected is , where H is the operator set; The stage requires that the operator weights be updated according to the following rules after each search stage: Define the number of times operator I is adopted in stage j, and the scores are and ; If the operator is used in stage j , then its weight in the next stage is , otherwise the weight remains unchanged, where is the reaction coefficient, set to 0.3; If the neighborhood solution Better than current solutions , then the neighborhood solution is retained; otherwise, the probability that the neighborhood solution is retained is , where the initial temperature .

8. An optimal scheduling system for electric vehicle charging network design, used to implement the optimal scheduling method for electric vehicle charging network design according to any one of claims 1 to 7, characterized in that: include: Function definition module, used to define charging function and wear cost function according to the actual charging process of the battery and the battery wear; A construction module is used to construct a mathematical model of the corresponding wear cost function according to the monotonically increasing or decreasing relationship between the wear cost and the charging state, and set constraints; The solution output module is used to solve the mathematical model of the wear cost function using the three-stage efficient heuristic algorithm of CWIGALNS to obtain the optimal scheduling solution.

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