CN109636067B - Electric automobile charging scheduling optimization method based on particle swarm algorithm - Google Patents

Abstract

A particle swarm algorithm-based electric vehicle charging scheduling optimization method comprises the following steps: 1) when the electric vehicle is in a low electric quantity value, a user sends a charging request to a server, and after receiving the request, the server collects the residual energy of the battery of the electric vehicle, the air-conditioning state, the current position of the electric vehicle and the distribution situation of peripheral charging stations, and meanwhile, the congestion situation of surrounding roads is referred to; 2) gridding the road, and describing the electric vehicle charging scheduling problem as an optimization problem; 3) aiming at the problem model, a particle swarm algorithm is adopted to select a proper target charging station and an optimal running path to the target charging station for the electric automobile; 4) as long as the server and the electric vehicle are in a connected state, the server can send information of the target charging station and the optimal path to the target charging station to the user. The invention improves the charging efficiency of the electric automobile and provides a more intelligent charging path planning scheme for users.

Description

Electric automobile charging scheduling optimization method based on particle swarm algorithm

Technical Field

The invention relates to the field of electric vehicle charging scheduling and driving path optimization, in particular to an electric vehicle charging scheduling optimization method based on a particle swarm algorithm.

Background

The traditional energy consumption rate is very fast, the regeneration rate is very slow, and the pollution to the environment is very large. In recent years, people have been exploring in the field of green travel, and electric vehicles are one of the representatives. The electric automobile uses batteries to replace traditional energy sources to provide power, and the energy utilization rate of the electric automobile is high, zero pollution and low noise, so the holding amount of the electric automobiles on the market is increased all the time. However, compared with the conventional energy vehicle, the electric vehicle also has the defects that the electric vehicle cannot continuously run for a long distance, the charging time is long, the configuration of related charging facilities is not reasonable, and the like.

If the electric vehicle in driving can provide a reasonable charging scheduling and driving path optimization scheme for the user according to the residual electric quantity of the battery and the using state of the charging station, the worry of the user about the driving distance of the electric vehicle can be reduced. Although the related research on the electric vehicle charging scheduling is still in the starting stage at present, a reliable and accurate method is provided by the scheme for optimizing the charging scheduling and the driving path of the electric vehicle based on the particle swarm optimization, the scheduling optimization method can enable a user to reasonably arrange the charging time and the charging driving path of the electric vehicle, the waiting time of the user is reduced, the inconvenience caused by unreasonable allocation of charging station resources to the user can be solved, and meanwhile, the popularization of the electric vehicle is promoted.

Disclosure of Invention

The invention provides an electric vehicle charging scheduling optimization method based on a particle swarm algorithm, aiming at overcoming the defect that the existing electric vehicle is low in charging efficiency, improving the charging efficiency of the electric vehicle and providing a more intelligent charging path planning scheme for a user.

The technical scheme adopted by the invention for solving the technical problems is as follows:

an electric vehicle charging scheduling optimization method based on a particle swarm algorithm comprises the following steps:

1) when the electric vehicle is in a state of low electric quantity value, a user firstly sends a charging request to the server, and the server firstly collects the residual energy of the battery of the electric vehicle, the air-conditioning state, the current position of the electric vehicle and the distribution situation of peripheral charging stations after receiving the request, and simultaneously refers to the situation of congestion of surrounding roads, and the method comprises the following steps:

step 1.1: the residual energy of the electric automobile is estimated by measuring the coulomb quantity flowing in and out and adopting a coulomb counting method, in the measuring process, the battery capacity takes ampere hours as a metering unit, and the calculation formula is as follows:

A_l＝A_max-A_u (1-1)

wherein, each parameter is defined as follows:

A_max: battery capacity at full charge;

A_u: the battery capacity currently in use;

A_l: remaining usable battery capacity;

E_max: the energy of the battery in a full state;

E_l: battery residual energy;

step 1.2: in the running process of the electric automobile, the time of the electric automobile arriving at a charging station is often influenced by the road congestion condition, a congestion coefficient epsilon is introduced to represent the road congestion condition, then the running time of the electric automobile on the road is calculated according to the congestion condition of each section of road, and the calculation formula is as follows:

wherein, each parameter is defined as follows:

num is the number of vehicles entering the road;

t is threshold capacity, which ensures the maximum number of vehicles passing through the road smoothly;

c, a critical value, namely the number of vehicles causing road congestion;

t, the driving time of the electric automobile on the road;

the average running time of the electric automobile passing through the road section smoothly;

step 1.3: calculating the total energy consumed by the electric automobile in the running process on the road according to the energy consumed by the electric automobile in the running process and the energy consumed by the air conditioner in the running process of the electric automobile, wherein the total energy consumed by the electric automobile in the running process on the road is as follows:

E₁＝d×E(v) (1-6)

E₂＝t×E (1-7)

E＝E₁+E₂ (1-8)

wherein, each parameter is defined as follows:

v is the running speed of the electric automobile on the road;

d is the length of the road;

e, energy consumption corresponding to the running of the electric automobile at the speed v;

E₁the total energy consumption of the electric automobile;

E₂the total energy consumption of the air conditioner of the electric automobile;

e, total energy consumption required by the electric automobile;

step 1.4: f charging stations allowing the electric vehicle to be charged are screened out, an index set I is introduced, wherein the index set I is {1,2_i}_i∈I；

2) Gridding the road, and assuming that the electric automobile can only be arranged on a horizontal road x between an intersection f and an intersection m_fmAnd intersection n and intersection m perpendicular road y_nmThe set of two adjacent road points of the road network in the up-running, horizontal and vertical directions is { x₁₁,x₁₂,…x_fmAnd { y }₁₁,y₁₂,…y_nmThe congestion coefficients in the horizontal and vertical directions are integrated into

And

f charging stations are sequentially arranged at the terminals of the road network, and in combination with the step 1), the electric vehicle charging scheduling problem is described as an optimization problem as follows:

s.t.x_fm,y_nm＝{0,1} (2-1)

x₁₁+y₁₁＝1 (2-2)

x_nm+y_nm＝x_nm'+y_n'm (2-3)

x_fm＝x_fm'+y_nm (2-4)

here, the parameters are defined as follows:

t_fwaiting time of the selected charging station;

the number of roads of the horizontal section;

the number of roads in the vertical section;

electric automobile is in horizontal highway section x_fmThe running energy consumption of (2);

electric automobile on vertical road section y_nmThe running energy consumption of (2);

E_l: residual energy of the electric vehicle;

where m '═ m-1 and n' ═ n-1, and if m '═ 0 and n' ═ 0, then the corresponding x_nm'、y_nm'、x_fm'Is 0;

3) aiming at the problem model, a particle swarm algorithm is adopted to select a proper target charging station and an optimal running path to the target charging station for the electric automobile, and the steps are as follows:

step 3.1: initializing a road network model, randomly generating congestion coefficients of horizontal and vertical road sections, and setting the current position of the electric automobileSetting a larger total initial running time T of the electric automobile as a starting point and each charging station as an end point_ini10000, the current best solution CBS ═ T_iniSetting the driving route as L, setting the current optimal driving route CBV as L, setting the iteration number k as 1 and enabling the current optimal driving route to be L

Step 3.2: initializing the number I of the groups, wherein the positions of the I groups are driving routes L reaching a target charging station I_ini＝{L_i}_1≤i≤IWherein L is_i＝{L_i,j}_1≤j≤Ni，N_iThe number of driving routes from a starting point to a charging station i is referred to, and the initialization iteration number iter is equal to 1;

step 3.3: if iter is less than or equal to I, executing step 3.4, otherwise executing step 3.7;

step 3.4: if k < N_iterThen calculate T_sumAnd executing step 3.5, otherwise, making iter be iter +1, and k be 1, and executing step 3.3;

step 3.5: if T is_sum＜T_iniThen, T is updated_ini＝T_sumUpdate L_ini＝L_iter,kAnd executing step 3.6, otherwise executing step 3.4;

step 3.6: updating k to k +1, and executing step 3.4;

step 3.7: outputting the shortest total running time CBS ═ T of the electric automobile_iniThe optimal driving route CBV of the electric automobile is L_ini；

4) And then, as long as the server and the electric vehicle are in a connected state, the server sends the information of the target charging station and the optimal path to the target charging station to the user.

The technical conception of the invention is as follows: firstly, when the electric quantity value of the electric automobile is low, a user needs to send a charging request to a server, and the server estimates the remaining mileage capable of driving according to the remaining energy of the battery of the electric automobile and the state of an air conditioner after receiving the request; and then, according to the current position of the electric vehicle and the distribution situation of the peripheral charging stations, and simultaneously referring to the road congestion situation, selecting the reachable optimal charging station, and planning the optimal driving path for the user of the electric vehicle. In the process of obtaining the optimal path by the algorithm, the Metropolis acceptance criterion can be effectively utilized to avoid solving local optimal points and find out global optimal points, so that the optimal solution of the electric vehicle driving path is obtained.

The invention has the advantages that 1, the robustness of the particle swarm algorithm is strong, the optimal path can be effectively obtained through searching, and the scheduling optimization problem of the electric automobile is reliably solved; 2. the particle swarm algorithm starts from a random solution, and an optimal solution is searched through iteration; 3. the particle swarm algorithm does not need to traverse all the conditions and then compare the advantages and the disadvantages of the conditions to obtain an optimal solution, so that the calculation complexity is reduced, the calculation time is greatly reduced, and the calculation efficiency is improved.

Drawings

FIG. 1 is a road network model diagram.

Detailed Description

The present invention is described in further detail below with reference to the attached drawing figures.

Referring to fig. 1, a method for optimizing a charging schedule of an electric vehicle based on a particle swarm algorithm, in other words, a charging schedule of an electric vehicle is optimized by using a particle swarm algorithm. In the invention, in a simplified road network model (as shown in figure 1), path selection is carried out by a particle swarm algorithm, and finally, an optimal path for charging is provided. The invention provides a particle swarm algorithm for an electric vehicle which is in urgent need of charging, and aims at the energy of the residual battery of the electric vehicle, the state information of the electric vehicle and the road congestion situation in a road network model to obtain an optimal charging station and an optimal charging path. The scheduling optimization method comprises the following steps:

1) when the electric vehicle is in a state of low electric quantity value, a user firstly sends a charging request to the server, and the server firstly collects the residual energy of the battery of the electric vehicle, the air-conditioning state, the current position of the electric vehicle and the distribution situation of peripheral charging stations after receiving the request, and simultaneously refers to the situation of congestion of surrounding roads, and the method comprises the following steps:

step 1.1: the residual energy of the electric automobile is estimated by measuring the coulomb quantity flowing in and out and adopting a coulomb counting method, in the measuring process, the battery capacity takes ampere hours as a metering unit, and the calculation formula is as follows:

A_l＝A_max-A_u (1-1)

wherein, each parameter is defined as follows:

A_max: battery capacity at full charge;

A_u: the battery capacity currently in use;

A_l: remaining usable battery capacity;

E_max: the energy of the battery in a full state;

E_l: battery residual energy;

step 1.2: in the running process of the electric automobile, the time of the electric automobile arriving at a charging station is often influenced by the road congestion condition, a congestion coefficient epsilon is introduced to represent the road congestion condition, then the running time of the electric automobile on the road is calculated according to the congestion condition of each section of road, and the calculation formula is as follows:

wherein, each parameter is defined as follows:

num is the number of vehicles entering the road;

t is threshold capacity, which ensures the maximum number of vehicles passing through the road smoothly;

c, a critical value, namely the number of vehicles causing road congestion;

t, the driving time of the electric automobile on the road;

the average running time of the electric automobile passing through the road section smoothly;

step 1.3: calculating the total energy consumed by the electric automobile in the running process on the road according to the energy consumed by the electric automobile in the running process and the energy consumed by the air conditioner in the running process of the electric automobile, wherein the total energy consumed by the electric automobile in the running process on the road is as follows:

E₁＝d×E(v) (1-6)

E₂＝t×E (1-7)

E＝E₁+E₂ (1-8)

wherein, each parameter is defined as follows:

v is the running speed of the electric automobile on the road;

d is the length of the road;

e, energy consumption corresponding to the running of the electric automobile at the speed v;

E₁the total energy consumption of the electric automobile;

E₂the total energy consumption of the air conditioner of the electric automobile;

e, total energy consumption required by the electric automobile;

step 1.4: f charging stations allowing the electric vehicle to be charged are screened out, an index set I is introduced, wherein the index set I is {1,2_i}_i∈I；

2) Gridding the road, and assuming that the electric automobile can only be arranged on a horizontal road x between an intersection f and an intersection m_fmAnd intersection n and intersection m perpendicular road y_nmThe set of two adjacent road points of the road network in the up-running, horizontal and vertical directions is { x₁₁,x₁₂,…x_fmAnd { y }₁₁,y₁₂,…y_nmThe congestion coefficients in the horizontal and vertical directions are integrated into

And

f charging stations are sequentially arranged at the terminals of the road network, and in combination with the step 1), the electric vehicle charging scheduling problem is described as an optimization problem as follows:

s.t.x_fm,y_nm＝{0,1} (2-1)

x₁₁+y₁₁＝1 (2-2)

x_nm+y_nm＝x_nm'+y_n'm (2-3)

x_fm＝x_fm'+y_nm (2-4)

here, the parameters are defined as follows:

t_fwaiting time of the selected charging station;

the number of roads of the horizontal section;

the number of roads in the vertical section;

electric automobile is in horizontal highway section x_fmThe running energy consumption of (2);

electric automobile on vertical road section y_nmThe running energy consumption of (2);

E_l: residual energy of the electric vehicle;

where m '═ m-1 and n' ═ n-1, and if m '═ 0 and n' ═ 0, then the corresponding x_nm'、y_nm'、x_fm'Is 0;

3) aiming at the problem model, a particle swarm algorithm is adopted to select a proper target charging station and an optimal running path to the target charging station for the electric automobile, and the steps are as follows:

step 3.1: initializing a road network model, randomly generating congestion coefficients of horizontal and vertical road sections, setting the current position of the electric automobile as a starting point, setting each charging station as a terminal point, and setting a larger total initial driving time T of the electric automobile_ini10000, the current best solution CBS ═ T_iniSetting the driving route as L, setting the current optimal driving route CBV as L, setting the iteration number k as 1 and enabling the current optimal driving route to be L

Step 3.2: initializing the number I of the groups, wherein the positions of the I groups are driving routes L reaching a target charging station I_ini＝{L_i}_1≤i≤IWherein

N_iThe number of driving routes from a starting point to a charging station i is referred to, and the initialization iteration number iter is equal to 1;

step 3.3: if iter is less than or equal to I, executing step 3.4, otherwise executing step 3.7;

step 3.4: if k < N_iterThen calculate T_sumAnd executing step 3.5, otherwise, making iter be iter +1, and k be 1, and executing step 3.3;

step 3.5: if T is_sum＜T_iniThen, T is updated_ini＝T_sumUpdate L_ini＝L_iter,kAnd executing step 3.6, otherwise executing step 3.4;

step 3.6: updating k to k +1, and executing step 3.4;

step 3.7: transfusion systemThe shortest total driving time CBS of the electric automobile is T_iniThe optimal driving route CBV of the electric automobile is L_ini；

4) And then, as long as the server and the electric vehicle are in a connected state, the server sends the information of the target charging station and the optimal path to the target charging station to the user.

Claims (1)

1. An electric vehicle charging scheduling optimization method based on a particle swarm algorithm is characterized by comprising the following steps:

1) when the electric vehicle is in a state of low electric quantity value, a user firstly sends a charging request to the server, and the server firstly collects the residual energy of the battery of the electric vehicle, the air-conditioning state, the current position of the electric vehicle and the distribution situation of peripheral charging stations after receiving the request, and simultaneously refers to the situation of congestion of surrounding roads, and the method comprises the following steps:

step 1.1: the residual energy of the electric automobile is estimated by measuring the coulomb quantity flowing in and out and adopting a coulomb counting method, in the measuring process, the battery capacity takes ampere hours as a metering unit, and the calculation formula is as follows:

A_l＝A_max-A_u (1-1)

wherein, each parameter is defined as follows:

A_max: battery capacity at full charge;

A_u: the battery capacity currently in use;

A_l: remaining usable battery capacity;

E_max: the energy of the battery in a full state;

E_l: battery residual energy;

step 1.2: in the running process of the electric automobile, the time of the electric automobile arriving at a charging station is often influenced by the road congestion condition, a congestion coefficient epsilon is introduced to represent the road congestion condition, then the running time of the electric automobile on the road is calculated according to the congestion condition of each section of road, and the calculation formula is as follows:

wherein, each parameter is defined as follows:

num is the number of vehicles entering the road;

t is threshold capacity, which ensures the maximum number of vehicles passing through the road smoothly;

c, a critical value, namely the number of vehicles causing road congestion;

t, the driving time of the electric automobile on the road;

the average running time of the electric automobile passing through the road section smoothly;

step 1.3: calculating the total energy consumed by the electric automobile in the running process on the road according to the energy consumed by the electric automobile in the running process and the energy consumed by the air conditioner in the running process of the electric automobile, wherein the total energy consumed by the electric automobile in the running process on the road is as follows:

E₁＝d×E(v) (1-6)

E₂＝t×E (1-7)

E＝E₁+E₂ (1-8)

wherein, each parameter is defined as follows:

v is the running speed of the electric automobile on the road;

d is the length of the road;

e, energy consumption corresponding to the running of the electric automobile at the speed v;

E₁the total energy consumption of the electric automobile;

E₂the total energy consumption of the air conditioner of the electric automobile;

e, total energy consumption required by the electric automobile;

step 1.4: g charging stations allowing the electric vehicle to be charged are screened out, an index set I is introduced, wherein the index set I is {1,2_i}_i∈I；

2) Gridding the road, and assuming that the electric automobile can only be arranged on a horizontal road x between an intersection f and an intersection m_fmAnd intersection n and intersection m perpendicular road y_nmThe set of two adjacent road points of the road network in the up-running, horizontal and vertical directions is { x₁₁,x₁₂,…x_fmAnd { y }₁₁,y₁₂,…y_nmThe congestion coefficients in the horizontal and vertical directions are integrated into

And

g charging stations are sequentially arranged at the terminal of a road network, and in combination with the step 1), the electric vehicle charging scheduling problem is described as an optimization problem as follows:

s.t.x_fm,y_nm＝{0,1} (2-1)

x₁₁+y₁₁＝1 (2-2)

x_nm+y_nm＝x_nm'+y_n'm (2-3)

x_fm＝x_fm'+y_nm (2-4)

here, the parameters are defined as follows:

t_gwaiting time of the selected charging station;

the number of roads of the horizontal section;

the number of roads in the vertical section;

electric automobile is in horizontal highway section x_fmThe running energy consumption of (2);

electric automobile on vertical road section y_nmThe running energy consumption of (2);

E_l: residual energy of the electric vehicle;

where m '═ m-1 and n' ═ n-1, and if m '═ 0 and n' ═ 0, then the corresponding x_nm'、y_nm'、x_fm'Is 0;

3) aiming at the problem model, a particle swarm algorithm is adopted to select a proper target charging station and an optimal running path to the target charging station for the electric automobile, and the steps are as follows:

step 3.1: initializing a road network model, randomly generating congestion coefficients of horizontal and vertical road sections, setting the current position of the electric automobile as a starting point, setting each charging station as a terminal point, and setting the total initial driving time T of the electric automobile_ini10000, the current best solution CBS ═ T_iniSetting the driving route as L, setting the current optimal driving route CBV as L, setting the iteration number k as 1 and enabling the current optimal driving route to be L

Step 3.2: initializing the number I of the groups, wherein the positions of the I groups are driving routes L reaching a target charging station I_ini＝{L_i}_1≤i≤IWherein L is_i＝{L_i,j}_1≤j≤Ni，N_iThe number of driving routes from a starting point to a charging station i is referred to, and the initialization iteration number iter is equal to 1;

step 3.3: if iter is less than or equal to I, executing step 3.4, otherwise executing step 3.7;

step 3.4: if k < N_iterThen calculate T_sum(L) and performing step 3.5, otherwise, let iter +1, k 1, and performing step 3.3;

step 3.5: if T is_sum(L)＜T_iniThen, T is updated_ini＝T_sum(L), update L_ini＝L_iter,kAnd executing step 3.6, otherwise executing step 3.4;

step 3.6: updating k to k +1, and executing step 3.4;

step 3.7: outputting the shortest total running time CBS ═ T of the electric automobile_iniThe optimal driving route CBV of the electric automobile is L_ini；

4) And then, as long as the server and the electric vehicle are in a connected state, the server sends the information of the target charging station and the optimal path to the target charging station to the user.

Images