CN105046356A - Electromobile endurance mileage optimization device and method thereof - Google Patents

Abstract

The invention discloses an electromobile endurance mileage optimization device and a method thereof. The device comprises a data acquisition system, a decision making system and an expert system. The data acquisition system comprises a mobile client, a vehicle-carried communication system and a charging station communication module. The decision making system comprises a first wireless communication module and a decision making module. The expert system comprises a second wireless communication module, a database and a path planning module. The expert system receives driving task attribute information, electromobile vehicle condition information and charging post work condition information and then searches the database. If there is matched data, an existing plan path is returned. If there is no matched data, the path planning module processes. By combining constraint conditions such as electromobile vehicle condition, charging post work condition, traffic condition, driving task attribute and the like, the path planning module establishes an optimization problem model, solves the optimization problem and provides multiple battery friendly routing solutions. According to the invention, the problem of range anxiety can be effectively alleviated, and work efficiency can be greatly raised. The device and the method are of deep and practical significance.

Description

A kind of electric automobile course continuation mileage optimization device and method

Technical field

The invention belongs to automobile technical field, relate to a kind of electric automobile course continuation mileage optimization device and method, especially relate to a kind of electric automobile course continuation mileage optimization device and method of battery friendly.

Background technology

In recent years, fossil energy is exhausted, and environmental pollution is serious, and research and development new-energy automobile becomes the only way of Resources for construction saving, friendly environment society.Under this background, electric automobile arises at the historic moment.But electric automobile is gone through years development and also failed to enter huge numbers of families, this is because its intrinsic limitation fails to break through always: energy storage is few, distance travelled is short, charging is slow, need to charge could ensure flying power by common charging device relay.At present, countries in the world electric automobile course continuation mileages is optimized correlative study and is mainly concentrated on automobile power cell modeling and charging electric vehicle to fields such as the impacts of electric network performance, but these researchs all fundamentally solve electric automobile how to find charging pile fast, improve the problems such as electric automobile course continuation mileage.

Under the contradiction that these are urgently to be resolved hurrily, set up electric automobile intelligent charge network that is intelligent, information-based, networking, by adopting the routing algorithm of battery sensitivity, optimize electric automobile course continuation mileage, for most suitable driving path planned by electric automobile, ensure can relay charging to be unlikely to cast anchor in stroke; Adopt driving task scheduling algorithm, Intelligent Task sorts, and provides optimized solution, realizes, with the multiple goal path planning of real-time constraint task time, increasing substantially work efficiency.Charging problems could be solved in high quality, greatly alleviate " the mileage anxiety " of electric automobile user, promote the development of ev industry.

Summary of the invention

The present invention adopts the routing algorithm of battery sensitivity, optimizes electric automobile course continuation mileage, for most suitable driving path planned by electric automobile, ensures can relay charging to be unlikely to cast anchor in stroke, alleviates " mileage anxiety " problem.

The technical scheme that device of the present invention adopts is: a kind of electric automobile course continuation mileage optimization device, is characterized in that: comprise data acquisition system (DAS), decision system and expert system; Described data acquisition system (DAS) comprises mobile client, in-vehicle communication system and charging station communication module; Decision system comprises the first wireless communication module and decision-making module; Expert system comprises the second wireless communication module, database and path planning module; Described data acquisition system (DAS), to be interconnected with the second wireless communication module by the communication module that arranges in mobile client, the first wireless communication module between decision system and expert system and to communicate.

The technical scheme that method of the present invention adopts is: a kind of electric automobile course continuation mileage optimization method, is characterized in that, comprise the following steps:

Step 1: user uses mobile client collection and uploads driving task attribute information in expert system;

Step 2: in-vehicle communication system collection also uploads electric automobile vehicle condition information in expert system;

Step 3: the collection of charging station communication module also uploads charging pile work information in expert system;

Step 4: expert system receives searching database after the data uploaded in step 1, step 2 and step 3, if there is matched data, returns one group of existing programme path, otherwise transfers to path planning module to process;

Step 5: path planning module, in conjunction with constraint condition, sets up optimization problem model, solves this optimization problem, carries out multiple goal path planning, provides the routing plan of many feasible battery friendly; Described constraint condition comprises electric automobile vehicle condition, charging pile operating mode, traffic, driving task attribute;

Step 6, after decision system receives the one group of programme path satisfying condition feasible, gets front k bar optimal route according to probability feasibility and returns to mobile client, transfer to user to select.

As preferably, the driving task attribute information described in step 1 comprises the geographic position of every task, closing time and precedence information.

As preferably, the electric automobile vehicle condition packets of information described in step 2 draws together current vehicle position and dump energy information.

As preferably, the charging pile work information described in step 3 comprises service condition and the failure condition information of charging pile.

As preferably, if have matched data described in step 4, return one group of existing programme path, its matched rule is: electric automobile vehicle condition information is consistent with driving task attribute information, and namely current vehicle position, dump energy information, the geographic position of task, closing time are consistent with precedence information.

As preferably, the specific implementation process of the multiple goal path planning described in step 5 comprises following sub-step:

Step 5.1: set up optimization problem mathematical model; Be provided with N number of task node, c _ijrepresent the transportation cost from i a to j, comprise distance and electricity; P _ijrepresent and expend electricity from an i to a j; Δ P _ijrepresent from an i to the charge capacity of a j; P ₀represent the initial value of electricity; s _irepresent and arrive the moment on i ground and the difference of initial time; t _iexpression is finished the work the time consumed at i; t _ijrepresent the time consumed to a j from an i; d _ibe expressed as in the i ground deadline of task and the difference of initial time; x _ijrepresent that whether vehicle is from an i to a j;

Objective function:

$\min z=\left(\underset{i}{\overset{N}{\Σ}}\underset{j}{\overset{N}{\Σ}}{c}_{ij}{x}_{ij}\right);$

Constraint:

$\underset{i}{\overset{N}{\Σ}}\underset{j}{\overset{N}{\Σ}}(P_{ij}-{\mathrm{\ΔP}}_{ij})x_{ij}\≤P_0;$

Task restriction:

$\left\{\begin{array}{c}{s}_i+t_i+t_{ij}=s_j,i,j=1,2,...,N\\ s_j\≤d_j,j=1,2,...,N\end{array}\right.;$

Decision variable:

x _ij＝0or1；

Step 5.2, local paths planning, if f (i, j) is the cost valuation from an i to a j, g (i, j) be actual cost from an i to a j, h (i, j) is the heuristic information from an i to a j, estimate that each present node arrives at the minimum cost of destination node, improvement A* algorithm is adopted to obtain optimal path, i.e. f (i, j); Work as P _ij>P _cur(P _currepresent that electric automobile is positioned at electricity when namely present node puts i) time, represent and need charging, if when at this moment meeting real-time constraint task time, then can charge from an i to a j, otherwise return a node proposition charge request and to lay equal stress on new search route;

Step 5.3: global path planning, using the weight of the f (i, j) in step 5.2 as every paths, adopts Christofides algorithm to build path and obtains optimum battery friendly multiple objective programming path.

As preferably, the improvement A* algorithm described in step 5.2, if S _curfor current place node, S ₀for initial time place node, S _pfor charging pile place node, S _tfor destination node, S _ifor arbitrary node in map, DEPTH is the depth of nesting, Δ P ₀for electric automobile every kilometer power consumption, L represents distance between two points; Then improve A* algorithm specific implementation process and comprise following sub-step:

Step 5.2.1: initialization, makes S _cur=S ₀; Structure Open table is for accessing node to be expanded, and initial time is S ₀; Structure Close table is for accessing the node expanded, and initial time is stored in S ₀; Structure PATH table is for accessing the node on optimal path;

Step 5.2.2: search for from S in Open table _curnearest node S _i, with f (S _cur, S _i) minimum and the node meeting task restriction and Constraint condition is father node

$\left\{\begin{array}{c}{s}_i+t_i+t_{ij}=s_j,i,j=1,2,...,N\\ s_j\≤d_j,j=1,2,...,N\end{array}\right.;$

$\underset{i}{\overset{N}{\Σ}}\underset{j}{\overset{N}{\Σ}}(P_{ij}-{\mathrm{\ΔP}}_{ij})x_{ij}\≤P_0;$

If meet this two conditions simultaneously, go to step 5.2.3;

This two conditions cannot be met simultaneously, go to step 5.2.5;

Step 5.2.3: if S _ifor destination node S _t, then the set of node stored in PATH table as optimal path, and remembers that weight path is terminate algorithm;

Step 5.2.4: if S _inot destination node S _t, then to S _iall forerunner state S _jupgrade, if g _j<g _i+ g (i, j), then by S _iput into Close list; Otherwise make g _j=g _i+ g (i, j), by S _ias S ₀next step route, makes S _cur=S _iand stored in PATH table; Return step 5.2.2;

Step 5.2.5: make S _cur=S ₀, DEPTH=DEPTH-1;

If DEPTH=0, go to step 5.2.6;

Otherwise it is just satisfied range L in charging pile be set to special joint { S _p1, S _p2..., S _pn, adopt Charge algorithm to calculate optimal path;

If meet task restriction and Constraint condition

$\left\{\begin{array}{c}{s}_i+t_i+t_{ij}=s_j,i,j=1,2,...,N\\ s_j\&le;d_j,j=1,2,...,N\end{array}\right.;$

$\underset{i}{\overset{N}{\&Sigma;}}\underset{j}{\overset{N}{\&Sigma;}}(P_{ij}-{\mathrm{\&Delta;P}}_{ij})x_{ij}\&le;P_0;$

Then exporting weight path is f (S ₀, S _t)=f (S ₀, S _p)+f (S _p, S _t), terminate algorithm;

If still cannot meet task restriction and Constraint condition simultaneously, go to step 5.2.6;

Step 5.2.6: exporting weight path output weight path is f (S ₀, S _t)=∞, terminates algorithm.

As preferably, the Charge algorithm described in step 5.2.5, specific implementation process comprises following sub-step:

Step 5.2.5.1: make DEPTH=0, maps { S _p1, S _p2..., S _pnbe { S ₀₁, S ₀₂..., S _0n, utilize the principle of step 5.2.1 to step 5.2.6 to calculate f (S _pi, S _t), i=1,2 ..., n, if f is (S _pi,s _t)=∞, then give up p _it (p _it represents a p _ithis route to some t) this route, otherwise calculate f (S ₀, S _pi);

Step 5.2.5.2: make DEPTH=DEPTH _pi, (DEPTH _pirepresent corresponding S _pithe depth of nesting) map { S _p1, S _p2..., S _pnbe { S _t1, S _t2..., S _tn, utilize the principle of step 5.2.1 to step 5.2.6 to calculate f (S ₀, S _pi), i=1,2 ..., n, if f is (S ₀, S _pi)=∞, then give up p _ithis node;

Step 5.2.5.3: to reservation node p _icalculate f (S respectively ₀, S _t)=f (S ₀, S _pi)+f (S _pi, S _t), i=1,2 ..., n, gets minimum value minf (S ₀, S _t)=f (S ₀, S _pi)+f (S _pi, S _t), i=1,2 ..., n is path weight value, and algorithm terminates.

As preferably, the implementation procedure that the employing Christofides algorithm described in step 5.3 builds path comprises following sub-step:

Step 5.3.1, by task node { S each in city map _t1, S _t2..., S _tn, retain special joint { S _pi, S _pi+1..., S _pjand start node form point set structure minimum spanning tree;

Step 5.3.2: using the weight of the f (i, j) in step 5.2 as every paths, if f is (S _i, S _j)=∞, then delete path ij, find the summit that interconnectedness is odd number in minimum spanning tree, and create minimal weight coupling, structure Eular loop;

Step 5.3.3: remove and repeat a little in Eular loop and minimum spanning tree, obtain path and optimal programming route.

Tool of the present invention has the following advantages:

1. adopt the route of battery sensitivity, optimize electric automobile course continuation mileage, avoid charging peak period and traffic congestion phase, scientific dispatch driving task, ensure can relay charging to be unlikely to cast anchor in stroke.In conjunction with constraint conditions such as electric automobile vehicle condition, charging pile operating mode, traffic, driving task attributes, provide the routing plan that many feasible;

2. Intelligent Task sequence, provides optimized solution, alleviates " mileage anxiety ";

3. realize the multiple goal path planning with real-time constraint task time, there is deep practical significance, increase substantially work efficiency.

Accompanying drawing explanation

Fig. 1: the device frame schematic diagram being the embodiment of the present invention.

Fig. 2: be the process flow diagram improving A* algorithm in the embodiment of the present invention.

Embodiment

Understand for the ease of those of ordinary skill in the art and implement the present invention, below in conjunction with drawings and Examples, the present invention is described in further detail, should be appreciated that exemplifying embodiment described herein is only for instruction and explanation of the present invention, is not intended to limit the present invention.

Ask for an interview Fig. 1, a kind of electric automobile course continuation mileage optimization device provided by the invention, comprises data acquisition system (DAS), decision system and expert system; Described data acquisition system (DAS) comprises mobile client, in-vehicle communication system and charging station communication module; Decision system comprises the first wireless communication module and decision-making module; Expert system comprises the second wireless communication module, database and path planning module; Data acquisition system (DAS), to be interconnected with the second wireless communication module by the communication module that arranges in mobile client, the first wireless communication module between decision system and expert system and to communicate.

A kind of electric automobile course continuation mileage optimization method provided by the invention, comprises the following steps:

Step 1: user uses mobile client collection and uploads driving task attribute information (comprising the information such as the geographic position of every task, closing time and priority) in expert system;

Step 2: in-vehicle communication system collection also uploads electric automobile vehicle condition information (comprising the information such as current vehicle position and dump energy) in expert system;

Step 3: the collection of charging station communication module also uploads charging pile work information (comprising the information such as service condition and failure condition of charging pile) in expert system;

Step 4: expert system receives searching database after the data uploaded in step 1, step 2 and step 3, if there is matched data, returns one group of existing programme path, otherwise transfers to path planning module to process;

Its matched rule is: electric automobile vehicle condition information is consistent with driving task attribute information, and namely current vehicle position, dump energy information, the geographic position of task, closing time are consistent with precedence information.

Step 5: path planning module, in conjunction with constraint condition, sets up optimization problem model, solves this optimization problem, carries out multiple goal path planning, provides the routing plan of many feasible battery friendly; Described constraint condition comprises electric automobile vehicle condition, charging pile operating mode, traffic, driving task attribute;

Wherein the specific implementation process of multiple goal path planning comprises following sub-step:

Step 5.1: set up optimization problem mathematical model; Be provided with N number of task node, c _ijrepresent the transportation cost from i a to j, comprise distance and electricity; P _ijrepresent and expend electricity from an i to a j; Δ P _ijrepresent from an i to the charge capacity of a j; P ₀represent the initial value of electricity; s _irepresent and arrive the moment on i ground and the difference of initial time; t _iexpression is finished the work the time consumed at i; t _ijrepresent the time consumed to a j from an i; d _ibe expressed as in the i ground deadline of task and the difference of initial time; x _ijrepresent that whether vehicle is from an i to a j;

Objective function:

$\min z=\left(\underset{i}{\overset{N}{\&Sigma;}}\underset{j}{\overset{N}{\&Sigma;}}{c}_{ij}{x}_{ij}\right);$

Constraint:

$\underset{i}{\overset{N}{\&Sigma;}}\underset{j}{\overset{N}{\&Sigma;}}(P_{ij}-{\mathrm{\&Delta;P}}_{ij})x_{ij}\&le;P_0;$

Task restriction:

$\left\{\begin{array}{c}{s}_i+t_i+t_{ij}=s_j,i,j=1,2,...,N\\ s_j\&le;d_j,j=1,2,...,N\end{array}\right.;$

Decision variable:

x _ij＝0or1；

Step 5.2, local paths planning, if f (i, j) is the cost valuation from an i to a j, g (i, j) be actual cost from an i to a j, h (i, j) is the heuristic information from an i to a j, estimate that each present node arrives at the minimum cost of destination node, improvement A* algorithm is adopted to obtain optimal path, i.e. f (i, j); Work as P _ij>P _cur(P _currepresent that electric automobile is positioned at electricity when namely present node puts i) time, represent and need charging, if when at this moment meeting real-time constraint task time, then can charge from an i to a j, otherwise return a node proposition charge request and to lay equal stress on new search route;

Ask for an interview Fig. 2, improve A* algorithm, if S _curfor current place node, S ₀for initial time place node, S _pfor charging pile place node, S _tfor destination node, S _ifor arbitrary node in map, DEPTH is the depth of nesting, Δ P ₀for electric automobile every kilometer power consumption, L represents distance between two points; Then improve A* algorithm specific implementation process and comprise following sub-step:

Step 5.2.1: initialization, makes S _cur=S ₀; Structure Open table is for accessing node to be expanded, and initial time is S ₀; Structure Close table is for accessing the node expanded, and initial time is stored in S ₀; Structure PATH table is for accessing the node on optimal path;

Step 5.2.2: search for from S in Open table _curnearest node S _i, with f (S _cur, S _i) minimum and the node meeting task restriction and Constraint condition is father node

$\left\{\begin{array}{c}{s}_i+t_i+t_{ij}=s_j,i,j=1,2,...,N\\ s_j\&le;d_j,j=1,2,...,N\end{array}\right.;$

$\underset{i}{\overset{N}{\&Sigma;}}\underset{j}{\overset{N}{\&Sigma;}}(P_{ij}-{\mathrm{\&Delta;P}}_{ij})x_{ij}\&le;p_0;$

If meet this two conditions simultaneously, go to step 5.2.3;

This two conditions cannot be met simultaneously, go to step 5.2.5;

Step 5.2.3: if S _ifor destination node S _t, then the set of node stored in PATH table as optimal path, and remembers that weight path is terminate algorithm;

Step 5.2.4: if S _inot destination node S _t, then to S _iall forerunner state S _jupgrade, if g _j<g _i+ g (i, j), then by S _iput into Close list; Otherwise make g _j=g _i+ g (i, j), by S _ias S ₀next step route, makes S _cur=S _iand stored in PATH table; Return step 5.2.2;

Step 5.2.5: make S _cur=S ₀, DEPTH=DEPTH-1;

If DEPTH=0, go to step 5.2.6;

Otherwise it is just satisfied range L in charging pile be set to special joint { S _p1, S _p2..., S _pn, adopt Charge algorithm to calculate optimal path;

If meet task restriction and Constraint condition

$\left\{\begin{array}{c}{s}_i+t_i+t_{ij}=s_j,i,j=1,2,...,N\\ s_j\&le;d_j,j=1,2,...,N\end{array}\right.;$

$\underset{i}{\overset{N}{\&Sigma;}}\underset{j}{\overset{N}{\&Sigma;}}(P_{ij}-{\mathrm{\&Delta;P}}_{ij})x_{ij}\&le;p_0;$

Then exporting weight path is f (S ₀, S _t)=f (S ₀, S _p)+f (S _p, S _t), terminate algorithm;

If still cannot meet task restriction and Constraint condition simultaneously, go to step 5.2.6;

Wherein the specific implementation process of Charge algorithm comprises following sub-step:

Step 5.2.5.1: make DEPTH=0, maps { S _p1, S _p2..., S _pnbe { S ₀₁, S ₀₂..., S _0n, utilize the principle of step 5.2.1 to step 5.2.6 to calculate f (S _pi, S _t), i=1,2 ..., n, if f is (S _pi, S _t)=∞, then give up p _it (p _it represents a p _ithis route to some t) this route, otherwise calculate f (S ₀, S _pi);

Step 5.2.5.2: make DEPTH=DEPTH _pi, (DEPTH _pirepresent corresponding S _pithe depth of nesting) map { S _p1, S _p2..., S _pnbe { S _t1, S _t2..., S _tn, utilize the principle of step 5.2.1 to step 5.2.6 to calculate f (S ₀, S _pi), i=1,2 ..., n, if f is (S ₀, S _pi)=∞, then give up p _ithis node;

Step 5.2.5.3: to reservation node p _icalculate f (S respectively ₀, S _t)=f (S ₀, S _pi)+f (S _pi, S _t), i=1,2 ..., n, gets minimum value minf (S ₀, S _t)=f (S ₀, S _pi)+f (S _pi, S _t), i=1,2 ..., n is path weight value, and algorithm terminates;

Step 5.2.6: exporting weight path output weight path is f (S ₀, S _t)=∞, terminates algorithm.

Step 5.3: global path planning, using the weight of the f (i, j) in step 5.2 as every paths, adopts Christofides algorithm to build path and obtains optimum battery friendly multiple objective programming path;

The implementation procedure adopting Christofides algorithm to build path comprises following sub-step:

Step 5.3.1, by task node { S each in city map _t1, S _t2..., S _tn, retain special joint { S _pi, S _pi+1..., S _pjand start node form point set structure minimum spanning tree;

Step 5.3.2: using the weight of the f (i, j) in step 5.2 as every paths, if f is (S _i, S _j)=∞, then delete path ij, find the summit that interconnectedness is odd number in minimum spanning tree, and create minimal weight coupling, structure Eular loop;

Step 5.3.3: remove and repeat a little in Eular loop and minimum spanning tree, obtain path and optimal programming route.

Step 6: after decision system receives the one group of programme path satisfying condition feasible, gets front k bar optimal route according to probability feasibility and returns to mobile client, transfer to user to select.

Should be understood that, the part that this instructions does not elaborate all belongs to prior art.

Should be understood that; the above-mentioned description for preferred embodiment is comparatively detailed; therefore the restriction to scope of patent protection of the present invention can not be thought; those of ordinary skill in the art is under enlightenment of the present invention; do not departing under the ambit that the claims in the present invention protect; can also make and replacing or distortion, all fall within protection scope of the present invention, request protection domain of the present invention should be as the criterion with claims.

Claims (10)

1. an electric automobile course continuation mileage optimization device, is characterized in that: comprise data acquisition system (DAS), decision system and expert system; Described data acquisition system (DAS) comprises mobile client, in-vehicle communication system and charging station communication module; Decision system comprises the first wireless communication module and decision-making module; Expert system comprises the second wireless communication module, database and path planning module; Described data acquisition system (DAS), to be interconnected with the second wireless communication module by the communication module that arranges in mobile client, the first wireless communication module between decision system and expert system and to communicate.

2. utilize the electric automobile course continuation mileage optimization device described in claim 1 to carry out a method for electric automobile course continuation mileage optimization, it is characterized in that, comprise the following steps:

Step 1: user uses mobile client collection and uploads driving task attribute information in expert system;

Step 2: in-vehicle communication system collection also uploads electric automobile vehicle condition information in expert system;

Step 3: the collection of charging station communication module also uploads charging pile work information in expert system;

Step 4: expert system receives searching database after the data uploaded in step 1, step 2 and step 3, if there is matched data, returns one group of existing programme path, otherwise transfers to path planning module to process;

Step 5: path planning module, in conjunction with constraint condition, sets up optimization problem model, solves this optimization problem, carries out multiple goal path planning, provides the routing plan of many feasible battery friendly; Described constraint condition comprises electric automobile vehicle condition, charging pile operating mode, traffic, driving task attribute;

Step 6: after decision system receives the one group of programme path satisfying condition feasible, gets front k bar optimal route according to probability feasibility and returns to mobile client, transfer to user to select.

3. method according to claim 2, is characterized in that: the driving task attribute information described in step 1 comprises the geographic position of every task, closing time and precedence information.

4. method according to claim 2, is characterized in that: the electric automobile vehicle condition packets of information described in step 2 draws together current vehicle position and dump energy information.

5. method according to claim 2, is characterized in that: the charging pile work information described in step 3 comprises service condition and the failure condition information of charging pile.

6. method according to claim 2, it is characterized in that: if having matched data described in step 4, return one group of existing programme path, its matched rule is: electric automobile vehicle condition information is consistent with driving task attribute information, and namely current vehicle position, dump energy information, the geographic position of task, closing time are consistent with precedence information.

7. method according to claim 2, is characterized in that: the specific implementation process of the multiple goal path planning described in step 5 comprises following sub-step:

Step 5.1: set up optimization problem mathematical model; Be provided with N number of task node, c _ijrepresent the transportation cost from i a to j, comprise distance and electricity; P _ijrepresent and expend electricity from an i to a j; △ P _ijrepresent from an i to the charge capacity of a j; P ₀represent the initial value of electricity; s _irepresent and arrive the moment on i ground and the difference of initial time; t _iexpression is finished the work the time consumed at i; t _ijrepresent the time consumed to a j from an i; d _ibe expressed as in the i ground deadline of task and the difference of initial time; x _ijrepresent that whether vehicle is from an i to a j;

Objective function:

$\min z=\left(\underset{i}{\overset{N}{\&Sigma;}}\underset{j}{\overset{N}{\&Sigma;}}{c}_{ij}{x}_{ij}\right);$

Constraint:

$\underset{i}{\overset{N}{\&Sigma;}}\underset{j}{\overset{N}{\&Sigma;}}(P_{ij}-{\mathrm{\&Delta;P}}_{ij})x_{ij}\&le;P_0;$

Task restriction:

$\left\{\begin{array}{c}{s}_i+t_i+t_{ij}=s_j,i,j=1,2,...,N\\ s_j\&le;d_j,j=1,2,...,N\end{array}\right.;$

Decision variable:

x _ij＝0or1；

Step 5.2, local paths planning, if f (i, j) is the cost valuation from an i to a j, g (i, j) be actual cost from an i to a j, h (i, j) is the heuristic information from an i to a j, estimate that each present node arrives at the minimum cost of destination node, improvement A* algorithm is adopted to obtain optimal path, i.e. f (i, j); Work as P _ij>P _curtime, represent and need charging, if when at this moment meeting real-time constraint task time, then can charge from an i to a j, otherwise return node proposition charge request and to lay equal stress on new search route; Wherein, P _currepresent that electric automobile is positioned at electricity when namely present node puts i;

Step 5.3: global path planning, using the weight of the f (i, j) in step 5.2 as every paths, adopts Christofides algorithm to build path and obtains optimum battery friendly multiple objective programming path.

8. method according to claim 7, is characterized in that: the improvement A* algorithm described in step 5.2, if S _curfor current place node, S ₀for initial time place node, S _pfor charging pile place node, S _tfor destination node, △ P ₀for arbitrary node in map, DEPTH is the depth of nesting, △ P ₀for electric automobile every kilometer power consumption, L represents distance between two points, then improve A* algorithm specific implementation process and comprise following sub-step:

Step 5.2.1: initialization, makes S _cur=S ₀; Structure Open table is for accessing node to be expanded, and initial time is S ₀; Structure Close table is for accessing the node expanded, and initial time is stored in S ₀; Structure PATH table is for accessing the node on optimal path;

Step 5.2.2: search for from S in Open table _curnearest node S _i, with f (S _cur, S _i) minimum and the node meeting task restriction and Constraint condition is father node

$\left\{\begin{array}{c}{s}_i+t_i+t_{ij}=s_j,i,j=1,2,...,N\\ s_j\&le;d_j,j=1,2,...,N\end{array}\right.;$

$\underset{i}{\overset{N}{\&Sigma;}}\underset{j}{\overset{N}{\&Sigma;}}(P_{ij}-{\mathrm{\&Delta;P}}_{ij})x_{ij}\&le;P_0;$

If meet this two conditions simultaneously, go to step 5.2.3;

This two conditions cannot be met simultaneously, go to step 5.2.5;

Step 5.2.3: if S _ifor destination node S _t, then the set of node stored in PATH table as optimal path, and remembers that weight path is $f(S_0,S_t)=\underset{i=0}{\overset{t-1}{\&Sigma;}}f(s_i,s_{i+1}),$ Terminate algorithm;

Step 5.2.4: if S _inot destination node S _t, then to S _iall forerunner state S _jupgrade, if g _j<g _i+ g (i, j), then by S _iput into Close list; Otherwise make g _j=g _i+ g (i, j), by S _ias S ₀next step route, makes S _cur=S _iand stored in PATH table; Return step 5.2.2;

Step 5.2.5: make S _cur=S ₀, DEPTH=DEPTH-1;

If DEPTH=0, go to step 5.2.6;

Otherwise will meet range L in charging pile be set to special joint { S _p1, S _p2..., S _pn, adopt Charge algorithm to calculate optimal path;

If meet task restriction and Constraint condition

$\left\{\begin{array}{c}{s}_i+t_i+t_{ij}=s_j,i,j=1,2,...,N\\ s_j\&le;d_j,j=1,2,...,N\end{array}\right.;$

$\underset{i}{\overset{N}{\&Sigma;}}\underset{j}{\overset{N}{\&Sigma;}}(P_{ij}-{\mathrm{\&Delta;P}}_{ij})x_{ij}\&le;P_0;$

Then exporting weight path is f (S ₀, S _t)=f (S ₀, S _p)+f (S _p, S _t), terminate algorithm;

If still cannot meet task restriction and Constraint condition simultaneously, go to step 5.2.6;

Step 5.2.6: exporting weight path output weight path is f (S ₀, S _t)=∞, terminates algorithm.

9. method according to claim 8, is characterized in that: the Charge algorithm described in step 5.2.5, and specific implementation process comprises following sub-step:

Step 5.2.5.1: make DEPTH=0, maps { S _p1, S _p2..., S _pnbe { S ₀₁, S ₀₂..., S _0n, utilize the principle of step 5.2.1 to step 5.2.6 to calculate f (S _pi, S _t), i=1,2 ..., n, if f is (S _pi,s _t)=∞, then give up p _ithis route of t, otherwise calculate f (S ₀, S _pi), wherein p _it represents a p _ito this route of a t;

Step 5.2.5.2: make DEPTH=DEPTH _pi, map { S _p1, S _p2..., S _pnbe { S _t1, S _t2..., S _tn, utilize the principle of step 5.2.1 to step 5.2.6 to calculate f (S ₀, S _pi), i=1,2 ..., n, if f is (S ₀, S _pi)=∞, then give up p _ithis node; Wherein DEPTH _pirepresent corresponding S _pithe depth of nesting;

Step 5.2.5.3: to reservation node p _icalculate f (S respectively ₀, S _t)=f (S ₀, S _pi)+f (S _pi, S _t), i=1,2 ..., n, gets minimum value minf (S ₀, S _t)=f (S ₀, S _pi)+f (S _pi, S _t), i=1,2 ..., n is path weight value, and algorithm terminates.

10. method according to claim 7, is characterized in that: the implementation procedure that the employing Christofides algorithm described in step 5.3 builds path comprises following sub-step:

Step 5.3.1, by task node { S each in city map _t1, S _t2..., S _tn, retain special joint { S _pi, S _pi+1..., S _pjand start node form point set structure minimum spanning tree;

Step 5.3.2: using the weight of the f (i, j) in step 5.2 as every paths, if f is (S _i, S _j)=∞, then delete path ij, find the summit that interconnectedness is odd number in minimum spanning tree, and create minimal weight coupling, structure Eular loop;

Step 5.3.3: remove and repeat a little in Eular loop and minimum spanning tree, obtain path and optimal programming route.