CN104301864A - Wireless communication networking method of electric car charging pile cluster - Google Patents

Abstract

The invention relates to a wireless communication networking method of an electric car charging pile cluster. According to the method, the core is that a geographical-location-information-based self-networking route control method of a wireless sensor network is provided. Node/charging pile position information is obtained by a non-ranging positioning algorithm. On the basis of a self-adaption dynamic cluster idea, a hierarchical network is constructed. In terms of the network environment of the charging occasion, a novel threshold defining method is provided by considering a partial node density and a distance factor between the node and the sink base station, thereby improving the network load balancing degree. Moreover, the hotspot problem is also relieved to a certain extent. In order to ensure connection of a larger-scale network, an auxiliary cluster head node is introduced; and the cluster nod and the sink node carry out route path selection according to a trust value. Furthermore, after limited circulation, a partial optimized route with the section upper limit performance index constraint is determined based on the route optimization model. Therefore, the provided method has the great economic benefit foreground relative to the wired framework; and the networking capability and the data processing capability of the management system can be effectively improved.

Description

The method for wireless communication networking of electric automobile charging pile cluster

Technical field:

The present invention relates to a kind of electric automobile charging pile Trunked Radio network-building method based on geographical location information, its core is a kind of private radio sensor network (WSN) MANET route control method, for meeting the data communication needs of electric automobile charging pile cluster management system, belong to wireless communication technology field.

Background technology:

The energy resource supply problem how solving electric automobile (EV) is electric automobile one of universal key issue faced fast.At present, the energy resource supply of electric automobile provides primarily of charging pile charging.Electric automobile charging pile is on the basis providing safety, efficiently charging service, need to carry out centralized watch and unified management management by the technological means of advanced person to the multiple charging pile of the same area, there is provided perfect cluster management system, to promote the development of ev industry.

There is the multiple watch-dogs such as charger, controller switching equipment and protection supervisory equipment in electric automobile charging pile cluster; cluster management system be contact electric automobile and cluster management center, power-management centre information taken point; need to carry out integrated, analysis to parameter configuration, watch-dog state and charging process real time information etc.; contain much information; to the reliability of communication and requirement of real-time high, therefore efficiently information interaction is the key factor that system is rationally run.Relative to conventional wireline communication mode, based on the cluster management system communication mode of wireless sensor network with its distinctive quick self organization ability and superior autgmentability, adaptivity, conveniently realizing charging service that is flexible and efficient, quick and safe, is the desired communication mode of electric automobile charging pile cluster management system.

But traditional wireless sensor network route control method is unsuitable for the network environment of noenergy constraint, the now challenge of network faces is no longer reduce energy consumption as far as possible and extend network lifetime, and is the performance of maximization network.Also do not have a kind of route control method can meet all demands at present, Chinese scholars to the research of wireless sensor network route control method all for particular network environment.Huang H. etc. propose EIGR control method, and consider that positional information and transmitting-receiving power do forwarding strategy, in time delay and Packet delivery ratio, performance is excellent.Miao Shihong etc. propose to meet network reliability requirement by multipath authentic communication routing mechanism for distribution line, channel quality and the sensor node jumping figure to Sink node is mainly considered during Path selection, ensure that the fast transport of data, but network throughput cannot ensure; Preetika etc. analyze and utilize wireless sensor network carry out the energy management of electric automobile and realize the feasibility of information system management and electronics charging, adopt the method that Cluster-Tree route is combined with AODVjr route, improve algorithm performance, but the network that the scale that is only applicable to is less, routed path also may not be best.The people such as Sun Wei point out that power distribution network interior joint adopts mains-supplied, there is not Power supply problem, propose a kind of route control method based on layering wireless sensor network topology and link-quality, utilize Layered Multipath balanced load, for controller switching equipment provides the data communication services of low Packet Error Rate, short time-delay, but algorithm to network topology structure change insensitive, can not ensure that whole node networks.For solving the problem, realize carrying out efficient data interaction by wireless sensor network between each charging pile and administrative center, need primary study to be suitable for the efficient wireless sensor network MANET route control method of electric automobile charging pile cluster management system.

Summary of the invention:

The present invention will overcome in the conventional cluster way to manage based on wired backbone and connects up, debugging and later maintenance, change expends the drawback of huge manpower and materials, be intended to improve management system networking capability and data-handling capacity, based on the communication requirement of cluster management system, inquire into suitable wireless communication network scheme, devise the double-deck group-net communication framework of electric automobile charging pile cluster management system, this framework is in units of region, region is embodied in certain charging station in city, each area configurations one group of wireless sensor network, for the data interaction at each charging pile and cluster management center, and a kind of wireless sensor network MANET route control method for electric automobile charging pile cluster management system is provided, each region is connected by GPRS private network mode in the heart with cluster management, layering uploading data.The present invention realizes carrying out efficient, real-time data interaction between each charging pile and administrative center, improve the service quality of charging pile cluster, propose a kind of electric automobile charging pile Trunked Radio network-building method, as shown in Figure 1, the detailed process of technical scheme is as follows:

1, determine EV charging pile cluster scale (charging pile number N) and distributed areas (length lt and width wt), node communication distance R, Sink node is positioned at edges of regions;

2, for ensureing that all charging piles network, introducing auxiliary leader cluster node, determining number and the position of the auxiliary leader cluster node needing to dispose according to sensitive zones size and node communication distance;

3, the acquisition of node geo-location information

Range-free localization algorithm: algorithm only needs a mobile beacon, supposes that the perceived distance of node is R _s, beaconing nodes is with speed V _mfrom (-R _s,-R _s) to (lt+R _s, wt+R _s) be linearly spaced apart l _iback and forth movement, and with frequency f _bperiodically broadcast packet is containing the packet of current location information; In the process of movement, as a certain node S distance beaconing nodes R to be positioned _stime, so now the positional information of beacon can participate in the location of node S; In reality, due to the hardware of beaconing nodes and the restriction of positioning time, the interval of beaconing nodes broadcast packe can not be infinitesimal, and the radio broadcast range of node presents certain degree of irregularity (DOI), as shown in Figure 2, being therefore considered to apart from node to be positioned is R _sposition actual be R ' with the distance of node to be positioned _s, there is certain error, namely as (1-DoI) R _s≤ R ' _s≤ (1+DoI) R _stime, just think that node to be positioned and beaconing nodes distance are R _s, the positional information of beaconing nodes can as one of reference coordinate of location S this moment; For improving positioning precision, the present invention adopts node dormancy mechanism that node to be positioned is in and participates in, in the triangle of three position compositions of location, then utilizing maximum-likelihood method to estimate node location;

As shown in Figure 2, position fixing process interior joint S receives beacon message 4 times altogether, and the positional information that first time receives is A ₁, the now not direct dormancy of record position information, dormancy time is t ₁, suppose node S dormancy t ₁after the positional information that receives first be A ₂, record position information, then dormancy t ₂, by that analogy, recorded A ₄then no longer receive the information block of beaconing nodes afterwards; The dormancy time in each stage is:

$\begin{array}{c}{t}_1=0.25\frac{R_s}{l_I}{T}_l+\frac{2(-R_s)\log (1-\mathrm{rand})}{(\mathrm{lt}+2R_s)f_B/V_m}{T}_l\\ t_2=\frac{2\sqrt{R_s^2-{(R_s-3l_I)}^2}-R_s\mathrm{DoI}}{\mathrm{lt}+2R_s}{T}_l\\ t_3=(\frac{2R_s}{l_I}-0.25\frac{R_s}{l_I}-\frac{R_s\mathrm{DoI}}{l_I})T_l\end{array}---\left(1\right)$

In formula, T _lfor the time of the opposite side cost that moves along a straight line from the side of region D, rand is used for generating the random number between (0,1);

When node S (x, y) to be positioned receives three positional information: A by dormancy mechanism ₂(x ₂, y ₂), A ₃(x ₃, y ₃), A ₄(x ₄, y ₄) time has:

$\left\{\begin{array}{c}{(x_2-x)}^2+{(y_2-y)}^2=R_s^2\\ {(x_3-x)}^2+{(y_3-y)}^2=R_s^2\\ {(x_4-x)}^2+{(y_4-y)}^2=R_s^2\end{array}\right.---\left(2\right)$

Above formula is converted into the system of linear equations of AX=b form

$\begin{array}{c}A=\left[\begin{array}{cc}2(x_2-x_4)& 2(y_2-y_4)\\ 2(x_3-x_4)& 2(y_3-y_4)\end{array}\right]\\ b=\left[\begin{array}{c}{x}_2^2-x_4^2+y_2^2-y_4^2\\ x_3^2-x_4^2+y_3^2-y_4^2\end{array}\right]X=\left[\begin{array}{c}x\\ y\end{array}\right]\end{array}---\left(3\right)$

The coordinate that the nonlinear IEM model method of use standard can obtain node to be positioned is:

$\hat{X}={{\left(\begin{array}{cc}{A}^T& A\end{array}\right)}^{-1}A}^{T}b---\left(4\right)$

4, to threshold value T _ndefinition

T _n＝2pλe ^{(-λ)[(-k)log(1+kδ)]} (5)

Wherein p is the probability that node is chosen as bunch head, thinks that the corresponding node of every platform charging pile, N are sensing network interior joint sum here, d _{a → B}for node A is to the distance of Node B, A, B can represent all types of node, s _nrepresent sensing node, d _maxfor region interior joint is to the maximum distance of Sink node, k, α and λ _minfor special parameter;

5, ordinary node to add bunch in process

In this stage, a value in each node Stochastic choice (0,1), if selected value is less than specific threshold T _n, so this node will utilize CSMA-MAC protocol broadcast ADV message, and announce oneself elected epicycle bunch head, ordinary node determines to add which bunch according to formula (6), becomes a member of this bunch, and notice bunch head;

$S_i\∈{Q_C}_j\⇔\left\{\begin{array}{c}(R\≥d_{S_i\→C_j})\\ \∀k\≠j,S_i\∉{Q_C}_k\\ \∀C_n\∈U_i,{N_C}_n\≥{N_C}_j\end{array}\right.---\left(6\right)$

S in formula _irepresent ordinary node, Q _cjrepresent that bunch head is C _jbunch, U _irepresent at node S _ileader cluster node set in communication range, N _cnfor with C _nfor bunch number of members of bunch head; When formula (6) represents that ordinary node adds bunch, not only consider distance, also consider the scale of each bunch that is communicated with it;

6, MANET route control method

Shown in accompanying drawing 3 is the topological structure schematic diagram of certain sensing network, bunch head in clustering architecture in the mode of TDMA be bunch in member node arrangement send the order of data, bunch head after the de-redundancy of necessity, adopt CSMA/CA mechanism by data along bunch between path be sent to Sink node; Between bunch, the transmission of data adopts the multihop routing mechanism inspired based on trust value, and between bunch, the selection in path follows the principles 1;

The down hops that rule 1. is selected be depart from that line between source node and destination node is minimum, distance destination node recently and the node be communicated with present node;

As shown in Figure 4, detailed process is as follows for route-determining process block diagram:

6.1 path selection process are from source node C _istart, make C _i=C _nh(next-hop node), determines C _nhadjacent cluster node (leader cluster node and auxiliary leader cluster node) set and judge that whether Sink node exists in, if, jump to step 6.3, otherwise calculate C _nhtrust value matrix:

$K_{\mathrm{Nh}}=\left[\begin{array}{cccc}{K}_{\mathrm{Nh},1}& K_{\mathrm{Nh},2}& ...& K_{\mathrm{Nh},\left|{{\mathrm{\Γ}}_C}_{\mathrm{Nh}}\right|}\end{array}\right]={\left(K_{\mathrm{Nh},j}\right)}_{1\×\left|{{\mathrm{\Γ}}_C}_{\mathrm{Nh}}\right|}---\left(7\right)$

K _{nh, j}represent node C _nhgive node C _jtrust value, measured by the function about distance, its calculate such as formula shown in (8):

$K_{\mathrm{Nh},j}=\frac{{\mathrm{lt}}^2+{\mathrm{wt}}^2}{2}-(d_{C_{\mathrm{Nh}}\→C_j}^2+d_{C_j\→\mathrm{Sink}}^2),C_{j\∈{{\mathrm{\Γ}}_C}_{\mathrm{Nh}}}---\left(8\right)$

6.2 from the node P that middle selection trust value is maximum _n, decision node P _nwhether at C _ito the path P ath of Sink _iin, if not, then by P _nadd Path _iin, otherwise, by P _nfrom middle deletion, repeats step 6.2 until determine P _n, and make P _n=C _nh;

6.3 repeat step 6.1,6.2 until C _nhwhen finally searching Sink node, node C _ipath P ath _inamely be selected;

6.4 as source node C _iwhen having traveled through all cluster node, i.e. i>m _rtime (i=1,2 ..., m _r, represent cluster node numbering, m _rthe cluster knot produced for previous cycle is counted), just determine the preliminary route of this circulation, define one be root with Sink node, the cluster node routing tree that is backbone;

7, routing optimality

When setting up clustering architecture, although introduce the selection of node density factor to leader cluster node had certain guidance quality, the position that bunch head occurs still has randomness, a trunk is only had for preventing routing tree, carry out coordination optimization to the multiple target such as throughput, time delay further, the present invention adopts the mode determination route of first breadth first search deep search again; After walking Route establishment originally, current route is carried out to the route assessment of a period of time, obtain following evaluate parameter: 1. mean transit delay D _r(time delay of the present invention's statistics is time delay because Channel Access Mechanism produces and the processing delay of node itself); 2. network throughput ThP _r, the total amount of data that namely in observing time, Sink node receives; After h circulation, from route storehouse, determine according to evaluate parameter the optimum network routing meeting cluster management system transmission request message, and broadcast respective cluster head; The determination of route can be reduced to the end-to-end uncertain course optimizing Solve problems of interval upper limit performance index constraint, in limited number of time circulation, seek optimal solution; The Route Optimization Model met under real-time condition is:

$\left\{\begin{array}{c}\max (M\left(1\right),M\left(2\right),...,M\left(r\right),...,M\left(h\right))\\ s.t.D_r<D_0\end{array}\right.---\left(9\right)$

Wherein

$M\left(r\right)=\left\{\begin{array}{cc}\mathrm{Th}{P}_r,& D_r\&le;D_V^{*}\\ 0,& D_r>D_V^{*}\end{array}\right.---\left(10\right)$

D in formula ₀for the maximum delay that information transmission in charging pile cluster management system allows, particularly, the propagation delay time of this circulation is only had to be less than D ₀time, be just likely confirmed as final route, for the time delay value of each circulation is by V time delay value after ascending sort, parameter V is obtained by experiment experience, value such as formula shown in (11), represent the maximum integer being less than u, h represents cycle-index.

Beneficial effect of the present invention is, devise the double-deck group-net communication framework of electric automobile charging pile cluster management system, as shown in Figure 5, pre-test has been carried out to its Wireless Networking, for the region charging station in accompanying drawing 5, a kind of wireless sensor network MANET route control method is proposed especially: carry out routed path selection based on geographical location information, the path selected is restrained towards Sink node, achieve the most jete route from source node to destination node, use scenes for charging pile is (densely distributed, noenergy retrains), effectively can reduce propagation delay time, Routing Optimization Algorithm impels routing tree toward many trunks future development, and in network throughput, mean transit delay etc., performance is excellent, relative to conventional wireline communication mode, based on the communication mode of wireless sensor network, due to its self-organization and extensive property, rapid networking is convenient, cost performance is high, be suitable for distributed treatment to improve data-handling capacity, be more suitable for building electric automobile charging pile cluster management system, conveniently realize charging service that is flexible and efficient, quick and safe.

Accompanying drawing illustrates:

Fig. 1 route control method block diagram of the present invention.

The irregular radio broadcasting pattern of Fig. 2.

Fig. 3 sensor network topology structural representation.

Fig. 4 route-determining process block diagram.

Fig. 5 cluster management system structure chart.

Fig. 6 routing optimality: 6 (a) Node distribution figure; 6 (b) path selection process; 6 (c) optimum routed path.

Charge Fig. 7 day EV distributed number figure.

The network throughput discharge curve determined under the different cycle-index of Fig. 8

Embodiment:

Below in conjunction with embodiment and accompanying drawing, the present invention is described in further detail, but embodiments of the present invention are not limited thereto.Embodiment is launched based on the wireless sensor network route-determining process in the EV charging pile cluster of region, and as shown in Figure 1, the method for wireless communication networking of electric automobile charging pile cluster, comprises the following steps general diagram:

1, determine EV charging pile cluster scale (charging pile number N) and distributed areas (length lt and width wt), node communication distance R, Sink node is positioned at edges of regions;

2, for ensureing that all charging piles network, introducing auxiliary leader cluster node, determining number and the position of the auxiliary leader cluster node needing to dispose according to sensitive zones size and node communication distance;

3, the acquisition of node geo-location information

Range-free localization algorithm: algorithm only needs a mobile beacon, supposes that the perceived distance of node is R _s, beaconing nodes is with speed V _mfrom (-R _s,-R _s) to (lt+R _s, wt+R _s) be linearly spaced apart l _iback and forth movement, and with frequency f _bperiodically broadcast packet is containing the packet of current location information; In the process of movement, as a certain node S distance beaconing nodes R to be positioned _stime, so now the positional information of beacon can participate in the location of node S; In reality, due to the hardware of beaconing nodes and the restriction of positioning time, the interval of beaconing nodes broadcast packe can not be infinitesimal, and the radio broadcast range of node presents certain degree of irregularity (DOI), as shown in Figure 2, being therefore considered to apart from node to be positioned is R _sposition actual be R ' with the distance of node to be positioned _s, there is certain error, namely as (1-DoI) R _s≤ R ' _s≤ (1+DoI) R _stime, just think that node to be positioned and beaconing nodes distance are R _s, the positional information of beaconing nodes can as one of reference coordinate of location S this moment; For improving positioning precision, the present invention adopts node dormancy mechanism that node to be positioned is in and participates in, in the triangle of three position compositions of location, then utilizing maximum-likelihood method to estimate node location;

As shown in Figure 2, position fixing process interior joint S receives beacon message 4 times altogether, and the positional information that first time receives is A ₁, the now not direct dormancy of record position information, dormancy time is t ₁, suppose node S dormancy t ₁after the positional information that receives first be A ₂, record position information, then dormancy t ₂, by that analogy, recorded A ₄then no longer receive the information block of beaconing nodes afterwards; The dormancy time in each stage is such as formula shown in (1);

When node S (x, y) to be positioned receives the positional information of three shown in accompanying drawing 2 by dormancy mechanism: A ₂(x ₂, y ₂), A ₃(x ₃, y ₃), A ₄(x ₄, y ₄) time, obtain equation group such as formula shown in (2);

Formula (2) is converted into the system of linear equations of AX=b form, and wherein A, b, X are expressed as shown in formula (3);

The nonlinear IEM model method of use standard can obtain the coordinate of node to be positioned, shown in (4);

4, to threshold value T _ndefinition such as formula shown in (5);

5, ordinary node to add bunch in process

In this stage, a value in each node Stochastic choice (0,1), if selected value is less than specific threshold T _n, so this node will utilize CSMA-MAC protocol broadcast ADV message, and announce oneself elected epicycle bunch head, ordinary node determines to add which bunch according to formula (6), becomes a member of this bunch, and notice bunch head.When formula (6) represents that ordinary node adds bunch, not only consider distance, also consider the scale of each bunch that is communicated with it;

6, MANET route control method

Bunch head in clustering architecture in the mode of TDMA be bunch in member node arrangement send the order of data, bunch head after the de-redundancy of necessity, adopt CSMA/CA mechanism by data along bunch between path be sent to Sink node; Between bunch, the transmission of data adopts the multihop routing mechanism inspired based on trust value, and between bunch, the selection in path follows the principles 1:

The down hops that rule 1. is selected be depart from that line between source node and destination node is minimum, distance destination node recently and the node be communicated with present node;

As shown in Figure 4, detailed process is as follows for route-determining process block diagram:

6.1 path selection process are from source node C _istart, make C _i=C _nh(next-hop node), determines C _nhadjacent cluster node (leader cluster node and auxiliary leader cluster node) set and judge that whether Sink node exists in, if, jump to step 6.3, otherwise calculate C _nhtrust value matrix, calculate such as formula shown in (7);

6.2 from the node P that middle selection trust value is maximum _n, decision node P _nwhether at C _ito the path P ath of Sink _iin, if not, then by P _nadd Path _iin, otherwise, by P _nfrom middle deletion, repeats step 6.2 until determine P _n, and make P _n=C _nh;

6.3 repeat step 6.1,6.2 until C _nhwhen finally searching Sink node, node C _ipath P ath _inamely be selected;

6.4 as source node C _iwhen having traveled through all cluster node, i.e. i>m _rtime (i=1,2 ..., m _r, represent cluster node numbering, m _rthe cluster knot produced for previous cycle is counted), just determine the preliminary route of this circulation, define one be root with Sink node, the cluster node routing tree that is backbone;

7, routing optimality

When setting up clustering architecture, although introduce the selection of node density factor to leader cluster node had certain guidance quality, the position that bunch head occurs still has randomness, a trunk is only had for preventing routing tree, carry out coordination optimization to the multiple target such as throughput, time delay further, the present invention adopts the mode determination route of first breadth first search deep search again.After walking Route establishment originally, current route is carried out to the route assessment of a period of time, obtain following evaluate parameter: 1. mean transit delay D _r(time delay of the present invention's statistics is time delay because Channel Access Mechanism produces and the processing delay of node itself); 2. network throughput ThP _r, the total amount of data that namely in observing time, Sink node receives.After h circulation, from route storehouse, determine according to evaluate parameter the optimum network routing meeting cluster management system transmission request message, and broadcast respective cluster head.Meet Route Optimization Model under real-time condition such as formula shown in (9).

Adopt a charging station being positioned at community parking field to be that example is analyzed in the present embodiment, it is 14400m that this charging station is positioned at area ²square region (lt=wt=120m) in, have 300 charging piles, charge power is 7kW, charging load power factor be 0.9, every day serves EV1200, and battery capacity is 32kWh; Sink node coordinate is (60m, 120m), and node communication distance R is 50m, and 4 auxiliary leader cluster nodes are uniformly distributed, and each Node distribution is as shown in accompanying drawing 6 (a).Node locating relative parameters setting: the movement rate V of beaconing nodes _mfor the frequency f of 10m/s, beaconing nodes broadcast position information _bbe 10 times/s, R _sfor 30m, straight wire spacing l _ifor 3m, DOI get 0.1.

To T _ndefinition in, p is 0.1, k is 2/3, λ _minbe 0.455, α be 0.1.

According to the EV charge data setting in table 1, and suppose that charging terminates rear user and rolled away from by EV immediately.The present invention is based on Monte Carlo simulation approach (MCS), each EV day charge requirement data of random generation, obtain intraday charging EV distributed number figure, as shown in Figure 7.

The charge data setting of table 1EV

Note: a ∨ b represents the higher value got between a and b.

As can be seen from accompanying drawing 7, every day about 20:00 charging evening peak time, the charging pile in cluster almost all participates in service, therefore selects the communication requirement of this period to carry out testing algorithm.

In accompanying drawing 6 (b), with C ₅(No. 85 nodes) illustrates the path selection process of Sink node for example.First, C ₅adjacent cluster node then K ₅=[7.72 × 10 ³, 4.88 × 10 ²,-3.89 × 10 ², 4.62 × 10 ³, 5.52 × 10 ³], according to step 6.2, next-hop node C _nh=C ₃, now ${{\mathrm{\&Gamma;}}_C}_3=\{C_1,C_4,C_5,C_{10},C_{11},C_{12},C_{13},C_{14},C_{15}\},$ And have K ₃=[1.22 × 10 ⁴, 1.13 × 10 ⁴,-9.09 × 10 ², 8.01 × 10 ³, 6.94 × 10 ³, 4.57 × 10 ³, 5.96 × 10 ³, 1.02 × 10 ⁴, 1.16 × 10 ⁴], therefore next-hop node C _nh=C ₁, for C ₁, due to therefore Sink node is in set in, C ₁sink node can be connected, such C ₅path just have selected, i.e. path ₅={ C ₃, C ₁, Sink}.

The deterministic process of parameter h: network is emulated by the parameter of above-mentioned setting, the network throughput discharge curve determined by the Route Optimization Model in step 7 under obtaining different cycle-index, as shown in Figure 8.Can find out, after cycle-index is greater than 120, throughput curve remains unchanged substantially, can obtain ideal routing tree when therefore thinking and cycle-index h is set as 120.

Often set up primary cluster structure, be called and once circulate after all cluster node path selections, so after the circulation that h is 120 times, determine the optimum network routing in above-mentioned circulation according to Route Optimization Model, selection result is as shown in accompanying drawing 6 (c).

As mentioned above, just the present invention can be realized preferably, above-described embodiment is only exemplary embodiments of the present invention, not be used for limiting practical range of the present invention, can carry out multiple change, amendment, replacement and modification to these embodiments when not departing from principle of the present invention and aim, scope of the present invention is by claim and equivalency thereof.

Claims (1)

1., according to the double-deck group-net communication framework of determined electric automobile (EV) charging pile cluster management system, the method for wireless communication networking of electric automobile charging pile cluster, is characterized in that, comprises the steps:

Step 1, determine EV charging pile cluster scale (charging pile number N) and distributed areas D (length lt and width wt), node communication distance R, Sink node is positioned at edges of regions;

Step 2, for ensureing that all charging piles networks, introducing auxiliary leader cluster node, determining number and the position of the auxiliary leader cluster node needing to dispose according to sensitive zones size and node communication distance;

The acquisition of step 3, node geo-location information

Range-free localization algorithm: algorithm only needs a mobile beacon, supposes that the perceived distance of node is R _s, beaconing nodes is with speed V _mfrom (-R _s,-R _s) to (lt+R _s, wt+R _s) be linearly spaced apart l _iback and forth movement, and with frequency f _bperiodically broadcast packet is containing the packet of current location information; In the process of movement, as a certain node S distance beaconing nodes R to be positioned _stime, so now the positional information of beacon can participate in the location of node S; In reality, due to the hardware of beaconing nodes and the restriction of positioning time, the interval of beaconing nodes broadcast packe can not be infinitesimal, and the radio broadcast range of node presents certain degree of irregularity (DOI), and being therefore considered to apart from node to be positioned is R _sposition actual be R ' with the distance of node to be positioned _s, there is certain error, namely as (1-DoI) R _s≤ R ' _s≤ (1+DoI) R _stime, just think that node to be positioned and beaconing nodes distance are R _s, the positional information of beaconing nodes can as one of reference coordinate of location S this moment; For improving positioning precision, the present invention adopts node dormancy mechanism that node to be positioned is in and participates in, in the triangle of three position compositions of location, then utilizing maximum-likelihood method to estimate node location;

Position fixing process interior joint S receives beacon message 4 times altogether, and the positional information that first time receives is A1, and the now not direct dormancy of record position information, dormancy time is t ₁, suppose node S dormancy t ₁after the positional information that receives first be A ₂, record position information, then dormancy t ₂, by that analogy, recorded A ₄then no longer receive the information block of beaconing nodes afterwards; The dormancy time in each stage is:

$\begin{array}{c}{t}_1=0.25\frac{R_s}{l_I}{T}_l+\frac{2(-R_s)\log (1-\mathrm{rand})}{(\mathrm{lt}+2R_s)f_B/V_m}{T}_l\\ t_2=\frac{2\sqrt{R_s^2-{(R_s-3l_I)}^2}-R_s\mathrm{DoI}}{\mathrm{lt}+2R_s}{T}_l\\ t_3=(\frac{2R_s}{l_I}-0.25\frac{R_s}{l_I}-\frac{R_s\mathrm{DoI}}{l_I})T_l\end{array}---\left(1\right)$

In formula, T _lfor the time of the opposite side cost that moves along a straight line from the side of region D, rand is used for generating the random number between (0,1);

When node S (x, y) to be positioned receives three positional information: A by dormancy mechanism ₂(x ₂, y ₂), A ₃(x ₃, y ₃), A ₄(x ₄, y ₄) time has:

$\left\{\begin{array}{c}{(x_2-x)}^2+{(y_2-y)}^2=R_s^2\\ {(x_3-x)}^2+{(y_3-y)}^2=R_s^2\\ {(x_4-x)}^2+{(y_4-y)}^2=R_s^2\end{array}\right.---\left(2\right)$

Above formula is converted into the system of linear equations of AX=b form

$\begin{array}{c}A=\left[\begin{array}{cc}2(x_2-x_4)& 2(y_2-y_4)\\ 2(x_3-x_4)& 2(y_3-y_4)\end{array}\right]\\ b=\left[\begin{array}{c}{x}_2^2-x_4^2+y_2^2-y_4^2\\ x_3^2-x_4^2+y_3^2-y_4^2\end{array}\right]X=\left[\begin{array}{c}x\\ y\end{array}\right]\end{array}---\left(3\right)$

The coordinate that the nonlinear IEM model method of use standard can obtain node to be positioned is:

$\hat{X}={{\left(\begin{array}{cc}{A}^T& A\end{array}\right)}^{-1}A}^{T}b---\left(4\right)$

Step 4, to threshold value T _ndefinition

T _n＝2pλe ^{(-λ)[(-k)log(1+kδ)]} (5)

Wherein p is the probability that node is chosen as bunch head, thinks that the corresponding node of every platform charging pile, N are sensing network interior joint sum here, d _{a → B}for node A is to the distance of Node B, A, B can represent all types of node, s _nrepresent sensing node, d _maxfor region interior joint is to the maximum distance of Sink node, k, α and λ _minfor special parameter;

Process during step 5, ordinary node add bunch

In this stage, a value in each node Stochastic choice (0,1), if selected value is less than specific threshold T _n, so this node will utilize CSMA-MAC protocol broadcast ADV message, and announce oneself elected epicycle bunch head, ordinary node determines to add which bunch according to formula (6), becomes a member of this bunch, and notice bunch head;

$S_i\&Element;{Q_C}_j\&DoubleLeftRightArrow;\left\{\begin{array}{c}(R\&GreaterEqual;d_{S_i\&RightArrow;C_j})\\ \&ForAll;k\&NotEqual;j,S_i\&NotElement;{Q_C}_k\\ \&ForAll;C_n\&Element;U_i,{N_C}_n\&GreaterEqual;{N_C}_j\end{array}\right.---\left(6\right)$

S in formula _irepresent ordinary node, Q _cjrepresent that bunch head is C _jbunch, U _irepresent at node S _ileader cluster node set in communication range, N _cnfor with C _nfor bunch number of members of bunch head;

Step 6, MANET route control method

Bunch head in clustering architecture in the mode of TDMA be bunch in member node arrangement send the order of data, bunch head after the de-redundancy of necessity, adopt CSMA/CA mechanism by data along bunch between path be sent to Sink node; Between bunch, the transmission of data adopts the multihop routing mechanism inspired based on trust value, and between bunch, the selection in path follows the principles 1:

The down hops that rule 1. is selected be depart from that line between source node and destination node is minimum, distance destination node recently and the node be communicated with present node;

The detailed process that route is determined is as follows:

6.1 path selection process are from source node C _istart, make C _i=C _nh(next-hop node), determines adjacent cluster node (leader cluster node and the auxiliary leader cluster node) set of CNh and judge that whether Sink node exists in, if, jump to step 6.3, otherwise calculate C _nhtrust value matrix:

$K_{\mathrm{Nh}}=\left[\begin{array}{cccc}{K}_{\mathrm{Nh},1}& K_{\mathrm{Nh},2}& ...& K_{\mathrm{Nh},\left|{{\mathrm{\&Gamma;}}_C}_{\mathrm{Nh}}\right|}\end{array}\right]={\left(K_{\mathrm{Nh},j}\right)}_{1\&times;\left|{{\mathrm{\&Gamma;}}_C}_{\mathrm{Nh}}\right|}---\left(7\right)$

K _{nh, j}represent node C _nhgive node C _jtrust value, measured by the function about distance, its calculate such as formula shown in (8):

$K_{\mathrm{Nh},j}=\frac{{\mathrm{lt}}^2+{\mathrm{wt}}^2}{2}-(d_{C_{\mathrm{Nh}}\&RightArrow;C_j}^2+d_{C_j\&RightArrow;\mathrm{Sink}}^2),C_{j\&Element;{{\mathrm{\&Gamma;}}_C}_{\mathrm{Nh}}}---\left(8\right)$

6.2 from the node P that middle selection trust value is maximum _n, decision node P _nwhether at C _ito the path P ath of Sink _iin, if not, then by P _nadd Path _iin, otherwise, by P _nfrom middle deletion, repeats step 6.2 until determine P _n, and make P _n=C _nh;

6.3 repeat step 6.1,6.2 until C _nhwhen finally searching Sink node, node C _ipath P ath _inamely be selected;

6.4 when source node Ci has traveled through all cluster node, namely determines the preliminary route of this circulation, define one be root with Sink node, the cluster node routing tree that is backbone;

Step 7, Route Optimization Model

When setting up clustering architecture, although introduce the selection of node density factor to leader cluster node had certain guidance quality, the position that bunch head occurs still has randomness, a trunk is only had for preventing routing tree, carry out coordination optimization to the multiple target such as throughput, time delay further, the present invention adopts the mode determination route of first breadth first search deep search again; After walking Route establishment originally, current route is carried out to the route assessment of a period of time, obtain following evaluate parameter: 1. mean transit delay D _r(time delay of the present invention's statistics is time delay because Channel Access Mechanism produces and the processing delay of node itself); 2. network throughput ThP _r, the total amount of data that namely in observing time, Sink node receives; After h circulation, from route storehouse, determine according to evaluate parameter the optimum network routing meeting cluster management system transmission request message, and broadcast respective cluster head; The determination of route can be reduced to the end-to-end uncertain course optimizing Solve problems of interval upper limit performance index constraint, in limited number of time circulation, seek optimal solution; The Route Optimization Model met under real-time condition is:

$\left\{\begin{array}{c}\max (M\left(1\right),M\left(2\right),...,M\left(r\right),...,M\left(h\right))\\ s.t.D_r<D_0\end{array}\right.---\left(9\right)$

Wherein

$M\left(r\right)=\left\{\begin{array}{cc}\mathrm{Th}{P}_r,& D_r\&le;D_V^{*}\\ 0,& D_r>D_V^{*}\end{array}\right.---\left(10\right)$

D in formula ₀for the maximum delay that information transmission in charging pile cluster management system allows, particularly, the propagation delay time of this circulation is only had to be less than D ₀time, be just likely confirmed as final route, for the time delay value of each circulation is by V time delay value after ascending sort, parameter V is obtained by experiment experience, value such as formula shown in (11), represent the maximum integer being less than u, h shows cycle-index.