CN105868942A - Ordered charging scheduling method for electric vehicle - Google Patents

Abstract

The invention provides an ordered charging scheduling method for an electric vehicle. The method comprises the steps of calculating out energy consumption of the electric vehicle in a current work condition according to a pre-established driving range predicting model of the electric vehicle; calculating out a driving range of the electric vehicle according to the energy consumption of the electric vehicle in the current work condition and a current SOC; calculating out a charging quantity of the electric vehicle according to a pre-established electric vehicle power battery service life model when a proportion between the driving range of the electric vehicle and a predicted driving range of the electric vehicle before next charging is smaller than a set threshold value; scheduling a charging process of the electric vehicle according to the charging quantity and departure time of the electric vehicle and a current load condition of a power distribution network by taking the minimum difference between peak and valley of the local-area power distribution network as an optimizing target. According to the ordered charging scheduling method, meeting a driving behavior characteristic of a driver is used as a base, the service life of a power battery of the electric vehicle is prolonged by reasonably guiding the electric vehicle to charge and discharge, the difference between peak and valley of a network load is reduced, and stable operation of a power network is guaranteed.

Description

The dispatching method that charges in order of electric automobile

Technical field

The present invention relates to charging electric vehicle technical field, particularly relate to the orderly charging of a kind of electric automobile Dispatching method.

Background technology

Ensure the sustainable supply of the energy, be a very important link of national energy security strategy, Formulation meets the energy resource safety strategy of China's sustainable development and has arrived instant stage.One side Face, improves energy environment, reduces the important common recognition that carbon emission is energy sustainable development.On the other hand, Fossil energy, as the important form of global energy, is easily subject to each side such as politics, economy, the attack of terrorism The impact in face.Energy-saving and emission-reduction and sustainable development make the conventional motor vehicle face using internal combustion engine as power source Face the destiny being eliminated, and use the electric automobile of clean energy resource will necessarily become following motor vehicles industry The new direction of development.Electric automobile is as a kind of novel electric power load, and its charging has randomness, intermittently The feature of property, scale charging electric vehicle will change the load condition that electrical network is current, adds bulk power grid one Peak load in it and the difference (peak-valley difference) of minimum load, affect power distribution network stable operation.The most such as How to solve the bigger peak-valley difference brought to electrical network because of charging electric vehicle, be the main aspect of the present invention.

Access the impact on power distribution network on a large scale for alleviating electric automobile, improve operation of power networks reliability and Economy, needs to reduce the random charging (unordered charging) of electric automobile, to charging electric vehicle as far as possible Guide, charge the most in order.Owing to current electric automobile is also in primary developmental stage, electric automobile Popularity rate the highest, the research therefore charged in order for electric automobile is very many and many joins to improve Network load situation or with reduce distribution network loss as target, but have ignored the charging wish of user, cause These researchs hardly result in application in practice.

In order to improve the responsiveness of electric automobile user, in addition to electric power netting safe running target, also need The basic demand of user to be paid close attention to and interests.Show according to auto industry research result: Yong Hu Buy and use during electric automobile, in addition to the cost performance of vehicle, the most it is of concern that drive Convenience (single battery continual mileage, driving comfort etc.), electrokinetic cell life-span, the facility of charging Property.If vehicle can be extended according to user's driving behavior characteristic and its discharge and recharge of user's request correct guidance Battery, reduces the peak-valley difference of electrical network simultaneously, and the user of electric automobile responsiveness of charging will have one Individual qualitative leap.But, in current prior art, battery life and user drive to rarely have research to consider Behavioral trait and the wish importance for the scheduling strategy that charges that charges.

Summary of the invention

The embodiment provides the dispatching method that charges in order of a kind of electric automobile, to realize considering The scheduling strategy that charges in order of the electric automobile of user's driving behavior characteristic.

To achieve these goals, this invention takes following technical scheme.

A kind of electric automobile charges dispatching method in order, including:

Electric automobile continual mileage forecast model according to pre-building calculates electric automobile current working Energy consumption, energy consumption and current state-of-charge SOC according to described electric automobile current working calculate described electricity The continual mileage of electrical automobile；

Predicted travel mileage before the continual mileage and charging next time of described electric automobile of described electric automobile Between ratio less than setting threshold value, work as according to charge volume, time departure and the power distribution network of described electric automobile Front load condition, utilizes the electric automobile power battery life model pre-build to calculate described electronic The charge volume of automobile, with the local minimum optimization aim of power distribution network peak-valley difference, the charging to described electric automobile Process is scheduling.

Further, the electric automobile continual mileage forecast model that described basis pre-builds calculates electricity The energy consumption of electrical automobile current working, including:

Pre-build electric automobile continual mileage forecast model, this electric automobile continual mileage forecast model bag Include: it is a fragment that definition electric automobile often travels setpoint distance, to each fragment setup parameter information, This parameter information includes average speed, maximal rate, the quadratic sum of speed, acceleration ratio, speed reducing ratio Example, at the uniform velocity ratio, idling ratio, outdoor temperature and air-conditioning power, choose setting quantity fragment and The parameter information of each fragment, individual to described setting quantity by PCA and fuzzy clustering algorithm Fragment calculates, and obtains multiple cluster centre, each cluster centre correspondence one operating mode, each cluster The parameter information at center includes: average speed, acceleration ratio, deceleration ratio, at the uniform velocity ratio, air-conditioning merit Rate, ambient temperature and energy consumption parameter；

Parameter information according to the current fragment of described electric automobile and described each cluster centre decapacitation consumption ginseng Parameter information outside number, calculates between the current fragment of described electric automobile and each cluster centre respectively Distance value, each distance value is compared, operating mode corresponding for the shortest for distance value cluster centre is made For the current working of described electric automobile, using the energy consumption parameter of cluster centre the shortest for described distance value as The energy consumption of described electric automobile current working.

Further, described the current fragment of described electric automobile and each cluster centre are calculated respectively Between distance value, including:

If the quantity of cluster centre is c, between fragment and each cluster centre that described electric automobile is current Distance value d_iComputing formula be:

d_i=| | x-c_i| |, i=1,2,3 ..., c

In formula: x is the parameter of the current fragment of described electric automobile, c_iCluster centre for cluster centre i is joined Number, c_i=(c_i1,c_i2,…,c_i6)。

Further, the described energy consumption according to described electric automobile current working and current state-of-charge SOC calculates the continual mileage L1 of described electric automobile, including:

Reading the state-of-charge SOC that described electric automobile is current, the battery capacity of electric automobile is Q, cluster The quantity at center is c.The long-term driving behavior characteristic of driver according to described electric automobile determines each operating mode The ratio of class, the ratio of each operating mode class is x₁:x₂:…:x_i:…:x_c-1:x_c, (1≤i≤c)), every kind of work The energy consumption of condition is respectively p₁,p₂,…,p_i,…,p_c-1,p_c, (1≤i≤c)), according to the SOC of Current vehicle, Calculating continual mileage L1, computing formula is:

$L1=\frac{Q\×SOC\×{\mathrm{\Σ}}_{i=1}^c{x}_i}{{\mathrm{\Σ}}_{j=1}^c(p_j\×x_j)}.$

Further, before the continual mileage of described described electric automobile and charging next time of described electric automobile Predicted travel mileage between ratio less than set threshold value, including:

If the predicted travel mileage before charging next time of described electric automobile is L, utilize electric automobile continual mileage The continual mileage of the described electric automobile that forecast model calculates is L1, if

L1 < (1+10%) L

Then determine the predicted travel before the continual mileage of described electric automobile and charging next time of described electric automobile Ratio between mileage is less than setting threshold value.

Further, described method also includes:

According to affecting the ambient temperature in electric automobile power battery life-span, charging current, discharge current, putting The electricity degree of depth and cycle-index set up electric automobile power battery life model, this electric automobile power battery longevity Life model includes: under the low discharge degree of depth and under high depth of discharge, the capacity of electric automobile power battery Decline amount and the relational expression of cycle-index, described electric automobile power battery life model shows at low discharge Under the degree of depth, use electric automobile power battery can extend the use longevity of the electrokinetic cell of described electric automobile Life.

Further, the described charge volume according to described electric automobile, time departure and power distribution network are current Load condition, utilize the electric automobile power battery life model that pre-builds to calculate described electronic vapour The charge volume of car, with the local minimum optimization aim of power distribution network peak-valley difference, the charging to described electric automobile Journey is scheduling, including:

Historical load data prediction according to power distribution network obtains power distribution network and works as daily load curve, is divided into N by one day The individual period, in the i-th period, power distribution network original loads size is P_i(i=1,2,3 ..., N), if meeting described The charge volume of electric automobile trip is S_E, the charging process of electric automobile is invariable power charging, its merit of charging Rate is Δ P, and batteries of electric automobile capacity is Q, and it is S that charging electric vehicle initiates SOC_S, arrive charging place Time is T_S, time departure is t, and the initiation of charge time is T_c, this charging of the most described electric automobile is required Charge capacity S_SOCComputational methods are as follows:

$S_{SOC}=\left\{\begin{array}{c}\left(1+10\%\right)S_E-S_S,\left(1+10\%\right)S_E<Q\\ Q-S_S,\left(1+10\%\right)S_E\&GreaterEqual;Q\end{array}\right.$

The time of staying T of electric automobile_stayFor

T_stay=t-T_S

If the electric automobile load charged in the i-th period is p_i, total n electric automobile fills Electricity, then

$p_i=\underset{k=1}{\overset{n}{\&Sigma;}}\mathrm{\&Delta;}P$

The total load P of power distribution network in the i-th period_sumiIt is charging electric vehicle load p_iWith original loads P_i's Superposition:

P_sumi=p_i+P_i

At the time (T that described electric automobile stops_S, t) interior, with the initiation of charge time T of described electric automobile_c The earliest and power distribution network peak-valley difference minimum as charging control object function, this object function i.e.:

$\left\{\begin{array}{c}\min \mathrm{var}\left(P_{sumi}\right)\\ \min T_c\end{array}\right.$

Wherein, var (P_sumi) it is P_sumiVariance function.P_maxFor the peak load of local power distribution network, Then P_sumiShould meet constraints:

P_sumi≤P_max

Additionally, the initiation of charge time T of described electric automobile_cAlso should meet constraints:

T_S≤T_c≤t

(t-T_c)ΔP≥S_SOC

Solve described object function and all of constraints by cyclic process, obtain rising of described electric automobile Begin charging interval T_c。

The technical scheme provided by embodiments of the invention described above is it can be seen that the present invention proposes to meet Electric automobile based on the driving behavior characteristic of driver and charging wish charges dispatching method in order, logical Cross correct guidance electric automobile discharge and recharge to extend the service life of electric automobile power battery, the most permissible Reduce the peak-valley difference of network load, the driver of enthusiasm to(for) charge dispatching method can be greatly improved, Ensure the stable operation of electrical network simultaneously, there is the most real meaning.

Aspect and advantage that the present invention adds will part be given in the following description, and these are by from following Description becomes obvious, or recognized by the practice of the present invention.

Accompanying drawing explanation

In order to be illustrated more clearly that the technical scheme of the embodiment of the present invention, institute in embodiment being described below The accompanying drawing used is needed to be briefly described, it should be apparent that, the accompanying drawing in describing below is only this Some bright embodiments, for those of ordinary skill in the art, are not paying creative work Under premise, it is also possible to obtain other accompanying drawing according to these accompanying drawings.

The dispatching party of charging in order of the electric automobile improving user's responsiveness that Fig. 1 provides for the embodiment of the present invention The process chart of method；

Fig. 2 is the inducing capacity fading amount relational expression schematic diagram with cycle-index of the electrokinetic cell of electric automobile；

Fig. 3 is charging load design sketch when the unordered charging of electric automobile and orderly charging in a day；

Fig. 4 is distribution network load design sketch when the unordered charging of electric automobile and orderly charging in a day.

Detailed description of the invention

Embodiments of the present invention are described below in detail, and the example of described embodiment is shown in the drawings, The most same or similar label represents same or similar element or has same or like merit The element of energy.The embodiment described below with reference to accompanying drawing is exemplary, is only used for explaining this Bright, and be not construed as limiting the claims.

Those skilled in the art of the present technique are appreciated that unless expressly stated, singulative used herein " one ", " one ", " described " and " being somebody's turn to do " may also comprise plural form.Will be further understood that , the wording used in the description of the present invention " includes " referring to there is described feature, integer, step Suddenly, operation, element and/or assembly, but it is not excluded that existence or add other features one or more, Integer, step, operation, element, assembly and/or their group.It should be understood that when we claim element quilt " connecting " or during " coupled " to another element, it can be directly connected or coupled to other elements, or Intermediary element can also be there is in person.Additionally, " connection " used herein or " coupling " can include nothing Line connects or couples.Wording "and/or" used herein includes that what one or more was associated lists item Any cell and all combinations.

Those skilled in the art of the present technique are appreciated that unless otherwise defined, all terms used herein (including technical term and scientific terminology) has and one of the those of ordinary skill in art of the present invention As understand identical meaning.Should also be understood that those terms defined in such as general dictionary should It is understood to that there is the meaning consistent with the meaning in the context of prior art, and unless as here one Sample defines, and will not explain by idealization or the most formal implication.

For ease of the understanding to the embodiment of the present invention, below in conjunction with accompanying drawing as a example by several specific embodiments It is further explained explanation, and each embodiment is not intended that the restriction to the embodiment of the present invention.

The present invention is directed to the electric automobile unordered charging negative effect to power distribution network, dynamic to extend electric automobile The service life of power battery sets out, it is proposed that meet electric vehicle driver driving behavior characteristic and charging meaning The dispatching method that charges in order being willing to.

The present invention uses the method extending the electric automobile power battery life-span to improve user for charging in order The responsiveness of dispatching method, has initially set up electric automobile continual mileage forecast model A1 and electric automobile moves Power battery service life model A2, reads the charge information of electric automobile, is inputted model A1 and A2, calculates The charge volume of this charging, then uses algorithm B (i.e. genetic algorithm) to calculate electric automobile and fills in order Electricity dispatching method, reaches optimum of both power distribution network and user.

The electric automobile improving user's responsiveness that the embodiment of the present invention proposes charges the place of dispatching method in order Reason flow chart is as shown in Figure 1.Key step is as follows:

Step 1: set up electric automobile continual mileage forecast model A1.Average speed, maximal rate, acceleration Ratio, deceleration ratio, at the uniform velocity ratio, idling ratio, outdoor temperature, air-conditioning use power etc. all can shadows Ringing the continual mileage of electric automobile, the present invention, from the angle of Electric Vehicles Driving Cycle, uses algorithm C (i.e. The algorithm that principal component analysis and fuzzy clustering algorithm combine), the driving cycle of electric automobile is known Not, the energy consumption of electric automobile is studied, it was predicted that the continual mileage of electric automobile.Prius with Toyota Company As a example by PHEV, its continual mileage under electric-only mode is predicted.This fills to define electric automobile Driving process before charging to next time after electricity is a circulation, and in each circulation, definition electric automobile is often gone Sailing 1km is a fragment.For each fragment of accurate description, it is ensured that do not have loss and the mistake of driving information Very, selecting and calculate 9 parameters for describing each fragment, these 9 parameters are respectively average speed, Big speed, the quadratic sum of speed, acceleration ratio, deceleration ratio, at the uniform velocity ratio, idling ratio, outdoor Temperature and air-conditioning power, the unit of each parameter is respectively km/h, km/h, (km/h)²、1、1、1、 And kW 1, DEG C.Wherein, each parameter relevant with acceleration used in parameter (accelerates ratio, deceleration Ratio, at the uniform velocity ratio and idling ratio) by speed calculation first derivative is obtained.According to this side Method, chooses the principal component analysis after 3368 fragments are carried out.

Table 1 below is each parameter value of front 5 fragments.

Table 1: each parameter value of front 5 fragments of electric automobile

Principal component analysis is exactly to replace the most more parameter with less several aggregate variables, and these Less aggregate variable can reflect the useful information of original parameter as much as possible, and is the most again nothing Closing, these aggregate variables are known as main constituent.If front e (e=1,2 ..., 9) individual main constituent tired When long-pending contribution rate is to 80% or 85%, this e main constituent can represent original variable and be analyzed.The present invention In used PCA, and realize its function by MATLAB.At the numeral of MATLAB Reason ability is relatively strong, is relatively suitable for present disclosure, so selecting it as implementation tool.Utilize MATLAB software carries out principal component analysis to each parameter in 3368 fragments, obtains 9 main constituents.Often The eigenvalue of individual main constituent and contribution rate are as described in Table 2.

The eigenvalue of each main constituent of table 2 and contribution rate

Main constituent	Eigenvalue	Contribution rate/%
			Y1	2.3992	34.27
Y2	1.4624	20.89
			Y3	1.0571	15.10
Y4	0.7321	10.46
			Y5	0.4382	6.69
Y6	0.4148	6.35
			Y7	0.2962	4.23
Y8	0.1568	1.54
			Y9	0.0432	0.47

Choose front 4 main constituents by principal component analysis principle, and carry out between characteristic parameter to main constituent relevant Property analyze, 4 main constituents are chosen in the past representative average speed, idling ratio, at the uniform velocity than Example, deceleration ratio, outdoor temperature and 6 parameters of air-conditioning power are used for cluster calculation.

The purpose of cluster is to be root by being classified things according to certain regular divide into several classes, classifying rules Determine according to the feature of object, be between of a sort things and there is certain similarity.But time a lot Wait and a certain class that is divided into strict for each object to be sorted is necessarily had its irrationality, therefore, in conjunction with mould Stick with paste sets theory process clustering problem and can make the application of cluster more rationally reliably.The present invention use fuzzy C-means clustering algorithm.Fuzzy C-means clustering is a kind of clustering method based on object function, and each is right As if it is under the jurisdiction of each cluster centre with certain degree of membership.The object of study of the present invention is to comprise 3368 Individual fragment and the data of 6 parameters, its observing matrix can be represented by following matrix

In matrix, one fragment of each behavior, each parameter being classified as fragment.Sample is drawn by fuzzy clustering exactly It is divided into c class (2≤c≤3368).Definition V={v₁,v₂,…,v_cIt is designated as the cluster centre of c class, wherein v_i={ v_i1,v_i2,…,v_i6}.Definition u_ikRepresent that kth fragment belongs to the degree of membership of the i-th class, wherein 0≤u_ik≤1,d_ik=| | x_k-v_i| |, represent that kth variable is to i-th center Distance.

Objective function is:

$J(U,V)=\underset{k=1}{\overset{3368}{\&Sigma;}}\underset{i=1}{\overset{c}{\&Sigma;}}{u}_{ik}^m{d}_{ik}$

Wherein U=(U_ik) c × 3368 are subordinated-degree matrix.J (U, V) illustrates each apoplexy due to endogenous wind sample to cluster centre Weighted quadratic distance sum, weight is sample x_kBelong to the m power of the degree of membership of the i-th class.

Fuzzy C-Means Clustering seeks U and V exactly so that J (U, V) gets minima.Specifically comprise the following steps that

First, number c of class, power exponent m are determined > 1 and initial subordinated-degree matrixHerein In take the uniform random number on [0,1] to determine initial subordinated-degree matrix.L=1 represents that the first step is repeatedly Generation.

Secondly, the cluster centre V of l step is calculated⁽⁰⁾:

$v_i^{\left(l\right)}=\underset{k=1}{\overset{3368}{\&Sigma;}}{\left(u_{ik}^{l-1}\right)}^m{x}_k/\underset{k=1}{\overset{3368}{\&Sigma;}}{\left(u_{ik}^{l-1}\right)}^m,i=(1,2,...c)$

Again, subordinated-degree matrix U is revised^(l), calculate l and walk object function J^(l)

$u_{ik}^{\left(l\right)}=1/\underset{j=1}{\overset{c}{\&Sigma;}}{(d_{ik}^{\left(l\right)}/d_{jk}^{\left(l\right)})}^{\frac{2}{m-1}},i=1,2,...,c;k=1,2,...,3368$

$J^{\left(l\right)}(U^{\left(l\right)},V^{\left(l\right)})=\underset{k=1}{\overset{3368}{\&Sigma;}}\underset{i=1}{\overset{c}{\&Sigma;}}{\left(u_{ik}^{\left(l\right)}\right)}^m{\left(d_{ik}^{\left(l\right)}\right)}^2$

Finally, to given degree of membership termination tolerance ω_u> 0, when Time, stop iteration.

Through above step, final subordinated-degree matrix and cluster centre can be tried to achieve so that object function The value of J (U, V) minimizes, and may determine that the ownership of all samples according to final subordinated-degree matrix U.

According to above-mentioned process, the parameter of fragment each to electric automobile carries out cluster analysis, to cluster number c Attempting respectively, finally find when c=12, each cluster centre can preferably reflect the row of electric automobile Sail operating mode, each cluster centre correspondence one operating mode.

The parameter of each cluster centre is as described in Table 3.Except above-mentioned average speed, idling ratio, even in table Outside speed ratio example, deceleration ratio, outdoor temperature and 6 parameters of air-conditioning power, further comprises under each operating mode Energy consumption parameter, its unit is kWh.

The parameter of each cluster centre of table 3

In table, each row parameter represents average speed the most successively, accelerates ratio, deceleration ratio, at the uniform velocity ratio, Air-conditioning power, ambient temperature and the cluster centre of energy consumption parameter.From temperature, each cluster centre Substantially become low temperature, room temperature, high temperature three kinds distribution, represent winter, spring and autumn and summer, explanation respectively The temperature obvious effect to electric automobile energy consumption.From the point of view of average speed, each cluster centre can be basic Being divided into low speed, middle low speed, middling speed, at a high speed four classes, this shows that the impact of energy consumption is also apparent from by speed. Accelerate, slow down, driving that at the uniform velocity ratio and air-conditioning power etc. mainly reflect electric vehicle driver Behavioral trait.According to each cluster centre, the method utilizing operating mode's switch, set up in the driving of electric automobile Journey forecast model A1.According to distance minimum principle, determine the classification of each fragment of electric automobile, permissible Traveling fragment is divided into 12 classes according to operating mode.Wherein, distance computing formula is:

d_i=| | x-c_i| |, i=1,2,3 ..., 12

In formula: x is the parameter of certain fragment, x=(x₁,x₂,…,x₆)；c_iFor the cluster centre parameter of class i, c_i=(c_i1,c_i2,…,c_i6)

By distance value d_iOperating mode corresponding to the shortest cluster centre, will as the current working of described electric automobile The energy consumption parameter of the cluster centre that described distance value is the shortest is as the energy consumption of described electric automobile current working.

Read the SOC (State of Charge, state-of-charge) that vehicle is current, the battery of electric automobile Capacity is Q,.The long-term driving behavior characteristic of driver according to described electric automobile determines each operating mode class Ratio, the ratio of each operating mode class is x₁:x₂:…:x_i:…:x_c-1:x_c(1≤i≤c), c=12), every kind of work The energy consumption of condition is respectively p₁,p₂,…,p_i,…,p_c-1,p_c, (1≤i≤c), c=12), according to Current vehicle SOC, calculate continual mileage L1, computing formula is

$L1=\frac{Q\&times;SOC\&times;{\mathrm{\&Sigma;}}_{i=1}^{12}{x}_i}{{\mathrm{\&Sigma;}}_{j=1}^{12}(p_j\&times;x_j)}$

Step 2: set up electric automobile power battery life model A2.

When setting up electric automobile power battery life model A2, selection environment temperature, charging current, electric discharge Electric current, depth of discharge and cycle-index are as the principal element affecting the electric automobile power battery life-span.Ring When border temperature selects, it is simulated according to the temperature value in the mixing four seasons, is followed successively by 10 DEG C → 25 DEG C → 40 DEG C →25℃→10℃.Rate of charge is generally relatively more fixing for the electrokinetic cell of electric automobile, chooses C/3 (wherein C is rate of charge, and computational methods are that the charging current of electric automobile power battery is divided by electric automobile The rated capacity of electrokinetic cell).Under the conditions of actual motion, the discharge current of electric automobile power battery becomes Changing relatively big, therefore select averaged discharge multiplying power, its value is about C/2.In general, electric powered motor is worked as When the capacity of battery is 70% the 80% of its nominal capacity, electric automobile power battery i.e. can not re-use. Claim electric automobile power battery to discharge into after fully charged and specify SOC corresponding depth of discharge, refill electricity The once circulation that process is electric automobile power battery.Under the research low discharge degree of depth, (50% electric discharge is deep respectively Degree) and high depth of discharge (80% depth of discharge) under the inducing capacity fading amount of electric automobile power battery with circulation The relation of number of times, obtains result as shown in Figure 2.Wherein, under the low discharge degree of depth and high depth of discharge Under, the inducing capacity fading amount of electric automobile power battery is with the relational expression of cycle-index as shown in Figure 2.From attached Fig. 2 finds out, under equal cycle-index, and electric automobile power battery inducing capacity fading under the low discharge degree of depth Amount is significantly lower than its inducing capacity fading amount under high depth of discharge.Electric automobile is used under the low discharge degree of depth Electrokinetic cell can effectively extend its service life.Current cycle-index according to described electric automobile is looked into Ask described relational expression, obtain the current inducing capacity fading amount of described power vehicle electrokinetic cell.Fill determining Anticipated row during electricity, before the continual mileage of described electric automobile meets charging next time of described electric automobile On the premise of sailing mileage, electric automobile power battery is kept to use under the low discharge degree of depth.

Step 3: propose electric automobile and charge in order dispatching method

In step 1 and step 2, propose method and the electrokinetic cell of electric automobile continual mileage prediction respectively Life-span prediction method, establishes electric automobile continual mileage forecast model A1 and electric automobile power battery longevity Life model A2.In step 3, in conjunction with charging wish and the time of staying of driver, according to current power distribution network Load condition, it is proposed that meet the charge electric automobile of wish of driver and charge in order dispatching method.This tune Degree method uses bilayer model, and upper layer model is user side, before charging starts, by electric vehicle driver Input the predicted travel mileage L before electric automobile current SOC, time departure t and charging next time, according to mould Type A1 prediction electric automobile continual mileage L1 under current SOC, if

L1 >=(1+10%) L

Show that current power automobile power is sufficient, electric vehicle driver decide whether to enter for electric automobile Row charging；In formula, 10% is the electricity surplus of vehicle, if current continual mileage is unsatisfactory for above formula or driver Be still to be charged, then this time still provides for charging and the most still is decided whether to be ready that accepting charging adjusts by driver Degree, if being unwilling, the most currently starting to charge up for electric automobile and being full of；If driver is ready to accept tune Degree, then enter underlying model i.e. grid side, calculates the charge volume S meeting vehicle driving according to model A2_E, And according to the time departure of driver's input and the current load condition of power distribution network, with local power distribution network peak valley The minimum optimization aim of difference, proposes corresponding dispatching method.

Power distribution network is obtained by the prediction of its historical load when daily load curve.The present invention was divided into 96 by one day Period, time interval is 15 minutes, and therefore in the i-th period, power distribution network original loads size is P_i(i=1,2,3 ..., 96).Additionally, the present invention assuming, the charging process of electric automobile is that invariable power fills Electricity, its charge power is Δ P.If batteries of electric automobile capacity is Q, charging electric vehicle initiates SOC and is S_S, driver arrive charging place time be T_S, time departure is t, and the initiation of charge time is T_c, then Charge capacity S needed for this charging of electric automobile_SOCComputational methods are as follows:

$S_{SOC}=\left\{\begin{array}{c}\left(1+10\%\right)S_E-S_S,\left(1+10\%\right)S_E<Q\\ Q-S_S,\left(1+10\%\right)S_E\&GreaterEqual;Q\end{array}\right.$

The time of staying T of electric automobile_stayFor

T_stay=t-T_S

If the electric automobile load charged in the i-th period is p_i, total n electric automobile fills Electricity, then

$p_i=\underset{k=1}{\overset{n}{\&Sigma;}}\mathrm{\&Delta;}P$

The total load P of power distribution network in the i-th period_sumiIt is charging electric vehicle load p_iWith original loads P_i's Superposition:

P_sumi=p_i+P_i

At the time (T that driver is stopped_S, t) in, with user's initiation of charge time the earliest and the peak of power distribution network The object function that paddy difference minimum controls as charging, i.e.

$\left\{\begin{array}{c}\min \mathrm{var}\left(P_{sumi}\right)\\ \min T_c\end{array}\right.$

Wherein, var (P_sumi) it is P_sumiVariance function.

P_maxFor the peak load of local power distribution network, then P_sumiShould meet constraints:

P_sumi≤P_max

Additionally, initiation of charge time T_cAlso should meet constraints:

T_S≤T_c≤t

(t-T_c)ΔP≥S_SOC

Above-mentioned this scheduling problem of charging in order that is, uses algorithm B to solve this problem.Choose each period Charge power, as chromosome, carries out binary coding, performs to intersect and mutation operation, and according to Constraints calculating target function, is carried out outstanding chromosome retaining and heavily inserting, is asked by cyclic process Solve described object function and all of constraints, obtain the initiation of charge time T of described electric automobile_c。

Step 4: have 100 electric automobiles in establishing Grid.It was a stage with one day, will be complete Charging load and distribution network load after it renewal show in design sketch, simultaneously by joining under unordered charging Network load shows in design sketch, to verify this tune that charges in order with the distribution network load under charging in order The effectiveness of degree method.Filling when accompanying drawing 3 is that in one day, electric automobile is respectively unordered charging and charges in order Electric load design sketch, distribution when accompanying drawing 4 is that in a day, electric automobile is respectively unordered charging and charges in order Net load design sketch.

In sum, traditional dispatching method of charging in order is many to improve distribution network load situation or to reduce Distribution network loss is target, and have ignored driving behavior characteristic and the charging wish of user, causes filling in order Electricity scheduling hardly results in application in practice.The embodiment of the present invention is for solving this problem, it is proposed that with full Electric automobile based on the driving behavior characteristic of foot driver and charging wish charges dispatching method in order, The service life of electric automobile power battery is extended by correct guidance electric automobile discharge and recharge, simultaneously can To reduce the peak-valley difference of network load, driver can be greatly improved for the positive of dispatching method of charging Property, ensure the stable operation of electrical network simultaneously, there is the most real meaning.

One of ordinary skill in the art will appreciate that: accompanying drawing is the schematic diagram of an embodiment, in accompanying drawing Module or flow process not necessarily implement necessary to the present invention.

As seen through the above description of the embodiments, those skilled in the art is it can be understood that arrive The present invention can add the mode of required general hardware platform by software and realize.Based on such understanding, The part that prior art is contributed by technical scheme the most in other words can be with software product Form embody, this computer software product can be stored in storage medium, such as ROM/RAM, magnetic Dish, CD etc., including some instructions with so that computer equipment (can be personal computer, Server, or the network equipment etc.) perform each embodiment of the present invention or some part institute of embodiment The method stated.

Each embodiment in this specification all uses the mode gone forward one by one to describe, identical between each embodiment Similar part sees mutually, and what each embodiment stressed is different from other embodiments Part.For device or system embodiment, owing to it is substantially similar to embodiment of the method, So describing fairly simple, relevant part sees the part of embodiment of the method and illustrates.Above retouched The Apparatus and system embodiment stated is only schematically, the wherein said unit illustrated as separating component Can be or may not be physically separate, the parts shown as unit can be or also may be used Not to be physical location, i.e. may be located at a place, or multiple NE can also be distributed to On.Some or all of module therein can be selected according to the actual needs to realize the present embodiment scheme Purpose.Those of ordinary skill in the art, in the case of not paying creative work, are i.e. appreciated that also Implement.

The above, the only present invention preferably detailed description of the invention, but protection scope of the present invention is not Being confined to this, any those familiar with the art, can in the technical scope that the invention discloses The change readily occurred in or replacement, all should contain within protection scope of the present invention.Therefore, the present invention Protection domain should be as the criterion with scope of the claims.

Claims (7)

1. an electric automobile charges dispatching method in order, it is characterised in that including:

Electric automobile continual mileage forecast model according to pre-building calculates electric automobile current working Energy consumption, energy consumption and current state-of-charge SOC according to described electric automobile current working calculate described electricity The continual mileage of electrical automobile；

Predicted travel mileage before the continual mileage and charging next time of described electric automobile of described electric automobile Between ratio less than setting threshold value, work as according to charge volume, time departure and the power distribution network of described electric automobile Front load condition, utilizes the electric automobile power battery life model pre-build to calculate described electronic The charge volume of automobile, with the local minimum optimization aim of power distribution network peak-valley difference, the charging to described electric automobile Process is scheduling.

Method the most according to claim 1, it is characterised in that the electricity that described basis pre-builds Electrical automobile continual mileage forecast model calculates the energy consumption of electric automobile current working, including:

Pre-build electric automobile continual mileage forecast model, this electric automobile continual mileage forecast model bag Include: it is a fragment that definition electric automobile often travels setpoint distance, to each fragment setup parameter information, This parameter information includes average speed, maximal rate, the quadratic sum of speed, acceleration ratio, speed reducing ratio Example, at the uniform velocity ratio, idling ratio, outdoor temperature and air-conditioning power, choose setting quantity fragment and The parameter information of each fragment, individual to described setting quantity by PCA and fuzzy clustering algorithm Fragment calculates, and obtains multiple cluster centre, each cluster centre correspondence one operating mode, each cluster The parameter information at center includes: average speed, acceleration ratio, deceleration ratio, at the uniform velocity ratio, air-conditioning merit Rate, ambient temperature and energy consumption parameter；

Parameter information according to the current fragment of described electric automobile and described each cluster centre decapacitation consumption ginseng Parameter information outside number, calculates between the current fragment of described electric automobile and each cluster centre respectively Distance value, each distance value is compared, operating mode corresponding for the shortest for distance value cluster centre is made For the current working of described electric automobile, using the energy consumption parameter of cluster centre the shortest for described distance value as The energy consumption of described electric automobile current working.

Method the most according to claim 2, it is characterised in that described calculates described electricity respectively Distance value between fragment and each cluster centre that electrical automobile is current, including:

If the quantity of cluster centre is c, between fragment and each cluster centre that described electric automobile is current Distance value d_iComputing formula be:

d_i=| | x-c_i| |, i=1,2,3 ..., c

In formula: x is the parameter of the current fragment of described electric automobile, c_iCluster centre for cluster centre i is joined Number, c_i=(c_i1,c_i2,…,c_i6)。

Method the most according to claim 1, it is characterised in that described according to described electric automobile The energy consumption of current working and current state-of-charge SOC calculate the continual mileage L1 of described electric automobile, bag Include:

Reading the state-of-charge SOC that described electric automobile is current, the battery capacity of electric automobile is Q, cluster The quantity at center is c.The long-term driving behavior characteristic of driver according to described electric automobile determines each operating mode The ratio of class, the ratio of each operating mode class is x₁:x₂:…:x_i:…:x_c-1:x_c(1≤i≤c), every kind of operating mode Energy consumption is respectively p₁,p₂,…,p_i,…,p_c-1,p_c, (1≤i≤c), according to the SOC of Current vehicle, calculate Continual mileage L1, computing formula is:

$L1=\frac{Q\&times;SOC\&times;{\mathrm{\&Sigma;}}_{i=1}^c{x}_i}{{\mathrm{\&Sigma;}}_{j=1}^c(p_j\&times;x_j)}.$

5. according to the method described in any one of Claims 1-4, it is characterised in that described is described electronic The ratio between predicted travel mileage before the continual mileage of automobile and charging next time of described electric automobile is less than Set threshold value, including:

If the predicted travel mileage before charging next time of described electric automobile is L, utilize electric automobile continual mileage The continual mileage of the described electric automobile that forecast model calculates is L1, if

L1 < (1+10%) L

Then determine the predicted travel before the continual mileage of described electric automobile and charging next time of described electric automobile Ratio between mileage is less than setting threshold value.

Method the most according to claim 5, it is characterised in that described method also includes:

According to affecting the ambient temperature in electric automobile power battery life-span, charging current, discharge current, putting The electricity degree of depth and cycle-index set up electric automobile power battery life model, this electric automobile power battery longevity Life model includes: under the low discharge degree of depth and under high depth of discharge, the capacity of electric automobile power battery Decline amount and the relational expression of cycle-index, described electric automobile power battery life model shows at low discharge Under the degree of depth, use electric automobile power battery can extend the use longevity of the electrokinetic cell of described electric automobile Life.

Method the most according to claim 6, it is characterised in that described according to described electric automobile The current load condition of charge volume, time departure and power distribution network, utilize the electric automobile pre-build to move Power battery service life model calculates the charge volume of described electric automobile, minimum excellent with local power distribution network peak-valley difference Change target, the charging process of described electric automobile is scheduling, including:

Historical load data prediction according to power distribution network obtains power distribution network and works as daily load curve, is divided into N by one day The individual period, in the i-th period, power distribution network original loads size is P_i(i=1,2,3 ..., N), if meeting described The charge volume of electric automobile trip is S_E, the charging process of electric automobile is invariable power charging, its merit of charging Rate is Δ P, and batteries of electric automobile capacity is Q, and it is S that charging electric vehicle initiates SOC_S, arrive charging place Time is T_S, time departure is t, and the initiation of charge time is T_c, this charging of the most described electric automobile is required Charge capacity S_SOCComputational methods are as follows:

$S_{SOC}=\left\{\begin{array}{c}(1+10\%)S_E-S_S,(1+10\%)S_E<Q\\ Q-S_S,(1+10\%)S_E\&GreaterEqual;Q\end{array}\right.$

The time of staying T of electric automobile_stayFor

T_stay=t-T_S

If the electric automobile load charged in the i-th period is p_i, total n electric automobile fills Electricity, then

$p_i=\underset{k=1}{\overset{n}{\&Sigma;}}\mathrm{\&Delta;}P$

The total load P of power distribution network in the i-th period_sumiIt is charging electric vehicle load p_iWith original loads P_i's Superposition:

P_sumi=p_i+P_i

At the time (T that described electric automobile stops_S, t) interior, with the initiation of charge time T of described electric automobile_c The earliest and power distribution network peak-valley difference minimum as charging control object function, this object function i.e.:

$\left\{\begin{array}{c}\min \mathrm{var}\left(P_{sumi}\right)\\ \min T_c\end{array}\right.$

Wherein, var (P_sumi) it is P_sumiVariance function.P_maxFor the peak load of local power distribution network, Then P_sumiShould meet constraints:

P_sumi≤P_max

Additionally, the initiation of charge time T of described electric automobile_cAlso should meet constraints:

T_S≤T_c≤t

(t-T_c)ΔP≥S_SOC

Solve described object function and all of constraints by cyclic process, obtain rising of described electric automobile Begin charging interval T_c。