CN110797931A - Electric vehicle charging method and system - Google Patents
Electric vehicle charging method and system Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0013—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries acting upon several batteries simultaneously or sequentially
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
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- H01M10/058—Construction or manufacture
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- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
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- H—ELECTRICITY
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- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
- H01M10/44—Methods for charging or discharging
- H01M10/441—Methods for charging or discharging for several batteries or cells simultaneously or sequentially
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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- H02J7/007—Regulation of charging or discharging current or voltage
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- H01M10/425—Structural combination with electronic components, e.g. electronic circuits integrated to the outside of the casing
- H01M2010/4271—Battery management systems including electronic circuits, e.g. control of current or voltage to keep battery in healthy state, cell balancing
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- H—ELECTRICITY
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- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M2220/00—Batteries for particular applications
- H01M2220/20—Batteries in motive systems, e.g. vehicle, ship, plane
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Abstract
An electric vehicle charging method and system comprises the following steps: based on each secondary control center, bringing the battery information and the charging time of the electric automobile into a secondary control center optimization model for calculation; substituting the calculation result and the predicted output of the new energy power generation into a two-stage optimization model for calculation to obtain a load guidance curve of each secondary control center; formulating a charging load following guidance curve of each electric automobile under the secondary control center based on the load guidance curve; and the electric automobile is charged according to the charging load following the guide curve. The method for the orderly charging load following of the electric automobile can be embedded into a layered control method, and the writing control problem of the electric automobile and the clean energy power generation during charging can be solved through the layered control method, so that the charging behavior of the electric automobile can be better matched with the intermittent new energy power generation, and economic benefits and social benefits are created for society, power grids and electric automobile operators.
Description
Technical Field
The invention relates to the fields of electric vehicle charging, new energy consumption, computer technology and the like, in particular to an electric vehicle charging method and system.
Background
The charging requirements of the electric automobile have certain controllability and certain randomness. In addition, the clean energy power generation mainly based on wind power generation and photovoltaic power generation is limited by natural conditions, and the output of the clean energy power generation is random and intermittent. How to consider the charging requirement of the electric automobile and the uncertainty of the clean energy power generation output, the defects of complex structure, large impact on a power grid and a battery and the like of the conventional high-power wired charging equipment are overcome, the ordered charging control suitable for large-scale electric automobiles and clean energy power generation is realized, the cleanness of the electric automobiles can be realized under a cooperative charging method, and the method is one of the difficulties of the current industry research.
Aiming at the aspect of a cooperative charging method of an electric vehicle and clean energy such as wind energy, solar energy and the like, an evaluation index is formulated based on an electric vehicle and power grid interaction platform framework at present, and the effect of the electric vehicle on absorbing new energy fluctuation under different interaction intentions is analyzed. For example, some research researches an EV and distributed energy optimization scheduling model under different time scales, and verifies that scientificity and effectiveness of stabilizing a power grid equivalent load and improving the utilization rate of distributed energy by using the charging load through scheduling the charging load under the model. Although the research comprehensively considers the combined operation optimization of the electric automobile, the distributed power supply and the energy storage system, the research is basically carried out from the angle of regulation and control of the electric automobile, the influence on the power demand, the load characteristic and the gasoline consumption cannot be well solved, and meanwhile, because the stability of the battery of the conventional electric automobile is not enough, the deviation exists in the calculation of the influence on the power demand, the load characteristic and the gasoline consumption.
Disclosure of Invention
In order to solve the technical problems in the prior art, the invention provides a charging method for an electric automobile.
The technical scheme provided by the invention is as follows:
based on each secondary control center, bringing the battery information and the charging time of the electric automobile into a pre-constructed secondary control center optimization model for calculation;
substituting the calculation results of all secondary control center optimization models and the predicted output of new energy power generation into a pre-constructed two-stage optimization model for calculation to obtain a load guidance curve of each secondary control center;
the secondary control center formulates a charging load following guidance curve of each electric automobile under the secondary control center based on the load guidance curve of the secondary control center;
the electric automobile is charged along the guide curve according to the charging load;
the battery of the electric automobile is as follows: a lithium ion battery with high storage performance.
Wherein the battery information of the electric vehicle includes: power, capacity, and battery state of charge, SOC;
the charging time includes: and charging start-stop time.
Preferably, the two-stage optimization model includes:
a first stage objective function with the total load peak clipping and valley filling as the target,
The constraint condition corresponding to the first stage objective function,
Second stage objective function and method aimed at stabilizing desired total load curve fluctuations at each secondary control center
And the constraint condition corresponding to the objective function of the second stage.
Wherein the first stage objective function is calculated as follows:
in the formula (f)1A first stage objective function taking total load peak clipping and valley filling as targets; gi，tGuiding the load of the electric automobile at the moment t of the ith secondary control center control area; di，tControlling the total conventional load of the area t moment for the ith secondary control center; ri，tAnd the new energy output at the moment t of the ith secondary control center control area is shown, wherein omega is the set of the secondary control centers, and tau is the optimization time interval.
Specifically, the constraint conditions corresponding to the first-stage objective function include:
Ri，t≤Gi，t+Di，t
in the formula (I), the compound is shown in the specification,
P i，trespectively setting the upper limit and the lower limit of the total charging power of the electric automobile at the moment t of the ith secondary control center;
E i，trespectively setting the upper limit and the lower limit of the total charging energy of the electric automobile at the moment t of the ith secondary control center; Δ t is the time interval;
R i，trespectively corresponding upper and lower limits of new energy output at the moment t of the ith secondary control center; and lambda is the set upper limit of the power abandoning proportion of the new energy.
Preferably, the calculation formula of the second stage objective function is as follows:
in the formula (f)2The load value is directed for time t.
The corresponding constraint conditions of the second stage objective function comprise:
in the formula (I), the compound is shown in the specification,
in order to be the weight coefficient,
is the minimum of the first stage objective function.
Preferably, the step of substituting the calculation results of the optimization models of all the secondary control centers and the predicted output of the new energy power generation into a pre-constructed two-stage optimization model for calculation to obtain the load guidance curves of all the secondary control centers includes:
substituting the calculation results of all secondary control center optimization models and the predicted output of new energy power generation into a two-stage optimization model, and solving the first-stage objective function to obtain a minimum value;
setting a weight coefficient;
and substituting the minimum value of the first-stage objective function and the weight coefficient into the second-stage objective function to solve to obtain a charging load guidance curve and new energy output of each secondary control center:
wherein the weighting factor is greater than 1.
Wherein the secondary control center optimization model comprises:
and the secondary control objective function and the corresponding secondary control constraint condition take the minimum Euclidean distance between the total charging load curve and the load guide curve of the electric automobile as a target.
Specifically, the secondary control objective function is as follows:
in the formula, PtIs the total charging load of the electric automobile at the time t, GtThe component of the load guidance curve at time t that the main control center is required to follow is given.
Specifically, the secondary control constraints include:
in the formula (I), the compound is shown in the specification,
P tthe upper limit and the lower limit of the total charging power of the electric automobile at the moment t; etCharged for electric vehicles at time tThe total energy of the plants is calculated, E tthe upper limit and the lower limit of the total charging energy of the electric automobile at the moment t;
wherein, the
P tSatisfies the following conditions:
P t＝0
wherein is the chargeable period of the jth electric vehicle, and τj＝[τbegin，j，τend，j]；
The sum of rated charging power of the electric automobile at the time t is contained in all chargeable periods; plimitIs the total power upper limit;
the above-mentioned
E tSatisfies the following conditions:
in the formula (I), the compound is shown in the specification,the energy required by the jth electric automobile.
Preferably, the bringing the battery information and the charging time of the electric vehicle into a pre-constructed secondary control center optimization model for calculation based on each secondary control center includes:
solving is carried out by adopting a direct solution method, a distributed algorithm or a probability transfer matrix method.
Preferably, the secondary control center formulates a charging load following guidance curve of each electric vehicle under the secondary control center based on the load guidance curve of the secondary control center, and the method includes: and setting the charging power of each electric automobile at each moment in the time range of the load guidance curve.
Preferably, the electric vehicle is charged according to the charging load following a guidance curve, and includes: the electric automobile adjusts the charging power according to the charging time and the charging power set at the charging time.
Preferably, the positive electrode of the lithium ion battery comprises a current collector, an active material layer and a carbon nanofiber layer, wherein the current collector, the active material layer and the carbon nanofiber layer are sequentially arranged, the carbon nanofiber layer is positioned on the surface of the active material layer, the active material layer comprises two active materials, the two active materials are lithium iron phosphate and lithium manganate, and the thickness of the carbon nanofiber layer is 2-4 microns.
Wherein, the current collector is selected from metal foil or conductive polymer with a metal layer plated on the surface.
The length of the carbon nanofiber in the carbon nanofiber layer is 50-100 mu m.
Preferably, the structure of the active material layer is a three-layer structure including a first active material layer, a second active material layer, and a third active material layer;
the thickness of the first active material layer is 10 to 12 μm, the thickness of the second active material layer is 25 to 30 μm, and the thickness of the third active material layer is 3 to 5 μm.
And the first active material layer comprises lithium manganate, the second active material layer comprises lithium iron phosphate and lithium manganate, and the third active material layer comprises lithium iron phosphate.
The invention also provides an electric vehicle charging system based on the same inventive concept, which comprises: the system comprises a secondary control center module, a main control center module and an electric vehicle charging calculation module;
the secondary control center computing module is configured to: based on each secondary control center, bringing the battery information and the charging time of the electric automobile into a pre-constructed secondary control center optimization model for calculation, and uploading the calculation result to a control center calculation module; the system is also used for formulating a charging load following guidance curve of each electric automobile under the secondary control center based on the load guidance curve of the secondary control center and issuing the curve to the corresponding electric automobile;
the control center calculation module: substituting the calculation results of all secondary control center optimization models and the predicted output of new energy power generation into a pre-constructed two-stage optimization model for calculation to obtain the load guidance curves of all the secondary control centers, and sending the load guidance curves of the secondary control centers to corresponding secondary control center modules;
electric automobile calculation module that charges: and the electric automobile is charged according to the charging load following the guide curve.
Compared with the prior art, the invention has the beneficial effects that:
1. the electric automobile charging hierarchical control method has good expandability, and all methods for realizing the ordered charging load following of the electric automobile can be embedded into the hierarchical control method. By the layered control method, the problem of write control of the electric automobile during charging and the clean energy power generation can be well solved, so that the charging behavior of the electric automobile can be better matched with the intermittent new energy power generation, and economic benefits and social benefits are created for society, power grids and electric automobile operators.
2. By using the lithium ion battery with high storage performance, the polymer conducting layer is present, so that the side reaction between the active substance and the electrolyte is relieved, and the decomposition of the electrolyte is avoided; meanwhile, lithium cobaltate provides higher energy density and rate capability, and the nickel cobalt lithium manganate is stable in performance and provides higher cycle life performance.
Drawings
FIG. 1 is a three-tier architecture of the present invention;
FIG. 2 is a flow chart of a method of the present invention;
FIG. 3 illustrates the conventional load and predicted wind power output of the secondary control center of the present invention;
FIG. 4 is a diagram of a secondary control center normal load versus a desired load for the present invention;
FIG. 5 is a total conventional load curve versus a total desired load curve (three regions) for the present invention;
FIG. 6 is a predicted wind power output versus an expected wind power output of the present invention;
fig. 7 shows the normal load and the desired load of the secondary control center according to the present invention.
Detailed Description
The basic idea of hierarchical control is to divide a control object into different hierarchies, and each hierarchy carries out control activities relatively independently on the basis of obeying the overall goal. The idea of layered control is clear, the expansion is easy, and the method is suitable for the optimization control of large-scale electric vehicles. Most of the layered control assumes that the charging situations and modes of all electric vehicles are consistent, and actually, the charging situations of the electric vehicles governed by different control centers are different, and the ordered charging control mode is also suitable according to local conditions. For example, electric vehicles corresponding to the jurisdiction are mainly charged in a centralized charging/converting station, and centralized control is suitable for the charging; if the electric automobile corresponding to the district is mainly charged in the widely distributed and sparse charging pile, distributed control is suitable to be adopted.
For a better understanding of the present invention, reference is made to the following description taken in conjunction with the accompanying drawings and examples.
Example 1: an orderly charging layered control method for an electric automobile with good expandability. As shown in fig. 1, the layering method proposed by the present invention is divided into: the system comprises a main control center, a secondary control center and an electric automobile.
Each layer carries out the control activities of each layer relatively independently in a complementary mode on the basis of obeying the main control target, and provides flexibility and independence for the self-control activities of each layer on the basis of meeting the overall control target.
The main control center establishes a two-stage optimization model with peak clipping and valley filling as targets according to the electric vehicle energy, power boundary and the like given by the secondary control centers and the output prediction, new energy abandoning rate and the like given by new energy power generation, calculates the electric vehicle charging load guidance curve and the new energy output of each secondary control center and issues the electric vehicle charging load guidance curve and the new energy output. The main control center part constraint conditions (upper and lower limits of power and energy) are obtained by the secondary control center through calculation and uploading.
Each secondary control center collects relevant information of the governed electric automobile group, calculates power, energy boundary and the like of the electric automobile charging load as constraints and uploads the constraints so as to ensure that a load guidance curve issued by the main control center can be followed; and each secondary control center controls the total charging load of the electric automobile group to follow the guide curve through an ordered charging control method.
Each secondary control center can select a centralized/distributed control method according to actual conditions to realize the following of the charging load of the electric automobile. Different secondary control centers can select different control methods according to the type and charging situation of the electric vehicle under jurisdiction, so that the expandability of the method is greatly improved.
Meanwhile, the method considers the electric automobile power, energy boundary constraint, new energy electricity abandoning proportion constraint, reverse power transmission constraint and the like, and ensures high-level consumption of new energy output.
1. A main control center:
and acquiring related constraints of the electric automobile and new energy power generation, and solving an electric automobile ordered charging optimization model containing new energy access. The optimal solution of the model is the electric vehicle charging load guidance curve and the new energy output which are issued to each secondary control center. And the charging load guide curve of the electric automobile is sent to each secondary control center.
The optimization model of the main control center is targeted to peak clipping and valley filling, so that a two-stage optimization model is established, and the total load curve fluctuation of each secondary control center is reduced, thereby being a multi-objective optimization problem. The optimization target of the first stage is total load peak clipping and valley filling, and the optimization target of the second stage is used for stabilizing the expected total load curve fluctuation of each secondary control center.
1.1 the optimization objective of the first stage is the peak clipping and valley filling of the total load, the objective function f1Comprises the following steps:
in the formula Gi，t、Di，t、Ri，tRespectively for the electric automobile guidance load, the total conventional load and the new energy output of the ith secondary control center control area at the moment t, wherein Ω represents the set of the secondary control centers, and τ is an optimization time period, wherein the optimization time period is a settable time range, such as 7: 00 to 9: 00. typically, a phase of optimization problem has multiple optimal solutions.
The constraint includes: and electric automobile power, energy boundary constraint, new energy electricity abandon proportion constraint, reverse power transmission constraint and the like of each secondary control center. The upper and lower limits of power and energy are uploaded by each secondary control center to obtain:
Ri，t≤Gi，t+Di，t(6)
in the formula (I), the compound is shown in the specification, P i，tthe upper limit and the lower limit of the total charging power of the electric automobile at the moment t of the ith secondary control center are set; E i，tthe upper limit and the lower limit of the total charging energy of the electric automobile at the moment t of the ith secondary control center are set; Δ t is the time interval. R i，tAnd the output of the new energy is the upper and lower limits corresponding to the ith secondary control center at the moment t.
The predicted output at the time t of the new energy can be set,R i，tcan be set to zero; and lambda is the set upper limit of the power abandoning proportion of the new energy.
1.2 second stage aimed at smoothing the expected Total load Curve fluctuations of the Secondary control centers, the objective function f2Comprises the following steps:
in the formula (f)2Directing load values for time t based on said objective function f2And forming a time and load value curve in the period tau.
In the second stage optimization model, in addition to the constraints appearing in the first stage, the following constraints are added:
in the formula
For the weighting factor, a value slightly larger than 1 may be set,
is the minimum value of the formula (1) after the optimization of the first stage is finished.
By solving the two-stage optimization model, the main control center can obtain the charging load guidance curve and the new energy output of each secondary control center.
2. The secondary control center:
the secondary control center can select a centralized or distributed control method according to actual situations to realize that the charging load of the electric automobile follows a guide curve and a target function f3The Euclidean distance between a total charging load curve and a load guide curve of the electric automobile is the minimum, namely:
in the formula PtIs the total charging load of the electric automobile at the time t, GtThe component of the load guidance curve at time t that the main control center is required to follow is given.
The constraints include power, energy boundary constraints, as follows:
in the formula (I), the compound is shown in the specification,
P tthe upper limit and the lower limit of the total charging power of the electric automobile at the moment t; etFor the total energy that the electric vehicle has been charged at time t,
E tthe upper and lower limits of the total charging energy of the electric automobile at the moment t. Formula (12) is total charging energy E of the electric vehicle at the moment t given by definitiontIs described in (1).
Upper and lower power limits of distributed control model of secondary control center P tSatisfies the following conditions:
P t＝0 (13)
wherein is the chargeable period of the jth electric vehicle, and τj＝[τbegin，j，τend，j]；
The sum of rated charging power of the electric automobile at the time t is contained in all chargeable periods; plimitIs the total power upper limit;
the above-mentioned E tSatisfies the following conditions:
in the formula (I), the compound is shown in the specification,
the energy required by the jth electric automobile.
Equation (15) represents that the lower limit of the total charging energy of the electric vehicle at the time t is all the electric vehicles which have finished charging at the time t (the time t is greater than or equal to the upper limit tau of the charging interval of the electric vehicles at the time t)end，t) The total energy required.
Equation (16) represents an electric vehicle in which the upper limit of the total charging energy of the electric vehicle at time t is set to the upper limit of the charging period τ of the electric vehicle which has started or completed charging at time t (time t is equal to or greater than the lower limit τ of the charging period τ of the electric vehicle at time t)begin，t) The total energy required.
The load following model of the secondary control center can be solved by adopting different optimization algorithms such as a direct solution (centralized algorithm), an ODC (optimized learning) algorithm (distributed algorithm), a probability transfer matrix method (distributed algorithm) and the like.
The direct solution method (centralized algorithm) is suitable for scenes such as a centralized charging/converting station and the like in which the number of controlled electric vehicles is not large and communication is relatively convenient, electric vehicles corresponding to the jurisdiction are mainly charged in the centralized charging/converting station, and the target function is the minimum Euclidean distance between a total charging load curve and a load guide curve of the electric vehicle.
An ODC (optimal localized charging) algorithm (distributed algorithm) is suitable for scenes that controlled electric vehicles such as dispersed charging piles are large in quantity and relatively inconvenient in communication, electric vehicles in corresponding jurisdictions are mainly charged in the widely distributed and sparse charging piles, in the algorithm, a secondary control center collects charging plans of the electric vehicles, control signals are calculated and broadcast to the electric vehicles, the electric vehicles locally solve optimization problems according to the control signals, the charging plans of the electric vehicles are corrected and fed back, and iteration is carried out until a control target is achieved.
According to the probability transfer matrix method (distributed algorithm), the basic flow is consistent with the ODC algorithm, electric vehicles corresponding to jurisdictions are mainly charged in widely distributed and sparse charging piles, but control signals are changed into a probability transfer matrix, meanwhile, the solution of an optimization problem is avoided locally, the requirement on equipment at the electric vehicle end is lower, and the calculation speed is higher.
The positive electrode of the lithium ion battery of the electric automobile comprises a current collector, an active material layer and a carbon nanofiber layer, wherein the current collector, the active material layer and the carbon nanofiber layer are sequentially arranged, the active material layer comprises two active materials, the two active materials are lithium iron phosphate and lithium manganate, and the thickness of the carbon nanofiber layer is 2-4 mu m.
The current collector is selected from metal foil or conductive polymer with a metal layer plated on the surface.
Wherein the length of the carbon nanofiber in the carbon nanofiber layer is 50-100 μm
Preferably, the structure of the active material layer is a three-layer structure including a first active material layer, a second active material layer, and a third active material layer;
the thickness of the first active material layer is 10 to 12 μm, the thickness of the second active material layer is 25 to 30 μm, and the thickness of the third active material layer is 3 to 5 μm.
Wherein, first active material layer includes lithium manganate and does not include lithium iron phosphate, second active material layer includes lithium iron phosphate and lithium manganate, third active material layer includes lithium iron phosphate and does not include lithium manganate.
The preparation method of the lithium ion battery comprises the following steps:
1) providing lithium iron phosphate particles with the average particle size D50 of 200nm, wherein the particle size distribution (D90-D10)/D50 of the lithium iron phosphate particles is 0.8, mixing the lithium iron phosphate particles, PVDF and sodium carboxymethylcellulose in a ratio of 100: 8: 4, and dispersing the mixture in NMP, wherein the NMP comprises 80% of deionized water and 20% of isopropanol; stirring for 4 hours to obtain first slurry, wherein the solid content of the first slurry is 40%;
2) providing lithium cobaltate particles with an average particle diameter D50 of 2 μm and a particle size distribution (D90-D10)/D50 of 0.4, mixing the lithium cobaltate particles, PVDF and sodium carboxymethyl cellulose in a ratio of 100: 4: 8, and dispersing in NMP; stirring for 6 hours to obtain a second slurry, wherein the solid content of the second slurry is 70%;
3) mixing the first slurry and the second slurry, and stirring for 1h to obtain a third slurry, wherein the mass ratio of the lithium iron phosphate particles to the lithium cobaltate particles in the third slurry is 20: 80;
4) providing an Al foil current collector;
5) coating the second slurry on the current collector, and drying to obtain a first active material layer with the thickness of 10 microns;
6) coating the third slurry on the first active material layer, and drying to obtain a second active material layer with the thickness of 20 mu m;
7) coating the first slurry on the second active material layer, and drying to obtain a third active material layer with the thickness of 3 mu m;
8) mixing carbon nanofibers with the length of 50 microns and a binder in a ratio of 9: 1, dispersing the mixture in MNP, stirring the mixture for 6 hours to obtain carbon nanofiber slurry, coating the carbon nanofiber slurry on the third active material layer, and drying the carbon nanofiber slurry to obtain a carbon nanofiber layer with the thickness of 2 microns;
9) and hot pressing to obtain the anode.
By using the lithium ion battery with high storage performance, the polymer conducting layer is present, so that the side reaction between the active substance and the electrolyte is relieved, and the decomposition of the electrolyte is avoided; conjugated electronic bonds in the conductive polymer can capture transition metal elements overflowing from the active substances, so that the storage performance of the battery is improved; active substance particles with different particle sizes are respectively mixed to prepare slurry, and PVDF and thickening agents with different contents are added according to different particle size ranges, so that the dispersibility of the slurry is improved; the particles with two particle size distributions are mixed to prepare the anode, and the small particles are filled in gaps of the large particles, so that the energy density is improved; the lithium cobaltate layer close to the current collector layer selects large-particle particles so as to obtain larger pores in the layer, improve the infiltration degree of the electrolyte and obtain better rate performance. The nickel cobalt lithium manganate layer far away from the current collector adopts small-particle particles and contains PVDF with higher content, so that the surface layer is more compact, the separation of the positive active substance from the positive electrode is avoided, the stability of the positive electrode is improved, and the cycle life is prolonged.
Example 2: based on the same inventive concept, the invention also provides an algorithm of the electric automobile ordered charging layered control model, and the flow of the algorithm is shown in fig. 2.
Firstly, the electric vehicle uploads information such as power, capacity, SOC (State of Charge), and Charge start-stop time to each secondary control center; each secondary control center calculates the electric automobile constraint and uploads the electric automobile constraint and the like to the main control center; the main control center solves a two-stage optimization model according to the information such as the constraint uploaded by the secondary control centers and the predicted output uploaded by the new energy power generation, and obtains a load guidance curve and the new energy output of each secondary control center; the secondary control center guides the charging load of the electric automobile to follow the guide curve through a centralized/distributed control method; and the electric automobile adjusts the charging plan according to the instruction issued by the secondary control center, so as to realize ordered charging.
Example 3: example simulation of the procedure of example 2 above using simulation
1. Emulation setting
The total number of A, B, C secondary control centers under the main control center is set, the load peak values of the daily gauge are 3715kW, 4000kW and 4500kW respectively, and the power upper limits are 4000kW, 4500kW and 5000kW respectively. A. The load of the distribution network region corresponding to the secondary center C is mainly residential life load, the load of the region corresponding to the secondary center B is mainly industrial and commercial load, and new energy power generation mainly based on wind power generation exists in the region B. The conventional load curve and the predicted wind power output (wind power output data from a wind power plant in a certain area in north China) of the corresponding area of the tertiary level center are shown in fig. 3.
Here, the rated charging power of the electric vehicle was 7kW, and the battery capacity was 32 kWh. The distribution of parameters such as the number of electric vehicles governed by the tertiary control center, arrival time and departure time, SOC and the like is shown in Table 1. It can be seen that the A, C secondary control center electric vehicle charging load is concentrated at night, while the B secondary control center electric vehicle charging load is concentrated during the day.
TABLE 1 basic controlled parameters of electric vehicles
In the aspect of the control method, the secondary center A controls the electric vehicle under jurisdiction by adopting a direct solution method, the secondary center B controls the electric vehicle under jurisdiction by adopting an ODC algorithm, and the secondary center C controls the electric vehicle under jurisdiction by adopting a probability transfer matrix method.
2 simulation results
Fig. 4 and 5 show the optimization results obtained by the main control center according to the two-stage optimization model. For the sake of comparison, the conventional load curve D and the expected total load curve L of the corresponding region of each secondary control center are actually shown in FIG. 4＊(conventional load curve D plus load guidance curve G minus new energy output R). Due to the advantages of the second stage of the modelThe goal is to stabilize the expected total load curve fluctuation of each secondary control center, and the expected total load curve of each zone is relatively smooth and has small fluctuation. Given in fig. 5 are a total normal load curve (three regions) and a total expected load curve (three regions), since the model optimization goal in the first stage is to minimize the total load variance, the total expected load curve is relatively smooth, and the control goal of electric vehicle charging load peak clipping and valley filling in the hierarchical control method is basically achieved. The relationship between the predicted wind power output and the optimized expected wind power output is shown in fig. 6. It can be found that the wind abandoning is needed at the time of the low valley of the partial load due to the restriction of the new energy power abandoning proportion, the restriction of the reverse power transmission and the like.
Fig. 7(a), (b), (c) show the load following situation of A, B, C tertiary control center, respectively. It can be found that although different load following methods are used, the charging load T of the electric automobile of each secondary control center basically follows the load guide curve G, the actual total load curve L' (the conventional load curve D is added with the charging load T of the electric automobile to reduce the new energy output R) and the expected total load curve L＊The deviation is not large. As can be seen from fig. 7(d), through the sequential charging hierarchical control of the electric vehicle, the total charging load curve of the electric vehicle substantially follows the total expected load curve, and the control target of peak clipping and valley filling is achieved.
Table 2 gives a quantitative analysis of the load following and load shifting effects of the hierarchical control method the average relative error α between the actual total load curve and the expected total load curve is used to measure the load following effect:
TABLE 2 analysis of load following and load shifting effects
The peak-to-valley difference reduction ratio β is used to measure the peak-to-valley clipping effect:
according to the definition, the smaller α is, the higher the coincidence degree of an actual total load curve and an expected total load curve is, the better the load following effect is, the larger β is, the larger the reduction degree of the peak-valley difference of the layered optimization control relative to the peak-valley difference of the original conventional load curve is, the better the peak load filling effect is, the better the load following effect is, the better the secondary control center B, C which adopts a distributed algorithm needing iteration and repeated convergence is, the chargeable time period of the electric vehicle of the secondary control center B is basically overlapped with the peak load time period, the charging load of the electric vehicle is difficult to be transferred to the low valley time period, so the charging load of the secondary control center B has poor peak load filling effect, only partial peak load is reduced through new energy power generation, the A, C secondary control center mainly belongs to the electric vehicle for the civil service commuting, the charging time period covers the low valley time period, the main application of the charging load filling of the electric vehicle is, the peak load filling effect is better, and the layered control method for realizing the ordered load following of the electric vehicle.
It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The present invention is not limited to the above embodiments, and any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are included in the scope of the claims of the present invention which are filed as the application.
Claims (20)
1. An electric vehicle charging method, comprising:
based on each secondary control center, bringing the battery information and the charging time of the electric automobile into a pre-constructed secondary control center optimization model for calculation;
substituting the calculation results of all secondary control center optimization models and the predicted output of new energy power generation into a pre-constructed two-stage optimization model for calculation to obtain a load guidance curve of each secondary control center;
the secondary control center formulates a charging load following guidance curve of each electric automobile under the secondary control center based on the load guidance curve of the secondary control center;
the electric automobile is charged along the guide curve according to the charging load;
the battery of the electric automobile is as follows: a lithium ion battery.
2. The method of charging an electric vehicle according to claim 1,
the battery information of the electric vehicle includes: power, capacity, and battery state of charge, SOC;
the charging time includes: and charging start-stop time.
3. The electric vehicle charging method of claim 1, wherein the two-stage optimization model comprises:
a first stage objective function with the total load peak clipping and valley filling as the target,
The constraint condition corresponding to the first stage objective function,
Second stage objective function and method aimed at stabilizing desired total load curve fluctuations at each secondary control center
And the constraint condition corresponding to the objective function of the second stage.
4. The method of charging an electric vehicle of claim 3, wherein the first stage objective function is calculated as follows:
in the formula (f)1A first stage objective function taking total load peak clipping and valley filling as targets; gi，tGuiding the load of the electric automobile at the moment t of the ith secondary control center control area; gi，tControlling the total conventional load of the area t moment for the ith secondary control center; ri，tAnd the new energy output at the moment t of the ith secondary control center control area is shown, wherein omega is the set of the secondary control centers, and tau is the optimization time interval.
5. The method of claim 4, wherein the constraints associated with the first-stage objective function include:
Ri，t≤Gi，t+Di，t
in the formula (I), the compound is shown in the specification,
respectively setting the upper limit and the lower limit of the total charging power of the electric automobile at the moment t of the ith secondary control center;
respectively setting the upper limit and the lower limit of the total charging energy of the electric automobile at the moment t of the ith secondary control center; Δ t is the time interval;
respectively corresponding upper and lower limits of new energy output at the moment t of the ith secondary control center; and lambda is the set upper limit of the power abandoning proportion of the new energy.
6. The method of charging an electric vehicle according to claim 4, wherein the second stage objective function is calculated as follows:
in the formula (f)2The load value is directed for time t.
7. The method according to claim 6, wherein the constraints associated with the second stage objective function include:
in the formula (I), the compound is shown in the specification,
in order to be the weight coefficient,
is the minimum of the first stage objective function.
8. The electric vehicle charging method according to claim 3, wherein the step of bringing the calculation results of the optimization models of all the secondary control centers and the predicted output of the new energy power generation into a pre-constructed two-stage optimization model for calculation to obtain the load guidance curves of the secondary control centers comprises the steps of:
substituting the calculation results of all secondary control center optimization models and the predicted output of new energy power generation into a two-stage optimization model, and solving the first-stage objective function to obtain a minimum value;
setting a weight coefficient;
and substituting the minimum value of the first-stage objective function and the weight coefficient into the second-stage objective function to solve to obtain a charging load guidance curve and new energy output of each secondary control center:
wherein the weighting factor is greater than 1.
9. The electric vehicle charging method of claim 1, wherein the secondary control center optimization model comprises:
and the secondary control objective function and the corresponding secondary control constraint condition take the minimum Euclidean distance between the total charging load curve and the load guide curve of the electric automobile as a target.
10. The method of charging an electric vehicle of claim 9, wherein the secondary control objective function is as follows:
in the formula, PtIs the total charging load of the electric automobile at the time t, GtThe component of the load guidance curve at time t that the main control center is required to follow is given.
11. The method of charging an electric vehicle of claim 10, wherein the secondary control constraints comprise:
in the formula (I), the compound is shown in the specification,
the upper limit and the lower limit of the total charging power of the electric automobile at the moment t; etFor the total energy that the electric vehicle has been charged at time t,the upper limit and the lower limit of the total charging energy of the electric automobile at the moment t;
wherein, the
Satisfies the following conditions:
wherein is the chargeable period of the jth electric vehicle, and τj＝[τbegin，j，τend，j]；
The sum of rated charging power of the electric automobile at the time t is contained in all chargeable periods; plimitIs the total power upper limit;
the above-mentioned
Satisfies the following conditions:
in the formula (I), the compound is shown in the specification,
the energy required by the jth electric automobile.
12. The method for charging the electric vehicle according to claim 11, wherein the step of bringing the battery information and the charging time of the electric vehicle into a pre-constructed optimization model of the secondary control center for calculation based on each secondary control center comprises the following steps:
solving is carried out by adopting a direct solution method, a distributed algorithm or a probability transfer matrix method.
13. The electric vehicle charging method according to claim 1, wherein the secondary control center formulates a charging load following guidance curve for each electric vehicle under the secondary control center based on the load guidance curve of the secondary control center, and the method comprises the following steps: and setting the charging power of each electric automobile at each moment in the time range of the load guidance curve.
14. The method of charging an electric vehicle according to claim 13, wherein the electric vehicle follows a guideline curve for charging according to the charging load, comprising: the electric automobile adjusts the charging power according to the charging time and the charging power set at the charging time.
15. The charging method of the electric vehicle according to claim 1, wherein the positive electrode of the lithium ion battery comprises a current collector, an active material layer and a carbon nanofiber layer on the surface of the active material layer, which are arranged in sequence;
the active material layer includes: lithium iron phosphate and lithium manganate;
the thickness of the carbon nanofiber layer is 2-4 μm.
16. The method of charging an electric vehicle according to claim 15, wherein the current collector is selected from a metal foil or a conductive polymer with a metal layer plated on the surface.
17. The method for charging an electric vehicle according to claim 15, wherein the carbon nanofibers in the carbon nanofiber layer have a length of 50 to 100 μm.
18. The charging method for an electric vehicle according to claim 15, wherein the active material layer has a three-layer structure including a first active material layer, a second active material layer, and a third active material layer;
the thickness of the first active material layer is 10 to 12 μm, the thickness of the second active material layer is 25 to 30 μm, and the thickness of the third active material layer is 3 to 5 μm.
19. The method of charging an electric vehicle according to claim 18, wherein the first active material layer comprises lithium manganate, the second active material layer comprises lithium iron phosphate and lithium manganate, and the third active material layer comprises lithium iron phosphate.
20. An electric vehicle charging system, comprising: the system comprises a secondary control center module, a main control center module and an electric vehicle charging calculation module;
the secondary control center computing module is configured to: based on each secondary control center, bringing the battery information and the charging time of the electric automobile into a pre-constructed secondary control center optimization model for calculation, and uploading the calculation result to a control center calculation module; the system is also used for formulating a charging load following guidance curve of each electric automobile under the secondary control center based on the load guidance curve of the secondary control center and issuing the curve to the corresponding electric automobile;
the control center calculation module: substituting the calculation results of all secondary control center optimization models and the predicted output of new energy power generation into a pre-constructed two-stage optimization model for calculation to obtain the load guidance curves of all the secondary control centers, and sending the load guidance curves of the secondary control centers to corresponding secondary control center modules;
electric automobile calculation module that charges: and the other electric vehicle is used for charging according to the charging load following the guide curve.
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