CN102570490B - Intelligent charge-discharge control method for electric vehicle - Google Patents
Intelligent charge-discharge control method for electric vehicle Download PDFInfo
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Abstract
The invention discloses an intelligent charge-discharge control method for an electric vehicle. The method comprises the following steps: acquiring voltage waveforms of a power grid; performing mathematical morphological transformation to the voltage waveforms of the power grid and extracting the corresponding form characteristic curve of the power grid; inputting the form characteristic curve of the power grid to a nerve network and judging the load status of the running power grid; and utilizing the load status information of the power grid, as well as the peak-valley progressive pricing and the charging requirement of users, to draw up the charge-discharge control strategy. According to the invention, the electric vehicle and the intelligent power grid are integrated to improve the charging economical efficiency and convenience for electric vehicle users; the running efficiency of the power grid is improved through intelligent charging management of the electric vehicle; and besides, the invention has significance on scientifically and reasonably planning the construction of charging facilities and distribution networks, reducing the construction investment on construction of the distribution networks, which is caused by large-scale electric vehicle charging, improving the facility utilization ratio and improving the economical efficiency in construction of the charging facilities.
Description
Technical field:
The present invention relates to charging electric vehicle field, be specifically related to a kind of Intelligent charge-discharge control method for electric vehicle.
Background technology:
The great special project of National 863 plan electric automobile plays great impetus to Development of Electric Vehicles.After electric automobile large-scale development, must new requirement be proposed to electrically-charging equipment and supporting power grid construction thereof.Under the construction situation of current national grid, a large amount of electric automobiles charge simultaneously, will cause electric load excessive, and net capacity can not meet the demands.Utilize the regulating strategy of electricity price between peak and valley, encouraging user is charging electric vehicle in low ebb period of network load, and discharged to electrical network by batteries of electric automobile in load peak time, can rationally build the capacity of power supply network, realize power grid operation and electric automobile user's overall efficiency.
As everyone knows, in the time that total active power of network system workload demand is greater than the gross output of generator, the voltage of network system and frequency just can decline; In the case of the gross output of generator is larger, network system voltage and frequency just can rise.Sometimes blindly charging of electric automobile, causes power network fluctuation larger, makes troubles to electrical network and user.
Summary of the invention:
For the deficiencies in the prior art, the present invention designs a kind of Intelligent charge-discharge control method for electric vehicle, and this control method is by the voltage status of the real-time electrical network detecting, and the feature of extracting line voltage form spectral line judges the load condition of electrical network.
By discharging and recharging tactful control algolithm, determine the charging and discharging state of electric automobile, reach the object that regulates network load balance, ensure the quality of power supply of mains supply system, make the safe and stable operation of charger.
A kind of Intelligent charge-discharge control method for electric vehicle provided by the invention, its improvements are, described method comprises the steps:
(1) gather grid voltage waveform;
(2) described grid voltage waveform is carried out to mathematical morphology conversion, extract corresponding electrical network morphological character curve;
(3) described electrical network morphological character curve is input to neural net, judges the load condition of operation of power networks;
(4) utilize described network load state information, formulate and discharge and recharge control strategy in conjunction with peak valley step price and user's charging demand.
Wherein, step (1) gathers power system voltage by voltage transformer.
Wherein, the corresponding electrical network morphological character of the described extraction of step (2) curve comprises the steps:
A. define morphology spectrum;
B. selecting structure element;
C. select the length of described structural element;
D. select the amplitude of described structural element;
E. extract morphology spectrum.
Wherein, step (3) comprises the steps:
1) choose operation of power networks state;
2) choose corresponding morphology spectrum data according to running status;
3) choose load condition neural net decision device;
4) described morphology spectrum data passed to described load condition decision device judge the load condition of operation of power networks; Described load condition comprises electrical network heavy service, network load balance movement and electrical network light(-duty) service state.
Wherein, described in step (4), discharging and recharging control strategy comprises the steps:
When described electrical network heavy service, improve charging electricity price, to reduce the quantity of electric automobile user charging or to encourage electric automobile user by electric energy feedback electrical network;
When network load balance movement, electric automobile is by demand charging.
When electrical network light(-duty) service state, reduce electricity price, to encourage electric automobile user to charge.
Wherein, be provided with filter circuit before described voltage transformer, power system voltage is passed to described voltage transformer after by filter circuit filtering and is gathered.
Compared with the prior art, beneficial effect of the present invention is:
1. the present invention carries out confession, the charging system research that influences each other by electric automobile is discharged and recharged, the line voltage characteristic of qualitative analysis and qualitative assessment electricity consumption peak valley phase, combine by a large amount of emulation experiments and field measurement, while studying electric automobile as adjustable load, electric power system is affected, make a kind of Intelligent charge-discharge control method for electric vehicle, the voltage status of the electrical network by real-time detection, the feature of extracting line voltage form spectral line judges the load condition of electrical network.By discharging and recharging tactful control algolithm, determine the charging and discharging state of electric automobile, reach the object that regulates network load balance, ensure the quality of power supply of mains supply system, make the safe and stable operation of charger.
2. the invention solves in the method for frequency measurement electric network state and can not well suppress harmonic component, amount of calculation is bigger than normal, to all once calculate each cycle, will take the too much processor time, it can not be taken into account the problem of computational accuracy and real-time and certainty of measurement and be subject to the problems such as the impact of voltage over zero is larger.
3. the present invention, by the fusion of electric automobile and intelligent grid, has improved economy and the convenience of electric automobile user charging; And can, by the intelligent management of charging electric vehicle, improve operation of power networks efficiency.
4. the present invention, to planning scientifically and rationally electrically-charging equipment and distribution network construction, reduces the power distribution network transformation construction investment that scale charging electric vehicle causes, improves facility utilization rate, and the economy of improving electrically-charging equipment construction is significant.
Brief description of the drawings
Fig. 1 is that morphology spectrum provided by the invention extracts flow chart.
Fig. 2 is electric network state identification neural network model figure provided by the invention.
Embodiment
Below in conjunction with accompanying drawing, the specific embodiment of the present invention is described in further detail.
The Intelligent charge-discharge control method for electric vehicle of the present embodiment comprises the steps:
(1) gather grid voltage waveform;
Data acquisition is mainly the collection to voltage signal, by accurate voltage transformer, the voltage signal of electrical network is gathered, and virtual voltage quantity of state is converted to the small-signal that electronic equipment can be processed.First voltage transformer will consider the rated voltage size in working line, and the input range of its less important consideration measurement mechanism requires and required precision.In practical power systems, the voltage in circuit is very large, and voltage transformer can convert virtual voltage to a little Voltage-output, and this output can reflect the size of virtual voltage electric current.
The power network signal that voltage transformer collects, exists high order harmonic component conventionally, like this spectral line characteristic of voltage waveform is caused to certain interference.So first the present embodiment is input to the signal of voltage transformer collection the input of low-pass filtering module.Low-pass filtering part can adopt after passive second-order low-pass filter circuit filtering high order harmonic component, being input in voltage transformer.
(2) described grid voltage waveform is carried out to mathematical morphology conversion, extract corresponding electrical network morphological character curve;
Generally, image is mostly gray level image, and gray scale morphology can be processed multivalue image effectively.For power system signal, the corresponding real-valued function of its sampling gained waveform, therefore the present embodiment adopts gray scale morphological transformation to carry out analyzing and processing to voltage run signal.
In mathematical morphology, propose four kinds of graphical analysis criterions such as overall covariance, local covariance, size distribution and connectedness and come various geometric parameters and the feature of Description Image, for example area, girth, degree of communication, granularity, skeleton and directivity etc.Wherein, size distribution criterion is by carrying out the area change after opening operation, the size distribution character of Description Image between measurement image and a series of different size structural element.Maragos introduces closed operation in definition, thereby size distribution is extended to the dual description of image and background, and is referred to as morphology spectrum.
Make f (x), x ∈ Rm, m=1,2 ..., m-1 is a nonnegative function, m is a natural number, the gray scale that now m is image.G (x) is a protruding structure function.F (x) morphology spectrum is defined as:
Wherein
be illustrated in the limited area of U (f (x)) in the domain of definition; In the time of λ>=0, be opening operation morphology spectrum, when λ < 0, be closed operation morphology spectrum;
The choosing of structural element in data and image processing:
The shape of structural element will require to determine according to the difference of processing signals, the structural element of generally not selecting waveform to differ greatly.Under the requirement of outstanding signal shape feature, in the process of constantly experiment, the present embodiment is selected the linear structural element about origin symmetry, more can give prominence to the shape facility of the voltage under different load state status.
The selection of structural element length:
Structural element length is to embody with the array element number of one-dimension array in the processing of program.Can obviously find out the increase along with structural element length from result, it is more level and smooth that waveform becomes.But structural element length is long, can damage again useful signal.In morphology spectrum leaching process, the length that constantly increases structural element is extracted morphology spectrum curve.The present embodiment draws, the structural element length of getting again in the process of 1 to 80 variation, the shape of gray level image and area change are all little, illustrate that original image has kept good shape facility, so the selection of rectilinear structure element is suitable.And be greater than after 80 when structural element length, the value of the spectral line of waveform is zero, illustrate that choosing of structural element length is now excessive, effectively the shape of reflected waveform.
Choosing of structural element amplitude:
In order to obtain good treatment effect, the amplitude of structural element, than the little order of magnitude of pending signal value, could obtain good effect.
The extraction of morphology spectrum:
Chosen after structural element, voltage waveform in various load condition situations is extracted to morphology spectrum, extraction procedure overall procedure as shown in Figure 1, comprising:
Start; Choose signal length; In 1-80 district, signal is expanded or corroded; Process; Obtain morphology spectrum value; Finish.Wherein, Se representative structure length of element; N representative is carried out erosion operation with structural element to signal; M representative is carried out dilation operation again to the signal after corroding.
The present embodiment amplifies difference, after being more conducive to, distinguishes.
(3) described electrical network morphological character curve is input to neural net, judges the load condition of operation of power networks;
The system model adopting and parameter be as shown in Table 1:
The present embodiment is chosen respectively electrical network heavy service period, each 300 of the morphology spectrum data of the actual measurement working voltage waveform in light load period and load balancing period, and totally 900 data samples are trained.
The present embodiment select BP neural net as load condition decision device for judging network load state.Select hidden layer to count the network structure model of L=1, paid the utmost attention to and adopted the feedforward network with single hidden layer configuration.The value of the morphology spectrum of voltage when input layer is three kinds of load conditions, gathers equally distributed 30 points on every spectral line, be 30x3 totally 90 input nodes, and 3 nodes of output layer, corresponding to 3 kinds of running statuses.It ought the first output node be 1 that the present embodiment is established, and second and third output node is to represent electrical network heavy service at 0 o'clock; If when the second output node is that 1, first and third output node is to represent network load balance movement at 0 o'clock; If when the 3rd output node is 1, first and second output node is to represent electrical network light(-duty) service at 0 o'clock.
The output of table 1. sample arranges table
The error dividing value of neural net is made as 10-6,, in the time that iterative computation mistiming difference E reaches 10-6, thinks and has learnt, and stops calculating Output rusults.In conjunction with the training result of different hidden layer network configurations, select the network that hidden layer number is 1
Structural model.Adjust through continuous test, hidden layer is made as and contains 40 nodes, neural network model as shown in Figure 2:
The transfer function of hidden layer is logarithm S type transfer function logsig, and the transfer function of output layer is line style purelin function.Under ideal conditions, represent that with output layer Y3 (j)=1 (or approaching at 1 o'clock) corresponding load condition occurs, otherwise be 0; If | when Y3 (j)-1| < 0.3, represent that pattern can identify, otherwise think do not have.It is 1.1 that learning rate is set, and uses traingdx training function, and train epochs is made as 600 steps.This network convergence precision after 7 steps is less than 10-6, meets required precision.Convergence curve in obtained error range after training.
(4) utilize described network load state information, formulate and discharge and recharge control strategy in conjunction with peak valley step price and user's charging demand.
IEC61000-4-30, in 2003, point out, the electric power system that is 50HZ for frequency, the fundamental measurement time interval of parameter value (as supply power voltage, harmonic wave, a harmonic wave and imbalance) should be the time interval in 10 cycles, namely using 0.2 second time interval as the interval of measuring.
In GB/T19862-2005 " power quality monitoring device General Requirement ", the memory function that records of power quality monitoring device is made to following regulation: a master record cycle of voltage deviation, frequency departure, tri-phase unbalance factor, Detecting Power Harmonics is 3s.Choose 0.3 second herein as the time interval of judging electric network state.
The control strategy of the present embodiment comprises: when electrical network is in light(-duty) service period, reduce electricity price and encourage electric automobile user to charge.In electrical network heavy service period, improve charging electricity price and reduce the quantity of user's charging or encourage user by electric energy feedback electrical network.In network load balance movement period, electric automobile is chargeable or do not fill by demand.Charging cost mainly decides with overall elapsed time, time-of-use tariffs and Spot Price.Also need to protect positive charge power at battery, in the limit range of charger and charging station.According to the state information of battery (being battery temperature, pressure, voltage), determine the charge rate limit, state-of-charge, safe condition.
The in the situation that of non-electric network fault, user habit is set to and has higher priority (for example charge time started can by for random setting) with respect to electric network information.Support not consider the control strategy that discharges and recharges of expense simultaneously.
Finally should be noted that: only illustrate that in conjunction with above-described embodiment technical scheme of the present invention is not intended to limit.Those of ordinary skill in the field are to be understood that: those skilled in the art can modify or be equal to replacement the specific embodiment of the present invention, but among the claim protection range that these amendments or change are all awaited the reply in application.
Claims (5)
1. an Intelligent charge-discharge control method for electric vehicle, is characterized in that, described method comprises the steps:
(1) gather grid voltage waveform;
(2) described grid voltage waveform is carried out to mathematical morphology conversion, extract corresponding electrical network morphological character curve;
(3) described electrical network morphological character curve is input to neural net, judges the load condition of operation of power networks;
Step (3) comprises the steps:
1) choose operation of power networks state;
2) choose corresponding morphology spectrum data according to running status;
3) choose load condition neural net decision device;
4) described morphology spectrum data passed to described load condition neural net decision device judge the load condition of operation of power networks; Described load condition comprises electrical network heavy service, network load balance movement and electrical network light(-duty) service state;
(4) utilize the load condition of described operation of power networks, formulate and discharge and recharge control strategy in conjunction with peak valley step price and user's charging demand.
2. control method as claimed in claim 1, is characterized in that, step (1) gathers power system voltage by voltage transformer.
3. control method as claimed in claim 1, is characterized in that, the corresponding electrical network morphological character of the described extraction of step (2) curve comprises the steps:
A. define morphology spectrum;
B. selecting structure element;
C. select the length of described structural element;
D. select the amplitude of described structural element;
E. extract morphology spectrum.
4. control method as claimed in claim 1, is characterized in that, discharges and recharges control strategy described in step (4) to comprise the steps:
When described electrical network heavy service, improve charging electricity price, to reduce the quantity of electric automobile user charging or to encourage electric automobile user by electric energy feedback electrical network;
When network load balance movement, electric automobile is by demand charging;
When electrical network light(-duty) service state, reduce electricity price, to encourage electric automobile user to charge.
5. control method as claimed in claim 2, is characterized in that, is provided with filter circuit before described voltage transformer, and power system voltage is passed to described voltage transformer after by filter circuit filtering and gathered.
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| CN105141000A (en) * | 2015-09-22 | 2015-12-09 | 国网山东东营市东营区供电公司 | Electric vehicle charging/discharging control method |
| CN107623149B (en) * | 2017-08-30 | 2019-12-10 | 深圳市盛路物联通讯技术有限公司 | Charging method and related equipment |
| CN108482134A (en) * | 2018-04-08 | 2018-09-04 | 范跃 | A kind of electric vehicle solar charging device based on machine learning |
| CN109934955B (en) * | 2019-02-28 | 2021-05-14 | 深圳智链物联科技有限公司 | Charging mode identification method and device, terminal equipment and storage medium |
| CN111025043B (en) * | 2019-11-13 | 2021-08-13 | 深圳猛犸电动科技有限公司 | Method for identifying charging behavior and terminal equipment |
| CN114204644B (en) * | 2021-12-16 | 2023-11-07 | 国网湖南省电力有限公司 | Charging and discharging control method, system and storage medium for energy storage system of electric automobile |
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