CN105184361B - The maximal efficiency tracking of electric automobile magnetic coupling wireless charging system - Google Patents
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Abstract
The invention discloses a kind of maximal efficiency tracking of electric automobile magnetic coupling wireless charging system, derives the mutual inductance between transmitting and receiving coil by primary power driving frequency first, and fitness function is changed into a function relevant with frequency；Population scale in general particle cluster algorithm is separately set, respectively maximum population scaleNmax=30 and smallest particles group's scaleNmin=2, population scale is gradually reduced as iterations increases.This particle cluster algorithm subtracts redundancy particle, changes population scale, simplifies algorithm, accelerate algorithm the convergence speed.It is evidence-based that particle cluster algorithm of the present invention not only causes particle scale to choose, and algorithm has larger ability of self-teaching and social learning's ability early stage in search, in the search later stage, accelerates convergence rate, the algorithm search time reduces.
Description
Technical field
The present invention relates to electric automobile magnetic coupling wireless charging system maximal efficiency finding method technical field, particularly magnetic
A kind of search technique field of system efficiency of transmission in coupled resonance radio energy transmission system, and in particular to electric automobile magnetic coupling
Close the maximal efficiency tracking of wireless charging system.
Background technology
The charging of electric automobile is always the focus of research, with the development of electric automobile, convenient various charging modes
It is becoming increasingly popular.And wireless charging technology is easy to use, safe, equipment laying dust is less, and the problems such as contactless loss, therefore
Received more and more attention in wireless charging technology in electric automobile field.Wireless power transmission mode mainly has 3 kinds：First
Kind is induction；Second is microwave radio formula；The third magnetic coupling resonance formula.The third its basic thought is based on magnetic coupling
Resonance principle is closed to realize：When power supply driving frequency reaches certain value, whole system is in resonance state, can now realize nothing
The high-effect energy transmission of line.Magnetic coupling resonance formula wireless power transmission mode has the advantages of long transmission distance, transimission power is big, but
The technology is also in the starting stage.
System effectiveness transmission optimal value is found, and the driving frequency for finding the system in system maximum transmitted efficiency is current
The focus of research.Particle cluster algorithm has advantage in the searching of Solving Multimodal Function value, but is directed to magnetic coupling radio energy and goes out
For defeated system, single or two extreme points occur in its system effectiveness function, when there is single extreme point situation, with basic grain
When swarm optimization is searched for it, of short duration stagnation behavior occurs in the algorithm search later stage；For algorithm in itself for, particle scale mistake
Conference causes algorithm to carry out unnecessary calculating, takes and calculates the time, and less scale then causes particle directly to miss the overall situation most
The figure of merit, or even can not find extreme point.By searching document, general particle scale is located between 20-40, but particle scale
Accurate choose all is that when solving problem, ceaselessly hit and miss experiment comes out according to individual all the time, very blindly.So pin
To case above, it is badly in need of finding a kind of algorithm for feature of magnetic coupling radio energy transmission system itself, solves system optimal
Efficiency find problem.How to be directed in magnetic coupling wireless power supply system and design a kind of algorithm to quickly find the maximum effect of system
Rate and corresponding Frequency point are urgent problems to be solved.
The content of the invention
Present invention solves the technical problem that it there is provided a kind of maximal efficiency of electric automobile magnetic coupling wireless charging system
Tracking, what the fitness function that the algorithm uses changed with the difference of mutual inductance between transmitting and receiving coil, calculating
Method starts that the mutual inductance between transmitting coil and receiving coil is first determined before searching for so that fitness function is changed into only with encouraging frequency
The relevant function of rate, then carries out optimizing with particle cluster algorithm.
The present invention adopts the following technical scheme that to solve above-mentioned technical problem, 1, electric automobile magnetic coupling wireless charging system
Maximal efficiency tracking, it is characterised in that：Population scale in general particle cluster algorithm is separately set, respectively most
Big population scale Nmax=30 and smallest particles group scale Nmin=2, population scale are gradual with iterations increase
Reduce, its specific implementation step is：
(1), initialization algorithm, including setting particle populations dimension D, maximum iteration MaxNum, while limit particle
Maximal rate vmax, initialization inertia weight w；
(2), directly set population maximum-norm Nmax as 30 and population smallest size Nmin be 2, random initializtion
The speed v of particle and the position of particle, primary group scale is set as maximum-norm Nmax=30, initialization iterations t
=1；
(3), using fitness functionCalculate the fitness function value of each particle of current population
fi, fiThe fitness function value of i-th of particle is represented, wherein
The π f of ω=2r, frFor current excitations frequency, ω is the angular frequency of excitation power supply, and M is to launch the mutual inductance between receiving coil, L1,
L2For transmitting coil and receiving coil inductance, C1, C2For electric capacity, RsFor the internal resistance of source, RLFor load resistance, R1, R2For in loop
Resistance；
(4) f, is usedi-bestThe adaptive optimal control degree functional value that i-th of particle searches when the t times iteration is represented, is used
fi-gbestRepresent when the t times iteration, the adaptive optimal control degree functional value that all particles search, start in particle cluster algorithm
Before iteration, f is seti-best=0, fi-gbest=0, the particle fitness function value f that will be obtained in step (3)iWith individual extreme value
fi-bestAnd global extremum fi-gbestCompare, if fi≤fi-best, then fi-best=fi, pi=xi, piRepresent fitness function
It is worth for fi-bestParticle position, xiBe be f to fitness function valueiThe position of particle, if fi≤fi-gbest, then
fi-gbest=fi, pg=xi, pgIt is that global optimum is f in particle populationsi-gbestParticle position；
(5), by formula Npresent=N max- (N max-N min) * t/ (MaxNum) update population scale, wherein
Npresent is the current scale of population, and Nmax is maximum population scale, and Nmin is smallest particles group's scale, and MaxNum is most
Big iterations, t is current iteration number, by formula
And formulaSpeed and the position of each particle are updated, then makes iterations t=t+1, turns to step (6),
Wherein vi t+1Represent the speed of i-th of particle of t+1 iteration, vi tRepresent the speed of current the t times iteration, i-th of particle, c1And c2
Studying factors are represented, rand represents the random number between [01], piExpression fitness function value is fi-bestParticle position, pgIt is
Global optimum is f in particle populationsi-gbestParticle position, xi t+1Represent i-th of particle position of t+1 iteration, xi tRepresent
The t times iteration, i-th of particle current location, w represent inertia weight；
(6), according to formulaCalculate particle fitness function value variance it
With favgFor the average value of all particles fitness function value, wherein if (fi-favg)>1, then a=max (fi-favg), it is no
Then, a=1, judge variance whether be equal to 0 or particle cluster algorithm whether reach maximum iteration, if it is not, then turn to step
(3) step (7), is if it is turned to；
(7) the global optimum p searched, is exportedg, pgIt is that global optimum is f in particle populationsi-gbestParticle position
Put, that is, frequency values corresponding to the optimal value searched；
(8), load current i is detected with current sensor2Peak value, if Δ for setting maximum current peak fluctuate model
Enclose, i2maxFor the load current peak detected, i2max(k) it is k-th of current cycle current peak of load, i2max(k+1) it is
The current peak of+1 current cycle of kth of load, judge | i2max(k+1)|-|i2max(k)|>Whether Δ is set up, if it is determined that
As a result it is yes, then turns to step (1), algorithm is restarted, if it is judged that being no, algorithm branches step (7).
The inventive method makes particle scale be gradually reduced with iterations increase, starts early stage, population scale in algorithm
Set higher value so that algorithm carries out global search, global optimum will not be missed, in the algorithm search later stage, if general grain
Of short duration stagnation behavior occurs in swarm optimization, and now, this algorithm eliminates redundancy particle because having simplified population scale,
It is not in of short duration stagnation behavior that algorithm, which is allowed for, in the later stage, and convergence rate is accelerated, and reduces the algorithm search time；And pass through
Searching document finds that population scale is generally located between 20-40, but particle scale is accurately chosen and do not known, but according to individual
People's experience is set.This algorithm sets particle maximum-norm and smallest size, make particle scale with iterations increase gradually by
Maximum-norm Nmax=30 is reduced to smallest size Nmin=2, and it is inaccurate to solve the problems, such as that algorithm scale is chosen.In addition, this
What the fitness function that invention algorithm uses changed with the difference of mutual inductance between transmitting and receiving coil, start to search in algorithm
The mutual inductance between transmitting coil and receiving coil is first determined before rope so that fitness function is changed into only relevant with driving frequency
Function, then carry out optimizing with particle cluster algorithm.It is turned to restart bar in addition, whether this algorithm setting load current becomes
Part, and when detection electric current changes, algorithm starts to restart, and re-searches for frequency corresponding to the maximum of system efficiency of transmission
Rate.
Brief description of the drawings
Fig. 1 particle swarm optimization algorithm flow charts of the present invention；
Fig. 2 is general particle cluster algorithm optimizing result analogous diagram；
Fig. 3 is particle swarm optimization algorithm optimizing result analogous diagram of the present invention；
Fig. 4 is that population scale increases reduction figure with iterations.
Specific implementation method
Particular content of the present utility model is described in detail with reference to accompanying drawing.The present invention passes primarily directed to magnetic coupling radio energy
Defeated system, with improved Particle Swarm Algorithm, reduce particle scale, algorithm can be quickly found out efficiency maximum point and its phase
Answer frequency.Illustrate below by way of specific instantiation and emulated with Matlab.Particle cluster algorithm flow is see Fig. 1, electronic vapour
The maximal efficiency tracking of car magnetic coupling wireless charging system, it comprises the following steps：
(1), initialization algorithm, including setting particle populations dimension D=1, maximum iteration MaxNum=200, simultaneously
Limit particle maximal rate vmax, initialization inertia weight w；
(2), directly set population maximum-norm Nmax as 30 and population smallest size Nmin be 2, random initializtion
The speed v of particle and the position of particle.Primary group scale is set as maximum-norm Nmax=30, initialization iterations t
=1, at present, population scale is configured without unified rule, is set generally according to optimizing object and personal experience.This
Algorithm only needs directly to set population maximum-norm as Nmax=30, can solve resonant mode electric energy dispensing device improving efficiency
Various situations.Smallest size is set in algorithm, population scale is gradually subtracted with the increase of iterations by maximum-norm Nmax
It is small to arrive smallest size Nmin, Nmin=2 in this algorithm；
(3), using fitness functionCalculate the fitness function value of each particle of current population
fi, fiThe fitness function value of i-th of particle is represented, wherein
The π f of ω=2r, frCurrent excitations frequency, ω are the angular frequency of excitation power supply, and M is to launch the mutual inductance between receiving coil, L1, L2
For transmitting coil and receiving coil inductance, C1, C2For electric capacity, RsFor the internal resistance of source, RLFor load resistance, R1, R2For electricity in loop
Resistance.This algorithm is first by current excitations frequency frAnd equation groupDerive transmitting and receiving coil
Between mutual inductance M,Then fitness function is derived again, this
The fitness function that algorithm uses is the function of efficiency and mutual inductance M.Shown equation group can be according to basic circuit theorem to whole system
System carries out analytical derivation and come out.WhereinRepresent coil L1Voltage,For input current,Load current, what this algorithm used
Fitness function is the function of efficiency and mutual inductance M, so when the change of the distance between two coils, when causing M also to change, is adapted to
Degree function can also change, and at this moment can obtain M according to current voltage driving frequency and equation group, further determine that system is worked as
Fitness function under front distance；
(4) f, is usedi-bestThe adaptive optimal control degree functional value that i-th of particle searches when the t times iteration is represented, is used
fi-gbestRepresent when the t times iteration, the adaptive optimal control degree functional value that all particles search.Algorithm start iteration it
Before, set fi-best=0, fi-gbest=0, the particle fitness function value f that will be obtained in step (3)iWith individual extreme value fi-bestAnd
Global extremum fi-gbestCompare, if fi≤fi-best, then fi-best=fi, pi=xi；piRepresent that fitness function value is
fi-bestParticle position, xiBe be f to fitness function valueiThe position of particle, if fi≤fi-gbest, then fi-gbest=
fi, pg=xi；pgIt is that global optimum is f in particle populationsi-gbestParticle position；
(5), by formula Npresent=N max- (N max-N min) * t/ (MaxNum) update population scale, wherein
Npresent is the current scale of population, and Nmax is maximum population scale, and MaxNum is maximum iteration, and t changes to be current
Generation number, by formulaAnd formula
Speed and the position of each particle are updated, then makes iterations t=t+1, turns to step (6), wherein vi t+1Represent t+1 times repeatedly
The speed of i-th of particle of generation, vi tRepresent the speed of current the t times iteration, i-th of particle, c1And c2Studying factors are represented, this
If c1=2, c2=2, rand represent the random number between [01].piExpression fitness function value is fi-bestParticle position, pg
It is that global optimum is f in particle populationsi-gbestParticle position, xi t+1Represent i-th of particle position of t+1 iteration, xi tGeneration
I-th of the iteration particle of table the t times current location, w represent inertia weight.It is 30 to initially set up population scale, be can solve the problem that existing
There is the situation that population scale chooses deficiency and causes algorithm to miss global optimum in technology, with the operation of algorithm, particle
Influence of the setting of scale to algorithmic statement is increasing, the side being gradually reduced using particle scale as iterations increases
Formula, population scale are simplified, and subtract redundancy particle so that particle cluster algorithm accelerates convergence in the search later stage, improves
The speed of service of algorithm；
(6), according to formulaCalculate particle fitness function value variance it
With favgFor the average value of all particles fitness function value, wherein if (fi-favg)>1, then a=max (fi-favg), it is no
Then, a=1.Judge variance whether be equal to 0 or algorithm whether reach maximum iteration, if it is not, then turn to step (3), such as
Fruit is then to turn to step (7)；
(7) the global optimum p searched, is exportedg, pgIt is that global optimum is f in particle populationsi-gbestParticle position
Put, that is, frequency values corresponding to the optimal value searched；
(8), load current i is detected with current sensor2Peak value, if Δ for setting maximum current peak fluctuate model
Enclose, i2maxFor the load current peak detected, i2max(k) it is k-th of current cycle current peak of load, i2max(k+1) it is
The current peak of+1 current cycle of kth of load, judge | i2max(k+1)|-|i2max(k)|>Whether Δ is set up, if it is determined that
As a result it is yes, then turns to step (1), algorithm is restarted；If it is judged that it is no, algorithm branches step (7).
In order to the advantage for understanding this algorithm that will be apparent that, general particle cluster algorithm is given in figs. 2 and 3 respectively
With particle swarm optimization algorithm optimizing result analogous diagram of the present invention, Fig. 4 is that population scale increases reduction figure with iterations.
The general particle cluster algorithm optimizing result figures of Fig. 2, curve is magnetic coupling radio energy transmission system efficiency and frequency in figure
The functional image of rate, five-pointed star is the optimal value that is searched in figure.Wherein 10.265 seconds algorithm used times, institute's search efficiency are maximum
It is worth for 0.70011, the Frequency point corresponding to maximum efficiency is 13544961.6816Hz.
Fig. 3 is particle swarm optimization algorithm optimizing result figure of the present invention, and its parameter setting is identical with general particle cluster algorithm, by
As a result as can be seen that in the case of low optimization accuracy identical, this particle swarm optimization algorithm spent time reduces than general algorithm
40%, there is certain advantage.
Fig. 4 represents the process that this algorithm population scale is gradually reduced with iterations increase.
Embodiment above describes the general principle of the present invention, main features and advantages, the technical staff of the industry should
Understand, the present invention is not limited to the above embodiments, the original for simply illustrating the present invention described in above-described embodiment and specification
Reason, under the scope for not departing from the principle of the invention, various changes and modifications of the present invention are possible, and these changes and improvements are each fallen within
In the scope of protection of the invention.
Claims (1)
1. the maximal efficiency tracking of electric automobile magnetic coupling wireless charging system, it is characterised in that：General population is calculated
Population scale in method is separately set, respectively maximum population scale Nmax=30 and smallest particles group scale Nmin=2,
Population scale is gradually reduced as iterations increases, and its specific implementation step is：
(1), initialization algorithm, including setting particle populations dimension D, maximum iteration MaxNum, while limit particle maximum
Speed vmax, initialization inertia weight w；
(2), directly set population maximum-norm Nmax as 30 and population smallest size Nmin be 2, random initializtion particle
Speed v and particle position, set primary group scale as maximum-norm Nmax=30, initialization iterations t=1；
(3), using fitness functionCalculate the fitness function value f of each particle of current populationi, fi
The fitness function value of i-th of particle is represented, wherein
The π f of ω=2r, frFor current excitations frequency, ω is the angular frequency of excitation power supply, and M is to launch the mutual inductance between receiving coil, L1,
L2For transmitting coil and receiving coil inductance, C1, C2For electric capacity, RsFor the internal resistance of source, RLFor load resistance, R1, R2For in loop
Resistance；
(4) f, is usedi-bestThe adaptive optimal control degree functional value that i-th of particle searches when the t times iteration is represented, uses fi-gbest
Represent when the t times iteration, adaptive optimal control degree functional value that all particles search, particle cluster algorithm start iteration it
Before, set fi-best=0, fi-gbest=0, the particle fitness function value f that will be obtained in step (3)iWith individual extreme value fi-bestAnd
Global extremum fi-gbestCompare, if fi≤fi-best, then fi-best=fi, pi=xi, piRepresent that fitness function value is
fi-bestParticle position, xiBe be f to fitness function valueiThe position of particle, if fi≤fi-gbest, then fi-gbest=
fi, pG=xi, pgIt is that global optimum is f in particle populationsi-gbestParticle position；
(5), by formula Npresent=Nmax- (Nmax-Nmin) * t/ (MaxNum) update population scale, and wherein Npresent is
The current scale of population, Nmax are maximum population scale, and Nmin is smallest particles group's scale, and MaxNum is greatest iteration time
Number, t is current iteration number, by formulaWith
FormulaSpeed and the position of each particle are updated, then makes iterations t=t+1, turns to step (6), its
InThe speed of i-th of particle of t+1 iteration is represented,Represent the speed of current the t times iteration, i-th of particle, c1And c2Generation
The table learning factor, rand represent the random number between [0,1], piExpression fitness function value is fi-bestParticle position, pgIt is
Global optimum is f in particle populationsi-gbestParticle position,I-th of particle position of t+1 iteration is represented,Represent
The t times iteration, i-th of particle current location, w represent inertia weight；
(6), according to formulaCalculate the variance sum of particle fitness function value, favg
For the average value of all particles fitness function value, wherein if (fi-favg)>1, then a=max (fi-favg), otherwise, a=
1, judge variance whether be equal to 0 or particle cluster algorithm whether reach maximum iteration, if it is not, then turn to step (3), such as
Fruit is then to turn to step (7)；
(7) the global optimum p searched, is exportedg, pgIt is that global optimum is f in particle populationsi-gbestParticle position, i.e.,
Frequency values corresponding to the optimal value searched；
(8), load current i is detected with current sensor2Peak value, if Δ for setting maximum current peak fluctuation range, i2max
For the load current peak detected, i2max(k) it is k-th of current cycle current peak of load, i2max(k+1) it is load
The current peak of+1 current cycle of kth, judge | i2max(k+1)|-|i2max(k)|>Whether Δ is set up, if it is judged that being
It is then to turn to step (1), algorithm is restarted, if it is judged that being no, algorithm branches step (7).
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| DE102017214741A1 (en) * | 2017-02-10 | 2018-08-16 | Robert Bosch Gmbh | A method for wireless energy transmission from an energy end device to a consumer and wireless energy end device for performing the method |
| CN107315903B (en) * | 2017-05-23 | 2020-05-08 | 浙江大学 | Intelligent electric field analysis system |
| CN107038323B (en) * | 2017-06-05 | 2024-01-12 | 江南大学 | Magnetic coupling structure optimization method for wireless charging system of electric automobile |
| CN114030386A (en) * | 2021-11-30 | 2022-02-11 | 国网浙江杭州市萧山区供电有限公司 | Electric vehicle charging control method based on user charging selection |
| CN116151053B (en) * | 2023-04-24 | 2023-07-07 | 南京信息工程大学 | Optimization method based on two-layer particle swarm algorithm for EVs wireless charging |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8118706B2 (en) * | 2008-06-30 | 2012-02-21 | Caterpillar Inc. | Machine having a multiple-ratio transmission |
| CN104300699A (en) * | 2014-11-07 | 2015-01-21 | 天津工业大学 | Magnetic coupling resonance type wireless power transmission self-adaptive impedance matching system |
| CN104810935A (en) * | 2015-05-12 | 2015-07-29 | 南京信息工程大学 | Resonant coupling type wireless power multi-load transmission method |
-
2015
- 2015-09-06 CN CN201510559048.0A patent/CN105184361B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8118706B2 (en) * | 2008-06-30 | 2012-02-21 | Caterpillar Inc. | Machine having a multiple-ratio transmission |
| CN104300699A (en) * | 2014-11-07 | 2015-01-21 | 天津工业大学 | Magnetic coupling resonance type wireless power transmission self-adaptive impedance matching system |
| CN104810935A (en) * | 2015-05-12 | 2015-07-29 | 南京信息工程大学 | Resonant coupling type wireless power multi-load transmission method |
Non-Patent Citations (2)
| Title |
|---|
| Multi-Objectove Particle Swarm Optimization Applied to the Design of Wireless Power Transfer Systems;Nazmul Hasan etal.;《2015 IEEE Wireless Power Transfer Conference》;20150702;第1-4页 * |
| 基于频率控制的磁耦合共振式无线电力传输系统传输效率优化控制;谭林林等;《中国科学》;20110730;第913-919页 * |
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Effective date of registration: 20171228 Address after: 518000 Guangdong city of Shenzhen province Nanshan District Guangdong streets Nanshan Technology Park Business Center, block B 21 layer B083 Patentee after: Jicheng wireless (Shenzhen) Co., Ltd. Address before: 453007 Xinxiang East Road, Makino District, Henan, No. 46 Patentee before: Henan Normal University |