CN105160395B - The improving efficiency inertial change particle swarm optimization of resonant mode electric energy dispensing device - Google Patents
The improving efficiency inertial change particle swarm optimization of resonant mode electric energy dispensing device Download PDFInfo
- Publication number
- CN105160395B CN105160395B CN201510561283.1A CN201510561283A CN105160395B CN 105160395 B CN105160395 B CN 105160395B CN 201510561283 A CN201510561283 A CN 201510561283A CN 105160395 B CN105160395 B CN 105160395B
- Authority
- CN
- China
- Prior art keywords
- particle
- algorithm
- scale
- current
- population
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
Abstract
The invention discloses a kind of improving efficiency inertial change particle swarm optimization of resonant mode electric energy dispensing device, belong to the finding method field of system efficiency of transmission in magnetic coupling radio energy transmission system.Technical scheme main points are：Population scale in general particle cluster algorithm is separately set, respectively maximum population scaleNmax=30 and smallest particles group's scaleNmin=2, population scale general trend is gradually reduced as iterations increases along the mode of inertial curve, is subtracted redundancy particle, is simplified algorithm, accelerates algorithm late convergence.It is evidence-based that the particle swarm optimization algorithm of the present invention not only causes particle scale to choose, and reduces in search later stage, quickening convergence rate, algorithm search time.
Description
Technical field
The invention belongs to magnetic coupling wireless power transmission technical field, more particularly to magnetic coupling radio energy transmission system
A kind of finding method field of middle system efficiency of transmission, and in particular to improving efficiency inertial change of resonant mode electric energy dispensing device
Particle swarm optimization.
Background technology
Electric energy transmission is always the major issue of academia's concern, and non-contact power supply technique is the heat studied in recent years
Point.Wireless power transmission mode mainly has 3 kinds：The first is induction；Second is microwave radio formula；The third magnetic coupling
Close resonant, three kinds of modes respectively have its advantage.It is real that magnetic coupling resonance formula electric energy transmission basic thought is based on magnetic coupling resonance principle
Existing, when power supply driving frequency reaches certain value, whole system is in resonant condition, can now realize wireless high-effect energy
Transmission.Magnetic coupling resonance formula wireless power transmission mode has long transmission distance than first way, compared with the second way
The advantages of transimission power is big, had obtained great concern in recent years, but the technology is also in the starting stage, especially to its efficiency of transmission
What the research of related fields was a lack of always.
Magnetic coupling radio energy transmission system efficiency is different, its efficiency-frequency at different electrical power driving frequency point
Curve is an one-dimensional functions.For a system, when the distance between dispatch coil is fixed, its efficiency of transmission function is with sharp
One or two extreme points occur in the change for encouraging frequency, and this allows for general algorithm (hill-climbing algorithm, simulated annealing
Deng) be easily trapped into local optimum and miss global optimum.Particle cluster algorithm can provide a kind of side for solving this problem
Method, although general particle cluster algorithm is more advantageous when solving general function optimization problem, it is directed to magnetic coupling
Resonant radio energy goes out for defeated system, and when the situation of system one extreme point of appearance, algorithm occurs in the search later stage
Of short duration stagnation behavior, it is impossible to Fast Convergent, it is longer to expend the time；And for algorithm in itself for, population scale set mistake
Conference causes algorithm to carry out unnecessary calculating, and less scale then causes algorithm directly to miss global optimum, or even looks for not
To extreme point, general population scale is located between 20-40, but the accurate selection of its population scale is all root all the time
According to individual when solving problem ceaselessly hit and miss experiment come out, very blindly.For case above, urgent need finds one kind and is directed to
The optimization method of feature of magnetic coupling radio energy transmission system itself finds problem to solve system effectiveness.Therefore, how to be directed to
System maximal efficiency and phase can be quickly found by designing a kind of optimization method in the characteristics of magnetic coupling wireless power supply system
The Frequency point answered is necessary.
The content of the invention
Present invention solves the technical problem that it there is provided a kind of improving efficiency inertial change of resonant mode electric energy dispensing device
Particle swarm optimization, this method is taken population scale gradually to be reduced with iterations increase in a manner of similar inertial curve,
Mainly solve conventional particle colony optimization algorithm in magnetic coupling radio energy transmission system occurs of short duration stop in searching process
The problem of stagnant phenomenon and algorithm population scale itself are chosen, makes particle cluster algorithm restrain rapidly, is quickly found out system
The optimal value of efficiency.
The present invention is to solve above-mentioned technical problem to adopt the following technical scheme that, the improving efficiency of resonant mode electric energy dispensing device
Inertial change particle swarm optimization, 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 with iterations increase and along
The mode of inertial curve is gradually reduced, and 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 f of each particle of current populationi,
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=Nmax* (e-(t/MaxNum))nPopulation scale is updated, wherein Npresent is particle
The current scale of group, Nmax are maximum population scale, and MaxNum is maximum iteration, and t is current iteration number, and n is control
The power exponent of population scale changing rule, the speed degree of population scale change is adjusted by parameter n, by formulaAnd formulaUpdate each grain
The speed of son and position, iterations t=t+1 is then made, turn to step (6), wherein vi t+1Represent t+1 iteration i-th
The speed of son, vi tRepresent the speed of current the t times iteration, i-th of particle, c1And c2Represent Studying factors, rand represent [01] it
Between random number, piExpression fitness function value is fi-bestParticle position, pgIt is that global optimum is in particle populations
fi-gbestParticle position, xi t+1Represent i-th of particle position of t+1 iteration, xi tIt is current to represent the t times iteration, i-th of particle
Position, 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 that will be searchedgAs output, pgIt is that global optimum is f in particle populationsi-gbest's
Particle position, 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 load current peak, i2max(k) it is k-th of current cycle current peak of load, i2max(k+1) it is the of load
The current peak of k+1 current cycle, judge | i2max(k+1)|-|i2max(k)|>Whether Δ is set up, if it is judged that be it is yes,
Step (1) is then turned to, particle cluster algorithm is restarted, if it is judged that being no, particle cluster algorithm turns to step (7).
The fitness function that particle cluster algorithm of the present invention uses changes with the change of mutual inductance between transmitting and receiving coil
Become, only first determine the mutual inductance between transmitting and receiving coil so that fitness function is changed into only relevant with driving frequency
Function, then scanned for particle cluster algorithm, wherein fitness function is the function of efficiency and frequency.This Particle Swarm Optimization
Method in actual applications by detecting initial excitation system when driving frequency used, and then calculate the mutual inductance of two coils.This hair
Bright take population scale is gradually reduced with iterations increase in a manner of similar inertial curve, mainly solves magnetic coupling
The phenomenon and the algorithm sheet of of short duration stagnation occurs in conventional particle group algorithm in searching process in radio energy transmission system
The problem of body population scale is chosen, makes particle cluster algorithm go to restrain, is quickly found out system effectiveness optimal value.This population is excellent
Change algorithm to set algorithm and restart condition, when detecting that algorithm is restarted, and re-searches for maximal efficiency when apart from load current changing
Value and its corresponding frequency.
Brief description of the drawings
Fig. 1 is the flow chart of particle swarm optimization algorithm of the present invention；
Fig. 2 is the result analogous diagram of general particle swarm optimization algorithm；
Fig. 3 is the result analogous diagram of particle swarm optimization algorithm of the present invention；
Fig. 4 is that population scale of the present invention increases reduction figure with iterations.
Specific implementation method
The particular content of the present invention is described in detail with reference to accompanying drawing.The present invention is primarily directed to magnetic coupling wireless power transmission system
System, with improved Particle Swarm Algorithm, reduce particle scale, algorithm can be quickly found out efficiency maximum point and its respective tones
Rate.Illustrate below by way of specific instantiation and emulated with Matlab.Optimization method flow is shown in Fig. 1, of the present invention
Technical scheme is：The improving efficiency inertial change particle swarm optimization of resonant mode electric energy dispensing device, is concretely comprised 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 f of each particle of current populationi,
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 receive
Mutual inductance M between coil,Then fitness letter is derived again
Number, the fitness function that this algorithm uses are the functions of efficiency and mutual inductance M.Shown equation group can be according to basic circuit theorem to whole
Individual system carries out analytical derivation and come out.WhereinRepresent coil L1Voltage,For input current,Load current, this algorithm
The fitness function of use is the function of efficiency and mutual inductance M, so when the change of the distance between two coils, causes M also to change
When, fitness function can also change, and at this moment can obtain M according to current voltage driving frequency and equation group, further really
Determine the fitness function under system current 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=Nmax* (e-(t/MaxNum))nPopulation scale is updated, wherein Npresent is population
Current scale, Nmax are maximum population scale, and MaxNum is maximum iteration, and t is current iteration number, and n is control grain
The power exponent of subgroup scale changing rule, population scale can adjust by parameter n and change speed degree, by many experiments, n
Can make when=2.3 population rule be gradually reduced in the way of similar to inertial curve, algorithm can Fast Convergent, reduce algorithm
Run time, there is larger advantage.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 i-th of particle rapidity of t+1 iteration, vi tRepresent current the t times iteration, i-th of particle rapidity, c1And c2Represent
This experiment of Studying factors c1=2, c2=2, rand represent the random number between [01].piExpression fitness function value is fi-best
Particle position, pgIt is that global optimum is f in particle populationsi-gbestParticle position, xi t+1Represent t+1 iteration i-th
Sub- position, xi tThe t times iteration, i-th of particle current location is represented, w represents inertia weight.This algorithm is searching particle cluster algorithm
No longer there is of short duration stagnation behavior in the rope later stage, and algorithm being capable of Fast Convergent；
(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 the analogous diagram of particle swarm optimization algorithm of the present invention, Fig. 4 is that population scale increases reduction figure with iterations.
The optimizing result figure of the general particle cluster algorithms of Fig. 2, curve is the efficiency of magnetic coupling radio energy transmission system in figure
With frequency function image, five-pointed star is the optimal value that is searched in figure, and the Frequency point corresponding to maximum efficiency is
13544961.6816Hz。
Fig. 3 is the optimizing result figure of particle swarm optimization algorithm of the present invention, and its parameter setting is identical with general particle cluster algorithm,
As can be seen that compared with general algorithm, in the case of its search result identical, 4.617000 seconds its algorithm used times, and it is general
Particle cluster algorithm is 10.265 seconds, and this particle swarm optimization algorithm consuming time reduces 55% than general particle cluster algorithm, saves
Time.
Fig. 4 is the process that population scale is gradually reduced with iterations increase, and it is that coast-down is bent that it, which reduces mode,
Line.
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 improving efficiency inertial change particle swarm optimization of resonant mode electric energy dispensing device, it is characterised in that：By general population
Population scale in algorithm is separately set, respectively maximum population scale Nmax=30 and smallest particles group's scale Nmin=
2, population scale is gradually reduced as iterations increases along the mode of inertial curve, 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,
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, 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=N max* (e-(t/MaxNum))nPopulation scale is updated, wherein Npresent is population
Current scale, Nmax are maximum population scale, and MaxNum is maximum iteration, and t is current iteration number, and n is control grain
The power exponent of subgroup scale changing rule, the speed degree of population scale change is adjusted by parameter n, by formulaAnd formulaUpdate each grain
The speed of son and position, iterations t=t+1 is then made, turn to step (6), whereinRepresent t+1 iteration i-th
The speed of son,Represent the speed of current the t times iteration, i-th of particle, c1And c2Studying factors are represented, rand represents [0,1]
Between random number, piExpression fitness function value is fi-bestParticle position, pgIt is that global optimum is in particle populations
fi-gbestParticle position,I-th of particle position of t+1 iteration is represented,It is current to represent the t times iteration, i-th of particle
Position, 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 that will be searchedgAs output, pgIt is that global optimum is f in particle populationsi-gbestParticle
Position, 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 fluctuation range, i2max
For load current peak, i2max(k) it is k-th of current cycle current peak of load, i2max(k+1) it is+1 electricity of kth of load
The current peak in cycle is flowed, is judged | i2max(k+1)|-|i2max(k)|>Whether Δ is set up, if it is judged that being yes, then turns to
Step (1), particle cluster algorithm is restarted, if it is judged that being no, particle cluster algorithm turns to step (7).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510561283.1A CN105160395B (en) | 2015-09-06 | 2015-09-06 | The improving efficiency inertial change particle swarm optimization of resonant mode electric energy dispensing device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510561283.1A CN105160395B (en) | 2015-09-06 | 2015-09-06 | The improving efficiency inertial change particle swarm optimization of resonant mode electric energy dispensing device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105160395A CN105160395A (en) | 2015-12-16 |
| CN105160395B true CN105160395B (en) | 2017-12-01 |
Family
ID=54801247
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510561283.1A Expired - Fee Related CN105160395B (en) | 2015-09-06 | 2015-09-06 | The improving efficiency inertial change particle swarm optimization of resonant mode electric energy dispensing device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105160395B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109347214A (en) * | 2018-07-16 | 2019-02-15 | 福州大学 | The searching method of multimodal extreme value in radio energy transmission system frequency modulation control |
Families Citing this family (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105387875B (en) * | 2015-12-24 | 2018-01-12 | 安徽工程大学 | A kind of improvement of method for planning path for mobile robot based on ant group algorithm |
| CN107862100A (en) * | 2016-09-21 | 2018-03-30 | 中国石油化工股份有限公司 | A kind of shot point method for correcting error based on particle cluster algorithm |
| CN108959729B (en) * | 2018-06-13 | 2022-05-06 | 西北工业大学 | Efficient and rapid power supply cluster construction method |
| CN110086350B (en) * | 2019-05-30 | 2021-01-15 | 南京邮电大学 | Isolated bidirectional DC-DC efficiency optimization method based on simulated annealing-hill climbing hybrid algorithm |
| CN110257835B (en) * | 2019-07-31 | 2020-04-28 | 承德前潮慧创科技有限公司 | Cathodic protection feed experiment box |
| CN115115224A (en) * | 2022-06-28 | 2022-09-27 | 中国矿业大学(北京) | Mining area vegetation carbon sink grading and change trend evaluation and analysis method |
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 CN201510561283.1A patent/CN105160395B/en not_active Expired - Fee Related
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 * |
| "基于改进粒子群算法的 ICPT 系统多约束多参数优化";左兰 等;《电工电能新技术》;20130430;第32卷(第2期);第76-81页 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109347214A (en) * | 2018-07-16 | 2019-02-15 | 福州大学 | The searching method of multimodal extreme value in radio energy transmission system frequency modulation control |
| CN109347214B (en) * | 2018-07-16 | 2021-12-28 | 福州大学 | Method for searching multi-peak extreme value in frequency modulation control of wireless power transmission system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105160395A (en) | 2015-12-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105160395B (en) | The improving efficiency inertial change particle swarm optimization of resonant mode electric energy dispensing device | |
| CN105184361B (en) | The maximal efficiency tracking of electric automobile magnetic coupling wireless charging system | |
| CN105140972B (en) | The frequency method for fast searching of high-transmission efficiency radio energy emission system | |
| CN105826997B (en) | A kind of closed loop control method for the charging of accumulator gamut | |
| CN104377839B (en) | The multiple feedback loop method of magnetic resonance coupling Wireless power transmission system | |
| CN104283393B (en) | Method for optimizing structure parameter of single-winding magnetic suspension switch reluctance machine | |
| CN103268065B (en) | The building method of axial mixed magnetic bearing ant group algorithm Fractional Order PID Controller | |
| CN109787531A (en) | A kind of switching magnetic-resistance hub motor forecast Control Algorithm | |
| CN102819652B (en) | Mechanical parameter optimization design method based on adaptive reverse differential evolution | |
| Nasir et al. | A novel hybrid spiral-dynamics bacterial-foraging algorithm for global optimization with application to control design | |
| CN103513574B (en) | The building method of axial mixed magnetic bearing fish-swarm algorithm Fractional Order PID Controller | |
| CN104362927A (en) | Asynchronous motor key state information tracking method based on improved particle swarm optimization | |
| Iordache et al. | On exact circuit analysis of frequency splitting and bifurcation phenomena in wireless power transfer systems | |
| CN104980069A (en) | Multipurpose optimization method for double closed-loop speed governing system of brushless DC motor | |
| CN108573300A (en) | Particle cluster algorithm feedback adaptive rate parameter optimization method and full-order state observer | |
| Ai et al. | Parameter estimation of fractional-order chaotic power system based on lens imaging learning strategy state transition algorithm | |
| CN105141016B (en) | Electric automobile wireless charging stake efficiency extreme point tracking during frequency bifurcated | |
| CN105184077B (en) | Cross short distance low-resonance electric energy transmission system improving efficiency population index method | |
| CN105978434B (en) | A kind of permanent magnet synchronous motor maximum torque per ampere control method and control system | |
| Lin et al. | Parameter identification of PMSM using immune clonal selection differential evolution algorithm | |
| CN114704936B (en) | Method and device for controlling direct current air conditioner and direct current air conditioner | |
| US20150349540A1 (en) | Apparatus and method for transmitting wireless power | |
| Wang et al. | Design of fractional order pi controller for permanent magnet synchronous motor | |
| CN104022716A (en) | Method for controlling maximum torque current ratio of excitation-variation synchronous motor through coefficient fitting | |
| CN109347214A (en) | The searching method of multimodal extreme value in radio energy transmission system frequency modulation control |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20190107 Address after: Room 2516, East Skirt Building A, 2nd Floor, Building 4, 3560 Qingsong Highway, Baihe Town, Qingpu District, Shanghai Patentee after: Zhongzhi Technology Co., Ltd. Address before: No. 46, Makino District, Makino District, Xinxiang, Henan Patentee before: Henan Normal University |
|
| TR01 | Transfer of patent right | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20171201 Termination date: 20180906 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |