CN114662368A - Friction nanometer generator power supply management system optimization method based on genetic algorithm - Google Patents
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Abstract
The invention discloses a friction nano generator power supply management system optimization method based on a genetic algorithm, which is characterized in that corresponding characteristics are obtained by carrying out simulation on different TENG forms, and the characteristics are matched by utilizing two main indexes of a power curve and an equivalent capacitance interval, so that a simulation result and measured data have stronger consistency. And then, an NSGA-II multi-target non-dominated sorting genetic algorithm is utilized, multi-target optimization can be simultaneously carried out on the output efficiency and the matching coefficient, the optimized output condition of the same circuit under the condition of different parameter combinations is realized, and specific different structure optimization comparison can be carried out on the TENG special power supply device. In the whole optimization design process, the parameter selection of circuit components can be quickly realized, the design of a special power management system under the conditions of different forms of loads and different forms of TENG can be accelerated, and a certain support is provided for landing of more applications of the TENG.
Description
Technical Field
The invention belongs to the technical field of system design optimization, relates to a power management system of a friction nano generator, and particularly relates to a genetic algorithm-based power management system optimization method of the friction nano generator.
Background
The technology of the internet of things has become a non-negligible part in life, and the sensor is used as the tail end of the internet of things and an interconnection system, so that the sensor plays a very important role. The problem of how to provide working energy for a large number of sensors in different environments becomes one of the main reasons for limiting the further development of the internet of things technology.
A Triboelectric Nanogenerator (TENG) is an emerging energy supply device based on the combination of a Triboelectric effect and electrostatic induction, and has incomparable advantages. Friction nanometer generator TENG mainly installs in the abundant environment of mechanical energy, can carry out the energy collection of unlimited number of times for the sensor energy supply theoretically, and can carry out complementary use with traditional battery, and the single live time of very big extension sensor, the problem that network node needs long-term energy supply among the wireless sensor network is solved to a certain extent.
TENG is used in sensors in two ways: (1) TENG is used as an energy collection module; (2) TENG acts as a Self-powered (Self-powered) sensor that does not require power. Whether it is used as an energy harvesting module or as a self-driven sensor module, at least two parts of TENG itself and its peripheral circuitry are required. In the prior art, the research on TENG and peripheral circuits thereof is still focused on a simple-structure, simple-motion TENG and a circuit mainly based on resistance-capacitance load, and the research mode mainly utilizes an analytical model and a circuit theory for analysis. However, in practical applications, TENG structure, motion mode or circuit structure are often complex, and it is difficult to obtain a better analytical solution result when an analytical model is used for analysis. In addition, for TENG of different forms, the output is not constant, and different TENG forms often have great influence on corresponding output conditions, thereby affecting the design of the system.
The TENG special energy collecting device has the characteristics of large voltage and small current output, large influence of environment change on output frequency and the like, and the traditional power management circuit is poor in direct applicability to the device, so that the design of a special power management system needs to be carried out aiming at the specific structure, the specific motion state and the like of the TENG. There are also many groups that currently have dedicated power management circuits for TENG[1]Studies were conducted, including related studies that attempted to perform specific TENG morphological output analyses using finite element models. The design optimization is carried out on the TENG power management system by utilizing a computer simulation optimization technology, so that the trial and error cost can be greatly reduced, the design period of the whole TENG power management circuit is shortened, and the design efficiency is greatly improved.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a genetic algorithm-based friction nano generator power management system optimization method, which is used for determining a corresponding power management circuit aiming at the selected TENG time sequence output, and optimally designing the determined power management circuit component parameters by adopting an NSGA-II genetic algorithm so as to avoid the output deviation caused by a power management system.
The friction nanometer generator power supply management system optimization method based on the genetic algorithm specifically comprises the following steps:
step one, finite element model simulation and output matching
The basic characterization parameters of the TENG comprise output open-circuit voltage and equivalent capacitance, and basic output characterization of the TENG in various forms can be realized by obtaining the output open-circuit voltage and the equivalent capacitance in a basic motion state. The finite element model simulation comprises three steps of static model drawing, simplification and dynamic characteristic value output. And comparing the simulation output of the finite element model with the measured value to obtain the simulation output matched with the actual output.
And s1.1, drawing a two-dimensional and three-dimensional finite element static general simulation model according to the structure and material parameters of the TENG, and measuring the actual output power curve and the capacitance change of the TENG.
And s1.2, simplifying the model obtained in the step s1.1 according to the actual motion state of the TENG in the using process and the difference of the capacitance and the voltage output, respectively obtaining a capacitance model aiming at the capacitance output and a voltage model aiming at the voltage output, and reducing the difficulty of simulation grid drawing and the difficulty of boundary condition setting.
And s1.3, simulating the movement process of TENG, taking the pure open-circuit voltage value in the voltage model as output open-circuit voltage, and taking the inter-electrode capacitance value in the capacitance model as output capacitance. And obtaining a complete multi-period equivalent capacitor output and voltage output time-varying sequence through interpolation and replication.
s1.4, drawing a power output curve and a capacitance output interval of the TENG finite element model according to the output time-varying sequence obtained in s1.3, comparing the power output curve and the capacitance output interval with the actually-measured power output curve and the capacitance output interval obtained in s1.1, returning to s1.2 when the simulation output is not matched with the actually-measured data, adjusting parameters in the capacitance model and the voltage model, repeating s1.3 and s1.4 until the output is matched, and storing a TENG representation time sequence output result at the moment.
Preferably, in s1.3, the movement process of TENG is simulated by means of deformation geometry or parametric scanning.
Preferably, in s1.4, the output change of the capacitance model is adjusted to match the measured capacitance magnitude change, and then the parameters affected by the environment in the model are adjusted until the output power curve of the model matches the measured output power curve.
Step two, power management system optimization design
Inputting structural parameters and material parameters of TENG and element parameters in the special power management circuit into an NSGA-II genetic algorithm, and obtaining the special power management circuit meeting design requirements and the element parameters thereof through iteration by taking high output efficiency and low matching points as optimization targets.
Preferably, the structural and material parameters of the TENG include electrode rotor speed, electrode effective area and friction material dielectric constant, thickness.
And s2.1, inputting the structural parameters and the material parameters of TENG and the element parameters in the special power management circuit into NSGA-II together for population initialization. Wherein the element parameters of the power management circuit are set to variable parameters.
And s2.2, obtaining a capacitance voltage time sequence output by the TENG finite element model through a finite element simulation and input matching method in the step one, inputting the capacitance voltage time sequence and element parameters set in the step 2.1 into the special power management circuit model, and simulating to obtain the output conditions of the load voltage and the current.
And s2.3, calculating the output efficiency and the matching coefficient of the special power management circuit under different element parameters, and completing the individual fitness calculation of the NSGA-II genetic algorithm. And then setting two optimization targets of high output efficiency and low matching coefficient, carrying out screening, copying, crossing and mutation operations on different parameter combinations by using an NSGA-II genetic algorithm to obtain a new parameter combination, outputting the parameter combination after sorting screening and N times of iterative optimization, and selecting according to actual use requirements to obtain the optimized special power supply management circuit.
Preferably, the dedicated power management circuit is a thyristor capacitor secondary charging type power management circuit.
Preferably, during each iterative optimization, 8 different sets of parameter combinations are generated.
Preferably, the number of iterations is set to 200.
The invention has the following beneficial effects:
a method for optimizing the design of a special power management system circuit for a friction nano generator based on a computer simulation technology is provided. Corresponding characteristics are obtained by carrying out simulation on different TENG forms, and characteristics are matched by using two main indexes of a power curve and an equivalent capacitance interval, so that a simulation result has stronger consistency with actually measured data. And then, an NSGA-II multi-target non-dominated sorting genetic algorithm is utilized, multi-target optimization can be simultaneously carried out on the output efficiency and the matching coefficient, the optimized output condition of the same circuit under the condition of different parameter combinations is realized, and specific different structure optimization comparison can be carried out on the TENG special power supply device. In the whole optimization design process, the parameter selection of circuit components can be quickly realized, the design of a special power management system under the conditions of different forms of loads and different forms of TENG can be accelerated, and a certain support is provided for landing of more applications of the TENG.
Drawings
FIG. 1 is a flow chart of finite element model simulation and output matching;
FIG. 2 is a flow chart of power management system optimization design;
FIG. 3 is a schematic diagram of a dedicated power management circuit used in an embodiment;
FIG. 4 is a schematic structural diagram of a simplified capacitor model in an embodiment;
FIG. 5 is a schematic structural diagram of a simplified voltage model in an embodiment;
FIG. 6 is a diagram of a simulation optimized single circuit parameter combination group arrangement in an embodiment.
Detailed Description
The invention is further explained below with reference to the drawings;
the friction nanometer generator power supply management system optimization method based on the genetic algorithm specifically comprises the following steps:
step one, finite element model simulation and output matching
The premise of the optimization design based on the computer simulation technology is that the simulation result is matched with the measured data, basic characterization parameters of the TENG comprise output open-circuit voltage and the size of an equivalent capacitor, and basic output characterization of the TENG in various forms can be achieved by obtaining the basic output open-circuit voltage and the size of the equivalent capacitor. As shown in FIG. 1, the finite element model simulation comprises three steps of static model drawing, simplification and dynamic characteristic value output.
And obtaining a time variable group file of the equivalent capacitance and the open-circuit voltage through finite element simulation. Firstly, the capacitance change of TENG is subjected to actual measurement and simulation, and the capacitance output change interval during simulation is ensured to be basically consistent with the capacitance output obtained through actual measurement. And then comparing the power curve graph output by simulation with a power curve graph obtained by actual test of an oscilloscope, and continuously adjusting parameters such as surface charge density and the like which are greatly influenced by the environment to obtain more consistent results, wherein the obtained capacitance and voltage time-varying array file can be considered to completely represent the actual TENG output state, and deriving a time-varying sequence file of CTENG and Voc.
Step two, power management system optimization design
As shown in fig. 2, the structural parameters and material parameters of TENG and the element parameters in the dedicated power management circuit are input into the NSGA-ii genetic algorithm, and the dedicated power management circuit and the element parameters thereof satisfying the design requirements are obtained through iteration with high output efficiency and low matching point as optimization targets. The method not only can realize transverse comparison of the TENG special power management circuit with the same function, but also can realize longitudinal comparison of a single circuit subjected to parameter optimization after genetic algorithm iteration, and finally obtains the selection of the component parameters of the optimal circuit with a given optimization target.
And s2.1, inputting the structural parameters and the material parameters of TENG and the element parameters in the special power management circuit into NSGA-II together for population initialization. Wherein the element parameters of the power management circuit are set to variable parameters. In order to realize a dc output with higher efficiency, the power management circuit structure used in this embodiment is as shown in fig. 3, and is a thyristor capacitor two-stage charging type power management circuit with better charging effect[2]And the part of the thyristor is used for controlling the charging energy of the multi-stage capacitor, so that direct current energy supply is performed for the subsequent load R1. The Voc and CTENG on the left represent matched TENG equivalent inputs, and the parameters to be optimized in the circuit include Cin, Cout, D5, D6, L1 and the values of the SCR thyristors.
And s2.2, obtaining a capacitance voltage time sequence output by the TENG finite element model through a finite element simulation and input matching method in the step one, inputting the capacitance voltage time sequence and element parameters set in the step 2.1 into the special power management circuit model, and simulating to obtain the output conditions of the load voltage and the current.
In the embodiment, a rotary TENG open-circuit voltage finite element simulation model is selected, specifically, a friction material of TENG is FEP, and the turntable is a stator single-side double electrode manufactured by utilizing a PCB manufacturing process. Because the simulation forms of the capacitance and the voltage are different, the thicknesses of the brass blades of the rotor and the thicknesses of the brass double electrodes of the stator are simplified to different degrees in the model simplification process, and the simplified capacitance model and the simplified voltage model are respectively shown in fig. 4 and 5. The simplified model can reduce the difficulty of simulation grid drawing and the difficulty of boundary condition setting.
And s2.3, calculating the output efficiency and the matching coefficient of the special power management circuit under different element parameters, and completing the individual fitness calculation of the NSGA-II genetic algorithm. The higher output efficiency and the reduced matching point, which is expressed by a matching coefficient, i.e., a reduction ratio of the best matching impedance, are selected as two optimization objectives. Screening, copying, crossing and mutating different parameter combinations by using an NSGA-II genetic algorithm to obtain new parameter combinations, sequencing, screening and repeating the iteration process for 200 times, wherein 8 different parameter combinations are generated in each iteration. After N iterations, the algorithm converges, and the output optimized parameter combination and the randomly generated parameter combination pair are as shown in fig. 6, where the star-shaped individual group represents the randomly generated individual output state, and the triangle group represents the optimized individual group output state, it can be seen that the optimized group is relatively close to the coordinate axis, and the basic requirement of at least one of the two indexes of high efficiency or low matching coefficient can be ensured. And the optimization results of each generation are recorded, so that the specific difference of two factors of the efficiency and the output matching point reduction of different circuit parameters in different iteration states can be compared, the circuit efficiency and the output matching point reduction effect of different parameter circuits can be compared visually in the optimal state, and the selection of the actual power management circuits special for the TENG is greatly benefited.
In addition to the above, the method can also perform simulation characterization for more complicated RLC loads, obtain different design modes of preferred power supplies and management structures for different load characterization results, and perform homomorphic TENG parameter optimization for a given specific power supply management circuit of the same type of TENG under the condition of completely characterizing different structure parameters.
[1]Xc A,Wei T B,Yu S A,et al.Power management and effective energy storage of pulsed output from triboelectric nanogenerator-ScienceDirect[J].Nano Energy,2019,61:517-532.
[2]Harmon W,Bamgboje D,Guo H,et al.Self-driven Power Management System for Triboelectric Nanogenerators[J].Nano Energy,2020,71(8):104642。
Claims (8)
1. A friction nano generator power supply management system optimization method based on genetic algorithm is characterized in that: the method specifically comprises the following steps:
step one, finite element model simulation and output matching
Drawing a static general simulation model according to the structure and material parameters of the TENG, and measuring the actual output power and capacitance of the TENG in different motion states; simplifying the static general simulation model to obtain a capacitance model and a voltage model, continuously adjusting parameters of the capacitance model and parameters of the voltage model until the output power of the model is consistent with the measured actual output power, storing a TENG representation time sequence output result, and obtaining a complete multi-period equivalent capacitance output and voltage output time-varying sequence through interpolation and replication;
step two, power management system optimization design
s2.1, inputting the structural parameters and the material parameters of TENG and the element parameters in the special power management circuit into NSGA-II together for population initialization; wherein the element parameter of the power management circuit is set to a variable parameter;
s2.2, obtaining a capacitance voltage time sequence output by the TENG finite element model through the finite element simulation and input matching method in the first step, inputting the capacitance voltage time sequence and element parameters set in the s2.1 into the special power management circuit model, and simulating to obtain the output condition of the load voltage and the current;
s2.3, calculating the output efficiency and the matching coefficient of the special power management circuit under different element parameters, and completing the individual fitness calculation of the NSGA-II genetic algorithm; and then setting two optimization targets of high output efficiency and low matching coefficient, carrying out screening, copying, crossing and mutation operations on different parameter combinations by using an NSGA-II genetic algorithm to obtain a new parameter combination, and outputting the parameter combination after sorting screening and N times of iterative optimization to obtain the optimized special power management circuit.
2. The friction nano-generator power management system optimization method based on genetic algorithm as claimed in claim 1, wherein: the first step specifically comprises:
s1.1, drawing a two-dimensional and three-dimensional finite element static universal simulation model according to the structure and material parameters of the TENG, and measuring the actual output power curve of the TENG and the change of the capacitance;
s1.2, simplifying the model obtained in the step s1.1 according to the actual motion state of the TENG in the using process and the difference of capacitance and voltage output, and respectively obtaining a capacitance model aiming at the capacitance output and a voltage model aiming at the voltage output;
s1.3, simulating a TENG movement process, taking a pure open-circuit voltage value in a voltage model as an output open-circuit voltage, and taking an inter-electrode capacitance value in a capacitance model as an output capacitance; obtaining a complete multi-period equivalent capacitance output and voltage output time-varying sequence through interpolation and replication;
and s1.4, drawing a power output curve of the TENG finite element model according to the output time-varying sequence obtained in the step s1.3, comparing the power output curve with the actually-measured power output curve obtained in the step s1.1, adjusting parameters in the capacitance model and the voltage model until the parameters are matched when the simulation output is not matched with the actually-measured data, and storing the TENG representation time sequence output result at the moment.
3. The friction nano-generator power management system optimization method based on genetic algorithm as claimed in claim 2, wherein: in s1.3, the course of movement of TENG is simulated by means of deformation geometry or parametric scanning.
4. The friction nanogenerator power management system optimization method based on genetic algorithm of claim 2, wherein the method comprises the following steps: in s1.4, the output change of the capacitance model is adjusted to be consistent with the actually measured capacitance change, and then parameters influenced by the environment in the model are adjusted until the output power curve of the model is matched with the actually measured output power curve.
5. The friction nano-generator power management system optimization method based on genetic algorithm as claimed in claim 1, wherein: the structural parameters and material parameters of TENG include electrode rotor speed, electrode effective area, and friction material dielectric constant and thickness.
6. The friction nano-generator power management system optimization method based on genetic algorithm as claimed in claim 1, wherein: the special power management circuit is a thyristor capacitor two-stage charging type power management circuit.
7. The friction nano-generator power management system optimization method based on genetic algorithm as claimed in claim 1, wherein: during each iterative optimization, 8 different sets of parameter combinations were generated.
8. The friction nano-generator power management system optimization method based on genetic algorithm as claimed in claim 1 or 7, wherein: the iterative optimization times are set to 200.
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