CN108019982A - A kind of semiconductor thermoelectric refrigeration device drive control method - Google Patents
A kind of semiconductor thermoelectric refrigeration device drive control method Download PDFInfo
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- CN108019982A CN108019982A CN201711215313.9A CN201711215313A CN108019982A CN 108019982 A CN108019982 A CN 108019982A CN 201711215313 A CN201711215313 A CN 201711215313A CN 108019982 A CN108019982 A CN 108019982A
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F25—REFRIGERATION OR COOLING; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS; MANUFACTURE OR STORAGE OF ICE; LIQUEFACTION SOLIDIFICATION OF GASES
- F25B—REFRIGERATION MACHINES, PLANTS OR SYSTEMS; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS
- F25B21/00—Machines, plants or systems, using electric or magnetic effects
- F25B21/02—Machines, plants or systems, using electric or magnetic effects using Peltier effect; using Nernst-Ettinghausen effect
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F25—REFRIGERATION OR COOLING; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS; MANUFACTURE OR STORAGE OF ICE; LIQUEFACTION SOLIDIFICATION OF GASES
- F25B—REFRIGERATION MACHINES, PLANTS OR SYSTEMS; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS
- F25B49/00—Arrangement or mounting of control or safety devices
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F25—REFRIGERATION OR COOLING; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS; MANUFACTURE OR STORAGE OF ICE; LIQUEFACTION SOLIDIFICATION OF GASES
- F25B—REFRIGERATION MACHINES, PLANTS OR SYSTEMS; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS
- F25B2321/00—Details of machines, plants or systems, using electric or magnetic effects
- F25B2321/02—Details of machines, plants or systems, using electric or magnetic effects using Peltier effects; using Nernst-Ettinghausen effects
- F25B2321/021—Control thereof
Abstract
The invention discloses a kind of semiconductor thermoelectric refrigeration device drive control method, using PWM Regulation Control type of drive, utilize semiconductor thermoelectric refrigeration device cold end, hot-side temperature, voltage, electric current and the refrigeration target temperature parameter gathered in real time, divide the output duty cycle P values of method combination self learning type artificial intelligence fuzzy model control PWM, the driving voltage and control cryogenic temperature of adaptive control thermoelectric cooler using numerical value.The present invention uses adaptive PWM Regulation Control type of drive, by the real-time parameter numerical analysis that refrigerating efficiency is influenced on the cold end of semiconductor thermoelectric cooler, hot-side temperature, voltage, electric current etc., selection plays the optimal drive voltage of refrigerating efficiency, by testing the refrigerating efficiency of its cooling stages up to 28%, the refrigerating efficiency in temperature control stage reaches 30%.
Description
Technical field
The present invention relates to a kind of control method, and in particular to a kind of semiconductor thermoelectric refrigeration device drive control method, belongs to
Field of intelligent control technology.
Background technology
Semiconductor thermoelectric refrigeration device is supplied to using the power consumption of the refrigeration plant 80% of semiconductor thermoelectric refrigeration mode, though
The theoretical refrigerating efficiency of right semiconductor thermoelectric refrigeration device can reach 32%, but due to semiconductor thermoelectric device refrigerating efficiency and work
Make the factors such as voltage, electric current, cold junction temperature, hot-side temperature, ambient temperature time correlation, and current thermoelectric cooling is mainly adopted
With electric current Drive Control Technique, driving current size is adjusted by the detection to the target temperature that freezes, the Drive Control Technique is only
Control one object of temperature is concerned about, not to being related to the control of semiconductor thermoelectric device refrigerating efficiency parameter, causes actually to make
Its refrigerating efficiency is generally 20%~25% in, it is therefore desirable to is improved by improving the Drive Control Technique of thermoelectric cooler
Semiconductor thermoelectric refrigeration efficiency, reduces refrigeration plant power consumption.And do not have preferable drive control method at present.
The content of the invention
Present invention is primarily intended in order to solve, conventional semiconductors thermoelectric cooling Drive Control Technique refrigerating efficiency is low to ask
Topic, there is provided (maximum is flat for a kind of low refrigeration target temperature (- 20 DEG C of lowest refrigerating temperature) for adapting to different external environments and low-power consumption
Equal power consumption 65W) semiconductor thermoelectric refrigeration equipment Drive Control Technique.
What the present invention was realized in：
A kind of semiconductor thermoelectric refrigeration device drive control method, using PWM Regulation Control type of drive, utilizes real-time collection
Semiconductor thermoelectric refrigeration device cold end, hot-side temperature, voltage, electric current and refrigeration target temperature parameter, divide method knot using numerical value
Close the output duty cycle P values of self learning type artificial intelligence fuzzy model control PWM, the driving of adaptive control thermoelectric cooler
Voltage and control cryogenic temperature.
In order to illustrate technical scheme, the drive control principle of the present invention is described as follows below：
Semiconductor thermoelectric refrigeration device work first is divided into refrigeration and temperature control both of which, refrigeration mode operating mode mainly according to
Low temperature locker cavity temperature is reduced to target cryogenic temperature by the maximum cooling capacity of thermoelectric cooling rapidly, close to after refrigeration target
Into temperature control operating mode, low temperature locker temperature conditioning unit component calculates outer by the parameters such as the temperature that collects, voltage, electric current, study
Boundary's environment temperature, system thermal leak rate, cavity temperature variation tendency, Fast Convergent PWM control P values, adjust thermoelectric cooler
Driving voltage, shortens the stabilization time for reaching refrigeration target temperature.
More detailed description below technical scheme.
A kind of semiconductor thermoelectric refrigeration device drive control method, thermoelectric cooler cold end is measured using sensor for measuring temperature
Temperature, hot-side temperature and cryogenic temperature, and the temperature information of measurement is passed into Acquisition Circuit, Acquisition Circuit by low-pass filtering and
Operational amplifier circuit forms, and the analog signal of the temperature information for being transmitted to sensor for measuring temperature is nursed one's health, and by after conditioning
Signal pass to DSP, DSP carries out A/D conversions to analog signal, runs self learning type artificial intelligence fuzzy control model, control
System output adjusts output control parameter P values, while drives output voltage and thermoelectric cooler operating current to feed back to DSP, utilizes
Artificial intelligence fuzzy control model constantly regulate P values；PWM drive controls are by drive control power supply, low-pass filter circuit and driving
Circuit composition is protected, mainly realizes the control to voltage regulating module；Voltage regulating module is made of pressure-adjustable DC/DC, by inputting PWM tune
Save thermoelectric cooling driving voltage (4V~14V).
Further scheme is：
Self learning type artificial intelligence FUZZY ALGORITHMS FOR CONTROL discrete model is as follows：
E is the warm variable Rate factor in formula, by carrying out Data Matching acquisition with the warm variable Rate curve of knowledge base；UtTo work as
Preceding temperature control cycle controlling value, is a constant within the t periods；V(Th, Tc, Qi, It) electric to work in single temperature control cycle time
Pressure value, ThFor thermoelectric cooler hot-side temperature, TcFor thermoelectric cooler cold junction temperature, QiFor the system produced in temperature control cycle time
Shown in cold, its cool and heat ends temperature with thermoelectric cooler, operating current and refrigeration magnitude relation equation below 2 and 3；Δ U is U
It is worth adjustment amount, passes through Ut-1And U0Obtain, calculate in detail as shown in formula 4.
Tht, Tct, ItBe the test parameter of t periods, be respectively the t periods thermoelectric cooler hot-side temperature, thermoelectricity
Refrigerator cold junction temperature, real-time current；QiThe refrigeration total amount produced for the thermoelectric cooler of t periods, calculation formula are as follows：
In formula：Φi-- refrigerating capacity, J；
Vt-- real-time input voltage, V；
It-- real-time current, A；
Tht-- hot-side temperature, K；
Tct-- cold junction temperature, K；
nt-- Temperature Difference Ratio Th/Tc；
ΔTt-- hot and cold side temperature difference Tht-Tct, K；
H --- program setting fixed sample time, S.
U in formula0It is by matching acquisition with the warm variable Rate curve data of knowledge base to warm variable Rate in the h periods.Ut-1
For the controlling value of a cycle before current temperature control cycle t.
This self learning type artificial intelligence FUZZY ALGORITHMS FOR CONTROL calculates one by the thermoelectric cooling running parameter to gathering in real time
The refrigerating capacity that thermoelectric cooler produces in the section time, with a preceding Ut-1Value, with reference to the warm variable Rate curve of knowledge base, determines
Δ U, substantially reduce convergence time, improve thermoelectricity intelligent drives control efficiency and temperature-controlled precision.
The present invention has the beneficial effect that compared with prior art：
(1) present invention use adaptive PWM Regulation Control type of drive, by the cold end to semiconductor thermoelectric cooler,
Hot-side temperature, voltage, electric current etc. influence the real-time parameter numerical analysis of refrigerating efficiency, and selection plays the optimal drive of refrigerating efficiency
Voltage, by testing the refrigerating efficiency of its cooling stages up to 28%, the refrigerating efficiency in temperature control stage reaches 30%.
(2) present invention is controlled and driven using PWM and powered using independent current source, can completely isolated drive circuit and DSP electricity
Road, has higher Electro Magnetic Compatibility and functional reliability；
(3) present invention uses self learning type artificial intelligence fuzzy control technology, by the qualitative experience of people, logical reasoning ability
Combined with mnemonic learning, improve Control system resolution and robustness, shorten cryogenic temperature stabilization time, possess quick control
Temperature.
Brief description of the drawings
Fig. 1 is composition schematic diagram of the present invention；
Fig. 2 is drive control schematic diagram of the present invention；
Fig. 3 is self learning type artificial intelligence fuzzy control model figure of the present invention；
Fig. 4 is semiconductor thermoelectric refrigeration device cooling parameters test curve figure of the present invention.
Embodiment
The present invention will be further described below in conjunction with the accompanying drawings.
Present invention composition is as shown in Figure 1：Including sensor for measuring temperature, Acquisition Circuit, DSP, PWM drive control, pressure regulation
Module and thermoelectric cooler.Sensor for measuring temperature is mainly used for measuring thermoelectric cooler cold junction temperature, hot-side temperature and refrigeration
Temperature, using PT100 as temperature sensor；Acquisition Circuit mainly nurses one's health the analog signal of sensor, by low pass filtered
Ripple and operational amplifier circuit composition；DSP is used to change the A/D for gathering analog signal, runs self learning type artificial intelligence fuzzy control
Model adjusts output control parameter P values with DSP outer extension memories knowledge base, learning data model, control output is deposited in, at the same time
Driving output voltage and thermoelectric cooler operating current feed back to DSP, utilize artificial intelligence fuzzy control model constantly regulate P
Value；PWM drive controls mainly realize the control to voltage regulating module, are protected by drive control power supply, low-pass filter circuit and driving
Circuit forms；Voltage regulating module is made of pressure-adjustable DC/DC, and thermoelectric cooling driving voltage (4V~14V) is adjusted by inputting PWM.
Fig. 2 is described as follows for drive control schematic diagram of the present invention：
DSP output pwm signals are transferred to the input terminal of drive control by light-coupled isolation and low-pass filtering in figure, and DSP is supplied
Power supply and drive control power supply are two-way independent current source, can completely isolated drive circuit and DSP circuit, improve mutual electromagnetism
Compatible performance and functional reliability, drive circuit driving voltage regulating module output thermoelectric cooler driving voltage 4V~14V.
Fig. 3 is described as follows for self learning type artificial intelligence fuzzy control model figure of the present invention：
Yr (t) is hot and cold side temperature, operating voltage, electric current and the target refrigeration temperature of the thermoelectric cooler gathered in real time in figure
The parameters such as degree, consider temperature delay inertia, store data in database, calculated initially by the data analysis of inference machine
Control parameter U values are put into interim storage area blackboard；Real time data is delivered separately to knowledge base and study ring by database at the same time
Section, calculates ambient temperature, system thermal leak rate, cavity temperature variation tendency, to initial in blackboard by knowledge base
Control parameter U values are modified output P (t), carry out the control of the driving parameter of cooling stages；Using learning link to history number
According to study, output adjustment amount Δ U, adjust output P values, carry out the temperature control stage drive parameter control.
Self learning type artificial intelligence FUZZY ALGORITHMS FOR CONTROL discrete model is as follows：
E is the warm variable Rate factor in formula, by carrying out Data Matching acquisition with the warm variable Rate curve of knowledge base；UtTo work as
Preceding temperature control cycle controlling value, is a constant within the t periods；V(Th, Tc, Qi, It) electric to work in single temperature control cycle time
Pressure value, ThFor thermoelectric cooler hot-side temperature, TcFor thermoelectric cooler cold junction temperature, QiFor the system produced in temperature control cycle time
Shown in cold, its cool and heat ends temperature with thermoelectric cooler, operating current and refrigeration magnitude relation equation below 2 and 3；Δ U is U
It is worth adjustment amount, passes through Ut-1And U0Obtain, calculate in detail as shown in formula 4.
Th, Tc, ItIt is the test parameter of t periods, QiThe refrigeration total amount produced for the thermoelectric cooler of t periods, meter
It is as follows to calculate formula：
In formula：Φi-- refrigerating capacity, J；
Vt-- real-time input voltage, V；
It-- real-time current, A；
Tht-- hot-side temperature, K；
Tct-- cold junction temperature, K；
nt-- Temperature Difference Ratio Th/Tc；
ΔTt-- hot and cold side temperature difference Tht-Tct, K；
H --- program setting fixed sample time, S.
U in formula0It is by matching acquisition with the warm variable Rate curve data of knowledge base to warm variable Rate in the h periods.
This self learning type artificial intelligence FUZZY ALGORITHMS FOR CONTROL calculates one by the thermoelectric cooling running parameter to gathering in real time
The refrigerating capacity that thermoelectric cooler produces in the section time, with a preceding Ut-1Value, with reference to the warm variable Rate curve of knowledge base, determines
Δ U, substantially reduce convergence time, improve thermoelectricity intelligent drives control efficiency and temperature-controlled precision.
Fig. 4 is described as follows for semiconductor thermoelectric refrigeration device cooling parameters test curve figure of the present invention：
Two stages of refrigeration and temperature control are divided into figure, the wherein fluctuation before temperature control adjusts section for drive control, from figure
It can be seen that being adjusted by tertiary voltage, control temperature has reached stabilization.In cooling stages, thermoelectric cooler operating voltage 12V,
Operating current 5.38A, -21 DEG C of cold junction temperature, 31.2 DEG C of hot-side temperature, in the temperature control stage, thermoelectric cooler operating voltage
10.8V, operating current 4.75A, -20.5 DEG C of cold junction temperature, 29 DEG C of hot-side temperature.It is former according to the work of semiconductor thermoelectric refrigeration device
Reason, its refrigerating efficiency (ηmax) calculation formula is as follows：
In formula：ηmax-- refrigerating efficiency；
Z -- thermoelectric cooler figure of merit；
Th-- thermoelectric cooler hot-side temperature, K；
Tc-- thermoelectric cooler cold junction temperature, K.
It is computed, thermoelectric cooler is 0.28 in cooling stages refrigerating efficiency, average power consumption 64.56W；Temperature control stage system
Cold efficiency is 0.3, average power consumption 51.3W.
Unspecified part of the present invention belongs to general knowledge well known to those skilled in the art
Although reference be made herein to invention has been described for explanatory embodiment of the invention, and above-described embodiment is only this hair
Bright preferable embodiment, embodiments of the present invention are simultaneously not restricted to the described embodiments, it should be appreciated that people in the art
Member can be designed that a lot of other modifications and embodiment, these modifications and embodiment will fall in principle disclosed in the present application
Within scope and spirit.
Claims (4)
- A kind of 1. semiconductor thermoelectric refrigeration device drive control method, it is characterised in that：Using PWM Regulation Control type of drive, profit With gather in real time semiconductor thermoelectric refrigeration device cold end, hot-side temperature, voltage, electric current and refrigeration target temperature parameter, using number Value divides the output duty cycle P values of method combination self learning type artificial intelligence fuzzy model control PWM, adaptive control thermoelectricity system The driving voltage and control cryogenic temperature of cooler.
- 2. semiconductor thermoelectric refrigeration device drive control method according to claim 1, it is characterised in that：Passed using temperature survey Sensor measurement thermoelectric cooler cold junction temperature, hot-side temperature and cryogenic temperature, and the temperature information of measurement is passed into collection electricity Road, Acquisition Circuit are made of low-pass filtering and operational amplifier circuit, for the simulation of the temperature information transmitted to sensor for measuring temperature Signal is nursed one's health, and the signal after conditioning is passed to DSP, and DSP carries out A/D conversions to analog signal, runs self learning type Artificial intelligence fuzzy control model, control output adjusts output control parameter P values, while drives output voltage and thermoelectric cooler Operating current feeds back to DSP, utilizes artificial intelligence fuzzy control model constantly regulate P values；PWM drive controls are by drive control electricity Source, low-pass filter circuit and Drive Protecting Circuit composition, mainly realize the control to voltage regulating module；Voltage regulating module is by pressure-adjustable DC/DC is formed, and thermoelectric cooling driving voltage is adjusted by inputting PWM.
- 3. semiconductor thermoelectric refrigeration device drive control method according to claim 2, it is characterised in that：The thermoelectric cooling is driven Dynamic voltage is 4V~14V.
- 4. semiconductor thermoelectric refrigeration device drive control method according to claim 2, it is characterised in that：The artificial intelligence of self learning type Energy FUZZY ALGORITHMS FOR CONTROL discrete model is as follows：E is the warm variable Rate factor in formula, by carrying out Data Matching acquisition with the warm variable Rate curve of knowledge base；UtFor current control Thermoperiod controlling value, is a constant within the t periods；V(Th, Tc, Qi, It) it is operating voltage value in single temperature control cycle time, ThFor thermoelectric cooler hot-side temperature, TcFor thermoelectric cooler cold junction temperature, QiFor the refrigerating capacity produced in temperature control cycle time, Shown in its cool and heat ends temperature with thermoelectric cooler, operating current and refrigeration magnitude relation equation below 2 and 3；Δ U adjusts for U values Amount, passes through Ut-1And U0Obtain, calculate in detail as shown in formula 4；Tht, Tct, ItBe the test parameter of t periods, be respectively the t periods thermoelectric cooler hot-side temperature, thermoelectric cooling Device cold junction temperature, real-time current；QiThe refrigeration total amount produced for the thermoelectric cooler of t periods, calculation formula are as follows：In formula：Φi-- refrigerating capacity, J；Vt-- real-time input voltage, V；It-- real-time current, A；Tht-- hot-side temperature, K；Tct-- cold junction temperature, K；nt-- Temperature Difference Ratio Th/Tc；ΔTt-- hot and cold side temperature difference Tht-Tct, K；H --- program setting fixed sample time, S；U in formula0It is by matching acquisition with the warm variable Rate curve data of knowledge base to warm variable Rate in the h periods；Ut-1To work as The controlling value of a cycle before preceding temperature control cycle t.
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Cited By (9)
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| CN109323482A (en) * | 2018-10-24 | 2019-02-12 | 中国石油天然气集团有限公司 | Semiconductor cooler and its refrigeration control method |
| CN111503935A (en) * | 2020-04-29 | 2020-08-07 | 深圳彩果科技有限公司 | Control system and method for semiconductor temperature adjusting device |
| CN112902494A (en) * | 2021-03-30 | 2021-06-04 | 联想(北京)有限公司 | Control method and electronic device |
| CN113348414A (en) * | 2018-12-17 | 2021-09-03 | 卡乐工业股份公司 | Method for testing the functional stability of a refrigerator |
| CN114739076A (en) * | 2021-01-07 | 2022-07-12 | 贵州海尔电器有限公司 | Semiconductor refrigeration equipment and control method thereof |
| CN114739077A (en) * | 2021-01-07 | 2022-07-12 | 贵州海尔电器有限公司 | Semiconductor refrigeration equipment and control method thereof |
| CN114739078A (en) * | 2021-01-07 | 2022-07-12 | 贵州海尔电器有限公司 | Semiconductor refrigeration equipment and control method thereof |
| CN114935222A (en) * | 2022-06-10 | 2022-08-23 | 中南大学 | Semiconductor refrigerator dynamic temperature distribution obtaining and refrigeration control method and system |
| CN115542969A (en) * | 2022-10-11 | 2022-12-30 | 浙江大学 | Automatic temperature control circuit |
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| CN114739077A (en) * | 2021-01-07 | 2022-07-12 | 贵州海尔电器有限公司 | Semiconductor refrigeration equipment and control method thereof |
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