Device-level thermoelectric effect parameter identification method and system based on least square method

Abstract

The invention provides a device-level thermoelectric effect parameter identification method and system based on a least square method. The function relation between thermoelectric effect parameters and the temperature of the hot end and the cold end of a thermoelectric device can be obtained. According to the method, dominant parameters of a working thermoelectric device are collected and measured, wherein the dominant parameters comprise the open-circuit voltage, the cold and hot end temperature, the cooling medium inlet and outlet temperature and the sizes of the thermoelectric device and thermoelectric particles; and under various temperature conditions, the dominant parameters are utilized to calculate thermoelectric effect parameters of the thermoelectric device. And for each thermoelectric effect parameter, constructing a function relationship between the thermoelectric effect parameter and the temperature of the hot end and the cold end by using a least square method, and completing identification of the device-level thermoelectric effect parameter. According to the method, macroscopic fitting of thermoelectric effect parameters is realized, and the precision of a simulation model is improved and the calculation time of the model is shortened by accurately identifying the thermoelectric effect parameters of a main component, namely a thermoelectric device, in the thermoelectric power generation system.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature difference power generation, and in particular to a method and system for identifying device-level thermoelectric effect parameters of a temperature difference power generation system. Background Art

[0002] Energy shortage is an important challenge facing the world. Saving energy and improving energy efficiency have become the core themes of industrial development. Thermoelectric power generation technology is a technology that directly converts thermal energy into electrical energy based on semiconductor thermoelectric materials. However, due to the high experimental cost, the development of thermoelectric power generation systems is difficult. Therefore, an accurate and convenient simulation model is needed to analyze the output characteristics and related parameters of the thermoelectric power generation system.

[0003] The error in the simulation of the thermoelectric power generation system is largely due to the model of its main component, the thermoelectric device. Therefore, the thermoelectric device needs to be accurately modeled when building the model. At present, the simulation of the thermoelectric power generation system is usually based on the system or the material. There is no identification scheme for the device-level thermoelectric effect parameters, and the device-level integrity is lacking.

[0004] Thermoelectric devices mainly involve three thermoelectric effect parameters in the energy conversion process, namely Seebeck coefficient, thermal conductivity and resistivity. Accurate calculation of these three parameters can greatly reduce model errors. However, the current fitting of these three parameters is mainly aimed at thermoelectric materials, and thermoelectric devices are modeled using the thermoelectric effect parameters of the materials, that is, the physical parameters. There are differences in the thermoelectric characteristics of materials and devices. For materials, they are composed of thermoelectric particles. The thermoelectric characteristics of thermoelectric particles change with temperature along the height direction of the thermoelectric particles. However, when the material is made into a thermoelectric device, it is not only the material itself that needs to be considered, but also the contact thermal resistance between the thermoelectric particles and the electrodes. Since the contact thermal resistance between the thermoelectric particles and the electrodes cannot be accurately calculated, it is impossible to accurately fit the material physical parameters, and a single-chip thermoelectric device contains a large number of thermoelectric particles. Each thermoelectric particle is modeled separately and the device model is synthesized, which greatly increases the amount of model calculation. Summary of the invention

[0005] In view of this, the present invention provides a method and system for identifying the device-level thermoelectric effect parameters of a temperature difference power generation system based on the least squares method, which performs macroscopic fitting on the Seebeck coefficient, thermal conductivity and resistivity of the thermoelectric device at the device level, and accurately identifies the thermoelectric effect parameters of the main components in the temperature difference power generation system, namely the thermoelectric devices, thereby improving the accuracy of the simulation model and shortening the calculation time of the model.

[0006] In order to solve the above technical problems, the present invention is implemented as follows.

[0007] A device-level thermoelectric effect parameter identification method based on the least squares method is based on the hot end temperature T h And the cold end temperature T c As the identification variable, the thermoelectric effect parameters and the hot end temperature T of the thermoelectric device are constructed. h And the cold end temperature T c The method comprises:

[0008] Step 1: Use a heating device and a heat dissipation device to provide a temperature difference for the thermoelectric device, and the thermoelectric device converts the temperature difference into electrical energy output;

[0009] Step 2: Collect and measure the explicit parameters of the thermoelectric device, including: the open circuit voltage U of the thermoelectric device, the internal resistance R of the thermoelectric device; the hot end temperature T of the thermoelectric device h And the cold end temperature T c ; Cooling medium inlet temperature T in the heat dissipation device lin and outlet temperature T lout ; The thickness of the thermoelectric device along the longitudinal heat transfer direction is H, and the area of ​​the thermoelectric device is S; The number of pairs of thermoelectric particles in the thermoelectric device is N, and the height h and cross-sectional area A of the thermoelectric particles;

[0010] Step 3: After the open circuit voltage U is stable, the thermoelectric effect parameters of the thermoelectric device are calculated using the collected explicit parameters, including the Seebeck coefficient α, thermal conductivity k, and resistivity ρ;

[0011] Step 4: modify the heating temperature of the heating device and / or the inlet temperature of the cooling medium of the heat dissipation device, repeat the centroid step 3, and record M groups of thermoelectric effect parameters of the thermoelectric device; M is a positive integer greater than 2;

[0012] Step 5: For each thermoelectric effect parameter of the thermoelectric device, use the least squares method to construct the thermoelectric effect parameter and the hot end temperature T of the thermoelectric device. h And the cold end temperature T c The functional relationship of is used to complete the identification of device-level thermoelectric effect parameters.

[0013] Preferably, the step 3 is:

[0014] For the Seebeck coefficient α, according to the relationship involved in the Seebeck constant U = α (T h -T c ), based on the measured open circuit voltage U divided by the hot end temperature T of the thermoelectric device h The cold end temperature of the thermoelectric device T c The difference between and is used to obtain the Seebeck coefficient under the current temperature conditions;

[0015] For thermal conductivity k, use Calculate the heat flux q flowing through the thermoelectric device; where c l is the specific heat capacity of cooling water, is the mass flow rate of cooling water; the calculated heat flux q and the collected hot end temperature T of the thermoelectric device h and the cold junction temperature T c , Substitute the measured thickness H and area S of the thermoelectric device into the heat transfer equation Get the thermal conductivity under the current temperature conditions;

[0016] For the resistivity ρ, substitute the measured internal resistance R of the thermoelectric device and the logarithm N, height h, and area S of the thermoelectric particles into the resistance calculation formula Get the resistivity under current temperature conditions.

[0017] Preferably, in step 5, the thermoelectric effect parameters and the hot end temperature T of the thermoelectric device are constructed. h And the cold end temperature T c After the functional relationship is established, three-dimensional surface fitting is further performed for each thermoelectric effect parameter.

[0018] Preferably, the functional relationship described in step 5 is an n-order polynomial, where n is a positive integer greater than or equal to 1.

[0019] Preferably, for the thermoelectric effect parameter x i , the functional relationship is x i (T h ,T c )=(a i T h +b i T c +c i ) n ; Among them, a i ,b i ,c i are the coefficients of the function that needs to be fitted.

[0020] The present invention also provides a device-level thermoelectric effect parameter identification system based on the least squares method, using the hot end temperature T h And the cold end temperature T c As the identification variable, the thermoelectric effect parameters and the hot end temperature T of the thermoelectric device are constructed. h And the cold end temperature T c Functional relationship of

[0021] The system includes an electric heating device, a heat dissipation device, a measuring component, a thermoelectric effect parameter calculation module, and a data fitting module;

[0022] The thermoelectric device as the identification object has its hot end heated by an electric heating device and its cold end cooled by a cooling medium introduced into a heat dissipation device.

[0023] The measurement component is used to collect and measure the explicit parameters of the thermoelectric device, including: the open circuit voltage U of the thermoelectric device, the internal resistance R of the thermoelectric device; the hot end temperature T of the thermoelectric device h And the cold end temperature T c ; Cooling medium inlet temperature T in the heat dissipation device lin and outlet temperature T lout ; The thickness of the thermoelectric device along the longitudinal heat transfer direction is H, and the area of ​​the thermoelectric device is S; the number of pairs of thermoelectric particles in the thermoelectric device is N, the height of the thermoelectric particles is h, and the cross-sectional area of ​​the thermoelectric particles is A;

[0024] Thermoelectric effect parameter calculation module, used to collect and obtain the explicit parameters from the measurement component, and after the open circuit voltage U is stable, use the explicit parameters to calculate the thermoelectric effect parameters of the thermoelectric device, including Seebeck coefficient α, thermal conductivity k, and resistivity ρ; after the heating device and / or the heat dissipation device are temperature adjusted, under different temperature conditions, the thermoelectric effect parameter calculation module calculates and records M groups of thermoelectric effect parameters of the thermoelectric device, and provides them to the data fitting module; M is a positive integer greater than 2;

[0025] The data fitting module is used to construct the thermoelectric effect parameters, the hot end temperature T of the thermoelectric device, and the thermoelectric effect parameters of each thermoelectric device using the least squares method. h And the cold end temperature T c The functional relationship of is used to complete the identification of device-level thermoelectric effect parameters.

[0026] Preferably, the thermoelectric effect parameter calculation module includes a Seebeck coefficient calculation module, a thermal conductivity calculation module and a resistivity calculation module;

[0027] Seebeck coefficient calculation module, used to calculate the Seebeck coefficient according to the relationship U=α(T h -T c ), based on the measured open circuit voltage U divided by the hot end temperature T of the thermoelectric device h The cold end temperature of the thermoelectric device T c The difference between and is used to obtain the Seebeck coefficient α under the current temperature conditions;

[0028] Thermal conductivity calculation module for using Calculate the heat flux q flowing through the thermoelectric device; where c l is the specific heat capacity of cooling water, is the mass flow rate of cooling water; the calculated heat flux q and the collected hot end temperature T of the thermoelectric device h and the cold junction temperature T c , Substitute the measured thickness H and area S of the thermoelectric device into the heat transfer equation Get the thermal conductivity k under the current temperature conditions;

[0029] The resistivity calculation module is used to substitute the internal resistance R of the thermoelectric device and the logarithm N, height h, and area S of the thermoelectric particles into the resistance calculation formula Obtain the resistivity ρ under current temperature conditions.

[0030] Preferably, the data fitting module constructs the thermoelectric effect parameters, the hot end temperature T of the thermoelectric device. h And the cold end temperature T c After the functional relationship is established, three-dimensional surface fitting is further performed for each thermoelectric effect parameter.

[0031] Preferably, the functional relationship constructed by the data fitting module for each thermoelectric effect parameter is an n-order polynomial, where n is a positive integer greater than or equal to 1; i , the functional relationship is x i (T h ,T c )=(a i T h +b i T c +c i ) n ; Among them, a i ,b i ,c i are the coefficients of the function that needs to be fitted.

[0032] Beneficial effects:

[0033] (1) The present invention does not model and identify the thermoelectric effect parameters of thermoelectric devices at the system level or material level, but directly starts from the device level of thermoelectric devices, and performs macroscopic fitting of thermoelectric effect parameters such as Seebeck coefficient, thermal conductivity, and resistivity of thermoelectric devices at the device level. The parameters selected during fitting consider that the thermoelectric effect parameters, i.e., the physical parameters, are only affected by the hot end temperature and the cold end temperature of the thermoelectric device, so the hot end temperature and the cold end temperature are selected as variables for function fitting. The explicit parameters required for fitting can be obtained by experimentally measuring the relevant explicit parameters of the thermoelectric device, and mathematical transformation is performed based on the relationship between the explicit parameters and the relevant thermoelectric effect parameters, thereby avoiding the complex process of directly measuring the relevant thermoelectric effect parameters.

[0034] (2) In order to solve the problem that it is difficult to carry out experiments on the temperature difference power generation system and the experiments are relatively complicated, the present invention discloses a device-level thermoelectric effect parameter identification method based on the least squares method. The explicit parameters required to be collected by the present invention can be accurately collected through experiments, thereby accurately identifying the thermoelectric effect parameters of the main components in the temperature difference power generation system, namely the thermoelectric devices, and improving the accuracy of the simulation model. This avoids the problem that it is difficult to carry out experiments on the temperature difference power generation system and the experiments are relatively complicated.

[0035] (3) The device-level thermoelectric effect parameter identification method based on the least squares method disclosed in the present invention directly fits the mathematical relationship between the device-level thermoelectric effect parameters and the temperatures of the hot end and the cold end of the thermoelectric device through the least squares method to form a one-to-one mapping surface. This is more convenient when modeling and there is no need to analyze the relevant material performance parameters and calculation process of the thermoelectric particles inside the thermoelectric device.

[0036] (4) The device-level thermoelectric effect parameter identification method based on the least squares method disclosed in the present invention greatly reduces the amount of calculation of the thermoelectric device part during the modeling of the temperature difference power generation system by directly fitting the device-level thermoelectric performance parameters, thereby shortening the calculation time of the model. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 The present invention discloses a schematic diagram of a parameter measurement device related to a device-level thermoelectric effect parameter identification method based on the least squares method.

[0038] Figure 2 The present invention discloses a method for identifying device-level thermoelectric effect parameters based on the least squares method. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments.

[0040] The present invention provides a device-level thermoelectric effect parameter identification scheme based on the least squares method. The scheme does not model and identify the thermoelectric effect parameters of thermoelectric devices at the system level or material level, but directly starts from the device level of the thermoelectric device, and performs macroscopic fitting of the thermoelectric effect parameters such as the Seebeck coefficient, thermal conductivity, and resistivity of the thermoelectric device at the device level. The parameters selected during fitting consider that the thermoelectric effect parameters, i.e., the physical property parameters, are only affected by the hot end temperature and the cold end temperature of the thermoelectric device, so the hot end temperature and the cold end temperature are selected as variables for function fitting.

[0041] In order to realize the scheme, the present invention detects the device open circuit voltage, heat flux flowing through the device, and device internal resistance of the thermoelectric device under the specified hot end temperature and specified temperature difference, and measures the relevant dimensions of the device and thermoelectric particles. According to the relationship between the Seebeck coefficient, thermal conductivity, resistivity and the three detection parameters and the size parameter, the relevant numerical calculation processing is performed to obtain the scatter plot of the Seebeck coefficient, thermal conductivity, resistivity and the cold end temperature and hot end temperature, and the scatter plot is fitted based on the least squares method to realize the device-level thermoelectric effect parameter identification based on the least squares method. The present invention measures the relevant explicit parameters of the thermoelectric device through experiments, and performs mathematical transformation according to the relationship between the explicit parameters and the relevant thermoelectric effect parameters, thereby avoiding the complex process of directly measuring the relevant thermoelectric effect parameters. Moreover, by accurately identifying the thermoelectric effect parameters of the main components in the temperature difference power generation system, namely the thermoelectric device, the accuracy of the simulation model can be improved.

[0042] Figure 1 The schematic diagram of the device-level thermoelectric effect parameter identification system based on the least square method of the present invention is shown. As shown in the figure, the system includes an electric heating device, a heat dissipation device, a measurement component, a thermoelectric effect parameter calculation module, and a data fitting module. The measurement component, the thermoelectric effect parameter calculation module, and the data fitting module are not shown.

[0043] The thermoelectric device as the identification object has its hot end heated by an electric heating device and its cold end cooled by a cooling medium introduced into a heat dissipation device. In practice, a fixture can be designed to fix the thermoelectric device.

[0044] The measuring components include multimeter, thermocouple, internal resistance meter and other equipment. The multimeter is used to monitor the open circuit voltage U of the thermoelectric device, and the thermocouple is used to monitor the hot end temperature T of the thermoelectric device. h And the cold end temperature T c , while monitoring the temperature T of the cooling water entering the heat sink lin and the temperature T of the heat sink lout ; Use an internal resistance meter to monitor the change of the internal resistance of the thermoelectric device. At the same time, a measuring device is used to measure the thickness H of the thermoelectric device along the longitudinal heat transfer direction, the area of ​​the thermoelectric device is S; the number of thermoelectric particles in the thermoelectric device N, the height h of the thermoelectric particles, and the cross-sectional area A of the thermoelectric particles.

[0045] The thermoelectric effect parameter calculation module is used to collect and obtain the explicit parameters from the measurement component. After the open-circuit voltage U is stabilized, the thermoelectric effect parameters of the thermoelectric device, including the Seebeck coefficient α, thermal conductivity k, and resistivity ρ, are calculated through the relevant correspondence between the explicit parameters and the thermoelectric effect parameters; and after the temperature of the heating device and / or the heat dissipation device is adjusted, the M groups of thermoelectric effect parameters of the thermoelectric device are repeatedly calculated and recorded under different temperature conditions.

[0046] The data fitting module is based on the M group of thermoelectric effect parameters and the corresponding thermoelectric device hot end temperature T h And the cold end temperature T c , a scatter plot of the thermoelectric effect parameters is obtained, where each of the three points consists of the thermoelectric effect parameters, the hot end temperature T of the thermoelectric device h And the cold end temperature T c The least squares method is used for fitting to obtain the functional relationship, and then a three-dimensional surface is drawn, and finally the device-level thermoelectric effect parameter identification based on the least squares method is realized.

[0047] Figure 2 The specific working process of the device-level thermoelectric effect parameter identification system based on the least squares method is shown, including the following steps:

[0048] Step 1: Control the heating device to heat the temperature to the specified value T H , control the cooling water inlet temperature of the heat dissipation device to be T lin Lower than T H At this point, a temperature difference forms across the thermoelectric device, and based on the Seebeck law of the thermoelectric device, the device begins to generate a potential difference.

[0049] In this embodiment, the specified value T H is 573K, and the cooling water inlet temperature is controlled to be T lin It is 293K.

[0050] Step 2: Collect relevant explicit parameters of the thermoelectric device.

[0051] In this step, the measurement component collects explicit parameters, specifically including the following sub-steps:

[0052] Step 2.1: Use a multimeter to collect the open circuit voltage U of the thermoelectric device, and use an internal resistance meter to collect the internal resistance R of the thermoelectric device.

[0053] Step 2.2: Use a thermocouple to collect the hot-end temperature T of the thermoelectric device h And the cold end temperature T c , Cooling water inlet temperature T lin and outlet temperature T lout .

[0054] Step 2.3: Calculate the difference between the current open circuit voltage and the previous open circuit voltage as ΔU. When ΔU is less than 0.01, it means that the open circuit voltage is stable. Record the above data [U, R, T h ,T c ,T lin ,T lout ].

[0055] Step 2.4: Measure the thickness of the thermoelectric device along the longitudinal heat transfer direction as H, and measure and calculate the area of ​​the thermoelectric device as S.

[0056] Step 2.5: Measure the number of pairs of thermoelectric particles N, the height h of the thermoelectric particles, and the cross-sectional area A of the thermoelectric particles in the thermoelectric device, ignoring the internal resistance of the copper electrode.

[0057] In this step, various data of thermoelectric devices and thermoelectric particles are collected and measured as explicit parameters.

[0058] Step 3: Calculate the Seebeck coefficient α, thermal conductivity k, and resistivity ρ based on the various relevant explicit parameters measured in step 3.

[0059] In this step, the thermoelectric effect parameter calculation module is responsible for calculating various thermoelectric effect parameters based on explicit parameters. The thermoelectric effect parameter calculation module includes the Seebeck coefficient calculation module, the thermal conductivity calculation module, and the resistivity calculation module, which are responsible for the calculation of the Seebeck coefficient, thermal conductivity, and resistivity, respectively. The specific calculation method is as follows:

[0060] For the Seebeck coefficient α, the relationship shown in formula (1) can be obtained according to the Seebeck law. Therefore, the open circuit voltage U can be divided by the hot end temperature T of the thermoelectric device. h The cold end temperature of the thermoelectric device T c The difference between the two gives the Seebeck coefficient at this temperature.

[0061] U=α(T h -T c ) (1)

[0062] For the thermal conductivity k, the heat flux q flowing through the thermoelectric device is calculated according to equation (2). According to the heat transfer equation (3), the calculated q and the hot end temperature T of the thermoelectric device can be used to calculate h , cold end temperature T c , thickness and area of ​​the thermoelectric device to obtain the thermal conductivity under the current temperature conditions.

[0063]

[0064]

[0065] where c l is the specific heat capacity of cooling water, is the mass flow rate of cooling water.

[0066] For the resistivity ρ, according to the relationship shown in the resistance calculation formula (4), the resistivity under the current temperature conditions can be calculated based on the internal resistance R of the thermoelectric device measured in step 3.3 and the logarithm N, height h, and area S of the thermoelectric particles measured in step 3.5.

[0067]

[0068] Step 4: Modify the specified value T of the heating temperature of the electric heating system H Or the cooling water inlet temperature value T lin Repeat steps 2 and 3 until the amount of data on the Seebeck coefficient, thermal conductivity, and resistivity of the thermoelectric device obtained is greater than or equal to M groups. M is a positive integer greater than 2.

[0069] Step 5: According to the M group data in step 4, the Seebeck coefficient α, thermal conductivity k, resistivity ρ and the hot end temperature T of the thermoelectric device are fitted according to the principle of least squares method. h , cold end temperature T c The functional relationship of .

[0070] In this step, the data fitting module constructs the functional relationship according to the M groups of data, which specifically includes the following sub-steps:

[0071] Step 5.1: In this embodiment, the functional relationship constructed for each thermoelectric effect parameter is an n-order polynomial, where n is a positive integer greater than or equal to 1. Preferably, n=2 is selected, and the Seebeck coefficient α and the hot end temperature T of the thermoelectric device are set h And the cold end temperature T c The relationship is: α(T h ,T c )=(a1T h +b1T c +c1) 2 , assuming that the thermal conductivity k is related to the hot end temperature T of the thermoelectric device h And the cold end temperature T c The relationship is: k(T h ,T c )=(a2T h +b2T c +c2) 2 , assuming that the resistivity ρ is related to the hot end temperature T of the thermoelectric device h And the cold end temperature T c The relationship is: ρ(T h ,T c )=(a3T h +b3T c +c3) 2 Among them, a1~a3, b1~b3, c1~c3 are the function coefficients that need to be fitted.

[0072] Step 5.2: There are M points to be fitted, and the M points are represented as:

[0073] (T hi ,T ci ,α i );i=1,2......M

[0074] (T hi,T ci ,k i );i=1,2......M

[0075] (T hi ,T ci ,ρ i );i=1,2......M

[0076] Set the objective function as:

[0077]

[0078]

[0079]

[0080] Step 5.3: Minimize s1, s2, and s3, that is, satisfy:

[0081]

[0082] Then we have:

[0083]

[0084]

[0085]

[0086] Step 5.4: Based on the equation obtained in step 5.3, the matrix representation is:

[0087]

[0088] Step 5.5: Solve the system of equations obtained in step 5.4 to obtain a j , b j , c j , bring the obtained parameters back to the plane equation listed in step 5.1, and obtain the functional relationship between the device-level Seebeck coefficient, thermal conductivity, resistivity and the hot-end temperature and cold-end temperature of the thermoelectric device.

[0089] Step 6: Draw a three-dimensional surface graph based on the functional relationship obtained in step 5.5, and finally realize the device-level thermoelectric effect parameter identification based on the least squares method.

[0090] In this step, the data fitting module draws a three-dimensional surface graph of each thermoelectric effect parameter based on the functional relationship.

[0091] This is the end of the process.

[0092] When the thermoelectric effect parameters of a thermoelectric device need to be determined at a certain moment, the hot end temperature T of the thermoelectric device at the current moment is obtained.h And the cold end temperature T c , substituting into the above function relationship or three-dimensional surface, we can get the corresponding Seebeck coefficient α, thermal conductivity k, and resistivity ρ.

[0093] The above specific embodiments only describe the design principle of the present invention. The shapes and names of the components in the description may be different and are not limited. Therefore, those skilled in the art in the field of the present invention may modify or replace the technical solutions recorded in the above embodiments; and these modifications and replacements do not deviate from the creative purpose and technical solutions of the present invention and should all fall within the protection scope of the present invention.

Claims

1. A device-level thermoelectric effect parameter identification method based on the least squares method, characterized in that: The hot end temperature of the thermoelectric device is T h And the cold end temperature T c As the identification variable, the thermoelectric effect parameters and the hot end temperature T of the thermoelectric device are constructed. h And the cold end temperature T c The method comprises: Step 1: Use a heating device and a heat dissipation device to provide a temperature difference for the thermoelectric device, and the thermoelectric device converts the temperature difference into electrical energy output; Step 2: Collect and measure the explicit parameters of the thermoelectric device, including: the open circuit voltage U of the thermoelectric device, the internal resistance R of the thermoelectric device; the hot end temperature T of the thermoelectric device h And the cold end temperature T c ; Cooling medium inlet temperature T in the heat dissipation device lin and outlet temperature T lout ; The thickness of the thermoelectric device along the longitudinal heat transfer direction is H, and the area of ​​the thermoelectric device is S; The number of pairs of thermoelectric particles in the thermoelectric device is N, and the height h and cross-sectional area A of the thermoelectric particles; Step 3: After the open circuit voltage U is stable, the thermoelectric effect parameters of the thermoelectric device are calculated using the collected explicit parameters, including the Seebeck coefficient α, thermal conductivity k, and resistivity ρ; Step 4: modify the heating temperature of the heating device and / or the inlet temperature of the cooling medium of the heat dissipation device, repeat the centroid step 3, and record M groups of thermoelectric effect parameters of the thermoelectric device; M is a positive integer greater than 2; Step 5: For each thermoelectric effect parameter of the thermoelectric device, use the least squares method to construct the thermoelectric effect parameter and the hot end temperature T of the thermoelectric device. h And the cold end temperature T c The functional relationship of is used to complete the identification of device-level thermoelectric effect parameters.

2. The method according to claim 1, characterized in that The step 3 is: For the Seebeck coefficient α, according to the relationship involved in the Seebeck constant U = α (T h -T c ), based on the measured open circuit voltage U divided by the hot end temperature T of the thermoelectric device h The cold end temperature of the thermoelectric device T c The difference between and is used to obtain the Seebeck coefficient under the current temperature conditions; For thermal conductivity k, use Calculate the heat flux q flowing through the thermoelectric device; where c l is the specific heat capacity of cooling water, is the mass flow rate of cooling water; the calculated heat flux q and the collected hot end temperature T of the thermoelectric device h and the cold junction temperature T c , Substitute the measured thickness H and area S of the thermoelectric device into the heat transfer equation Get the thermal conductivity under the current temperature conditions; For the resistivity ρ, substitute the measured internal resistance R of the thermoelectric device and the logarithm N, height h, and area S of the thermoelectric particles into the resistance calculation formula Get the resistivity under current temperature conditions.

3. The method according to claim 1 or 2, characterized in that In step 5, the thermoelectric effect parameters and the hot end temperature T of the thermoelectric device are constructed. h And the cold end temperature T c After the functional relationship is established, three-dimensional surface fitting is further performed for each thermoelectric effect parameter.

4. The method according to claim 1, characterized in that The functional relationship described in step 5 is an n-order polynomial, where n is a positive integer greater than or equal to 1.

5. The method according to claim 4, characterized in that For the thermoelectric effect parameter x i , the functional relationship is x i (T h ,T c )=(a i T h +b i T c +c i ) n ; Among them, a i ,b i ,c i are the coefficients of the function that needs to be fitted.

6. A device-level thermoelectric effect parameter identification system based on the least squares method, characterized in that: The hot end temperature of the thermoelectric device is T h And the cold end temperature T c As the identification variable, the thermoelectric effect parameters and the hot end temperature T of the thermoelectric device are constructed. h And the cold end temperature T c Functional relationship of The system includes an electric heating device, a heat dissipation device, a measuring component, a thermoelectric effect parameter calculation module, and a data fitting module; The thermoelectric device as the identification object has its hot end heated by an electric heating device and its cold end cooled by a cooling medium introduced into a heat dissipation device. The measurement component is used to collect and measure the explicit parameters of the thermoelectric device, including: the open circuit voltage U of the thermoelectric device, the internal resistance R of the thermoelectric device; the hot end temperature T of the thermoelectric device h And the cold end temperature T c ; Cooling medium inlet temperature T in the heat dissipation device lin and outlet temperature T lout ; The thickness of the thermoelectric device along the longitudinal heat transfer direction is H, and the area of ​​the thermoelectric device is S; the number of pairs of thermoelectric particles in the thermoelectric device is N, the height of the thermoelectric particles is h, and the cross-sectional area of ​​the thermoelectric particles is A; Thermoelectric effect parameter calculation module, used to collect and obtain the explicit parameters from the measurement component, and after the open circuit voltage U is stable, use the explicit parameters to calculate the thermoelectric effect parameters of the thermoelectric device, including Seebeck coefficient α, thermal conductivity k, and resistivity ρ; after the heating device and / or the heat dissipation device are temperature adjusted, under different temperature conditions, the thermoelectric effect parameter calculation module calculates and records M groups of thermoelectric effect parameters of the thermoelectric device, and provides them to the data fitting module; M is a positive integer greater than 2; The data fitting module is used to construct the thermoelectric effect parameters, the hot end temperature T of the thermoelectric device, and the thermoelectric effect parameters of each thermoelectric device using the least squares method. h And the cold end temperature T c The functional relationship of is used to complete the identification of device-level thermoelectric effect parameters.

7. The system according to claim 6, characterized in that The thermoelectric effect parameter calculation module includes a Seebeck coefficient calculation module, a thermal conductivity calculation module and a resistivity calculation module; Seebeck coefficient calculation module, used to calculate the Seebeck coefficient according to the relationship U=α(T h -T c ), based on the measured open circuit voltage U divided by the hot end temperature T of the thermoelectric device h The cold end temperature of the thermoelectric device T c The difference between and is used to obtain the Seebeck coefficient α under the current temperature conditions; Thermal conductivity calculation module for using Calculate the heat flux q flowing through the thermoelectric device; where c l is the specific heat capacity of cooling water, is the mass flow rate of cooling water; the calculated heat flux q and the collected hot end temperature T of the thermoelectric device h and the cold junction temperature T c , Substitute the measured thickness H and area S of the thermoelectric device into the heat transfer equation Get the thermal conductivity k under the current temperature conditions; The resistivity calculation module is used to substitute the internal resistance R of the thermoelectric device and the logarithm N, height h, and area S of the thermoelectric particles into the resistance calculation formula Obtain the resistivity ρ under current temperature conditions.

8. The system according to claim 6, characterized in that The data fitting module constructs the thermoelectric effect parameters, the hot end temperature T of the thermoelectric device h And the cold end temperature T c After the functional relationship is established, three-dimensional surface fitting is further performed for each thermoelectric effect parameter.

9. The system according to claim 6, characterized in that The functional relationship constructed by the data fitting module for each thermoelectric effect parameter is an n-order polynomial, where n is a positive integer greater than or equal to 1; for the thermoelectric effect parameter xi, the functional relationship is x i (T h ,T c )=(a i T h +b i T c +c i ) n ; Among them, a i ,b i ,c i are the coefficients of the function that needs to be fitted.

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