Seven-parameter extraction method of photovoltaic cell double-diode model
By employing regional linearization and a seven-parameter extraction method based on a photovoltaic cell dual-diode model, the problems of low computational efficiency, limited accuracy, and poor stability in existing methods are solved, achieving high-precision and high-stability parameter extraction that is suitable for engineering applications.
Patent Information
- Application Number
- CN202511816335.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-04
- Publication Date
- 2026-03-13
AI Technical Summary
Existing methods for extracting seven parameters from dual diodes in photovoltaic cells suffer from low computational efficiency, limited accuracy, sensitivity to initial values, and poor convergence stability, especially with a decrease in model accuracy under low light conditions.
A regional linearization processing strategy was adopted. By establishing the equivalent circuit topology of the photovoltaic cell with two diodes, a seven-parameter mathematical model was constructed. The series resistance, parallel resistance, reverse saturation current and management factors were extracted step by step using dark-state current-voltage characteristic data. Combined with the calculation of photocurrent, the accuracy of the parameters was evaluated through model verification.
It significantly improves the stability and accuracy of parameter extraction, reduces computational complexity, avoids sensitivity to initial values, and improves the convergence stability of the algorithm, making it suitable for engineering applications.
Smart Images
Figure CN121659758A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of new energy photovoltaic power generation, specifically a method for extracting seven parameters of a photovoltaic cell dual-diode model. Background Technology
[0002] In modern photovoltaic (PV) power generation systems, accurate PV cell modeling is crucial for system performance prediction and optimal control. Current PV cell modeling methods are mainly divided into two categories: single-diode five-parameter models and dual-diode seven-parameter models. While the single-diode modeling method has relatively low computational complexity and is widely used in engineering applications, it has inherent limitations in describing PV cell characteristics. This method fails to fully consider the recombination mechanism of charge carriers in the depletion region of the PN junction, leading to a significant decrease in model accuracy under low-light conditions. In contrast, the dual-diode seven-parameter modeling method can more accurately describe the physical characteristics of PV cells, especially exhibiting higher modeling accuracy under complex lighting conditions.
[0003] However, existing seven-parameter extraction methods have many technical bottlenecks: Analytical methods: Although computationally efficient, they often require the introduction of simplified assumptions and the neglect of the influence of some key parameters, which limits the accuracy of the model.
[0004] Numerical solution methods typically require the introduction of auxiliary parameters such as temperature coefficient and equivalent resistance to construct a set of constraint equations. This method is extremely sensitive to the initial value setting and is prone to getting trapped in local optima or even having no solution.
[0005] Intelligent optimization algorithms, including particle swarm optimization, genetic algorithms, artificial neural networks, and simulated annealing, while possessing good global search and nonlinear processing capabilities, generally suffer from poor convergence stability, long computation time, and complex parameter tuning. Therefore, they do not meet current requirements. To address this, we propose a seven-parameter extraction method for a photovoltaic cell dual-diode model. Summary of the Invention
[0006] The purpose of this invention is to provide a method for extracting seven parameters of a photovoltaic cell dual-diode model, in order to address the numerous technical bottlenecks of existing seven-parameter extraction methods mentioned in the background section. Analytical methods: Although computationally efficient, they often require the introduction of simplified assumptions and the neglect of the influence of some key parameters, which limits the accuracy of the model.
[0007] Numerical solution methods typically require the introduction of auxiliary parameters such as temperature coefficient and equivalent resistance to construct a set of constraint equations. This method is extremely sensitive to the initial value setting and is prone to getting trapped in local optima or even having no solution.
[0008] Intelligent optimization algorithms, including particle swarm optimization, genetic algorithms, artificial neural networks, simulated annealing, etc., have good global search capabilities and nonlinear processing capabilities, but they generally suffer from poor convergence stability, long computation time, and complex parameter tuning.
[0009] To achieve the above objectives, the present invention provides the following technical solution: a method for extracting seven parameters of a photovoltaic cell dual-diode model, the method comprising the following steps: S1: Establish the dual-diode equivalent circuit topology of the photovoltaic cell and clarify the physical meaning of the seven parameters; S2: Based on the IV characteristics of photovoltaic cells, a seven-parameter mathematical model of dual diodes is constructed; S3: Acquisition of dark-state current-voltage characteristic data of photovoltaic cells; S4: Calculation of photocurrent parameters; S5: Determine the photogenerated current based on the extracted series and parallel resistances; S6: Model validation and accuracy assessment.
[0010] Preferably, in S1: S1.1: Photocurrent I ph Characterized by the charge carriers generated in a photovoltaic cell under illumination. S1.2: Reverse saturation current of the first diode I 01 : Characteristic parameters reflecting the diffusion current mechanism; S1.3: Reverse saturation current of the second diode I 02 Characteristic parameters that characterize the recombination current mechanism; S1.4: Ideal factor of the first diode n 1: Dimensionless parameters describing the carrier transport characteristics in the diffusion region; S1.5: Ideal factor of the second diode n 2: Dimensionless parameters describing the recombination characteristics of carriers in the depletion region; S1.6: Series resistor R s Internal resistance and loss parameters in the equivalent circuit; S1.7: Parallel resistor R sh : Equivalent resistance characterizing the leakage current characteristics of a battery.
[0011] Preferably, in S2: Based on the IV characteristics of photovoltaic cells, a seven-parameter mathematical model for dual diodes is constructed:
[0012] in,I This refers to the output current of the photovoltaic cell. V This refers to the output voltage of the photovoltaic cell. q is the electron charge constant, with a value of 1.602e. -19 C; k is the Boltzmann constant, with a value of 1.381e. -23 J / K; T The temperature of the photovoltaic cell.
[0013] Preferably, in S3: In a completely dark room environment, a forward bias voltage is applied to the photovoltaic cells, and dark-state current response data across the entire voltage scan range is collected using a high-precision power measurement instrument to construct a high-resolution database of forward dark-state current-voltage characteristics.
[0014] Preferably, in S4: The dark-state current-voltage characteristic curve is divided into four characteristic voltage ranges to realize parallel resistance. R sh Series resistors R s The reverse saturation current of the second diode I 02 Second diode ideal factor n 2. Reverse saturation current of the first diode I 01 First diode ideal factor n Step-by-step extraction of 1: In the low voltage region ( V (<0.2V), dark current is mainly controlled by the parallel resistor, and a simplified mathematical model is established:
[0015] Through least squares linear regression analysis, dark-state current I d and voltage V The data is linearly fitted, and the slope is 1 / R sh Thus, a parallel resistor is obtained. R sh ; In the high voltage region ( V >0.6V), select four equally spaced sampling points A1 ( V 1, I d1 A2 V 2, I d2 A3 V 3, I d3 ) and A4 (V 4, I d4 Using the calculation formula:
[0016] This calculation formula accurately extracts the series resistance. R s ; In the low to medium voltage range (0.2V < V <0.5V), within this range, the second diode effect dominates, and a mathematical relationship can be established:
[0017]
[0018]
[0019] By constructing In( I D2 )and V j The linear relationship, with the y-intercept being In( I 02 The slope is 1 / ( n 2 kT / q This determines the reverse saturation current of the second diode. I 02 Second diode ideal factor n 2; In the medium to high voltage range (0.5V < V <0.6V), the characteristics of the first diode become apparent in this range, establishing a mathematical relationship:
[0020]
[0021]
[0022]
[0023] By constructing In( I D - I D2 )and V j Linear regression analysis, with the ordinate intercept being In( I 01 The slope is 1 / ( n 1 kT / q This determines the reverse saturation current of the first diode.I 01 and the ideal factor of the first diode n 1.
[0024] Preferably, in S5: Combined with the extracted series resistance R s Parallel resistor R sh Short-circuit current I under standard test conditions for photovoltaic cells sc Calculate the photocurrent I p .
[0025] Preferably, in S6: A complete dual-diode equivalent circuit model was constructed using the extracted seven parameters. By comparing the IV characteristic curves predicted by the model with the measured data, the mean relative error (MRE) and maximum relative error (MaxRE) were used as evaluation indicators to judge the accuracy of parameter extraction and the reliability of the model.
[0026] Compared with the prior art, the beneficial effects of the present invention are: In terms of accuracy improvement, this invention effectively avoids the interference of varying lighting conditions on parameter extraction through dark-state testing, significantly improving the stability and accuracy of parameter extraction. Regarding computational efficiency, it employs a regional linearization strategy, cleverly transforming the complex nonlinear optimization problem into multiple simple linear regression problems, greatly reducing computational complexity. In terms of robustness, it successfully avoids the sensitivity of traditional optimization algorithms to initial values, significantly improving the algorithm's convergence stability. In terms of practicality, the required testing equipment is relatively simple, easy to implement in engineering, and has good prospects for industrial application. In summary, this invention not only achieves a significant theoretical breakthrough but also demonstrates excellent practical value and broad market prospects in engineering applications. Attached Figure Description
[0027] Figure 1 This is a schematic diagram of the technology implementation process; Figure 2 This is a diagram of the equivalent circuit topology of a photovoltaic cell with two diodes. Figure 3 This is a partitioned diagram of the dark-state current-voltage characteristic curve of a photovoltaic cell. Figure 4 This is a comparison and verification chart between model predictions and measured data. Detailed Implementation
[0028] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.
[0029] Please see Figures 1 to 4 Example 1: A method for extracting seven parameters of a photovoltaic cell dual-diode model, the method steps are as follows: S1: As Figure 2 The dual-diode equivalent circuit topology of the photovoltaic cell is established as shown, and the physical meaning of the seven parameters is clarified. S1.1: Photocurrent I ph Characterized by the charge carriers generated in a photovoltaic cell under illumination. S1.2: Reverse saturation current of the first diode I 01 : Characteristic parameters reflecting the diffusion current mechanism; S1.3: Reverse saturation current of the second diode I 02 Characteristic parameters that characterize the recombination current mechanism; S1.4: Ideal factor of the first diode n 1: Dimensionless parameters describing the carrier transport characteristics in the diffusion region; S1.5: Ideal factor of the second diode n 2: Dimensionless parameters describing the recombination characteristics of carriers in the depletion region; S1.6: Series resistor R s Internal resistance and loss parameters in the equivalent circuit; S1.7: Parallel resistor R sh : Equivalent resistance characterizing the leakage current characteristics of a battery.
[0030] S2: Based on the IV characteristics of photovoltaic cells, a seven-parameter mathematical model for dual diodes is constructed:
[0031] in, I This refers to the output current of the photovoltaic cell. V This refers to the output voltage of the photovoltaic cell. q The electron charge constant is 1.602e -19 C; k For Boltzmann's constant, 1.381e -23 J / K; T The temperature of the photovoltaic cell.
[0032] S3: Acquisition of dark-state current-voltage characteristic data of photovoltaic cells; In a completely dark room environment, a forward bias voltage is applied to the photovoltaic cells, and dark-state current response data across the entire voltage scan range is collected using a high-precision power measurement instrument to construct a high-resolution database of forward dark-state current-voltage characteristics.
[0033] S4: Implementation of regional parameter extraction algorithm for dark-state current-voltage characteristic curve; The dark-state current-voltage characteristic curve is divided into four characteristic voltage ranges to achieve the connection resistance. R sh Series resistors R s The reverse saturation current of the second diode I 02 Second diode ideal factor n 2. Reverse saturation current of the first diode I 01 First diode ideal factor n Step-by-step extraction of 1: In the low voltage region ( V (<0.2V), dark current is mainly controlled by the parallel resistor, and a simplified mathematical model is established:
[0034] Through least squares linear regression analysis, dark-state current I d and voltage V The data is linearly fitted, and the slope is 1 / R sh Thus, a parallel resistor is obtained. R sh .
[0035] In the high voltage region ( V >0.6V), select four equally spaced sampling points A1 ( V 1, I d1 A2 V 2, I d2 A3 V 3, I d3 ) and A4 ( V 4, I d4 Using the calculation formula:
[0036] This algorithm accurately extracts the series resistance. R s .
[0037] In the low to medium voltage range (0.2V < V <0.5V), within this range, the second diode effect dominates, and a mathematical relationship can be established:
[0038]
[0039]
[0040] By constructing In( I D2 )and V j The linear relationship, with the y-intercept being In( I 02 The slope is 1 / ( n 2 kT / q This determines the reverse saturation current of the second diode. I 02 Second diode ideal factor n 2.
[0041] In the medium to high voltage range (0.5V < V <0.6V), the characteristics of the first diode become apparent in this range, establishing a mathematical relationship:
[0042]
[0043]
[0044]
[0045] By constructing In( I D - I D2 )and V j Linear regression analysis, with the ordinate intercept being In( I 01 The slope is 1 / ( n 1 kT / q This determines the reverse saturation current of the first diode. I 01 and the ideal factor of the first diode n 1.
[0046] S5: Calculation of photocurrent parameters: Combined with the extracted series resistance R s Parallel resistor R sh Short-circuit current I under standard test conditions for photovoltaic cells sc Calculate the photocurrent I ph :
[0047] S6: Model validation and accuracy assessment; Construct a complete double-diode equivalent circuit model using the extracted seven parameters. By comparing the I-V characteristic curves predicted by the model with the measured data, the mean relative error (MRE) and the maximum relative error (MaxRE) are used as evaluation indicators to judge the accuracy of parameter extraction and the reliability of the model: Among them, the calculation formula for the mean relative error is:
[0048] When MRE ≤ 2% and MaxRE ≤ 3%, the accuracy of parameter extraction is excellent, the model reliability is high, and it is suitable for high-precision simulation analysis and system optimization design; When 2% < MRE ≤ 5% and 3% < MaxRE ≤ 8%, the accuracy of parameter extraction is good, the model reliability is relatively high, and it can meet the general engineering application requirements; When 5% < MRE ≤ 10% and 8% < MaxRE ≤ 15%, the accuracy of parameter extraction is qualified, the model reliability is average, and it is recommended to optimize the test conditions or increase the number of sampling points; When MRE > 10% or MaxRE > 15%, the accuracy of parameter extraction is insufficient, the model reliability is low, and it is necessary to re-collect data and extract parameters.
[0049] Example 2: Select a monocrystalline silicon photovoltaic cell as the test object, and perform parameter extraction experiments according to the method of the present invention in a standard laboratory environment (temperature 25°C). During the experiment, under completely shaded conditions, a precision source measurement instrument is used to perform voltage scanning on the photovoltaic cell. The scanning range is 0 - 0.7V, the sampling interval is set to 0.01V, and a total of 71 effective data points are obtained.
[0050] Construct a seven-parameter model based on the obtained experimental data, and compare and analyze the predicted results of the model with the measured I-V characteristic curves.
[0051] As Figure 4 The results show that the mean relative error (MRE) between the predicted value of the model and the measured value is less than 1.5%, and the maximum relative error (MaxRE) does not exceed 2.8%, fully verifying that the method of the present invention has high precision and good reliability.
[0052] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.
Claims
1. A method for extracting seven parameters of a photovoltaic cell dual-diode model, characterized in that, The method steps are as follows: S1: Establish the dual-diode equivalent circuit topology of the photovoltaic cell and clarify the physical meaning of the seven parameters; S2: Based on the IV characteristics of photovoltaic cells, a seven-parameter mathematical model of dual diodes is constructed; S3: Acquisition of dark-state current-voltage characteristic data of photovoltaic cells; S4: Calculation of photocurrent parameters; S5: Determine the photogenerated current based on the extracted series and parallel resistances; S6: Model validation and accuracy assessment.
2. The method for extracting seven parameters of a photovoltaic cell dual-diode model according to claim 1, characterized in that: In S1: S1.1: Photocurrent I ph Characterized by the charge carriers generated in a photovoltaic cell under illumination. S1.2: Reverse saturation current of the first diode I 01 : Characteristic parameters reflecting the diffusion current mechanism; S1.3: Reverse saturation current of the second diode I 02 Characteristic parameters that characterize the recombination current mechanism; S1.4: Ideal factor of the first diode n 1: Dimensionless parameters describing the carrier transport characteristics in the diffusion region; S1.5: Ideal factor of the second diode n 2: Dimensionless parameters describing the recombination characteristics of carriers in the depletion region; S1.6: Series resistor R s Internal resistance and loss parameters in the equivalent circuit; S1.7: Parallel resistor R sh : Equivalent resistance characterizing the leakage current characteristics of a battery.
3. The method for extracting seven parameters of a photovoltaic cell dual-diode model according to claim 2, characterized in that: In S2: Based on the IV characteristics of photovoltaic cells, a seven-parameter mathematical model for dual diodes is constructed: in, I This refers to the output current of the photovoltaic cell. V This refers to the output voltage of the photovoltaic cell. q is the electron charge constant, with a value of 1.602e. -19 C; k is the Boltzmann constant, with a value of 1.381e. -23 J / K; T The temperature of the photovoltaic cell.
4. The method for extracting seven parameters of a photovoltaic cell dual-diode model according to claim 3, characterized in that: In S3: In a completely dark room environment, a forward bias voltage is applied to the photovoltaic cells, and dark-state current response data across the entire voltage scan range is collected using a high-precision power measurement instrument to construct a high-resolution database of forward dark-state current-voltage characteristics.
5. The method for extracting seven parameters of a photovoltaic cell dual-diode model according to claim 4, characterized in that: In S4: The dark-state current-voltage characteristic curve is divided into four characteristic voltage ranges to realize parallel resistance. R sh Series resistors R s The reverse saturation current of the second diode I 02 Second diode ideal factor n 2. Reverse saturation current of the first diode I 01 First diode ideal factor n Step-by-step extraction of 1: In the low voltage region ( V (<0.2V), dark current is mainly controlled by the parallel resistor, and a simplified mathematical model is established: Through least squares linear regression analysis, dark-state current I d and voltage V The data is linearly fitted, and the slope is 1 / R sh Thus, a parallel resistor is obtained. R sh ; In the high voltage region ( V >0.6V), select four equally spaced sampling points A1 ( V 1, I d1 A2 V 2, I d2 A3 V 3, I d3 ) and A4 ( V 4, I d4 Using the calculation formula: This calculation formula accurately extracts the series resistance. R s ; In the low to medium voltage range (0.2V < V <0.5V), within this range, the second diode effect dominates, and a mathematical relationship can be established: By constructing In( I D2 )and V j The linear relationship is given by the y-intercept Ln( I 02 The slope is 1 / ( n 2 kT / q This determines the reverse saturation current of the second diode. I 02 Second diode ideal factor n 2; In the medium to high voltage range (0.5V < V <0.6V), the characteristics of the first diode become apparent in this range, establishing a mathematical relationship: By constructing In( I D - I D2 )and V j Linear regression analysis, with the ordinate intercept being In( I 01 The slope is 1 / ( n 1 kT / q This determines the reverse saturation current of the first diode. I 01 and the ideal factor of the first diode n 1.
6. The method for extracting seven parameters of a photovoltaic cell dual-diode model according to claim 5, characterized in that: In S5: Combined with the extracted series resistance R s Parallel resistor R sh Short-circuit current I under standard test conditions for photovoltaic cells sc Calculate the photocurrent I p .
7. The method for extracting seven parameters of a photovoltaic cell dual-diode model according to claim 6, characterized in that: In S6: A complete dual-diode equivalent circuit model was constructed using the extracted seven parameters. By comparing the IV characteristic curves predicted by the model with the measured data, the mean relative error (MRE) and maximum relative error (MaxRE) were used as evaluation indicators to judge the accuracy of parameter extraction and the reliability of the model.