A photovoltaic cell voltage-current characteristic curve testing system based on integrated circuit

Abstract

The application relates to the technical field of photovoltaic cell testing, and particularly discloses a photovoltaic cell volt-ampere characteristic curve testing system based on an integrated circuit, which comprises a programmable power supply module, a high-precision data acquisition module, a core control and processing module and a man-machine interaction module. The core control and processing module dynamically adjusts the scanning voltage step length based on the curvature estimation of real-time data acquisition, and performs battery type identification and fault early warning simultaneously while encrypting sampling in the region with sharp curve change and sparsely sampling in the region with gentle curve change. The application realizes the balance between testing precision and efficiency, and provides online diagnosis capability.

Description

Technical Field

[0001] This invention belongs to the field of photovoltaic cell testing technology, specifically relating to a photovoltaic cell current-voltage characteristic curve testing system based on integrated circuits. Background Technology

[0002] As a crucial component of clean energy, photovoltaic power generation relies heavily on the performance evaluation and quality testing of its core component, photovoltaic cells, as a key technological aspect of the industry's development. The current-voltage characteristic curve is the core basis for characterizing the output characteristics of photovoltaic cells, directly reflecting key parameters such as open-circuit voltage, short-circuit current, and maximum power point. It is of decisive significance for cell performance analysis, system matching, and fault diagnosis.

[0003] The current-voltage characteristic curve testing system based on electronic load or data acquisition card is the main technical means to achieve the above evaluation. This type of system controls the load or applies a scanning voltage to simultaneously acquire the voltage and current data points of the battery, and finally plots a complete current-voltage characteristic curve.

[0004] Linear scanning or fixed-step scanning strategies are used to acquire data points. This scanning method has inherent technical limitations: the scanning step size is preset and uniform, making it impossible to adaptively adjust according to the actual shape of the photovoltaic cell's current-voltage characteristic curve. This results in insufficient data point density in critical regions where the curve changes drastically, such as near the maximum power point, making it difficult to accurately capture the subtle characteristics of the power inflection point; while in relatively flat regions, such as near the open-circuit voltage or short-circuit current, unnecessary oversampling occurs, wasting test time and computational resources. Furthermore, this mechanical scanning process is completely independent of the real-time response of the tested cell; the system cannot analyze and determine the cell type or potential fault status based on the acquired data during the scanning process. Summary of the Invention

[0005] The purpose of this invention is to provide a photovoltaic cell current-voltage characteristic curve testing system based on integrated circuits, in order to solve the problems in the prior art that the fixed step size scanning strategy leads to insufficient sampling accuracy in the key area of ​​the current-voltage characteristic curve, redundant sampling in the flat area, and the disconnect between the testing process and the real-time status of the cell, making online analysis and type identification impossible.

[0006] This invention provides a photovoltaic cell current-voltage characteristic curve testing system based on integrated circuits. The system includes a programmable power supply module, a high-precision data acquisition module, a core control and processing module, and a human-machine interface module. The programmable power supply module applies precisely controllable scanning voltage or current excitation to the photovoltaic cell under test. The high-precision data acquisition module synchronously and in real-time acquires the response voltage and response current signals of the photovoltaic cell under test under excitation and converts them into digital quantities. The core control and processing module integrates an adaptive scanning strategy generation unit, a real-time characteristic analysis and type identification unit, and a curve reconstruction and parameter extraction unit. The human-machine interface module receives user commands and displays test results and system status.

[0007] The programmable power supply module includes a digital-to-analog converter, a power amplifier circuit, and an output protection circuit. The digital-to-analog converter receives digital control commands from the core control and processing module and converts them into an analog voltage reference signal. The power amplifier circuit amplifies this analog voltage reference signal to provide the scanning excitation required to drive the photovoltaic cell under test. The output protection circuit integrates overvoltage, overcurrent, and reverse current protection functions to ensure the safety of the testing process and the integrity of the device under test.

[0008] The high-precision data acquisition module includes a voltage sampling channel, a current sampling channel, and a synchronous sampling analog-to-digital converter. The voltage sampling channel uses a high-input-impedance differential amplifier directly connected to the two ends of the photovoltaic cell under test to accurately measure its terminal voltage. The current sampling channel employs a solution based on a precision sampling resistor and instrumentation amplifier, or integrates an isolated current sensor, to accurately measure the current flowing through the cell. The synchronous sampling analog-to-digital converter ensures that the voltage and current signals are sampled and converted into digital quantities simultaneously; its sampling rate and resolution are dynamically configured by the core control and processing module.

[0009] The core control and processing module, serving as the system's intelligent hub, has an internal adaptive scanning strategy generation unit that initiates testing based on preset initial scanning parameters and dynamically adjusts subsequent scanning step sizes according to real-time feedback data from the high-precision data acquisition module. This unit incorporates a curvature estimation algorithm, the specific process of which is as follows: The system acquires voltage and current data from the current sampling point and several preceding historical sampling points, calculates the first derivative (dynamic conductance) of the volt-ampere characteristic curve at the current sampling point using numerical differentiation, and further estimates its second derivative (curvature).

[0010] The system sets a first curvature threshold and a second curvature threshold, where the first curvature threshold is higher than the second curvature threshold. When the estimated absolute value of curvature is greater than the first curvature threshold, it is determined that the current region is in a region of drastic curve change, and the adaptive scanning strategy generation unit immediately reduces the subsequent voltage scan step size to a first preset step size value, which is significantly smaller than the initial step size. When the estimated absolute value of curvature is less than the second curvature threshold, it is determined that the current region is in a region of gentle curve change, and the adaptive scanning strategy generation unit increases the subsequent voltage scan step size to the second preset step size value, which is greater than the initial step size. When the absolute value of curvature is between the second curvature threshold and the first curvature threshold, the current scan step size remains unchanged.

[0011] Through this closed-loop feedback mechanism, the system achieves high-density sampling in key areas such as near the maximum power point, while performing sparse sampling in flat areas close to the open-circuit voltage and short-circuit current. This significantly improves the overall testing efficiency while ensuring the accuracy of capturing key curve features.

[0012] Furthermore, the real-time characteristic analysis and type identification unit within the core control and processing module operates in parallel during the scanning process. This unit continuously receives real-time data streams from the high-precision data acquisition module and performs online parameter estimation and pattern matching. The online parameter estimation process uses the least squares method to fit the approximate equivalent circuit model parameters of the current local area based on several recently acquired data points, including estimated values ​​for series and parallel resistances. The pattern matching process compares the estimated parameters with a database of various typical photovoltaic cell fault characteristics pre-stored in the module's non-volatile memory.

[0013] The feature library contains standard parameter ranges for normal monocrystalline silicon cells, polycrystalline silicon cells, and thin-film cells, as well as abnormal parameter characteristics corresponding to typical fault modes such as hot spots, aging, microcracks, and bypass diode failure. When the real-time estimated parameters continuously deviate from the normal range and the degree of matching with a certain fault feature library entry exceeds a preset confidence threshold, the real-time characteristic analysis and type identification unit will generate warning information containing the suspected fault type and confidence level, and will provide real-time prompts to the user through the human-machine interaction module. At the same time, it can trigger the interruption of the scanning process or switch to a targeted fine diagnostic scanning mode.

[0014] After completing all adaptive scans, the curve reconstruction and parameter extraction unit receives all acquired non-uniformly distributed data points. This unit first uses an algorithm based on cubic spline interpolation to smooth and fit all data points, generating a complete and smooth volt-ampere characteristic curve. Subsequently, based on this fitted curve, the unit accurately calculates the key performance parameters of the photovoltaic cell: determining the open-circuit voltage by finding points where the current is zero, determining the short-circuit current by finding points where the voltage is zero, determining the maximum power point and its corresponding voltage and current values ​​by iterating through the product of power and voltage and finding the maximum value, and further calculating the fill factor and conversion efficiency. All calculation results, along with the fitted curve, are sent to the human-computer interaction module for display and output.

[0015] In one embodiment of the present invention, the human-computer interaction module includes an LCD touch screen and physical buttons. The LCD touch screen is used to graphically display the real-time scanning process, dynamically updated volt-ampere characteristic curves, real-time estimated parameters, type identification results, and the final test report. The physical buttons are used to provide basic control functions such as emergency stop, start test, and mode selection. The module also integrates a standard communication interface for uploading test data and reports to a host computer or data center.

[0016] Furthermore, the system's adaptive scanning strategy generation unit also integrates scanning termination condition judgment logic. This logic continuously monitors the collected current values. When the current values ​​corresponding to multiple consecutive scanning points are lower than a preset near-zero current threshold, and the voltage continues to increase while the current does not change significantly, it is determined that the voltage has reached the vicinity of the actual open-circuit voltage, and the voltage scanning is automatically terminated to avoid invalid overscanning.

[0017] In a preferred embodiment of the present invention, the synchronous sampling analog-to-digital converter in the high-precision data acquisition module is a 24-bit resolution model with a sampling rate of not less than 100 kHz, to ensure that extremely high measurement accuracy is maintained even when the scan step size is reduced for high-speed sampling. The input impedance of the voltage sampling channel is not less than 10 megohms to minimize the impact on the output characteristics of the photovoltaic cell under test.

[0018] Furthermore, the core control and processing module is implemented using a system-on-a-chip (SoC) that integrates a high-performance processor core, a digital-to-analog converter controller, an analog-to-digital converter interface, and ample storage resources. The adaptive scanning strategy generation unit, real-time characteristic analysis and type recognition unit, and curve reconstruction and parameter extraction unit are integrated into this chip as firmware or hardware-accelerated logic, thereby achieving high-speed, low-latency data processing and closed-loop control.

[0019] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention fundamentally changes the traditional fixed-step scanning mechanical mode by introducing an adaptive scanning strategy based on real-time curvature estimation. The system can dynamically adjust the sampling density according to the local morphology of the current-voltage characteristic curve, automatically increasing sampling density in the region of maximum power with drastic changes to accurately capture the power inflection point characteristics, and automatically reducing sampling density in the region of smooth flow to reduce redundant data. This data-driven intelligent sampling method significantly shortens the overall testing time while ensuring the testing accuracy in critical areas, and improves the overall efficiency and resource utilization of the testing system.

[0020] 2. This invention innovatively integrates online real-time characteristic analysis and type identification functions, elevating the testing process from a passive data acquisition process to an active online diagnostic process. While scanning, the system analyzes the battery's real-time response in parallel. Through parameter estimation and pattern matching, it can instantly identify the basic battery type and detect potential fault signs. This capability enables the system not only for performance evaluation but also as an effective tool for rapid quality screening and early fault warning on the production line, enhancing the system's application value and practicality.

[0021] 3. This invention employs a highly integrated system-on-a-chip (SoC) as the core control and processing module, integrating complex algorithms such as adaptive scan control, real-time data analysis, and curve reconstruction into a single chip. This architecture reduces communication latency between internal modules, achieving millisecond-level closed-loop control of scan excitation and data acquisition, ensuring the real-time response of the adaptive strategy. Simultaneously, the integrated design improves system reliability, reduces hardware complexity and cost, and facilitates the large-scale application and promotion of this testing system. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the overall technical solution architecture of the present invention; Figure 2 This is a schematic diagram of the core principle framework of the adaptive scanning strategy generation unit in this invention; Figure 3 This is a logical flow diagram of the real-time feature analysis and type identification unit in this invention; Figure 4 This is a schematic diagram of the multi-level interaction relationship and data flow between the core control and processing module, the programmable power supply, and the data acquisition module in this invention. Detailed Implementation

[0023] This invention provides a photovoltaic cell current-voltage characteristic curve testing system based on integrated circuits, the overall technical architecture of which is shown in the attached figure. Figure 1As shown in the figure. The system consists of four main functional units: a programmable power supply module, a high-precision data acquisition module, a core control and processing module, and a human-machine interface module. These units communicate with each other via a high-speed internal bus or dedicated interface to achieve low-latency, high-synchronization data exchange and command control, thus forming a closed-loop, adaptive, and online diagnostic intelligent testing platform. The following will be discussed in conjunction with the attached... Figures 1 to 4 The specific implementation methods of each component of the system are described in detail.

[0024] The programmable power supply module, acting as the system's excitation source, is responsible for applying a controllable scanning voltage or current signal to the photovoltaic cell under test. Internally, this module comprises three key substructures: a digital-to-analog converter (DAC), a power amplifier circuit, and an output protection circuit. The DAC receives digital control commands from the core control and processing module. These commands represent the target output voltage value in 16-bit or higher resolution digital form and are converted into a corresponding analog voltage reference signal. This analog voltage reference signal is then fed into the power amplifier circuit for power amplification, enabling it to drive the photovoltaic cell under test load.

[0025] The power amplifier circuit employs a linear regulator or switching mode power regulation architecture with a bandwidth of at least 10 kHz to ensure good dynamic response performance even when the adaptive scanning strategy requires rapid adjustment of the output voltage. The output protection circuit integrates multiple safety mechanisms, including overvoltage protection, overcurrent protection, and reverse current protection. The overvoltage protection threshold is set at 1.2 times the nominal open-circuit voltage of the photovoltaic cell under test; the overcurrent protection threshold is preset to 1.5 times the maximum short-circuit current of the cell under test; and the reverse current protection uses unidirectional conduction devices or active detection logic to prevent reverse current surges from external power supplies or energy storage components, thus comprehensively ensuring the safety of the device under test and the test equipment.

[0026] The high-precision data acquisition module is used to synchronously and in real-time acquire the terminal voltage and output current response signals of the photovoltaic cell under test under excitation, and accurately convert them into digital quantities that can be analyzed by the core control and processing module. This module consists of a voltage sampling channel, a current sampling channel, and a synchronous sampling analog-to-digital converter. The voltage sampling channel uses a high-input-impedance differential amplifier directly connected to the positive and negative terminals of the photovoltaic cell under test, with an input impedance of no less than 10 megohms, to minimize the load effect on the operating state of the cell under test. The differential amplifier has a common-mode rejection ratio of no less than 90 dB and a gain error of less than 0.1%, ensuring accurate extraction of weak voltage signals even in environments with strong electromagnetic interference.

[0027] The current sampling channel employs two optional schemes: The first is a non-isolated structure based on a precision sampling resistor and instrumentation amplifier, with the sampling resistor value ranging from 1 milliohm to 10 milliohms and a temperature coefficient better than 50 ppm / degree Celsius. This, combined with a high-precision instrumentation amplifier, amplifies and filters the microvolt-level voltage drop. The second scheme is an integrated isolated current sensor solution, utilizing the Hall effect or magnetic modulation principle to achieve high-precision current measurement under electrical isolation, suitable for applications with high safety isolation requirements. The synchronous sampling analog-to-digital converter is the core of the entire data acquisition module, and it must meet the technical specifications of 24-bit effective resolution and a sampling rate of not less than 100 kHz.

[0028] The converter simultaneously triggers sampling and quantization operations on both the voltage and current analog signals on the same clock edge, ensuring strict alignment of the two signals in the time domain and eliminating phase errors introduced by asynchronous sampling. The converter's reference voltage source is provided by a low-noise, low-temperature-drift reference source, with long-term stability better than 10ppm / year, guaranteeing measurement consistency across the entire measurement range. All acquired raw data is temporarily stored in a local buffer in 32-bit floating-point format and transmitted in real time to the core control and processing module via a high-speed serial interface (such as SPI or LVDS).

[0029] The core control and processing module, serving as the intelligent hub of the entire system, undertakes multiple tasks, including scanning strategy generation, real-time data analysis, curve reconstruction, and parameter extraction. This module is preferably implemented using a highly integrated system-on-a-chip (SoC). This chip integrates a high-performance processor core (with a clock speed of at least 500 MHz), a dedicated digital-to-analog converter controller, an analog-to-digital converter interface controller, a large-capacity on-chip memory (including at least 512 kilobytes of SRAM and 4 megabytes of flash memory), and various standard communication peripherals. The adaptive scanning strategy generation unit, real-time characteristic analysis and type recognition unit, and curve reconstruction and parameter extraction unit are all deployed within this chip as embedded firmware or hardware acceleration logic, thereby achieving millisecond-level or even microsecond-level closed-loop control response speeds.

[0030] The workflow of the adaptive scanning strategy generation unit is shown in the attached figure. Figure 2 As shown. This unit first initiates the scanning process based on the initial test conditions (including starting voltage, ending voltage, initial scan step size, etc.) set by the user through the human-computer interaction module. During the scanning process, the unit continuously receives real-time voltage-current data pairs from the high-precision data acquisition module and performs curvature estimation calculations based on the current sampling point and several preceding historical sampling points (typically 3 to 5). The core of the curvature estimation algorithm lies in the dynamic perception of the local morphology of the volt-ampere characteristic curve. The system first calculates the first derivative at the current point, i.e., the dynamic conductance, using the central difference method:

[0031] in, and They represent the first The current and voltage values ​​at each sampling point are then used. Based on this, the second derivative, i.e., the curvature, is further estimated using a three-point quadratic fitting or finite difference method. The system presets two curvature thresholds: the first curvature threshold... With the second curvature threshold And satisfy When the estimated absolute value of curvature When the system determines that the voltage-current characteristic curve is in a region of drastic change (such as near the maximum power point), the adaptive scanning strategy generation unit immediately reduces the subsequent voltage scanning step size to the first preset step size value. This value is typically 1 / 5 to 1 / 10 of the initial step size, for example, decreasing from an initial 0.1 volt to 0.01 volt. When If the system determines that the current position is in a flat region of the curve (such as near the open-circuit voltage or short-circuit current terminal), then the subsequent scanning step size is increased to the second preset step size value. This value can be 2 to 5 times the initial step size, for example, increasing to 0.3 volts. When the absolute value of curvature is between and During this process, the current scan step size remains unchanged. This dynamic adjustment mechanism ensures that sampling points are densely distributed in critical areas and sparsely distributed in non-critical areas, significantly improving data validity and testing efficiency.

[0032] Furthermore, the adaptive scanning strategy generation unit also integrates scanning termination condition judgment logic. This logic continuously monitors the acquired current value sequence. When the current value corresponding to three or more consecutive scanning points is lower than the preset near-zero current threshold (e.g., 1 mA), and the voltage continues to increase while the current does not show a significant upward trend (i.e., the current change rate is less than 0.1 mA / volt), the system determines that it has approached the actual open-circuit voltage, automatically terminates the voltage scanning process, avoids redundant sampling in invalid areas, saves testing time, and prevents unnecessary high-voltage stress on the battery.

[0033] The real-time feature analysis and type recognition unit runs in parallel with the adaptive scanning strategy generation unit during the scanning process. Its implementation logic is shown in the attached figure. Figure 3 As shown, this unit continuously receives the real-time data stream output from the high-precision data acquisition module and selects several newly acquired data points (the window size is usually 8 to 16 points) using a sliding window method for online parameter estimation. The estimation process uses the weighted least squares method to fit the local current-voltage characteristic curve segment, establishing a simplified single-diode equivalent circuit model, and then calculating the series resistance from it. With parallel resistor The real-time estimated value. Specifically, the model equation is:

[0034] in, For photocurrent, It is the reverse saturation current. For diode ideality factor, The number of batteries connected in series. This is the thermal voltage. Under the assumption of local linearization, it can be solved quickly using an iterative algorithm. and The obtained parameter estimates are then fed into the pattern matching engine. This engine accesses a photovoltaic cell feature library pre-stored in the system-on-a-chip non-volatile memory.

[0035] The feature library contains standard parameter ranges for normal monocrystalline silicon cells, polycrystalline silicon cells, and thin-film cells (e.g., monocrystalline silicon cells). Typically less than 0.1 ohms, (greater than 100 ohms), and anomalous feature templates for typical failure modes, including hot spots (manifested as localized hot spots). Significantly increased), aging ( Faults can be caused by various factors, including: a decrease in fill factor, microcracks (sudden current drop, curve distortion), and bypass diode failure (abnormal current plateau in a specific voltage range). Pattern matching uses Euclidean distance or cosine similarity to measure the degree of matching between real-time parameters and each template. When the matching degree of a fault template exceeds a preset confidence threshold (e.g., 95%) and the duration exceeds a preset window (e.g., 5 consecutive sampling periods), the real-time characteristic analysis and type identification unit generates an early warning message containing the suspected fault type, confidence percentage, and suggested actions. This message is transmitted to the human-machine interface module for real-time display via an internal message queue and can trigger an interrupt signal, causing the system to pause the current scan and switch to a targeted fine-tuning diagnostic mode (e.g., performing ultra-fine step scans in the suspected fault voltage range or applying pulse excitation to verify the diode status).

[0036] The curve reconstruction and parameter extraction unit starts working after completing all adaptive scans. Because the adaptive scan strategy results in a non-uniform distribution of sampling points on the voltage axis, directly plotting the original data points would lead to discontinuous or distorted curves. Therefore, this unit first preprocesses all acquired data points to remove outliers caused by noise or transient interference (identified using the 3σ criterion or median filtering). Subsequently, an algorithm based on cubic spline interpolation is used to perform global smoothing fitting on the effective data points. Cubic spline interpolation, while ensuring the continuity of the first and second derivatives of the curve, preserves the local features of the original data to the greatest extent, generating a smooth, continuous, and physically meaningful current-voltage characteristic curve.

[0037] Based on this fitted curve, the unit performs precise extraction of key performance parameters: open-circuit voltage. The short-circuit current is determined by finding the point on the fitted curve where the current value is closest to zero, with an accuracy of up to 0.001 volts. The maximum power point is determined by finding the point where the voltage value is closest to zero, with an accuracy of 0.1 mA; By traversing all points on the fitted curve The value is obtained by locating its global maximum value, and the corresponding voltage is recorded. With current ; Fill factor According to the formula Calculation; Conversion efficiency The calculation is then normalized by combining the effective area of ​​the tested battery with the irradiance under standard test conditions. All parameter results, along with the fitted curve data, are packaged and sent to the human-computer interaction module via the internal bus.

[0038] The human-computer interaction module, acting as a bridge between the user and the system, comprises an LCD touchscreen display and physical buttons. The LCD touchscreen uses a 7-inch or larger color TFT-LCD with a resolution of 800×480 or higher, supporting multi-layer overlay display. During testing, the screen dynamically updates the plotting process of the volt-ampere characteristic curve in real time, using different colors to indicate key information such as the current scanning area, sampled points, and the location of the maximum power point; simultaneously, it displays the real-time estimated... , The screen displays values ​​such as current power and scanning progress; when the real-time characteristic analysis and type identification unit issues an early warning, a prominent prompt box pops up on the screen, displaying the fault type and confidence level.

[0039] After the test, the screen switches to the results page, displaying the final fitted curve, all key parameter values, battery type identification results, and health status assessment. Physical buttons include an emergency stop button, a start / pause button, and a mode selection knob, providing basic control capabilities that remain operational even in situations where the touchscreen fails or in bright sunlight. Furthermore, the human-machine interface module integrates standard communication interfaces, including USB 2.0, RS-485, and Ethernet interfaces, supporting the uploading of complete test data packages (including original sampling points, fitted curves, parameter results, and diagnostic logs) in JSON or CSV format to host computer software or a remote data center, facilitating batch analysis and quality traceability.

[0040] The multi-level interaction relationships and data flow of the entire system are shown in the attached figure. Figure 4As shown, the core control and processing module is located at the top level. It sends digital control commands to the programmable power supply module, configures sampling parameters and reads data from the high-precision data acquisition module, and pushes status information and result data to the human-machine interface module. Although there is no direct data exchange between the programmable power supply module and the high-precision data acquisition module, they are physically coupled through the photovoltaic cell under test, and their excitation and response form a closed-loop test circuit. The functional units within the core control and processing module achieve efficient collaboration through shared memory and an event-driven mechanism: the adaptive scan strategy generation unit updates the scan command queue after each step size decision; the real-time characteristic analysis and type identification unit extracts samples from the data stream for analysis at fixed intervals (e.g., every 10 milliseconds); and the curve reconstruction and parameter extraction unit starts after the scan end event is triggered. This architecture ensures that the system maintains high real-time performance while possessing powerful concurrent processing capabilities.

[0041] In summary, the integrated circuit-based photovoltaic cell current-voltage characteristic curve testing system described in this embodiment achieves rapid, accurate, and intelligent evaluation of photovoltaic cell performance by deeply integrating three core technologies: adaptive scanning, real-time diagnostics, and high-precision data acquisition. The system not only efficiently acquires high-fidelity current-voltage characteristic curves but also simultaneously identifies cell type and provides early fault warnings during the testing process, significantly enhancing the functionality and practical value of the testing system.

[0042] Example 2: Based on the previous examples, this example optimizes the current sampling channel design of the high-precision data acquisition module to adapt to the testing needs of photovoltaic modules of different sizes. Specifically, the current sampling channel adopts a modular, scalable multi-range automatic switching architecture. This architecture includes multiple parallel precision sampling branches, each corresponding to a different range. For example, the first branch is suitable for small current measurements of 0 to 1 ampere, using a 10 milliohm sampling resistor; the second branch is suitable for medium currents of 1 to 10 amperes, using a 1 milliohm sampling resistor; and the third branch is suitable for large currents of 10 to 100 amperes, using a 0.1 milliohm sampling resistor.

[0043] Each branch is equipped with an electronic switch (such as a MOSFET relay) at its front end, and the core control and processing module dynamically selects the active branch based on the estimated short-circuit current of the tested battery or the initial sampled value. The switching logic incorporates a hysteresis mechanism to prevent frequent switching near the range boundaries. Furthermore, the gain of the instrumentation amplifier for each branch is programmable to match the voltage drop range generated by different sampling resistors, ensuring that the analog-to-digital converter always operates within its optimal linear range. This design allows a single test system to seamlessly cover a full range of test objects, from single small-power cells to large photovoltaic modules, without requiring hardware replacement, greatly improving the equipment's versatility and cost-effectiveness.

[0044] Meanwhile, this embodiment enhances the real-time characteristic analysis and type identification unit of the core control and processing module. In addition to pattern matching based on equivalent circuit parameters, this unit adds a deep learning-assisted identification mechanism based on the shape characteristics of the volt-ampere curve. Before leaving the factory, the system trains a lightweight convolutional neural network model using a large number of labeled samples (including measured curves of normal and various faulty batteries). This model is compressed and embedded in a dedicated neural network accelerator on the system-on-a-chip. During testing, real-time acquired volt-ampere data segments are normalized and input into the model, which outputs the probability distribution of each fault category. This probability distribution is weighted and fused with the traditional parameter matching results to form the final comprehensive diagnostic conclusion. This fusion strategy significantly improves the identification accuracy of complex or compound faults (such as those with both microcracks and aging), maintaining high diagnostic reliability even when parameter characteristics are atypical.

[0045] In addition, the human-machine interface module in this embodiment adds wireless communication functionality. Besides the wired interface, the module integrates a dual-mode wireless module with Wi-Fi 6 and Bluetooth 5.0, supporting remote monitoring of the testing process, receiving alert notifications, and downloading test reports via a mobile terminal application. Wireless communication employs the AES-256 encryption protocol to ensure data transmission security. This function is particularly suitable for on-site operation and maintenance scenarios in distributed photovoltaic power plants, allowing technicians to complete high-voltage string testing operations from a safe distance, improving operational safety and convenience.

[0046] Through the above improvements, this embodiment further expands the application boundaries of the system, enhances diagnostic capabilities, and improves the flexibility of human-computer interaction, making it more suitable for large-scale industrial deployments and complex field environments.

Claims

1. A photovoltaic cell current-voltage characteristic curve testing system based on integrated circuits, characterized in that, include: A programmable power supply module for applying precisely controllable scanning voltage or current excitation to the photovoltaic cell under test; A high-precision data acquisition module is used to synchronously and in real time acquire the response voltage and response current signals of the photovoltaic cell under test under excitation, and convert them into digital quantities; The core control and processing module is used to dynamically adjust the subsequent scanning step size based on the real-time feedback data from the high-precision data acquisition module, and to perform online parameter estimation and pattern matching in parallel during the scanning process, as well as to reconstruct curves and extract parameters from the non-uniformly distributed data points after completing all adaptive scans. The human-computer interaction module is used to receive user commands and display test results and system status; The core control and processing module integrates an adaptive scanning strategy generation unit, a real-time characteristic analysis and type identification unit, and a curve reconstruction and parameter extraction unit. The adaptive scanning strategy generation unit starts the test based on preset initial scanning parameters and has a built-in curvature estimation algorithm; The real-time feature analysis and type recognition unit continuously receives real-time data streams from the high-precision data acquisition module during the scanning process and performs online parameter estimation and pattern matching. After completing all adaptive scans, the curve reconstruction and parameter extraction unit receives all the non-uniformly distributed data points collected and uses an algorithm based on cubic spline interpolation to smooth and fit all data points to generate a complete and smooth volt-ampere characteristic curve. Subsequently, based on the volt-ampere characteristic curve, the unit accurately calculates the key performance parameters of the photovoltaic cell. The curvature estimation algorithm obtains the voltage and current data of the current sampling point and several previous historical sampling points, calculates the first derivative of the current-voltage characteristic curve at the current sampling point, i.e., the dynamic conductance, through numerical differentiation, and further estimates its second derivative, i.e., the curvature. The system sets a first curvature threshold and a second curvature threshold, wherein the first curvature threshold is higher than the second curvature threshold; when the estimated absolute value of curvature is greater than the first curvature threshold, it is determined that the current area is in a region of drastic curve change, and the adaptive scanning strategy generation unit immediately reduces the subsequent voltage scanning step size to a first preset step size value, which is significantly smaller than the initial step size; When the estimated absolute value of curvature is less than the second curvature threshold, it is determined that the current region is a flat curve region. The adaptive scanning strategy generation unit then increases the subsequent voltage scanning step size to the second preset step size value, which is greater than the initial step size. When the absolute value of curvature is between the second curvature threshold and the first curvature threshold, the current scan step size remains unchanged.

2. The photovoltaic cell current-voltage characteristic curve testing system based on integrated circuits according to claim 1, characterized in that, The online parameter estimation process is based on several newly acquired data points. The least squares method is used to fit the approximate equivalent circuit model parameters of the current local area, including the estimated values ​​of series resistance and parallel resistance. The pattern matching process compares the estimated parameters with a library of typical photovoltaic cell fault features pre-stored in the module's non-volatile memory; When the real-time estimated parameters continuously deviate from the normal range and the degree of matching with a certain fault feature library entry exceeds the preset confidence threshold, the real-time characteristic analysis and type identification unit will generate a warning message containing the suspected fault type and confidence level.

3. The photovoltaic cell current-voltage characteristic curve testing system based on integrated circuits according to claim 2, characterized in that, The key performance parameters include open-circuit voltage, short-circuit current, maximum power point power and its corresponding voltage and current values, fill factor and conversion efficiency.

4. The photovoltaic cell current-voltage characteristic curve testing system based on integrated circuits according to claim 3, characterized in that, The programmable power module includes a digital-to-analog converter, a power amplifier circuit, and an output protection circuit. The digital-to-analog converter receives digital control commands from the core control and processing module and converts them into analog voltage reference signals. The power amplifier circuit amplifies the analog voltage reference signal to provide the scanning excitation required to drive the photovoltaic cell under test. The output protection circuit integrates overvoltage, overcurrent, and reverse current protection functions.

5. The photovoltaic cell current-voltage characteristic curve testing system based on integrated circuits according to claim 4, characterized in that, The high-precision data acquisition module includes a voltage sampling channel, a current sampling channel, and a synchronous sampling analog-to-digital converter. The voltage sampling channel uses a high input impedance differential amplifier directly connected to both ends of the photovoltaic cell under test to accurately measure its terminal voltage. The current sampling channel adopts a solution based on a precision sampling resistor and an instrumentation amplifier, or integrates an isolated current sensor, for accurately measuring the current flowing through the battery; The synchronous sampling analog-to-digital converter ensures that voltage and current signals are sampled and converted into digital quantities at the same time, and its sampling rate and resolution are dynamically configured by the core control and processing module.

6. The photovoltaic cell current-voltage characteristic curve testing system based on integrated circuits according to claim 5, characterized in that, The adaptive scanning strategy generation unit also integrates scanning termination condition judgment logic; The scanning termination condition judgment logic continuously monitors the collected current value. When the current value corresponding to multiple consecutive scanning points is lower than the preset near-zero current threshold, and the voltage continues to increase while the current does not change significantly, it is determined that the voltage has reached the vicinity of the actual open circuit voltage and the voltage scanning is automatically terminated.

7. The photovoltaic cell current-voltage characteristic curve testing system based on integrated circuits according to claim 6, characterized in that, The core control and processing module is implemented by a system-on-a-chip that integrates a high-performance processor core, a digital-to-analog converter controller, an analog-to-digital converter interface, and sufficient storage resources. The adaptive scanning strategy generation unit, real-time feature analysis and type recognition unit, and curve reconstruction and parameter extraction unit are integrated into the chip in the form of firmware or hardware acceleration logic.

8. The photovoltaic cell current-voltage characteristic curve testing system based on integrated circuits according to claim 7, characterized in that, The human-computer interaction module includes an LCD touch screen and physical buttons; The LCD touch screen is used to graphically display the real-time scanning process, dynamically updated volt-ampere characteristic curves, real-time estimated parameters, type identification results, and the final test report. The physical buttons are used to provide basic control functions such as emergency stop, start test, and mode selection. The human-computer interaction module also integrates a standard communication interface for uploading test data and reports to a host computer or data center.

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