Electronic load device with self-learning function

Abstract

The invention discloses an electronic load device with a self-learning function, which comprises a power loop, a DAC (Digital-to-Analog Converter) module, a voltage sampling loop, a current sampling loop, an ADC (Analog-to-Digital Converter) module, a man-machine interaction module, a high-speed data processing unit and a storage unit, and is characterized in that the high-speed data processing unit comprises a PI regulator and a data analysis unit; according to the invention, the PI parameter autonomous learning of the electronic load in different application scenes can be realized without the assistance of an oscilloscope and the participation of professional debugging personnel, so that the debugging process is simplified, the dependence on the professional personnel is reduced, and the adaptability and the intelligent level of the electronic load device are improved.

Description

Technical Field

[0001] This invention relates to an electronic load device, and more particularly to an electronic load device with a self-learning function. Background Technology

[0002] Currently, an electronic load device is an electronic device capable of simulating real-world load conditions. It consumes electrical energy by controlling the conduction of internal power devices (such as MOSFETs) or transistors. It primarily operates in the following four modes:

[0003] Constant Current Mode (CC): In this mode, the electronic load maintains a constant current value regardless of changes in the power supply voltage. This is helpful for testing the power supply's output performance under constant current conditions.

[0004] Constant Voltage Mode (CV): The electronic load will maintain a constant voltage value, which is suitable for testing the output capability and stability of the power supply under constant voltage conditions.

[0005] Constant resistance mode (CR): The electronic load will behave as a resistor with a specific resistance value. This resistance value can be changed by adjusting the internal parameters, thereby simulating different load conditions.

[0006] Constant power mode (CP): In this mode, the electronic load will consume a constant power value, which helps to test the output efficiency and stability of the power supply under specific power conditions.

[0007] Based on the above four working modes, electronic loads are mainly used in the following scenarios;

[0008] Power supply testing: Electronic loads are essential testing tools in the research and development and production of power supplies. They can be used to test parameters such as output voltage, current, and power of the power supply in order to evaluate its performance and stability.

[0009] Energy research: In the field of energy conversion and storage, such as solar panels and electric vehicle batteries, electronic loads can be used to simulate output efficiency and stability under different load conditions.

[0010] Electronic product development: In the process of developing electronic products, electronic loads can be used to verify the correctness of circuit design and ensure that the circuit can output the expected voltage and current under normal operating conditions.

[0011] Laboratory teaching and research experiments: Electronic loads are also one of the commonly used tools in laboratory teaching and research experiments in majors such as electronic engineering and power electronics.

[0012] Given the wide range and complexity of applications for electronic load devices, PI control technology has become the dominant automatic control strategy. However, as application scenarios continue to change, the transfer function of PI control for electronic loads also needs to be adjusted. This means that electronic load devices require targeted tuning of PI parameters for different power supply characteristics or application scenarios.

[0013] Traditional debugging processes are complex, requiring the use of an oscilloscope to observe voltage and current waveforms. Debuggers must finely adjust parameters based on the waveforms displayed on the oscilloscope to ensure that the voltage and current waveforms of the electronic load are stable during the load-bearing process, without oscillations or spikes. This process often relies on professional electronic load R&D engineers. Existing technology

[0014] The industry has developed a PI parameter tuning technique for electronic load devices, designed to meet parameter optimization needs in specific application scenarios. For example... Figure 1 As shown, the R&D team of the electronic load device adopted a systematic approach for a specific test object: manually adjusting the PI parameters by observing changes in the waveform on the oscilloscope. This adjustment process continued until the waveform at the input terminal of the electronic load device changed from its initial state (e.g., ...). Figure 2 As shown, there are obvious oscillations or spikes in voltage and current, which transform into an ideal state (such as...). Figure 3 As shown, the waveform has virtually no oscillations or spikes, thus achieving precise adjustment of the PI parameters.

[0015] Disadvantages of existing technology:

[0016] 1) Current technology relies on specialized testing equipment, such as oscilloscopes, to monitor the voltage and current waveforms of electronic loads during operation. This process is not only complex to debug but also costly. Debugging personnel need to rely on experience and knowledge to make qualitative judgments about the waveforms, which makes it difficult to ensure the accuracy of the judgments because there is a lack of a clear quantitative standard to evaluate whether the waveforms meet the actual requirements.

[0017] 2) The involvement of professional commissioning personnel is essential. They need to judge whether the waveforms meet the standards based on the monitored voltage and current waveforms, combined with their professional knowledge and experience. However, this judgment often remains at the qualitative stage, lacking precise quantitative standards, thus affecting the accuracy and reliability of the judgment.

[0018] 3) Even with professional debugging personnel, from a substandard waveform (such as...) Figure 2 Adjust to a suitable waveform (as shown) Figure 3 As shown in the image, this also requires a significant amount of time. This not only reduces debugging efficiency but also increases the project's time cost.

[0019] 4) When the application scenario changes, existing debugging methods require debugging personnel to constantly readjust parameters to adapt to the new scenario. However, this process is often not fast or efficient enough to quickly meet new requirements, thus limiting the flexibility and adaptability of electronic load devices.

[0020] In view of this, the present invention is hereby proposed. Summary of the Invention

[0021] The purpose of this invention is to provide an electronic load device with self-learning function to solve the above-mentioned technical problems existing in the prior art.

[0022] The objective of this invention is achieved through the following technical solution:

[0023] The electronic load device with self-learning function of the present invention includes a power circuit, a DAC module, a voltage sampling circuit, a current sampling circuit, an ADC module, a human-machine interaction module, a high-speed data processing unit, and a storage unit. The high-speed data processing unit includes a PI regulator and a data analysis unit.

[0024] The power circuit is connected to the signal output terminal of the DAC module, and the PI regulator is connected to the adjustment input terminal of the DAC module.

[0025] The voltage sampling circuit is connected to the ADC module and the voltage oscillation detection circuit;

[0026] The current sampling circuit is connected to the ADC module and the current oscillation detection circuit;

[0027] The ADC module, voltage oscillation detection circuit, and current oscillation detection circuit are respectively connected to the data analysis unit;

[0028] The PI controller and the data analysis unit are respectively connected to the storage unit;

[0029] The human-computer interaction module is connected to the high-speed data processing unit.

[0030] Compared with existing technologies, the electronic load device with self-learning function provided by this invention can achieve autonomous learning of PI parameters of the electronic load in different application scenarios without the need for oscilloscope assistance or professional debugging personnel. This innovation not only simplifies the debugging process but also reduces reliance on professional personnel, improving the adaptability and intelligence level of the electronic load device. Attached Figure Description

[0031] Figure 1 A schematic diagram of common PI parameter adjustment methods for electronic load devices;

[0032] Figure 2The normal waveform at the input port of the electronic load exhibits oscillation and spikes;

[0033] Figure 3 The normal waveform at the input port of the electronic load is basically free of oscillations and spikes;

[0034] Figure 4 This is a schematic diagram of the design scheme of an electronic load device with self-learning function according to an embodiment of the present invention;

[0035] Figure 5 This is a flowchart illustrating the self-learning process of an electronic load device with self-learning function according to an embodiment of the present invention. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them, and do not constitute a limitation on the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the protection scope of the present invention.

[0037] First, the following explanations are provided for the terms that may be used in this article:

[0038] The term "and / or" means that either or both can be achieved simultaneously. For example, X and / or Y means that it includes both "X" or "Y" as well as the three cases of "X and Y".

[0039] The terms "comprising," "including," "containing," "having," or other similar semantic descriptions should be interpreted as non-exclusive inclusion. For example, including a technical feature element (such as raw material, component, ingredient, carrier, dosage form, material, size, part, component, mechanism, device, step, process, method, reaction conditions, processing conditions, parameter, algorithm, signal, data, product or article of manufacture, etc.) should be interpreted as including not only the expressly listed technical feature element, but also other technical feature elements that are not expressly listed and are well-known in the art.

[0040] The contents not described in detail in the embodiments of this invention are prior art known to those skilled in the art. Where specific conditions are not specified in the embodiments of this invention, they shall be performed according to conventional conditions in the art or conditions recommended by the manufacturer. Where the manufacturers of the reagents or instruments used in the embodiments of this invention are not specified, they are all conventional products that can be purchased commercially.

[0041] The electronic load device with self-learning function of the present invention includes a power circuit, a DAC module, a voltage sampling circuit, a current sampling circuit, an ADC module, a human-machine interaction module, a high-speed data processing unit, and a storage unit. The high-speed data processing unit includes a PI regulator and a data analysis unit.

[0042] The power circuit is connected to the signal output terminal of the DAC module, and the PI regulator is connected to the adjustment input terminal of the DAC module.

[0043] The voltage sampling circuit is connected to the ADC module and the voltage oscillation detection circuit;

[0044] The current sampling circuit is connected to the ADC module and the current oscillation detection circuit;

[0045] The ADC module, voltage oscillation detection circuit, and current oscillation detection circuit are respectively connected to the data analysis unit;

[0046] The PI controller and the data analysis unit are respectively connected to the storage unit;

[0047] The human-computer interaction module is connected to the high-speed data processing unit.

[0048] The power circuit performs a load-bearing operation on the target object under test according to the set value of the DAC module;

[0049] The DAC module is used to receive the analog signal output by the PI regulator and adjust its output accordingly.

[0050] The PI regulator adjusts the DAC output value according to the current operating mode of the device to ensure optimal system performance.

[0051] The voltage sampling circuit acquires the input voltage of the load and conditions it for subsequent processing by the ADC module and the voltage oscillation detection circuit.

[0052] The current sampling circuit acquires and conditions the input current of the load for subsequent processing by the ADC module and the current oscillation detection circuit.

[0053] The ADC module is used to convert conditioned analog voltage or current signals into digital signals for in-depth analysis and processing by the data analysis unit.

[0054] The data analysis unit is used to analyze the oscillations and spikes in the device during the learning process to ensure the stable operation of the system.

[0055] The high-speed data processing unit integrates the functions of a PI controller and a data analysis unit. Based on the decision results of the data analysis unit, it adjusts the parameters of the PI controller in real time to achieve rapid response and precise control.

[0056] The storage unit is used to save the learned PI parameters and provide storage space for newly learned PI parameters, enabling the device to quickly find the corresponding PI parameters. At the same time, newly learned parameters are also stored here, continuously enriching the database so that the device can adapt to new scenarios more quickly.

[0057] The human-computer interaction module initiates a self-learning process and allows users to set the maximum voltage, current, and power thresholds that the device under test can withstand during the self-learning process, in order to ensure the safety of self-learning and prevent damage to the device under test due to improper parameter settings.

[0058] In summary, the self-learning electronic load device of this invention can autonomously learn the PI parameters of the electronic load in different application scenarios without the need for an oscilloscope or professional debugging personnel. This innovation not only simplifies the debugging process but also reduces reliance on professional personnel, improving the adaptability and intelligence of the electronic load device.

[0059] To more clearly demonstrate the technical solution and its effects provided by the present invention, the embodiments of the present invention will be described in detail below with reference to specific examples.

[0060] This invention relies on an advanced digital control system for electronic loads, aiming to solve the challenges of PI (proportional-integral) parameter tuning when electronic loads are applied in various scenarios. These problems include high tuning costs, low efficiency, and a complex tuning process requiring a high degree of expertise. Electronic loads with self-learning capabilities provide an effective solution, effectively addressing these challenges.

[0061] Example 1

[0062] The design scheme of the electronic load device with self-learning function of the present invention is as follows: Figure 4 As shown:

[0063] The core structure of this device is based on the following key components:

[0064] (1) Power circuit: This circuit performs load operation on the target object under test according to the set value of DAC (digital-to-analog converter).

[0065] (2) DAC module: This module is responsible for receiving the analog signal output by the PI (proportional-integral) regulator and adjusting its output accordingly.

[0066] (3) PI regulator: The PI regulator flexibly adjusts the output value of the DAC according to the current working mode of the device to ensure the optimal performance of the system.

[0067] (4) Voltage sampling circuit: This circuit is responsible for collecting the input voltage of the load and conditioning it so that the ADC (analog-to-digital converter) and voltage oscillation detection circuit can perform subsequent processing.

[0068] (5) Current sampling circuit: Similar to the voltage sampling circuit, this circuit collects the input current of the load and conditions it to facilitate processing by the ADC and the current oscillation detection circuit.

[0069] (6) ADC module: This module is responsible for converting the conditioned voltage or current analog signal into a digital signal for the data analysis unit to perform in-depth analysis and processing.

[0070] (7) Data Analysis Unit: This unit focuses on analyzing the oscillations and spikes in the learning process of the device to ensure the stable operation of the system.

[0071] (8) High-speed data processing unit: This unit integrates the functions of PI controller and data analysis unit. Based on the decision results of data analysis unit, the parameters of PI controller are adjusted in real time to achieve fast response and precise control.

[0072] (9) Storage Unit: As the system's "intelligence repository," the storage unit not only stores the learned PI parameters but also provides storage space for newly learned and applied PI parameters. This is equivalent to a database, enabling the device to quickly find the corresponding PI parameters. At the same time, newly learned parameters are also stored here, continuously enriching the database so that the device can adapt to new scenarios more quickly.

[0073] (10) Human-computer interaction module: This module is designed to initiate the self-learning process and allow users to set the maximum voltage, current and power thresholds that the device under test (UUT) can withstand during the self-learning process, so as to ensure the safety of self-learning and prevent damage to the device under test due to improper parameter settings.

[0074] like Figure 5 The diagram shown is a flowchart of the device's self-learning process.

[0075] The beneficial effects of the technical solution of this invention are as follows:

[0076] 1) Autonomous Monitoring and Quantitative Evaluation: This electronic load device has a built-in advanced waveform monitoring and analysis system, eliminating the need for external professional testing equipment (such as oscilloscopes). It can monitor voltage and current waveforms in real time and automatically evaluate whether the waveforms meet actual requirements through quantitative standards, thereby eliminating debugging complexity, reducing costs, and ensuring the accuracy and consistency of the evaluation.

[0077] 2) Intelligent Judgment and Reduced Human Reliance: Utilizing advanced algorithms, this device can automatically quantify and analyze monitored voltage and current waveforms without the direct involvement of professional technicians. This not only reduces reliance on specialized knowledge but also improves the accuracy and reliability of judgments, avoiding errors caused by human judgment discrepancies.

[0078] 3) Efficient Waveform Adjustment and Cost Reduction: Through a self-learning algorithm, the device can quickly adjust from unqualified waveforms to suitable waveforms, significantly improving debugging efficiency and reducing project time costs. This means users can obtain electronic load devices that meet their needs more quickly, thereby accelerating the product launch process.

[0079] 4) Flexible Adaptability and Rapid Response: When application scenarios change, the device can quickly adjust parameters to adapt to new requirements. Its self-learning function enables the device to automatically learn and adapt to different application scenarios, thereby improving the flexibility and adaptability of the electronic load device and ensuring the stability and performance of the device in different scenarios.

[0080] The components used in the specific implementation, such as the power circuit, DAC (digital-to-analog converter) module, PI (proportional-integral) controller, voltage sampling circuit, current sampling circuit, ADC (analog-to-digital converter) module, data analysis unit, high-speed data processing unit, storage unit, and human-machine interface, are not limited to specific models or functional modules. Any module that meets the design requirements and has the corresponding functions can be flexibly assembled and integrated according to this solution to collaboratively achieve the required functional goals.

[0081] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims. The information disclosed in the background section is intended only to enhance the understanding of the overall background technology of the present invention and should not be construed as an admission or implication in any way that such information constitutes prior art known to those skilled in the art.

Claims

1. An electronic load device with self-learning function, characterized in that, It includes a power circuit, a DAC module, a voltage sampling circuit, a current sampling circuit, an ADC module, a human-machine interaction module, a high-speed data processing unit, and a storage unit. The high-speed data processing unit includes a PI regulator and a data analysis unit. The power circuit is connected to the signal output terminal of the DAC module, and the PI regulator is connected to the adjustment input terminal of the DAC module. The voltage sampling circuit is connected to the ADC module and the voltage oscillation detection circuit; The current sampling circuit is connected to the ADC module and the current oscillation detection circuit; The ADC module, voltage oscillation detection circuit, and current oscillation detection circuit are respectively connected to the data analysis unit; The PI controller and the data analysis unit are respectively connected to the storage unit; The human-computer interaction module is connected to the high-speed data processing unit.

2. The electronic load device with self-learning function according to claim 1, characterized in that: The power circuit performs a load-bearing operation on the target object under test according to the set value of the DAC module; The DAC module is used to receive the analog signal output by the PI regulator and adjust its output accordingly. The PI regulator adjusts the DAC output value according to the current operating mode of the device to ensure optimal system performance. The voltage sampling circuit acquires the input voltage of the load and conditions it for subsequent processing by the ADC module and the voltage oscillation detection circuit. The current sampling circuit acquires and conditions the input current of the load for subsequent processing by the ADC module and the current oscillation detection circuit. The ADC module is used to convert conditioned analog voltage or current signals into digital signals for in-depth analysis and processing by the data analysis unit. The data analysis unit is used to analyze the oscillations and spikes in the device during the learning process to ensure the stable operation of the system. The high-speed data processing unit integrates the functions of a PI controller and a data analysis unit. Based on the decision results of the data analysis unit, it adjusts the parameters of the PI controller in real time to achieve rapid response and precise control. The storage unit is used to save the learned PI parameters and provide storage space for newly learned PI parameters, enabling the device to quickly find the corresponding PI parameters. At the same time, newly learned parameters are also stored here, continuously enriching the database so that the device can adapt to new scenarios more quickly. The human-computer interaction module initiates a self-learning process and allows users to set the maximum voltage, current, and power thresholds that the device under test can withstand during the self-learning process, in order to ensure the safety of self-learning and prevent damage to the device under test due to improper parameter settings.

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