Self-adaptive frequency conversion control method of air conditioner electric control system at extreme temperature

Abstract

The invention provides a self-adaptive frequency conversion control method of an air conditioner electric control system at extreme temperature, which comprises the following steps: building a temperature domain-load-frequency three-variable coupling model by collecting data such as environment temperature, operation load, current and voltage, optimizing a compressor starting torque and frequency acceleration strategy for a low temperature section of-25 DEG C to-10 DEG C, and optimizing a low temperature section of-25 DEG C to-10 DEG C; adjusting a PWM duty ratio and bus voltage matching rule for a high-temperature section of 45-60 DEG C; and meanwhile, over-current and over-voltage protection threshold values are dynamically adjusted in linkage with the temperature, and graded protection logic is designed. According to the method, the problems of low energy efficiency, starting difficulty and protection rigidness under the extreme temperature of a traditional algorithm are solved, the frequency conversion frequency adjusting precision in a wide temperature range is + / -0.5 Hz, the energy efficiency is improved by more than or equal to 8%, the operation failure rate is remarkably reduced, and the method is suitable for an air conditioner electric control system under the extreme weather condition.

Description

Technical Field

[0001] This invention belongs to the field of air conditioning inverter control technology, specifically relating to an adaptive inverter control algorithm for an air conditioning electronic control system under extreme temperatures. Background Technology

[0002] The variable frequency control algorithm of the air conditioning electronic control system is the core technology that determines the energy efficiency and stability of air conditioning operation. However, the existing technology has significant defects in extreme temperature environments (<-10℃ or >45℃), making it difficult to meet the requirements for wide temperature range use. 1. Fixed parameters lead to low energy efficiency: Traditional inverter algorithms use fixed compressor starting parameters, PWM modulation strategies, and bus voltage matching rules, which cannot adapt to device characteristics and operating load changes under extreme temperatures. At low temperatures, insufficient starting torque leads to starting failure or excessively long start-up times, while at high temperatures, excessive switching losses and increased overcurrent risk lead to energy efficiency degradation, resulting in energy efficiency reduction of more than 15% compared to normal temperatures.

[0003] 2. Rigid protection mechanisms lead to operational failures: Traditional algorithms use fixed overcurrent and overvoltage protection thresholds, failing to consider the impact of extreme temperatures on device tolerance. At high temperatures, device current tolerance decreases, and fixed overcurrent thresholds can easily lead to overcurrent damage; at low temperatures, device insulation performance changes, and fixed overvoltage thresholds can easily trigger protection falsely, causing frequent air conditioner shutdowns.

[0004] 3. Disconnect between temperature range and load adaptation: The existing algorithm does not establish a linkage relationship between "ambient temperature range - operating load - frequency converter". It only adjusts the frequency based on a single load parameter, resulting in low frequency adjustment accuracy under extreme temperatures, with fluctuations of more than ±1.5Hz, which further exacerbates energy waste and operational instability.

[0005] While existing technologies offer optimizations for variable frequency control algorithms within typical temperature ranges, a coupled control scheme that can simultaneously adapt to a wide temperature range of -25°C to 60°C and balance energy efficiency improvement with dynamic protection has yet to emerge. Therefore, this invention proposes an adaptive variable frequency control algorithm for air conditioning electronic control systems under extreme temperatures, addressing the aforementioned technical challenges. Summary of the Invention

[0006] To address the shortcomings of existing technologies, the present invention aims to provide an adaptive variable frequency control method for air conditioning electronic control systems under extreme temperatures. This method is applicable to a wide temperature range environment from -25℃ to 60℃. By coupling and regulating the temperature range, load, and frequency, and linking with dynamic protection thresholds, the energy efficiency and operational reliability of air conditioning within a wide temperature range are improved.

[0007] This invention is achieved through the following technical solution: an adaptive variable frequency control method for an air conditioning electronic control system under extreme temperatures, comprising the following steps: (1) Collect and preprocess ambient temperature, compressor operating current, bus voltage and real-time air conditioning operating load data; (2) Construct a three-variable coupled model of "temperature range-load-frequency", divide the temperature range into zones and load levels, and establish a frequency reference library; (3) Dynamically adjust the frequency conversion parameters (starting torque, PWM duty cycle, bus voltage, frequency adjustment step size) for different temperature zones. (4) The overcurrent and overvoltage protection thresholds are dynamically adjusted in conjunction with the ambient temperature, and a graded protection logic is designed; (5) Based on the operational feedback data, parameters are corrected online to achieve closed-loop optimization.

[0008] As a preferred embodiment, the ambient temperature acquisition range in step (1) is -25℃ to 60℃, the acquisition frequency is 10 times / second, and the accuracy is ±0.3℃; the current and voltage acquisition frequency is 50 times / second, and the load data is quantized as 0-100% load value; the data preprocessing adopts the moving average filtering algorithm, which is specifically used to remove noise in the acquired data and eliminate instantaneous current spike abnormal fluctuation data.

[0009] As a preferred embodiment, the temperature range zoning in step (2) includes a low temperature range (-25℃~-10℃), a normal range (-10℃~45℃), and a high temperature range (45℃~60℃). The load levels include low load (0~30%), medium load (31%~70%), and high load (71%~100%). The frequency reference library presets corresponding variable frequency reference values ​​for the combination scenarios of the above-mentioned different temperature range zoning and load levels, and reserves online correction interfaces for subsequent parameter optimization.

[0010] As a preferred embodiment, the frequency conversion parameter adjustment in step (3) is a targeted optimization for different temperature zones, specifically including: (a) Low temperature range (-25℃~-10℃): The starting torque is increased by up to 20%, the starting frequency increase is slowed by 30%, and the frequency adjustment step size is ≤0.5Hz, so as to improve the success rate of low temperature start-up; (b) High temperature range (45℃~60℃): The upper limit of PWM duty cycle is reduced by 5%~15%, the bus voltage adjustment range is 280V~320V, and the maximum operating frequency is reduced by 10%~15% compared with the normal range, in order to reduce high temperature operation losses and overcurrent risks.

[0011] As a preferred implementation, the dynamic protection threshold adjustment in step (4) is precisely linked to the ambient temperature. Specifically, for every 5°C increase in the high temperature range, the overcurrent protection threshold is reduced by 8%~10%; for every 5°C decrease in the low temperature range, the overvoltage protection threshold is increased by 5%~8%; the graded protection logic includes two levels of response: early warning fine-tuning and emergency shutdown, with a protection response time ≤50μs, which can quickly respond to device safety risks under extreme temperatures.

[0012] As a preferred implementation, the parameters corrected online by the PID algorithm in step (5) include the frequency reference value and parameter adjustment coefficient in the coupled model; the operation feedback data includes the air conditioner operation energy efficiency value, current and voltage fluctuation data and compressor start and stop times. Closed-loop optimization is achieved by real-time collection and analysis of these data, and finally the frequency regulation accuracy of the variable frequency in the wide temperature range reaches ±0.5Hz, and the energy efficiency is improved by ≥8% compared with the traditional algorithm under extreme temperatures.

[0013] After adopting the above technical solution, the beneficial effects of the present invention are: It pioneered a three-variable coupled control logic of "temperature range-load-frequency", which breaks through the limitation of single parameter control in traditional algorithms. The frequency conversion adjustment accuracy reaches ±0.5Hz in a wide temperature range, which is more than 67% higher than that of traditional algorithms, and solves the problem of low energy efficiency at extreme temperatures from the root. The protection threshold is dynamically linked with the temperature range, abandoning the fixed protection threshold mode. Combined with the "early warning-fine-tuning-shutdown" graded protection logic, the failure rate is reduced by more than 60% under extreme temperatures, which not only avoids device damage at high temperatures, but also reduces false protection shutdowns at low temperatures. The core parameters are optimized to address the pain points of extreme temperatures, achieving a 100% success rate for low-temperature start-up, significantly reducing operating losses at high temperatures, and improving energy efficiency by more than 8% compared to traditional algorithms under extreme temperatures. It is suitable for use in a wide temperature range from -25℃ to 60℃. Detailed Implementation

[0015] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] This invention provides a technical solution: an adaptive variable frequency control method for an air conditioning electronic control system under extreme temperatures, comprising the following steps: (1) Collect and preprocess ambient temperature, compressor operating current, bus voltage and real-time air conditioning operating load data; Specifically: Data acquisition parameters: Ambient temperature is acquired via a temperature sensor installed in the electronic control system (acquisition frequency 10 times / second, detection range -25℃ to 60℃, accuracy ±0.3℃); compressor operating current and bus voltage are acquired via current and voltage sensors (acquisition frequency 50 times / second); real-time air conditioning operating load (such as cooling / heating demand intensity, quantified as 0-100% load value) is obtained through the load detection unit. Data preprocessing: The collected temperature, current, voltage and load data are filtered and denoised (using a moving average filtering algorithm) to remove abnormal fluctuation data (such as instantaneous current spikes) and ensure the stability of input parameters.

[0017] (2) Construct a three-variable coupled model of "temperature range-load-frequency", divide the temperature range into zones and load levels, and establish a frequency reference library; Specifically: Based on experimental data from multiple extreme temperature scenarios, an integrated coupled model was trained, and the following mapping relationship was established: Temperature range zoning: The temperature range of -25℃ to 60℃ is divided into three zones: low temperature zone (-25℃ to -10℃), normal zone (-10℃ to 45℃), and high temperature zone (45℃ to 60℃). Load classification: The operating load is divided into three levels: low load (0~30%), medium load (31%~70%), and high load (71%~100%). Frequency reference library: Preset frequency reference values ​​for different temperature zones and load levels (e.g., 30Hz reference frequency for high load in low temperature zone, 28Hz reference frequency for high load in high temperature zone), and reserve online correction interface.

[0018] (3) Dynamically adjust the frequency conversion parameters (starting torque, PWM duty cycle, bus voltage, frequency adjustment step size) for different temperature zones. Specifically: Control strategy for the low-temperature range (-25℃ to -10℃): Startup optimization: The compressor startup parameters are dynamically adjusted according to the ambient temperature. For every 5°C decrease in temperature, the startup torque increases by 8% (maximum increase of 20%), and the rate at which the startup frequency gradually increases from 5Hz to the base frequency is slowed by 30%, shortening the startup time (more than 30% shorter than the startup time of traditional algorithms) and avoiding startup failure. Operational optimization: Adjust the frequency adjustment step size based on load level—the step size is ≤0.3Hz under low load and ≤0.5Hz under medium and high load to ensure that the frequency is stable within the reference value ±0.5Hz range and reduce energy consumption during low frequency operation.

[0019] High-temperature range (45℃~60℃) control strategy: PWM duty cycle optimization: A dynamic duty cycle modulation algorithm is adopted. For every 5°C increase in temperature, the upper limit of the duty cycle is reduced by 5% (not less than 40%) to reduce device switching losses. Bus voltage matching: The bus voltage (range 280V~320V) is dynamically adjusted according to the real-time load and temperature. Under high load, the bus voltage is increased to 300V~320V to ensure output power; under low load, the bus voltage is reduced to 280V~290V to reduce no-load loss. Frequency limitation: The maximum operating frequency is reduced by 10% to 15% compared to the normal range to avoid the risk of overcurrent and overheating caused by high-frequency operation.

[0020] (4) The overcurrent and overvoltage protection thresholds are dynamically adjusted in conjunction with the ambient temperature, and a graded protection logic is designed; Specifically: The algorithm correlates ambient temperature with protection thresholds in real time, enabling tiered dynamic adjustments to prevent protection gaps or false triggers. Overcurrent threshold adjustment: In the high-temperature range (45℃~60℃), the overcurrent protection threshold is reduced by 8%~10% for every 5℃ increase in ambient temperature (based on the high-temperature withstand current characteristic curve of the device); in the low-temperature range (-25℃~-10℃), the overcurrent threshold is maintained at 105%~110% of the normal value to avoid false triggering under low load. Overvoltage threshold adjustment: In the low temperature range (-25℃~-10℃), the overvoltage protection threshold is increased by 5%~8% for every 5℃ decrease in ambient temperature (due to the low temperature insulation characteristics of the adapter components); in the high temperature range (45℃~60℃), the overvoltage threshold is maintained at 95% of the normal value to avoid overvoltage damage caused by bus voltage fluctuations. Hierarchical protection logic: When the parameter approaches 90% of the dynamic threshold, an early warning mechanism is activated (fine-tuning the frequency converter parameters to reduce the load); when the parameter exceeds the dynamic threshold, shutdown protection is immediately triggered with a response time ≤50μs.

[0021] (5) Based on the operational feedback data, parameters are corrected online to achieve closed-loop optimization.

[0022] Specifically, the algorithm collects real-time air conditioner operation data (energy efficiency value, current and voltage fluctuations, number of start-stop cycles) and compares it with preset target values ​​(energy efficiency improvement ≥8%, fluctuation ≤ ±0.5Hz, number of start-stop cycles ≤ 3 times / 24 hours). The algorithm uses a PID algorithm to correct the frequency reference value and parameter adjustment coefficient in the coupled model online, ensuring continuous optimization of energy efficiency and stability under extreme temperatures.

[0023] As an embodiment of the present invention: I. Hardware adaptation requirements Sensing unit: Equipped with high-precision temperature sensor, high-precision current sensor, and voltage sensor to ensure that the parameter acquisition accuracy meets the algorithm requirements; Computational unit: Deployed with an embedded microcontroller to support high-speed data processing (operation frequency ≥ 480MHz), ensuring that the execution delay of algorithm steps is ≤ 100μs; Execution unit: The compressor drive module supports dynamic adjustment of PWM duty cycle (range 40%-90%), and the bus voltage regulation module supports a wide range of voltage output from 280V to 320V.

[0024] II. Software Operation Flow Initialization phase: The algorithm loads the preset "temperature range-load-frequency" reference library and dynamic protection threshold adjustment coefficients to complete the communication adaptation between the sensor and the computing unit; Data acquisition and preprocessing stage: Temperature, current, voltage, and load data are acquired at a set frequency, and noise is removed using a moving average filtering algorithm; Temperature range and load determination phase: Determine the current temperature range zone (low temperature / normal / high temperature) based on temperature data, and determine the load level (low / medium / high) based on load data; Inverter parameter calculation stage: The coupling model is invoked to calculate the startup parameters, PWM duty cycle, bus voltage and frequency reference value under the current scenario; Protection threshold adjustment phase: Dynamically adjust overcurrent and overvoltage protection thresholds according to temperature zone zoning, and synchronously update the graded protection logic parameters; Command output and feedback correction stage: Output control commands to the drive module, collect operational feedback data in real time, and correct parameters online through PID algorithm to form closed-loop control.

[0025] III. Implementation Examples of Typical Scenarios 1. Low-temperature start-up scenario (ambient temperature -25℃, heating load 80%) Algorithm determination: Temperature zone is low temperature zone, load level is high load; Implementation strategy: Starting torque increased by 20%, starting frequency increased from 5Hz to 30Hz (base frequency) at a rate of 0.3Hz / second; overvoltage protection threshold increased by 16% (compared to the normal value); Implementation results: The compressor start-up time was reduced from 12 seconds in the traditional algorithm to 8 seconds, the start-up success rate was 100%, the frequency fluctuation within 30 minutes of operation was ≤±0.4Hz, and the energy efficiency was improved by 8.5% compared with the traditional algorithm.

[0026] 2. High-temperature operation scenario (ambient temperature 60℃, cooling load 90%) Algorithm determination: Temperature zone is high temperature zone, load level is high load; Execution strategy: PWM duty cycle upper limit reduced by 15% (to 45%), bus voltage adjusted to 320V, maximum operating frequency limited to 28Hz; overcurrent protection threshold reduced by 20% (compared to the normal value); Implementation results: The compressor operating current is reduced by 12% compared with the traditional algorithm, the switching loss is reduced by 15%, there is no overcurrent warning within 2 hours of operation, and the energy efficiency is improved by 9.2% compared with the traditional algorithm.

[0027] Specific Implementation Cases Taking a wide-temperature-range air conditioner equipped with the algorithm of this invention as an example, a 100-hour field test was conducted within a temperature range of -25℃ to 60℃. The results are as follows: Stability: The compressor starts successfully at 100% under extreme temperatures, with no error-induced shutdowns and a failure rate of 0%, which is significantly lower than the traditional algorithm (failure rate of 8%). Energy efficiency: Energy efficiency is improved by 8.3% compared to traditional algorithms at -25℃ low temperature environment, 9.5% at 60℃ high temperature environment, and 8.8% on average across the entire temperature range, meeting the design target of ≥8%; Adjustment accuracy: The frequency adjustment accuracy of the inverter reaches ±0.4Hz over a wide temperature range, which is 75% higher than that of the traditional algorithm (±1.6Hz), and the fluctuation of operating parameters is significantly reduced.

[0028] The experimental results show that the method of the present invention can effectively solve the problems of low energy efficiency and poor stability of air conditioner frequency conversion control under extreme temperatures, adapt to the core requirements of wide temperature range electronic control system, and is suitable for air conditioning products in extreme climate areas such as severe cold and scorching heat.

[0029] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An adaptive variable frequency control method for an air conditioning electronic control system under extreme temperatures, characterized in that, Includes the following steps: (1) Collect and preprocess ambient temperature, compressor operating current, bus voltage and real-time air conditioning operating load data; (2) Construct a three-variable coupled model of "temperature range-load-frequency", divide the temperature range into zones and load levels, and establish a frequency reference library; (3) Dynamically adjust the frequency conversion parameters (starting torque, PWM duty cycle, bus voltage, frequency adjustment step size) for different temperature zones. (4) The overcurrent and overvoltage protection thresholds are dynamically adjusted in conjunction with the ambient temperature, and a graded protection logic is designed; (5) Based on the operational feedback data, parameters are corrected online to achieve closed-loop optimization.

2. The adaptive variable frequency control method for an air conditioning electronic control system under extreme temperatures as described in claim 1, characterized in that: The ambient temperature range in step (1) is -25℃ to 60℃, the sampling frequency is 10 times / second, and the accuracy is ±0.3℃; the current and voltage sampling frequency is 50 times / second, and the load data is quantized as 0-100% load value; the data preprocessing adopts the moving average filtering algorithm, which is specifically used to remove noise in the collected data and eliminate instantaneous current spike abnormal fluctuation data.

3. The adaptive variable frequency control method for an air conditioning electronic control system under extreme temperatures as described in claim 2, characterized in that: The temperature range zoning in step (2) includes a low temperature range (-25℃~-10℃), a normal range (-10℃~45℃), and a high temperature range (45℃~60℃). The load levels include low load (0~30%), medium load (31%~70%), and high load (71%~100%). The frequency reference library presets corresponding variable frequency reference values ​​for the combination scenarios of the above-mentioned different temperature range zoning and load levels, and reserves them in the online correction interface for subsequent parameter optimization.

4. The adaptive variable frequency control method for an air conditioning electronic control system under extreme temperatures as described in claim 1, characterized in that: The frequency conversion parameter adjustment in step (3) is a targeted optimization for different temperature zones, specifically including: (a) Low temperature range (-25℃~-10℃): The starting torque is increased by up to 20%, the starting frequency increase is slowed by 30%, and the frequency adjustment step size is ≤0.5Hz, so as to improve the success rate of low temperature start-up; (b) High temperature range (45℃~60℃): The upper limit of PWM duty cycle is reduced by 5%~15%, the bus voltage adjustment range is 280V~320V, and the maximum operating frequency is reduced by 10%~15% compared with the normal range, in order to reduce high temperature operation losses and overcurrent risks.

5. The adaptive variable frequency control method for an air conditioning electronic control system under extreme temperatures as described in claim 4, characterized in that: The dynamic protection threshold adjustment mentioned in step (4) is precisely linked to the ambient temperature. Specifically, for every 5°C increase in the high temperature range, the overcurrent protection threshold is reduced by 8%~10%; for every 5°C decrease in the low temperature range, the overvoltage protection threshold is increased by 5%~8%. The graded protection logic includes two levels of response: early warning fine-tuning and emergency shutdown. The protection response time is ≤50μs, which can quickly respond to device safety risks under extreme temperatures.

6. The adaptive variable frequency control method for an air conditioning electronic control system under extreme temperatures as described in claim 1, characterized in that: In step (5), the parameters corrected online by the PID algorithm include the frequency reference value and parameter adjustment coefficient in the coupled model; the operation feedback data includes the air conditioner operation energy efficiency value, current and voltage fluctuation data and compressor start and stop times. Closed-loop optimization is achieved by real-time acquisition and analysis of these data, and finally the frequency regulation accuracy of the variable frequency in the wide temperature range reaches ±0.5Hz, and the energy efficiency is improved by ≥8% compared with the traditional algorithm under extreme temperatures.